Author: "Ramakrishnan A" / Topic: computer science - Searchworks@Jio Institute Digital Library Search Results

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1. Detection of Mild Cognitive Impairment Using Kernel Density Estimation Based Texture Analysis of the Corpus Callosum in Brain MR Images

Author: Amrutha Veluppal, Deboleena Sadhukhan, Ramakrishnan Swaminathan, and Venugopal Gopinath
Subjects: Computer science, business.industry, Kernel density estimation, Biomedical Engineering, Biophysics, Pattern recognition, Texture (music), Corpus callosum, Linear discriminant analysis, Support vector machine, Level set, Artificial intelligence, Mr images, business, Mass screening
Abstract: Objectives Mild Cognitive Impairment (MCI) is the prodromal stage of Alzheimer's disease (AD), which is a progressive and fatal neurodegenerative disorder. Detection of MCI condition can enable early diagnosis resulting in timely intervention to delay the disease progression. Onset of MCI causes tissue alterations in Corpus Callosum (CC) of the brain. Texture analysis of brain Magnetic Resonance (MR) images aids in characterising these imperceptible changes. In this study, Kernel Density Estimation (KDE) technique is used to analyse the textural variations in CC to detect MCI condition. Materials and method The pre-processed brain MR images are obtained from a public access database. Reaction Diffusion level set is employed to segment CC from sagittal slices of the images. Kernel density estimation method is applied to study the local intensity variations within the segmented CC. Statistical features quantifying these variations are extracted from the KDE values. These features are used to differentiate MCI condition using linear classifiers based on discriminant analysis and support vector machine. The results are compared with conventional Grey Level Co-occurrence Matrix (GLCM) features for validation. Results The KDE-based texture features extracted from CC show significant variation between normal and MCI classes. Results demonstrate that this approach can differentiate MCI condition with high accuracy and specificity of 81.3% and 82.7%, respectively. The KDE-based features perform better when compared with GLCM features for distinguishing MCI. Conclusions The KDE-based texture features are able to capture the subtle changes occurring in CC at the MCI stage. This technique achieves comparable performance to other state-of-the-art methods with reduced number of features. Efficiency of the KDE-based texture analysis confirms that the proposed computer assisted technique can be used for mass screening of MCI, which can aid in handling the disease severity.
Published: 2022

2. The Seattle report on database research

Author: Alon Halevy, Surajit Chaudhuri, Juliana Freire, Dan Suciu, Volker Markl, Michael J. Franklin, Joseph M. Hellerstein, Sergey Melnik, Fatma Ozcan, Chandrasekaran Mohan, David G. Andersen, AnHai Doan, Raghu Ramakrishnan, Stratos Idreos, Tim Kraska, Tova Milo, Anastasia Ailamaki, Magdalena Balazinska, Thomas Neumann, Michael Stonebraker, Beng Chin Ooi, Raluca Ada Popa, Andrew Pavlo, Donald Kossmann, Alvin Cheung, Jignesh M. Patel, Christopher Ré, Peter Bailis, Luna Dong, Peter Boncz, Daniel J. Abadi, Philip A. Bernstein, and Sailesh Krishnamurthy
Subjects: General Computer Science, Computer science, 020204 information systems, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, Library science, 02 engineering and technology, 0509 other social sciences, 050905 science studies, Database research, Software, Information Systems
Abstract: Approximately every five years, a group of database researchers meet to do a self-assessment of our community, including reflections on our impact on the industry as well as challenges facing our research community. This report summarizes the discussion and conclusions of the 9th such meeting, held during October 9-10, 2018 in Seattle.
Published: 2022

3. Non-Pharmaceutical Interventions as Controls to mitigate the spread of epidemics: An analysis using a spatiotemporal PDE model and COVID–19 data

Author: Faray Majid, Subramanian Ramakrishnan, Shelley Ehrlich, Aditya M. Deshpande, Manish Kumar, and Michael Gray
Subjects: 0209 industrial biotechnology, 2019-20 coronavirus outbreak, Coronavirus disease 2019 (COVID-19), Computer science, Applied Mathematics, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 020208 electrical & electronic engineering, Psychological intervention, Model parameters, 02 engineering and technology, computer.software_genre, Computer Science Applications, 020901 industrial engineering & automation, Control and Systems Engineering, 0202 electrical engineering, electronic engineering, information engineering, Data mining, Electrical and Electronic Engineering, Instrumentation, computer
Abstract: We investigate the spatiotemporal dynamics and control of an epidemic using a partial differential equation (PDE) based Susceptible-Latent-Infected-Recovered (SLIR) model. We first validate the model using empirical COVID-19 data corresponding to a period of 45 days from the state of Ohio, United States. Upon optimizing the model parameters in the learning phase of the analysis using actual infection data from a period of the first 30 days, we then find that the model output closely tracks the actual data for the next 15 days. Next, we introduce a control input into the model to represent the Non-Pharmaceutical Intervention of social distancing. Implementing the control using two distinct schemes, we find that in both cases the control input is able to significantly mitigate the infection spread. In addition to opening a novel pathway towards the characterization, analysis and implementation of Non-Pharmaceutical Interventions across multiple geographical scales using Control frameworks, our results highlight the importance of first-principles based PDE models in understanding the spatiotemporal dynamics of epidemics triggered by novel pathogens.
Published: 2022

4. An Enhanced Memetic Algorithm for Feature Selection in Big Data Analytics with MapReduce

Author: Nandhagopal Nachimuthu and Umanesan Ramakrishnan
Subjects: Computer science, business.industry, Big data, Feature selection, Machine learning, computer.software_genre, Theoretical Computer Science, Computational Theory and Mathematics, Artificial Intelligence, Memetic algorithm, Artificial intelligence, business, computer, Software
Published: 2022

5. Graph-Based Namespaces and Load Sharing for Efficient Information Dissemination

Author: Mohammad Jahanian, Jiachen Chen, and Kadangode K. Ramakrishnan
Subjects: Computer Networks and Communications, business.industry, Computer science, Graph based, Load sharing, Information Dissemination, Electrical and Electronic Engineering, business, Software, Computer Science Applications, Computer network
Published: 2021

6. Impact of minimal sufficient balance, minimization, and stratified permuted blocks on bias and power in the estimation of treatment effect in sequential clinical trials with a binary endpoint

Author: Steven D. Lauzon, Jody D. Ciolino, Wenle Zhao, Michael D. Hill, Paul J. Nietert, and Viswanathan Ramakrishnan
Subjects: Statistics and Probability, Selection bias, Clinical Trials as Topic, Randomization, Epidemiology, Computer science, media_common.quotation_subject, Logistic regression, Article, Random Allocation, Bias, Health Information Management, Research Design, Statistics, Covariate, Computer Simulation, Minification, Predictability, Randomness, Probability, Balance (ability), media_common
Abstract: Minimization is among the most common methods for controlling baseline covariate imbalance at the randomization phase of clinical trials. Previous studies have found that minimization does not preserve allocation randomness as well as other methods, such as minimal sufficient balance, making it more vulnerable to allocation predictability and selection bias. Additionally, minimization has been shown in simulation studies to inadequately control serious covariate imbalances when modest biased coin probabilities (≤0.65) are used. This current study extends the investigation of randomization methods to the analysis phase, comparing the impact of treatment allocation methods on power and bias in estimating treatment effects on a binary outcome using logistic regression. Power and bias in the estimation of treatment effect was found to be comparable across complete randomization, minimization, and minimal sufficient balance in unadjusted analyses. Further, minimal sufficient balance was found to have the most modest impact on power and the least bias in covariate-adjusted analyses. The minimal sufficient balance method is recommended for use in clinical trials as an alternative to minimization when covariate-adaptive subject randomization takes place.
Published: 2021

7. Random Forest Bagging and X-Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data

Author: Mounir Hamdi, V. Vijayakumar, Ketan Kotecha, M. Poongodi, Rajesh Kumar Dhanaraj, Vinothsaravanan Ramakrishnan, and Lalitha Krishnasamy
Subjects: Technology, SQL, Article Subject, Computer Networks and Communications, Computer science, TK5101-6720, Similarity measure, computer.software_genre, Random forest, Similarity (network science), Bayesian information criterion, Telecommunication, Data mining, False positive rate, Electrical and Electronic Engineering, Cluster analysis, Time complexity, computer, Information Systems, computer.programming_language
Abstract: In the current ongoing crisis, people mostly rely on mobile phones for all the activities, but query analysis and mobile data security are major issues. Several research works have been made on efficient detection of antipatterns for minimizing the complexity of query analysis. However, more focus needs to be given to the accuracy aspect. In addition, for grouping similar antipatterns, a clustering process was performed to eradicate the design errors. To address the above-said issues and further enhance the antipattern detection accuracy with minimum time and false positive rate, in this work, Random Forest Bagging X-means SQL Query Clustering (RFBXSQLQC) technique is proposed. Different patterns or queries are initially gathered from the input SQL query log, and bootstrap samples are created. Then, for each pattern, various weak clusters are constructed via X-means clustering and are utilized as the weak learner (clusters). During this process, the input patterns are categorized into different clusters. Using the Bayesian information criterion, the similarity measure is employed to evaluate the similarity between the patterns and cluster weight. Based on the similarity value, patterns are assigned to either relevant or irrelevant groups. The weak learner results are aggregated to form strong clusters, and, with the aid of voting, a majority vote is considered for designing strong clusters with minimum time. Experiments are conducted to evaluate the performance of the RFBXSQLQC technique using the IIT Bombay dataset using the metrics like antipattern detection accuracy, time complexity, false-positive rate, and computational overhead with respect to the differing number of queries. The results revealed that the RFBXSQLQC technique outperforms the existing algorithms by 19% with pattern detection accuracy, 34% minimized time complexity, 64% false-positive rate, and 31% in terms of computational overhead.
Published: 2021

8. Programming Abstractions for Managing Workflows on Tiered Storage Systems

Author: Lavanya Ramakrishnan and Devarshi Ghoshal
Subjects: Workflow, Hardware and Architecture, Computer science, business.industry, Software engineering, business
Abstract: Scientific workflows in High Performance Computing ( HPC ) environments are processing large amounts of data. The storage hierarchy on HPC systems is getting deeper, driven by new technologies (NVRAMs, SSDs, etc.) There is a need for new programming abstractions that allow users to seamlessly manage data at the workflow level on multi-tiered storage systems, and provide optimal workflow performance and use of storage resources. In previous work, we introduced a software architecture Managing Data on Tiered Storage for Scientific Workflows (MaDaTS ) that used a Virtual Data Space ( VDS ) abstraction to hide the complexities of the underlying storage system while allowing users to control data management strategies. In this article, we detail the data-centric programming abstractions that allow users to manage a workflow around its data on the storage layer. The programming abstractions simplify data management for scientific workflows on multi-tiered storage systems, without affecting workflow performance or storage capacity. We measure the overheads and effectiveness introduced by the programming abstractions of MaDaTS. Our results show that these abstractions can optimally use the storage capacity in lesser capacity storage tiers, and simplify data management without adding any performance overheads.
Published: 2021

9. Design of State Feedback LQR Based Dual Mode Fractional-Order PID Controller using Inertia Weighted PSO Algorithm: For Control of an Underactuated System

Author: Rajesh Ramakrishnan and Deepa Subramaniam Nachimuthu
Subjects: Control theory, Underactuation, Robustness (computer science), Computer science, Mechanical Engineering, Full state feedback, Aerospace Engineering, PID controller, Ocean Engineering, Performance improvement, Reference model, Industrial and Manufacturing Engineering, Inverted pendulum
Abstract: Several control strategies are proposed and developed to enhance the performance of the various underactuated systems, particularly inverted pendulum. This paper presents the dual-mode fractional-order control with a reference model for pitch and angle control of an inverted pendulum. An inertia weighted PSO is utilized for optimal tuning of the FOPID parameters to ensure an optimal balance between local and global search. A cost function of this algorithm is framed based on the error between the reference model output and actual system output along with time-domain performance criteria. The reference model-based tuning improves the performance of the controller and steady-state error tracking. In addition, an optimal state feedback LQR is implemented using pole placement design in the feedback to stabilize and improve the robustness of the inverted pendulum. Compared to the conventional PID, the proposed structure illustrates the FOPID structure has significant performance improvement in both with and without disturbance conditions.
Published: 2021

10. An Intelligent Artificial Bee Colony and Adaptive Bacterial Foraging Optimization Scheme for Reliable Breast Cancer Diagnosis

Author: Sipahi Hande, Gu Haijun, Turgutalp Bengisu, Tadi Beni Yaghoub, Velmurugan Devadasan, Chen Yihua, Ece Gurdal Enise, Suresh Joseph K, M. Abdel-Daim Mohamed, Michael Gromiha M., Tanvir Kabir, Punitha S., Li Huan-Ting, N. Bin-Jumah May, K. Mohapatra Ranjan, Shanmugam Anusuya, Peng Yangrui, Turkoz Acar Ebru, Yarim Mine, Ramakrishnan Chandrasekaran, Sahab Uddin, Hafeez Abdul, Atiah Alhusadi Nesrin, Zhu Xiaoyong, Deniz Inci, Ashraf Ghulam, Amuthan A., Abdeen Ahmed, and Perveen Asma
Subjects: Scheme (programming language), General Computer Science, Computer science, business.industry, Foraging, medicine.disease, Machine learning, computer.software_genre, Breast cancer, medicine, Artificial intelligence, business, computer, computer.programming_language
Abstract: Breast cancer is essential to be detected in primitive localized stage for enhancing the possibility of survival since it is considered as the major malediction to the women society around the globe. Most of the intelligent approaches devised for breast cancer necessitate expertise that results in reliable identification of patterns that conclude the presence of oncology cells and determine the possible treatment to breast cancer patients in order to enhance their survival feasibility. Moreover, the majority of the existing schemes of the literature incur intensive labor and time, inducing a predominant impact over the diagnosis time utilized for detecting breast cancer cells. An Intelligent Artificial Bee Colony and Adaptive Bacterial Foraging Optimization (IABC-ABFO) scheme is proposed for facilitating a better rate of local and global searching ability in selecting the optimal features subsets and optimal parameters of ANN considered for breast cancer diagnosis. In the proposed IABC-ABFO approach, the traditional ABC algorithm used for cancer detection is improved by integrating an adaptive bacterial foraging process in the onlooker bee and the employee bee phase that results in optimal exploitation and exploration. The investigation of results of the proposed IABC-ABFO approach facilitating the use of the Wisconsin breast cancer dataset showed a mean classification accuracy of 99.52% which is higher than the existing breast cancer detection schemes.
Published: 2021

11. CPU Overclocking: A Performance Assessment of Air, Cold Plates, and Two-Phase Immersion Cooling

Author: Mark Shaw, Robert Lankston, Ioannis Manousakis, Majid Jalili, Husam A. Alissa, Marcus Fontoura, Ricardo Bianchini, Ashish Raniwala, Pulkit A. Misra, Brijesh Warrier, Íñigo Goiri, Rich Baca, Monroe Mark Alan, Bharath Ramakrishnan, Christian L. Belady, and Washington Kim
Subjects: Air cooling, Overclocking, Computer cooling, Computer science, Thermal resistance, Clock rate, Heat transfer, Electrical and Electronic Engineering, Heat sink, Industrial and Manufacturing Engineering, Automotive engineering, Electronic, Optical and Magnetic Materials, Coolant
Abstract: Computing capacity has always been on the upward climb due to the constant technological improvements in semiconductor manufacturing and packaging industry. This growth in computing capability is usually accompanied by a steep rise in heat flux density associated with the electronic component (CPUs or GPUs, for example). High-performance computing (HPC) data centers often employ several of these high-performance devices for crunching their enormous artificial intelligence (AI) or scientific computing workloads. State-of-the-art air cooling technologies throttle after a certain heat flux level and would require bigger heat sinks driving enormous airflow through it, which may not be desirable from a data center operation standpoint. Hence, a lookout for advanced thermal management techniques is quite imperative. In this article, a commercially available intel overclockable CPU i9-9900k was exercised with high performance workloads while employing and evaluating three different cooling technologies namely air-cooled, cold plates, and two-phase (2P) immersion. The high heat carrying capacity associated with cold plates and 2P immersion techniques outperformed the air-cooled solution by constantly yielding higher CPU clock rates up to 41% utilizing cold plates and 51% employing 2P immersion. The performance of cold plates and 2P immersion was evaluated at different functional points (different coolant operating temperatures and 2P coolant types for example) to understand how the technologies would respond in a legacy or modern data center operation. It was again observed that both 2P immersion technology and cold plates provided the least thermal resistance (as low as 0.247 °C/W) path to heat transfer and therefore provided higher computational performance and efficiency compared to air cooling. Further improvement in performance is not limited by cooling but by packaging constraints. Advanced packaging techniques coupled with minimizing the interfacial resistance can reap enhanced performance and energy efficiency gains using these modes of cooling. This article demonstrates the potential increase in virtual machine (VM) performance that can be attained using cold plates or 2P immersion. Authors recommend adoption of liquid cooling in early chip design and architecture phases. Finally, the article provides a generic overclocking methodology that the industry can use to qualify postturbo server class chip performance.
Published: 2021

12. Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

Author: Shinjae Yoo, Logan Ward, Nikoli Dryden, Ramakrishnan Kannan, Rajeev Thakur, Bert Debusschere, Ganesh Sivaraman, Sutanay Choudhury, Zhengchun Liu, Neeraj Kumar, Peter Nugent, Francis J. Alexander, Sudip K. Seal, Shantenu Jha, James A. Ang, David Pugmire, Li Tan, Ian Foster, Yunzhi Huang, Paul M. Welch, Cristina Garcia Cardona, Sivasankaran Rajamanickam, Thomas Proffen, Ai Kagawa, Malachi Schram, Byung-Jun Yoon, Jamaludin Mohd-Yusof, Erin McCarthy, Tiernan Casey, Sotiris S. Xantheas, Vinay Ramakrishniah, Jan Balewski, Sayan Ghosh, Brian Van Essen, Michael M. Wolf, Christine Sweeney, J. Austin Ellis, Peter Harrington, Jong Choi, Yosuke Oyama, Naoya Maruyama, Satoshi Matsuoka, Jenna A. Bilbrey, Kevin G. Yager, Anthony M. DeGennaro, Travis Johnston, and Ryan Chard
Subjects: Co-design, ComputerSystemsOrganization_COMPUTERSYSTEMIMPLEMENTATION, Active learning (machine learning), Statistical learning, Computer science, business.industry, Machine learning, computer.software_genre, Exascale computing, Theoretical Computer Science, Hardware and Architecture, Reinforcement learning, Center (algebra and category theory), Artificial intelligence, business, computer, Software
Abstract: Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.
Published: 2021

13. Spatio-Temporal Event Forecasting Using Incremental Multi-Source Feature Learning

Author: Yuyang Gao, Jieping Ye, Naren Ramakrishnan, Feng Chen, Liang Zhao, Yanfang Ye, and Chang-Tien Lu
Subjects: Hierarchy, General Computer Science, Computer science, business.industry, Feature selection, Machine learning, computer.software_genre, Missing data, Set (abstract data type), Feature (machine learning), Artificial intelligence, Online algorithm, business, computer, Feature learning, Multi-source
Abstract: The forecasting of significant societal events such as civil unrest and economic crisis is an interesting and challenging problem which requires both timeliness, precision, and comprehensiveness. Significant societal events are influenced and indicated jointly by multiple aspects of a society, including its economics, politics, and culture. Traditional forecasting methods based on a single data source find it hard to cover all these aspects comprehensively, thus limiting model performance. Multi-source event forecasting has proven promising but still suffers from several challenges, including (1) geographical hierarchies in multi-source data features, (2) hierarchical missing values, (3) characterization of structured feature sparsity, and (4) difficulty in model’s online update with incomplete multiple sources. This article proposes a novel feature learning model that concurrently addresses all the above challenges. Specifically, given multi-source data from different geographical levels, we design a new forecasting model by characterizing the lower-level features’ dependence on higher-level features. To handle the correlations amidst structured feature sets and deal with missing values among the coupled features, we propose a novel feature learning model based on an N th-order strong hierarchy and fused-overlapping group Lasso. An efficient algorithm is developed to optimize model parameters and ensure global optima. More importantly, to enable the model update in real time, the online learning algorithm is formulated and active set techniques are leveraged to resolve the crucial challenge when new patterns of missing features appear in real time. Extensive experiments on 10 datasets in different domains demonstrate the effectiveness and efficiency of the proposed models.
Published: 2021

14. An enhanced network intrusion detection system for malicious crawler detection and security event correlations in ubiquitous banking infrastructure

Author: Sadhana Selvakumar, Sivaraman Eswaran, D Daniel, Vakula Rani, and Jayabrabu Ramakrishnan
Subjects: Password, General Computer Science, Computer science, Event (computing), Bayesian network, Intrusion detection system, Computer security, computer.software_genre, Session (web analytics), Theoretical Computer Science, Identification (information), Web crawler, Cluster analysis, computer
Abstract: Purpose In the recent era, banking infrastructure constructs various remotely handled platforms for users. However, the security risk toward the banking sector has also elevated, as it is visible from the rising number of reported attacks against these security systems. Intelligence shows that cyberattacks of the crawlers are increasing. Malicious crawlers can crawl the Web pages, crack the passwords and reap the private data of the users. Besides, intrusion detection systems in a dynamic environment provide more false positives. The purpose of this research paper is to propose an efficient methodology to sense the attacks for creating low levels of false positives. Design/methodology/approach In this research, the authors have developed an efficient approach for malicious crawler detection and correlated the security alerts. The behavioral features of the crawlers are examined for the recognition of the malicious crawlers, and a novel methodology is proposed to improvise the bank user portal security. The authors have compared various machine learning strategies including Bayesian network, support sector machine (SVM) and decision tree. Findings This proposed work stretches in various aspects. Initially, the outcomes are stated for the mixture of different kinds of log files. Then, distinct sites of various log files are selected for the construction of the acceptable data sets. Session identification, attribute extraction, session labeling and classification were held. Moreover, this approach clustered the meta-alerts into higher level meta-alerts for fusing multistages of attacks and the various types of attacks. Originality/value This methodology used incremental clustering techniques and analyzed the probability of existing topologies in SVM classifiers for more deterministic classification. It also enhanced the taxonomy for various domains.
Published: 2021

15. Revisiting Network Telemetry in COIN: A Case for Runtime Programmability

Author: Reza Rejaie, Walter Willinger, Chris Misa, and Ramakrishnan Durairajan
Subjects: Computer Networks and Communications, Computer science, business.industry, Distributed computing, Cloud computing, Operation scheduling, Scheduling (computing), Software, Hardware and Architecture, Telemetry, Overhead (computing), Static data, business, Edge computing, Information Systems
Abstract: Applications based on the compute-in-the-network (COIN) paradigm require flexible network telemetry data to drive effective allocation decisions. Telemetry systems collect such data based on queries specifying the precise traffic metrics or features required. Recent advances in programmable switch hardware have led to highly efficient methods to compute query results using in-network resources. However, current approaches fail to meet the simultaneous requirements of dynamic traffic loads, diverse query types, and query dynamics. In this work, we argue that telemetry systems should be cast as active runtime schedulers rather than static data sources. COIN-based applications can then dynamically submit telemetry queries on the fly and react to their results in a closed-loop fashion – enabling a new generation of telemetry-driven reactive applications. As a first step toward this vision, we present a single-switch telemetry operation scheduling method. Our empirical evaluation demonstrates that such a method can reduce overhead by an order of magnitude compared to worst case allocations. This initial exploration opens the door to a multi-tier scheduling framework combining switch hardware with software-based compute platforms to meet the telemetry demands of future COIN-based applications.
Published: 2021

16. ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

Author: Abbas, Ali, Abdukahil, Sheryl Ann, Abdulkadir, Nurul Najmee, Abe, Ryuzo, Abel, Laurent, Absil, Lara, Jabal, Kamal Abu, Zayyad, Hiba Abu, Acharya, Subhash, Acker, Andrew, Adachi, Shingo, Adam, Elisabeth, Adriano, Enrico, Adrião, Diana, Ageel, Saleh Al, Ahmed, Shakeel, Aiello, Marina, Ainscough, Kate, Airlangga, Eka, Aisa, Tharwat, Hssain, Ali Ait, Tamlihat, Younes Ait, Akimoto, Takako, Akmal, Ernita, Qasim, Eman Al, Al-dabbous, Tala, Al-Fares, Abdulrahman, Alalqam, Razi, Alberti, Angela, Alegesan, Senthilkumar, Alegre, Cynthia, Alessi, Marta, Alex, Beatrice, Alexandre, Kévin, Alfoudri, Huda, Ali, Adam, Ali, Imran, Shah, Naseem Ali, Sheikh, Naseem Ali, Alidjnou, Kazali Enagnon, Aliudin, Jeffrey, Alkhafajee, Qabas, Allavena, Clotilde, Allou, Nathalie, Altaf, Aneela, Alves, João, Alves, João Melo, Alves, Rita, Amaral, Maria, Amira, Nur, Ammerlaan, Heidi, Ampaw, Phoebe, Andini, Roberto, Andrejak, Claire, Angheben, Andrea, Angoulvant, François, Ansart, Séverine, Anthonidass, Sivanesen, Antonelli, Massimo, de Brito, Carlos Alexandre Antunes, Anwar, Kazi Rubayet, Apriyana, Ardiyan, Arabi, Yaseen, Aragao, Irene, Arancibia, Francisco, Araujo, Carolline, Arcadipane, Antonio, Archambault, Patrick, Arenz, Lukas, Arlet, Jean-Benoît, Arnold-Day, Christel, Aroca, Ana, Arora, Lovkesh, Arora, Rakesh, Artaud-Macari, Elise, Aryal, Diptesh, Asaki, Motohiro, Asensio, Angel, Ashley, Elizabeth, Ashraf, Muhammad, Asif, Namra, Asim, Mohammad, Assie, Jean Baptiste, Asyraf, Amirul, Atique, Anika, Attanyake, A. M. Udara Lakshan, Auchabie, Johann, Aumaitre, Hugues, Auvet, Adrien, Axelsen, Eyvind W., Azemar, Laurène, Azoulay, Cecile, Bach, Benjamin, Bachelet, Delphine, Badr, Claudine, Bævre-Jensen, Roar, Baig, Nadia, Baillie, J. Kenneth, Baird, J Kevin, Bak, Erica, Bakakos, Agamemnon, Bakar, Nazreen Abu, Bal, Andriy, Balakrishnan, Mohanaprasanth, Balan, Valeria, Bani-Sadr, Firouzé, Barbalho, Renata, Barbosa, Nicholas Yuri, Barclay, Wendy S., Barnett, Saef Umar, Barnikel, Michaela, Barrasa, Helena, Barrelet, Audrey, Barrigoto, Cleide, Bartoli, Marie, Bartone, Cheryl, Baruch, Joaquín, Bashir, Mustehan, Basmaci, Romain, Basri, Muhammad Fadhli Hassin, Battaglini, Denise, Bauer, Jules, Rincon, Diego Fernando Bautista, Dow, Denisse Bazan, Beane, Abigail, Bedossa, Alexandra, Bee, Ker Hong, Begum, Husna, Behilill, Sylvie, Beiruti, Karine, Beishuizen, Albertus, Beljantsev, Aleksandr, Bellemare, David, Beltrame, Anna, Beltrão, Beatriz Amorim, Beluze, Marine, Benech, Nicolas, Benjiman, Lionel Eric, Benkerrou, Dehbia, Bennett, Suzanne, Bento, Luís, Berdal, Jan-Erik, Bergeaud, Delphine, Bergin, Hazel, Sobrino, José Luis Bernal, Bertoli, Giulia, Bertolino, Lorenzo, Bessis, Simon, Betz, Adam, Bevilcaqua, Sybille, Bezulier, Karine, Bhatt, Amar, Bhavsar, Krishna, Bianchi, Isabella, Bianco, Claudia, Bidin, Farah Nadiah, Singh, Moirangthem Bikram, Bin Humaid, Felwa, Bin Kamarudin, Mohd Nazlin, Bissuel, François, Biston, Patrick, Bitker, Laurent, Bitton, Jonathan, Blanco-Schweizer, Pablo, Blier, Catherine, Bloos, Frank, Blot, Mathieu, Blumberg, Lucille, Boccia, Filomena, Bodenes, Laetitia, Bogaarts, Alice, Bogaert, Debby, Boivin, Anne-Hélène, Bolze, Pierre-Adrien, Bompart, François, Bonelli, Patrizia, Bonfasius, Aurelius, Borges, Diogo, Borie, Raphaël, Bosse, Hans Martin, Botelho-Nevers, Elisabeth, Bouadma, Lila, Bouchaud, Olivier, Bouchez, Sabelline, Bouhmani, Dounia, Bouhour, Damien, Bouiller, Kévin, Bouillet, Laurence, Bouisse, Camile, Bounphiengsy, Thipsavanh, Bountthasavong, Latsaniphone, Boureau, Anne-Sophie, Bourke, John, Bouscambert, Maude, Bousquet, Aurore, Bouziotis, Jason, Boxma, Bianca, Boyer-Besseyre, Marielle, Boylan, Maria, Bozza, Fernando Augusto, Braconnier, Axelle, Braga, Cynthia, Brandenburger, Timo, Monteiro, Filipa Brás, Brazzi, Luca, Breen, Dorothy, Breen, Patrick, Brickell, Kathy, Browne, Alex, Browne, Shaunagh, Brozzi, Nicolas, Brunvoll, Sonja Hjellegjerde, Brusse-Keizer, Marjolein, Buchtele, Nina, Buesaquillo, Christian, Bugaeva, Polina, Buisson, Marielle, Buonsenso, Danilo, Burhan, Erlina, Burrell, Aidan, Bustos, Ingrid G., Butnaru, Denis, Cabie, André, Cabral, Susana, Caceres, Eder, Cadoz, Cyril, Callahan, Mia, Calligy, Kate, Calvache, Jose Andres, Caminiti, Caterina, Camões, João, Campana, Valentine, Campbell, Paul, Campisi, Josie, Canepa, Cecilia, Cantero, Mireia, Caraux-Paz, Pauline, Cárcel, Sheila, Cardellino, Chiara Simona, Cardoso, Filipa, Cardoso, Filipe, Cardoso, Nelson, Cardoso, Sofia, Carelli, Simone, Carlacci, Francesca, Carlier, Nicolas, Carmoi, Thierry, Carney, Gayle, Carqueja, Inês, Carret, Marie-Christine, Carrier, François Martin, Carroll, Ida, Carson, Gail, Carvalho, Leonor, Casanova, Maire-Laure, Cascão, Mariana, Casey, Siobhan, Casimiro, José, Cassandra, Bailey, Castañeda, Silvia, Castanheira, Nidyanara, Castor-Alexandre, Guylaine, Castrillón, Henry, Castro, Ivo, Catarino, Ana, Catherine, François-Xavier, Cattaneo, Paolo, Cavalin, Roberta, Cavalli, Giulio Giovanni, Cavayas, Alexandros, Ceccato, Adrian, Cervantes-Gonzalez, Minerva, Chair, Anissa, Chakveatze, Catherine, Chan, Adrienne, Chand, Meera, Auger, Christelle Chantalat, Chapplain, Jean-Marc, Chas, Julie, Chatterjee, Allegra, Chaudry, Mobin, Iñiguez, Jonathan Samuel Chávez, Chen, Anjellica, Chen, Yih-Sharng, Cheng, Matthew Pellan, Cheret, Antoine, Chetta, Alfredo Antonio, Chiarabini, Thibault, Chica, Julian, Chidambaram, Suresh Kumar, Tho, Leong Chin, Chirouze, Catherine, Chiumello, Davide, Cho, Hwa Jin, Cho, Sung-Min, Cholley, Bernard, Chommanam, Danoy, Chopin, Marie-Charlotte, Chow, Ting Soo, Chow, Yock Ping, Christy, Nathaniel, Chua, Hiu Jian, Chua, Jonathan, Cidade, Jose Pedro, Herreros, José Miguel Cisneros, Citarella, Barbara Wanjiru, Ciullo, Anna, Clarke, Emma, Clarke, Jennifer, Granado, Rolando Claure-Del, Clohisey, Sara, Cobb, Perren J., Codan, Cassidy, Cody, Caitriona, Coelho, Alexandra, Coles, Megan, Colin, Gwenhaël, Collins, Michael, Colombo, Sebastiano Maria, Combs, Pamela, Connor, Marie, Conrad, Anne, Contreras, Sofía, Conway, Elaine, Cooke, Graham S., Copland, Mary, Cordel, Hugues, Corley, Amanda, Cornelis, Sabine, Cornet, Alexander Daniel, Corpuz, Arianne Joy, Cortegiani, Andrea, Corvaisier, Grégory, Costigan, Emma, Couffignal, Camille, Couffin-Cadiergues, Sandrine, Courtois, Roxane, Cousse, Stéphanie, Cregan, Rachel, D’Orleans, Charles Crepy, Croonen, Sabine, Crowl, Gloria, Crump, Jonathan, Cruz, Claudina, Berm, Juan Luis Cruz, Rojo, Jaime Cruz, Csete, Marc, Cucino, Alberto, Cullen, Ailbhe, Cummings, Matthew, Curley, Ger, Curley, Gerard, Curlier, Elodie, Curran, Colleen, Custodio, Paula, D’Amico, Federico, D’Aragon, Frédérick, D’Ortenzio, Eric, da Silva Filipe, Ana, Da Silveira, Charlene, Dabaliz, Al-Awwab, Dagens, Andrew, Dahl, John Arne, Dahly, Darren, Dalton, Heidi, Dalton, Jo, Daly, Seamus, Daneman, Nick, Daniel, Corinne, Dankwa, Emmanuelle A, Dantas, Jorge, de Boer, Mark, de Loughry, Gillian, de Mendoza, Diego, De Montmollin, Etienne, de Oliveira França, Rafael Freitas, Oliveira, Ana Isabel de Pinho, De Rosa, Rosanna, De Rose, Cristina, de Silva, Thushan, de Vries, Peter, Deacon, Jillian, Dean, David, Debard, Alexa, DeBenedictis, Bianca, Debray, Marie-Pierre, DeCastro, Nathalie, Dechert, William, Deconninck, Lauren, Decours, Romain, Defous, Eve, Delacroix, Isabelle, Delaveuve, Eric, Delavigne, Karen, Delfos, Nathalie M., Deligiannis, Ionna, Dell’Amore, Andrea, Delmas, Christelle, Delobel, Pierre, Delsing, Corine, Demonchy, Elisa, Denis, Emmanuelle, Deplanque, Dominique, Depuydt, Pieter, Desai, Mehul, Descamps, Diane, Desvallées, Mathilde, Dewayanti, Santi, Dhanger, Pathik, Diallo, Alpha, Diamantis, Sylvain, Dias, André, Dias, Andrea, Diaz, Juan Jose, Diaz, Priscila, Diaz, Rodrigo, Didier, Kévin, Diehl, Jean-Luc, Dieperink, Wim, Dimet, Jérôme, Dinot, Vincent, Diop, Fara, Diouf, Alphonsine, Dishon, Yael, Djossou, Félix, Docherty, Annemarie B., Doherty, Helen, Dondorp, Arjen M, Dong, Andy, Donnelly, Christl A., Donnelly, Maria, Donohue, Chloe, Donohue, Sean, Donohue, Yoann, Doran, Peter, Dorival, Céline, Douangdala, Phouvieng, Douglas, James Joshua, Douma, Renee, Dournon, Nathalie, Downer, Triona, Downey, Joanne, Downing, Mark, Drake, Tom, Driscoll, Aoife, Dror, Amiel A., Dryden, Murray, Fonseca, Claudio Duarte, Dubee, Vincent, Dubos, François, Dubot-Pérès, Audrey, Ducancelle, Alexandre, Duculan, Toni, Dudman, Susanne, Duggal, Abhijit, Dunand, Paul, Dunning, Jake, Duplaix, Mathilde, Durante-Mangoni, Emanuele, Durham III, Lucian, Dussol, Bertrand, Duthoit, Juliette, Duval, Xavier, Dyrhol-Riise, Anne Margarita, Ean, Sim Choon, Echeverria-Villalobos, Marco, Economopoulos, Giorgio, Edelstein, Michael, Egan, Siobhan, Eggesbø, Linn Margrete, Eira, Carla, El Sanharawi, Mohammed, Elapavaluru, Subbarao, Elharrar, Brigitte, Ellerbroek, Jacobien, Ellingjord-Dale, Merete, Eloy, Philippine, Elshazly, Tarek, Elyazar, Iqbal, Enderle, Isabelle, Endo, Tomoyuki, Eng, Chan Chee, Engelmann, Ilka, Enouf, Vincent, Epaulard, Olivier, Escher, Martina, Esperatti, Mariano, Esperou, Hélène, Esposito-Farese, Marina, Estevão, João, Etienne, Manuel, Ettalhaoui, Nadia, Everding, Anna Greti, Evers, Mirjam, Fabre, Isabelle, Fabre, Marc, Faheem, Amna, Fahy, Arabella, Fairfield, Cameron J., Fakar, Zul, Fareed, Komal, Faria, Pedro, Farooq, Ahmed, Fateena, Hanan, Fatoni, Arie Zainul, Faure, Karine, Favory, Raphaël, Fayed, Mohamed, Feely, Niamh, Feeney, Laura, Fernandes, Jorge, Fernandes, Marília Andreia, Fernandes, Susana, Ferrand, François-Xavier, Devouge, Eglantine Ferrand, Ferrão, Joana, Ferrari, Carlo, Ferraz, Mário, Ferreira, Benigno, Ferreira, Isabel, Ferreira, Sílvia, Ferrer-Roca, Ricard, Ferriere, Nicolas, Ficko, Céline, Figueiredo-Mello, Claudia, Finlayson, William, Fiorda, Juan, Flament, Thomas, Flateau, Clara, Fletcher, Tom, Florio, Letizia Lucia, Flynn, Brigid, Flynn, Deirdre, Fogliazza, Federica, Foley, Claire, Foley, Jean, Fomin, Victor, Fonseca, Tatiana, Fontela, Patricia, Forsyth, Simon, Foster, Denise, Foti, Giuseppe, Fourn, Erwan, Fowler, Robert A., Fraher, Marianne, Franch-Llasat, Diego, Fraser, Christophe, Fraser, John F, Freire, Marcela Vieira, Ribeiro, Ana Freitas, French, Craig, Friedrich, Caren, Fritz, Ricardo, Fry, Stéphanie, Fuentes, Nora, Fukuda, Masahiro, Argin, G, Gaborieau, Valérie, Gaci, Rostane, Gagliardi, Massimo, Gagnard, Jean-Charles, Gagné, Nathalie, Gagneux-Brunon, Amandine, Gaião, Sérgio, Skeie, Linda Gail, Gallagher, Phil, Curto, Elena Gallego, Gamble, Carrol, Gani, Yasmin, Garan, Arthur, Garcia, Rebekha, Barrio, Noelia García, Garcia-Diaz, Julia, Garcia-Gallo, Esteban, Garimella, Navya, Garofalo, Federica, Garot, Denis, Garrait, Valérie, Gauli, Basanta, Gault, Nathalie, Gavin, Aisling, Gavrylov, Anatoliy, Gaymard, Alexandre, Gebauer, Johannes, Geraud, Eva, Morlaes, Louis Gerbaud, Germano, Nuno, Ghisulal, Praveen Kumar, Ghosn, Jade, Giani, Marco, Giaquinto, Carlo, Gibson, Jess, Gigante, Tristan, Gilg, Morgane, Gilroy, Elaine, Giordano, Guillermo, Girvan, Michelle, Gissot, Valérie, Gitaka, Jesse, Giwangkancana, Gezy, Glikman, Daniel, Glybochko, Petr, Gnall, Eric, Goco, Geraldine, Goehringer, François, Goepel, Siri, Goffard, Jean-Christophe, Goh, Jin Yi, Golob, Jonathan, Gomes, Rui, Gomez, Kyle, Gómez-Junyent, Joan, Gominet, Marie, Gonçalves, Bronner P., Gonzalez, Alicia, Gordon, Patricia, Gorelik, Yanay, Gorenne, Isabelle, Goubert, Laure, Goujard, Cécile, Goulenok, Tiphaine, Grable, Margarite, Graf, Jeronimo, Grandin, Edward Wilson, Granier, Pascal, Grasselli, Giacomo, Grazioli, Lorenzo, Green, Christopher A., Greene, Courtney, Greenhalf, William, Greffe, Segolène, Grieco, Domenico Luca, Griffee, Matthew, Griffiths, Fiona, Grigoras, Ioana, Groenendijk, Albert, Lordemann, Anja Grosse, Gruner, Heidi, Gu, Yusing, Guarracino, Fabio, Guedj, Jérémie, Guego, Martin, Guellec, Dewi, Guerguerian, Anne-Marie, Guerreiro, Daniela, Guery, Romain, Guillaumot, Anne, Guilleminault, Laurent, de Castro, Maisa Guimarães, Guimard, Thomas, Haalboom, Marieke, Haber, Daniel, Habraken, Hannah, Hachemi, Ali, Hackmann, Amy, Hadri, Nadir, Haidri, Fakhir, Hakak, Sheeba, Hall, Adam, Hall, Matthew, Halpin, Sophie, Hameed, Jawad, Hamer, Ansley, Hamers, Raph L., Hamidfar, Rebecca, Hammarström, Bato, Hammond, Terese, Han, Lim Yuen, Haniffa, Rashan, Hao, Kok Wei, Hardwick, Hayley, Harrison, Ewen M., Harrison, Janet, Harrison, Samuel Bernard Ekow, Hartman, Alan, Hasan, Mohd Shahnaz, Hashmi, Junaid, Hashmi, Madiha, Hayes, Ailbhe, Hays, Leanne, Heerman, Jan, Heggelund, Lars, Hendry, Ross, Hennessy, Martina, Henriquez-Trujillo, Aquiles, Hentzien, Maxime, Hernandez-Montfort, Jaime, Herr, Daniel, Hershey, Andrew, Hesstvedt, Liv, Hidayah, Astarini, Higgins, Dawn, Higgins, Eibhilin, Hinchion, Rita, Hinton, Samuel, Hiraiwa, Hiroaki, Hirkani, Haider, Hitoto, Hikombo, Ho, Antonia, Ho, Yi Bin, Hoctin, Alexandre, Hoffmann, Isabelle, Hoh, Wei Han, Hoiting, Oscar, Holt, Rebecca, Holter, Jan Cato, Horby, Peter, Horcajada, Juan Pablo, Hoshino, Koji, Hoshino, Kota, Houas, Ikram, Hough, Catherine L., Houltham, Stuart, Hsu, Jimmy Ming-Yang, Hulot, Jean-Sébastien, Huo, Stella, Hussain, Iqbal, Ijaz, Samreen, Illes, Hajnal-Gabriela, Imbert, Patrick, Imran, Mohammad, Sikander, Rana Imran, Imtiaz, Aftab, Inácio, Hugo, Dominguez, Carmen Infante, Ing, Yun Sii, Iosifidis, Elias, Ippolito, Mariachiara, Isgett, Sarah, Isidoro, Tiago, Ismail, Nadiah, Isnard, Margaux, Istre, Mette Stausland, Itai, Junji, Ito, Asami, Ivulich, Daniel, Jaafar, Danielle, Jaafoura, Salma, Jabot, Julien, Jackson, Clare, Jamieson, Nina, Jaquet, Pierre, Jassat, Waasila, Jaud-Fischer, Coline, Jaureguiberry, Stéphane, Javidfar, Jeffrey, Jaworsky, Denise, Jego, Florence, Jelani, Anilawati Mat, Jenum, Synne, Jimbo-Sotomayor, Ruth, Joe, Ong Yiaw, García, Ruth N. Jorge, Jørgensen, Silje Bakken, Joseph, Cédric, Joseph, Mark, Joshi, Swosti, Jourdain, Mercé, Jouvet, Philippe, June, Jennifer, Jung, Anna, Jung, Hanna, Juzar, Dafsah, Kafif, Ouifiya, Kaguelidou, Florentia, Kaisbain, Neerusha, Kaleesvran, Thavamany, Kali, Sabina, Kalicinska, Alina, Kalleberg, Karl Trygve, Kalomoiri, Smaragdi, Kamaluddin, Muhammad Aisar Ayadi, Kamaruddin, Zul Amali Che, Kamarudin, Nadiah, Kamineni, Kavita, Kandamby, Darshana Hewa, Kandel, Chris, Kang, Kong Yeow, Kanwal, Darakhshan, Kanyawati, Dyah, Karpayah, Pratap, Karsies, Todd, Kartsonaki, Christiana, Kasugai, Daisuke, Kataria, Anant, Katz, Kevin, Kaur, Aasmine, Kawasaki, Tatsuya, Kay, Christy, Keane, Hannah, Keating, Seán, Kedia, Pulak, Kelly, Andrea, Kelly, Aoife, Kelly, Claire, Kelly, Niamh, Kelly, Sadie, Kelly, Yvelynne, Kelsey, Maeve, Kennedy, Ryan, Kennon, Kalynn, Keomany, Sommay, Kernan, Maeve, Kerroumi, Younes, Keshav, Sharma, Kestelyn, Evelyne, Khalid, Imrana, Khalid, Osama, Khalil, Antoine, Khan, Coralie, Khan, Irfan, Khan, Quratul Ain, Khanal, Sushil, Khatak, Abid, Khawaja, Amin, Kherajani, Krish, Kho, Michelle E., Khoo, Denisa, Khoo, Ryan, Khoo, Saye, Khoso, Nasir, Kiat, Khor How, Kida, Yuri, Kihuga, Harrison, Kiiza, Peter, Granerud, Beathe Kiland, Kildal, Anders Benjamin, Kim, Jae Burm, Kimmoun, Antoine, Kindgen-Milles, Detlef, King, Alexander, Kitamura, Nobuya, Kjetland, Eyrun Floerecke Kjetland, Klenerman, Paul, Klont, Rob, Bekken, Gry Kloumann, Knight, Stephen R, Kobbe, Robin, Kodippily, Chamira, Vasconcelos, Malte Kohns, Koirala, Sabin, Komatsu, Mamoru, Korten, Volkan, Kosgei, Caroline, Kpangon, Arsène, Krawczyk, Karolina, Krishnan, Sudhir, Krishnan, Vinothini, Kruglova, Oksana, Kumar, Deepali, Kumar, Ganesh, Kumar, Mukesh, Vecham, Pavan Kumar, Kuriakose, Dinesh, Kurtzman, Ethan, Kusumastuti, Neurinda Permata, Kutsogiannis, Demetrios, Kutsyna, Galyna, Kyriakoulis, Konstantinos, L’Her, Erwan, Lachatre, Marie, Lacoste, Marie, Laffey, John G., Lagrange, Marie, Laine, Fabrice, Lairez, Olivier, Lakhey, Sanjay, Lalueza, Antonio, Lambert, Marc, Lamontagne, François, Langelot-Richard, Marie, Langlois, Vincent, Lantang, Eka Yudha, Lanza, Marina, Laouénan, Cédric, Laribi, Samira, Lariviere, Delphine, Lasry, Stéphane, Lath, Sakshi, Latif, Naveed, Launay, Odile, Laureillard, Didier, Lavie-Badie, Yoan, Law, Andrew, Law, Andy, Lawrence, Cassie, Lawrence, Teresa, Le, Minh, Le Bihan, Clément, Le Bris, Cyril, Le Falher, Georges, Le Fevre, Lucie, Le Hingrat, Quentin, Le Maréchal, Marion, Le Mestre, Soizic, Le Moal, Gwenaël, Le Moing, Vincent, Le Nagard, Hervé, Le Turnier, Paul, Leal, Ema, Santos, Marta Leal, Lee, Biing Horng, Lee, Heng Gee, Lee, James, Lee, Su Hwan, Lee, Todd C., Lee, Yi Lin, Leeming, Gary, Lefebvre, Bénédicte, Lefebvre, Laurent, Lefevre, Benjamin, LeGac, Sylvie, Lelievre, Jean-Daniel, Lellouche, François, Lemaignen, Adrien, Lemee, Véronique, Lemeur, Anthony, Lemmink, Gretchen, Lene, Ha Sha, Lennon, Jenny, León, Rafael, Leone, Marc, Leone, Michela, Lepiller, Quentin, Lescure, François-Xavier, Lesens, Olivier, Lesouhaitier, Mathieu, Lester-Grant, Amy, Letizia, Andrew, Levy, Bruno, Levy, Yves, Levy-Marchal, Claire, Lewandowska, Katarzyna, Bassi, Gianluigi Li, Liang, Janet, Liaquat, Ali, Liegeon, Geoffrey, Lim, Kah Chuan, Lim, Wei Shen, Lima, Chantre, Lina, Bruno, Lina, Lim, Lind, Andreas, Lingad, Maja Katherine, Lingas, Guillaume, Lion-Daolio, Sylvie, Lissauer, Samantha, Liu, Keibun, Livrozet, Marine, Lizotte, Patricia, Loforte, Antonio, Lolong, Navy, Loon, Leong Chee, Lopes, Diogo, Lopez-Colon, Dalia, Loschner, Anthony L., Loubet, Paul, Loufti, Bouchra, Louis, Guillame, Lourenco, Silvia, Lovelace-Macon, Lara, Low, Lee Lee, Lowik, Marije, Loy, Jia Shyi, Lucet, Jean Christophe, Bermejo, Carlos Lumbreras, Luna, Carlos M., Lungu, Olguta, Luong, Liem, Luque, Nestor, Luton, Dominique, Lwin, Nilar, Lyons, Ruth, Maasikas, Olavi, Mabiala, Oryane, MacDonald, Sarah, Machado, Moïse, Macheda, Gabriel, Sanchez, Juan Macias, Madhok, Jai, Madiha, Hashmi, de la Calle, Guillermo Maestro, Magara, Jacob, Maglietta, Giuseppe, Mahieu, Rafael, Mahy, Sophie, Maia, Ana Raquel, Maier, Lars S., Maillet, Mylène, Maitre, Thomas, Majori, Maria, Malfertheiner, Maximilian, Malik, Nadia, Mallon, Paddy, Maltez, Fernando, Malvy, Denis, Mammi, Patrizia, Manda, Victoria, Mandei, Jose M., Mandelbrot, Laurent, Manetta, Frank, Mankikian, Julie, Manning, Edmund, Manuel, Aldric, Malaque, Ceila Maria Sant’Ana, Marino, Daniel, Marino, Flávio, Markowicz, Samuel, Eid, Charbel Maroun, Marques, Ana, Marquis, Catherine, Marsh, Brian, Marsh, Laura, Marshal, Megan, Marshall, John, Martelli, Celina Turchi, Martin, Dori-Ann, Martin, Emily, Martin-Blondel, Guillaume, Martin-Loeches, Ignacio, Martin-Quiros, Alejandro, Martinelli, Alessandra, Martinot, Martin, Martins, Ana, Martins, João, Martins, Nuno, Rego, Caroline Martins, Martucci, Gennaro, Martynenko, Olga, Marwali, Eva Miranda, Marzukie, Marsilla, Jimenez, Juan Fernado Masa, Maslove, David, Mason, Phillip, Mason, Sabina, Masood, Sobia, Nor, Basri Mat, Matan, Moshe, Fernandes, Henrique Mateus, Mathew, Meghena, Mathieu, Daniel, Mattei, Mathieu, Matulevics, Romans, Maulin, Laurence, Maxwell, Michael, Maynar, Javier, Mayxay, Mayfong, Mazzoni, Thierry, Mc Evoy, Natalie, Mc Sweeney, Lisa, McArthur, Colin, McCarthy, Aine, McCarthy, Anne, McCloskey, Colin, McConnochie, Rachael, McDermott, Sherry, McDonald, Sarah E., McElroy, Aine, McElwee, Samuel, McEneany, Victoria, McGeer, Allison, McKay, Chris, McKeown, Johnny, McLean, Kenneth A., McNally, Paul, McNicholas, Bairbre, McPartlan, Elaine, Meaney, Edel, Mear-Passard, Cécile, Mechlin, Maggie, Meher, Maqsood, Mehkri, Omar, Mele, Ferruccio, Melo, Luis, Memon, Kashif, Mendes, Joao, Menkiti, Ogechukwu, Menon, Kusum, Mentré, France, Mentzer, Alexander J., Mercier, Emmanuelle, Mercier, Noémie, Merckx, Antoine, Mergeay-Fabre, Mayka, Mergler, Blake, Merson, Laura, Meschi, Tiziana, Mesquita, António, Meta, Roberta, Metwally, Osama, Meybeck, Agnès, Meyer, Dan, Meynert, Alison M., Meysonnier, Vanina, Meziane, Amina, Mezidi, Mehdi, Michelagnoli, Giuliano, Michelanglei, Céline, Michelet, Isabelle, Mihelis, Efstathia, Mihnovit, Vladislav, Miranda-Maldonado, Hugo, Misnan, Nor Arisah, Mohamed, Nik Nur Eliza, Mohamed, Tahira Jamal, Moin, Asma, Molina, David, Molinos, Elena, Molloy, Brenda, Mone, Mary, Monteiro, Agostinho, Montes, Claudia, Montrucchio, Giorgia, Moore, Sarah, Moore, Shona C., Cely, Lina Morales, Moro, Lucia, Tutillo, Diego Rolando Morocho, Morton, Ben, Motherway, Catherine, Motos, Ana, Mouquet, Hugo, Perrot, Clara Mouton, Moyet, Julien, Mudara, Caroline, Mufti, Aisha Kalsoom, Muh, Ng Yong, Muhamad, Dzawani, Mullaert, Jimmy, Müller, Fredrik, Müller, Karl Erik, Munblit, Daniel, Muneeb, Syed, Munir, Nadeem, Munshi, Laveena, Murphy, Aisling, Murphy, Lorna, Murris, Marlène, Murthy, Srinivas, Musaab, Himed, Mutti, Carlotta, Muvindi, Himasha, Muyandy, Gugapriyaa, Myrodia, Dimitra Melia, Mohd-Hanafiah, Farah Nadia, Nagpal, Dave, Narasimhan, Mangala, Narayanan, Nageswaran, Khan, Rashid Nasim, Nazerali-Maitland, Alasdair, Neant, Nadège, Neb, Holger, Necsoi, Coca, Nekliudov, Nikita A., Nelwan, Erni, Neto, Raul, Neumann, Emily, Neves, Bernardo, Ng, Pauline Yeung, Ng, Wing Yiu, Nghi, Anthony, Ngure, Jane, Nguyen, Duc, Choileain, Orna Ni, Leathlobhair, Niamh Ni, Nichol, Alistair, Nitayavardhana, Prompak, Nonas, Stephanie, Noordin, Nurul Amani Mohd, Noret, Marion, Norharizam, Nurul Faten Izzati, Norman, Lisa, Notari, Alessandra, Noursadeghi, Mahdad, Nowicka, Karolina, Nowinski, Adam, Nseir, Saad, Nunez, Jose I., Nurnaningsih, Nurnaningsih, Nusantara, Dwi Utomo, Nyamankolly, Elsa, Nygaard, Anders Benteson, Brien, Fionnuala O., Callaghan, Annmarie