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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Easwaramoorthy, Sathishkumar Veerappampalayam Subramani, Neelakandan Hoong, Angela Lee Siew Sunway University School of Engineering and Technology Selangor Darul Ehsan47500 Malaysia R.M.K Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331530631

The COVID-19 epidemic has negatively affected features of human beings and diverse sectors, particularly the healthcare industry. This modality led to the formation of novel life patterns that people need to face to reduce the spreading of the pandemic by promising social distance, amongst others. For that reason, investigators have discovered several deep learning (DL) and machine learning (ML) based techniques to quickly diagnose COVID-19 patients using X-ray images. ML-based approaches can decrease costs and take less time for treatment. Nevertheless, maintaining patient privacy poses challenges inside such third-person-controlled models, potentially decreasing to protect patients from possible discomfort and disgrace. However, BC technology provides the potential to safely store complex HealthCare data anonymously, without needing third-person interference. In this study, we introduced a Leveraging Blockchain Technology and Artificial Intelligence using a Smart Contract-Driven COVID-19 Pandemic Detection (LBCTAI-SCCPD) model. The main aim of the LBCTAI-SCCPD model is to investigate the absence or presence of COVID-19 from medical images. To accomplish that, the LBCTAI-SCCPD model is a blockchain-based COVID-19 recognition infrastructure, which contains a smart contracting method for uploading COVID-19-positive case-oriented information with blockchain. At the initial step, the LBCTAI-SCCPD technique employs an adaptive median filtering (AMF) approach to pre-process the input image. In addition, the complex patterns and features in the images can be derived from CapsNet model. Furthermore, the radial basis function neural network (RBFNN) model can be used for a precise COVID-19 process of classification. Lastly, the dung beetle optimization (DBO) algorithm is used for optimal hyperparameter selection of the RBFNN model. To demonstrate the better performance of the LBCTAI-SCCPD technique, a sequence of simulations is executed on the benchmark dataset. The stimulat

关键词： COVID-19

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Enhancing Fintech Fraud Detection through Graph Convolutional Neural Networks 1

Enhancing Fintech Fraud Detection through Graph Convolutiona...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Pavan, V. Omprakash, Omprakash Bugge, Bhagya Prasad Thangamani, S. Perada, Anvesh Suneetha, Sampathirao Manipal Academy of Higher Education Manipal Law School Manipal India Jain University School of Law Bengaluru India S.R.K.R Engineering College Department of Electronics and Communication Engineering Bhimavaram India Nandha Engineering College Department of Information Technology Erode India Drexel University Department of Electrical and Computer Engineering PhiladelphiaPA United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798331529246

The swift advancement of fintech has transformed financial services, offering users seamless digital experiences. Nonetheless, this expansion has also presented intricate cybersecurity difficulties, especially in fraud detection. This research investigates novel methods for addressing fintech fraud through the utilization of GCNN and the ResNet101-C model. GCNN adeptly addresses dynamic fraud trends via graph-based feature aggregation, whereas ResNet101-C, augmented with dropout and fully connected layers, attains enhanced classification performance. Preprocessing procedures, such as imputation, noise reduction, and feature standardization, enhance data quality, whereas feature extraction emphasizes essential transaction variables like frequency and value. Assessment of synthetic datasets reveals that ResNet101-C achieves an accuracy of 93.46%, surpassing leading models. These results underscore the transformational capability of GCNN and ResNet101-C in tackling the dynamic nature of fraud, providing scalable, precise, and privacy-preserving solutions for the financial sector. © 2025 IEEE.

关键词： Noise abatement

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IoT-Enabled Smart Irrigation System with ABiLSTM for Optimized Water Management and Crop Yields 3

IoT-Enabled Smart Irrigation System with ABiLSTM for Optimiz...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Chaturvedi, Abhay Rao, Praveena Venkata Maruti Prasad, S. Malini, T. Prabagar, S. Bharathi, K. Gla University Department of Electronics and Communication Engineering Mathura India Institute of Aeronautical Engineering Department of Civil Engineering Hyderabad India Aditya Institute of Technology & Management Department of Bs & H Tekkali India Sri Krishna College of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India School of Advanced Computing Alliance University Department of Computer Science and Engineering Bangalore India Erode Sengunthar Engineering College Department of Agricultural Engineering Perundurai India

