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Nowadays, literature review is a necessary task when trying to solve a given problem. However, an exhaustive literature review is very time-consuming in today's vast literature landscape. It can take weeks, even if looking only for abstracts or surveys. Moreover, choosing a method among others, and targeting searches within relevant problem and solution domains, are not easy tasks. These are especially true for young researchers or engineers starting to work in their field. Even if surveys that provide methods used to solve a specific problem already exist, an automatic way to do it for any use case is missing, especially for those who don't know the existing literature. Our proposed tool, SARBOLD-LLM, allows discovering and choosing among methods related to a given problem, providing additional information about their uses in the literature to derive decision-making insights, in only a few hours. The SARBOLD-LLM comprises three modules: (1: Scopus search) paper selection using a keyword selection scheme to query Scopus API; (2: Scoring and method extraction) relevancy and popularity scores calculation and solution method extraction in papers utilizing OpenAI API (GPT 3.5); (3: Analyzes) sensitivity analysis and post-analyzes which reveals trends, relevant papers and methods. Comparing the SARBOLD-LLM to manual ground truth using precision, recall, and F1-score metrics, the performance results of AI in the oncology case study are 0.68, 0.9, and 0.77, respectively. SARBOLD-LLM demonstrates successful outcomes across various domains, showcasing its robustness and effectiveness. The SARBOLD-LLM addresses engineers more than researchers, as it proposes methods and trends without adding pros and cons. It is a useful tool to select which methods to investigate first and comes as a complement to surveys. This can limit the global search and accumulation of knowledge for the end user. However, it can be used as a director or recommender for future implementation to solve a problem. Highlights: Automated support for literature choice and solution selection for any use case. A generalized keyword selection scheme for literature database queries. Trends in literature: detecting AI methods for a case study using Scopus and OpenAI. A better understanding of the tool by sensitivity analyzes for Scopus and OpenAI. Robust tool for different domains with promising OpenAI performance results. [ABSTRACT FROM AUTHOR]

EU-US data transfers for health research remain a particularly thorny issue in view of the stringent rules of the EU General Data Protection Regulation (GDPR) and the challenges related to US mass surveillance programs, particularly the manner in which US law enforcement and national security agencies can access personal data originating from the EU. Since the entry into force of the GDPR, evidence of impeded collaborations is increasing, particularly in the case of sharing data with US public institutions. The adoption of a new EU-US adequacy decision in July 2023 does not hold the promise for a long-lasting solution due to the risks of being challenged and invalidated – yet again – at the Court of Justice of the EU. As the research community is calling for answers, the new proposal for a European Health Data Space regulation may hold a key to solving some of the existing issues. In this paper, we critically discuss the current rules and outline a possible way forward for transfers between public bodies. [ABSTRACT FROM AUTHOR]

The metaverse is seen as a future generation of the Internet in which the virtual and the real merge into a common world. Technologies such as IoT, cloud computing, artificial intelligence or the reality–virtuality continuum underpin the metaverse and condition its evolution. This paper presents a bibliometric study on the WoS database (from 1995 to 2022) to obtain a comprehensive and non-subjective understanding of the metaverse. The study identifies the main subject areas and sources, the leading countries and authors. It also analyzes the evolution over time and the core and future research themes. Extended reality, blockchain, artificial intelligence and sensors are identified as core themes, while building information modeling, digital twins and governance emerge as future themes. Based on the bibliometric study, a general layered metaverse architecture is proposed that streamlines open challenges in the metaverse to assist researchers and companies in introducing innovative and disruptive improvements. [ABSTRACT FROM AUTHOR]

This paper is a literature review of the intersection field between Artificial Intelligence (AI) and Customer Experience (CX). We analyzed and synthesized the most recent and prominent literature on the subject, providing an overview of the state of the art, through articles found in the Scopus database. Among the main findings, it is noteworthy that this intersection appears as an interdisciplinary topic of interest in the fields of Computer Science, Business and Management, and Engineering. Additionally, studies often examine conversational agents such as chatbots and voicebots, as well as machine learning prediction models and recommendation systems as a way to improve the Customer Experience. The most common sectors in the review are tourism, banking and e-commerce. Other segments and technologies appear less and may be underrepresented, thus a scope for future research agenda. Despite the existing literature, it is observed that there is still substantial space for expansion and exploration, especially considering the emergence of new generative Artificial Intelligence models. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the model-, code-, and data-sharing practices in the current radiomics research landscape and to introduce a radiomics research database. Methods: A total of 1254 articles published between January 1, 2021, and December 31, 2022, in leading radiology journals (European Radiology, European Journal of Radiology, Radiology, Radiology: Artificial Intelligence, Radiology: Cardiothoracic Imaging, Radiology: Imaging Cancer) were retrospectively screened, and 257 original research articles were included in this study. The categorical variables were compared using Fisher's exact tests or chi-square test and numerical variables using Student's t test with relation to the year of publication. Results: Half of the articles (128 of 257) shared the model by either including the final model formula or reporting the coefficients of selected radiomics features. A total of 73 (28%) models were validated on an external independent dataset. Only 16 (6%) articles shared the data or used publicly available open datasets. Similarly, only 20 (7%) of the articles shared the code. A total of 7 (3%) articles both shared code and data. All collected data in this study is presented in a radiomics research database (RadBase) and could be accessed at https://github.com/EuSoMII/RadBase. Conclusion: According to the results of this study, the majority of published radiomics models were not technically reproducible since they shared neither model nor code and data. There is still room for improvement in carrying out reproducible and open research in the field of radiomics. Clinical relevance statement: To date, the reproducibility of radiomics research and open science practices within the radiomics research community are still very low. Ensuring reproducible radiomics research with model-, code-, and data-sharing practices will facilitate faster clinical translation. Key Points: • There is a discrepancy between the number of published radiomics papers and the clinical implementation of these published radiomics models. • The main obstacle to clinical implementation is the lack of model-, code-, and data-sharing practices. • In order to translate radiomics research into clinical practice, the radiomics research community should adopt open science practices. [ABSTRACT FROM AUTHOR]

Muslims aim to recite and memorize the Holy Quran correctly. However, traditional recitation verification approaches depend on humans who may not be available. On the other hand, Artificial Intelligence (AI) capabilities assist in developing intelligent recitation verification tools based on speech recognition techniques. This study aims to overview the current state of the intelligent Quran recitation recognition and verification solutions and highlight the related open issues. A systematic literature review was performed on the published paper since 2006 up to date to answer six research questions. The research questions covered the speech recognition techniques and methods used to develop Quran recitation recognition and verification models, the database and tools used, and the existing mobile application supporting real-time intelligent Quran recitation verification services. Based on the review results, a taxonomy of the Quran recitation recognition and verification techniques was generated, including traditional and end-to-end speech recognition methods. Moreover, the limitations of the existing AI-based Quran recitation verification applications were reported. Additionally, the available Quran audio datasets and tools capable of dealing with Quranic speech were identified. In conclusion, several open issues can be addressed in future research, e.g., considering AI-based approaches to ensure sequence recitation and recognize diacritics-based errors. [ABSTRACT FROM AUTHOR]

