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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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Cyber attack prediction on IoT and IIOT devices using ensemble based predictive analytic model

Cyber attack prediction on IoT and IIOT devices using ensemb...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Priyan Malarvizhi Kumar Tayyaba Shahwar C Gokulnath Jeeva Selvaraj Department of Data Science University of North Texas Denton USA Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of ISE Jain University Global Campus Bangalore India

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

The accelerated connectivity across almost all entities of the globe has eventually escalated the number of Internet of Things (IoT) and Industrial IoT (IIoT) devices. These objects need seamless internet access which makes them vulnerable to various types of cyber-attacks at various layers. Many innovative and efficient solutions are developed for preventing these attacks and protecting the devices and data from malicious access. However, new forms of attacks continue to surge every year, which necessitates the development of more adaptive solutions by harnessing the attack profile data. The proposed research work constructs a heterogeneous ensemble model consisting of different types of base machine learning algorithms. These models explore the network profile data of the IoT and IIoT devices to extract inherent and latent features from it and predicts the network activity. Later, the predictions of the individual base learners are aggregated using a meta learner model to give the final prediction. The performance of the proposed ensemble model is validated using standard prediction metrics to assess its performance. As future research extension many base learners could be integrated to form a more efficient and powerful ensemble model with novel aggregation techniques.

关键词： Performance evaluation Analytical models Adaptation models Surge protection Predictive models Feature extraction Surges Cyberattack Standards Industrial Internet of Things

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RayLoc: Wireless Indoor Localization via Fully Differentiable Ray-tracing

arXiv

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arXiv 2025年

作者： Han, Xueqiang Zheng, Tianyue Han, Tony Xiao Luo, Jun Department of Computer Science and Engineering Southern University of Science and Technology China Huawei Technologies Co. Ltd China College of Computing and Data Science Nanyang Technological University Singapore

Wireless indoor localization has been a pivotal area of research over the last two decades, becoming a cornerstone for numerous sensing applications. However, conventional wireless localization methods rely on channel state information to perform blind modelling and estimation of a limited set of localization parameters. This oversimplification neglects many sensing scene details, resulting in suboptimal localization accuracy. To address this limitation, this paper presents a novel approach to wireless indoor localization by reformulating it as an inverse problem of wireless ray-tracing, inferring scene parameters that generates the measured CSI. At the core of our solution is a fully differentiable ray-tracing simulator that enables backpropagation to comprehensive parameters of the sensing scene, allowing for precise localization. To establish a robust localization context, RayLoc constructs a high-fidelity sensing scene by refining coarse-grained background model. Furthermore, RayLoc overcomes the challenges of sparse gradient and local minima by convolving the signal generation process with a Gaussian kernel. Extensive experiments showcase that RayLoc outperforms traditional localization baselines and is able to generalize to different sensing environments. © 2025, CC BY-NC-ND.

关键词： Inverse problems

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Deep Dynamic Probabilistic Canonical Correlation Analysis

Deep Dynamic Probabilistic Canonical Correlation Analysis

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shiqin Tang Shujian Yu Yining Dong S. Joe Qin Department of Data Science City University of Hong Kong Hong Kong Department of Artificial Intelligence Vrije Universiteit Amsterdam Amsterdam Netherlands Department of Computing and Decision Science Lingnan University Hong Kong

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper presents Deep Dynamic Probabilistic Canonical Correlation Analysis (D 2 PCCA), a model that integrates deep learning with probabilistic modeling to analyze nonlinear dynamical systems. Building on the probabilistic extensions of Canonical Correlation Analysis (CCA), D 2 PCCA captures nonlinear latent dynamics and supports enhancements such as KL annealing for improved convergence and normalizing flows for a more flexible posterior approximation. D 2 PCCA naturally extends to multiple observed variables, making it a versatile tool for encoding prior knowledge about sequential datasets and providing a probabilistic understanding of the system’s dynamics. Experimental validation on real financial datasets demonstrates the effectiveness of D 2 PCCA and its extensions in capturing latent dynamics.

