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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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An Intelligent Voice-Recognition Wheelchair system for Disabled Persons

An Intelligent Voice-Recognition Wheelchair System for Disab...

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2023 International Conference on Sustainable Computing and Smart systems, ICSCSS 2023

作者： Kalpana, A.V. Venkataramanan, V. Charulatha, G. Geetha, G. Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Chennai India D.J Sanghvi College of Engineering Department of Electronics and Communication Engineering Maharashtra Mumbai India Peri Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India B v Raju Institute of Technology Department of Computer Science Engineering Telangana Hyderabad India

ISBN: (纸本)9798350333602

This study illustrates the creation of an intelligent voice-recognition wheelchair for disabled persons who cannot manually man-oeuvre their wheelchairs. Using voice recognition, the patient operates the wheelchair, and a GPS device included in the wheelchair can track the patient's position and communicate the data to a Smartphone. To carry out the people's orders, the voice module V3 records and can recognize the patient's voice. This module converts the voice commands into letters, subsequently transmitted to the Wi-Fi module to operate the wheelchair. The Wi-Fi module sends commands to the motor drive for the wheels. In addition, three levels of motor speed adjustment are available: low, medium, and high. This device also utilizes an Infrared sensor to automatically identify obstacles, allowing the patient's family members to know exactly where they are. Disabled People can be used this device, which simultaneously provides a voice-controlled wheelchair, obstacle detection, motor speed control, and patient GPS tracking via mobile phone;it will be a successful solution for disabled people across the world. © 2023 IEEE.

关键词： Wheelchairs

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Fashionable Four Wheeler Parking to Reduce the Human Intervention and to Enhance the Flexibility in Parking system Using IoT 3

Fashionable Four Wheeler Parking to Reduce the Human Interve...

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3rd International Conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Manoj, P. Varun, C.M. Keerthi Kumar, N. Padhye, Pradip Kumari, K. Aruna Das, Puja Sarkar, Swagata Vidyavardhaka College of Engineering Department of Civil Engineering Mysore India R.M.K. Engineering College Department of Computer Science and Business System Kavaraipettai India Bms Institute of Technology and Management Department of Mechanical Engineering Bengaluru India School of Management Dr. Vishwanath Karad Mit World Peace University Pune India Gitam University Department of Mathematics Visakhapatnam India Hiralal Mazumder Memorial College for Women Department of Computer Science Kolkata India Sri Sai Ram Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350336238

Due to the lack of a mechanism in place to check online for parking availability, it is now quite difficult to find a parking spot in congested regions. Imagine being able to access information about parking slot availability on your phone and not having to move around to do so. The cutting-edge parking system powered by IoT can overcome this issue. The IoT-based parking system makes it simple to check the availability of parking spaces online. With the aid of this device, the parking system might be fully automated. You might have automatic admission, payment, and exit processes. A NodeMCU-based IOT-based car parking system is being created using five IR sensors, two servo motors, and NodeMCU. Three IR sensors are utilised to determine whether parking spaces are available, and two IR sensors are used at the entry and exit gates to identify vehicles. Based on the sensor value, servo motors control the gates' opening and closing. In this part, we'll show you how to upload data to the cloud that is reachable from anywhere using the Adafruit IO platform. © 2023 IEEE.

关键词： Infrared detectors

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Next-Generation Artificial Intelligence Solutions for Comprehensive Analysis and Improvement of Heat Dissipation in High-Intensity Boiling Environments

Next-Generation Artificial Intelligence Solutions for Compre...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Gadiyar, H. Manoj T. Kumar, S Vinod Raj, Vijilius Helena Yadav, Dinesh Kumar Hasoni, Mortada Kareem Sabi Lakhanpal, Sorabh Canara Engineering College Department of Computer Science and Business System India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Applied Sciences Bangalore India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology AlTalb Collage of Nursing Dhi Qar64001 Iraq Lovely Professional University Phagwara India

