25 results on 'LN cat08778a'
Search Results
2. Big data, IoT, and machine learning : tools and applications.
- Author
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Agrawal, Rashmi, Paprzycki, Marcin, and Gupta, Neha
- Subjects
Big data ,Internet of things ,Machine learning - Abstract
Summary: The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them. Features Addresses the complete data science technologies workflow Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning Covers data processing and security solutions in IoT and Big Data applications Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems Presents security issues and data migration techniques of NoSQL databases
- Published
- 2021
3. Machine Learning Design Patterns: solutions to common challenges in data preparation, model building, and MLOps.
- Author
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Lakshmanan, Valliappa, Robinson, Sara, and Munn, Michael
- Subjects
Machine learning ,Big data ,Design patterns - Abstract
Summary: The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hundreds of experts into advice you can easily follow. The authors, three Google Cloud engineers, describe 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the most appropriate remedy for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure that models are treating users fairly.
- Published
- 2021
4. Demystifying big data, machine learning, and deep learning for healthcare analytics / edited by Pradeep Nijalingappa, Sandeep Kautish, Sheng Lung Peng.
- Author
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Nijalingappa, Pradeep, Kautish, Sandeep, and Peng, Sheng Lung
- Subjects
Medical informatics ,Machine learning ,Big data - Abstract
Summary: "Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians"-- Provided by publisher.
- Published
- 2021
5. Codeless Data Structures and Algorithms: Learn DSA Without Writing a Single Line of Code.
- Author
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Subero, Armstrong
- Subjects
Coding and algorithm theory ,Algorithm analyis and Problem complexity ,Big data ,Machine learning - Abstract
Summary: In the era of self-taught developers and programmers, essential topics in the industry are frequently learned without a formal academic foundation. A solid grasp of data structures and algorithms (DSA) is imperative for anyone looking to do professional software development and engineering, but classes in the subject can be dry or spend too much time on theory and unnecessary readings. Regardless of your programming language background, Codeless Data Structures and Algorithms has you covered. In this book, author Armstrong Subero will help you learn DSAs without writing a single line of code. Straightforward explanations and diagrams give you a confident handle on the topic while ensuring you never have to open your code editor, use a compiler, or look at an integrated development environment. Subero introduces you to linear, tree, and hash data structures and gives you important insights behind the most common algorithms that you can directly apply to your own programs. Codeless Data Structures and Algorithms provides you with the knowledge about DSAs that you will need in the professional programming world, without using any complex mathematics or irrelevant information. Whether you are a new developer seeking a basic understanding of the subject or a decision-maker wanting a grasp of algorithms to apply to your projects, this book belongs on your shelf. Quite often, a new, refreshing, and unpretentious approach to a topic is all you need to get inspired.
- Published
- 2020
6. Advances in deep learning.
- Author
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Wani, Arif M.
- Subjects
Education--Data processing ,Machine learning ,Big data - Abstract
Summary: This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.
- Published
- 2019
7. Deep learning through sparse and low-rank modeling.
- Author
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Wang, Zhangyang, Fu, Yun, and Huang, Thomas S.
- Subjects
Machine learning ,Big data ,Data mining - Abstract
Summary: Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.
- Published
- 2019
8. Development and Analysis of Deep Learning Architectures.
- Author
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Pedrycz, Witold and Chen, Shyi-Ming
- Subjects
Machine learning ,Deep learning ,Big data - Abstract
Summary: This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.
- Published
- 2019
9. Applications of machine learning in wireless communications.
- Author
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He, Ruisi and Zhiguo Ding
- Subjects
Wireless communication systems ,Analysis ,Data mining ,Data processing ,Machine learning ,Radio ,Telecommunication ,Big Data ,data analysis ,data mining ,learning (artificial intelligence) ,radiocommunication ,telecommunication computing - Abstract
Summary: In such an era of big data where data mining and data analysis technologies are effective approaches for wireless system evaluation and design, the applications of machine learning in wireless communications have received a lot of attention recently. Machine learning provides feasible and new solutions for the complex wireless communication system design. It has been a powerful tool and popular research topic with many potential applications to enhance wireless communications, e.g. radio channel modelling, channel estimation and signal detection, network management and performance improvement, access control, resource allocation. However, most of the current researches are separated into different fields and have not been well organized and presented yet. It is therefore difficult for academic and industrial groups to see the potentialities of using machine learning in wireless communications. It is now appropriate to present a detailed guidance of how to combine the disciplines of wireless communications and machine learning.
