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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Puyam, Kelvin Singh, Nishant Aruna, M. SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350375237

Gesture recognition holds paramount significance in facilitating communication for individuals utilizing sign language to convey phrases and expressions. We present an innovative approach to gesture recognition in this article tailored for interpreting hand signs to derive meaningful phrases, such as 'I love you,' 'please,' 'sorry,' and 'goodbye.' Our proposed methodology integrates a multi-box detector (SSD) with dynamic hand gesture analysis to achieve real-time and accurate recognition of hand signs across various languages and cultural contexts. By harnessing the power of deep learning SSD for object detection, our system ensures swift responses while maintaining precision. Additionally, the incorporation of dynamic hand gesture analysis techniques enhances the system's adaptability to diverse sign styles and linguistic nuances, thus fostering better communication access. By means of meticulous experimentation, we establish the effectiveness of our methodology in precisely identifying and deciphering real-time sign language gestures, attaining noteworthy results in terms of both speed and accuracy. This study enhances assistive technology for hard-of-hearing and deaf communities, thereby promoting accessibility and inclusiveness in communication environments that are accommodated to the linguistic requirements of a variety of individuals. © 2024 IEEE.

关键词： Gesture recognition

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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Hydrogen Energy Storage System:Review on Recent Progress

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Energy engineering 2025年第1期122卷 1-39页

作者： MilleniumWong Hadi Nabipour Afrouzi Research Centre for Sustainable Technologies Faculty of EngineeringComputing and ScienceSwinburne University of TechnologyJalan Simpang TigaKuchingSarawak93350Malaysia College of Engineering Faculty of ComputingEngineering and the Built EnvironmentBirmingham City UniversityBirminghamWest MidlandsEnglandB47XGUK

A hydrogen energy storage system(HESS)is one of the many risingmodern green innovations,using excess energy to generate hydrogen and storing it for various *** that,there have been many discussions about commercializing HESS and improving it ***,the design and sizing process can be overwhelming to comprehend with various sources to examine,and understanding optimal design methodologies is crucial to optimize a HESS *** that,this review aims to collect and analyse a wide range of HESS studies to summarise recent *** different collections of studies are studied,one was sourced by the main author for preliminary readings,and another was obtained via *** findings from the Web of Science platform were also examined for amore comprehensive *** findings include the People’sRepublic of China has been active in HESS research,as most works and active organizations originate from this *** has been mainly researched to support power generation and balance load demands,with financial analysis being the common scope of *** is a common tool used for HESS design,modelling,and optimization as it can handle complex *** neural network(ANN)has the potential to be used to model the HESS,but additional review is required as a formof future *** a commercialization perspective,pressurized hydrogen tanks are ideal for hydrogen storage in a HESS,but other methods can be considered after additional research and *** this review,it can be implied that modelling works will be the way forward for HESS research,but extensive collaborations and additional review are ***,this review summarized various takeaways that future research works on HESS can use.

关键词： Hydrogen energy storage system VOSViewer design review sizing

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Graph-Theoretic Approaches in Semantic Web Service Orchestration and Discovery: A Comprehensive Literature Survey 5

Graph-Theoretic Approaches in Semantic Web Service Orchestra...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Vignesh, T.V. Vijaya, K. Minu, R.I. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies School of Computing Tamil Nadu Kattankulathur603203 India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Semantic Web Services (SWS) have become a powerful means for automating the discovery, composition, and orchestration of web services by providing them with semantic descriptions. This paper provides a comprehensive survey of graph-theoretic approaches in the orchestration and discovery of SWS, systematically categorizing and analyzing various methodologies. We examine how graph-based models contribute to enhancing the efficiency, accuracy, scalability, performance, reliability, and predictability of SWS processes. Furthermore, the paper discusses research gaps, challenges, and potential future directions, offering insights into emerging solutions and state-of-the-art techniques in the field. The survey also integrates recent advancements such as hybrid frameworks combining knowledge graphs and semantic web services, service clustering using graph embedding, and enhanced recommendation models. © 2024 IEEE.

