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Improving Energy Harvesting System from Ambient RF Sources in Social Systems with Overcrowding

International Journal of Crowd Science 2025年第1期9卷 13-28页

作者： Agieb, Ramy Amer, Ayman Mansour, Ibrahim Solyman, Ahmed Yahya, Khalid Samir, Ahmed Department of Electrical Engineering Faculty of Engineering MTI University Cairo11511 Egypt Faculty of Engineering Zarqa University Zarqa13110 Jordan School of Computing Engineering and Built Environment Glasgow Caledonian University GlasgowG4 0BA United Kingdom College of Engineering University of Business and Technology Jeddah22233 Saudi Arabia Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Istanbul Gelisim University Istanbul34310 Turkey

This paper presents a novel approach to enhance energy harvesting systems from ambient Radio Frequency (RF) sources in overcrowded environments. In environments like shopping malls, coffee shops, and airports, where wireless devices are prevalent, the electromagnetic energy emitted by these devices can be harvested and converted into electrical energy to power small devices, specifically those associated with the Social Internet of Things (SIoT). However, due to the high density of devices in such environments, the RF signals can be weak, resulting in low energy harvesting efficiency. This study focuses on developing technologies for wireless power transfer through a radio frequency ambient energy harvesting scheme, specifically designing to improve energy harvesting systems in crowded social environments. Recognizing the growing importance of energy harvesting for low-power devices in intelligent environments, our proposed method utilizes the ambient environment to capture energy in the downlink radio frequency range of the GSM-900 band. The system architecture comprises four main stages: a supercapacitor, a Villard voltage doubler circuit with seven stages, a lumped element matching network, and a microstrip patch antenna. The voltage doubler circuit is designed and simulated using the Agilent Advanced Design System (ADS) 2014 environment, and simulations and tests are conducted across different input power levels. Throughout the study, several key factors are identified as crucial to the system’s efficiency, including the frequency band, input power level, voltage doubler circuit design, impedance matching, diode selection, number of rectification stages, and load resistance. The proposed method demonstrates significant potential in enhancing the energy harvesting efficiency from ambient RF sources in crowded social environments. By providing a sustainable power source for SIoT devices in such settings, our approach contributes to the advancement of energy harves

关键词： Slot antennas

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Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms

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Current Science 2025年第7期128卷 728-736页

作者： Rama, P. Angayarkanni, V. Theresa, W. Gracy Pandiaraj, A. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai600 123 India

The detection of skin cancer holds paramount importance worldwide due to its impact on global health. While deep convolutional neural networks (DCNNs) have shown potential in this domain, current approaches often struggle with fine-grained variability in skin lesion features, imbalanced datasets and inadequate augmentation. The objectives of our study encompass building a model that not only enhances accuracy but also reduces training time and cost, improves dimensionality reduction during classification and segmentation, and is evaluated on larger datasets for robustness. We enhance the learning process using an improved nature-inspired optimisation algorithm tailored specifically for skin cancer classification tasks. This algorithm optimises model parameters to maximise classification accuracy while minimising computational overhead. Our proposed architecture leverages a gold standard dermatological image dataset meticulously curated and validated by experts. This dataset spans eight distinct classes, including actinic keratosis, basal cell carcinoma, melanoma, nevus, and others, ensuring a diverse representation of skin conditions. The balanced class distribution in the training and test sets enables fair evaluation and robustness assessment of our deep learning models. The developed algorithm showcases novelty in its approach, contributing to advancements in early skin cancer detection technologies. This study introduces an optimised deep learning model designed for the early detection of skin cancer, achieving a notable accuracy of 97.48%. Our study aims to contribute significantly to the medical field by providing a reliable and efficient tool for dermatologists and healthcare practitioners to assist in diagnosing skin conditions accurately and promptly, ultimately improving patient outcomes and healthcare efficiency. © (2025), (Indian Academy of Sciences). All rights reserved.