关键词： Deep learning Analytical models Correlation Buildings Signal processing Probabilistic logic Encoding Nonlinear dynamical systems Speech processing Convergence

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Autonomous Multi-Objective Optimization Using Large Language Model

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IEEE Transactions on Evolutionary Computation 2025年

作者： Huang, Yuxiao Wu, Shenghao Zhang, Wenjie Wu, Jibin Feng, Liang Tan, Kay Chen Department of Data Science and Artificial Intelligence Hong Kong Polytechnic University Hong Kong Department of Electrical and Elctronics Engineering Hong Kong Polytechnic University Hong Kong Chongqing University College of Computer Science Chongqing Key Laboratory of Big Data Intelligence and Privacy Computing Chongqing400044 China

Multi-objective optimization problems (MOPs) are ubiquitous in real-world applications, presenting a complex challenge of balancing multiple conflicting objectives. Traditional multi-objective evolutionary algorithms (MOEAs), though effective, often rely on domain-specific expertise for improved optimization performance, hindering adaptability to unseen MOPs. In recent years, the Large Language Models (LLMs) has revolutionized software engineering by enabling the autonomous generation and refinement of programs. Leveraging this breakthrough, we propose a new LLM-based framework that autonomously designs MOEAs for solving MOPs. The proposed framework includes a robust testing module to refine the generated MOEA through error-driven dialogue with LLMs, a dynamic selection strategy along with informative prompting-based crossover and mutation to fit textual optimization pipeline. Our approach facilitates the design of MOEA without the extensive demands for expert intervention, thereby speeding up the innovation of MOEA. Empirical studies across various MOP categories validate the robustness and superior performance of our proposed framework. © 1997-2012 IEEE.

关键词： Multiobjective optimization

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Self-supervised representation learning for geospatial objects: A survey

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Information Fusion 2025年 123卷

作者： Chen, Yile Huang, Weiming Zhao, Kaiqi Jiang, Yue Cong, Gao College of Computing and Data Science Nanyang Technological University Singapore Department of Physical Geography and Ecosystem Science Lund University Sweden School of Computer Science University of Auckland New Zealand

The proliferation of various data sources in urban and territorial environments has significantly facilitated the development of geospatial artificial intelligence (GeoAI) across a wide range of geospatial applications. However, geospatial data, which is inherently linked to geospatial objects, often exhibits data heterogeneity that necessitates specialized fusion and representation strategies while simultaneously being inherently sparse in labels for downstream tasks. Consequently, there is a growing demand for techniques that can effectively leverage geospatial data without heavy reliance on task-specific labels and model designs. This need aligns with the principles of self-supervised learning (SSL), which has garnered increasing attention for its ability to learn effective and generalizable representations directly from data without extensive labeled supervision. This paper presents a comprehensive and up-to-date survey of SSL techniques specifically applied to or developed for geospatial objects in three primary vector geometric types: Point, Polyline, and Polygon. We systematically categorize various SSL techniques into predictive and contrastive methods, and analyze their adaptation to different data types for representation learning across various downstream tasks. Furthermore, we examine the emerging trends in SSL for geospatial objects, particularly the gradual advancements towards geospatial foundation models. Finally, we discuss key challenges in current research and outline promising directions for future investigation. By offering a structured analysis of existing studies, this paper aims to inspire continued progress in integrating SSL with geospatial objects, and the development of geospatial foundation models in a longer term. © 2025 Elsevier B.V.

关键词： Self-supervised learning

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Application of Generative Artificial Intelligence for Epidemic Modeling 24

Application of Generative Artificial Intelligence for Epidem...

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Proceedings of the Winter Simulation Conference

作者： Hannah Danielle Ladera Villaplana Jaeyoung Kwak Michael H. Lees Hongying Li Wentong Cai College of Computing and Data Science Nanyang Technological University Singapore Singapore Informatics Institute University of Amsterdam Amsterdam The Netherlands Department of Fluid Dynamics Institute of High Performance Computing Singapore Singapore

ISBN: (纸本)9798331534202

Epidemic models have become increasingly useful, especially in the wake of the recent COVID-19 pandemic, emphasizing the crucial role of human behavior in the spread of disease. There has been a recent rise in the usage and popularity of generative artificial intelligence (GenAI), such as ChatGPT especially with its ability to mimic human behavior. In this study, we demonstrate a novel application of GenAI for epidemic modeling. We employed GenAI for creating agents living in a hypothetical town in simulations and simulating their behavior within the context of an ongoing pandemic. We performed a series of simulations to quantify the impact of agent traits and the availability of information for health condition, virus, and government guidelines on the disease spread patterns in terms of peak time and epidemic duration. We also characterized the most influential factors in agents' decision-making using random forest model.