关键词： Knowledge graph

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Leveraging HPC with GAN-CNN Integration for Enhanced Tuberculosis Detection using Synthetic Image Generation 3

Leveraging HPC with GAN-CNN Integration for Enhanced Tubercu...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Manasa, Pokala Veeramalla, Vaishnavi Vanusha, D. Vathana, D. Robinsha, Delsi S. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603203 India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603203 India

ISBN: (纸本)9798331532420

Tuberculosis (TB) remains a significant global health challenge, especially in remote areas with limited medical resources. Early and accurate diagnosis is crucial to control its spread. This project harnesses high-performance computing (HPC) to address two key issues: managing unreliable medical data and improving TB diagnosis. Given the imbalance between healthy and TB-affected samples, Generative Adversarial Networks (GANs) are used to generate synthetic images, enriching the dataset and making it more diverse. This helps train Convolutional Neural Networks (CNNs), which are highly effective in medical image analysis. By leveraging HPC, we accelerate the training of CNNs on large, augmented datasets, significantly reducing processing time while maintaining accuracy. This system improves TB detection by combining GAN-enhanced data and CNN models, enabling faster and more reliable diagnosis. © 2024 IEEE.

关键词： X ray apparatus

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Gray-Hole Attack Minimization in IoMT with 5G Based D2D Networks

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Computer Systems Science & engineering 2022年第9期42卷 1289-1303页

作者： V.Balaji P.Selvaraj Department of Computing Technologies College of Engineering and TechnologyFaculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia

Reliable transmission is vital to the success of the next generation ofcommunications technologies and Fifth Generation (5G) networks. Many sensitive applications, such as eHealth and medical services, can benefit from a 5G network. The Internet of Medical Things (IoMT) is a new field that fosters themaintenance of trust among various IoMT Device to Device (D2D) modern technologies. In IoMT the medical devices have to be connected through a wirelessnetwork and constantly needs to be self-configured to provide consistent and effi-cient data transmission. The medical devices need to be connected with sophisticated protocols and architecture to handle the synergy of the monitoring ***, one of the commonly used algorithms in D2D communication is the Optimized Link State Routing protocol (OLSR). The OLSR is considerably good ateffectively utilizing the bandwidth and reserving the paths. One of the majorattack against the OLSR is the Node isolation attack, also known as the Gray holedenial of service attack. The Gray hole attack exploits the vulnerabilities presentwith sharing the topological information of the network. The attackers may usethis topological information to maliciously disconnect the target nodes from theexisting network and stops rendering the communication services to the victimnode. Hence, considering the sensitivity and security concerns of the data usedin e-Health applications, these types of attacks must be detected and disabledproactively. In this work, a novel Node Authentication (NA) with OLSR is proposed. The simulation experiments illustrated that the proposed protocol has anexcellent Packet Delivery Ratio, minimal End-End delay, and minimal Packet losswhen compared to the Ad-hoc On-Demand Distance Victor (AODV) protocol andthe proposed authentication scheme was able to protect the OLSR protocol from anode isolation attack.

关键词： 5G AODV D2D IoMT OLSR security issues

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YOLOv8-Based Model for Precise Corrosion Segmentation in Industrial Imagery

YOLOv8-Based Model for Precise Corrosion Segmentation in Ind...

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2024 International Conference on Computational Intelligence for Green and Sustainable technologies, ICCIGST 2024

作者： Chandu, Seethala Devi Varma, Sai Ranjani, M. SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies Chennai India SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing and Technologies Chennai India

ISBN: (纸本)9798350381887

A significant risk to the longevity and intelligence of fundamental infrastructure, such bridges and pipelines, is corrosion. Easy finding and erosion monitoring are important for predicting additional setbacks and ensuring transparency in security. This investigation offers a creative method of erosion discovery by utilizing the YOLOv8 question discovery computation. It also works in tandem with the WANDB stage to facilitate efficient show planning and organization. Said to offer a new method of managing videos by combining the powerful visualization powers of the W&B stage with YOLOv8, an enhanced version of the YOLO protest demonstration. YOLOv8 refers to a state-of-the-art development within the YOLO family, intended to surpass expectations in real-time protest finding tasks with increased productivity and accuracy. Additionally, considered looking into the use of cloud-based virtual machines as an affordable alternative to traditional setups like purplish blue for show organization and inspection, with a focus on the W&B Cloud advertising. One of YOLOv8's unique features is its single-pass localization method, which handles the entire video outline in a single forward pass over the neural network. This increases the pace of discovery and eliminates the need for intricate post-processing. Improved question localization and classification accuracy are encouraged by secured boundary boxes and network cell designs. The real-time position optimization of YOLOv8's engineering makes it particularly suitable for video feeds. Furthermore, by employing the Weights & Predispositions stage, analysts and experts can visualize and monitor the performance of the YOLOv8 demonstration during video preparation. W&B provides easily navigable dashboards and visualizations that enable real-time tracking of important metrics, demonstrate behavior, and pinpoint the site of protests. This integration provides a deeper comprehension of the model's features and potential areas of improv

