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6th International Workshop on Artificial Intelligence and Education, WAIE 2024

作者： Marutschke, Daniel Moritz Kirjakovski, Atanas Bachtiar, Fitra A. Hananto, Valentinus Roby Kovacs, Mate Brockmann, Patricia Kryssanov, Victor V. Kyoto University of Advanced Science Faculty of Economics and Business Administration Kyoto Japan International Balkan University Department of Psychology Skopje Macedonia Universitas Brawijaya Faculty of Computer Science Malang Indonesia College of Information Science and Engineering Ritsumeikan University Osaka Japan Nuremberg Institute of Technology Faculty of Computer Science Nuremberg Germany

ISBN: (纸本)9798350378894

The necessity for professionals to make predictions using digital data has been recognized for many decades. With its roots in statistical analysis and later implemented as data analytics, Data science emerged as a widely accepted discipline. The data science life cycle begins by setting a goal and ends with reporting/reflecting on the gained results. It incorporates known analytical techniques, including modern artificial intelligence and machine learning techniques. While often considered an independent discipline, both engineering and liberal arts colleges have recognized the need for an understanding of data science in the general workforce. The literature still suggests a disconnect between the study fields and how the Internet of Things could be leveraged in Project-Based Learning (PBL) style data science education. In this paper, the authors propose a conceptual framework to teach multidisciplinary data science at university level to engineering and liberal arts students using data from real-world Internet of Things (IoT) projects. © 2024 IEEE.

关键词： Adversarial machine learning

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Psychological structure of human trust toward autonomous vehicles, using structural equation model

Advances in Transportation Studies

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Advances in Transportation Studies 2024年 64卷 73-90页

作者： Park, H. Lee, K. Kim, S.-H. Department of Industrial and Manufacturing Systems Engineering College of Engineering and Computer Science University of Michigan-Dearborn DearbornMI48128 United States Department of Marketing Global Business and Economics College of Business and Public Management Kean University UnionNJ07083 United States

This study aimed to validate the influence of latent variables on human trust in autonomous vehicles and elucidate the underlying psychological framework. A survey with 114 queries, drawn from previous studies on trust factors, was conducted online, garnering 195 valid responses. Employing correlation and factor analysis, 51 queries were identified as significantly impacting trust levels. A structural equation model incorporating these 51 variables was constructed, revealing five higher-level psychological constructs: "Interpersonal," "System Feature," "Risk Perception," "Behavioral Intention," and "Trust," each comprising sub-factors derived from associated queries. Notably, the "Interpersonal" construct exhibited the greatest influence on "Behavioral Intention" and "Trust," while "Risk Perception" showed a negative correlation with trust, indicating higher perceived risk diminishes trust. The findings highlight the importance of understanding user interpersonal characteristics in enhancing trust in autonomous vehicles. Unlike previous studies focusing on individual variables, this research unveiled a comprehensive psychological structure by categorizing variables, factors, and constructs hierarchically. The developed model facilitates comprehension, prediction, and enhancement of user trust, guiding the development of tailored trust-fostering strategies and interventions for specific user groups and contexts. © 2024, Aracne Editrice. All rights reserved.

关键词： Risk perception

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Digitization and Secure Process of Forensic Data Management Using Blockchain

Digitization and Secure Process of Forensic Data Management ...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Indhumathi, R. Mathivanan, P. Mahalakshmi, K. Subha, K. Krishna, Nanda S. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Business Systems Coimbatore India Surya Group of Institutions Department of Computer Science and Engineering Villuppuram India

ISBN: (纸本)9798350315127

Cyber threats and data manipulation are the main concerns in this technological society. The investigator finds it challenging to submit evidence to the court because of the information breach as continuity documentation of records is very critical for safeguarding against criminal events. Blockchain-based digital forensic data management systems have been developed to deal with this issue of Privacy preservation for evidence items. Blockchain technology provides authentication, immutability, traceability, resilience, and distributed trust for forensic data. In order to ensure security and ease of use, the system utilizes onion architecture. Data can be stored and analysed whenever required. They provide trust between the examiners and the system. The system is a multi-platform system that provides effective usage where it can be accessed and used from anywhere. The honest information is make sure to follow the crime investigator of the steps. if we use the propose system of block chain, it is can't be altered the evidence during the submission of court. The storing data of each block is verify and added to the blockchian. The creation of safe database was implemented by a decentralised network with bitcoin and networks of cryptocurrency. In proposed system, the blockchain technology helps in can't able to access and altered the data during the investigation periods. This system is used to implements the easily accessed and used in anywhere. © 2023 IEEE.