ISBN: (纸本)9798350373783

This research delves into the realm of AI-driven heat dissipation, offering a suite of algorithms to address the challenges posed by high-intensity boiling environments. The proposed predictive modeling algorithm, rooted in linear regression, provides a systematic and robust approach through 20 meticulously designed steps. This algorithm, along with neural networks, reinforcement learning, generative adversarial networks (GANs), and genetic algorithms, forms a versatile toolbox for thermal management. Linear regression, through its step-by-step predictive modeling process, ensures accurate heat dissipation predictions. Neural networks, employing backpropagation, showcase adaptability and complexity, excelling in real-time adaptation. Reinforcement learning, specifically Q-Learning, introduces dynamic decision-making strategies, vital for navigating complex thermal scenarios. GANs contribute by generating realistic synthetic data, an invaluable asset for simulating high intensity boiling environments. Genetic algorithms, resembling natural selection, prove versatile and scalable in evolving optimal solutions. The research evaluates these algorithms comprehensively, demonstrating the proposed method's superiority in prediction accuracy, energy efficiency, adaptability, and overall performance. The proposed predictive modeling algorithm stands out as a benchmark, showcasing its potential for practical implementation and optimization in diverse scenarios. This comprehensive exploration sets the stage for innovative solutions to the intricate challenges of thermal management, as continuous adaptation and fine-tuning remain pivotal in this dynamic landscape. © 2024 IEEE.

关键词： Generative adversarial networks

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Attentive Feature Segmentation model for Stroke detection on MRI images

Attentive Feature Segmentation model for Stroke detection on...

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BIT Conference (BITCON), International

作者： S.E. Viswapriya D. Rajeswari Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Medical image segmentation plays a crucial role in treatment planning, disease diagnosis, and guiding surgical navigation. Research should focus on medical image segmentation to achieve substantial breakthroughs or fill knowledge gaps that could lead to discoveries and improvements. Deep learning (DL) techniques have recently become necessary in creating advanced models, particularly for medical image segmentation. This paper introduces a novel segmentation algorithm specifically designed for MRI images. Initially, the paper will discuss the introduction and challenges in medical image segmentation. Later, developing a novel attentive feature-based segmentation algorithm is focused and their corresponding evaluation steps are measured. The evaluation metrics are finally compared with other existing segmentation algorithms and concluded that the proposed model surpasses the cutting-edge methods by notable differences in performance, showing the adaptability and overall effectiveness of the proposed model.

关键词： Deep learning Image segmentation Adaptation models Magnetic resonance imaging Semantic segmentation Surgery Computer architecture Feature extraction Medical diagnostic imaging Testing

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Phylogenetic-Informed Generative Adversarial Network for Predicting Mutations in SARS-CoV-2

Phylogenetic-Informed Generative Adversarial Network for Pre...

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Networking and Communications (ICNWC), International Conference on

作者： Vijay Chandar Vishnu G Nair Rajeswari D Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

In the pursuit of developing more effective vaccines against viruses like SARS-CoV-2, understanding their potential mutations is crucial. In order to generate protein sequences, this study presents a unique framework that synergistically blends phylogenetic data with Generative Adversarial Networks (GANs). The proposed method combines the strengths of two models: TEMPO, a transformer-based model proficient in leveraging phylogenetic data for mutation detection in SARS-CoV-2, and MutaGAN, a GAN-based model adept in generating synthetic protein sequences. The phylogenetic data from TEMPO is used in this novel design as the input for the MutaGAN generator, replacing the conventional random noise input used in GANs. The generator may now create synthetic protein sequences that are impacted by the evolutionary past included in the phylogenetic data resulting in the creation of artificial protein sequences that are more biologically significant and lifelike. The discriminator portion of the model, similar to MutaGAN, is still in charge of determining whether or not the input sequences comprise a valid parent-child pair and uses sequences produced by the first GAN as extra negative cases. This prevented the model from going too far off its planned route. Additional investigation into this fascinating confluence of bioinformatics and AI may be possible as a result of this study’s conclusions. This approach has tremendous promise in many applications, such as the production of more powerful vaccinations, protein engineering, medication design, and improving our knowledge of biological systems, by forecasting how SARS-CoV-2 could change. A precision of 0.662 and an accuracy of around 0.671, together with a Matthews Correlation Coefficient (MCC) score of 0.313, show that the framework performs very well. These scores show that the framework performs better than a number of cutting-edge baseline techniques. The synthetic protein sequences generated by the framework not only bea

关键词： Proteins Protein engineering Biological system modeling Generative adversarial networks Transformers data models Generators

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Implementation of CBDC system for the Customised Development of Finance Service system

Implementation of CBDC System for the Customised Development...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Devansh Mehra A Sri Vamsi Prabhu Kavin Department of Data Science and Business Systems School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India