O., O’Callaghan, Annmarie, O’Donnell, Max, O’Halloran, Sophie, O’Hearn, Katie, O’Neil, Conar, O’Shea, Linda, O’Sullivan, Miriam, Occhipinti, Giovanna, OConnor, Derbrenn, Ogston, Tawnya, Ogura, Takayuki, Oh, Tak-Hyuk, Ohshimo, Shinichiro, Oldakowska, Agnieszka, Oliveira, João, Oliveira, Larissa, Olliaro, Piero L., Ong, David S. Y., Ong, Jee Yan, Oosthuyzen, Wilna, Opavsky, Anne, Openshaw, Peter, Orakzai, Saijad, Orozco-Chamorro, Claudia Milena, Orquera, Andrés, Ortoleva, Jamel, Osatnik, Javier, Othman, Siti Zubaidah, Otiku, Paul, Ouamara, Nadia, Ouissa, Rachida, Owyang, Clark, Oziol, Eric, Pagadoy, Maïder, Pages, Justine, Palacios, Amanda, Palacios, Mario, Palmarini, Massimo, Panarello, Giovanna, Panda, Prasan Kumar, Paneru, Hem, Pang, Lai Hui, Panigada, Mauro, Pansu, Nathalie, Papadopoulos, Aurélie, Parducci, Paolo, Oña, Edwin Fernando Paredes, Parke, Rachael, Parker, Melissa, Parra, Briseida, Parrini, Vieri, Pasha, Taha, Pasquier, Jérémie, Pastene, Bruno, Patauner, Fabian, Patel, Drashti, Pathmanathan, Mohan Dass, Patrão, Luís, Patricio, Patricia, Patrier, Juliette, Patrizi, Laura, Patterson, Lisa, Pattnaik, Rajyabardhan, Paul, Christelle, Paul, Mical, Paulos, Jorge, Paxton, William A., Payen, Jean-François, Peariasamy, Kalaiarasu, Jiménez, Miguel Pedrera, Peek, Giles J., Peelman, Florent, Peiffer-Smadja, Nathan, Peigne, Vincent, Pejkovska, Mare, Pelosi, Paolo, Peltan, Ithan D., Pereira, Rui, Perez, Daniel, Periel, Luis, Perpoint, Thomas, Pesenti, Antonio, Pestre, Vincent, Petrou, Lenka, Petrov-Sanchez, Ventzislava, Petrovic, Michele, Pettersen, Frank Olav, Peytavin, Gilles, Pharand, Scott, Phonemixay, Ooyanong, Piagnerelli, Michael, Picard, Walter, Picone, Olivier, de Piero, Maria, Pierobon, Carola, Piersma, Djura, Pimentel, Carlos, Pinto, Raquel, Pires, Catarina, Pironneau, Isabelle, Piroth, Lionel, Pisi, Roberta, Pitaloka, Ayodhia, Pius, Riinu, Piva, Simone, Plantier, Laurent, Png, Hon Shen, Poissy, Julien, Pokeerbux, Ryadh, Pokorska-Spiewak, Maria, Poli, Sergio, Pollakis, Georgios, Ponscarme, Diane, Popielska, Jolanta, Porto, Diego Bastos, Post, Andra-Maris, Postma, Douwe F., Povoa, Pedro, Póvoas, Diana, Powis, Jeff, Prapa, Sofia, Praphasiri, Viladeth, Preau, Sébastien, Prebensen, Christian, Preiser, Jean-Charles, Prinssen, Anton, Pritchard, Mark G., Priyadarshani, Gamage Dona 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Eoin, Wham, Murray, Whelan, Bryan, White, Nicole, Wicky, Paul Henri, Wiedemann, Aurélie, Wijaya, Surya Otto, Wille, Keith, Willems, Suzette, Williams, Virginie, Wils, Evert-Jan, Wong, Calvin, Wong, Teck Fung, Wong, Xin Ci, Wong, Yew Sing, Wright, Natalie, Xian, Gan Ee, Xian, Lim Saio, Xuan, Kuan Pei, Xynogalas, Ioannis, Yacoub, Sophie, Yakop, Siti Rohani Binti Mohd, Yamazaki, Masaki, Yazdanpanah, Yazdan, Hing, Nicholas Yee Liang, Yelnik, Cécile, Yeoh, Chian Hui, Yerkovich, Stephanie, Yiaye, Touxiong, Yokoyama, Toshiki, Yonis, Hodane, Yousif, Obada, Yuliarto, Saptadi, Zaaqoq, Akram, Zabbe, Marion, Zacharowski, Kai, Zahid, Masliza, Zahran, Maram, Zaidan, Nor Zaila Binti, Zambon, Maria, Zambrano, Miguel, Zanella, Alberto, Zawadka, Konrad, Zaynah, Nurul, Zayyad, Hiba, Zoufaly, Alexander, Zucman, David, Fryer, Daniel Vidali, Duque, Sara, Fuentes, Yuli V., Baruch, Joaquin, Beane, Abi, Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), CHU Pontchaillou [Rennes], ARN régulateurs bactériens et médecine (BRM), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Génétique Moléculaire des Virus à ARN - Molecular Genetics of RNA Viruses (GMV-ARN (UMR_3569 / U-Pasteur_2)), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Centre National de Référence des virus des infections respiratoires (dont la grippe) - National Reference Center Virus Influenzae [Paris] (CNR - laboratoire coordonnateur), Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), University of La Sabana = Universitad de la Sabana, University of Queensland [Brisbane], University of British Columbia (UBC), University of Oxford, The investigators acknowledge: This work is part of the Grand Challenges ICODA pilot initiative, delivered by Health Data Research UK and funded by the Bill & Melinda Gates Foundation and the Minderoo Foundation. The philanthropic support of the donors to the University of Oxford’s COVID-19 Research Response Fund, UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z and 220757/Z/20/Z], the Bill & Melinda Gates Foundation [OPP1209135], the National Institute for Health Research (NIHR, award CO-CIN-01), the Medical Research Council (MRC, grant MC_PC_19059), the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE)(award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927), Liverpool Experimental Cancer Medicine Centre (grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013), NIHR Clinical Research Network (infrastructure support), CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and was coordinated out of Sunnybrook Research Institute, the endorsement of the Irish Critical Care- Clinical Trials Group, co-ordinated in Ireland by the Irish Critical Care- Clinical Trials Network at University College Dublin and funded by the Health Research Board of Ireland [CTN-2014-12], Rapid European COVID-19 Emergency Response research (RECOVER) [H2020 project 101003589], European Clinical Research Alliance on Infectious Diseases (ECRAID) [965313], COVID clinical management team, AIIMS, Rishikesh, India, Cambridge NIHR Biomedical Research Centre, the dedication and hard work of the Groote Schuur Hospital Covid ICU Team, the Groote Schuur nursing and University of Cape Town registrar bodies coordinated by the Division of Critical Care at the University of Cape Town, Wellcome Trust fellowship [205228/Z/16/Z], the Liverpool School of Tropical Medicine, the University of Oxford, the dedication and hard work of the Norwegian SARS-CoV-2 study team, the Research Council of Norway grant no 312780, a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner, Innovative Medicines Initiative Joint Undertaking under Grant Agreement No. 115523 COMBACTE, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies, in-kind contribution, preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection, Stiftungsfonds zur Förderung der Bekämpfung der Tuberkulose und anderer Lungenkrankheiten of the City of Vienna, Project Number: APCOV22BGM, Italian Ministry of Health 'Fondi Ricerca corrente–L1P6' to IRCCS Ospedale Sacro Cuore–Don Calabria, Australian Department of Health grant (3273191), Gender Equity Strategic Fund at University of Queensland, Artificial Intelligence for Pandemics (A14PAN) at University of Queensland, the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS, CE170100009), the Prince Charles Hospital Foundation, Australia, UK Medical Research Council Clinical Research Training Fellowship MR/V001671/1, Instituto de Salud Carlos III, Ministerio de Ciencia, Spain, Brazil, National Council for Scientific and Technological Development Scholarship number 303953/2018-7, Firland Foundation, Shoreline, Washington, USA, the French COVID cohort (NCT04262921) is sponsored by INSERM and is funding by the REACTing (REsearch & ACtion emergING infectious diseases) consortium and by a grant of the French Ministry of Health (PHRC n°20-0424). This work uses Data/Material provided by patients and collected by the NHS as part of their care and support #DataSavesLives. The Data/materials used for this research were obtained from ISARIC4C. ISARIC4C Investigators collated the COVID-19 Clinical Information Network (CO-CIN) data., European Project: 101003589, H2020-SC1-PHE-CORONAVIRUS-2020,RECOVER(2020), Bill & Melinda Gates Foundation, Minderoo Foundation, Commonwealth Foundation, Wellcome Trust, National Institute for Health Research (UK), Medical Research Council (UK), University of Liverpool, Imperial College London, Canadian Institutes of Health Research, Sunnybrook Research Institute, Health Research Board (Ireland), European Commission, All India Institute of Medical Science, University of Cape Town, Research Council of Norway, Innovative Medicines Initiative, Governo Italiano, University of Queensland, Australian Research Council, Prince Charles Hospital Foundation, Instituto de Salud Carlos III, Institut National de la Santé et de la Recherche Médicale (France), Conselho Nacional de Desenvolvimento Científico e Tecnológico (Brasil), Garcia-Gallo, Esteban, Merson, Laura, Kennon, Kalynn, Kelly, Sadie, Citarella, Barbara Wanjiru, Fryer, Daniel Vidali, Shrapnel, Sally, Lee, Jame, Duque, Sara, Fuentes, Yuli V, Balan, Valeria, Smith, Sue, Wei, Jia, Gonçalves, Bronner P, Russell, Clark D, Sigfrid, Louise, Dagens, Andrew, Olliaro, Piero L, Baruch, Joaquin, Kartsonaki, Christiana, Dunning, Jake, Rojek, Amanda, Rashan, Aasiyah, Beane, Abi, Murthy, Sriniva, Reyes, Luis Felipe, ISARIC Clinical Characterization Group: Ali Abbas, Sheryl Ann Abdukahil, Nurul Najmee Abdulkadir, Ryuzo Abe, Laurent Abel, Lara Absil, Kamal Abu Jabal, Hiba Abu Zayyad, Subhash Acharya, Andrew Acker, Shingo Adachi, Elisabeth Adam, Enrico Adriano, Diana Adrião, Saleh Al Ageel, Shakeel Ahmed, Marina Aiello, Kate Ainscough, Eka Airlangga, Tharwat Aisa, Ali Ait Hssain, Younes Ait Tamlihat, Takako Akimoto, Ernita Akmal, Eman Al Qasim, Tala Al-Dabbous, Abdulrahman Al-Fares, Razi Alalqam, Angela Alberti, Senthilkumar Alegesan, Cynthia Alegre, Marta Alessi, Beatrice Alex, Kévin Alexandre, Huda Alfoudri, Adam Ali, Imran Ali, Naseem Ali Shah, Naseem Ali Sheikh, Kazali Enagnon Alidjnou, Jeffrey Aliudin, Qabas Alkhafajee, Clotilde Allavena, Nathalie Allou, Aneela Altaf, João Alves, João Melo Alves, Rita Alves, Maria Amaral, Nur Amira, Heidi Ammerlaan, Phoebe Ampaw, Roberto Andini, Claire Andrejak, Andrea Angheben, François Angoulvant, Séverine Ansart, Sivanesen Anthonidass, Massimo Antonelli, Carlos Alexandre Antunes de Brito, Kazi Rubayet Anwar, Ardiyan Apriyana, Yaseen Arabi, Irene Aragao, Francisco Arancibia, Carolline Araujo, Antonio Arcadipane, Patrick Archambault, Lukas Arenz, Jean-Benoît Arlet, Christel Arnold-Day, Ana Aroca, Lovkesh Arora, Rakesh Arora, Elise Artaud-Macari, Diptesh Aryal, Motohiro Asaki, Angel Asensio, Elizabeth Ashley, Muhammad Ashraf, Namra Asif, Mohammad Asim, Jean Baptiste Assie, Amirul Asyraf, Anika Atique, A M Udara Lakshan Attanyake, Johann Auchabie, Hugues Aumaitre, Adrien Auvet, Eyvind W Axelsen, Laurène Azemar, Cecile Azoulay, Benjamin Bach, Delphine Bachelet, Claudine Badr, Roar Bævre-Jensen, Nadia Baig, J Kenneth Baillie, J Kevin Baird, Erica Bak, Agamemnon Bakakos, Nazreen Abu Bakar, Andriy Bal, Mohanaprasanth Balakrishnan, Valeria Balan, Firouzé Bani-Sadr, Renata Barbalho, Nicholas Yuri Barbosa, Wendy S Barclay, Saef Umar Barnett, Michaela Barnikel, Helena Barrasa, Audrey Barrelet, Cleide Barrigoto, Marie Bartoli, Cheryl Bartone, Joaquín Baruch, Mustehan Bashir, Romain Basmaci, Muhammad Fadhli Hassin Basri, Denise Battaglini, Jules Bauer, Diego Fernando Bautista Rincon, Denisse Bazan Dow, Abigail Beane, Alexandra Bedossa, Ker Hong Bee, Husna Begum, Sylvie Behilill, Karine Beiruti, Albertus Beishuizen, Aleksandr Beljantsev, David Bellemare, Anna Beltrame, Beatriz Amorim Beltrão, Marine Beluze, Nicolas Benech, Lionel Eric Benjiman, Dehbia Benkerrou, Suzanne Bennett, Luís Bento, Jan-Erik Berdal, Delphine Bergeaud, Hazel Bergin, José Luis Bernal Sobrino, Giulia Bertoli, Lorenzo Bertolino, Simon Bessis, Adam Betz, Sybille Bevilcaqua, Karine Bezulier, Amar Bhatt, 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Shankar, Shaikh Sharjeel, Pratima Sharma, Catherine A Shaw, Victoria Shaw, Ashraf Sheharyar, Dr Rajesh Mohan Shetty, Rohan Shetty, Haixia Shi, Nisreen Shiban, Mohiuddin Shiekh, Takuya Shiga, Nobuaki Shime, Hiroaki Shimizu, Keiki Shimizu, Naoki Shimizu, Sally Shrapnel, Pramesh Sundar Shrestha, Shubha Kalyan Shrestha, Hoi Ping Shum, Nassima Si Mohammed, Ng Yong Siang, Jeanne Sibiude, Bountoy Sibounheuang, Atif Siddiqui, Louise Sigfrid, Piret Sillaots, Catarina Silva, Maria Joao Silva, Rogério Silva, Benedict Sim Lim Heng, Wai Ching Sin, Dario Sinatti, Budha Charan Singh, Punam Singh, Pompini Agustina Sitompul, Karisha Sivam, Vegard Skogen, Sue Smith, Benjamin Smood, Coilin Smyth, Michelle Smyth, Morgane Snacken, Dominic So, Tze Vee Soh, Lene Bergendal Solberg, Joshua Solomon, Tom Solomon, Emily Somers, Agnès Sommet, Myung Jin Song, Rima Song, Tae Song, Jack Song Chia, Michael Sonntagbauer, Azlan Mat Soom, Arne Søraas, Camilla Lund Søraas, Alberto Sotto, Edouard Soum, Ana Chora Sousa, Marta Sousa, Maria Sousa Uva, Vicente Souza-Dantas, Alexandra Sperry, Elisabetta Spinuzza, B P Sanka Ruwan Sri Darshana, Shiranee Sriskandan, Sarah Stabler, Thomas Staudinger, Stephanie-Susanne Stecher, Trude Steinsvik, Ymkje Stienstra, Birgitte Stiksrud, Eva Stolz, Amy Stone, Adrian Streinu-Cercel, Anca Streinu-Cercel, Samantha Strudwick, Ami Stuart, David Stuart, Decy Subekti, Gabriel Suen, Jacky Y Suen, Asfia Sultana, Charlotte Summers, Dubravka Supic, Deepashankari Suppiah, Magdalena Surovcová, Suwarti Suwarti, Andrey A Svistunov, Sarah Syahrin, Konstantinos Syrigos, Jaques Sztajnbok, Konstanty Szuldrzynski, Shirin Tabrizi, Fabio S Taccone, Lysa Tagherset, Shahdattul Mawarni Taib, Ewa Talarek, Sara Taleb, Jelmer Talsma, Renaud Tamisier, Maria Lawrensia Tampubolon, Kim Keat Tan, Le Van Tan, Yan Chyi Tan, Clarice Tanaka, Hiroyuki Tanaka, Taku Tanaka, Hayato Taniguchi, Huda Taqdees, Arshad Taqi, Coralie Tardivon, Pierre Tattevin, M Azhari Taufik, Hassan Tawfik, Richard S Tedder, Tze Yuan Tee, João Teixeira, Sofia Tejada, Marie-Capucine Tellier, Sze Kye Teoh, Vanessa Teotonio, François Téoulé, Pleun Terpstra, Olivier Terrier, Nicolas Terzi, Hubert Tessier-Grenier, Adrian Tey, Alif Adlan Mohd Thabit, Anand Thakur, Zhang Duan Tham, Suvintheran Thangavelu, Vincent Thibault, Simon-Djamel Thiberville, Benoît Thill, Jananee Thirumanickam, Shaun Thompson, David Thomson, Emma C Thomson, Surain Raaj Thanga Thurai, Duong Bich Thuy, Ryan S Thwaites, Andrea Ticinesi, Paul Tierney, Vadim Tieroshyn, Peter S Timashev, Jean-François Timsit, Bharath Kumar Tirupakuzhi Vijayaraghavan, Noémie Tissot, Jordan Zhien Yang Toh, Maria Toki, Kristian Tonby, Sia Loong Tonnii, Antoni Torres, Margarida Torres, Rosario Maria Torres Santos-Olmo, Hernando Torres-Zevallos, Michael Towers, Tony Trapani, Théo Treoux, Huynh Trung Trieu, Cécile Tromeur, Ioannis Trontzas, Tiffany Trouillon, Jeanne Truong, Christelle Tual, Sarah Tubiana, Helen Tuite, Jean-Marie Turmel, Lance C W Turtle, Anders Tveita, Pawel Twardowski, Makoto Uchiyama, P G Ishara Udayanga, Andrew Udy, Roman Ullrich, Alberto Uribe, Asad Usman, Timothy M Uyeki, Cristinava Vajdovics, Luís Val-Flores, Piero Valentini, Ana Luiza Valle, Amélie Valran, Ilaria Valzano, Stijn Van de Velde, Marcel van den Berge, Machteld Van der Feltz, Job van der Palen, Paul van der Valk, Nicky Van Der Vekens, Peter Van der Voort, Sylvie Van Der Werf, Marlice van Dyk, Laura van Gulik, Jarne Van Hattem, Carolien van Netten, Gitte Van Twillert, Ilonka van Veen, Noémie Vanel, Henk Vanoverschelde, Pooja Varghese, Michael Varrone, Shoban Raj Vasudayan, Charline Vauchy, Shaminee Veeran, Aurélie Veislinger, Sebastian Vencken, Sara Ventura, Annelies Verbon, James Vickers, José Ernesto Vidal, César Vieira, Deepak Vijayan, Joy Ann Villanueva, Judit Villar, Pierre-Marc Villeneuve, Andrea Villoldo, Nguyen Van Vinh Chau, Gayatri Vishwanathan, Benoit Visseaux, Hannah Visser, Chiara Vitiello, Manivanh Vongsouvath, Harald Vonkeman, Fanny Vuotto, Noor Hidayu Wahab, Suhaila Abdul Wahab, Nadirah Abdul Wahid, Marina Wainstein, Wan Fadzlina Wan Muhd Shukeri, Chih-Hsien Wang, Steve Webb, Jia Wei, Katharina Weil, Tan Pei Wen, Sanne Wesselius, T Eoin West, Murray Wham, Bryan Whelan, Nicole White, Paul Henri Wicky, Aurélie Wiedemann, Surya Otto Wijaya, Keith Wille, Suzette Willems, Virginie Williams, Evert-Jan Wils, Calvin Wong, Teck Fung Wong, Xin Ci Wong, Yew Sing Wong, Natalie Wright, Gan Ee Xian, Lim Saio Xian, Kuan Pei Xuan, Ioannis Xynogalas, Sophie Yacoub, Siti Rohani Binti Mohd Yakop, Masaki Yamazaki, Yazdan Yazdanpanah, Nicholas Yee Liang Hing, Cécile Yelnik, Chian Hui Yeoh, Stephanie Yerkovich, Touxiong Yiaye, Toshiki Yokoyama, Hodane Yonis, Obada Yousif, Saptadi Yuliarto, Akram Zaaqoq, Marion Zabbe, Kai Zacharowski, Masliza Zahid, Maram Zahran, Nor Zaila Binti Zaidan, Maria Zambon, Miguel Zambrano, Alberto Zanella, Konrad Zawadka, Nurul Zaynah, Hiba Zayyad, Alexander Zoufaly, David Zucman, Durante-Mangoni, Emanuele, Critical care, Anesthesiology, Peri-operative and Emergency medicine (CAPE), Microbes in Health and Disease (MHD), Garcia-Gallo E., Merson L., Kennon K., Kelly S., Citarella B. W. , Fryer D. V. , Shrapnel S., Lee J., Duque S., Fuentes Y. V. , et al., and Medical Microbiology & Infectious Diseases
Subjects: Eğitim, Social Sciences and Humanities, Information Security and Reliability, Social Sciences (SOC), Sosyal Bilimler ve Beşeri Bilimler, Epidemiology, EDUCATION & EDUCATIONAL RESEARCH, Temel Bilimler (SCI), BİLGİSAYAR BİLİMİ, BİLGİ SİSTEMLERİ, MATHEMATICS, Sociology, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Prospective Studies, COMPUTER SCIENCE, INFORMATION SYSTEMS, STATISTICS & PROBABILITY, Matematik, Bilgisayar Bilimi Uygulamaları, Computer Sciences, Bilgi Güvenliği ve Güvenilirliği, EĞİTİM VE EĞİTİM ARAŞTIRMASI, BİLGİ BİLİMİ VE KÜTÜPHANE BİLİMİ, Bilgi sistemi, Computer Science Applications, Kütüphane ve Bilgi Bilimleri, Hospitalization, Natural Sciences (SCI), Physical Sciences, Engineering and Technology, Sosyal Bilimler (SOC), Bilgisayar Bilimi, Statistics, Probability and Uncertainty, Information Systems, Human, Statistics and Probability, Humans, Pandemics, SARS-CoV-2, COVID-19, SOCIAL SCIENCES, GENERAL, Library and Information Sciences, Education, SDG 3 - Good Health and Well-being, Library Sciences, INFORMATION SCIENCE & LIBRARY SCIENCE, İstatistik ve Olasılık, Sosyal ve Beşeri Bilimler, Bilgisayar Bilimleri, Social Sciences & Humanities, Engineering, Computing & Technology (ENG), Sosyoloji, Pandemic, İSTATİSTİK & OLASILIK, Mühendislik, Bilişim ve Teknoloji (ENG), İstatistik, Olasılık ve Belirsizlik, Sosyal Bilimler Genel, COMPUTER SCIENCE, Prospective Studie, Fizik Bilimleri, Viral infection, Mühendislik ve Teknoloji, Kütüphanecilik
Abstract: The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use., This work is part of the Grand Challenges ICODA pilot initiative, delivered by Health Data Research UK and funded by the Bill & Melinda Gates Foundation and the Minderoo Foundation. The philanthropic support of the donors to the University of Oxford’s COVID-19 Research Response Fund; UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z and 220757/Z/20/Z]; the Bill & Melinda Gates Foundation [OPP1209135]; the National Institute for Health Research (NIHR; award CO-CIN-01); the Medical Research Council (MRC; grant MC_PC_19059); the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE)(award 200907); NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927); Liverpool Experimental Cancer Medicine Centre (grant C18616/A25153); NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013); NIHR Clinical Research Network (infrastructure support); CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and was coordinated out of Sunnybrook Research Institute; the endorsement of the Irish Critical Care- Clinical Trials Group, co-ordinated in Ireland by the Irish Critical Care- Clinical Trials Network at University College Dublin and funded by the Health Research Board of Ireland [CTN-2014-12]; Rapid European COVID-19 Emergency Response research (RECOVER) [H2020 project 101003589]; European Clinical Research Alliance on Infectious Diseases (ECRAID) [965313]; COVID clinical management team, AIIMS, Rishikesh, India; Cambridge NIHR Biomedical Research Centre; the dedication and hard work of the Groote Schuur Hospital Covid ICU Team; the Groote Schuur nursing and University of Cape Town registrar bodies coordinated by the Division of Critical Care at the University of Cape Town; Wellcome Trust fellowship [205228/Z/16/Z]; the Liverpool School of Tropical Medicine; the University of Oxford; the dedication and hard work of the Norwegian SARS-CoV-2 study team; the Research Council of Norway grant no 312780; a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner; Innovative Medicines Initiative Joint Undertaking under Grant Agreement No. 115523 COMBACTE, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies, in-kind contribution; preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection; Stiftungsfonds zur Förderung der Bekämpfung der Tuberkulose und anderer Lungenkrankheiten of the City of Vienna, Project Number: APCOV22BGM; Italian Ministry of Health “Fondi Ricerca corrente–L1P6” to IRCCS Ospedale Sacro Cuore–Don Calabria; Australian Department of Health grant (3273191); Gender Equity Strategic Fund at University of Queensland; Artificial Intelligence for Pandemics (A14PAN) at University of Queensland; the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS, CE170100009); the Prince Charles Hospital Foundation, Australia; UK Medical Research Council Clinical Research Training Fellowship MR/V001671/1; Instituto de Salud Carlos III, Ministerio de Ciencia, Spain; Brazil, National Council for Scientific and Technological Development Scholarship number 303953/2018-7; Firland Foundation, Shoreline, Washington, USA; the French COVID cohort (NCT04262921) is sponsored by INSERM and is funding by the REACTing (REsearch & ACtion emergING infectious diseases) consortium and by a grant of the French Ministry of Health (PHRC n°20-0424).
Published: 2022