关键词： Convolutional neural networks

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Telugu Language Analysis with XLM-RoBERTa: Enhancing Parts of Speech Tagging for Effective Natural Language Processing

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SN Computer Science 2025年第2期6卷 1-13页

作者： Bharathi Mohan, G. Prasanna Kumar, R. Krishna Jayanth, K. Doss, Srinath Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Faculty of Engineering and Technology Botho University Gaborone Botswana

This research investigates the efficacy of XLM-RoBERTa, a potent deep learning architecture rooted in transformer networks, for Part-of-Speech (POS) tagging—a foundational task in Natural Language Processing (NLP). The study narrows its focus to address the formidable UCD Telugu dataset, renowned for its intricate morphological characteristics that pose challenges to accurate tagging. Through meticulous fine-tuning of the pre-trained XLM-RoBERTa model. This study adeptly discerns and exploit intricate syntactic and semantic patterns underpinning the interplay between lexical elements and their respective POS tags. The rigorous evaluation regimen, complemented by a comprehensive statistical comparison with the Multilingual BERT model, conclusively establishes XLM-RoBERTa’s superiority. This advanced model attains a remarkable accuracy score of 91%, decisively surpassing the Multilingual BERT model, which achieves an accuracy of 88%. These findings not only underscore XLM-RoBERTa’s prowess in precise POS tagging, especially in the context of morphologically rich languages like Telugu, but also carry significant implications for the broader NLP community. This research unlocks valuable insights and positions XLM-RoBERTa as an exemplar of reliability and robustness in POS tagging. Furthermore, it extends an invitation for further exploration and innovation in deep learning architectures within the NLP domain. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bert Multilingual Bert NLP Parts-of-speech tagging Telugu XLM-RoBERTa

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Artificial intelligence-inspired comprehensive framework for desertification prediction

International Journal of Cognitive Computing in Engineering

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International Journal of Cognitive computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Safe Spaces Analysis for Mental Health 12th

Safe Spaces Analysis for Mental Health

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12th International Conference on Recent Trends in computing, ICRTC 2024

作者： Kelawala, Harsh Shah, Zeal Selvaraj, P. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur 603203 India

ISBN: (纸本)9789819789450

Mental health is a paramount concern in contemporary urban environments, necessitating comprehensive approaches to understanding its determinants and formulating effective interventions. This research project adopts a multifaceted methodology to explore the factors influencing mental well-being in urban areas, specifically focusing on cities within Gujarat. By integrating Geographic Information Systems (GIS) technology and predictive modeling techniques, we analyze various socio-environmental factors spanning air quality, noise pollution, green space availability, educational attainment, population density, income levels, and employability. At the same time, the GIS approach encompasses all major cities across India, the Random Forest algorithm is tailored specifically to the cities of Gujarat, reflecting the unique socio-geographical context of the region. Through rigorous data analysis and visualization, we identify spatial patterns and correlations, enabling the identification of areas most vulnerable to mental health challenges and prioritizing targeted interventions. This work presents our research's methodology, findings, and implications, offering valuable insights for policymakers and stakeholders striving to cultivate safe and supportive urban environments conducive to optimal mental health outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Interbank Liability Analyzing Tool To Manage Short-Term Liquidity Among Banks And Facilitate Liability Limits For Daily Operations 5

Interbank Liability Analyzing Tool To Manage Short-Term Liqu...

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5th International Conference on Advanced Research in computing, ICARC 2025

作者： Wasantha Kumara, H.P. Samith Malhari Bamunawita, Karishani Harshani Jayasekara, H.R. Lasanthika Kingston University Faculty of Engineering Computing and the Environment United Kingdom Esoft Metro Campus School of Computing Bambalapitiya Sri Lanka

ISBN: (纸本)9798331530983

The "Interbank Fund Transfer Liability Management System"seeks to simplify interbank financial transfers, strengthen security, and lower risks. To maximize risk management, limit liability, and manage real-time net positions between banks, it incorporates a Liability Manager tool. By utilizing sophisticated forecasting algorithms, historical data, market circumstances, holidays, events, and bank preferences, the system determines liability limits for every financial institution. This improves financial institutions' capacity to plan and manage interbank fund transfer liabilities efficiently. the 'Interbank Liability Analyzing Tool' represents a significant advancement in the field of interbank liquidity management. Its dynamic and adaptable approach to setting liability limits, coupled with its user-friendly interface and potential for integration with central bank systems, positions it as a valuable asset for financial institutions and regulators alike. the tool represents a significant advancement in interbank liquidity management. Its dynamic and adaptable approach to setting liability limits, coupled with its user-friendly interface and potential for integration with central bank systems, positions it as a valuable asset for financial institutions and regulators. Insights gained from this research pave way for future enhancements and explorations, promising a more resilient and efficient financial ecosystem. © 2025 IEEE.