Technology is paving innovative ways to provide financial services and improve the efficiency of financial systems. Since it is a dynamic field of research, it is important to look back on the ever-changing field of financial technology. This paper aims to analyse the existing research on financial technology through a bibliometric approach. The data were gathered from the Scopus database using secondary sources, and the analysis is presented descriptively along with science-mapping techniques. This paper offers an overview of the influential journals, authors, and organizations contributing to financial technology research. The study focuses on citation, cocitation, bibliographic coupling, and coauthorship analysis within the collected corpus. It is worth noting that this study is limited by the use of only one database, Scopus and excludes grey literature, this could lead to skewed results but this can be an arena for future research. [ABSTRACT FROM AUTHOR]

Data missing has always occurred in data processing. To solve this problem, researchers have improved the process methods of the missing data with diverse strategies, which range from directly deleting missing data samples to using artificial intelligence technology to filling in incomplete data. The processing methods of the missing data have been improved. Generative adversarial imputation network (GAIN) is a kind of neural network which has an excellent performance in missing data imputation. A number of publications that research and cite the GAIN model show a significant growth trend after GAIN was proposed in 2018. GAIN has been studied and improved by many scholars in their specific fields. However, few studies have systematically surveyed the GAIN model's development trends on missing data from its birth to the present, which result in a lack of comprehensive information about GAINs general performance in different fields. In this review, we summarize the development of the GAIN model in missing data imputation from 2018 to 2022. Based on the WOS database, 32 publications are selected according to the PRISMA statement. The outcome of this paper is from the following aspects: (1) analyzing the publication information and application fields quantitatively; (2) expounding the GAIN-based models, classification, and research trends; (3) elaborating the model attributes and missing data mechanism; and (4) summarizing the existing issues and proposing the future directions. Above all, this paper can help scholars gain further insight into the missing data issues and better understand the optimized directions of GAIN models. [ABSTRACT FROM AUTHOR]

This study employs bibliometric analysis to investigate the evolving research landscape of Artificial intelligence (AI) within Africa, focusing on the years 2013 to 2022. The central objective is to discern and analyze AI studies conducted in Africa, using a dataset compiled from research papers within the Scopus database. By conducting a comprehensive analysis, this research uncovers crucial insights, including primary authors, influential journals and publishers, nations with the highest research productivity, noteworthy funding sources, influential organizations, and prevalent research domains. Additionally, the study examines year-by-year growth trends and authorship patterns. Employing the VOSviewer software, it creates visual representations that illustrate the dynamic evolution of AI research within the African context. Notably, the analysis of 1646 publications reveals a significant increase in publications over the last decade, with South Africa emerging as a global leader in AI development, and the IEEE, Elsevier, and Springer as prominent publishers. The study also highlights the leading institutions, with the University of the Witwatersrand, University of Johannesburg, University of KwaZulu-Natal, University of Cape Town, and University of Pretoria at the forefront of AI research in Africa. The National Research Foundation is identified as the primary funder supporting AI research across the continent. In conclusion, this research aims to provide a comprehensive understanding of AI's role in addressing African challenges, fostering innovation, and contributing to the continent's technological advancement, shedding light on prevalent research areas and significant funding sources in the process. [ABSTRACT FROM AUTHOR]

This paper presents AutoTemplate, an innovative data preprocessing protocol, addressing the crucial need for high-quality chemical reaction datasets in the realm of machine learning applications in organic chemistry. Recent advances in artificial intelligence have expanded the application of machine learning in chemistry, particularly in yield prediction, retrosynthesis, and reaction condition prediction. However, the effectiveness of these models hinges on the integrity of chemical reaction datasets, which are often plagued by inconsistencies like missing reactants, incorrect atom mappings, and outright erroneous reactions. AutoTemplate introduces a two-stage approach to refine these datasets. The first stage involves extracting meaningful reaction transformation rules and formulating generic reaction templates using a simplified SMARTS representation. This simplification broadens the applicability of templates across various chemical reactions. The second stage is template-guided reaction curation, where these templates are systematically applied to validate and correct the reaction data. This process effectively amends missing reactant information, rectifies atom-mapping errors, and eliminates incorrect data entries. A standout feature of AutoTemplate is its capability to concurrently identify and correct false chemical reactions. It operates on the premise that most reactions in datasets are accurate, using these as templates to guide the correction of flawed entries. The protocol demonstrates its efficacy across a range of chemical reactions, significantly enhancing dataset quality. This advancement provides a more robust foundation for developing reliable machine learning models in chemistry, thereby improving the accuracy of forward and retrosynthetic predictions. AutoTemplate marks a significant progression in the preprocessing of chemical reaction datasets, bridging a vital gap and facilitating more precise and efficient machine learning applications in organic synthesis. Scientific contribution: The proposed automated preprocessing tool for chemical reaction data aims to identify errors within chemical databases. Specifically, if the errors involve atom mapping or the absence of reactant types, corrections can be systematically applied using reaction templates, ultimately elevating the overall quality of the database. [ABSTRACT FROM AUTHOR]

In recent years, spatial transcriptomics (ST) research has become a popular field of study and has shown great potential in medicine. However, there are few bibliometric analyses in this field. Thus, in this study, we aimed to find and analyze the frontiers and trends of this medical research field based on the available literature. A computerized search was applied to the WoSCC (Web of Science Core Collection) Database for literature published from 2006 to 2023. Complete records of all literature and cited references were extracted and screened. The bibliometric analysis and visualization were performed using CiteSpace, VOSviewer, Bibliometrix R Package software, and Scimago Graphica. A total of 1467 papers and reviews were included. The analysis revealed that the ST publication and citation results have shown a rapid upward trend over the last 3 years. Nature Communications and Nature were the most productive and most co-cited journals, respectively. In the comprehensive global collaborative network, the United States is the country with the most organizations and publications, followed closely by China and the United Kingdom. The author Joakim Lundeberg published the most cited paper, while Patrik L. Ståhl ranked first among co-cited authors. The hot topics in ST are tissue recognition, cancer, heterogeneity, immunotherapy, differentiation, and models. ST technologies have greatly contributed to in-depth research in medical fields such as oncology and neuroscience, opening up new possibilities for the diagnosis and treatment of diseases. Moreover, artificial intelligence and big data drive additional development in ST fields. Spatial transcriptomics (ST) technologies and application prospects. (1) Imaging-based approaches, including in situ sequencing (ISS)—where transcripts are amplified and sequenced in tissue—and ISH-based approaches—where imaging probes are sequentially hybridized in tissue. (2) NGS-based techniques, in which positional information is encoded onto transcripts prior to NGS sequencing. [ABSTRACT FROM AUTHOR]