- Published
- 2019
10. Data science.
- Author
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Kelleher, John D. and Tierney, Brendan
- Subjects
Big data ,Machine learning ,Data mining ,Quantitative research - Abstract
Summary: "The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges."--Provided by publisher.
- Published
- 2018
11. The deep learning revolution.
- Author
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Sejnowski, Terrence J.
- Subjects
Machine learning ,Big data ,Artificial intelligence - Abstract
Summary: How deep learning-from Google Translate to driverless cars to personal cognitive assistants-is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
- Published
- 2018
12. Cloud computing for machine learning and cognitive applications.
- Author
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Hwang, Kai
- Subjects
Cloud computing ,Machine learning ,Data mining ,Big data - Abstract
Summary: The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies. This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage, transmission and mashup, and AI techniques to analyze this data. This textbook is designed to train college students to master modern cloud computing systems in operating principles, architecture design, machine learning algorithms, programming models and software tools for big data mining, analytics, and cognitive applications. The book will be suitable for use in one-semester computer science or electrical engineering courses on cloud computing, machine learning, cloud programming, cognitive computing, or big data science. The book will also be very useful as a reference for professionals who want to work in cloud computing and data science. Cloud and Cognitive Computing begins with two introductory chapters on fundamentals of cloud computing, data science, and adaptive computing that lay the foundation for the rest of the book. Subsequent chapters cover topics including cloud architecture, mashup services, virtual machines, Docker containers, mobile clouds, IoT and AI, inter-cloud mashups, and cloud performance and benchmarks, with a focus on Google’s Brain Project, DeepMind, and X-Lab programs, IBKai HwangM SyNapse, Bluemix programs, cognitive initiatives, and neurocomputers. The book then covers machine learning algorithms and cloud programming software tools and application development, applying the tools in machine learning, social media, deep learning, and cognitive applications. All cloud systems are illustrated with big data and cognitive application examples.
- Published
- 2017
13. Analysis of multivariate and high-dimensional data.
- Author
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Koch, Inge
- Subjects
Multivariate analysis ,Big data ,Multidimensional data ,Machine learning - Abstract
Summary: "'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text integrates the two strands into a coherent treatment, drawing together theory, data, computation and recent research. The theoretical framework includes formal definitions, theorems and proofs, which clearly set out the guaranteed 'safe operating zone' for the methods and allow users to assess whether data is in or near the zone. Extensive examples showcase the strengths and limitations of different methods in a range of cases: small classical data; data from medicine, biology, marketing and finance; high-dimensional data from bioinformatics; functional data from proteomics; and simulated data. High-dimension, low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code and problem sets complete the package. The text is suitable for graduate students in statistics and researchers in data-rich disciplines"-- Provided by publisher.
- Published
- 2014
14. Machine learning in action.
- Author
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Harrington, Peter
- Subjects
Machine learning ,Machine learning -- Handbooks, manuals, etc ,Big data ,Apriori algorithm - Abstract
Summary: Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About this Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos About the Author Peter Harrington is a professional developer and data scientist. He holds five US patents and his work has been published in numerous academic journals.
- Published
- 2012
15. Pattern recognition and machine learning.
- Author
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Bishop, Christopher M.
