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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Velusamy, Dhanalakshmi Harish, U. Lingesh, P. Kaviyarasan, P. Cyril Poornaraj, J. Prem, D. Sharan, K. Jeppiaar Institute of Technology Department of Computer Science and Engineering Chennai India Jeppiaar Institute of Technology Bachelor of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368109

Around the world, kidney diseases are a major open prosperity concern. Environmental components, tall salt levels, and additional minutes are all risk factors. Kidney dissatisfaction can be a critical prosperity issue, and it has gotten to be more transcendent in afterward a long time, with horribleness and mortality extending by 100% over the ultimate decade. Several kidney sicknesses, tallying those caused by renal stones and rankles, can be easily recognized through CT imaging. In separate, manual examination of a CT picture is time-consuming and requires a portion of effort. Deep learning methods, checking 3D CNNs, can be utilized to recognize plans of hurt and make assurance easier. By planning these calculations on clarified datasets, they can distinguish kidney stones and rankles with tall accuracy. Despite its potential to assist inside the early recognizing verification and treatment of ailments, modified revelation has to be utilized with caution and not as an elective to restorative information. © 2024 IEEE.

关键词： Deep learning

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Age-of-Information-Aware Federated Learning

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Journal of computer science & technology 2024年第3期39卷 637-653页

作者：徐殷肖明军吴晨吴杰周津锐孙贺 Student Member CCF 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China School of Data Science University of Science and Technology of ChinaHefei 230026China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122U.S.A. CCF IEEE

Federated learning(FL)is an emerging privacy-preserving distributed computing paradigm,enabling numerous clients to collaboratively train machine learning models without the necessity of transmitting clients’private datasets to the central *** most existing research where the local datasets of clients are assumed to be unchanged over time throughout the whole FL process,our study addresses such scenarios in this paper where clients’datasets need to be updated periodically,and the server can incentivize clients to employ as fresh as possible datasets for local model *** primary objective is to design a client selection strategy to minimize the loss of the global model for FL loss within a constrained *** this end,we introduce the concept of“Age of Information”(AoI)to quantitatively assess the freshness of local datasets and conduct a theoretical analysis of the convergence bound in our AoI-aware FL *** on the convergence bound,we further formulate our problem as a restless multi-armed bandit(RMAB)***,we relax the RMAB problem and apply the Lagrangian Dual approach to decouple it into multiple ***,we propose a Whittle’s Index Based Client Selection(WICS)algorithm to determine the set of selected *** addition,comprehensive simulations substantiate that the proposed algorithm can effectively reduce training loss and enhance the learning accuracy compared with some state-of-the-art methods.

关键词： federated learning Age of Information restless multi-armed bandit Whittle’s index

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Investigating the potential adverse effects of virtual reality-based learning system usage: from UTAUT2 and PPVITU perspectives

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Interactive Learning Environments 2024年第9期32卷 5106-5125页

作者： Wang, Yu-Yin Chuang, Yu-Wei Department of Computer Science and Information Management Providence University Taichung Taiwan Bachelor Program in Business and Management National Yunlin University of Science and Technology Yunlin Taiwan

A review of the literature shows that much academic effort has been expended studying information system usage and information technology adoption. However, these theories/models based on psychological research are not specific to the virtual reality context and may not fully capture the nature of virtual reality-based learning system (VR-BLS) usage behavior in the virtual environment. Given the limited utility of existing theories/models, this study aims to build a theoretical model and to validate the psychological and physiological effects of using VR-BLSs. To explore students’ usage intentions, this study integrates the extended unified theory of acceptance and use of technology (UTAUT2), representing the psychological effects, and the perceived physiological vulnerability to IT usage (PPVITU), representing the physiological effects. We empirically examine the research model using a cross-sectional survey, collecting data from a sample of 352 students, and utilizing partial least squares (PLS) for the analysis. Our findings show that the behavioral intention to use VR-BLSs is determined by performance expectancy, social influence, and facilitating conditions. Moreover, PPVITU negatively moderates the association between social influence and the behavioral intention to use VR-BLSs. The results of this study enhance our understanding of the relationship between students’ VR-BLS usage behavior and VR-based education. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： extended unified theory of acceptance and use of technology (UTAUT2) perceived physiological vulnerability to IT usage (PPVITU) system usage virtual reality-based education Virtual reality-based learning