ISBN: (纸本)9798331508456

An efficient management of water resources in agriculture is now crucial in light of the growing global population and increased demand for water. Innovative solutions are needed to improve water efficiency since conventional irrigation methods are not sustainable anymore. Through the use of precision agriculture and the Internet of Things (IoT), smart irrigation has revolutionised water management, leading to increased crop yields. In order to improve the efficiency of decision-making, this research presents an Internet of Things (IoT) smart irrigation system that uses SA-BiLSTM (Self-Attention Bidirectional Long Short-Term Memory). Using Min-Max and Z-score normalisation approaches for feature scaling, the methodology includes data preparation, feature selection, and model training. Methods based on ranking, such as the Chi-Square Test, Pearson Correlation Coefficient (PCC), and Mutual Information (MI), were used to prioritise feature selection. Classification and prediction testing showed that the proposed SA-BiLSTM model outperformed state-of-the-art models like HGAT and Linear SVM, with an astounding 95.63% accuracy. Improved resource allocation and irrigation effectiveness, leading to increased agricultural output, are highlighted by the results, which highlight the effectiveness of advanced machine learning approaches in precision agriculture. © 2025 IEEE.

关键词： Resource allocation

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Power Optimized Multiple-UAV Error-Free Network in Cognitive Environment

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computers, Materials & Continua 2023年第5期75卷 3189-3201页

作者： Shakti Raj Chopra Parulpreet Singh Ahmed Alhussen Nitin Mittal MohdAnul Haq School of Electronics and Electrical Engineering Lovely Professional UniversityPhagwaraPunjab144411India Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia University Centre for Research and Development Chandigarh UniversityMohaliPunjab140413India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Many extensive UAV communication networks have used UAV cooperative *** networking services can be offered using unmanned aerial vehicles(UAVs)as aerial base *** only is coverage maximization,but also better connectivity,a fundamental design challenge that must be *** number of applications for unmanned aerial vehicles(UAVs)operating in unlicensed bands is fast expanding as the Internet of Things(IoT)*** bands,however,have become overcrowded as the number of systems that use them *** Radio(CR)and spectrum allocation approaches have emerged as a potential approach for resolving spectrum scarcity in wireless networks,and hence as technological solutions for future generations,from this *** a result,combining CR with UAVs has the potential to give significant benefits for large-scale UAV *** paper examines existing research on the subject of UAV covering and proposes a multi-UAV cognitive-based error-free model for energy-efficient *** maximization,power control,and enhanced connection quality are the three steps of the proposed *** satisfy the desired signal-to-noise ratio,the covering zone efficacy is investigated as a function of the distance among UAVs stationed in a specific geographic region depending on multiple deployment configurations like as rural,suburban,and urban macro deployment scenarios of the ITU-R M.2135 standard(SNR).

关键词： UAV networks multi-UAV coverage energy-efficient FER SNR

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Soft,full Wheatstone bridge 3D pressure sensors for cardiovascular monitoring

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npj Flexible Electronics 2024年第1期8卷 910-917页

作者： Yoonseok Park Haiwen Luan Kyeongha Kwon Ted S.Chung Seyong Oh Jae-Young Yoo Gooyoon Chung Junha Kim Suhyeon Kim Sung Soo Kwak Junhwan Choi Hoang-Phuong Phan Seonggwang Yoo Hyoyoung Jeong Jaeho Shin Sang Min Won Hong-Joon Yoon Yei Hwan Jung John A.Rogers Department of Advanced Materials Engineering for Information and Electronics Integrated Education Institute for Frontier Science&Technology(BK21 Four)Kyung Hee UniversityYongin-siGyeonggi-do 17104Republic of Korea Department of Mechanical and Aerospace Engineering University of California San DiegoLa JollaCA 92093USA School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea Department of Mechanical Engineering Columbia UniversityNew YorkNYUSA Division of Electrical Engineering Hanyang University ERICAAnsan 15588Republic of Korea Department of Semiconductor Convergence Engineering Sungkyunkwan UniversitySuwonRepublic of Korea Biomedical Research Division Korea Institute of Science and TechnologySeoul 02792Republic of Korea Department of Chemical Engineering Dankook UniversityYonginGyeonggi-do 16890Republic of Korea School of Mechanical and Manufacturing Engineering Tyree Institute of Health EngineeringUniversity of New South WalesSydneyNSW 2052Australia Querrey Simpson Institute for Bioelectronics Northwestern University EvanstonEvanstonIL 60208USA Department of Electrical and Computer Engineering University of California DavisDavisCA 95616USA Department of Electrical and Computer Engineering Sungkyunkwan University2066Seobu-roJangan-guSuwon-siGyeonggi-do 16419Republic of Korea Department of Electronic Engineering Gachon UniversitySeongnamsiGyeonggi-do 13120Republic of Korea Department of Electronic Engineering Hanyang UniversitySeoulRepublic of Korea Department of Materials Science and Engineering Biomedical EngineeringNeurological SurgeryChemistryMechanical EngineeringElectrical Engineering and Computer Science Simpson Querrey Institute and Feinberg Medical SchoolCenter for Bio-Integrated ElectronicsNorthwestern University EvanstonEvanstonIL 60208USA