- Subjects
Pattern perception ,Machine learning ,Programming language ,Big Data - Published
- 2006
16. Open Data and Energy Analytics.
- Author
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Nastasi, Benedetto, Manfren, Massimiliano, Noussan, Michel, and Nastasi, Benedetto
- Subjects
Research & information: general ,data envelopment analysis ,Kohonen self-organizing maps ,factor analysis ,multiple regression ,energy efficiency ,social media ,energy-consuming activities ,energy consumption ,machine learning ,ontology ,energy performance certificate ,heating energy demand ,buildings ,data mining ,classification ,regression ,decision tree ,support vector machine ,random forest ,artificial neural network ,open data ,electrification modelling ,Malawi ,OnSSET ,MESSAGEix ,reproducibility ,collaborative work ,open modelling and data ,data-handling ,integrated assessment modelling ,data pre- and post-processing ,space heating ,domestic hot water ,market assessment ,EU28 ,district heating ,data analytics ,big data ,forecasting ,energy ,polygeneration ,clustering ,kNN ,pattern recognition ,heating ,building stock ,heat map ,spatial analysis ,heat density map ,building performance simulation ,parametric modelling ,energy management ,model calibration ,Passive House ,energy planning ,energy potential mapping ,urban energy atlas ,urban energy transition ,energy data ,data-aware planning ,spatial planning ,open data analytics ,smart cities ,open energy governance ,urban database ,energy mapping ,building dataset ,energy modelling - Abstract
Summary: Open data and policy implications coming from data-aware planning entail collection and pre- and postprocessing as operations of primary interest. Before these steps, making data available to people and their decision-makers is a crucial point. Referring to the relationship between data and energy, public administrations, governments, and research bodies are promoting the construction of reliable and robust datasets to pursue policies coherent with the Sustainable Development Goals, as well as to allow citizens to make informed choices. Energy engineers and planners must provide the simplest and most robust tools to collect, process, and analyze data in order to offer solid data-based evidence for future projections in building, district, and regional systems planning. This Special Issue aims at providing the state-of-the-art on open-energy data analytics; its availability in the different contexts, i.e., country peculiarities; and its availability at different scales, i.e., building, district, and regional for data-aware planning and policy-making. For all the aforementioned reasons, we encourage researchers to share their original works on the field of open data and energy analytics. Topics of primary interest include but are not limited to the following: 1. Open data and energy sustainability; 2. Open data science and energy planning; 3. Open science and open governance for sustainable development goals; 4. Key performance indicators of data-aware energy modelling, planning, and policy; 5. Energy, water, and sustainability database for building, district, and regional systems; 6. Best practices and case studies.
17. Algorithms in Decision Support Systems.
- Author
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García-Díaz, Vicente and García-Díaz, Vicente
- Subjects
History of engineering & technology ,semi-supervised learning ,transfer learning ,radar emitter ,decision support systems ,population health management ,big data ,machine learning ,deep learning ,personalized patient care ,Nonlinear regression ,interactive platform ,component-based approach ,software architecture ,Eclipse-RCP (Rich Client Platform) ,spatial prediction ,rule-based expert systems ,tennis hitting technique ,computer algebra systems ,Groebner bases ,Boolean logic ,data envelopment analysis ,dimensionality reduction ,ensembles ,exhaustive state space search ,entropy ,associative classification ,class association rule ,vertical data representation ,classification ,algorithm evaluation ,parallel algorithms ,multi-objective optimization ,train rescheduling ,very large-scale decision support systems ,very large-scale data and program cores of information systems ,meta-database ,teleological meta-database ,thematic list ,indicators list ,computational methods list ,geographically dispersed systems ,external sources - Abstract
Summary: This book aims to provide a new vision of how algorithms are the core of decision support systems (DSSs), which are increasingly important information systems that help to make decisions related to unstructured and semi-unstructured decision problems that do not have a simple solution from a human point of view. It begins with a discussion of how DSSs will be vital to improving the health of the population. The following article deals with how DSSs can be applied to improve the performance of people doing a specific task, like playing tennis. It continues with a work in which authors apply DSSs to insect pest management, together with an interactive platform for fitting data and carrying out spatial visualization. The next article improves how to reschedule trains whenever disturbances occur, together with an evaluation framework. The final works focus on different relevant areas of DSSs: 1) a comparison of ensemble and dimensionality reduction models based on an entropy criterion; 2) a radar emitter identification method based on semi-supervised and transfer learning; 3) design limitations, errors, and hazards in creating very large-scale DSSs; and 4) efficient rule generation for associative classification. We hope you enjoy all the contents in the book.