Speech emotion recognition (SER) has gained significant attention due to its several application fields, such as mental health, education, and human-computer interaction. However, the accuracy of SER systems is hindered by high-dimensional feature sets that may contain irrelevant and redundant information. To overcome this challenge, this study proposes an iterative feature boosting approach for SER that emphasizes feature relevance and explainability to enhance machine learning model performance. Our approach involves meticulous feature selection and analysis to build efficient SER systems. In addressing our main problem through model explainability, we employ a feature evaluation loop with Shapley values to iteratively refine feature sets. This process strikes a balance between model performance and transparency, which enables a comprehensive understanding of the model's predictions. The proposed approach offers several advantages, including the identification and removal of irrelevant and redundant features, leading to a more effective model. Additionally, it promotes explainability, facilitating comprehension of the model's predictions and the identification of crucial features for emotion determination. The effectiveness of the proposed method is validated on the SER benchmarks Toronto emotional speech set (TESS), Berlin Database of Emotional Speech (EMO-DB), Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), and Surrey Audio-Visual Expressed Emotion (SAVEE) dataset, outperforming state-of-the-art methods. These results highlight the potential of the proposed technique in developing accurate and explainable SER systems. To the best of our knowledge, this is the first work to incorporate model explainability into an SER framework. The source code of this paper is publicly available via this https://github.com/alaaNfissi/Unveiling-Hidden-Factors-Explainable-AI-for-Feature-Boosting-in-Speech-Emotion-Recognition. [ABSTRACT FROM AUTHOR]

The measurement of electricity consumption at 15-minute granularity, including for households, is increasingly mandated in the EU and this also allows, once sufficient data have been collected, the prediction of future consumption at the same time intervals. In this paper, we present preliminary results of the industry project that aims to build AI models for next-day electricity consumption at 15-minute granularity. We have identified the main influencing factors, developed scripts and databases to collect data about these features and about the past electricity consumption at 15-minute granularity for each measuring point, and, finally, developed three AI models to predict the future electricity consumption for each 15-minute interval and each measurement point. We provide descriptive analyses for all measuring points that were in the database in April 2022 and show that for computing the prediction of accumulated electricity consumption at 15-minute granularity, it is much more accurate (in terms of mean absolute percentage error – MAPE) to compute the prediction for each measuring point and accumulate these predictions. An evaluation of the models on the list of the 10 outstanding measuring points (according to the data provider) shows that our predictions achieve very good MAPE. Additionally, we have provided an evaluation of possible ways of parallelization within R, and laid out results of a computational study using parallel, doParallel, and foreach R libraries. [ABSTRACT FROM AUTHOR]

Purpose: The industry of the future through digital twins is constantly seeking efficiency, competitiveness, and innovation, particularly in the case of the diagnosis of rotating structures. Their use in the maintenance of systems has been the subject of several research in recent years by studying simulated vibration behaviour of several operating modes, as well as building a substantial database. Method: However, the limitations in its implementation lie in the accuracy and reliability of the simulated data, which can lead to a misdiagnosis of the state of the system. The study presented in this paper proposes a method of diagnosis based on the digital twin using a digital and experimental database of a gearbox classified with the Multiclass Support Vector Machine (MSVM). To come up with an outcome, experimental data are collected on the test bench in different modes of operation, then for each of these modes, simulations are made on the numerical model created to generate data, which will be updated on those from the physical model. Results: Using a classification algorithm, data from the numerical model are used to train the model and experimental data are used for testing. The results provide an average of 81% of classification accuracy. Conclusion: Finally, from the dynamic model of a gearbox developed in this study, several operating modes can be generated and used in case of lack of experimental data, to do a diagnostic. [ABSTRACT FROM AUTHOR]

Natural Language Processing (NLP) has many applications such as Speech recognition, Speech understanding, and Speech synthesis. Several approaches have been proposed in the literature in dealing with NLP. This paper describes an ongoing research project that tackles Speech Arabic Synthesis using multi-agent system techniques. The system consists of five modules (agents): the User Interface Agent (UIA), the Facilitator Agent (FA), the Preprocessing Agent (PPA), the Orthographic and Phonetic Transcription Agent, and the Speech Generation Agent. These agents are communicating with each other to construct agent sub societies representing the user input. All the agents are cognitive, work together, and communicate with the Knowledge-Base and the Sound Segments Database to generate Arabic speech signals. We used 800 Arabic sentences and asked 10 listeners with different levels of knowledge of the Arab language to accomplish the evaluation perception process. The system presents in general a Success Rate of 86% for the set of 800 tested sentences. [ABSTRACT FROM AUTHOR]

Amidst of all the tumors, breast tumor is the main source for a high incidence mortality rate among women. In the past decades, this mortality rate has tremendously reduced by early detection and diagnosis of the tumors using different modalities. Mammograms have significantly contributed towards the detection of masses and calcifications which assists radiologists in decision making. With the advent of Artificial Intelligence (AI) techniques, several breast mass segmentation methods are devised to assists radiologists in detection of tumor masses and lesions. UNet and its variants are widely used deep learning techniques for mass segmentation in mammograms. Although, UNet exhibits an outstanding performance, it has certain limitations, where the extraction of the fine-grained features is still resilient. In this paper, a AU 2 Net is proposed where two UNets are cascaded and are connected through skip connections. The attention mechanism and Atrous Spatial Pyramid Pooling (ASPP) are introduced to learn more contextual based features for an enhanced tumor segmentation. The proposed model is validated on Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM). The images are preprocessed and are augmented using conventional and deep convolutional Generative Adversarial Network based approaches. The proposed method achieved a 92.3% dice score and 89.6% Intersection over Union (IoU) on CBIS-DDSM dataset. [ABSTRACT FROM AUTHOR]

Purpose: Metaverse is becoming an alternative world in which technology and virtual experiences are mixed with real life, and it holds the promise of changing our way of living. Healthcare is already changing thanks to Metaverse and its numerous applications. In particular, Urology and urologic patients can benefit in many ways from Metaverse. Methods: A non-systematic literature review identified recently published studies dealing with Metaverse. The database used for this review was PubMed, and the identified studies served as the base for a narrative analysis of the literature that explored the use of Metaverse in Urology. Results: Virtual consultations can enhance access to care and reduce distance and costs, and pain management and rehabilitation can find an incredible support in virtual reality, reducing anxiety and stress and improving adherence to therapy. Metaverse has the biggest potential in urologic surgery, where it can revolutionize both surgery planning, with 3D modeling and virtual surgeries, and intraoperatively, with augmented reality and artificial intelligence. Med Schools can implement Metaverse in anatomy and surgery lectures, providing an immersive environment for learning, and residents can use this platform for learning in a safe space at their own pace. However, there are also potential challenges and ethical concerns associated with the use of the metaverse in healthcare. Conclusions: This paper provides an overview of the concept of the metaverse, its potential applications, challenges, and opportunities, and discusses the implications of its development in Urology. [ABSTRACT FROM AUTHOR]