关键词： Demonstrations

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Cyberbullying Advisory Model 15

Cyberbullying Advisory Model

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15th International Conference on Advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Dhulipala, Sai Kowshik Jaswanth, Vungarala Ragunthar, T. Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331300579

In the current digital environment, cyberbullying remains a significant threat to online safety and well-being. This study explores the technological advancements and strategies utilized in the project, providing compelling evidence of its effectiveness in reshaping the landscape of cyberbullying recovery. To tackle this urgent issue, we introduce the "Cyberbullying Advisory," an innovative AI-powered solution. Unlike traditional technologies, our platform is specifically designed to offer a dedicated space for cyberbullying victims across different online platforms, allowing them to share their stories and begin a journey towards emotional healing. Notably, it goes a step further by providing legal guidance to assist users in navigating the complexities of online harassment. Incorporating technologies such as Natural Language Processing (NLP), Natural Language Understanding (NLU), and Deep Learning, our models utilize task-specific datasets to implement key features like Toxicity Level Detection, Empathetic Response, and Intent Recognition. By leveraging the BERT Model for improved user interaction, these technologies enable real-time empathetic responses and personalized legal advice, revolutionizing the user experience in combating cyberbullying. The integration of AI-driven analysis and supervised learning techniques ensures accurate identification and categorization of toxic content, allowing users to receive tailored support. The uniqueness of our project lies in the seamless integration of these components, emphasizing precision through the use of task-specific datasets. By providing victims with empathetic and context-aware responses, our platform serves as a steadfast ally, offering assistance to those impacted by cyberbullying across diverse digital platforms. © Grenze Scientific Society, 2024.

关键词： Phishing

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Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

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Computer Systems Science & engineering 2023年第8期46卷 2141-2157页

作者： J.Prabakaran P.Selvaraj Department of Networking communications Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Department of Computing Technologies Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia

In healthcare systems,the Internet of Things(IoT)innovation and development approached new ways to evaluate patient data.A cloud-based platform tends to process data generated by IoT medical devices instead of high storage,and computational *** this paper,an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography(CT)images of patients with pneumonia,Covid-19,tuberculosis(TB),and ***,the CT images are captured and transmitted to the fog node through IoT *** the fog node,the image gets modified into a convenient and efficient format for further *** encryption Standard(AES)algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating ***,the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning *** different pre-trained deep learning architectures(Inception-ResNet-v2,VGG-19,ResNet-50)used transfer learning is adopted for feature *** softmax of heterogeneous base classifiers assists to make individual *** a meta-classifier,the ensemble approach is employed to obtain final optimal *** predicted image is consigned to the recurrent neural network with long short-term memory(RNN-LSTM)for severity analysis,and the patient is directed to seek therapy based on the *** proposed method achieved 98.6%accuracy,0.978 precision,0.982 recalls,and 0.974 F1-score on five class *** experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.

关键词： Intelligent health care cloud computing fog computing ensemble learning RNN-LSTM

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Plant Disease Detection and Classification using Convolutional Neural Networks (CNN) 15

Plant Disease Detection and Classification using Convolution...

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15th International Conference on Advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Sreenivasan, Mahalakshmi Selvaraj, P. Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331300579

Farmers and producers may suffer significant financial losses due to plant diseases. Effective control of plant diseases requires early identification and categorization of the illnesses. However, traditional plant disease detection techniques are frequently labor- and time-intensive. Plant disease detection has found a possible collaborator in deep learning and machine learning. Consequently, a novel deep-learning technique is proposed for classifying and diagnosing plant illnesses. It uses a transfer learning methodology built on a convolutional neural network architecture consisting of 5 convolutional, 2 fully- connected, and 3 max pooling layers. The algorithm has a 98% accuracy rate when tested on a publicly accessible dataset of images of plant diseases. The CNN model was trained with the PlantVillage dataset, which is available on Kaggle. From the results and a comparison with previous works, it is evident that the chosen approach performed well in classifying plant diseases with expected accuracy. © Grenze Scientific Society, 2024.

关键词： Plant diseases

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