18. Applications in Electronics Pervading Industry, Environment and Society. Sensing Systems and Pervasive Intelligence.
- Author
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Saponara, Sergio, De Gloria, Alessandro, Bellotti, Francesco, and Saponara, Sergio
- Subjects
Technology: general issues ,model-based design ,FPGA ,HDL code generation ,wearable sensors ,embedded devices ,face recognition ,face verification ,biometric sensors ,deep learning ,distillation ,convolutional neural networks ,spatial transformer network ,video coding ,discrete cosine transform ,directional transform ,VLSI ,alternative representations to float numbers ,posit arithmetic ,Deep Neural Networks (DNNs) ,neural network activation functions ,surface electromyography ,event-driven ,functional electrical stimulation ,embedded system ,resampling ,interpolating polynomial ,polyphase filter ,digital circuit design ,ASIC ,bitmap indexing ,processing in memory ,memory wall ,big data ,internet of things ,intelligent sensors ,autonomous driving ,cyber security ,HW accelerator ,on-chip random number generator (RNG) ,SHA2 ,ASIC standard-cell ,machine learning ,edge computing ,edge analytics ,ANN ,k-NN ,SVM ,decision trees ,ARM ,X-Cube-AI ,STM32 Nucleo ,rad-hard ,PLL (phase-locked loop) ,SEE (single event effects) ,Spacefibre ,TID (total ionization dose) ,charge pump ,phase/frequency detector ,frequency divider ,ring oscillator ,LC-tank oscillator ,SpaceFibre ,rad-hard circuits ,radiation effects ,high-speed data transfer ,support attitude ,inertial measurement unit ,coal mining ,unscented Kalman filter ,quaternion ,gradient descent ,research data collection and sharing ,connected and automated driving ,deployment and field testing ,vehicular sensors ,impact assessment ,knowledge management ,collaborative project methodology ,n/a - Abstract
Summary: This book features the manuscripts accepted for the Special Issue "Applications in Electronics Pervading Industry, Environment and Society-Sensing Systems and Pervasive Intelligence" of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the "Applications in Electronics Pervading Industry, Environment and Society" (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs.
19. The Application of Computer Techniques to ECG Interpretation.
- Author
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Macfarlane, Peter and Macfarlane, Peter
- Subjects
Medicine ,electrocardiographic imaging (ECGI) ,heart failure (HF) ,cardiac resynchronization therapy (CRT) ,ultrasound ,strain ,speckle tracking echocardiography ,in silico ,electrophysiology ,electrocardiogram ,ECG ,cardiac disease ,arrhythmia ,ischemia ,standardization ,computerized ECG ,personalized medicine ,telemedicine ,digital ECG data interchange protocol ,eHealth ,ECG equipment ,computerized electrocardiograph ,ECG analysis algorithms ,computerized ECG interpretation ,interatrial block ,partial interatrial block ,advanced interatrial block ,atypical patterns ,electrocardiogram (ECG) ,automated ECG analysis ,CSE study ,age ,sex ,race ,historical aspects ,electronic cohort ,mortality ,big data ,telehealth ,alarm fatigue ,annotation of ECG data ,arrhythmia alarms ,intensive care unit ,patient monitoring ,ambulatory ECG ,machine learning ,deep learning ,pattern recognition ,noise reduction ,Holter ECG ,ECG interpretation ,artificial intelligence ,body surface mapping ,electrocardiographic imaging ,image processing ,clinical applications ,n/a - Abstract
Summary: This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field.