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2025, CC BY.

关键词： Mobile edge computing

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Suicidal ideation data augmentation and recognition technology based on large language models

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Acta Psychologica Sinica 2025年第6期57卷 987-1001页

作者： Zhang, Yanbo Huang, Feng Mo, Liuling Liu, Xiaoqian Zhu, Tingshao CAS Key Laboratory of Behavioral Science Institute of Psychology Chinese Academy of Sciences Beijing 100101 China Department of Psychology University of Chinese Academy of Sciences Beijing 100049 China Department of Data Science College of Computing City University of Hong Kong 999077 Hong Kong Department of Social Psychology School of Sociology Nankai University Tianjin 300350 China

Suicide constitutes a significant global public health challenge, with the World Health Organization reporting substantial annual mortality rates. Traditional suicide detection methods primarily depend on self-assessment scales and clinical evaluations, which require considerable resources and rely on patients actively seeking assistance. The integrated motivational-volitional (IMV) model offers a theoretical framework for comprehending suicidal behavior progression, with suicidal ideation serving as a critical risk indicator. While text-based analysis presents a promising non-invasive approach for early identification, it encounters technical challenges due to limited annotated data and linguistic complexity. Large Language Models (LLMs) offer unprecedented capabilities in language understanding and generation, potentially addressing these challenges through their ability to comprehend diverse expressions of suicidal ideation and generate high-quality training data. This research employed a two-stage design leveraging LLMs to address the challenge of limited training data for suicidal ideation recognition. In Study I, we selected ChatGLM3-6B and Qwen-7B-Chat as foundation LLMs and implemented both zero-shot and few-shot learning approaches combined with supervised learning strategies. We extracted examples from an original dataset of Weibo comments to create high-quality training data for the LLMs. Comparative experiments evaluated model performance, with human coders assessing the quality of LLM-generated texts using established suicide risk evaluation criteria. In Study II, we evaluated the impact of LLM-based data augmentation on recognition models by comparing traditional machine learning approaches with LLM-based methods trained on both original and augmented datasets, measuring performance through accuracy and true negative rate metrics. In Study I, the two self-developed LLM-based models demonstrated excellent performance in suicidal ideation data augmentati

关键词： artificial intelligence data augmentation large language models suicidal ideation suicide text recognition

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Efficient acne classification based on Resnet18 3

Efficient acne classification based on Resnet18

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3rd International Conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Cheng, Shi Sun, Mingze Huang, Zhongyi Wang, Yushan Fu, Jiaxuan School of Engineering University of Manchester ManchesterM13 9PL United Kingdom School of Computing Science University of Glasgow GlasgowG12 8RZ United Kingdom School of Data Science and Big Data Nanjing Institute of Technology Nanjing210000 China The Affiliated High School Renmin University of China Beijing100083 China International Department Nanjing Jinling High School Nanjing21000 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

In this study, we investigated the mechanical performance of lightweight composite slabs, specifically focusing on the application of an integrated face recognition and acne classification pipeline. We propose a comprehensive approach that commences with facial detection, proceeds to the acne region localization, and concludes with acne classification utilizing ResNet18 architecture. The aim is to develop a highly accurate and efficient system for acne classification, particularly beneficial for remote patients or those seeking a convenient self-assessment tool. Our dataset, though limited in size and diversity, is iteratively refined and augmented to mitigate data scarcity issues. Through experimental evaluations, we demonstrate that the combination of ResNet18 and Support Vector Machine (SVM) achieves an accuracy rate of 96%, significantly outperforming traditional manual feature extraction methods. Additionally, visualization techniques such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are employed to gain insights into the data distribution and the effectiveness of automatically extracted features. This study highlights the potential of deep learning for acne detection and underscores the importance of dataset refinement for enhancing model performance. © 2025 SPIE.

关键词： Support vector machines

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