17. Environmentally adaptive automated recognition of underwater mines with synthetic aperture sonar imagery

Author: T. Scott Brandes, Patrick Rabenold, Bradley Marchand, Sowmya Ramakrishnan, Brett Ballard, and Ethan Lockhart
Subjects: Acoustics and Ultrasonics, Computer science, business.industry, Active learning (machine learning), Deep learning, Detector, Cognitive neuroscience of visual object recognition, Pattern recognition, Arts and Humanities (miscellaneous), Feature (machine learning), Synthetic aperture sonar, Clutter, Segmentation, Artificial intelligence, business
Abstract: This work demonstrates that automated mine countermeasure (MCM) tasks are greatly facilitated by characterizing the seafloor environment in which the sensors operate as a first step within a comprehensive strategy for how to exploit information from available sensors, multiple detector types, measured features, and target classifiers, depending on the specific seabed characteristics present within the high-frequency synthetic aperture sonar (SAS) imagery used to perform MCM tasks. This approach is able to adapt as environmental characteristics change and includes the ability to recognize novel seabed types. Classifiers are then adaptively retrained through active learning in these unfamiliar seabed types, resulting in improved mitigation of challenging environmental clutter as it is encountered. Further, a segmentation constrained network algorithm is introduced to enable enhanced generalization abilities for recognizing mine-like objects from underrepresented environments within the training data. Additionally, a fusion approach is presented that allows the combination of multiple detectors, feature types spanning both measured expert features and deep learning, and an ensemble of classifiers for the particular seabed mixture proportions measured around each detected target. The environmentally adaptive approach is demonstrated to provide the best overall performance for automated mine-like object recognition.
Published: 2021

18. Knowledge Graph-Based Approaches to Drug Repurposing for COVID-19

Author: Srinivasan Ramakrishnan, Jacob Al-Saleem, Catherine J. Saveson, Natalie A. Ciancetta, Chris Gessner, Qiongqiong Zhou, and Roger Granet
Subjects: Biomedical knowledge, Coronavirus disease 2019 (COVID-19), SARS-CoV-2, Computer science, Process (engineering), General Chemical Engineering, Drug Repositioning, COVID-19, General Chemistry, Library and Information Sciences, Antiviral Agents, Data science, Article, Pattern Recognition, Automated, Computer Science Applications, Drug repositioning, Knowledge graph, Humans, Graph (abstract data type), Pandemics, Repurposing, Host protein
Abstract: The COVID-19 pandemic has motivated researchers all over the world in trying to find effective drugs and therapeutics for treating this disease. To save time, much effort has focused on repurposing drugs known for treating other diseases than COVID-19. To support these drug repurposing efforts, we built the CAS Biomedical Knowledge Graph and identified 1350 small molecules as potentially repurposable drugs that target host proteins and disease processes involved in COVID-19. A computer algorithm-driven drug-ranking method was developed to prioritize those identified small molecules. The top 50 molecules were analyzed according to their molecular functions and included 11 drugs in clinical trials for treating COVID-19 and new candidates that may be of interest for clinical investigation. The CAS Biomedical Knowledge Graph provides researchers an opportunity to accelerate innovation and streamline the investigative process not just for COVID-19 but also in many other diseases.
Published: 2021

19. Automated detection of muscle fatigue conditions from cyclostationary based geometric features of surface electromyography signals

Author: Karthick P A, Divya Bharathi K, and S. Ramakrishnan
Subjects: Computer science, Cyclostationary process, Physics::Medical Physics, Fast Fourier transform, Biomedical Engineering, Spectral correlation density, Bioengineering, Humans, Muscle, Skeletal, Extreme learning machine, Fourier Analysis, Artificial neural network, Electromyography, business.industry, Spectral density, Pattern recognition, General Medicine, Computer Science Applications, Human-Computer Interaction, Multilayer perceptron, Muscle Fatigue, Artificial intelligence, business, Algorithms, Energy (signal processing), Muscle Contraction
Abstract: In this study, an attempt has been made to develop an automated muscle fatigue detection system using cyclostationary based geometric features of surface electromyography (sEMG) signals. For this purpose, signals are acquired from fifty-eight healthy volunteers under dynamic muscle fatiguing contractions. The sEMG signals are preprocessed and the epochs of signals under nonfatigue and fatigue conditions are considered for the analysis. A computationally effective Fast Fourier transform based accumulation algorithm is adapted to compute the spectral correlation density coefficients. The boundary of spectral density coefficients in the complex plane is obtained using alpha shape method. The geometric features, namely, perimeter, area, circularity, bending energy, eccentricity and inertia are extracted from the shape and the machine learning models based on multilayer perceptron (MLP) and extreme learning machine (ELM) are developed using these biomarkers. The results show that the cyclostationarity increases in fatigue condition. All the extracted features are found to have significant difference in the two conditions. It is found that the ELM model based on prominent features classifies the sEMG signals with a maximum accuracy of 94.09% and F-score of 93.75%. Therefore, the proposed approach appears to be useful for analysing the fatiguing contractions in neuromuscular conditions.
Published: 2021

20. High performance computation of human computer interface for neurodegenerative individuals using eye movements and deep learning technique

