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9th International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Ramachandran, Sharad Aishwarya, B. Pushpalatha, M. Poornima, S. Venkatesh, K. Srm Institute of Science And Technology Department of Networking And Communications kattankulathur India Srm Institute of Science And Technology Department of Computing Technologies kattankulathur India

ISBN: (纸本)9798350365092

Detecting surface defects in anodized automobile components on a serial production line is a formidable task that demands precision, given theseparts' critical role in ensuring vehicle safety and performance. This research proposes a ground-breaking system that tackles these challenges by minimizing hardware and leveraging software processing, rendering it automated, versatile, affordable, and precise. The proposed method adopts the Mask R-CNN algorithm for defect detection, enabling pixel-level instance segmentation and bounding the defects with boxes. Moreover, a multi-label classifier is deployed to distinguish between defective and non-defective components and predict the type of defect. The system's design also accounts for the computational time needed to ensure the system is suitable for high-speed conveyor belts. By accurately detecting defects and preventing defective components from causing catastrophic consequences, the proposed approach has the potential to revolutionize defect detection in serial production lines, making the process faster, more reliable, and safer © 2024 IEEE.

关键词： computer vision

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Exploring Deep Learning Methods for Diabetic Retinopathy Detection and Classification 5

Exploring Deep Learning Methods for Diabetic Retinopathy Det...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Tomy, Minnu C. Malathy, C. Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350361155

The prevalence of diabetes, a chronic condition influenced by lifestyle factors, has experienced a significant surge across several age cohorts. The incidence of diabetes has exhibited a more pronounced upward trend in underdeveloped and developing nations compared to developed nations. Diabetes has a significant impact on multiple organs inside the human body, including the retina. Diabetic Retinopathy is a diabetic consequence characterized by retinal blood vessel swelling and the development of lesions, resulting in impaired vision in affected individuals. Failure to recognize Diabetic Retinopathy (DR) in its early stages may result in the development of visual impairment or blindness. Fundus images of the retina are examined by ophthalmologists as a component of the manual diagnostic approach for diabetic retinopathy (DR). In contrast to computer-aided diagnostic technology, traditional diagnostic methods are characterized by time-consuming procedures, high costs, and a susceptibility to misdiagnosis. In recent times, deep learning has gained significant prominence as a highly favored approach, demonstrating enhanced performance across several fields, with a notable emphasis on the examination and categorization of medical images. This study provides a comprehensive survey of deep learning techniques employed in the identification and categorization of diabetic retinopathy. Additionally, it examines the several publicly accessible datasets containing retinal fundus images. © 2024 IEEE.

关键词： Classification (of information)

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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M-Bi-GRU-CNN: a hybrid deep learning model with optimized feature selection for enhanced crop yield prediction

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Multimedia Tools and Applications 2025年 1-25页

作者： J, Madhuri M, Indiramma N, Nagarathna Department of Computer Science and Engineering Bangalore Institute of Technology Karnataka Bengaluru560004 India Department of Computer Science and Engineering BMS College of Engineering Karnataka Bengaluru560019 India

Weather variability significantly impacts crop yield, posing challenges for large-scale agricultural operations. This study introduces a deep learning-based approach to enhance crop yield prediction accuracy. A Multi-Head Attention-based Bi-Directional Gated Recurrent Unit (M-Bi-GRU) integrated with a Convolutional Neural Network (CNN) is proposed. The publicly available Crop Recommendation dataset is pre-processed using data cleaning and Min–Max normalization to eliminate inconsistencies. Then, the Adaptive Reptile Search Optimization (ARSO) algorithm is used to identify the most relevant features for the prediction task. The M-Bi-GRU-CNN model uses these selected features to predict crop yields. The implementation is carried out in Python, and the performance is measured using a variety of metrics. Experimental results demonstrate that the proposed model outperforms existing methods, achieving a Mean Absolute Error (MAE) of 0.45, Mean Absolute Percentage Error (MAPE) of 0.07, and Root Mean Square Error (RMSE) of 0.28. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Dual image based secured reversible data hiding scheme exploiting huffman compression tree combining bit-reversal permutation technique

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Multimedia Tools and Applications 2025年第9期84卷 5653-5698页

作者： Datta, Kankana Jana, Biswapati Chakraborty, Mamata Dalui Department of Computer Applications Haldia Institute of Technology HIT Campus Hatiberia Haldia Purba Medinipur West Bengal Haldia India Department of Computer Science Vidyasagar University West Bengal Midnapore India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India

In the area of data hiding and information security, the greater need is to ensure high embedding capacity of the stego media without hampering the visual quality while ensuring the tightest possible security. Now, it has become one of the biggest challenge to meet all these requirements. Embedding secret messages in dual images is one of the latest techniques that aims to cater such needs. However, there are very few data hiding schemes that are currently using dual images, but are suffering from several shortcomings, such as security loophole and poor visual quality with limited hiding capacity. In this paper, a dual image-based reversible data hiding scheme has been proposed where multiple bits can be embedded within a pair of pixels dynamically by Huffman compression where different pseudo codes are generated for different chunk of secret bits which are embedded by Bit-Reversal Permutation (BRP) and X-OR operations. Finally, pixels are interchanged between the dual images based on odd-even properties of the pixel values. The functional efficiency of the proposed scheme is evaluated by comparing the relevant parameters of different state-of-the-art schemes. It is established that the proposed scheme provides more than 64 dB PSNR value on average after embedding a maximum of 3, 27, 680 bits of secret data which ensures good visual quality and high embedding capacity as compared to other state-of-the-art schemes thereby benefitting innumerable applications across public and private sectors. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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Enhanced Database Encryption System 5

