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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Satone, Aditya P. Daronde, Subodh Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Due to the wealth of information accessible on many insurance issues, including travel and auto insurance, individuals are becoming more and more impacted by these kinds of insurance in today's society. But this tendency also makes room for dishonest practices. Insurance fraud is harmful and unlawful, regardless of who commits it - the vendor or the customer. Fraud can come from both the customer and the vendor. The consumer may commit fraud by filing exaggerated claims or using out-of-date policies, while the vendor may market insurance from phoney firms or neglect to submit premiums. This paper presents a comparative analysis of various classification algorithms, including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Linear Regression (LR), and Polynomial Regression, for detecting insurance fraud. The performance of these algorithms is evaluated using key metrics such as Precision, Recall, and F1-Score. The analysis reveals that the bestperforming model is the Random Forest, with a Mean Squared Error (MSE) of 6.50, outperforming the other techniques. © 2025 IEEE.

关键词： Random forests

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An IoT-Assisted Real-Time Urban Air Pollution Data Collection system 5th

An IoT-Assisted Real-Time Urban Air Pollution Data Collectio...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Ghose, Banani Minsar, Sk. Ghosh, Udayan Arif Mohammad, Sk. Mallick, Adil Hossain Rehena, Zeenat Department of Information Technology Haldia Institute of Technology West Bengal Haldia India Department of Computer Science and Engineering Aliah University West Bengal Kolkata India

ISBN: (纸本)9789819745395

Air pollution is one of the innumerable menaces an individual comes across every moment. Countless means to make urban lives smoother are indebted to witnessing air pollution and other types of pollution. It is contributed by unrestricted urbanization, traffic, industrial emissions, etc., causing life-threatening diseases;even death. Monitoring air pollution should be the primary objective of any society. For an effective air pollution monitoring system to run and deliver a real-time Air Quality Index (AQI) a prompt IoT-enabled air pollution sensor-based data collection system is a prerequisite. In this letter, a real-time hardware-based IoT-enabled air pollution-related data collection module is designed. This will be portable and will be recording both pollutant concentrations and meteorological factors. It consists of a number of sensors and a Wi-Fi module. Collected data will be directly sent to the cloud for further analysis. Thus, as a part of the air pollution monitoring system, this module will be capable of recording real-time data, that will lead to real-time air health monitoring. It will assist urban residents and policymakers in taking decisions that will guide them to have healthier lives. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Air quality

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Enabling Robust Security in MQTT-Based IoT Networks with Dynamic Resource-Aware Key Sharing 4

Enabling Robust Security in MQTT-Based IoT Networks with Dyn...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kaganurmath, Sharadadevi Cholli, Nagaraj Department of Computer Science Engineering Global Academy of Technology Bengaluru India Department of Information Science Engineering RV College of Engineering Bengaluru India

This paper presents a novel approach aimed at developing a secure secret key-sharing system optimized for resource-constrained Internet of Things (IoT) devices. Focusing on the MQTT protocol, the research endeavors to establish secure communication channels between IoT devices and brokers, thereby enhancing the overall security of MQTT-based IoT deployments. This research introduces a novel Dynamic Lightweight Authentication for MQTT (DLA-MQTT) mechanism designed to the unique needs of IoT devices operating under the MQTT protocol. The DLA-MQTT mechanism leverages an innovative lightweight Generalized Feedback Shift Register (GFSR)-based Pseudo-Random Number Generator (PRNG) to generate ephemeral keys, ensuring secure communication while addressing the limitations of computational power and energy resources. Through a detailed comparative analysis with existing cryptographic solutions, the DLA-MQTT mechanism demonstrates superior performance in terms of computational overhead, energy consumption, and execution time, while maintaining robust security against common attacks such as Man-in-the-Middle (MitM) and Denial of Service (DoS). he proposed algorithm's adaptability and scalability are validated using the Cooja simulator, where simulated IoT networks are subjected to various threat scenarios. The results confirm the DLA-MQTT mechanism's efficacy, showcasing a significant reduction in resource utilization without compromising the strength of the security provided. © 2025 The Authors. Published by Elsevier B.V.

