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A Comprehensive Integrated Security Model with Advanced Access Control for Enhancing WSN computing in IoT Frameworks

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Tehnicki Vjesnik 2025年第3期32卷 1040-1046页

作者： Navaneethan, S. Arun, A. Department of Networking & Communications School of Computing SRM Institute of Science and Technology SRM Nagar Kattan Kulathur Tamilnadu 603203 India Department of Networking & Communications School of Computing Faculty of Engineering & Technology SRM Institute of Science and Technology Kattan Kulathur Tamilnadu 603203 India

Wireless Sensor Network (WSN) computing has emerged as a pivotal intermediary process addressing latency challenges inherent in Cloud-oriented Internet of Things (IoT) services. However, this advancement brings forth significant security and privacy concerns exacerbated by the proliferation of network nodes and the vast volumes of real-time data generated within IoT frameworks. To mitigate these issues, this research proposes an Integrated Security Model with Advanced Access Control (ISM-AAC) Mechanism. The ISM-AAC integrates Functional Encryption (FE) and Role-Based Access Control (RBAC) to fortify communication security. The model employs a comprehensive approach encompassing Access Control Processes, Integrated Encryption Processes, and Re-Encryption Processes to enhance data confidentiality and integrity. The detailed framework includes phases such as system initialization, user management, and access permission administration, ensuring robust security throughout data transactions. Evaluation of the model's efficacy is conducted based on various security metrics pertinent to WSN computing environments. This study contributes to the advancement of secure computing in IoT by proposing a sophisticated ISM-AAC framework that not only addresses current security challenges but also anticipates future threats in the landscape of WSN computing. The findings underscore the model's effectiveness in safeguarding sensitive IoT data and lay the groundwork for future enhancements in IoT security protocols. © 2025, Strojarski Facultet. All rights reserved.

关键词： Access control models

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Joint contrastive learning and belief rule base for named entity recognition in cybersecurity

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Cybersecurity 2025年第2期8卷 174-187页

作者： Chenxi Hu Tao Wu Chunsheng Liu Chao Chang College of Electronic Engineering National University of Defense TechnologyHefeiChina Department of Computing The Hong Kong Polytechnic UniversityHong KongChina

Named Entity Recognition(NER)in cybersecurity is crucial for mining information during cybersecurity *** methods rely on pre-trained models for rich semantic text embeddings,but the challenge of anisotropy may affect subsequent encoding ***,existing models may struggle with noise *** address these issues,we propose JCLB,a novel model that Joins Contrastive Learning and Belief rule base for NER in *** utilizes contrastive learning to enhance similarity in the vector space between token sequence representations of entities in the same category.A Belief Rule Base(BRB)is developed using regexes to ensure accurate entity identification,particularly for fixed-format phrases lacking ***,a Distributed Constraint Covariance Matrix Adaptation Evolution Strategy(D-CMA-ES)algorithm is introduced for BRB parameter *** results demonstrate that JCLB,with the D-CMA-ES algorithm,significantly improves NER accuracy in cybersecurity.

关键词： Named entity recognition Cybersecurity Contrastive learning Belief rule base

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YOLOCSP-PEST for Crops Pest Localization and Classification

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Computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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An Innovative Deep Learning-Based Predictive Maintenance Model for Smart Automotive System

An Innovative Deep Learning-Based Predictive Maintenance Mod...

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

作者： Indumathi, V. Ashokkumar, C. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

This research presents an innovative deep learning-based predictive maintenance model designed for smart automotive systems, utilizing the EnsembleAE-Boost (EAE-Boost) algorithm. The primary objective of the proposed model is to enhance the prediction accuracy and fault detection capabilities of smart vehicles through advanced machine learning techniques. The EAE-Boost algorithm, an ensemble model integrating auto encoders and boosting, is compared with existing algorithms, namely K-Nearest Neighbors (KNN), Deep Neural Networks (DNN), and Extreme Gradient Boosting (XGBoost), based on key simulation metrics, including accuracy, precision, prediction time, and fault detection rate. Simulation results demonstrate that the EAE-Boost algorithm outperforms the existing algorithms, achieving superior performance in all metrics. Notably, the proposed method showed significant improvements in accuracy and fault detection rate, with a reduction in prediction time, highlighting its potential for real-time maintenance applications in smart automotive systems. The findings underscore the effectiveness of the proposed deep learning model in enhancing vehicle reliability, safety, and operational efficiency. © 2025 IEEE.

