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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Ambekar, Namrata Govind Thokchom, Surmila Department of Computer Science and Engineering National Institute of Technology Meghalaya Shillong793003 India

ISBN: (纸本)9798331504106

With the rapid growth of Internet of Things (IoT) technology, there is an increase in the dangers of malware attacks. The underlying threat in these attacks is invading people’s privacy and security through the use of vulnerabilities in IoT devices. The most known malware type is zero-day, which is a newly innovated malware that has no background;hence, no training has been initiated to be undetectable and unstoppable from violent software attacks. Traditional intrusion detection systems (IDS) often face difficulties in identifying these new and evolving threats since they rely on pre-established attack patterns or signatures. This study aims to develop a new method of IoT malware traffic classification using unsupervised learning-based variational autoencoder (UL-VAE). It is demonstrated that UL-VAEs are effective in capturing the latent representations of benign software images. In this manner, zero-day malware can be suspected whenever the represented images deviate from the benign ones. The examination of experimental results is conducted utilising an image-based IoT malware binary dataset. The UL-VAE detects zero-day attacks with an accuracy of 0.9600, a false positive rate (FPR) of 0.0203, and a false negative rate (FNR) of 0.0363, significantly improving some of the existing IDS methodologies. This study emphasises the potential of UL-VAE to enhance cybersecurity strategies against emerging and unknown threats. ©2024 IEEE.

关键词： Network intrusion

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Effects of Aging at 550℃ on α'-Martensitic Transformation of High Si-Bearing Metastable Austenitic Stainless Steel Weld Metal

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Acta Metallurgica Sinica(English Letters) 2024年第9期37卷 1467-1479页

作者： Yakui Chen Shitong Wei Dong Wu Shanping Lu Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of SciencesShenyang110016China School of Materials Science and Engineering University of Science and Technology of ChinaShenyang110016China CAS Key Laboratory of Nuclear Materials and Safety Assessment Institute of Metal ResearchChinese Academy of SciencesShenyang110016China

The high Si-bearing 15Cr-9Ni-Nb metastable austenitic stainless steel weld metal was prepared via gas tungsten arc welding and then processed by stabilized heat treatment(SHT)at 850℃ for 3 *** effects of 550℃ aging on the α'-martensitic transformation of the as-welded and the SHT weld metals were *** results showed that the weld metal had poor thermal stability of *** precipitation of NbC during the 850℃ SHT made the thermal stability of the local matrix decrease and led to the formation of a large amount of C-depleted α'-*** precipitation of coarse σ-phase at the δ-ferrite led to the Cr-depleted zone and the formation of Cr-depleted α'-martensite at the early stage of 550℃ *** homogenized diffusion of C and Cr in the matrix during 550℃ aging led to the restoration of austenitic thermal stability and the decrease of α'-martensite *** C-depleted α'-martensite content in the SHT weld metal decreased rapidly at the early stage of aging due to the fast diffusion rate of the C atom in the matrix,while the Cr-depleted α'-martensite decreased at the later stage of aging due to the decreased diffusion rate of the Cr.

关键词： Metastable austenitic stainless steel weld metal α'-martensitic transformation δ-ferrite σ-phase

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Ensemble based Satin Bower Bird Algorithms for Cyberbullying Detection

Ensemble based Satin Bower Bird Algorithms for Cyberbullying...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Gnanabaskaran, A.G. Kavinilavu, S. Kowshika, V. Logesh, A.S. Nivitha, K. K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637 215 India Vellore Institute of Technology Computer Science and Engineering Vellore632014 India

ISBN: (纸本)9798350359688

Cyberbullying has become a pervasive issue in online communities, necessitating the development of robust detection methods to mitigate its harmful effects. In this paper, we propose an innovative approach to cyberbullying detection inspired by the complex mating behaviors of Satin Bower Birds. Our algorithm leverages ensemble learning techniques to combine the strengths of multiple classifiers, enhancing the accuracy and reliability of detection. We present a comprehensive methodology that integrates bio-inspired optimization principles with machine learning frameworks, facilitating the extraction of diverse features from user interactions and content. Through extensive experimentation on real-world datasets, we demonstrate the effectiveness of our ensemble-based Satin Bower Bird algorithm in accurately identifying instances of cyberbullying behavior. The results highlight the potential of bio-inspired algorithms to address social challenges in online environments and underscore the importance of interdisciplinary research in advancing cyberbullying prevention efforts. © 2024 IEEE.