Population biobanks are an increasingly important infrastructure to support research and will be a much-needed resource in the delivery of personalised medicine. Artificial intelligence (AI) systems can process and cross-link very large amounts of data quickly and be used not only for improving research power but also for helping with complex diagnosis and prediction of diseases based on health profiles. AI, therefore, potentially has a critical role to play in personalised medicine, and biobanks can provide a lot of the necessary baseline data related to healthy populations that will enable the development of AI tools. To develop these tools, access to personal data, and in particular, sensitive data, is required. Such data could be accessed from biobanks. Biobanks are a valuable resource for research but accessing and using the data contained within such biobanks raise a host of legal, ethical, and social issues (ELSI). This includes the appropriate consent to manage the collection, storage, use, and sharing of samples and data, and appropriate governance models that provide oversight of secondary use of samples and data. Biobanks have developed new consent models and governance tools to enable access that address some of these ELSI-related issues. In this paper, we consider whether such governance frameworks can enable access to biobank data to develop AI. As Italy has one of the most restrictive regulatory frameworks on the use of genetic data in Europe, we examine the regulatory framework in Italy. We also look at the proposed changes under the European Health Data Space (EHDS). We conclude by arguing that currently, regulatory frameworks are misaligned and unless addressed, accessing data within Italian biobanks to train AI will be severely limited. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence (AI) across firms and industries warrants a line of research focused on determining its overall effect on economic variables. As a general-purpose technology (GPT), for example, AI helps in the production, marketing, and customer acquisition of firms, increasing their productivity and consumer reach. Aside from these, other effects of AI include enhanced quality of services, improved work accuracy and efficiency, and increased customer satisfaction. Hence, this study aims to gauge the impact of AI on the economy, specifically on long-run economic growth. This study conjectures a positive relationship between AI and economic growth. To test this hypothesis, this study makes use of a panel dataset of countries from 1970 to 2019, and the number of AI patents as a measure of AI. A text search query is performed to distinguish AI patents from other types of innovations in a public database. Employing fixed effects and generalized method of moments (GMM) estimation, this paper finds a positive relationship between AI and economic growth, which is higher than the effect of the total population of patents on growth. Furthermore, other results indicate that AI's influence on growth is more robust among advanced economies, and more evident towards the latter periods of the dataset. [ABSTRACT FROM AUTHOR]

The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It is made available by the National Health Service Artificial Intelligence (NHS AI) Lab to support the development of machine learning tools focused on Coronavirus Disease 2019 (COVID-19). A bespoke cleaning pipeline for NCCID, developed by the NHSx, was introduced in 2021. We present an extension to the original cleaning pipeline for the clinical data of the database. It has been adjusted to correct additional systematic inconsistencies in the raw data such as patient sex, oxygen levels and date values. The most important changes will be discussed in this paper, whilst the code and further explanations are made publicly available on GitLab. The suggested cleaning will allow global users to work with more consistent data for the development of machine learning tools without being an expert. In addition, it highlights some of the challenges when working with clinical multi-center data and includes recommendations for similar future initiatives. [ABSTRACT FROM AUTHOR]

In this paper a program and methodology for bibliometric mining of research trends and directions is presented. The method is applied to the research area Big Data for the time period 2012 to 2022, using the Scopus database. It turns out that the 10 most important research directions in Big Data are Machine learning, Deep learning and neural networks, Internet of things, Data mining, Cloud computing, Artificial intelligence, Healthcare, Security and privacy, Review, and Manufacturing. The role of Big Data research in different fields of science and technology is also analysed. For four geographic regions (North America, European Union, China, and The Rest of the World) different activity levels in Big Data during different parts of the time period are analysed. North America was the most active region during the first part of the time period. During the last years China is the most active region. The citation scores for documents from different regions and from different research directions within Big Data are also compared. North America has the highest average citation score among the geographic regions and the research direction Review has the highest average citation score among the research directions. The program and methodology for bibliometric mining developed in this study can be used also for other large research areas. Now that the program and methodology have been developed, it is expected that one could perform a similar study in some other research area in a couple of days. [ABSTRACT FROM AUTHOR]

This study is extensive as a whole and relies on a detailed bibliometric analysis to gain a better understanding of the transformation landscape in artificial intelligence (AI)-related entrepreneurship. A thorough analysis of data, taken from the Scopus database, results in clarified trends identifying AI as an increasingly important component of entrepreneurship from various perspectives. We extracted the bibliographic record from Scopus for 197 documents on AI in entrepreneurship research. By conducting bibliometric analysis, we tried to understand the top-influential and productive papers, authors, sources, and the significant contributing countries and institutions that use AI in entrepreneurship research. This study also intends to identify the most influential and productive writers and development of the discipline of AI in entrepreneurship. The insights identified at the base of this analysis are not only beneficial for exploring the rapidly developing field of AI entrepreneurship. They are also beneficial with respect to several practical implications for different stakeholders and fields. While the above distinguishes provide crucial findings that should be considered for further research within the rapidly changing sector of AI entrepreneurship, they have important implications for various concerned parties, of which researchers, practitioners, and policy makers not the least. The found themes and sub-themes, including business process automation, technology-based innovation, AI-reliant business models, as well as AI role in facilitating startup happenings, represent directions in which the academical research along with practical application is set to get into more detail in the future. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has grown considerably in the present environment and acts as a subject still possessing substantial space for development, making most of its noteworthy advancements in the current era. While considering the chess game based on AI, the end-to-end pipeline was employed, in which the prior knowledge concerning chess is not essential, but in contrast, the efficient learning makes the method more efficient for making the optimal move. This paper proposes the Hybrid Fly-based artificial neural network (Hybridfly-ANN) model for attaining the maximum possible legal moves by rectifying the drawbacks of conventional position evaluation strategies. The Portable Game Notation (PGN) file acts as the input database comprising the details and the games played by the players over 1 month. The possible legal moves corresponding to the opponent's single move are evaluated through the proposed method and the mini-max algorithm is utilized for evaluating the best optimal move. The implication of the research relies on the proposed Hybrid Fly optimization algorithm that tunes the ANN classifier's weight optimally, leading to enhanced system performance by hybridizing the behavior of the Photinus and ephemera flies. Thus, the ANN's weights tuned with the proposed Hybrid Fly optimization's global best solution reduces the training loss thereby, improving the prediction accuracy. The effectiveness of the proposed method is examined in terms of the evaluation metrics which showed the average accuracy above 98% and minimal computation time of 180 secs. [ABSTRACT FROM AUTHOR]

One of the key components of artificial intelligence algorithms is machine learning, involving a variety of fields, and has been applied in many artificial intelligence systems, including computer vision algorithm, radio network algorithm, medical diagnosis algorithm and intelligent robot system algorithm. In the form of machine learning algorithm, the machine learning module of the algorithm is first used to calculate the consumption, the main performance modules are optimized and improved, and the system data under database optimization are obtained, and selected the optimized structure of the database for calculation, data analysis for the calculation results. Finally, in the design of the database optimization system, the separation of the database system storage engine is studied, and the database optimization form under the data processing structure is proposed. In terms of performance and functionality and reliability, it helps to solve the loss problem caused by big data processing in transmission. In the research of data intensive downward moving calculation process, the optimized solution of data processing is estimated. The results show that using computer terminal sampling comparison to select the executable data processing scheme. The result of this paper shows that it can improve the calculation efficiency of data optimization system query. [ABSTRACT FROM AUTHOR]

