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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： L. N. Jayanthi Kannan Shanmugam Challapalli Suma Sagar B S S. P. Maniraj T. R. GaneshBabu Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of Gaming Technology School of Computing Science and Engineering VIT Bhopal University Bhopal Madhapradesh India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College (A) Bhimavaram Andhra Pradesh India Department of Electrical and Electronics Engineering REVA University Bengaluru Karnataka India Department of Data Science and Business Systems Faculty of Engineering & Technology Kattankulathur - Chennai Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience.

关键词： Surveys Text mining Cloud computing Technological innovation Accuracy Medical services Strategic planning Market research Natural language processing Real-time systems

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A Bibliometric Analysis and Research Landscape of Artificial Intelligence in Education

A Bibliometric Analysis and Research Landscape of Artificial...

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IEEE International Conference on Control System, computing and engineering (ICCSCE)

作者： Samuel-Soma M. Ajibade Bayan Issa Muhammed Basheer Jasser Farrukh Hassan Ghassan Saleh ALDharhani Ismail Ahmed Al-Qasem Al-Hadi Kayode Akinlekan Akintoye Almighty Cortezo Tabuena Department of Computing and Information Systems School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkiye Faculty of Informatics Engineering University of Aleppo Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia Faculty of Information Science and Technology UKM University Bangi Selangor Malaysia Department of Computer Science The Federal Polytechnic Ado Ekiti Ekiti State Nigeria College of Arts and Sciences University of the City of Valenzuela Philippines

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or $64.5 \%$ of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or $6.44 \%$ of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National Science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students’ learning patterns.

关键词： Deep learning Education Bibliometrics Neural networks Market research Prediction algorithms Stakeholders Artificial intelligence Socioeconomics Meteorology

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Leveraging AI-Generated Content for Synthetic Electronic Health Record Generation With Deep Learning-Based Diagnosis Model

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Abdel-Khalek, Sayed Algarni, Abeer D. Amoudi, Ghada Alkhalaf, Salem Alhomayani, Fahad Mohammed Kathiresan, Shankar Department of Mathematics and Statistics College of Science Taif University Taif Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Information Systems Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer College of Science and Arts in Ar Rass Qassim University Ar Rass Saudi Arabia College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Consumer electronics have transformed the way we interact with technology, improving convenience and connectivity in day-to-day lives. In the healthcare sector, recent technologies have resulted in enhanced diagnosis, treatment, and patient care. Wearables, artificial intelligence-based data analytics, and telemedicine transform the way of monitoring and managing health, fostering a proactive approach to well-being. The popularity of ChatGPT is proven great potential for AI-generated content (AIGC) that has formed a major impact on the artificial intelligence (AI) community and accelerates the reconsidering of the prospects of general AI. The AIGC is also exposed as a considerable scope to impulse healthcare electronics (HE). Although generative AI has achieved popularity like the formation of images, it could be employed for producing synthetic tabular information. The production of synthetic electronic health records (EHR) undertakes to increase the utilization of machine learning (ML) methods that commonly function with massive quantities of data. ML will identify non-intuitive classifier patterns that permit a new integration of patient feature predictive ability. Currently, deep learning (DL) techniques are effectively utilized in EHR data from medical domains. DL methods excellently captured the significant and beneficial features and patterns from the comprehensive medical information in EHR data. This study presents AI-generated content for Synthetic Electronic Health Record Generation with a Deep Learning-based Diagnosis (SEHRG-DLD) Model. The focus of the SEHRG-DLD technique is to initially generate the synthetic EHR data and then analyze the medical data for disease diagnosis using the DL model. The SEHRG-DLD technique comprises a two-stage process: synthetic data generation and disease diagnosis. At the initial stage, the SEHRG-DLD technique uses the ChatGPT tool to generate synthetic EHR data. Then, the SEHRG-DLD technique undergoes the disease diagnosi

关键词： Diagnosis

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Behavlet Analytics for Player Profiling and Churn Prediction 22nd

Behavlet Analytics for Player Profiling and Churn Prediction

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22nd International Conference on Human-Computer Interaction,HCII 2020

作者： Charles, Darryl Cowley, Benjamin Ultan School of Computing Engineering and Intelligent Systems Ulster University Newtownabbey United Kingdom Data Science Faculty of Educational Sciences University of Helsinki Helsinki Finland Cognitive Science Faculty of Arts University of Helsinki Helsinki Finland