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Comments on a Key Agreement Scheme for the V2G System 14th

Comments on a Key Agreement Scheme for the V2G System

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14th International Conference on Frontier Computing, FC 2024

作者： Chang, Ya-Fen Chang, Wen-Ting Tai, Wei-Liang Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789819623945

Vehicle-to-grid (V2G) technology supporting bidirectional power transfer allows electric vehicles (EVs) to contribute and consume energy bidirectionally. Because the specific properties and requirements of V2G, Khan et al. proposed a key agreement scheme for the V2G system to ensure the security of communication between a vehicle user and the grid server. However, after analyzing Khan et al.’s scheme, we find that it suffers from some flaws such that it cannot ensure anonymity, resist impersonation attack, man-in-the-middle attack and denial-of-service attack, and work as expected. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anonymity

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Optimization Model for Outpatient Services Capacity Management in Different Patient Conditions and Diseases

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第3期51卷 260-266页

作者： Poningsih Sihombing, Poltak Zarlis, Muhammad Tulus student of Universitas Sumatera Utara Indonesia Universitas Sumatera Utara Indonesia Department of Computer Science and Information Technology Universitas Bina Nusantara Jakarta Indonesia Universitas Sumatera Utara Indonesia

The high population in cities such as Medan, the capital of North Sumatra Province, Indonesia, is observed to have led to the need for more patient clinics with good health services. These clinics are required to have optimal capacity management to meet the health services expectation of patient. However, the main problem identified is uncertainty associated with the number of outpatients, including the first visit (FV) and revisit (RV). This research was conducted to propose optimization model to determine the minimal capacity necessary for a particular breach risk in order to achieve the intended lead time. The intention was to fix the constraints of the prior model by selecting the equivalent deterministic individual opportunity restrictions depending on the probability knowledge distribution. The purpose of model was to optimize the management of outpatient requests not fulfilled completely, the limited range of appointment times for returning patient, and the average appointment lead time for old patient. The results showed that the probability of the number of FV patient arriving at time unit i and provided with an appointment at time unit j being greater than or equal to their stochastic arrival in time unit i was 0.51. Each test was found to be different due to the application of random values generated from computer memory. It was observed that the probability value recorded was higher than previous model. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Optimization

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Combining Remote Sensing Dataset, Machine Learning, and Explainable AI for Detailed Human Development Index Monitoring in East Java, Indonesia

Combining Remote Sensing Dataset, Machine Learning, and Expl...

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2024 International Conference on Information technology Research and Innovation, ICITRI 2024

作者： Aprianto, Kasiful Saputra, Irwansyah Riana, Dwiza Universitas Nusa Mandiri Master of Computer Science Student Faculty of Information Technology Jakarta Indonesia Universitas Nusa Mandiri Faculty of Information Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350376210

This study integrates satellite imagery, machine learning, and explainable AI to enhance the predictive accuracy and interpretability of the Human Development Index (HDI) in East Java, Indonesia. Using advanced models, particularly Random Forest Regression, the research achieved an R-squared value of 0.8709, explaining approximately 87% of the variance in HDI. Feature importance analysis identified Night Time Light (NTL) as the most influential predictor with an importance score of 0.4910, indicating a strong correlation with urban development and economic activity. Other significant predictors included the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), reflecting the importance of environmental conditions in development metrics. SHAP (SHapley Additive exPlanations) analysis provided insights into individual feature contributions, enhancing model transparency and supporting targeted policy interventions. For instance, higher NTL was associated with increased HDI, highlighting urbanization's role in development, while pollutants like NO2 and SO2 negatively impacted HDI. This study underscores the effectiveness of integrating satellite data and machine learning in socioeconomic analyses and offers a robust framework for future HDI monitoring. © 2024 IEEE.