关键词： Adversarial machine learning

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A Comprehensive Review of Air Quality Prediction Techniques: Methods, Performance and Challenges 8

A Comprehensive Review of Air Quality Prediction Techniques:...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sellamuthu, Suseela Niranjan Ayush Manori, Vaidik Chaurasia, Yash Mohan, Prakarsh Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

Air quality is what makes the places we live in habitable and affects the well-being and productivity of the people. Not paying attention to air quality can be dangerous, which has raised alarm among scientists, researchers, and the general public because it has been getting worse over the past decades. This paper will look at various methods and techniques for monitoring and predicting Air Quality Indexes (AQI) and how complicated it is to predict air quality exactly. Diverse machine learning models have been developed as well as data-driven approaches to improve the accuracy and credibility of air pollution forecasts. The dynamic nature of environmental conditions may explain why despite advances, predicting air quality remains a difficult task due to many interfering variables. This paper also proposes an approach for the creation of an efficient alert system. By using current data that offers predictive models that should help with time-sensitive real-time alerts about poor air quality to minimize any negative effects from such occurrences, these predictions should provide up-to-date results on air pollution indices. Such an alert system could be integrated with existing operational techniques, which would improve air quality management and as a result, the safety of life and productivity of individuals. © 2024 IEEE.

关键词： Prediction models

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Drought and Salinity Stress Classification in Soyabean Crops: Comparative Analysis of Machine Learning Models

Drought and Salinity Stress Classification in Soyabean Crops...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Madhumitaa, P.S. Ragavi, Chagamreddy Kiranmayi, Chinthamani Venkatesan, M. Prabhavathy, P. National Institute of Technology Department of Computer Science & Engineering Puducherry India Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798331540685

Soyabean cultivation encounters significant obstacles attributed to abiotic stresses like drought and salinity, especially in areas characterized by tropical climates. In this paper, we seek to fill gaps identified in previous research efforts, particularly the lack of comprehensive analysis regarding genotypic factors and the underutilization of morphological characteristics in machine learning algorithms for Soyabean genotype selection. We propose a novel approach to bridge these gaps by employing six morphological variables in machine learning models. Through the process of training the models using specific parameters, plants are categorized into either the Control group or the Stress Group, which comprises of genotypes facing drought or salinity stress. Various models such as Random Forest Classifier (RFC), Support Vector Machine (SVM), Gradient Boosting (GB), k-Nearest Neighbors (kNN) and Naive Bayes algorithms are included, offering an in-depth comparative analysis of the performance and feature importance under each model used. However, the Support Vector Machine was identified as the most accurate algorithm with an accuracy of 96.79% in stress classification of Soyabean genotypes. Incorporating morphological parameters in machine learning models enhances the accuracy and efficiency of Stress Classification process, increasing the sustainability of Soyabean production and its ability to withstand environmental adversities. © 2024 IEEE.

关键词： Abiotic

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Enhancing Data Security and Efficiency: A Hybrid Cloud Approach to Secure Authorized Deduplication

Enhancing Data Security and Efficiency: A Hybrid Cloud Appro...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suresh Kumar, K. Preethi, S.T. Mahalakshmi, P. Hariharasudhan, S. Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India S A Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

As cloud services grow, data deduplication has become crucial for reducing storage costs and improving resource utilization by eliminating duplicate data. However, maintaining data security and user privacy in deduplication-enabled environments remains a significant challenge due to increasing risks of unauthorized access and data breaches. To address this, convergent encryption has been proposed, allowing data encryption before outsourcing while supporting deduplication. This paper introduces a hybrid cloud approach for secure authorized deduplication, combining public and private clouds to optimize efficiency, data integrity, and confidentiality. Our solution uses a dual-layer authentication and encryption mechanism, where key generation is tied to user privileges and file ownership. The control plane is managed by the private cloud, handling sensitive metadata and keys, while the public cloud stores deduplicated data to ensure cost efficiency. We further introduce differential user privileges in duplicate checks, a feature lacking in traditional systems. To validate our approach, we developed a prototype and conducted testbed experiments. The results show substantial reductions in storage overhead and latency, with minimal performance impact. Security analysis confirms that our scheme meets the required security guarantees, providing a scalable and efficient solution for cloud deduplication while addressing privacy and performance concerns. © 2024 IEEE.

关键词： Data integrity

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Favourable subpopulation migration strategy for travelling salesman problem

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International Journal of Business Intelligence and Data Mining 2022年第3期20卷 299-326页

作者： Chandar, Abhishek Srinivasan, Akshay Anand, G. Paavai Department of Computer Science and Engineering SRM Institute of Computer Science and Technology Tamil Nadu Chennai600026 India

Most of the existing migration strategies are complex in nature which results in high computation time. Since the introduction of parallel genetic algorithms, they are known for obtaining better optimal solutions at the cost of more computation. Migration has played a significant role in maintaining genetic diversity of the population. This paper aims to propose a new migration strategy that improves the performance of the parallel genetic algorithm in solving NP hard problems. In order to assess its behaviour, various travelling salesman problem datasets are considered since it is a perfect example of an NP hard problem. The algorithm is based on an island model approach where one subpopulation is nurtured by the other subpopulations by taking turns during the migration process to progressively reach the optimal solution. In turn, the nurtured subpopulation provides a feedback loop to the subpopulation with poor fitness value based on certain criteria to be met. An extensive study was conducted to understand the optimal parameters values in which the proposed algorithm works best. © 2022 Inderscience Enterprises Ltd.