20. Big Data in Dental Research and Oral Healthcare.
- Author
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Joda, Tim and Joda, Tim
- Subjects
Medicine ,digital transformation ,rapid prototyping ,augmented and virtual reality (AR/VR) ,artificial intelligence (AI) ,machine learning (ML) ,personalized dental medicine ,tele-health ,patient-centered outcomes ,integrated care, medical-dental integration, simulation model, dental research ,oral medicine ,oral healthcare ,dentistry ,gerodontology ,elderly patient ,big data ,Big Data ,digital dentistry ,oral health ,ethical issues ,dental education ,augmented reality (AR) ,virtual reality (VR) ,artificial intelligence ,AI ,machine learning ,ML ,cone beam computed tomography (CBCT) ,intraoral scanning ,facial scanning ,healthcare cost ,medical healthcare cost ,dental healthcare cost ,zero-inflated model ,neural network - Abstract
Summary: Progress in information technology has fostered a global explosion of data generation. Accumulated big data are now estimated to be 4.4 zettabytes in the digital universe; and trends predict an exponential increase in the future. Health data are gathered from professional routine care and other expanded sources including the social determinants of health, such as Internet of Things. Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of stored health data, dental medicine is edging into its fourth stage of digitization using new technologies including augmented and virtual reality, artificial intelligence, and blockchain. Big data collaborations involve interactions between a diverse range of stakeholders with analytical, technical and political focus. In oral healthcare, data technology has many areas of application: prognostic analysis and predictive modeling, the identification of unknown correlations of diseases, clinical decision support for novel treatment concepts, public health surveys and population-based clinical research, as well as the evaluation of healthcare systems. The objective of this Special Issue is to provide an update on the current knowledge with state-of-the-art theory and practical information on human and social perspectives that determine the uptake of technological innovations in big data science in the field of dental medicine. Moreover, it will focus on the identification of future research needs to manage the continuous increase in health data and to accomplish its clinical translation for patient-centered research and personalized dentistry. This Special Issue welcomes all types of studies and reviews considering the perspectives of different stakeholders on technological innovations for big data science in all dental disciplines. Kind regards,
21. Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation.
- Author
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Cheungpasitporn, Wisit, Thongprayoon, Charat, Kaewput, Wisit, and Cheungpasitporn, Wisit
- Subjects
Medicine ,tacrolimus ,C/D ratio ,tacrolimus metabolism ,everolimus ,conversion ,kidney transplantation ,gut microbiome ,renal transplant recipient ,diarrhea ,immunosuppressive medication ,gut microbiota ,16S rRNA sequencing ,butyrate-producing bacteria ,Proteobacteria ,torquetenovirus ,immunosuppression ,transplantation ,immunosuppressed host ,outcome ,renal transplantation ,Goodpasture syndrome ,anti-GBM disease ,epidemiology ,hospitalization ,outcomes ,acute kidney injury ,risk prediction ,artificial intelligence ,patent ductus arteriosus ,conservative management ,blood pressure ,eradication ,interferon-free regimen ,hepatitis C infection ,kidney transplant ,allograft steatosis ,lipopeliosis ,transplant numbers ,live donors ,public awareness ,Google TrendsTM ,machine learning ,big data ,nephrology ,chronic kidney disease ,NLR ,PLR ,RPGN ,predictive value ,hemodialysis ,withdrawal ,cellular crescent ,global sclerosis ,procurement kidney biopsy ,glomerulosclerosis ,minimally-invasive donor nephrectomy ,robot-assisted surgery ,laparoscopic surgery ,organ donation ,living kidney donation ,MeltDose® ,LCPT ,renal function ,liver transplantation ,metabolism ,erythropoietin ,fibroblast growth factor 23 ,death ,weekend effect ,in-hospital mortality ,comorbidity ,dialysis ,elderly ,klotho ,α-Klotho ,FGF-23 ,kidney donor ,Nephrology ,CKD-MBD ,CKD-Mineral and Bone Disorder ,deceased donor ,Eurotransplant Senior Program ,risk stratification ,intensive care ,kidney transplant recipients ,long-term outcomes ,graft failure ,cardiovascular mortality ,lifestyle ,inflammation ,vascular calcification ,bone mineral density ,dual-energy X-ray absorptiometry ,living donation ,repeated kidney transplantation ,graft survival ,prolonged ischaemic time ,patient survival ,pre-emptive transplantation ,metabolomics ,urine ,acute rejection ,allograft ,cystatin C ,hyperfiltration ,kidney injury molecule (KIM)-1 ,tubular damage ,genetic polymorphisms ,(cardiac) surgery ,inflammatory cytokines ,clinical studies ,chronic kidney disease (CKD) ,no known kidney disease (NKD) ,ICD-10 billing codes ,phenotyping ,electronic health record (EHR) ,estimated glomerular filtration rate (eGFR) ,machine learning (ML) ,generalized linear model network (GLMnet) ,random forest (RF) ,artificial neural network (ANN), clinical natural language processing (clinical NLP) ,discharge summaries ,laboratory values ,area under the receiver operating characteristic (AUROC) ,area under the precision-recall curve (AUCPR) ,fibrosis ,extracellular matrix ,collagen type VI ,living-donor kidney transplantation ,ethnic disparity - Abstract
Summary: In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid-base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes.