Variations in parameters associated with the ambient environment can introduce noise in soft,body-worn *** example,many piezoresistive pressure sensors exhibit a high degree of sensitivity to fluctuations in temperature,thereby requiring active compensation *** research presented here addresses this challenge with a multilayered 3D microsystem design that integrates four piezoresistive sensors in a full-Wheatstone bridge *** optimized layout of the sensors relative to the neutral mechanical plane leads to both an insensitivity to temperature and an increased sensitivity to pressure,relative to previously reported devices that rely on similar operating *** this 3D pressure sensor into a soft,flexible electronics platform yields a system capable of real-time,wireless measurements from the surface of the *** above the radial and carotid arteries yields high-quality waveforms associated with pulsatile blood flow,with quantitative correlations to blood *** results establish the materials and engineering aspects of a technology with broad potential in remote health monitoring.

关键词： Wheat bridge thereby

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS 15

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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Enhancing Agricultural IoT with Predictive Analytics and Reinforcement Learning Models 2

Enhancing Agricultural IoT with Predictive Analytics and Rei...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Krishnan, R. Santhana Raj, J. Relin Francis Christopher, V. Bibin Nandini, N. Muthu, A. Essaki Puvanadevi, C. SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Ssveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India

ISBN: (纸本)9798350317558

This research explores the augmentation of Agricultural Internet of Things (IoT) systems through the integration of advanced predictive analytics and reinforcement learning models. A novel algorithm, termed "CropQL,"is proposed to optimize crop yield prediction and resource allocation in precision agriculture. CropQL amalgamates the strengths of the CropNet IoT model with Q-Learning for reinforcement learning and Random Forest for predictive analytics. Simulation analyses are conducted to evaluate the performance of CropQL against established algorithms, employing diverse metrics such as precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The proposed CropQL algorithm exhibits superior predictive accuracy, demonstrating a significant enhancement in crop yield forecasting when compared to baseline algorithms. Precision and recall metrics reveal the algorithm's efficacy in minimizing false positives and negatives, ensuring precision agriculture systems make informed decisions. F1 score indicates the balanced optimization of precision and recall, reinforcing the algorithm's robustness. AUC-ROC analysis further corroborates the superior discriminative power of CropQL in distinguishing between positive and negative instances. In comparison to existing algorithms, including AgriSensNet, PrecisionCropNet, and IoTFarmGuard, CropQL consistently outperforms in predictive accuracy and resource optimization. The simulation results underscore the efficacy of the proposed algorithm in enhancing the efficiency of agricultural IoT systems, providing a promising avenue for sustainable precision farming. © 2024 IEEE.

关键词： Agricultural IoT CropQL Precision Agriculture Predictive Analytics Reinforcement Learning Sustainable Farming

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FreqEmo: A Twitter Dataset Comprising Users' Tweeting Frequency and Emotions

FreqEmo: A Twitter Dataset Comprising Users' Tweeting Freque...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Agrawal, Aryan Arora, Anshika Chakraborty, Pinaki Netaji Subhas University of Technology Department of Electronics and Communication Engineering New Delhi India School of Computer Science Engineering and Technology Bennett University Greater Noida India Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350379716