关键词： Shift registers

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A privacy-preserving approach for detecting smishing attacks using federated deep learning

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International Journal of information technology (Singapore) 2025年第1期17卷 547-553页

作者： Remmide, Mohamed Abdelkarim Boumahdi, Fatima Ilhem, Bousmaha Boustia, Narhimene LRDSI Laboratory Department of Computer Science Faculty of Sciences University of Blida 1 Blida Algeria Electronics Department Faculty of Technology University of Blida 1 Blida Algeria

Smishing is a type of social engineering attack that involves sending fraudulent SMS messages to trick recipients into revealing sensitive information. In recent years, it has become a significant threat to mobile communications. In this study, we introduce a novel smishing detection method based on federated learning, which is a decentralized approach ensuring data privacy. We develop a robust detection model within a federated learning framework based on deep learning methods such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). Our experiments show that the federated learning method using Bi-LSTM achieves an accuracy of 88.78%, highlighting its effectiveness in tackling smishing detection while preserving user privacy. This approach not only offers a promising solution to smishing attacks but also lays the groundwork for future research in mobile security and privacy-preserving machine learning. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Bi-LSTM Data privacy Federated learning Smishin detection Social engineering

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Opticurve: an optimized informer-curvelet framework for enhanced hyperspectral image segmentation and classification

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International Journal of information technology (Singapore) 2025年 1-16页

作者： Shaw, Kailash Wou Onn, Choo Li, Baihua Department of Computer Science and Engineering (AIML) Vishwakarma Institute of Technology Maharashtra Pune 411037 India Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Negeri Sembilan Nilai 71800 Malaysia Department of Computer Science Loughborough University Loughborough LE11 3TU United Kingdom

Hyperspectral image (HSI) classification is crucial for applications in climate action, land use analysis, disaster risk reduction, and informed decision-making, given the complex spatial and spectral variations inherent in HSI data. Traditional methods struggle with accurately capturing these variations, necessitating more advanced techniques. This work introduces an Optimized Linformer-Curvelet (OptiCurve) Framework that integrates CNN-based feature extraction, Curvelet Transform for spatial detail capture, and Linformer for efficient feature representation. By combining these techniques, the model enhances HSI segmentation and classification, supporting improved outcomes in critical areas like environmental monitoring and disaster response. The framework is validated on four standard HSI datasets-Indian Pines, Pavia University, Kennedy Space Center, and Houston University-showing significant performance improvements over existing methods. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Climate action Disaster risk reduction Feature extraction Hyperspectral image classification Land use Linformer-Curvelet transform

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Modulation Assessment With IQD Correction for Two-Way Sequential AAF Relaying Transmissions

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IEEE Access 2025年 13卷 72389-72401页

作者： Mostafa, Hala Marey, Mohamed Mohamad Almustafa, Khaled Katrib, Juliano Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Menoufia University Faculty of Engineering Shebeen El-Kom6132711 Egypt Department of Electrical and Computer Engineering Hawally32093 Kuwait Hawally32093 Kuwait

Modulation assessment (MA) is a fundamental element in the development of adaptive radios that can interact effectively with their context to optimize resource usage. This assessment has been extensively studied in the literature over the past several decades. Nevertheless, there have been a few experiments conducted on relaying systems. Previous explorations of MA for relaying broadcasts have neglected the presence of in-phase and quadrature-phase discrepancies (IQD). However, these discrepancies exist in practical wireless transmissions due to manufacturing faults in the transceiver components, which can lead to substantial performance deterioration. In this paper, we create an original maximum-likelihood (ML) solution to the MA challenge in the circumstance of these discrepancies for amplify-and-forward (AAF) two-way sequential relaying transmissions (TWRT). By assuming that each terminal autonomously creates its own IQD, we depart a stage closer to effectively implementing transmissions in actual scenarios. We deploy an expectation-maximization (EM) tool to execute the indicated solution using an iterative approach. We take use of the data duplication inherent in AAF-TWRT signals to support the offered solution. During each repetition, the soft content provided by the detection procedure is utilized to develop the a posteriori expectations of the broadcast information. These expectations are then included in the assessment process as if they were known symbols. Additionally, the proposed solution involves a supplementary duty, which involves calculating the IQD parameters that are initially triggered at each terminal. The simulation results have validated the viability of the proposed architecture by demonstrating its superior MA performance compared to the standard ones. Based on the results, the offered solution reaches convergence within five cycles and achieves exceptional assessment accuracy across a broad spectrum of signal-to-noise ratios. Moreover, the m

关键词： Signal to noise ratio

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Intelligent Anti-tamper systems: Safeguarding Critical Infrastructure in the Era of Proliferating Interconnected Devices and IoBT 6th

Intelligent Anti-tamper Systems: Safeguarding Critical Infra...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Jena, Shubhashish Behera, H.S. Jena, Gunamani Department of Information Technology VSSUT Odisha Burla India Department of Computer Science and Engineering BVCE Andhra Pradesh Odalarevu India