22. Data Science for Economics and Finance. Methodologies and Applications.
- Author
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Consoli, Sergio, Reforgiato Recupero, Diego, Saisana, Michaela, and Consoli, Sergio
- Subjects
Data mining ,Machine learning ,Business mathematics & systems ,Public administration ,Information retrieval ,Data Mining and Knowledge Discovery ,Machine Learning ,Business Information Systems ,Big Data/Analytics ,Computer Appl. in Administrative Data Processing ,Information Storage and Retrieval ,IT in Business ,Computer and Information Systems Applications ,Open Access ,Data Mining ,Big Data ,Data Analytics ,Decision Support Systems ,Semantics and Reasoning ,Expert systems / knowledge-based systems ,Information technology: general issues ,Data warehousing - Abstract
Summary: This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
23. Data Science in Healthcare.
- Author
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Hulsen, Tim and Hulsen, Tim
- Subjects
Medicine ,Pharmacology ,data sharing ,data management ,data science ,big data ,healthcare ,depression ,psychological treatment ,task sharing ,primary care ,pilot study ,non-specialist health worker ,training ,digital technology ,mental health ,COVID-19 ,SARS-CoV-2 ,pneumonia ,computed tomography ,case fatality rate ,social distancing ,smoking ,metabolically healthy obese phenotype ,metabolic syndrome ,obesity ,coronavirus ,machine learning ,social media ,apache spark ,Twitter ,Arabic language ,distributed computing ,smart cities ,smart healthcare ,smart governance ,Triple Bottom Line (TBL) ,thoracic pain ,tree classification ,cross-validation ,hand-foot-and-mouth disease ,early-warning model ,neural network ,genetic algorithm ,sentinel surveillance system ,outbreak prediction ,artificial intelligence ,vascular access surveillance ,arteriovenous fistula ,end stage kidney disease ,dialysis ,kidney failure ,chronic kidney disease (CKD) ,end-stage kidney disease (ESKD) ,kidney replacement therapy (KRT) ,risk prediction ,naïve Bayes classifiers ,precision medicine ,machine learning models ,data exploratory techniques ,breast cancer diagnosis ,tumors classification ,n/a - Abstract
Summary: Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management.
24. Introducing data science : big data, machine learning, and more, using Python tools.
- Author
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Cielen, Davy, Meysman, Arno, and Ali, Mohamed
- Subjects
Data mining ,Big data ,Machine learning ,Python (Computer program language) ,Python ,Massendaten - Abstract
Summary: "Introducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You'll explore data visualization, graph databases, the use of NoSQL, and the data science process. You'll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels"--Back cover.
25. Mastering machine learning with python in six steps: a practical implementation guide to predictive data analytics using Python.
- Author
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Swamynathan, Manohar
- Subjects
Computer science ,Computing methodologies ,Big data ,Open source ,Machine learning ,Computers - Machine theory ,Python - Programming language ,Data mining - Abstract
Summary: Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book's approach is based on the "Six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. You'll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you'll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation. https://www.apress.com/in/book/9781484228654
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