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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 overview Scientific Cloud computing Web-based cloud services issues and challenges 5

An overview Scientific Cloud computing Web-based cloud servi...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Majumder, Sayan Raju, Kommisetti Murthy Arya, Nancy Shaik, Nazeer Tiwari, Sanjay Singh, Akanksha Department of cyber security The Heritage Academy Kolkata India Department of Electronics and Communication Engineering Shri Vishnu Engineering College For Women Bhimavaram India Department of Computer Science and Engineering Shree Guru Gobind Singh Tricentenary University Gurugram India Department Of Computer Science and Engineering Srinivasa Ramanujan Institute Of Technology-Autonomous-Anantapur Andhra Pradesh India Department of Computer Science and Engineering ARYA Institute of Engineering Technology and Management Jaipur India Computer Science and Engineering Babu Banarasi Das University Lucknow India

ISBN: (纸本)9798400709418

This research investigates the crucial role of cloud computing services in web development for creating scalable applications. Our study explores the benefits, challenges, and best practices associated with leveraging these services. Real-world case studies will be examined to showcase their significance. Additionally, we examine the integration of blockchain technologies in cloud-based web development and their potential impact on scalability. This paper presents a comprehensive analysis of the indispensability of cloud computing services in building scalable applications for the web. This research project delves into the transformative impact of cloud computing services on the field of web development. By providing scalable and flexible infrastructure, these services have revolutionized the way applications are built. Through an in-depth analysis of benefits, challenges, and best practices, along with real- world case studies, the project highlights the indispensable nature of cloud computing in creating scalable web applications. Furthermore, the exploration of blockchain integration within cloud-based web development offers insights into potential advancements in scalability and opens new avenues for innovation in the field. Additionally, the integration of blockchain technologies in cloud-based web development is investigated, providing insights into its potential impact on scalability and fostering innovation in the field. The research presents a comprehensive analysis of the indispensability of cloud computing services in creating scalable web applications and highlights their transformative impact on the development process. © 2023 ACM.

关键词： Blockchain

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A Comparative Market Research and Trend Analysis of Volatility in Decentralized and Regulated Markets 3

A Comparative Market Research and Trend Analysis of Volatili...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Mittal, Anamika Hariharasitaraman, S. Subramanian, R. Raja School of Computing Science and Engineering Vit Bhopal University Division of Cyber Security and Digital Forensics Madhya Pradesh India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350361186

Decentralized markets like Bitcoin and Ethereum, which utilize blockchain technology, offer advantages such as increased transparency, lower transaction costs, and quicker settlement times when compared to regulated markets. However, these markets are also known for their higher volatility. On the other hand, traditional regulated markets such as stock and commodity exchanges have also been subject to volatility due to macroeconomic factors such as inflation, geopolitical tensions, and policy changes. Therefore, it is crucial to compare and analyze the volatility of both decentralized and regulated markets to understand their behaviour and potential risks. This paper aims to conduct comparative market research and trend analysis of volatility in decentralized and regulated markets, investigating the contributing factors such as market size, liquidity, regulations, and the impact of market events like economic downturns or regulatory changes. The study will also examine historical trends, explore the correlation between the volatility of these markets, assess the potential impact of market volatility on investors and traders, and analyze how different trading strategies and investor behaviours can affect market volatility. The research could provide valuable insights into the behaviour of decentralized and regulated markets, which could be useful for investors, traders, policymakers, and other stakeholders. The paper will conclude with a discussion of the findings and their implications for traders and investors in both types of markets. © 2024 IEEE.

关键词： Blockchain

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Federated Learning with Blockchain for Privacy-Preserving Data Sharing in Internet of Vehicles

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China Communications 2023年第3期20卷 69-85页

作者： Wenxian Jiang Mengjuan Chen Jun Tao School of Cyber Science and Engineering Southeast UniversityNanjing 211189China College of Computer Science and Technology Huaqiao UniversityXiamen 361021China Key Lab of CNII MOESoutheast UniversityNanjing 211189China Purple Mountain Laboratories:Networking Communications and Security Nanjing 211111China

Data sharing technology in Internet of Vehicles(Io V)has attracted great research interest with the goal of realizing intelligent transportation and traffic ***,the main concerns have been raised about the security and privacy of vehicle *** mobility and real-time characteristics of vehicle data make data sharing more difficult in Io *** emergence of blockchain and federated learning brings new *** this paper,a data-sharing model that combines blockchain and federated learning is proposed to solve the security and privacy problems of data sharing in Io ***,we use federated learning to share data instead of exposing actual data and propose an adaptive differential privacy scheme to further balance the privacy and availability of ***,we integrate the verification scheme into the consensus process,so that the consensus computation can filter out low-quality *** data shows that our data-sharing model can better balance the relationship between data availability and privacy,and also has enhanced security.