ISBN: (纸本)9789819780891

By 2025, the expected 50 billion connected devices worldwide will heighten risks of physical and direct attacks on critical systems. The Internet of Battlefield Things (IoBT) and embedded devices in industrial controls and infrastructure further amplify the threat. Traditional anti-tamper systems, limited by deterministic responses to specific attack types, are becoming inadequate due to advanced stealthier attacks. Intelligent defenses are essential for extended operational life and keeping pace with evolving threats. This study advocates for sophisticated anti-tamper solutions using machine learning to bolster physical security. The proposed system can detect various activities, discern normal operations from anomalies and known attacks, and has a stratified response and recovery mechanism, enhancing longevity and minimizing false positives. The growing IoBT usage necessitates better anti-tamper designs, and this research also explores the system’s resilience against adversarial learning attacks. We applied decision tree, SVM, and bagging classifiers and found that bagging classifier achieves 100% accuracy, outperforming the other used algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Critical infrastructures

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Confidence-Based Trustscoring system for Identification of Secure Agent Platforms

Confidence-Based Trustscoring System for Identification of S...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Joseph, Adri Jovin John Marikkannu, P. Mariappan, Marikkannan Sri Ramakrishna Institute of Technology Department of Information Technology Coimbatore India Anna University Department of Information Technology Regional Campus Coimbatore Coimbatore India Government College of Engineering Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798331509828

Autonomous mobile agent systems can accomplish multiple tasks in an asynchronous and dynamic manner in platforms where there is excessive network load, network interruption and in slow networks. However, the decisions and execution depend on the platforms visited by the agents. Since the platforms may influence the decisions made by the agents, it is crucial that the trustable platforms are selected. Trust score is one parameter that helps mobile agents to select the appropriate platform. This work proposes a minor modification of the existing trust score calculation which has a significant impact on the safety and performance of mobile agent systems when deployed in a conventional distributed environment or in a modern cloud environment for completion of tasks. This work introduces a confidence-based approach for the five parameters namely persistence, competence, reputation, credibility, integrity which determine the trust score. This will provide a clear decision-making environment for the mobile agents to choose an appropriate platform. © 2025 IEEE.

关键词： Cloud platforms

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Perception-Centric Explainable AI: Bridging Cognitive Theories and HCI Design for Enhanced User Experience 16

Perception-Centric Explainable AI: Bridging Cognitive Theori...

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16th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Alhasan, Sara Alnanih, Reem Computer Science Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

Artificial Intelligence (AI) systems increasingly influence high-stakes decision-making, necessitating explainability tailored to non-technical users. This research investigates integrating cognitive theories, Human-computer Interaction (HCI) principles, and Explainable AI (XAI) techniques to enhance user perception, a core cognitive function. A perception-focused framework was developed and tested using a medical symptom checker tool. The study involved 20 participants divided into control and experimental groups. The experimental group used an enhanced tool with features like visual hierarchy and consistency, while the control group used a basic version with minimal explainability. Participants completed pre-test, testing, and post-test phases, including surveys and objective tasks measuring recognition and recall. Results showed that 90% of the experimental group achieved "Good Performance" in recognition tasks compared to 20% in the control group. Subjective feedback indicated significantly higher un-derstandability, with an average rating of 4.8 out of 5 for the enhanced tool versus 3.1 for the control. These findings underscore the effectiveness of combining HCI principles and cognitive theories, such as Gestalt and Feature Integration, in reducing cognitive load and improving clarity. This multidisciplinary approach advances the design of intuitive, user-centered AI systems, fostering trust and accessibility. © 2025 Elsevier B.V.. All rights reserved.

关键词： Cognitive Functions Cognitive Theories Explainable Artificial Intelligence (XAI) Human-Centered XAI Human-computer Interaction (HCI) Medical Diagnosis Tool

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Enhancing Image Search Engine Accuracy Using Psychological Intention Diagrams 3

Enhancing Image Search Engine Accuracy Using Psychological I...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Aswathi, K.P. Yadwad, Sunita Raj, D. Nirmal Rastogi, Shailesh Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Parul University Faculty of Engineering and Technology Department of Computer science and Engineering Vadodara India Pune India Pune India

ISBN: (纸本)9798331508456

As the Internet becomes increasingly image-centric, users face substantial challenges in efficiently locating desired images due to the inherent limitations of image search engines in interpreting visual content and keyword ambiguity. This paper introduces an innovative solution employing psychological intention diagrams to enhance search accuracy. These diagrams harness historical data to depict the search intentions associated with specific keywords, reflecting both user and web designer perspectives. By integrating these diagrams, our proposed search engine significantly refines its ability to predict and retrieve the most relevant images. The implementation of this system involves constructing two types of intention diagrams: one based on past user queries and another derived from the intentions of web designers for cases without historical user data. This dual approach not only captures the semantic essence of images more effectively but also addresses the multi-faceted meanings of search keywords. Our experimental results confirm that incorporating psychological dimensions into image search mechanisms substantially improves precision, facilitating more accurate and user-aligned image retrieval. This pioneering approach is the first to apply psychological profiling in enhancing image search engine functionalities, marking a significant advancement in digital image retrieval technologies. © 2025 IEEE.

关键词： Content based retrieval

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