ISBN: (纸本)9783030601270

Players exhibit varying behaviour from each other when playing games. Indeed, a player’s own behaviour will change as they learn to play and changing behaviour may also be indicative of when they are prematurely about to quit playing permanently (known as ‘churn’). There can be many reasons for player churn including finding a game too easy, too hard, or just not understanding what they must do. The accurate prediction of player churn is important as it allows a publisher or developer to understand and intervene to improve retention and thus increase revenue. Profiling player behaviours through their actions in-game can facilitate personalization, by adapting gameplay for different types of player to enhance player enjoyment and reduce churn rate. Behavlets are data-features that encode short activity sequences (‘atoms’ of play), which represent an aspect of playing style or player personality traits, e.g. aggressiveness or cautiousness tendencies. Previously we have shown how Behavlets can be used to model variation between players. In this paper, we focus on Behavlet sequences and how process mining and entropy-based analysis can profile evolving behaviour, predict player churn, and adapt play to potentially increase enjoyment. © 2020, Springer Nature Switzerland AG.

关键词： Entropy

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Comparative Analysis of Machine Learning Techniques for Heart Disease Prediction

Comparative Analysis of Machine Learning Techniques for Hear...

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Ubiquitous computing and Intelligent Information Systems (ICUIS), International Conference on

作者： N. Susitha H. Selvi N.B. Mahesh Kumar VR. Nagarajan Syed Sumera Ali L. Sathish Kumar Vinayaka Mission's Research Foundation (DU) Paiyanoor OMR Chennai TamilNadu India Saveetha College of Liberal Arts and Sciences Chennai TamilNadu India School of Computing Science and Hindusthan Institute of Technology Coimbatore Tamil Nadu India Faculty of Arts Science Commerce and Management Karpagam Academy of Higher Education Coimbatore Tamil Nadu India CSMSS Chh Shahu College of Engineering Aurangabad Maharashtra India SCSE VIT Bhopal University Kothrikalan Madhya Pradesh India

Due to its substantial effects on society, efforts to enhance heart disease diagnosis and treatment have increased. Through data mining and the archiving of medical records, improved patient management is now possible thanks to the fusion of technology and medical diagnoses. Understanding the interaction between risk factors in the histories of patients and their impact on their prospects for heart disease is of utmost importance. This study aims to accurately predict cardiac disease by analyzing various data elements from patients. The selection and feature extraction methods are the most efficient choices for prediction system for heart disease. The following variables, such as age, sex, occupation, smoking, obesity, diet, exercise, mental stress levels, type of chest pain, history of chest pain, pressure, ECG, and results, have all been identified as significant factors in diagnosing heart disease. Various machine learning methods, such as Multilayer Perceptron (MLP), Naive Bayes (NB), K-nearest Neighbor (K-NN) and Support Vector Machine (SVM) were utilized to analyze the heart disease dataset. These datasets consisted of one with all features and another with only selected features. The objective was to compare the performance of these methods and determine which ones yielded accurate predictions. The results revealed that random forest, using the chosen features, outperformed other artificial intelligence algorithms, including those using all input features, achieving a maximum accuracy rate of 90%. As a support framework for predicting cardiac disease in its early stages, the suggested approach shows promise. This study advances the prognosis of cardiac disease, enables prompt therapies, and enhances patient outcomes by fusing the strength of data-driven AI algorithms with thorough feature selection.

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Robust and digital hyper-polarization protocol of nuclear spins via magic sequential sequence

arXiv

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arXiv 2025年

作者： Li, Haiyang Liao, Hao Wang, Ping Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Hyper-polarization of nuclear spins is crucial for advancing nuclear magnetic resonance (NMR) and quantum information technologies, as nuclear spins typically exhibit extremely low polarization at room temperature due to their small gyro-magnetic ratios. A promising approach to achieving high nuclear spin polarization is transferring the polarization of electron to nuclear spin. The nitrogen-vacancy (NV) center in diamond has emerged as a highly effective medium for this purpose, and various hyper-polarization protocols have been developed. Among these, the pulsed polarization (PulsePol) method has been extensively studied due to its robustness against static energy shifts of the electron spin. In this study, we introduce a sequential polarization protocol and identify a series of magic and digital sequences for hyper-polarizing nuclear spins. Notably, we demonstrate that some of these magic sequences exhibit significantly greater robustness compared to the PulsePol protocol in the presence of finite half π pulse duration of the protocol. This enhanced robustness positions our protocol as a more suitable candidate for hyper-polarizing nuclear spins species with large gyromagnetic ratios and also ensures better compatibility with high-efficiency readout techniques at high magnetic fields. Additionally, the generality of our protocol allows for its direct application to other solid-state quantum systems beyond the NV center. © 2025, CC BY.

关键词： Chemical shift

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Web ranking of higher education institutions: An SEO analysis

Web ranking of higher education institutions: An SEO analysi...

