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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

International Journal of Crowd science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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Enhancing Road Safety with Real-Time Helmet Detection and E-Challan Issuance using YOLO and OCR 1

Enhancing Road Safety with Real-Time Helmet Detection and E-...

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1st IEEE International Conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Deepa, S. Vinoth Kumar, C.N.S. Duraivelu, Vivek Easwaran, Balamurugan Suguna, R. Aparna, H. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Srm Institute of Science and Technology College of Engineering and Technology Department of Networking and Communications Tamilnadu Chennai India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narsapur502313 India Texila American University School of It Zambia Bannari Amman Institute of Technology Department of Computer Science and Business Systems Tamilnadu Sathyamangalam India Kongu Engineering College Perundurai Tamilnadu Erode638 060 India

ISBN: (纸本)9798350372748

This study proposes an integrated system combining Optical Character Recognition (OCR) and YOLO (You Only Look Once) for enforcing helmet laws and improving traffic safety. A user-friendly PyQT GUI facilitates vehicle registration, creating an organized CSV database for future reference. YOLOv5, an advanced object detection algorithm, identifies key elements like motorcycles, helmets, and number plates from input images. The primary objective is detecting motorcyclists without helmets, followed by OCR extraction of text from license plates. The extracted text is compared with registered bike information in the CSV file, generating e-challans and fines for matches. Automatic email notifications ensure timely communication and compliance with traffic regulations. The system's implementation not only aids helmet enforcement but also acts as a deterrent for future violations, fostering a safer driving environment. With scalability and practicality, this approach can integrate seamlessly into existing traffic control systems, leveraging AI technologies like YOLOv5 and OCR for effective problem-solving and enhanced road safety. © 2024 IEEE.

关键词： Object detection

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A Portable Electrocardiography Arrhythmia Classification Using Time-series Augmented Signals and Convolutional Neural Network

A Portable Electrocardiography Arrhythmia Classification Usi...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chang, Yu-Chieh Chang, Tzu-Shan Lin, Tu-Liang National Yunlin University of Science and Technology Department of Electronic Engineering Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Taiwan National Chiayi University Department of Management Information Systems Taiwan

ISBN: (纸本)9798350324174

Owing to the rapid development of artificial intelligence (AI) technology in recent years, numerous scholars have applied it to classify arrhythmias using electrocardiography (ECG). However, raw ECG data contain high- and low-frequency noise, which can affect the accuracy of the classification results. To address this issue, we propose using autoencoder processing to reduce noise. Furthermore, most previous studies have utilized a segmented one-dimensional time series to train the AI classification model. In contrast, our approach involves transforming the segmented time series into a two-dimensional image, which allows for better feature coverage to highlight the differences between the various types of arrhythmias. This approach can improve the accuracy of the AI classification model. Finally, we developed a portable ECG arrhythmia classification model by integrating the D1 mini development board and AD8232 ECG sensor. We worked in cooperation with the doctors of Dalin Tzu Chi Hospital to perform field tests on the model in the hospital. © 2023 IEEE.

关键词： Electrocardiography

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A Comparison of Different Optimization Techniques in AlexNet for Plant Disease Classification: SGD, Adadelta, ASGD and Adagrad 5

A Comparison of Different Optimization Techniques in AlexNet...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Ponnila, P. Bazila Banu, A. Bannari Amman Institute of Technology Department of Information Technology Erode Tamilnadu Sathyamangalam India Kpr Institute of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India

ISBN: (纸本)9798331504403

Plant diseases are a biggest challenge for the farmers. Agriculture relies heavily on its production, and diagnosis of plant disease in an accurate manner will help in identification of the appropriate disease. If correct identification was not performed, there might be a significant financial loss that could have an impact on both farmers and the national economy. For resolving this issue, the AlexNet was employed, a convolutional neural network method. In this research, AlexNet is used to classify the diseases occurring in chilli, cauliflower and sunflower. This AlexNet model, with Stochastic Gradient Descent(SGD) algorithm optimization techniques had achieved 82% accuracy for the overall classification of the test images of chilli plant, 98% accuracy for the overall classification of the test images of cauliflower plant and 91% accuracy for the overall classification of the test images of sunflower plant;with Adadelta optimization 86%, 99% and 93% accuracy was achieved for the overall classification of the test pictures of chilli plant, cauliflower plant and sunflower plant respectively. After using the Asynchronous Stochastic Gradient(ASGD) optimization with AlexNet model, the accuracy obtained are 88% for the test images of chilli plant, 98% for the test images of cauliflower plant and 91% for the test images of sunflower plant;and finally, AdaGrad optimization with AlexNet achieved accuracy of 80% for the test images of chilli plant, 9 8% for the test images of cauliflower plant and 93% for the test images of sunflower plant. © 2024 IEEE.