Author: Thangam Palaniswamy, Rajesh Doss, Raddad Faqihi, Dowlath Fathima, Jayabrabu Ramakrishnan, and Karthik Srinivasan
Subjects: medicine.diagnostic_test, business.industry, Computer science, High performance computation, Deep learning, Interface (computing), Eye movement, Mobile robot, Electrooculography, Theoretical Computer Science, Mode (computer interface), Hardware and Architecture, Human–computer interaction, medicine, Feedforward neural network, Artificial intelligence, business, Software, Information Systems
Abstract: Disabilities due to neurodegenerative disease are rapidly increasing in number. The need for rehabilitative devices to achieve a normal and comfortable life in the absence of biochannels has also increased. The activities of biochannels can be easily replaced by implementing rehabilitative devices. The electrooculography (EOG)-based human–computer interface (HCI) is one of the most important techniques for enabling disabled persons to enjoy a normal life. The technique of measuring the cornea-retina potential difference is called EOG. The technology for converting thoughts to different control patterns to activate external devices is called the HCI. In this paper, we carried out a study on ten subjects aged 20–30 years using a five-electrode signal acquisition system (AD T26). The subject performances were experimentally verified by implementing periodogram features with a feedforward neural network trained on a nature-inspired algorithm. The analysis was conducted offline and online to evaluate the achievement of the developed HCI. The study showed an average classification accuracy of 93.93% for four tasks, with 95% accuracy for the offline mode and 90.12% for the online mode. The participating subjects drove the mobile robot in all directions frequently and quickly with a recognition accuracy of 98.12%. The study confirmed that the four tasks (related to driving the external device) performed by the subjects were convenient to perform in real time.
Published: 2021

21. Framework for Formal Verification of Machine Learning Based Complex System‐of‐System

Author: Ramakrishnan Raman, Yogananda Jeppu, and Nikhil Gupta
Subjects: Computer science, business.industry, Complex system, Software engineering, business, Formal verification
Published: 2021

22. PLANC

Author: Ramakrishnan Kannan, Michael A. Matheson, Koby Hayashi, Grey Ballard, Haesun Park, and Srinivas Eswar
Subjects: Workstation, Exploit, business.industry, Computer science, Applied Mathematics, PLANC, Computation, Multiplicative function, 020206 networking & telecommunications, Low-rank approximation, 010103 numerical & computational mathematics, 02 engineering and technology, 01 natural sciences, law.invention, Software, law, Scalability, 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, business, computer, Algorithm, computer.programming_language
Abstract: We consider the problem of low-rank approximation of massive dense nonnegative tensor data, for example, to discover latent patterns in video and imaging applications. As the size of data sets grows, single workstations are hitting bottlenecks in both computation time and available memory. We propose a distributed-memory parallel computing solution to handle massive data sets, loading the input data across the memories of multiple nodes, and performing efficient and scalable parallel algorithms to compute the low-rank approximation. We present a software package called Parallel Low-rank Approximation with Nonnegativity Constraints, which implements our solution and allows for extension in terms of data (dense or sparse, matrices or tensors of any order), algorithm (e.g., from multiplicative updating techniques to alternating direction method of multipliers), and architecture (we exploit GPUs to accelerate the computation in this work). We describe our parallel distributions and algorithms, which are careful to avoid unnecessary communication and computation, show how to extend the software to include new algorithms and/or constraints, and report efficiency and scalability results for both synthetic and real-world data sets.
Published: 2021

23. A repeatable and reproducible modelling workflow using the Vegetation Optimality Model and RENKU

Author: Rok Roskar, Stan Schymanski, Chandrasekhar Ramakrishnan, and Remko C. Nijzink
Subjects: modelling, Workflow, Computer science, medicine, renku, medicine.symptom, Vegetation (pathology), reproducibility, Remote sensing, Optimality model
Abstract: Numerical experiments become more and more complex, resulting in workflows that are hard to repeat or reproduce. Even though many journals and funding agencies now require open access to data and model code, the linkages between these elements are often still poorly documented or even completely missing. The software platform Renku (https://renkulab.io/), developed by the Swiss Data Science Center, aims at improving reproducibility and repeatability of the entire scientific workflow. Data, scripts and code are stored in an online repository, and Renku records explicitly all the steps from data import to the generation of final plots, in the form of a knowledge graph. In this way, all output files have a history attached, including linkages to scripts and input files used generate them. Renku can visualize the knowledge graph, to show all scientific links between inputs, outputs, scripts and models. It enables easy re-use and reproduction of the entire workflow or parts thereof. In the test case presented here, the Vegetation Optimality Model (VOM, Schymanski et al., 2009) is applied along six study sites of the North-Australian Tropical Transect to simulate observed canopy-atmosphere exchange of water and carbon dioxide. The VOM optimizes vegetation properties, such as rooting depths and canopy properties, in order to maximize the Net Carbon Profit, i.e. the total carbon taken up by photosynthesis minus all the carbon costs of the plant organs involved. The vegetation is schematized as one big leaf for trees and one leaf for seasonal grasses, and is combined with a water balance model. Flux tower measurements of evaporation and CO2-assimilation, and remotely sensed vegetation cover are used for model evaluation, in addition to meteorological data as input for the model. A numerical optimization, the Shuffled Complex Evolution, is used to optimize the vegetation properties for each individual site by repeatedly running the model with different parametrizations and computing the net carbon profit over 20 years. The optimization was repeated several times for each site to analyze the sensitivity of the results to a range of different input parameters. This case demonstrates an example of a complex numerical experiment with all its associated challenges concerning documenting model choices, large datasets and a variety of pre- and post- processing steps. Renku assured the repeatability and reproducibility of this experiment, by documenting this in a proper and systematic way. We demonstrate how Renku helped us to repeat analyses and update results, and we will present the knowledge graph of this experiment. References Schymanski, S.J., Sivapalan, M., Roderick, M.L., Hutley, L.B., Beringer, J., 2009. An optimality‐based model of the dynamic feedbacks between natural vegetation and the water balance. Water Resources Research 45. https://doi.org/10.1029/2008WR006841, Funded by the Luxembourg National Research Fund, ATTRACT programme (A16/SR/11254288)
Published: 2022
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24. A Novel Approach to Optimize Water and Fertilizers in Agriculture using IoT

Author: D.Louisa Mary and M. Ramakrishnan
Subjects: business.industry, Computer science, Agriculture, Agricultural engineering, Internet of Things, business, GeneralLiterature_MISCELLANEOUS
Abstract: This paper presents a new method for supplying nutrients to the farm. This novel method uses IoT and Wireless Sensor Network in Farming. Soil Moisture sensor and humidity sensor monitor the soil properties. These results are updated to the IoT Server. Based on the values for humidity of the soil, drip irrigation to the plant is on/off through solenoid valve. In addition this paper concentrates on quantitative analysis of requirement of nutrients for the plant to avoid overdose or insufficiency of them. This work trains the machine using precision agriculture for supplying required nutrient to the plant using solenoid on/off valve through drip irrigation. It also alerts the user by sending SMS (Short Messaging Service) through GSM to the user on fertigation date and pesticides spraying date. This will be very helpful for the farmers to remember the fertigation date in their various activities. The limitation of this work is that the fertilizers used will only be liquid in state. This technology can also be improved to a large level agricultural production.
Published: 2021

25. Hybrid fuzzy logic and artificial Flora optimization algorithm-based two tier cluster head selection for improving energy efficiency in WSNs

Author: Ramakrishnan Anandkumar
Subjects: Mathematical optimization, Fitness function, Computer Networks and Communications, Computer science, Network packet, Node (networking), 020206 networking & telecommunications, 02 engineering and technology, Fuzzy logic, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Cluster analysis, Wireless sensor network, Software, Selection (genetic algorithm), Efficient energy use
Abstract: Wireless Sensor Network (WSN) is the one of the hot area of research in which energy stability and network lifetime are considered to be the twin challenges during its application. Clustering is the optimum energy efficiency strategy that organizes the sensor nodes into potential groups for the objective of attaining energy stability and network lifetime. In this energy potent clustering process, cluster head selection is determined to be highly significant in order to balance energy among the nodes sensor nodes. Moreover, two-tier cluster head selection that includes temporary and final cluster head is identified to be challenging in WSNs. In this paper, Hybrid Fuzzy Logic and Artificial Flora Optimization Algorithm (FL-AFA)-based Two Tier Cluster Head Selection is proposed for improving energy efficiency and prolog network lifetime. This FL-AFA scheme achieved the cluster head selection in two stages, such as, i) Temporary Cluster Head (TCH) selection using FL and, ii) Final Cluster Head (FCH) selection using AFA. In the first stage, the concept of fuzzy logic applied over the input parameters of residual energy (RE), distance to BS (DTBS), and node degree (NDE). In the second stage, the benefits of AFA is employed for computing the fitness function through distance to nearby nodes (DNN), cluster compactness estimation factor (CCEF), and position estimation (PE). Simulation experiments of the proposed FL-AFA scheme and the benchmarked schemes are conducted based on the evaluation metrics of energy efficiency, network lifetime, average delay, and packet delivery ratio (PDR) under the impact of different sensor nodes. .
Published: 2021

26. A Trusted Water Fall Model for Efficient Data Transmission in VANET

Author: Usha Mohanakrishnan, Sathiamoorthy Jayaraman, and Ashween Ramakrishnan
Subjects: Vehicular ad hoc network, business.industry, Network packet, Computer science, ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS, 020206 networking & telecommunications, Throughput, 02 engineering and technology, Computer Science Applications, Broadcasting (networking), Cognitive radio, Optimized Link State Routing Protocol, Waterfall model, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Electrical and Electronic Engineering, business, Computer network, Communication channel
Abstract: Vehicular adhoc networks are slowly gaining importance as the demand for faster communication increases. The vehicular nodes need to communicate securely via an established and effective route. Control and protocol information between the vehicles is transmitted using the technique of broadcasting. The nodes are never stagnant and even though their energy rarely dissipates, the high mobility of the nodes poses quite a problem. Lack of an established infrastructure is a major issue and needs to be addressed in order to achieve maximum performance. We have designed a novel waterfall model focusing on a robust broadcasting method. The OLSR protocol is enhanced using a robust MPR technique, which helps in mitigating the circulation of duplicate packets within the network. The RMPR subset of the OLSR protocol is capable of handling transmission errors and the hidden and exposed terminal problems prevalent in the VANET environment. The inclusion of a waterfall model enables the proposed technique to maximize throughput and minimize the delay in the network. The waterfall model works in two major phases. The first phase incorporates the Cognitive radio scheme for effective channel allocation and utilization.The RMPR technique is implemented in the second phase which focuses on utilizing the neighbor nodes to transmit the packets successfully towards the destination. The RMPR scheme aims to categorize the neighboring nodes as either one hop or 2-hop neighboring nodes. Additionally, the uncategorized nodes are further probed to find a second subset of 2-hop nodes which would speed up the transmission. The implementation of these two important phases into the classic waterfall model helps to streamline the activities and helps in streamlining the network activities.The proposed waterfall model RMPR technique is analyzed with protocols like MMPR–OLSR and the OLSR protocols to determine the effectiveness of the proposed protocol. The performance analysis is carried out using the NS2 simulator. The techniques that are compared are evaluated using major network parameters like throughput, delay, PDR and channel utilization. It is evident that the proposed protocol is able to maximize the PDR than the existing techniques.
Published: 2021

27. Secured Message Broadcasting in VANET using Blowfish Algorithm with Oppositional Deer Hunting Optimization

Author: B. Ramakrishnan and M. Selvi
Subjects: Vehicular ad hoc network, Blowfish algorithm, Computer Networks and Communications, business.industry, Computer science, Applied Mathematics, Deer hunting, Computer Science Applications, Broadcasting (networking), business, Safety Research, Software, Information Systems, Computer network
Abstract: Emergency Message broadcasting is an important process in VANET. Security and reliable transmission are the two major concerns in message broadcasting. VANET is open to unauthorized nodes, hackers, misbehaving vehicles, malicious attackers etc without security. Without valid confirmation of authorized vehicles, these types of attacks may occur. To enhance the reliability in message broadcasting, some existing techniques are used. They transmit the data without much delay but they didn’t provide any trusted authentication. So hackers, malicious nodes, unauthorized vehicles may easily interrupt the emergency messages. Also Brute force attack, Man in Middle attack are not identified and eliminated. In this research, a trust based secured broadcasting mechanism is developed which ensures the metrics such as security, privacy, integrity, trust etc. The major intension of this research is to reduce latency and provide high reliable, secure and efficient communication over the vehicles. The data such as vehicle position, location, speed, and other traffic information’s are generated and stored in a separate table. A network is created with varying densities. A path is generated for message broadcasting between source and destination based on the specific gateway estimated. Here, Optimal Wireless Access in Vanet (OWAV) Protocol is employed to gather vehicle related information to reduce the delay. Blowfish encryption algorithm along with Oppositional Deer Hunting Optimization (ODHO) is used to store the trusted vehicles location to avoid unauthorized tracking. The performance of the proposed research is analyzed with various metrics such as Packet delivery ratio (PDR), transmission delay, encryption time, hroughput, computational overhead etc. The efficiency of the research is compared with other existing methods.
Published: 2021

28. SEGMENTATION OF BRAIN STRUCTURES IN ALZHEIMER MR IMAGES USING SPATIAL FUZZY CLUSTERING LEVEL SET

Author: Sreelakshmi Shaji and Ramakrishnan Swaminathan
Subjects: Level set (data structures), Fuzzy clustering, business.industry, Computer science, Segmentation, Pattern recognition, Artificial intelligence, Mr images, business
Abstract: Alzheimer’s Disease (AD) is an irreversible neurodegenerative disorder that affects brain structures. Corpus Callosum (CC) atrophy and Lateral ventricle (LV) enlargement are useful structural biomarkers in distinguishing the preclinical stages of AD. The shape of CC appears to be homogeneous from normal controls to AD images and LV shows shape dissimilarity across subjects. Therefore, effective methods to segment CC and LV are essential to characterize the magnitude of morphometric changes. In this study, an attempt has been made to segment CC and LV from MR brain images using the Spatial Fuzzy Clustering based Level Set (SFC-LS) method. For this, T1-weighted MR images of AD, Mild Cognitive Impairment (MCI), and normal controls are obtained from a public database. Spatial fuzzy clustering forms the initial contour for the level set and regularizes the evolution of curve. The segmented images are validated against ground truth using standard measures. Results indicate that SFC-LS is able to segment CC and LV with automated contour initialization. The final contours obtained are sharp and distinct with a high validation performance of accuracy and specificity greater than 97% for normal controls, MCI, and AD. A dice score of 83% and 84% is achieved in segmenting CC and LV respectively. As structural changes in CC and LV have the potential to predict the early stages of AD, the proposed approach seems to be clinically significant.
Published: 2021

29. ROTATIONAL MOMENT SHAPE FEATURE EXTRACTION AND DECISION TREE BASED DISCRIMINATION OF MILD COGNITIVE IMPAIRMENT CONDITIONS USING MR IMAGE PROCESSING

Author: Ravi Dadsena, Deboleena Sadukhan, and Ramakrishnan Swaminathan
Subjects: Moment (mathematics), business.industry, Computer science, Feature extraction, Decision tree, Pattern recognition, Artificial intelligence, Mr images, Cognitive impairment, business
Abstract: Mild Cognitive Impairment (MCI) is the preclinical, asymptomatic stage for Alzheimer’s condition, which affects a large amount of the aging population around the world. Detection of MCI condition can ensure timely intervention needed for handling the disease severity. Morphological alterations of the Lateral Ventricle (LV) are considered a significant biomarker for diagnosing MCI conditions. This work aims at analyzing the shape alterations of LV from brain Magnetic Resonance (MR) images using Rotational moment shape features and differentiating MCI conditions using Decision Tree (DT) based classification. Trans-axial brain MR images are obtained from a publicly available OASIS database. Segmentation of LV is performed using the Reaction Diffusion level set, and the results are validated against Ground Truth. Rotational moment shape features are extracted from the segmented LV images. DT is implemented for the differentiation of control and MCI subjects. Results show that Rotational moment shape features are able to capture the alterations of LV in control and MCI subjects (p
Published: 2021

30. Name Space Analysis: Verification of Named Data Network Data Planes

Author: Mohammad Jahanian and Kadangode K. Ramakrishnan
Subjects: Computer Networks and Communications, business.industry, Computer science, Testbed, 020206 networking & telecommunications, 02 engineering and technology, Computer Science Applications, Data modeling, Test case, Reachability, Scalability, Header, 0202 electrical engineering, electronic engineering, information engineering, Forwarding plane, Electrical and Electronic Engineering, business, Name space, Protocol (object-oriented programming), Software, Codebase, Computer network
Abstract: Named Data Networking (NDN) has a number of forwarding behaviors, strategies, and protocols proposed by researchers and incorporated into the codebase, to enable exploiting the full flexibility and functionality that NDN offers. This additional functionality introduces complexity, motivating the need for a tool to help reason about and verify that basic properties of an NDN data plane are guaranteed. This paper proposes Name Space Analysis (NSA), a network verification framework to model and analyze NDN data planes. NSA can take as input one or more snapshots, each representing a particular state of the data plane. It then provides the verification result against specified properties. NSA builds on the theory of Header Space Analysis, and extends it in a number of ways, e.g., supporting variable-sized headers with flexible formats, introduction of name space functions, and allowing for name-based properties such as content reachability and name leakage-freedom. These important additions reflect the behavior and requirements of NDN, requiring modeling and verification foundations fundamentally different from those of traditional host-centric networks. For example, in name-based networks (NDN), host-to-content reachability is required, whereas the focus in host-centric networks (IP) is limited to host-to-host reachability. We have implemented NSA and identified a number of optimizations to enhance the efficiency of verification. Results from our evaluations, using snapshots from various synthetic test cases and the real-world NDN testbed, show how NSA is effective, in finding errors pertaining to content reachability, loops, and name leakage, has good performance, and is scalable.
Published: 2021