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2020 International Conference on Computation, Automation and Knowledge Management, ICCAKM 2020

作者： Halibas, Alrence Santiago Cherian, Anju Matthew Pillai, Indu Govinda Reazol, Leslyn Bonachita Delvo, Erbeth Gerald Sumondong, Genevive Halasan Faculty of Computing Sciences Gulf College Muscat Oman College of Engineering and Architecture La Salle University Ozamiz City Philippines Ozamiz City School of Arts and Trades Dept. of Educ. Ozamiz City Philippines

ISBN: (纸本)9781728106663

There are numerous ranking systems that provide web rankings for colleges and universities around the world. The aim of this paper is to present the findings of SEO audit and analysis of 34 HEI websites in Oman in terms of web presence and impact. The study used the SEO audit metrics-domain authority score, linking root domain, ranking keywords, and spam score which are provided by Moz. The findings revealed that only one (1) HEI has achieved a good quality for domain authority while all the others have obtained a low quality only for domain authority. The same HEI has scored high on the linking root domains and ranking keywords. However, 24 % of the HEIs have attained a zero spam score while 76% of the HEIs have attained a low spam score. The study recommends that HEIs should exert more efforts in improving their SEO to increase visibility and impact on the web. © 2020 IEEE.

关键词： Domain Authority SEO University ranking Web ranking Webometrics

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Generalization error bound for hyperbolic ordinal embedding

arXiv

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arXiv 2021年

作者： Suzuki, Atsushi Nitanda, Atsushi Wang, Jing Xu, Linchuan Cavazza, Marc Yamanishi, Kenji School of Computing and Mathematical Sciences Faculty of Liberal Arts and Sciences University of Greenwich United Kingdom Department of Artificial Intelligence Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Japan Department of Computing Hong Kong Polytechnic University Hong Kong Department of Mathematical Informatics Graduate School of Information Science and Technology University of Tokyo Japan

Hyperbolic ordinal embedding (HOE) represents entities as points in hyperbolic space so that they agree as well as possible with given constraints in the formof entity i ismore similar to entity j than to entity k. It has been experimentally shown that HOE can obtain representations of hierarchical data such as a knowledge base and a citation network effectively, owing to hyperbolic space's exponential growth property. However, its theoretical analysis has been limited to ideal noiseless settings, and its generalization error in compensation for hyperbolic space's exponential representation ability has not been guaranteed. The difficulty is that existing generalization error bound derivations for ordinal embedding based on the Gramian matrix do not work in HOE, since hyperbolic space is not inner-product space. In this paper, through our novel characterization of HOE with decomposed Lorentz Gramian matrices, we provide a generalization error bound of HOE for the first time, which is at most exponential with respect to the embedding space's radius. Our comparison between the bounds of HOE and Euclidean ordinal embedding shows that HOE's generalization error is reasonable as a cost for its exponential representation ability. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge based systems

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A school attendance management system by using facemask recognition based on the internet of things (IoT) approach

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AIP Conference Proceedings 2024年第1期2898卷

作者： A. R. A. Nazren Muhammad Syakir Shafie Wan Aida Nadia Wan Abdullah M. I. Jais I. Ismail M. W. Nasrudin C. M. N. C. Isa Centre of Excellence Advanced Computing (ADVCOMP) Faculty of Electronic Engineering Technology (FKTEN) Universiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Faculty of Electronic Engineering Technology (FKTEN) Universiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia School of Computing (SOC) College of Arts and Sciences Universiti Utara Malaysia (UUM) Sintok Kedah Malaysia

This project focuses primarily on developing a student attendance management system using a Raspberry Pi machine with Deep Learning and Computer Vision technologies. The main objective of this project is to assist school authorities who are struggling to maintain student attendance, especially during this global pandemic of COVID-19. This is done by implementing technology of facemask recognition, Quick Response (QR) code as data identification, temperature detection, storing the data in the database, and lastly, user monitoring. In addition, students can protect themselves from Covid-19 disease using this method based on the Standard Operating Procedure (SOP) implementation. The initial stage of this system is recognizing whether the students wear masks properly or not. Then, the QR code is scanned to provide the identity of the students and the data is saved. The Raspberry Pi is programmed to continuously monitor students at the school entrance. The board is set up to collect temperature data from the K3 Pro device and eventually uploaded the data to the Google Sheet database together with QR code identity data. Later, the data can be viewed through an android smartphone and the system can be controlled by the school authorities in charge. Every single process of this system is run and updated based on real-time data recording.

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on Computer-Supported Cooperative Work and Social computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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