CBDC–as-a-problem looks like a mania of interest from central banks and financial institutions all over the world. It may describe CBDC technology and CBDC technology development, the positive aspects of CBDC and its future perspectives as follows: The CBDC is linked to the existing banking systems in the past which was based on the past findings of the blockchain, DLT, etc., that possessed the qualities the previous ones did not have – security, trust, and transparency. Innovation, design and CBDC safety that falls below this paper are discussed in this section below. The paper proceeds to show several paths that central banks are taking to CBDC development given the regulation and changing dynamic of the financial world. However, the studies do not end there; information on the potential of CBDC for financial stability, monetary policy formulation trajectories, transaction privacy, and the role of banks is included. Given that in field of finance the CBDCs are becoming more and more important, this research paper shall ensure to provide consistent background to policymakers, financial institutions and society at large on CBDC and its structural implications. This guarantees that everyone is in the know so it will be possible for them to come up with compliant plans and decisions informed by the knowledge in this changing landscape that simply does not stop evolving.

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A Novel and Accurate Method of Diagnosis via AI and ML for Smart HC system 4

A Novel and Accurate Method of Diagnosis via AI and ML for S...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Elizabeth, J. Ruby Emmanuel, Mathews Aswini, E. Pathak, Shriram Bhuvaneswari, M. Choubey, Abhishek Nehru Institute of Engineering and Technology Department of Computer Science and Business System Tamil Nadu T.M.Palayam India Sathyabhama University of Science and Technology chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering India Ies Insititute of Technology and Management Ies University Department of Mechanical Engineering M.P. Bhopal India Karpagam Academy of Higher Education Department of Biomedical Engineering Coimbatore India Sreenidhi Institute of Science and Technology ghatkesar Hyderabad Yamnampet India

ISBN: (纸本)9798350360165

Recent breakthroughs in the internet of Issues (IoT), computing through the cloud, and the use of AI have changed the standard healthcare system, making a case for smart health care. By combining key technologies including IoT and AI, there is a major improvement in medical services. The merging of IoT and AI offers multiple possibilities within the healthcare industry. This study piece presents a new artificial intelligence- and internet of things convergence-based framework for disease detection in smart health care systems, emphasizing especially on cardiovascular diseases and diabetes. The suggested model includes separate steps, including data collection, preparation, classification, and parameter modification. Utilizing Internet of Things devices like smartwatches along with sensors enables smooth collection of information, while AI methods harness this data for the detection of illnesses. The technique presents a new approach, applying to the Crowd Search Optimizer algorithm-based Flowed Long and Short-Term Memory (CSO-CLSTM) approach to disease detection. CSO is applied to improve both 'weights' while 'bias' factors of the CLSTM framework, while the isolation Fg (iForest) method is implemented to remove outliers. Through testing with health care information, the CSO-LSTM model got excellent accuracy ratings of 96.16% when 97.26% in identifying heart disease while diabetes, respectively. Consequently, the suggested CSO-LSTM model appears as a solid and reliable method of disease evaluation in digital healthcare systems, showing its potential for broad application. © 2024 IEEE.

关键词： Electronic health record

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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A Framework for Deepfake Detection using Convolutional Neural Network and Deep Features

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Procedia Computer science 2025年 258卷 3640-3648页

作者： Soundarya B C Gururaj H L Naveen Kumar C M Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Business System Malnad College of Engineering Hassan India

With the advancement of Artificial Intelligence, facial recognition has become a crucial biometric feature. Deepfake technology leverages AI and can create hyper-realistic digitally manipulated videos of people appearing to say or do things that never occurred. The emergence of Generative Adversarial Networks (GANs) has further enabled the creation of fake visual content with astonishing realism. This technology has diverse applications, such as in the film industry, where it allows for video recreation without reshooting, creating awareness videos, restoring the voices of those who have lost them, and updating movie scenes at low cost. However, this rapid advancement also presents significant challenges. The proliferation of synthetic images raises severe concerns about their societal impact, particularly in terms of potential misuse for harassment and blackmail. Therefore, developing robust deepfake detection models is imperative. This study evaluates the performance of a proposed ResNet34 model in deepfake detection. We utilize the FaceForensics++ dataset to train and assess the model, incorporating images generated by four popular deepfake techniques. Our experimental results demonstrate that integrating linear ternary patterns (LTP) and edge detection-based features with the modified ResNet34 model achieves superior performance, attaining 97.5% accuracy and surpassing other approaches.

关键词： Deepfake Artificial intelligence Deep learning

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