关键词： Blockchain

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AI-Driven Real-Time Exercise Repetition Counting System 10

AI-Driven Real-Time Exercise Repetition Counting System

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10th International Conference on Signal Processing and Communication, ICSC 2025

作者： Lal, Ratan Koirala, Rikriti Thota, Sai Swathi Shaik, Khaja Nayab Rasool School of Computer Science and Information Systems Northwest Missouri State University Maryville MO United States Northwest Missouri State University Faculty in the School of Computer Science and Information Systems United States School of Computer Science and Information Systems Northwest Missouri State University United States

ISBN: (纸本)9798331511890

Real-time exercise detection is a challenging problem. Hence, we present a real-time exercise detection system that addresses the challenge of accurately monitoring exercises without the need for an expensive, real-life coach. Our approach leverages machine learning techniques to detect errors in exercises and provide actionable feedback, making fitness training more accessible and affordable. First, we collect a dataset, capturing multiple video frames with joint landmarks represented as key points in each image. These images are labeled with exercise actions such as curls, presses, and squats. Then, we perform data cleaning and splitting. After that we have trained and evaluated two machine learning models: Long Short-Term Memory (LSTM) networks and Random Forest based on metrics, namely, accuracy, precision, recall, F1 score. From our experimental results, we have found that the LSTM model outperforms the Random Forest, demonstrating its superior effectiveness in real-time exercise detection. Our results highlight the potential of AI-driven systems to provide personalized fitness guidance, opening doors to innovative solutions in the health and fitness industry. © 2025 IEEE.

关键词： Exercise Detection LSTM Machine Learning Random Forest Real-Time

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Lung sound disease detection using attention over pre-trained efficientnet architecture

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Multimedia Tools and Applications 2024年 1-35页

作者： Nair, Anuja Vadher, Himanshu Patel, Pal Vyas, Tarjni Bhatt, Chintan Bruno, Alessandro Department of Computer Science & amp Engineering School of Technology Nirma University Gujarat Ahmedabad382481 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar382007 India Department of Business Law Economics Consumer Behaviour ’Carlo A. Ricciardi’ Faculty of Communication IULM University Milan20143 Italy

Air pollution has been on the rise for quite a while now and with it is the increasing number of cases involving respiratory diseases. These respiratory diseases range from the mild ones to the most severe ones. Therefore, we need to detect these diseases. The most efficient way of checking the health of the lung is through the use of lung sounds. But the lung sounds carry with them the minute variations that a doctor or any human sometimes misses. To tackle the same, this paper proposes a deep learning model that detects a person’s lung disease by processing lung sounds. The ICBHI 2017 dataset comprising the lung sound audios and their corresponding labels is used to train the proposed model. The dataset consists of lung sounds and some additional background noise has been removed using the bandpass Butterworth filter, whose frequency range has been set to 250Hz - 2000Hz. Later, the amount of data is multiplied through data augmentation for audio signals. The augmented data is later converted to mel spectrograms, which are, in turn, fed into the deep learning model. The model that gives the best accuracy is EfficientNet-B0 + attention with a training accuracy of 99.72%, validation accuracy of 99.82%, precision, recall and f1-scores of 99.82% as well. Furthermore, a comparison of the pre-trained model’s training and inference times has been performed. Although the proposed model takes a significant amount of training time, it takes the least amount of inference time compared to the other pre-trained models that have been tested. © The Author(s), under exclusive licence to Springer science+business Media, LLC, part of Springer Nature 2024.