关键词： Convolutional neural networks

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Reinforcement Learning-Based Output Stabilization Control for Nonlinear systems With Generalized Disturbances 25

Reinforcement Learning-Based Output Stabilization Control fo...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Tang, Zezhi Rossiter, J Anthony Dong, Yi Panoutsos, George University Of Sheffield Department Of Automatic Control And Systems Engineering SheffieldS13JD United Kingdom University Of Southampton Department Of Electronics And Computer Science Southampton United Kingdom

ISBN: (纸本)9798350340266

This paper proposes a new disturbance observer (DO)-based reinforcement learning (RL) control approach for nonlinear systems with unmatched (generalized) disturbances. While a nonlinear disturbance observer (NDO) is utilized to measure the plant uncertainties, disturbances can exist in the plant via distinct channels from those of the control signals;so-called mismatched disturbances are theoretically difficult to attenuate within the channel of the system's states. A generalized disturbance observer-based compensator is implemented to address the uncertainty cancellation problem by removing the influence of uncertainties from the output channels. Con-currently, a composite actor-critic RL scheme is utilized for approximating the optimal control policy as well as the ideal value function pertaining to the compensated system by solving a Hamilton-Jacobi-Bellman (HJB) equation for both online and offline iterations simultaneously. Stability analysis verifies the convergence of the proposed framework. Simulation results are included to illustrate the effectiveness of the proposed scheme. © 2024 IEEE.

关键词： Nonlinear systems

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Protecting Your Attention during Distributed Graph Learning: Efficient Privacy-Preserving Federated Graph Attention Network

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1949-1964页

作者： Zhou, Jinhao Wu, Jun Ni, Jianbing Wang, Yuntao Pan, Yanghe Su, Zhou Waseda University Graduate School of Information Production and Systems Fukuoka808-0135 Japan Queen's University Department of Electrical and Computer Engineering KingstonONK7L 3N6 Canada Xi'An Jiaotong University School of Cyber Science and Engineering Xi'an710049 China

Federated graph attention networks (FGATs) are gaining prominence for enabling collaborative and privacy-preserving graph model training. The attention mechanisms in FGATs enhance the focus on crucial graph features for improved graph representation learning while maintaining data decentralization. However, these mechanisms inherently process sensitive information, which is vulnerable to privacy threats like graph reconstruction and attribute inference. Additionally, their role in assigning varying and changing importance to nodes challenges traditional privacy methods to balance privacy and utility across varied node sensitivities effectively. Our study fills this gap by proposing an efficient privacy-preserving FGAT (PFGAT). We present an attention-based dynamic differential privacy (DP) approach via an improved multiplication triplet (IMT). Specifically, we first propose an IMT mechanism that leverages a reusable triplet generation method to efficiently and securely compute the attention mechanism. Second, we employ an attention-based privacy budget that dynamically adjusts privacy levels according to node data significance, optimizing the privacy-utility trade-off. Third, the proposed hybrid neighbor aggregation algorithm tailors DP mechanisms according to the unique characteristics of neighbor nodes, thereby mitigating the adverse impact of DP on graph attention network (GAT) utility. Extensive experiments on benchmarking datasets confirm that PFGAT maintains high efficiency and ensures robust privacy protection against potential threats. © IEEE.

关键词： Differential privacy

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Physics-Informed Neural Networks for Solving Blood Flows 26

Physics-Informed Neural Networks for Solving Blood Flows

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Chang, Yao-Chung Lin, Yu-Shan Hu, Jeu-Jiun National Taitung University Department of Computer Science and Information Engineering Taitung Taiwan University Rd. Department of Information Science and Management Systems 369 Sec. 2 University Rd. Taitung Taiwan

ISBN: (纸本)9791188428120

The Physics-informed Neural Networks Deep Learning (PINN) framework has been introduced with the primary objective of advancing the field of blood flow simulations. PINN Deep Learning involves data-driven training for flow prediction and can incorporate the understanding of physical laws described by partial differential equations (PDEs). This paper employs the PINN Method for simulating blood flows. Multiple test cases will be computed and compared with other numerical and experimental results to validate the approach. The results demonstrate that the PINN method functions as expected, and validation against experimental and other researchers' results ensures the generation of meaningful output data and the prudent selection of parameters. © 2024 Global IT Research Institute - GIRI.