关键词： blockchain federated learning privacy data sharing Internet of Vehicles

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Low Resource Chinese Geological Text Named Entity Recognition Based on Prompt Learning

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Journal of Earth Science 2024年第3期35卷 1035-1043页

作者： Hang He Chao Ma Shan Ye Wenqiang Tang Yuxuan Zhou Zhen Yu Jiaxin Yi Li Hou Mingcai Hou State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Institute of Sedimentary GeologyCollege of Computer Science and Cyber SecurityChengdu University of TechnologyChengdu 610059China Key Laboratory of Deep-Time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources Chengdu University of TechnologyChengdu 610059China School of Information Engineering China University of Geosciences(Beijing)Beijing 100083China

Geological reports are a significant accomplishment for geologists involved in geological investigations and scientific research as they contain rich data and textual *** the rapid development of science and technology,a large number of textual reports have accumulated in the field of ***,many non-hot topics and non-English speaking regions are neglected in mainstream geoscience databases for geological information mining,making it more challenging for some researchers to extract necessary information from these *** Language Processing(NLP)has obvious advantages in processing large amounts of textual *** objective of this paper is to identify geological named entities from Chinese geological texts using NLP *** propose the RoBERTa-Prompt-Tuning-NER method,which leverages the concept of Prompt Learning and requires only a small amount of annotated data to train superior models for recognizing geological named entities in low-resource dataset *** RoBERTa layer captures context-based information and longer-distance dependencies through dynamic word ***,we conducted experiments on the constructed Geological Named Entity Recognition(GNER)*** experimental results show that the proposed model achieves the highest F1 score of 80.64%among the four baseline algorithms,demonstrating the reliability and robustness of using the model for Named Entity Recognition of geological texts.

关键词： Prompt Learning Named Entity Recognition(NER) low resource geological text text information mining big data geology.

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Revolutionizing prostate cancer diagnosis: Unleashing the potential of an optimized deep belief network for accurate Gleason grading in histological images

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International Journal of Intelligent Networks 2024年第1期5卷 241-254页

作者： Angel Latha Mary, S. Siva Subramanian, S. Priyanka, G. Vijayakumar, T. Alagumalai, Suganthi IT Department SNS College of Technology Coimbatore India Department of of BME Rathinam Technical Campus Coimbatore India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of AIDS Dr.Mahalingam College of Engineering and Technology Pollachi Coimbatore India Cyber Operations 3223 Hanover St Palo AltoCA94304 United States

PC (Prostate Cancer) is the second highest cause of death due to cancer in men globally. Proper detection and treatment are critical for halting or controlling the growth and spread of cancer cells within the human organism. However, evaluating these sorts of images is difficult and time-consuming, requiring histopathological image recognition as the most reliable method for treating PC because of its distinct visual characteristics. Risk evaluation and treatment planning rely heavily on histological image-based Gleason grading of prostate tumors. This work introduces an innovative approach to histological image analysis for prostate cancer diagnosis and Gleason grading. The Elephant Herding Optimization-based Hyper-parameter Convolutional Deep Belief Network (CDBN-EHO) is presented alongside a grading network head-optimized deep belief network technique for multi-task prediction. Leveraging an effective Bayesian inference method, fully linked Conditional Random Field (CRF) techniques are utilized for segmentation, with pairwise boundary capacities determined by a linear mixture of Gaussian kernels. The multi-task approach aims to enhance performance by incorporating contextual information, leading to breakthrough results in the identification of epithelial cells and the grading of Gleason scores. The objective of this study is to demonstrate the effectiveness of the optimized deep belief network technique in improving diagnostic accuracy and efficiency for prostate cancer diagnosis and Gleason grading in histological images. © 2024 The Authors

关键词： Grading

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A Lossless Relational Data Watermarking Scheme Based on Uneven Partitioning 20th

A Lossless Relational Data Watermarking Scheme Based on Une...

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20th International Conference on Information security and Cryptology, Inscrypt 2024

作者： Xu, Wenhao Xian, Hequn College of Computer Science and Technology Qingdao University Qingdao266071 China Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Chinese Academy of Sciences Beijing100085 China Cryptograph and Cyber Security Whampoa Institute Guangzhou510000 China

ISBN: (纸本)9789819647309

In modern relational databases, data security and integrity are crucial, particularly in large-scale, unevenly distributed big data environments. This paper proposes a lossless relational data watermarking scheme based on uneven partitioning, addressing the non-uniformity of data by managing data partitioning within the database. By selecting key attributes and setting appropriate thresholds, data is allocated to partitions of varying sizes and volumes. Different watermark intensities are embedded based on these partitions. Traditional robust watermarking schemes modify carriers to maintain data quality but often lack robustness. To improve this, the proposed scheme uses watermark encoding algorithms to integrate data bits with watermark information, storing the generated auxiliary data. During watermark extraction, the watermark is recovered from this auxiliary data through decoding algorithms. This method enhances watermark capacity by encoding at least one attribute of each data tuple, avoiding original data modifications and common distortion issues. Experimental results show that this scheme, combining uneven partitioning with high-capacity lossless watermarks, offers superior robustness and security compared to traditional methods. It provides an efficient and reliable option for data protection in relational databases, particularly suitable for large-scale and dynamically changing data environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data privacy

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A Robust Artificial Intelligence Enabled Methodology to Predict Online Consumers Behaviour Using Hybrid Deep Learning Strategy