31. An automated classification of HEp-2 cellular shapes using Bag-of-keypoint features and Ant Colony Optimization

Author: Kanchana Devanathan, Kavitha Ganesan, and Ramakrishnan Swaminathan
Subjects: Contrast enhancement, business.industry, Computer science, Ant colony optimization algorithms, 0206 medical engineering, Visibility (geometry), Biomedical Engineering, Pattern recognition, Feature selection, 02 engineering and technology, 020601 biomedical engineering, Support vector machine, Feature (computer vision), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Cell structure, Artificial intelligence, business
Abstract: In this study, an attempt has been made to differentiate HEp-2 cellular shapes using Bag-of-keypoint features and optimization. For this, the images are considered from a publicly available database. To increase the cell structure visibility, the images are pre-processed using edge-sensitive local contrast enhancement. Further, the Speeded-up Robust Feature (SURF) keypoints are extracted and Bag-of-keypoints for each shape are generated. These features are subjected to Ant Colony Optimization (ACO) algorithm for feature selection. The optimal features obtained are then fed to Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) classifiers. Results show that the ACO algorithm can identify the optimal features that characterize the cellular shapes. SVM and kNN are able to differentiate between the shapes with an average classification accuracy of 93.6% and 94.8% respectively. Since differential diagnosis of HEp-2 cellular shapes is significant in the disease-specific prognosis and treatment, this study seems to be clinically relevant.
Published: 2021

32. Assessing Container Network Interface Plugins: Functionality, Performance, and Scalability

Author: Kadangode K. Ramakrishnan, Sameer G. Kulkarni, and Shixiong Qi
Subjects: Computer Networks and Communications, Computer science, business.industry, 020206 networking & telecommunications, Cloud computing, 02 engineering and technology, Network interface, computer.software_genre, Network interface controller, Container (abstract data type), Scalability, 0202 electrical engineering, electronic engineering, information engineering, Operating system, Plug-in, Orchestration (computing), Electrical and Electronic Engineering, business, computer, Host (network)
Abstract: Kubernetes, an open-source container orchestration platform, has been widely adopted by cloud service providers (CSPs) for its advantages in simplifying container deployment, scalability, and scheduling. Networking is one of the central components of Kubernetes, providing connectivity between different Pods (a group of containers) both within the same host and across hosts. To bootstrap Kubernetes networking, the Container Network Interface (CNI) provides a unified interface for the interaction between container runtimes. There are several CNI implementations, available as open-source ‘CNI plugins’. While they differ in functionality and performance, it is a challenge for a cloud provider to differentiate and choose the appropriate plugin for their environment. In this article, we compare the various open-source CNI plugins available from the community, qualitatively, and through detailed quantitative measurements. With our experimental evaluation, we analyze the overheads and bottlenecks for each CNI plugin, especially because of the interaction with the datapath/iptables as well as the host network stack. Overlay tunnel offload support in the network interface card plays a significant role in achieving the good performance of CNIs that use overlay tunnels for inter-host Pod-to-Pod communication. We also study scalability with an increasing number of Pods, as well as with HTTP workloads, and briefly evaluate Pod startup latency. Our measurement results inform the outline of an ideal CNI environment for Kubernetes.
Published: 2021

33. THE USE OF AN ADAPTIVE LEARNING SYSTEM FOR A - LEVEL MODULE IN MALAYSIA

Author: Ramakrishnan Ramakrishnan, Kalaimagal Ramakrishnan Selvaraj, and Raadikesh Ramalingam
Subjects: business.industry, Computer science, Adaptive learning, Artificial intelligence, Machine learning, computer.software_genre, business, computer
Published: 2021

34. Energy Efficient of IDS Using Fuzzy Logic for Lifetime Improvement in Wireless Sensor Network

Author: M. Ramakrishnan, S. Venkatesan, and Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)
Subjects: Intrusion Detection System, Fuzzy Logic, Lifetime of Network, Clustering, Routing protocol, Environmental Engineering, 100.1/ijeat.C22140210321, Computer science, Real-time computing, General Engineering, 2249-8958, Wireless sensor network, Fuzzy logic, Computer Science Applications, Efficient energy use
Abstract: The Wireless Sensor Network (W.S.N.) comprises little batteries fueled sensor gadgets with restricted energy assets. The Sensor hubs used to monitor the physical screen or conditionsbased on normal, theinformation must be private organization to primary area. The Most significant obstacles in a sensing the remote in the particular network which used to make an efficient energy framework. Clustering is the one of the major process in the sensor network based on wireless which used to drag out the life time of an organization lifetime which in turn reduce the energy utilization of the network. It includes gathering hubs into groups and choosing bunch heads (CH) for all the groups. CH gather information from separate group hubs and forward the collected data to the fundamental corner. This paper proposes novel fluffy various dynamic methodology measures: “Energy Efficient Optimal Cluster Head Selection utilizing Fuzzy Logic (EEOCH-FL)” for Wireless Sensor Network. Fluffy different boundary dynamic methodology is used to choose C.H.s utilizing three standards: leftover energy, fixation, the right ways from the principle hubs, and base station. The life cycle of Clustering hub and Clustering Head are grouped, clustering hub which transmitted all data to the Cluster Header Leader (CHL). The bunch head pioneers sent collected information to the Base Station (B.S.) from that point forward. The determination of bunch heads, group head pioneers is controlled and monitored by utilizing a fluffy rationale. The information transmission measure is per-shaped by the briefest energy way chosen to apply Dijkstra Algorithm. The reenactment results show that this methodology is more potent in boosting the availability inside each bunch. Furthermore, the reproduction aftereffects of this examination are contrasted and different conventions LEACH and CEELRP to assess the proposed steering convention's presence. The assessment reasons that convention of steering of this proposed work proved to be an effective in utilization of an energy.
Published: 2021

35. An Exploration of Embodied Visual Exploration

Author: Santhosh K. Ramakrishnan, Dinesh Jayaraman, and Kristen Grauman
Subjects: Scope (project management), Computer science, media_common.quotation_subject, 02 engineering and technology, Benchmarking, Exploration problem, Data science, Empirical research, Artificial Intelligence, Embodied cognition, Perception, 0202 electrical engineering, electronic engineering, information engineering, Robot, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Architecture, Software, media_common
Abstract: Embodied computer vision considers perception for robots in novel, unstructured environments. Of particular importance is the embodied visual exploration problem: how might a robot equipped with a camera scope out a new environment? Despite the progress thus far, many basic questions pertinent to this problem remain unanswered: (i) What does it mean for an agent to explore its environment well? (ii) Which methods work well, and under which assumptions and environmental settings? (iii) Where do current approaches fall short, and where might future work seek to improve? Seeking answers to these questions, we first present a taxonomy for existing visual exploration algorithms and create a standard framework for benchmarking them. We then perform a thorough empirical study of the four state-of-the-art paradigms using the proposed framework with two photorealistic simulated 3D environments, a state-of-the-art exploration architecture, and diverse evaluation metrics. Our experimental results offer insights and suggest new performance metrics and baselines for future work in visual exploration. Code, models and data are publicly available.
Published: 2021

36. Flexible Process Planning Design and Resource Optimization

Author: V Thamaraiselvan and N Ramakrishnan
Subjects: Resource (project management), Process management, Computer science, Process (engineering)
Abstract: Manufacturing processes undergo various transformations based on technological advancements and industry expectations. At initial times, during the period of first Industrial revolution, products were getting manufactured using human capabilities and skills. Then, electrically-driven motors, machines and conveyors were used. Now in the fourth Industrial revolution, Industries are equipped with automation, robots and cyber-physical systems. But adopting such technologies requires a high capital investment which cannot be accommodated by some start-ups. In this research paper, a case of a start-up which manufactures straws and vessel scrubbers in an eco-friendly manner as an initiative towards sustainability has been discussed. The key product of the company is biodegradable straws which are manufactured from coconut leaves through a set of processes. The scope of the research is to develop a labour-intensive process planning model. Managing contemporary issues is a big task for the company because of the dynamic nature of the environment. In this case, the demand for straws changes based on customer preference and accordingly, the manufacturing processes need to be revised. Hence, developing a rigid planning model is not an effective solution, so that, flexible manufacturing process needs to be developed. There is always a scope of improvement towards betterment and optimization in a manufacturing process. It is not necessary that the improvements should lead to drastic results. Japanese manufactures who bring the Toyota Production System (TPS) believe that small improvements in a continual basis will fetch better results in terms of quality, efficiency and lead time reduction. Once the model is developed, areas of improvement to optimize the resources are found out and iterations of process planning model were carried over to improve the efficiency. Through the study, it is found that the development of a flexible process plan is required to compete with the changing business scenarios.
Published: 2021

37. Leveraging Business Data Analytics and Machine Learning Techniques for Competitive Advantage: Case Study Evidence from Small Businesses

Author: Kannan Ramakrishnan, Mohamed Haryz Izzudin Mohamed Rafy, Siti Nurfara Alia Mohd Salamun, Haziq Riza Abdul Rasid, Sukinurlin Yusuf, Intan Soraya Rosdi, and Rathimala Kannan
Subjects: Computer science, business.industry, Affinity analysis, Small business, Machine learning, computer.software_genre, Competitive advantage, Case method, Leverage (negotiation), Analytics, Data analysis, Artificial intelligence, business, Research question, computer
Abstract: Data analytics is the essential component in deriving insights from data obtained from multiple sources. It represents the technology, methods and techniques used to obtain insights from massive datasets. As data increases, companies are looking for ways to gain relevant business insights underneath layers of data and information, to help them better understand new business ventures, opportunities, business trends and complex challenges. However, to date, while the extensive benefits of business data analytics to large organizations are widely published, micro, small, and medium sized organisations have not fully grasped the potential benefits to be gained from data analytics using machine learning techniques. This study is guided by the research question of how data analytics using machine learning techniques can benefit small businesses. Using the case study method, this paper outlines how small businesses in two different industries i.e. healthcare and retail can leverage data analytics and machine learning techniques to gain competitive advantage from the data. Details on the respective benefits gained by the small business owners featured in the two case studies provide important answers to the research question.
Published: 2021

38. Online Review Characteristics and Information Asymmetry Is it easy to switch between Online Shopping sites? A Case Study of Reviews from Amazon and Flipkart

Author: S. Riasudeen, S. Madhavan, Ramakrishnan Venkatesakumar, Sudhakar Vijayakumar, and G. Vidyashankar
Subjects: Product (business), Information asymmetry, Computer science, Process (engineering), Helpfulness, Staffing, Advertising, Social media, Variation (game tree), Desk
Abstract: Consumer characteristics and store attributes decide the store choice decision of consumers. To facilitate the switching process, physical formats create identical layout structures, shelf designs, staffing and billing desk. In similar lines, online stores also create features like similar website characteristics like the menu, creating shopping basket options, comparing product and billing process. Similar to Word-of-Mouth (WOM), online stores encourage and facilitate electronic Word-of-Mouth (eWOM) communications through Online Consumer Reviews (OCR) in their websites. Many online buyers use the reviews of others, social media content and blogs in their decision process. To understand the distribution characteristics of the online reviews, in this research work, we analyze the online review from Amazon and Flipkart for the masks and sanitizers. In a review, star rating, review length and helpfulness of a review are visual characteristics that communicate the content faster than words and no research works compare their variation between two online sellers. We prove that there are significant differences exist in the distribution of review characteristics between the online retailers’ reviews. Two websites reviews vary in terms of star rating, review length and helpfulness votes. There are variations between Amazon and Flipkart reviews in general, and differences are observed in their brands sold also. Since the review characteristics and their distributions are unknown, this information asymmetry creates constraints for store switching behavior of online consumers.
Published: 2021

39. Survey of spiking in the mouse visual system reveals functional hierarchy

Author: Luke Esposito, John W. Phillips, Lydia Ng, Maggie Chvilicek, Kara Ronellenfitch, Corbett Bennett, Kael Dai, Peter A. Groblewski, John Galbraith, Jackie Swapp, Brian Hu, Ross Hytnen, Fuhui Long, Emily Gelfand, R.D. Young, India Kato, Linzy Casal, Greggory Heller, Jennifer Luviano, Xiaoxuan Jia, Ben Sutton, Michael A. Buice, Saskia E. J. de Vries, Shiella Caldejon, Sam Seid, Tamina K. Ramirez, Thuyahn Nguyen, Wayne Wakeman, Chelsea Nayan, Philip R. Nicovich, Roald Dietzman, Nicole Hancock, Colin Farrell, Carol L. Thompson, David Feng, Erika Jessett, Hongkui Zeng, Elizabeth Liang, Shawn R. Olsen, Kristen Turner, Jérôme Lecoq, Derric Williams, Katelyn Johnson, Jose Melchior, Stefan Mihalas, Hannah Choi, Sam Gale, Jennifer D. Whitesell, Ramakrishnan Iyer, Kat North, Melissa Reding, Dillan Brown, Yang Li, Kiet Ngo, Séverine Durand, Robert Howard, Amy Bernard, Anton Arkhipov, Julie A. Harris, Ali Williford, Yazan N. Billeh, Marina Garrett, Sophie Lambert, Tyler Mollenkopf, Arielle Leon, Marius Pachitariu, Michael Oliver, Nicolas Cain, Gabriel Koch Ocker, Daniel J. Denman, Justin T. Kiggins, Joshua H. Siegle, R. Clay Reid, Douglas R. Ollerenshaw, David Reid, Cliff Slaughterbeck, David Sullivan, Jed Perkins, Ruweida Ahmed, Daniel Millman, Jung Hoon Lee, Kyla Mace, Christof Koch, Andrew Cho, Nile Graddis, Timothy C. Cox, Peter Ledochowitsch, Miranda Robertson, Michelle Stoecklin, and Sarah A. Naylor
Subjects: 0301 basic medicine, Hierarchy, Retina, Multidisciplinary, Neocortex, Visual perception, genetic structures, Computer science, Direct observation, Visual task, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, medicine.anatomical_structure, medicine, Latency (engineering), Neuroscience, 030217 neurology & neurosurgery, Coding (social sciences)
Abstract: The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically1. However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset-part of the Allen Brain Observatory2-that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas3. We find that four classical hierarchical measures-response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale-are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas.
Published: 2021

40. Electrooculogram-aided intelligent sensing and high-performance communication control system for massive ALS individuals

Author: Mythily R, Mervin Retnadhas, Tintu Thomas Uthup, Jayabrabu Ramakrishnan, T. Akila, and Ramkumar Sivasakthivel
Subjects: 020203 distributed computing, medicine.medical_specialty, Computer science, Communication control, 02 engineering and technology, Audiology, Theoretical Computer Science, Probabilistic neural network, High performance communication, Age groups, Hardware and Architecture, Muscle dysfunction, Control system, 0202 electrical engineering, electronic engineering, information engineering, medicine, Software, Group performance, Information Systems
Abstract: Neurodegenerative disease was one of the progressive diseases that affect the brain’s neurons and cause muscle dysfunction. There is a need to focus on these challenges for interfaces to communicate with others. We analyze three categories of subjects from 15 subjects between different age groups from 20 to 55 to investigate the efficiency to promote the human–computer interface using eye activities. Observed signals were estimated with root mean square for 11 tasks and classified with probabilistic neural network. In the experimental analysis, it determines that subjects belonging to young-aged group performance were appreciated and obtained the average mean classification accuracy of 94.00%, the subjects belonging to old-aged group performance were medium and obtained the average mean classification accuracy of 93.27%, and subjects belonging to amyotrophic lateral sclerosis (ALS) performance were low and obtained the average mean classification accuracy of 90.37%. The study examined that young-aged subjects’ performance was marginally high compared with the other two categories of subjects. Our study concluded that first maximum performances were obtained for young-aged subjects and secondary performance was attained for old-aged subjects, and ALS subjects obtained minimum performance compared to other subjects who participated in this analysis. Finally, our study concluded that designing intelligent sensing and high-performance communication control systems for massive ALS-affected subjects was minimal. They also need more training than other categories of subjects in this experiment.
Published: 2021

41. A Survey on the Usage of Pattern Recognition and Image Analysis Methods for the Lifestyle Improvement on Low Vision and Visually Impaired People

Author: S. Malathi, Abhishek Kumar, M. Anitha, V. D. Ambeth Kumar, Rashid Ali, V.D. Ashok Kumar, and M. Ramakrishnan
Subjects: Computer science, Visually impaired, 02 engineering and technology, Braille, 01 natural sciences, Computer Graphics and Computer-Aided Design, Field (computer science), Domain (software engineering), Image (mathematics), 010309 optics, Human–computer interaction, 0103 physical sciences, Pattern recognition (psychology), 0202 electrical engineering, electronic engineering, information engineering, Key (cryptography), 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Pattern matching
Abstract: According to World Health Organization in 2017 nearly 253 million people are visually impaired of whom 36 million are blind. Braille books involve the tactile format that helps the visually impaired people to gain knowledge but only a limited resource is available. Enormous papers and studies describe the method for obtaining machine readable document from textual image. In upcoming days character recognition might serve a key role to create a paperless environment that helps the visually impaired people to gain enormous amount of educational material. Handwritten script recognition is gaining vital importance in today’s electronically interconnected society. In the field of machine learning and pattern matching handwritten has gained lot of attention. This paper first summarizes the pros and cons of technologies developed for the visually impaired people in terms of education material obtained from text image and handwritten image. Along with that it presents a performance comparison of different methods. Finally, it describes the future research work in this domain.
Published: 2021