关键词： Blood

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Joint Design of Probabilistic Constellation Shaping and Precoding for Multi-user VLC systems

Joint Design of Probabilistic Constellation Shaping and Prec...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Nguyen, Thang K. Pham, Thanh V. Le, Hoang D. Nguyen, Chuyen T. Pham, Anh T. The University of Aizu Computer Communications Lab. Japan Shizuoka University Department of Mathematical and Systems Engineering Japan Hanoi University of Science & Technology School of Electrical and Electronic Engineering Viet Nam

ISBN: (纸本)9798350351255

This paper proposes a joint design of probabilistic constellation shaping (PCS) and precoding to enhance the sum-rate performance of multi-user visible light communications (VLC) broadcast channels subject to signal amplitude constraint. In the proposed design, the transmission probabilities of bipolar M-pulse amplitude modulation (M-PAM) symbols for each user and the transmit precoding matrix are jointly optimized to improve the sum-rate performance. The joint design problem is shown to be a complex non-convex problem due to the non-convexity of the objective function. To tackle the problem, the firefly algorithm (FA), a nature-inspired heuristic optimization approach, is employed to solve a local optima to the original non-convex optimization problem. The FA-based approach, however, suffers from high computational complexity. Therefore, we propose a low-complexity design based on zero-forcing (ZF) precoding, which is solved using an alternating optimization (AO) approach. Simulation results reveal that the proposed joint design with PCS significantly improves the sum-rate performance compared to the conventional design with uniform signaling. Some insights into the optimal symbol distributions of the two joint design approaches are also provided. © 2024 IEEE.

关键词： Convex optimization

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Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

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computers, Materials & Continua 2022年第7期72卷 141-156页

作者： R.Gopi Mahantesh Mathapati B.Prasad Sultan Ahmad Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal Department of Computer Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India Department of Computer Science and Engineering RajaRajeswari College of EngineeringBengaluru560074India Department of Information Technology Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a15347Yemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

VehicularAd hoc Network(VANET)has become an integral part of Intelligent Transportation systems(ITS)in today’s *** is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real *** is susceptible to security issues,particularly DoS attacks,owing to maximum unpredictability in ***,effective identification and the classification of attacks have become the major requirements for secure data transmission in *** the same time,congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections,for a *** this motivation,the current research paper presents an intelligent DoS attack detection with Congestion Control(IDoS-CC)technique for *** presented IDoSCC technique involves two-stage processes namely,Teaching and Learning Based Optimization(TLBO)-based Congestion Control(TLBO-CC)and Gated Recurrent Unit(GRU)-based DoS detection(GRU-DoSD).The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the *** algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel *** is applied to avoid congestion on ***,GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the *** outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.

关键词： VANET intelligent transportation systems congestion control attack detection dos attack deep learning

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Quantifying, Activating and Rewarding Flexibility in Renewable Energy Communities

Quantifying, Activating and Rewarding Flexibility in Renewab...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Allard, Julien Vallée, François De Grève, Zacharie Stegen, Thomas Glavic, Mevludin Cornélusse, Bertrand University of Mons Power Systems and Markets Research Group Electrical Power Engineering Unit Mons Belgium University of Liège Department of Electrical Engineering and Computer Science Liège Belgium

ISBN: (纸本)9789531842976

Demand-side flexibility from renewable energy community members increases the benefits of local production and exchanges. To effectively harness this flexibility, end-users must be rewarded for their efforts regarding the shifted energy volumes and the associated discomfort. This work quantifies these last two quantities for three types of loads (electric vehicles, thermal loads and wet appliances). Then, a two-step model is proposed which first optimally activates the offered flexibility and then shares the benefits of this activation among the renewable energy community members through a rule-based or an optimization-based approach. Results show that the community framework can further increase the benefits brought by flexible demand. © 2024 IEEE.

关键词： Demand response

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