Infant cry is one of the first distinctive and informative life signals observed after birth. Neonatologists and automatic assistive systems can analyse infant cry to early-detect pathologies. These analyses extensively use reference expert-curated databases containing annotated infant-cry audio samples. However, these databases are not publicly accessible because of their sensitive data. Moreover, the recorded data can under-represent specific phenomena or the operational conditions required by other medical teams. Additionally, building these databases requires significant investments that few hospitals can afford. This paper describes an open-source workflow for infant-cry detection, which identifies audio segments containing high-quality infant-cry samples with no other overlapping audio events (e.g. machine noise or adult speech). It requires minimal training because it trains an LSTM-with-self-attention model on infant-cry samples automatically detected from the recorded audio through cluster analysis and HMM classification. The audio signal processing uses energy and intonation acoustic features from 100-ms segments to improve spectral robustness to noise. The workflow annotates the input audio with intervals containing infant-cry samples suited for populating a database for neonatological and early diagnosis studies. On 16 min of hospital phone-audio recordings, it reached sufficient infant-cry detection accuracy in 3 neonatal care environments (nursery—69%, sub-intensive—82%, intensive—77%) involving 20 infants subject to heterogeneous cry stimuli, and had substantial agreement with an expert's annotation. Our workflow is a cost-effective solution, particularly suited for a sub-intensive care environment, scalable to monitor from one to many infants. It allows a hospital to build and populate an extensive high-quality infant-cry database with a minimal investment. [ABSTRACT FROM AUTHOR]

With the ongoing digitization of the manufacturing industry and the ability to bring together data from manufacturing processes and quality measurements, there is enormous potential to use machine learning and deep learning techniques for quality assurance. In this context, predictive quality enables manufacturing companies to make data-driven estimations about the product quality based on process data. In the current state of research, numerous approaches to predictive quality exist in a wide variety of use cases and domains. Their applications range from quality predictions during production using sensor data to automated quality inspection in the field based on measurement data. However, there is currently a lack of an overall view of where predictive quality research stands as a whole, what approaches are currently being investigated, and what challenges currently exist. This paper addresses these issues by conducting a comprehensive and systematic review of scientific publications between 2012 and 2021 dealing with predictive quality in manufacturing. The publications are categorized according to the manufacturing processes they address as well as the data bases and machine learning models they use. In this process, key insights into the scope of this field are collected along with gaps and similarities in the solution approaches. Finally, open challenges for predictive quality are derived from the results and an outlook on future research directions to solve them is provided. [ABSTRACT FROM AUTHOR]

Computational intelligence (CI) has become one of the most useful and successful tools for dealing with uncertainties and complex problems in many fields, such as neural networks, genetic algorithms, and swarm intelligence, artificial intelligence, risk management, financial monitoring, etc. With the development of CI, abundant publications have arisen related to many research directions and hotspots. Based on the technical support from bibliometrics and the corresponding approach as well as the content analysis, this study conducts a science mapping analysis and a coherent knowledge picture of the research field in CI. The research contributes to clear future development trends and provides more ideas for scholars in this field. First, this paper focuses on the fundamental characteristics of CI publications, including annual numbers, term co-occurrence, and hot research directions, as the preliminary exploration of this field. Then, according to the widely used core database, i.e., Web of Science (WoS), and the technologies of software, VOS viewer, and CiteSpace, the productive institutions, authors, and journals are explored. Next, the corresponding internal characteristics of the CI research are analyzed, including the citation features of countries/regions, institutions, journals and authors. Furthermore, to analyze the development trend of research hotspots, the keywords of all CI publications are studied: (a) classifying them into three phases in chronological order aimed; (b) implementing the burst detection algorithm to intuitively reflect the scientific research in the field of CI. Finally, this paper provides a relatively throughout perspective for the CI articles and reviews and discloses the future development trend, which will help the scholars interested in this area conduct deep research. We conclude this bibliometric overview with the limitations and recommendations for future research in the field of CI. [ABSTRACT FROM AUTHOR]

Parkinson's is the second most common neurodegenerative disease, affecting nearly 8.5M people and steadily increasing. In this research, Multimodal Deep Learning is investigated for the Prodromal stage detection of Parkinson's Disease (PD), combining different 3D architectures with the novel Excitation Network (EN) and supported by Explainable Artificial Intelligence (XAI) techniques. Utilizing data from the Parkinson's Progression Markers Initiative, this study introduces a joint co-learning approach for multimodal fusion, enabling end-to-end training of deep neural networks and facilitating the learning of complementary information from both imaging and clinical modalities. DenseNet with EN outperformed other models, showing a substantial increase in accuracy when supplemented with clinical data. XAI methods, such as Integrated Gradients for ResNet and DenseNet, and Attention Heatmaps for Vision Transformer (ViT), revealed that DenseNet focused on brain regions believed to be critical to prodromal pathophysiology, including the right temporal and left pre-frontal areas. Similarly, ViT highlighted the lateral ventricles associated with cognitive decline, indicating their potential in the Prodromal stage. These findings underscore the potential of these regions as early-stage PD biomarkers and showcase the proposed framework's efficacy in predicting subtypes of PD and aiding in early diagnosis, paving the way for innovative diagnostic tools and precision medicine. [ABSTRACT FROM AUTHOR]

Steel construction is increasingly using thin-walled profiles to achieve lighter, more cost-effective structures. However, analyzing the behavior of these elements becomes very complex due to the combined effects of local buckling in the thin walls and overall global buckling of the entire column. These factors make traditional analytical methods difficult to apply. Hence, in this research work, the strength of bi-axially loaded track and channel cold formed composite column has been estimated by applying three AI-based symbolic regression techniques namely (GP), (EPR) and (GMDH-NN). These techniques were selected because their output models are closed form equations that could be manually used. The methodology began with collecting a 90 records database from previous researches and conducting statistical, correlation and sensitivity analysis, and then the database was used to train and validate the three models. All the models used local and global slenderness ratios (λ, λc, λt) and relative eccentricities (ex/D, ey/B) as inputs and (F/Fy) as output. The performances of the developed models were compared with the predicted capacities from two design codes (AISI and EC3). The results showed that both design codes have prediction error of 33% while the three developed models showed better performance with error percent of 6%, and the (EPR) model is the simplest one. Also, both correlation and sensitivity analysis showed that the global slenderness ratio (λ) has the main influence on the strength, then the relative eccentricities (ex/D, ey/B) and finally the local slenderness ratios (λc, λt). [ABSTRACT FROM AUTHOR]

The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is poised to be a main accelerator in the field. The question is then how to best benefit from recent advances in AI and how to generate, format and disseminate data to enable future breakthroughs in AI-guided drug discovery. We present here the recommendations of a working group composed of experts from both the public and private sectors. Robust data management requires precise ontologies and standardized vocabulary while a centralized database architecture across laboratories facilitates data integration into high-value datasets. Lab automation and opening electronic lab notebooks to data mining push the boundaries of data sharing and data modeling. Important considerations for building robust machine-learning models include transparent and reproducible data processing, choosing the most relevant data representation, defining the right training and test sets, and estimating prediction uncertainty. Beyond data-sharing, cloud-based computing can be harnessed to build and disseminate machine-learning models. Important vectors of acceleration for hit and chemical probe discovery will be (1) the real-time integration of experimental data generation and modeling workflows within design-make-test-analyze (DMTA) cycles openly, and at scale and (2) the adoption of a mindset where data scientists and experimentalists work as a unified team, and where data science is incorporated into the experimental design. Artificial intelligence is greatly accelerating research in drug discovery, but its development is still hindered by the lack of available data. Here the authors present data management and data science recommendations to help reach AI's potential in the field. [ABSTRACT FROM AUTHOR]