关键词： Traveling salesman problem

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Optimizing IoT Workloads for Minimal Time and Energy with UAV Networks Using Deep Q Network 18

Optimizing IoT Workloads for Minimal Time and Energy with UA...

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18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Singhal, Priyansh Chauhan, Harshit Hazra, Abhishek Joshi, Piyush Indian Institute of Information Technology Sri City Department of Computer Science and Engineering India

ISBN: (纸本)9798350387278

The rapid growth in the use of IoT devices has highlighted significant challenges due to their limited computational power and battery life, often resulting in long task execution times and potential battery depletion. The problem we address is the inefficiency in task completion and high battery usage in IoT devices. Our key objective is to minimize time delays and energy consumption in IoT workloads. To solve this problem, we propose a partial offloading algorithm for IoT devices, utilizing Unmanned Aerial Vehicles (UAVs) due to their larger battery capacity and greater computational power. UAVs provide flexibility in task management by moving to high-demand areas. Given the battery constraints of UAVs, a network of UAVs is used to share tasks among neighboring UAVs, balancing the load. Additionally, tasks can be offloaded to a ground-based cloud server to prevent overloading the UAV network. Our main contribution is the implementation of a Deep Q Network (DQN) reinforcement learning approach to manage the dynamic nature of task distribution. We achieved a significant reduction in both time delays and energy consumption. Our results displayed approximately a 15-fold reduction in total delay and a 6-fold reduction in total energy consumption, demonstrating the effectiveness of DQNs in this task offloading scenario. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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YOLO-NAS Based Deep Learning Approach for Breast Lesion Identification 2

YOLO-NAS Based Deep Learning Approach for Breast Lesion Iden...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Sonu Thakur, Ankita Kumar, Rajeev Bhadoria, Robin Singh National Institute of Technology Department of Computer Science and Engineering Himachal Pradesh Hamirpur India

ISBN: (纸本)9798350360523

Early and accurate breast cancer detection is crucial for effective intervention and improved patient outcomes. Conventional methods often encounter challenges in achieving the required accuracy. To address this, we advocate for the utilization of the YOLO-NAS architecture, renowned for its proficiency in object detection. Implementation of YOLO-NAS explores the efficacy of breast cancer identification, utilizing a diverse dataset for comprehensive evaluation. The employed model demonstrates exceptional accuracy, boasting a mean Average Precision (mAP) value of 87.7 and an impressive recall of 98%. Despite its computational demands, the model's promising performance suggests its potential as a valuable tool in medical diagnostics. Ongoing efforts will focus on optimizing computational efficiency and ensuring the seamless integration of the model into clinical practices for widespread and effective usage. © 2024 IEEE.

关键词： Object detection

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Hybrid attention residual deep convolution learning network for bio medical image analysis

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Journal of Intelligent and Fuzzy Systems 2025年第4期48卷 537-554页

作者： Sundar Raj, A. Naidu, Paruchuri Chandra Babu Sivakumar, S. Senthilkumar, S. Department of Biomedical Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam India Department of Electrical and Electronics Engineering BSA Crescent Institute of Science and Technology Vandalur Tamilnadu Chennai India Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Tamilnadu Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam India

Biomedical image analysis has progressed significantly with the integration of artificial intelligence, presenting new opportunities for early diagnosis and treatment of diseases with high mortality rates, such as skin cancer. This research work introduces a novel Hybrid Attention Residual Deep Convolution Learning (HARDCL) Model designed to enhance the accuracy of skin lesion classification, which is often challenging due to the subtle characteristics of lesions. The HARDCL Network employs an innovative hybrid attention mechanism within a residual learning framework, effectively distinguishing between benign and melanoma skin lesions with a high degree of precision. The encoder-decoder network segments the lesion from dermoscopic images, followed by feature extraction and classification, achieving a maximum accuracy of 97.11%, precision of 98.10%, recall of 98.81%, and an F1-score of 98.45%. This performance surpasses conventional deep learning algorithms such as AlexNet, InceptionV3, and ResNet18, demonstrating the effectiveness of the attention-based features in identifying critical lesion characteristics. However, despite its strengths, the HARDCL Network’s computational intensity and the need for a substantial dataset for optimal training highlight areas for further refinement. The proposed network advances the field of skin cancer diagnostics, offering a robust tool for medical professionals and setting a foundation for future enhancements in automated biomedical image analysis techniques. © 2025 – IOS Press. All rights reserved.

关键词： Deep learning

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