A Robust Artificial Intelligence Enabled Methodology to Pred...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Sophia, P. Suresh, G.R. Kiruthika, B. Juliet, N. Chadge, R. Al Sherideh, Alaa Sri Sai Ram Institute of Technology Department of Cse Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Ece Mamallapuram Chennai India Atharva College of Engineering Mumbai India S.A. Engineering College Department of Master of Computer Applications Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India College of Information Technology Zarqa University Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Marketing and consumer behavior predictions are two areas where Artificial Intelligence (AI) is finding widespread use. Evaluating the accuracy of AI -based consumer behavior prediction is the focus of this article. Evidence from research in this area suggests that AI can help sift through mountains of data pertaining to customer performance. It includes looking at things like customer surveys, purchasing history, and behaviour. Furthermore, this sector may make use of deep learning techniques for consumer behavior forecasting. All sorts of advertising and marketing choices can benefit from this data. The Hybrid Learning for Behavioral Prediction (HLBP) method, first proposed in this study, integrates AI -assisted learning with categorization logic for processing. In order to assess the efficacy of the suggested program, this model is cross-validated with the traditional learning model known as Recurrent Neural Network (RNN). Even more encouraging is the possibility that AI can aid businesses in fine-tuning their advertising strategies. As a result, advertising may be more precisely targeted, and marketing budgets can be more efficiently used. Here, AI in a marketing context may provide customized experiences for consumers, such making better product recommendations and enhancing the shopping experience overall. In order to maximize the effectiveness of online advertising efforts, artificial intelligence can be employed. This research delves into the potential of artificial neural networks to identify customer behavior utilizing data collected from conventional surveys. Neural networks outperform classical discriminant analysis in most cases, demonstrating their strong discriminate potential. © 2024 IEEE.

关键词： Budget control

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Failed Yet Stored: A First Look at DNS Negative Caching

Failed Yet Stored: A First Look at DNS Negative Caching

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Zeng, Meng Zhu, Yujia Li, Baiyang Zhang, Yuedong Liu, Qingyun Fang, Binxing Chinese Academy of Sciences Institute of Information Engineering China University of Chinese Academy of Sciences School of Cyber Security China National Computer Network Emergency Response Technical Team/Coordination of China China School of Computer Science and Technology ShenZhen China

ISBN: (纸本)9798350367942

Caching is a critical method for enhancing the efficiency and the security of the Domain Name System (DNS). Initially, only successful domain name resolution results were cached. To mitigate failures in DNS transactions (e.g. NXDomain), the IETF proposed standards, further developed into RFC 9520 as of December 2023. In addition to the basic implementation, RFC 9520 standardizes more sophisticated forms of negative caching. This new standard aims to reduce redundant query retries in DNS traffic and protect resolvers from Denial of Service (DoS) *** this study, we present a comprehensive examination of the specific implementations of Negative Caching in resolvers. We designed and validated a method for measuring negative caching and conducted experiments on 44 public resolvers, including their Do53, DoH, and DoT interfaces. Our findings indicate that while public resolvers generally implement various types of negative caching, some exhibit unexpected cache handling behaviors when encountering specific negative responses. Additionally, we discovered that most public resolvers modify the TTL value of negative responses before returning them to clients. Despite the lack of explicit TTL values for newly specified negative responses, we devised a method to approximate the default TTL values used by public resolvers. © 2024 IEEE.

关键词： Denial-of-service attack

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A Powerful Load Balancing Algorithm for Cloud Performance Evaluation 5

A Powerful Load Balancing Algorithm for Cloud Performance Ev...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Chandar, J. Praveen Chidambaranathan, C.M. Anbarasan, Anbarasa Kumar Karthikram, A. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Information Technology and Engineering Vellore Institute of Technology Vellore India School of Computers Madanapalle Institute of Technology & Science Department of Cyber Security Madanapalle India

ISBN: (纸本)9798350321425

The energy consumption of data centers plays a significant role in the cost estimation of maintenance, performance evaluation, and environmental pollution in cloud computing. Carbon emissions are one of the major drawback of data centers in this scenario. To manage the data center temperature as well as the necessity to reduce power consumption while satisfying customer demands. This is extremely beneficial to both the data center and the environment. The data center is a collection of servers from which consumers can benefit by using their services. In this context, cloud service providers are working hard to reduce energy usage and avoid environmental contamination. This research work describes the novel algorithm for scheduling virtual machines in servers in order to consume a minimal amount of energy. The weight approximation algorithm arranges servers and virtual machines in decreasing order depending on their configuration to allocate VMs with jobs to optimize power usage. The power consumption of the server is calculated before and after VM allocation in this algorithm. To avoid CRAC modifications, the critical threshold of each server is maintained in this instance. If it exceeds the server's critical threshold, the VM is moved to another ideal server to avoid migration. In this method of VM allocation, the ordered tasks are allocated to the server using round-robin fashion. The model is simulated using cloudsim tools, and the server's performance and energy consumption are better than the random selection algorithm. © 2023 IEEE.

关键词： Virtual machine

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