关键词： Adversarial machine learning

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Application of Liquid State Machine for Accurate Rainfall Prediction

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Procedia computer science 2025年 258卷 1554-1564页

作者： Meet K Patel Mohini Darji Bansari Patel Student of Bachelor of Technology Department of Computer Science & Engineering Devang Patel Institute of Advance Technology and Research (DEPSTAR) Faculty of Technology and Engineering (FTE) Charotar University of Science and Technology (CHARUSAT) Changa 388421 Gujarat India

Adequate rainfall prediction is important in many aspects, the most important being planning in agriculture, correct management of water resources, and early warnings in the event of impending natural disasters. Traditional techniques have been unsuccessful in handling nonlinear and dynamic characteristics of atmospheric processes. In this research, we look into the usage of Liquid State Machines, a type of spiking neural network, in rainfall prediction. One of the main reasons behind this choice is that the LSMs are known to handle temporal data with dynamic memory capabilities robustly, so they would turn out to be the right choice in modeling nonlinear and time-dependent patterns inherent in rainfall data. This will include preprocessing the dataset to normalize the input features and create lag features to capture the temporal dependencies. Then, the constructed high-dimensional state of the reservoir has to be mapped back to the desired output—rainfall amount or quantity—which involves building a model for LSM with an input layer, a dynamic reservoir or liquid state, and then the readout layer. The model will be trained using backpropagation across time to minimize the mean squared error between the anticipated and real rainfall quantities. For this purpose, the LSM model is trained and tested on a dataset built from the historical weather and rainfall data of the Gujarat region. Various metrics such as MSE, RMSE, MAE, and R² are used to quantify the accuracy of the predictions. The LSM model accurately predicts rainfall by capturing temporal and nonlinear interactions in the data.

关键词： Prediction Rainfall LSM Gujarat Time series

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Machine Learning for Forecasting Depression and Anxiety in University students 1

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8th International Conference on Information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Biswas, Tamal Bhattacharya, Diptendu Rudrapal, Dwijen Roy, Srijan National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India M Tech Student National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India

ISBN: (数字)9789819702107

ISBN: (纸本)9789819702091

The timely identification of mental health issues enables experts to more effectively provide treatment and enhance the well-being of patients. Mental health pertains to an individual’s emotional, mental, and interpersonal state, influencing their thoughts, emotions, and behaviors. It remains crucial across all life phases, spanning childhood, adolescence, and adulthood. Historically, categorizing mental health problems among college students demanded significant effort and time from psychologists. This research engaged five machine learning methods to classify such issues swiftly. The effectiveness of these methods was evaluated based on different standards. The five methods included logistic regression, KNN classification, decision tree classification, random forest, and stacking. A comparison and implementation of these methods revealed that stacking yielded the highest accuracy, predicting 82.20. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： students

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Based-on computer Vision Applications for Bus Stop Passenger Detection System 3

Based-on Computer Vision Applications for Bus Stop Passenger...

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3rd IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Chang, Yu-Cheng Tsai, Hua-Wen Huang, Chao-Yi Wu, Zong-Rong Department of Computer Information and Network Engineer Lunghwa University of Science and Technology Taiwan Department of Bachelor's Program in Intelligent Robot National Yunlin University of Science and Technology Taiwan

ISBN: (纸本)9798350333862

Passenger transport is one of the most common ways of commuting in Taiwan. It plays an important role in the transportation system due to its large number of stations, dense frequency, and cheap transportation. Due to the unfriendly transportation environment and a large number of passengers, a blind spot of passenger transportation exists, which leads to traffic accidents at the station. We research to make the Bus Stop Passenger Detection System. Taking the object detection of Wheelchairs into consideration, it is more convenient to assist the disabled to find the passenger transportation system, which makes Taiwan's transportation system more convenient. © 2023 IEEE.

关键词： Object detection

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