The purposes were to assess the efficacy of AI-generated radiology reports in terms of report summary, patient-friendliness, and recommendations and to evaluate the consistent performance of report quality and accuracy, contributing to the advancement of radiology workflow. Total 685 spine MRI reports were retrieved from our hospital database. AI-generated radiology reports were generated in three formats: (1) summary reports, (2) patient-friendly reports, and (3) recommendations. The occurrence of artificial hallucinations was evaluated in the AI-generated reports. Two radiologists conducted qualitative and quantitative assessments considering the original report as a standard reference. Two non-physician raters assessed their understanding of the content of original and patient-friendly reports using a 5-point Likert scale. The scoring of the AI-generated radiology reports were overall high average scores across all three formats. The average comprehension score for the original report was 2.71 ± 0.73, while the score for the patient-friendly reports significantly increased to 4.69 ± 0.48 (p < 0.001). There were 1.12% artificial hallucinations and 7.40% potentially harmful translations. In conclusion, the potential benefits of using generative AI assistants to generate these reports include improved report quality, greater efficiency in radiology workflow for producing summaries, patient-centered reports, and recommendations, and a move toward patient-centered radiology. [ABSTRACT FROM AUTHOR]

ChatMOF is an artificial intelligence (AI) system that is built to predict and generate metal-organic frameworks (MOFs). By leveraging a large-scale language model (GPT-4, GPT-3.5-turbo, and GPT-3.5-turbo-16k), ChatMOF extracts key details from textual inputs and delivers appropriate responses, thus eliminating the necessity for rigid and formal structured queries. The system is comprised of three core components (i.e., an agent, a toolkit, and an evaluator) and it forms a robust pipeline that manages a variety of tasks, including data retrieval, property prediction, and structure generations. ChatMOF shows high accuracy rates of 96.9% for searching, 95.7% for predicting, and 87.5% for generating tasks with GPT-4. Additionally, it successfully creates materials with user-desired properties from natural language. The study further explores the merits and constraints of utilizing large language models (LLMs) in combination with database and machine learning in material sciences and showcases its transformative potential for future advancements. LLMs can be augmented with tools to increase their capabilities. Here, authors have developed an artificial intelligence system called ChatMOF combining LLMs and specialised libraries and utilities to predict and generate metal-organic frameworks. [ABSTRACT FROM AUTHOR]

Recent advances in imaging technologies have led to a significant increase in the adoption of stereoscopic images. However, despite this proliferation, in-depth research into the complex analysis of the visual content of these stereoscopic images is still relatively rare. The advent of stereoscopic imaging has brought a new dimension to visual content. These images offer a higher level of visual detail, making them increasingly common in a variety of fields, including medicine and industrial applications. However, exploiting the full potential of stereoscopic images requires a deeper understanding. By exploiting the capabilities of octonion moments and the power of artificial intelligence, we aim to break new ground by introducing a novel method for classifying stereoscopic images. The proposed method is divided into two key stages: The first stage involves data preprocessing, during which we strive to construct a balanced database divided into three distinct categories. In addition, we extract the stable Octonion Krawtchouk moments (SOKM) for each image, leading to a database of moment images with dimensions of 128 × 128 × 1. In the second step, we train a convolutional neural network (CNN) model using this database, with the aim of discriminating between different categories. Standard measures such as precision, accuracy, recall, F1 score, and ROC curves are used to assess the effectiveness of our method. These measures provide a quantitative assessment of the performance of our object classification approach for stereoscopic images. [ABSTRACT FROM AUTHOR]

The utilization of artificial intelligence (AI) in healthcare is on the rise, demanding increased accessibility to (public) medical data for benchmarking. The digitization of healthcare in recent years has facilitated medical data scientists' access to extensive hospital data, fostering AI-based research. A notable addition to this trend is the Salzburg Intensive Care database (SICdb), made publicly available in early 2023. Covering over 27 thousand intensive care admissions at the University Hospital Salzburg from 2013 to 2021, this dataset presents a valuable resource for AI-driven investigations. This article explores the SICdb and conducts a comparative analysis with the widely recognized Medical Information Mart for Intensive Care - version IV (MIMIC-IV) database. The comparison focuses on key aspects, emphasizing the availability and granularity of data provided by the SICdb, particularly vital signs and laboratory measurements. The analysis demonstrates that the SICdb offers more detailed information with higher data availability and temporal resolution for signal data, especially for vital signs, compared to the MIMIC-IV. This is advantageous for longitudinal studies of patients' health conditions in the intensive care unit. The SICdb provides a valuable resource for medical data scientists and researchers. The database offers comprehensive and diverse healthcare data in a European country, making it well suited for benchmarking and enhancing AI-based healthcare research. The importance of ongoing efforts to expand and make public datasets available for advancing AI applications in the healthcare domain is emphasized by the findings. [ABSTRACT FROM AUTHOR]

Humans' ability to manage their emotions has a big impact on their ability to plan and make decisions. In order to better understand people and improve human–machine interaction, researchers in affective computing and artificial intelligence are investigating the detection and recognition of emotions. However, different cultures have distinct ways of expressing emotions, and the existing emotion recognition datasets and models may not effectively capture the nuances of the Indian population. To address this gap, this study proposes custom-built lightweight Convolutional Neural Network (CNN) models that are optimized for accuracy and computational efficiency. These models are trained and evaluated on two Indian emotion datasets: The Indian Spontaneous Expression Dataset (ISED) and the Indian Semi Acted Facial Expression Database (iSAFE). The proposed CNN model with manual feature extraction provides remarkable accuracy improvement of 11.14% for ISED and 4.72% for iSAFE datasets as compared to baseline, while reducing the training time. The proposed model also surpasses the accuracy produced by pre-trained ResNet-50 model by 0.27% ISED and by 0.24% for the iSAFE dataset with significant improvement in training time of approximately 320 s for ISED and 60 s for iSAFE dataset. The suggested lightweight CNN model with manual feature extraction offers the advantage of being computationally efficient and more accurate compared to pre-trained model making it a more practical and efficient solution for emotion recognition among Indians. [ABSTRACT FROM AUTHOR]

Existing natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training. This study aims to evaluate ChatGPT's capacity to extract information from free-text medical notes efficiently and comprehensively. We developed a large language model (LLM)-based workflow, utilizing systems engineering methodology and spiral "prompt engineering" process, leveraging OpenAI's API for batch querying ChatGPT. We evaluated the effectiveness of this method using a dataset of more than 1000 lung cancer pathology reports and a dataset of 191 pediatric osteosarcoma pathology reports, comparing the ChatGPT-3.5 (gpt-3.5-turbo-16k) outputs with expert-curated structured data. ChatGPT-3.5 demonstrated the ability to extract pathological classifications with an overall accuracy of 89%, in lung cancer dataset, outperforming the performance of two traditional NLP methods. The performance is influenced by the design of the instructive prompt. Our case analysis shows that most misclassifications were due to the lack of highly specialized pathology terminology, and erroneous interpretation of TNM staging rules. Reproducibility shows the relatively stable performance of ChatGPT-3.5 over time. In pediatric osteosarcoma dataset, ChatGPT-3.5 accurately classified both grades and margin status with accuracy of 98.6% and 100% respectively. Our study shows the feasibility of using ChatGPT to process large volumes of clinical notes for structured information extraction without requiring extensive task-specific human annotation and model training. The results underscore the potential role of LLMs in transforming unstructured healthcare data into structured formats, thereby supporting research and aiding clinical decision-making. [ABSTRACT FROM AUTHOR]