关键词： Risk management

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Towards a Unified Definition of Social XR

Towards a Unified Definition of Social XR

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Moslavac, Mirta Vlahović, Sara Skorin-Kapov, Lea University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9798331514846

Social Extended Reality (Social XR) is an evolving domain within Extended Reality (XR), encompassing immersive technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). Despite its growing significance in both research and practice, the term 'Social XR' remains ambiguously defined, with inconsistent usage across academic and industry contexts. This paper seeks to address this challenge by synthesising existing definitions and interpretations of Social XR, aiming to encourage a clearer, unified conceptualisation. Through an analysis of current research, we identify key themes underpinning the concept of Social XR, such as shared immersive 3D environments, blending of virtual and physical elements, real-time multiuser interaction, and user representation. Thus, we propose a comprehensive definition of Social XR, aiming to stimulate further discussion, encouraging refinement toward a coherent understanding of the term. Additionally, we build on previous work and introduce the XR3C Model, for a better understanding of collaboration within immersive environments and its relation to Social XR. © 2025 IEEE.

关键词： collaboration Extended reality (XR) immersive experience multimedia communication social computing

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Exploration and Exploitation Mechanism in Pairwise Test Case Generation: A Systematic Literature Review

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IEEE Access 2025年 13卷 82342-82377页

作者： Yahaya, Muhammad Sabo Hashim, Ahmad Sobri B. Oluwagbemiga Balogun, Abdullateef Aminu Muazu, Aminu Sabo Usman, Fatima Adamu Aliyu, Dahiru Uwaisu Muhammad, Abdullahi Universiti Teknologi PETRONAS Faculty of Science Management and Computing Department of Computing Perak32610 Malaysia Faculty of Engineering and Technology Federal Polytechnic Computer Engineering Technology Department Katsina Daura824101 Nigeria Umaru Musa Yar’adua University Faculty of Natural and Applied Sciences Computer Science Department Katsina820102 Nigeria Gombe State University Faculty of Science Computer Science Department Gombe760101 Nigeria Federal University Dutse Faculty of Computing Computer Science Department Jigawa 720223 Nigeria

Software systems are essential in modern life and require rigorous testing to ensure reliability. Pairwise testing, a combinatorial testing methodology, optimizes test case generation by minimizing redundancy while maintaining defect detection effectiveness. This review systematically examines the role of metaheuristic algorithms in pairwise test case generation, focusing on their exploration and exploitation mechanisms to address local optima challenges. Covering research from 2014 to 2024, the review evaluates hybrid and metaheuristic strategies, including Pairwise Migrating Birds Optimization-Based Strategies (PMBOS), Pairwise Gravitational Search Algorithm Strategy (PGSAS), Pairwise hybrid Artificial Bee Colony (PhABC), Genetic and Particle Swarm Optimization (GAPSO) algorithm, Hybrid Optimization Algorithm (HOA), and Parameter Free Choice Function based Hyper-Heuristic (PCFHH), among others. These methods consistently outperform traditional techniques by reducing test suite sizes and enhancing fault detection capabilities. The findings underscore the growing adoption of the one-test-at-a-time (OTAT) approach over the one-parameter-at-a-time (OPAT) approach and identify key challenges in scalability, hyperparameter tuning, and constraint handling, highlighting the need for hybrid strategies and adaptive optimization techniques. This review offers valuable insights into advancing Software testing methodologies and presents recommendations for future research to improve efficiency, scalability, and fault detection in pairwise testing. © 2013 IEEE.

关键词： Software reliability

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Lightweight secure image encryption: a tent map chaos theory approach

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Multimedia Tools and Applications 2025年 1-20页

作者： Odeh, Ammar Taleb, Anas Abu Alhajahjeh, Tareq Navarro, Francisco Ayesh, Aladdin Department of Computer Science Princess Sumaya University of Technology Amman1196 Jordan Faculty of Computing Engineering and Media De Montfort University LeicesterLE1 9BH United Kingdom Faculty of Computing University of Aberdeen King’s College AberdeenAB24 3 FX United Kingdom

In the digital era, securing image data against unauthorized access is a critical challenge, particularly in applications such as cloud storage, IoT security, and medical imaging. This study presents a lightweight and secure image encryption algorithm based on the Tent Map chaotic system, which enhances encryption strength while maintaining computational efficiency. The proposed approach effectively obfuscates image content, achieving a uniform histogram distribution indicative of robust encryption. Comparative analysis with existing methods demonstrates its superiority, particularly in security performance metrics, including entropy (7.996), NPCR (99.65%), and UACI (33.4724%). Additionally, image quality assessments using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) confirm that the algorithm preserves image integrity while ensuring strong encryption. The algorithm’s lightweight nature makes it suitable for large-scale applications, such as real-time encryption in cloud environments, IoT security, and multimedia protection. These results highlight the effectiveness of the proposed method in balancing security, efficiency, and image quality, making it a practical solution for modern image encryption challenges. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Signal to noise ratio

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