关键词： Predictive maintenance

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Adaptive sail fish optimizer based optimized region selection for robust video steganography

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International Journal of Information technology (Singapore) 2025年第4期17卷 2417-2427页

作者： Babu, J. Suresh Niranjana, G. Ramana, Kadiyala Department of Computing Science & Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology (Autonomous) Hyderabad India

Securing data transmission in a digital era is a difficult one due to the broad application of the Internet, personal computers, and mobile phones for communication. Traditional video steganography techniques sometimes fail to identify the best pixels for encoding hidden data, resulting in lowerquality and robustness. To solve this issue, this article proposes an innovative steganography technique that improves the security and quality of data hiding. The proposed method takes cover video and secret images as input. The Structural Similarity Index Measurement (SSIM) first collects key frames from the input video. The adaptive sailfish optimizer (ASFO) is then used to find the best regions within these keyframes to insert the secret data in. To improve security, the discrete wavelet transform (DWT) is applied to the specified region, and the LL band is used for the hiding process. The secret image is encrypted by Adaptive elliptical curve cryptography (AECC) and transformed to binary bits, which then get embedded in the chosen band. This method leads to the construction of a stego-video. The extraction procedure is repeated using the same approach, which ensures secure and robust data transmission. Performance metrics, including PSNR, BER, NAE, TAF, and NC are analyzed to compare each method's efficacy. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Adaptive elliptical curve cryptography And stego-video Discrete wavelet transform Structural similarity index measurement Video steganography

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Detection of diabetic retinopathy and age-related macular degeneration using DenseNet based neural networks

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Multimedia Tools and Applications 2025年第1期84卷 289-316页

作者： Singh, Manpinder Dalmia, Saiba Ranjan, Ranjeet Kumar School of Computing DIT University Dehradun248009 India CSE Department Thapar Institute of Engineering & Technology Patiala147004 India

The eyes are the organ of sight and one of the most highly developed sensory organs in our body which covers a larger part of the brain. One of the most common problems that are spreading from kids to adults is an eye disorder which can be defined as abnormal functioning of the eye that can lead to vision disturbance. Examples of major eye problems are cataracts, glaucoma, etc. Artificial Intelligence has helped benefit research in the medical field. Ocular diseases can be detected automatically through Computer Vision and Deep Learning models when high-quality medical eye fundus images are provided to them. Inspired by this, we proposed three deep learning models based on the DenseNet pre-trained model for Ocular Disease Detection. Since the amount of eye scans being performed is rapidly growing at a much faster rate than the interpretation of the scan results, our proposed models could help automate the process thus increasing speed and efficiency. The proposed model performed the detection with an accuracy of 77%, 86%, and 98% for Ocular Disease Detection, Diabetic Retinopathy, and Age-Related Macular Degeneration tasks respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Computer vision

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Deep Learning Approach for Identifying Red Lesions in Retinal Fundus Imagery 6th

Deep Learning Approach for Identifying Red Lesions in Retina...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Divyashrree, R. Dhawan, Samarth Saranya, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9789819774258