The prevalence of autism spectrum disorder (ASD) has risen rapidly in recent decades. Owing to its success across disciplines, the use of artificial intelligence (AI) in the screening of ASD has emerged as a prominent solution. We conducted a bibliometric analysis on AI-powered ASD screening research with a unit of 2090 publications retrieved from Scopus database in the period 2010–2021. Our findings show, among other things, that the annual growth rate of publications was 33.05% and scientific production drastically increased 23-fold from 22 in 2010 to 509 in 2021 with nearly two thirds (1307; 62,54%) of the retrieved documents being published between 2019–2021. The USA was the global leader in terms of scientific output with 730 publications followed by China (255), and India (251). Stanford university, the scientific journal NeuroImage, and Dennis P. Wall were the most globally prolific institution, publication source, and author, respectively. Using VOSviewer's clustering algorithms, keyword and topic analysis identified neuroimaging techniques and genetic research as hot and emerging research trends. Interestingly, three of the top ten prolific authors were women, indicating a significant milestone for gender rebalancing efforts in the AI workforce. The findings will help both experienced and aspiring scientists better understand the structure and current state of knowledge, uncover patterns of collaboration, and identify emerging trends in ASD research using AI. [ABSTRACT FROM AUTHOR]

Purpose: Little is known about the scientific literature regarding the new revolutionary tool, ChatGPT. We aim to perform a bibliometric analysis to identify ChatGPT-related publications in obstetrics and gynecology (OBGYN). Study design: A bibliometric study through PubMed database. We mined all ChatGPT-related publications using the search term "ChatGPT". Bibliometric data were obtained from the iCite database. We performed a descriptive analysis. We further compared IF among publications describing a study vs. other publications. Results: Overall, 42 ChatGPT-related publications were published across 26 different journals during 69 days. Most publications were editorials (52%) and news/briefing (22%), with only one (2%) research article identified. Five (12%) publications described a study performed. No ChatGPT-related publications in OBGYN were found. The leading journal by the number of publications was Nature (24%), followed by Lancet Digital Health and Radiology (7%, for both). The main subjects of publications were ChatGPT's scientific writing quality (26%) and a description of ChatGPT (26%) followed by tested performance of ChatGPT (14%), authorship and ethical issues (10% for both topics).In a comparison of publications describing a study performed (n = 5) vs. other publications (n = 37), mean IF was lower in the study-publications (mean 6.25 ± 0 vs. 25.4 ± 21.6, p <.001). Conclusions: The study highlights main trends in ChatGPT-related publications. OBGYN is yet to be represented in this literature. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop and test an artificial intelligence (AI)-based algorithm for detecting common technical errors in canine thoracic radiography. The algorithm was trained using a database of thoracic radiographs from three veterinary clinics in Italy, which were evaluated for image quality by three experienced veterinary diagnostic imagers. The algorithm was designed to classify the images as correct or having one or more of the following errors: rotation, underexposure, overexposure, incorrect limb positioning, incorrect neck positioning, blurriness, cut-off, or the presence of foreign objects, or medical devices. The algorithm was able to correctly identify errors in thoracic radiographs with an overall accuracy of 81.5% in latero-lateral and 75.7% in sagittal images. The most accurately identified errors were limb mispositioning and underexposure both in latero-lateral and sagittal images. The accuracy of the developed model in the classification of technically correct radiographs was fair in latero-lateral and good in sagittal images. The authors conclude that their AI-based algorithm is a promising tool for improving the accuracy of radiographic interpretation by identifying technical errors in canine thoracic radiographs. [ABSTRACT FROM AUTHOR]

The mapping and characterisation of building footprints is a challenging task due to inaccessibility and incompleteness of the required data, thus hindering the estimation of loss caused by natural and anthropogenic hazards. Major advancements have been made in the collaborative mapping of buildings with platforms like OpenStreetMap, however, many parts of the world still lack this information or the information is outdated. We created a semi-automated workflow for the development of elements-at-risk (EaR) databases of buildings by detecting building footprints using deep learning and characterising the footprints with building occupancy information using building morphological metrics and open-source auxiliary data. The deep learning model was used to detect building EaR footprints in a city in Kerala (India) with an F1 score of over 76%. The footprints were classified into 13 building occupancy types along with information such as average number of floors, total floor space area, building density, and percentage of built-up area. We analysed the transferability of the approach to a different city in Kerala and obtained an almost similar F1 score of 74%. We also examined the exposure of the buildings and the associated occupancies to floods using the 2018 flood susceptibility map of the respective cities. We notice certain shortcomings in our research particularly, the need for a local expert and good quality auxiliary data to obtain reasonable building occupancy information, however, our research contributes to developing a rapid method for generating a building EaR database in data-scarce regions with attributes of occupancy types, thus supporting regional risk assessment, disaster risk mitigation, risk reduction initiatives, and policy developments. [ABSTRACT FROM AUTHOR]

Recognizing faces from the video employs artificial intelligence-based computer technology. This includes applications such as law enforcement, biometrics, security, personal safety, etc., for tracking and enabling surveillance in real-time. However, detection and recognition of the face from the video are influenced by the change in variation of pose, brightness, occlusion, expression, and resolutions. While facial images are simple to detect, others may necessitate the use of specialized software. To address those challenges we propose an efficient face detection and recognition system with optimal features. Initially, the keyframes are extracted by the Key Frame Extraction method which utilizes Wavelet Information. Subsequently, the characteristics such as holo-entropy, appearance features, SURF feature, and multi-angle movement feature are extracted. The Oppositional Grass Hopper Optimization Algorithm is used to identify optimal features from this large feature set. The extracted features are classified using the fuzzy classifier that was designed. The proposed method's performance is validated using several benchmark video datasets and its efficiency is measured in terms of keyframe extraction time, sensitivity, accuracy, and specificity by comparing it to other state-of-the-art approaches. The proposed method outperforms previous state-of-the-art methods in terms of recognition accuracy. [ABSTRACT FROM AUTHOR]

Recent advances in large language models (LLMs) have demonstrated remarkable successes in zero- and few-shot performance on various downstream tasks, paving the way for applications in high-stakes domains. In this study, we systematically examine the capabilities and limitations of LLMs, specifically GPT-3.5 and ChatGPT, in performing zero-shot medical evidence summarization across six clinical domains. We conduct both automatic and human evaluations, covering several dimensions of summary quality. Our study demonstrates that automatic metrics often do not strongly correlate with the quality of summaries. Furthermore, informed by our human evaluations, we define a terminology of error types for medical evidence summarization. Our findings reveal that LLMs could be susceptible to generating factually inconsistent summaries and making overly convincing or uncertain statements, leading to potential harm due to misinformation. Moreover, we find that models struggle to identify the salient information and are more error-prone when summarizing over longer textual contexts. [ABSTRACT FROM AUTHOR]