关键词： Butterworth filters

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An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient-Boosting and Fuzzy Rule-Based Models

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5771-5785页

作者： Li, Jinbo Liu, Peng Chen, Long Pedrycz, Witold Ding, Weiping Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China China Unicom Research Institute Beijing100048 China Henan University International Business School Zhengzhou47500 China University of Macau Department of Computer and Information Science 999078 China University of Alberta Department of Electrical and Computer Engineering EdmontonAB T6R 2V4 Canada City University of Macau Faculty of Data Science 999078 China

The integration of different learning paradigms has long been a focus of machine learning research, aimed at overcoming the inherent limitations of individual methods. Fuzzy rule-based models excel in interpretability and have seen widespread application across diverse fields. However, they face challenges such as complex design specifications and scalability issues with large datasets. The fusion of different techniques and strategies, particularly gradient boosting, with fuzzy rule-based models offers a robust solution to these challenges. This article proposes an integrated fusion framework that merges the strengths of both paradigms to enhance model performance and interpretability. At each iteration, a fuzzy rule-based model is constructed and controlled by a dynamic factor to optimize its contribution to the overall ensemble. This control factor serves multiple purposes: it prevents model dominance, encourages diversity, acts as a regularization parameter, and provides a mechanism for dynamic tuning based on model performance, thus mitigating the risk of overfitting. Additionally, the framework incorporates a sample-based correction mechanism that allows for adaptive adjustments based on feedback from a validation set. Experimental results substantiate the efficacy of the presented gradient-boosting framework for fuzzy rule-based models, demonstrating performance enhancement, especially in terms of mitigating overfitting and complexity typically associated with many rules. By leveraging an optimal factor to govern the contribution of each model, the framework improves performance, maintains interpretability, and simplifies the maintenance and update of the models. © 2024 IEEE.

关键词： Optimization

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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A Process Oriented Integration Model for Smart Health Services

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Intelligent Automation & Soft Computing 2023年第2期35卷 1369-1386页

作者： Farzana Kausar Gondal Syed Khuram Shahzad Muhammad Arfan Jaffar Muhammad Waseem Iqbal Department of Computer Science Superior UniversityLahore54600Pakistan Department of Informatics and Systems University of Management and TechnologyLahore54000Pakistan Faculty of Computer Science&IT Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan

Cities are facing challenges of high rise in population number and con-sequently need to be equipped with latest smart services to provide luxuries of life to its *** integrated solutions are also a need to deal with the social and environmental challenges,caused by increasing ***,the development of smart services’integrated network,within a city,is facing the bar-riers including;less efﬁcient collection and sharing of data,along with inadequate collaboration of software and *** to resolve these issues,this paper recommended a solution for a synchronous functionality in the smart services’integration process through modeling *** this integration modeling solution,atﬁrst,the service participants,processes and tasks of smart services are identiﬁed and then standard illustrations are developed for the better understand-ing of the integrated service group *** process modeling and notation(BPMN)language based models are developed and discussed for a devised case study,to test and experiment i.e.,for remote healthcare from a smart *** research is concluded with the integration process model application for the required data sharing among different service *** outcomes of the modeling are better understanding and attaining maximum automation that can be referenced and replicated.

关键词： Process modeling business process business process integration business process modeling and notation

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An AI-driven Approach for Tailoring Java Programming Instructions 3

An AI-driven Approach for Tailoring Java Programming Instruc...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Thiyagarajan, Gomathi Vijayalakshmi, V. Department of Computer Applications CMR Institute of Technology Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331527549

AI and virtual assistants are transforming higher education by using digital tools to enhance teaching and learning in ways that go beyond traditional methods. These digital tools are not merely supplementary aids but are increasingly gaining popularity in educationalsetting offering innovative approaches that transcend conventional methods. In this research we introduce ByteBuddy a virtual assistant which assists instructors on Java ***'s framework is designed in a way to integrate easily into educational context. This study reveals the architecture of ByteBuddy, the process model that captured the behavior of students in the learning environment and the observation of its application in java instructions. By applying this study, we provide profound insights on how the effective use of ByteBuddy supplements the teaching practices and measure learning outcomes. © 2025 IEEE.

关键词： Java programming language

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Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

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computers, Materials & Continua 2024年第9期80卷 5029-5045页

作者： Sannasi Chakravarthy Bharanidharan Nagarajan Surbhi Bhatia Khan Vinoth Kumar Venkatesan Mahesh Thyluru Ramakrishna Ahlam AlMusharraf Khursheed Aurungzeb Department of Electronics and Communication Engineering Bannari Amman Institute of TechnologySathyamangalam638402India School of Computer Science Engineering and Information Systems(SCORE) Vellore Institute of TechnologyVellore632014India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India Department of Management College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpura140401India

Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast *** addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input ***,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of *** this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS *** accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing ***,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.

关键词： EfficientNet mammograms breast cancer Explainable AI deep-learning transfer learning

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