One dangerous side effect of diabetes that affects the eyes is called diabetic retinopathy. It happens as a result of alterations in the retina’s blood vessels, which can cause harm and even blindness. The development of red lesions in the retina is one typical sign of diabetic retinopathy. These red lesions may be a sign of microaneurysms or hemorrhages, which are important characteristics to identify and track as diabetic retinopathy progresses. This process becomes even more demanding in the early stages of the disease where symptoms are less pronounced in the images. Leveraging machine-based learning for medical image analysis, especially deep learning algorithms, has demonstrated its capability to enhance the early diagnosis of diabetic retinopathy, providing a more efficient and effective alternative to traditional manual assessments. The proposed work aims to improve the efficiency of early detection of diabetic retinopathy by utilizing Deep Learning (16-layer CNN) to determine a person’s likelihood of having diabetic retinopathy using a 02-Class model that compiles a variety of datasets, specifically IDRiD dataset. The entire dataset contains 18,590 fundus photographs, in which 3662 are allocated for training, 1928 for validation, and 13,000 for testing. All datasets had a similar distribution of output classes, which is a fundamental property of this type of data. Using the IDRiD dataset, the suggested Red Lesion detection system’s specificity and sensitivity were found to be 98% and 89%, respectively, with an accuracy of 95.32%. Using the MESSIDOR dataset, the Diabetic Retinopathy Severity Classification System’s specificity, sensitivity, and accuracy were found to be 93.8, 92.3, and 94%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ophthalmology

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A Novel Proactive AI-Based Agents Framework for an IoE-Based Smart Things Monitoring System with Applications for Smart Vehicles

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Computers, Materials & Continua 2025年第2期82卷 1839-1855页

作者： Meng-Hua Yen Nilamadhab Mishra Win-Jet Luo Chu-En Lin Department of Electronic Engineering National Chin-Yi University of TechnologyTaichung411030Taiwan School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India Department of Refrigeration Air-Conditioning and Energy EngineeringNational Chin-Yi University of TechnologyTaichung411030Taiwan

The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one *** featured in the IoE include embedding,networking,and sensing *** achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked *** IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel *** Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this *** STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor *** STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle *** information is utilized to locate the vehicle in an accident or *** findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have ***,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and *** our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV *** framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.

关键词： Artificial intelligence(AI) proactive AI-based learning agents(PLA) internet of everything(IoE) smart things monitoring system(STMS) cloud processing system driving monitoring assistance system(MAS) smart vehicles

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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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Graph Convolutional Networks for SEO: A Comprehensive Framework for Healthcare Information Ranking

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Journal of The Institution of Engineers (India): Series B 2025年 1-16页

作者： RR, Prianka M, Vedaraj R, Anto Arockia Rosaline Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Kavaraipettai Thiruvallur India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India

Internet’s remarkable surge, ubiquitous accessibility, and serviceability have increased users’ dependency on web services for fast search and recovery of wide sources of information. Search engine optimization (SEO) has become paramount in healthcare industries, which helps patients enhance and understand their health status based on their records. In the context of healthcare, it is more significant to improve search results from specific keywords related to clinical conditions, treatments, and healthcare services. So, this research work proposes a Graph Convolutional Network (GCN)-based Search Engine Optimization (SEO) algorithm for healthcare applications. The algorithm utilizes two distinct datasets: MIMIC-III Clinical Database and Consumer Health Search Queries to optimize search engine rankings for health related queries. Following data acquisition, data pre-processing is performed for better enrichment of analysis. The preprocessing steps involve data cleaning, data integration, feature engineering, and knowledge graph construction procedures to remove noisy data, integrate medical data with user search behavior, compute significant features, and construct knowledge graphs, correspondingly. The relation between the data entities is examined within constructed graph through link analysis. The pre-processed data including medical knowledge weights, content relevance scores, and user interaction signals are processed further on GCN model with Adam-tuned weights and bias for ranking healthcare data based on relevance score in response to user query using cosine similarity. The search relevance estimation indicators namely recall, precision, f1-score, and normalized discounted cumulative gain (NDCG) are computed to measure search optimization performance. The proposed GCN-SEO approach benchmarked its effectiveness over existing methods in optimizing web searches related to healthcare with a high performance rate of 95.75% accuracy and 48.25 s dwell time. This sho

关键词： Clinical research

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