Over the past few years, the explainable artificial intelligence (XAI) model receives a broad desire for investigation. The natural language processing (NLP) commune is reaching the fundamental change too – constructing a set of paradigms, which describe the preference on a few chief jobs devoid of influencing the execution. Abstractive Text Summarization (ATS) remains the job of building summary sentences by fusing factualities out of disparate source sentences and compressing them into a smaller portrayal when sustaining data content and comprehensive sense. This remains extremely arduous and long-drawn-out for people to physically summarize huge text documents. This study proffers Ontology-based Knowledge Aware Multi-focus Conditional Generative Adversarial Network (OKAM-CGAN) for novel documents. This could build novel sentences by analyzing many finer pieces than sentences, especially, semantic phrases. The proffered OKAM-CGAN comprises 3 prime portions – ontology aware knowledge-based document representation module, multitask and multi-focus learning unit, and an adversarial network unit. Experiential assessment is performed by correlating with advanced methodologies like RNN-W, CopyNet, GCU, Seq2Seq, and KESG concerning ROUGE scores. Consequently, it is observed that the proffered OKAM-CGAN attains 42.1% of ROUGE-L, 40% of accuracy, 45%of precision, and 53% of recall for the CNN/Daily Mail database and 45% of ROUGE-L, 4% of accuracy, 54% of precision, and 57% of recall for the Edmunds database. [ABSTRACT FROM AUTHOR]

Deep learning is a multilayer neural network learning algorithm which emerged in recent years. It has brought a new wave to machine learning and making artificial intelligence and human-computer interaction spread with big strides. India is a heritage country where traditions, religions and languages are quite varied. MODI script is one of the oldest written forms of media. Most of the early written knowledge on subjects like medicine, Buddhist ideology, food habits and horoscope has been written using MODI script. MODI is one of the languages that present special challenge to OCR. The main challenge in MODI script is that it is mostly cursive and few characters look similar. The deep learning methods like InceptionV3 and RestNet on Modi script seems not experimented yet as per literature review. This motivates to apply the deep learning methods to offline handwritten character recognition using Residual and InceptionV3 framework. The handwritten Modi barakhadi dataset is prepared by collecting samples from around 25 different people. The dataset of size 7721 is experimented. The individual characters are cut and pre-processed using Otsu binarization technique. The performance evaluation of pre-processed data is done using both algorithms on the real-world handwritten character database written by different people. Processed images recognized through RestNet50 gives testing accuracy of 94.552% with precision of the model 0.86. Processed images recognized through InceptionV3 gives testing accuracy of 93.923% with precision of the model 0.843. [ABSTRACT FROM AUTHOR]

Artificial intelligence has been successfully applied in various fields, one of which is computer vision. In this study, a deep neural network (DNN) was adopted for Facial emotion recognition (FER). One of the objectives in this study is to identify the critical facial features on which the DNN model focuses for FER. In particular, we utilized a convolutional neural network (CNN), the combination of squeeze-and-excitation network and the residual neural network, for the task of FER. We utilized AffectNet and the Real-World Affective Faces Database (RAF-DB) as the facial expression databases that provide learning samples for the CNN. The feature maps were extracted from the residual blocks for further analysis. Our analysis shows that the features around the nose and mouth are critical facial landmarks for the neural networks. Cross-database validations were conducted between the databases. The network model trained on AffectNet achieved 77.37% accuracy when validated on the RAF-DB, while the network model pretrained on AffectNet and then transfer learned on the RAF-DB results in validation accuracy of 83.37%. The outcomes of this study would improve the understanding of neural networks and assist with improving computer vision accuracy. [ABSTRACT FROM AUTHOR]

Sensors and smart equipment are frequently used in biomechanical systems and applications in sports and rehabilitation to measure various physical quantities. Various sensors, measuring different parameters, can produce a large amount of data at high speeds and volumes that must be stored for real-time or post-processing and analysis. In addition to sensor data, metadata is an important component and can vary between biomechanical applications. Currently however, each application typically has its own unique data flow and storage solution. In this research, we present a universal data model solution that can be applied to any sensor-based biomechanical application in sport and physical rehabilitation. Our proposed cloud platform architecture allows for the manipulation of sensor data and metadata using a combination of Big Data and conventional techniques. The main idea of this research is to develop a platform that allows a universal way for any biomechanical application to handle its data regardless of the type of data and metadata. This is achieved by creating a universal data model, and implementing this data model in a generalized architecture using a graph database. We demonstrate the benefits of this approach using examples from existing biomechanical systems and describe the development of the cloud platform architecture and the underlying data model. We also provide an example of the use of this platform in a sport shooting application. This approach is unique in that it allows data from different sources and applications to be stored and processed using the same procedures and techniques, facilitating data analysis and application development. We envision this system will expand to multiple different biomechanical applications in the future. We expect that in time, the ability to compare various data and store different biomechanical datasets will become necessity. With the advantages of modern recommender systems and utilization of artificial intelligence, huge amounts of relevant and well-prepared data with useful metadata are required thus having such system is an important advantage for future biomechanical systems development. With the increase of people's awareness and usage of devices that increase well-being and quality of life, presented platform and similar systems will play a pivotal role in shaping the future lifestyle. [ABSTRACT FROM AUTHOR]

Objectives: In many countries, workers who developed asbestosis due to their occupation are eligible for government support. Based on the results of clinical examination, a team of pulmonologists determine the eligibility of patients to these programs. In this Dutch cohort study, we aim to demonstrate the potential role of an artificial intelligence (AI)-based system for automated, standardized, and cost-effective evaluation of applications for asbestosis patients. Methods: A dataset of n = 523 suspected asbestosis cases/applications from across the Netherlands was retrospectively collected. Each case/application was reviewed, and based on the criteria, a panel of three pulmonologists would determine eligibility for government support. An AI system is proposed, which uses thoracic CT images as input, and predicts the assessment of the clinical panel. Alongside imaging, we evaluated the added value of lung function parameters. Results: The proposed AI algorithm reached an AUC of 0.87 (p < 0.001) in the prediction of accepted versus rejected applications. Diffusion capacity (DLCO) also showed comparable predictive value (AUC = 0.85, p < 0.001), with little correlation between the two parameters (r-squared = 0.22, p < 0.001). The combination of the imaging AI score and DLCO achieved superior performance (AUC = 0.95, p < 0.001). Interobserver variability between pulmonologists on the panel was estimated at alpha = 0.65 (Krippendorff's alpha). Conclusion: We developed an AI system to support the clinical decision-making process for the application to the government support for asbestosis. A multicenter prospective validation study is currently ongoing to examine the added value and reliability of this system alongside the clinic panel. Key Points: • Artificial intelligence can detect imaging patterns of asbestosis in CT scans in a cohort of patients applying for state aid. • Combining the AI prediction with the diffusing lung function parameter reaches the highest diagnostic performance. • Specific cases with fibrosis but no asbestosis were correctly classified, suggesting robustness of the AI system, which is currently under prospective validation. [ABSTRACT FROM AUTHOR]