Social media is now indispensable, used by all social, economic, age, and demographic groups. It provides extensive user-generated data, offering insights into users' mental states. This paper introduces a novel Twitter dataset comprising users' tweeting frequency and emotions shared. The dataset is based on Tweets collected from the USA and India during a 60-day period using adjectives representing emotions. Further, for frequently tweeting users, NRC emotion lexicon has been implemented which extracted sentiment and emotion scores of the users. The final dataset includes users' tweeting patterns along with the distribution of emotions and sentiments in their tweets. This dataset has been made publicly available and this project aims to help researchers develop models to assess users' emotional states based on social media activity without self-reporting, with applications in correlating usage patterns with well-being, clustering users by emotions and tweeting patterns, and analyzing temporal, spatial, and demographic trends. To provide baseline performance, k-means clustering is implemented in this study to cluster the users with similar usage patterns. Clusters are analyzed to determine the types of emotions and sentiments shared by users within each cluster. Based on the analysis, it is concluded that as Twitter usage increases within the clusters, the proportion of users dominated by negative sentiment also rises significantly, peaking at 41.67%. Additionally, the percentage of users whose predominant emotion is joy decreases with increased usage, reaching a low of 8.33% as opposed to the peak value of 69.44% in the group characterized by least usage, while the percentage of users dominated by anger and disgust grows to 17.24% and 4.17% as their usage intensifies. © 2024 IEEE.

关键词： Tweets

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Web-Based Platform Development for Interactive 3D Visual Exhibition of Painting Artwork 5

Web-Based Platform Development for Interactive 3D Visual Exh...

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5th IEEE International Conference on computer and informatics engineering, IC2IE 2022

作者： Yuly, Ade Rahma Nugrahani, Fitria Lazuardi, Muhammad Azhar Rijalullah, Muhammad Syaikhan Ramadhan, Muhammad Ridwan Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia

ISBN: (数字)9781665453387

ISBN: (纸本)9781665453387

We can use technological capabilities as a solution during a pandemic. We use virtual exhibitions as one of the solutions, as it is not only implementing multimedia capabilities but also for artists and viewers to present artworks with a different experience than conventional exhibitions. In this study, we worked with artists who could not exhibit their works due to social restrictions on community activities. We use the implementation of WebGL on the exhibition platform to provide an interactive experience for users to enjoy the work more freely. We built the platform using the mixed Rapid Application Development (RAD) and Multimedia Development Life Cycle (MDLC) methods by Luther to make the work efficient. The novelty in this research is that the platform not only presents works of art but also becomes a repository for storing work data so that we can update the data on the dashboard to store multimedia data. Tests conducted on Beta Testing of users with the System Usability Scale (SUS) showed that the virtual exhibition in terms of adjective ratings included the Good category and acceptability ranges including Acceptable. We hope that with this platform, the multimedia application will become more dynamic to solve community problems under challenging conditions such as Covid 19. © 2022 IEEE.

关键词： Life cycle

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Development of Gamification and Live Coding-Based Programming Learning Platform to Foster Learning Motivation of Vocational Students

Development of Gamification and Live Coding-Based Programmin...

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2023 International Conference on Electrical and Information Technology, IEIT 2023

作者： Hidayat, Wahyu Nur Dwi Prasetyo, Wiji Zahiroh, Ayu Najmatus Maydi Syahri, Esa Pramudheva Afdloly, Mochammad Mu'iz Sari, Rahajeng Kartika Universitas Negeri Malang Department of Electrical Engineering and Informatics Malang Indonesia Smk Negeri 10 Malang Departement of Computer Network Engineering and Telecommunications Malang Indonesia

ISBN: (纸本)9798350327298

The current utilization of e-learning has not been able to increase participation and learning experience and motivate students to achieve educational goals. This research focuses on developing gamification-based learning media in Object-Oriented Programming (OOP) subjects to increase student learning motivation, determine the feasibility level of learning media, and determine the increase in student learning motivation. The method of the research is design thinking It is used to identify and better understand users in the use of applications, find problems faced by users, and can explore new ideas in the creation of appropriate application features in accordance with user needs. The results of expert media validation from the teacher, lecturer, and programmer amounted to 89.78% in the good category, for the material validation results of 91.33% in the very good category. Media use was implemented by 23 students of class XI RPL SMKN 12 Malang. The user trial results showed a percentage of 83.48% with good criteria. The student learning motivation test results showed a percentage of 81.26% with very high criteria. This shows the effect of gamification implementation in fostering student learning motivation. Gamification-based media has been tested for feasibility by media experts, material experts, and students. Therefore, the media is feasible and can be used as a learning support media in object-oriented programming subjects in Vocational High school. © 2023 IEEE.

关键词： Students

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