42. Analysis of Dynamics of EMG Signal Variations in Fatiguing Contractions of Muscles Using Transition Network Approach

Author: Navaneethakrishna Makaram, P. A. Karthick, and Ramakrishnan Swaminathan
Subjects: Muscle fatigue, Receiver operating characteristic, medicine.diagnostic_test, business.industry, Computer science, Feature extraction, Pattern recognition, Electromyography, Signal, Naive Bayes classifier, Feature (computer vision), medicine, Artificial intelligence, Electrical and Electronic Engineering, Entropy (energy dispersal), business, Instrumentation
Abstract: The measurement and analysis of the electrical activity of muscle provide information that aids in the control of assistive devices. The investigation of these signals under varied physiological conditions, such as fatigue, enables reliable control. Muscle fatigue is a muscular condition associated with loss of muscle function. The early detection of muscle fatigue using surface Electromyography (sEMG)-based electrical measurements is challenging due to the nonlinear variations of the signal. In this work, an attempt has been made to understand the effect of dynamic nonlinear variations in the characteristics of the signal to develop a reliable fatigue index. The methodology involves the acquisition of myoelectric signals from the biceps brachii muscle of 52 healthy participants during dynamic contractions. The acquired signals are preprocessed and are analyzed with symbolic transition networks. Features such as symbolic entropy, network entropy, uniformity, and, minimum and maximum effective degrees (EDs) are extracted for further analysis. Appropriate decision boundaries are established for each feature using receiver operator characteristics (ROCs) and machine learning algorithms. The results indicate a decrease in signal complexity with fatigue. All the extracted features show a statistically significant difference (p < 0.05) between both conditions. Symbolic entropy achieves an accuracy of 89%, and the maximum ED yields an accuracy of 90% based on thresholds estimated with ROC. Furthermore, only a marginal improvement is observed with the combination of these features and the Naive Bayes classifier. It appears that the proposed maximum ED could be used as a reliable fatigue index in real-time applications for the improvement of rehabilitation efficacy.
Published: 2021

43. Decoding Imagined Speech From EEG Using Transfer Learning

Author: Jerrin Thomas Panachakel and Ramakrishnan Angarai Ganesan
Subjects: General Computer Science, medicine.diagnostic_test, Computer science, Imagined speech, Speech recognition, Feature extraction, General Engineering, imagined speech, Coherence (statistics), Electroencephalography, transfer learning, electroencephalogram, speech imagery, TK1-9971, Brain–computer interface, Sliding window protocol, medicine, General Materials Science, Electrical engineering. Electronics. Nuclear engineering, Transfer of learning, Representation (mathematics), Decoding methods
Abstract: We present a transfer learning-based approach for decoding imagined speech from electroencephalogram (EEG). Features are extracted simultaneously from multiple EEG channels, rather than separately from individual channels. This helps in capturing the interrelationships between the cortical regions. To alleviate the problem of lack of enough data for training deep networks, sliding window-based data augmentation is performed. Mean phase coherence and magnitude-squared coherence, two popular measures used in EEG connectivity analysis, are used as features. These features are compactly arranged, exploiting their symmetry, to obtain a three dimensional “image-like” representation. The three dimensions of this matrix correspond to the alpha, beta and gamma EEG frequency bands. A deep network with ResNet50 as the base model is used for classifying the imagined prompts. The proposed method is tested on the publicly available ASU dataset of imagined speech EEG, comprising four different types of prompts. The accuracy of decoding the imagined prompt varies from a minimum of 79.7% for vowels to a maximum of 95.5% for short-long words across the various subjects. The accuracies obtained are better than the state-of-the-art methods, and the technique is good in decoding prompts of different complexities.
Published: 2021

44. Troubleshooting unstable molecules in chemical space

Author: Raghunathan Ramakrishnan, Salini Senthil, and Sabyasachi Chakraborty
Subjects: Chemical Physics (physics.chem-ph), Electron density, 010304 chemical physics, Computer science, FOS: Physical sciences, General Chemistry, Troubleshooting, 010402 general chemistry, Topology, 01 natural sciences, Quantum chemistry, Chemical space, 0104 chemical sciences, Chemistry, Workflow, Physics - Chemical Physics, 0103 physical sciences, Bijection, Molecule, Quantum
Abstract: A key challenge in automated chemical compound space explorations is ensuring veracity in minimum energy geometries—to preserve intended bonding connectivities. We discuss an iterative high-throughput workflow for connectivity preserving geometry optimizations exploiting the nearness between quantum mechanical models. The methodology is benchmarked on the QM9 dataset comprising DFT-level properties of 133 885 small molecules, wherein 3054 have questionable geometric stability. Of these, we successfully troubleshoot 2988 molecules while maintaining a bijective mapping with the Lewis formulae. Our workflow, based on DFT and post-DFT methods, identifies 66 molecules as unstable; 52 contain –NNO–, and the rest are strained due to pyramidal sp2 C. In the curated dataset, we inspect molecules with long C–C bonds and identify ultralong candidates (r > 1.70 Å) supported by topological analysis of electron density. The proposed strategy can aid in minimizing unintended structural rearrangements during quantum chemistry big data generation., A high-throughput workflow for connectivity preserving geometry optimization minimizes unintended structural rearrangements during quantum chemistry big data generation.
Published: 2021

45. Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Author: Hassan Haes Alhelou, B. Santhosh Kumar, C. Ramakrishnan, M. Premkumar, Pradeep Jangir, and G. Nalinipriya
Subjects: chaotic generator, Optimization problem, General Computer Science, Mean squared error, Computer science, Estimation theory, 020209 energy, Photovoltaic system, General Engineering, Chaotic, 02 engineering and technology, 021001 nanoscience & nanotechnology, Chaotic-gradient-based optimizer (CGBO), Power (physics), TK1-9971, photovoltaics, Local optimum, Control theory, Robustness (computer science), gradient-based optimizer (GBO), 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, Electrical engineering. Electronics. Nuclear engineering, 0210 nano-technology, parameter estimation
Abstract: When discussing the commercial applications of photovoltaic (PV) systems, one of the most critical problems is to estimate the efficiency of a PV system because current (I) – voltage (V) and power (P) – voltage (V) characteristics are highly non-linear. It should be noted that most of the manufacturer’s datasheets do not have complete information on the electrical equivalent parameters of PV systems that are necessary for simulating an effective PV module. Compared to conventional approaches, computational optimization and global research strategies are more acceptable as an effective alternative to parameter estimation of solar PV modules. Recently, a Gradient-based optimizer (GBO) is reported to solve the engineering design optimization problems. However, the basic GBO algorithm is stuck in local optima when handling complex non-linear problems. In this sense, this paper presents a new optimization technique called the Chaotic-GBO (CGBO) algorithm to derive the parameters of PV modules while offering precise I-V and P-V curves. To this end, the CGBO algorithm is based on a chaotic generator to obtain the PV parameters combined with the GBO algorithm. There are five case studies considered to validate the performance of the proposed CGBO algorithm. A quantitative and qualitative performance evaluation reveals that the proposed CGBO algorithm has improved results than other state-of-the-art algorithms in terms of accuracy and robustness when obtaining PV parameters. The average RMSE values and runtime of five case studies are equal to 9.8427E-04, 2.3700E-04, 2.4251E-03, 4.3524E-03 and 1.8349E-03, and 18.44, 17.78, 18.18, 18.28 and 17.97, respectively. The results proved the superiority of the proposed CGBO algorithm over the different selected algorithms. For future research, this study will be backed up with external support at https://premkumarmanoharan.wixsite.com/mysite.
Published: 2021

46. Sensitivity enriched multi-criterion decision making process for novel railway switches and crossings − a case study

Author: Hitesh C. Boghani, Ramakrishnan Ambur, Marcelo Blumenfeld, Roger M. Goodall, Neil Gofton, Roger Dixon, Louis Saade, Clive J. Roberts, Christopher P. Ward, Miquel Morata, and Otto Plasek
Subjects: Process (engineering), Computer science, 0211 other engineering and technologies, Transportation, 02 engineering and technology, Railway tracks, 0502 economics and business, 021108 energy, Sensitivity (control systems), Decision-making, Reliability (statistics), Switches and crossings (S&C), 050210 logistics & transportation, Pugh matrix, Mechanical Engineering, 05 social sciences, lcsh:TA1001-1280, Multiple-criteria decision analysis, Industrial engineering, lcsh:HE1-9990, Weighting, Ranking, Automotive Engineering, Performance improvement, lcsh:Transportation engineering, lcsh:Transportation and communications, Sensitivity analysis, Multi-criterion decision making
Abstract: Background Despite their important role in railway operations, switches and crossings (S&C) have changed little since their conception over a century ago. It stands now that the existing designs for S&C are reaching their maximum point of incremental performance improvement, and only a radical redesign can overcome the constraints that current designs are imposing on railway network capacity. This paper describes the process of producing novel designs for next generation switches and crossings, as part of the S-CODE project. Methods Given the many aspects that govern a successful S&C design, it is critical to adopt multi criteria decision making (MCDM) processes to identify a specific solution for the next generation of switches and crossings. However, a common shortcoming of these methods is that their results can be heavily influenced by external factors, such as uncertainty in criterium weighting or bias of the evaluators, for example. This paper therefore proposes a process based on the Pugh Matrix method to reduce such biases by using sensitivity analysis to investigate them and improve the reliability of decision making. Results In this paper, we analysed the influences of three different external factors, measuring the sensitivity of ranking due to (a) weightings, (b) organisational and (c) discipline bias. The order of preference of the results was disturbed only to a minimum while small influences of bias were detected. Conclusions Through this case study, we believe that the paper demonstrates an effective case study for a quantitative process that can improve the reliability of decision making.
Published: 2021

47. Incorporating Artificial Fish Swarm in Ensemble Classification Framework for Recurrence Prediction of Cervical Cancer

Author: Mazin Abed Mohammed, Jaroslav Frnda, Jothilakshmi Ramakrishnan, G. Senthilkumar, Prayag Tiwari, Mohammad Shorfuzzaman, Deepak Gupta, Manikandan Ramachandran, Anna University, Mazharul Uloom College, University of Zilina, SASTRA, Guru Gobind Singh Indraprastha University, Department of Computer Science, Taif University, University Of Anbar, Aalto-yliopisto, and Aalto University
Subjects: Artificial intelligence, General Computer Science, cervical cancer, Computer science, Feature extraction, Feature selection, 02 engineering and technology, risk score, Machine learning, computer.software_genre, 03 medical and health sciences, feature selection, 0302 clinical medicine, Lasso (statistics), Classifier (linguistics), 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, Imputation (statistics), business.industry, recurrence prediction, Deep learning, the Internet of Things (IoT), General Engineering, Swarm behaviour, TK1-9971, Recurrent neural network, 030220 oncology & carcinogenesis, 020201 artificial intelligence & image processing, Electrical engineering. Electronics. Nuclear engineering, business, computer
Abstract: Funding Information: This work was supported in part by the Grant System of University of Zilina No. 1/2020 under Project 7962, in part by the Slovak Grant Agency for Science (VEGA) under Grant 1/0157/21, and in part by the Taif University Researchers, Taif University, Taif, Saudi Arabia, under Grant TURSP-2020/79. Publisher Copyright: © 2013 IEEE. IoT has facilitated predominant advancements in cancer research by incorporating Artificial intelligence (AI) that enables human decision-makers to achieve a better decision. Recently, Least Absolute Shrinkage and Selection Operator (LASSO) classifier has launched in predicting recurrence cancer genes in the cervix. At the initial phase, the recurrence gene expression of lncRNA is collected from Geo Datasets. Secondly, data imputation, accomplished with Mode and Mean Missing method (MMM-DI). Thirdly, feature selection is compassed using Hilbert-Schmidt independence criterion with Diversity based Artificial Fish Swarm (HSDAFS). In the HSDA.FS algorithm, the diversity parameter is added based on the gene value, and their risk score of the lncRNAs is computed using the Artificial intelligence (AI) technique. Finally, recurrence prediction, an ENSemble Classification Framework (ENSCF), is proposed based on recurrent neural networks. The prognostic factor is computed with a risk score of nine lncRNA signatures for300 samples taken from GSE44001. The Chi-Square method has been used to obtain statistical results. The survival of the patient with recurrence cervical cancer is shown using the proposed model.
Published: 2021

48. The RADx Tech Deployment Core: A Model for Academic/Government Based Support of Large-Scale Manufacturing and Implementation of Medical Technologies

Author: Sreeram Ramakrishnan, A. E. Hosoi, Manuel Kingsley, Susan Moreira, Brian Walsh, Nancy Gagliano, and Paul Tessier
Subjects: NIH, Government, SARS-CoV-2, End user, Process (engineering), Computer science, RADx Tech: A New Paradigm for MedTech Development, COVID-19, Public policy, Bridge (nautical), Engineering management, RADx, Software deployment, Scale (social sciences), device testing, Test plan
Abstract: This paper explores how the approach, process, and learnings of the RADxSM Tech Deployment Core in its support of manufacturing, deployment, and implementation of medical technologies is creating a replicable model for the future. Initially, the key construct of the RADx Tech Deployment Core was helping companies manufacture, commercialize, and develop a digital infrastructure for the purpose of SARS-CoV-2 testing and reporting. However, the team and RADx Tech leadership soon realized that the larger infrastructure to deploy testing in non-clinical environments was nonexistent and that wrap-around services were required to build the necessary bridge between manufacturing and end users. Furthermore, the unique communities that required testing (e.g., manufacturing plants, transportation hubs, K-12 schools, etc.) had different infrastructure requirements and outsized needs for education and support around testing plan implementation. The Deployment Core, therefore, quickly scaled a team to help to complete the picture and provide guidance to end users and ultimately help shape public policy around a useful data model.
Published: 2021

49. FOCUS-DB: An Online Comprehensive Database on Food Additive Safety

Author: Deepak Kumar Sachan, Ramakrishnan Parthasarathi, and Shweta Singh Chauhan
Subjects: Food Safety, food.ingredient, Acceptable daily intake, Resource (biology), Databases, Factual, Database, Computer science, business.industry, General Chemical Engineering, Food additive, digestive, oral, and skin physiology, General Chemistry, Library and Information Sciences, Food safety, computer.software_genre, Risk Assessment, Computer Science Applications, food, Health hazard, Food products, Food Additives, business, Risk assessment, computer
Abstract: Processing and packaging food has greatly exaggerated the use of food additives in different types of food products. Safety assessment to determine the pharmacokinetic and toxicological properties of food additives is imperative and experimentally challenging. Several resources of food additives properties have been collated; however, information remains partial, scattered, and not readily accessible, particularly for food safety. To address the concern related to the potential health hazard of food additives, we have developed the Food-Additive-Consumption-Safety Database (FOCUS-DB). Presently, the database comprises 2885 food additives, distributed into 18 categories with 40,800 collected data points, 89,435 predicted data points, and 14,425 external links. The dynamic web interface of the FOCUS-DB resource enables a risk assessment of additives, their approval status in various regulatory authorities, physicochemical properties, acceptable daily intake, GHS signals, biological pathways, predicted pharmacokinetic parameters, and various toxicity endpoint values. FOCUS-DB supports the exploration of food additives; it is beneficial for both the regulatory authorities and industries to optimize the usage limits of the additives and formulations. This database is a promising tool that helps understand the relationship between food additives and toxicity, which could be used to develop a future food safety framework.
Published: 2020

50. DeepInterpolation: fusion of multiple interpolations and CNN to obtain super‐resolution

Author: Ramakrishnan Angarai Ganesan and Ram Krishna Pandey
Subjects: Complex data type, Computer science, business.industry, Deep learning, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020206 networking & telecommunications, 02 engineering and technology, Convolutional neural network, Manifold, Image (mathematics), Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, Image scaling, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, Electrical and Electronic Engineering, business, Algorithm, Image resolution, Software, Interpolation
Abstract: The authors propose architectures that learn end-to-end mapping functions to improve the spatial resolution of the input natural images. The models are unique in forming non-linear combinations of three image interpolation techniques using the convolutional neural network. Another proposed architecture uses a skip connection with nearest-neighbour interpolation, achieving almost similar results. The architectures have been carefully designed to ensure that the reconstructed images lie precisely in the manifold of high-resolution images, thereby preserving the high-frequency components with fine details. They have compared with the state-of-the-art and recent deep learning-based natural image super-resolution techniques and found that their methods can preserve the sharp details in the image, while also obtaining comparable or better peak-signal-to-noise ratio values than them. Since their methods use image interpolations and a shallow convolutional neural network (CNN) with a fewer number of smaller filters, the computational cost is kept low. They have reported the results of the best two proposed architectures on five standard data sets for an upscale factor of 2. Their methods generalise well in most cases, which is evident from the better results obtained with increasingly complex data sets. For four times upscaling, they have designed similar architectures for comparing with other methods.
Published: 2020

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