Enhanced Database Encryption System

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, Piyush Hanif, Laraib Srinivas, L.N.B. Srm Institute of Science and Technology College of Engineering and Technology Department of Networking and Communications Kattankulatur India

ISBN: (纸本)9798350383522

This study, in particular, highlights the critical nature of Impacts of the Enhanced Database Encryption. System within contemporary enterprises. It places significant focus on critical goals, such as data consolidation [1]. Preservation of integrity, and enhanced security controls. Moreover, It emphasizes the essential nature of Database Management System. (DBMS) in streamlining various operational domains including customer relationship management, supply chain optimization and advanced analytics. Furthermore, the paper emphasizes the importance of dealing with such data privacy issues. regulatory compliance, as well as setting up a strong data governance frameworks. These efforts collectively contribute to the development of stable and highly reliable databases that are necessary for contemporary organizations that want to protect their strategic resources and preserve the faith in the digital-centered landscape. © 2024 IEEE.

关键词： Advanced analytics Data consolidation Data governance framework Integrity preservation

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A user-adaptive privacy-preserving authentication of IoMT using zero knowledge proofs with ECC

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Multimedia Tools and Applications 2025年 1-32页

作者： Misra, Garima Hazela, Bramah Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Department of Computer Science & Engineering Pranveer Singh Institute of Technology Utter Pradesh Kanpur India

The Internet of Things (IoT) has developed into a crucial component for meeting the connection needs of the current smart healthcare systems. The Internet of Medical Things (IoMT) consists of medical devices that are connected to patients in order to sense their medical parameters and exchange that information with healthcare professionals so they can deliver remote healthcare services. The need to protect the patient’s private information from being misused makes IoMT security an urgent issue that merits further study. Elliptic Curve cryptography (ECC), a popular substitute for traditional public key encryption like RSA, has several advantages. ECC is a prime contender for implementation on constrained devices with confined memory and speed, as well as wireless communication protocols that use low power like IoMT environment. The economic complexity of the current security protocols makes them inappropriate, hence new security protocols must be developed for resource-constrained and diverse IoT networks. This work suggests an authenticated key agreement protocol for the IoMT that is based on elliptic curve encryption based zero-knowledge proof (ZKP) protocols. The proposed mechanism completes in order of milliseconds up to 300 ms for 160 bits. The research will include the comparison between computational delays in traditional protocols and ZKP-ECC protocol. This paper provides a protocol to obtain privacy of data in IoMT environment on the basis of zero knowledge phenomenon and anonymous communication by ZKP-ECC. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Authentication Protocol

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Integrated Engroove Leach Clustering protocol with Artificial Bee Colony Optimization for Energy Efficient Routing in WSN 4

Integrated Engroove Leach Clustering protocol with Artificia...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Basanthi, Y. Kalaiselvi, K. Murugan, V. Senthil SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur603203 India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331515720

Wireless Sensor Networks (WSNs) plays a prominent role in numerous operations, such as environmental monitoring, healthcare, and military operations. WSN is a distributed environment which comprises multiple sensor nodes. The major constraint in WSN is energy as node is a tiny structure battery power is limited. Energy conservation very important factor in WSN. This research study presents an integrated approach combining the Engroove Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol with the Artificial Bee Colony (ABC) algorithm to optimize cluster head (CH) selection and routing. The proposed method seeks to balance energy consumption, extend network lifetime, and enhance scalability. Simulation results show that our approach is more effective than traditional LEACH and other existing protocols. © 2024 IEEE.

关键词： Energy efficiency

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DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

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computers, Materials & Continua 2023年第3期74卷 6429-6444页

作者： A.Arokiaraj Jovith S.Rama Sree Gudikandhula Narasimha Rao K.Vijaya Kumar Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Networking and Communications College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science Engineering Aditya Engineering CollegeSurampalemAndhra Pradesh533437India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of *** accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of *** quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression *** article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage *** proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data ***,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded *** addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably *** order to generate optimal codebook for LBG,the WSA is applied to *** performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several *** comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

关键词： DNA computing data storage image compression vector quantization ws algorithm space saving

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Advancing emotion recognition via EEG signals using a deep learning approach with ensemble model

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 143-154页

作者： Immanuel, Rajeswari Rajesh Sangeetha, S.K.B. Department of Computer Science and Engineering SRM Institute of Science and Technology Faculty of Engineering and Technology Vadapalani Campus Tamil Nadu Chennai India

Human emotions are the mind's responses to external stimuli, and due to their dynamic and unpredictable nature, research in this field has become increasingly important. There is a growing trend in utilizing deep learning and machine learning techniques for emotion recognition through EEG (electroencephalogram) signals. This paper presents an investigation based on a real-time dataset that comprises 15 subjects, consisting of 7 males and 8 females. The EEG signals of these subjects were recorded during exposure to video stimuli. The collected real-time data underwent preprocessing, followed by the extraction of features using various methods tailored for this purpose. The study includes an evaluation of model performance by comparing the accuracy and loss metrics between models applied to both raw and preprocessed data. The paper introduces the EEGEM (Electroencephalogram Ensemble Model), which represents an ensemble model combining LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) to achieve the desired outcomes. The results demonstrate the effectiveness of the EEGEM model, achieving an impressive accuracy rate of 95.56%. This model has proven to surpass the performance of other established machine learning and deep learning techniques in the field of emotion recognition, making it a promising and superior tool for this application. © 2024 - IOS Press. All rights reserved.

关键词： Convolutional neural networks

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