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5th International Ethical Hacking Conference, eHaCON 2024

作者： Chakraborty, Mohuya Palit, Sudip Kumar Cybersecurity Centre of Excellence Institute of Engineering and Management University of Engineering and Management Kolkata India Department of Computer Science and Engineering Institute of Engineering and Management University of Engineering and Management Kolkata India

ISBN: (纸本)9789819784561

This paper focuses on the security aspects of the Three-level Mobility Model (TLMM) in the next-generation networks. Prior research has indicated that hierarchical architectures outperform other network topologies in terms of handoff delay, location update, and signaling overhead in mobile networks. The optimality test of the time-bandwidth product proved the supremacy of the three-tiered architecture of mobile networks in literature. However, earlier works of TLMM have not addressed the security issues. Designing a security protocol for TLMM is therefore the goal of this research. Our solution to the current research gap is a lightweight blockchain-based security protocol that can be used for local, regional, and global mobility. The work’s uniqueness resides in its proposal of a protocol to establish a peer-to-peer network where users can communicate with the data stored in a secure distributed ledger, reducing the amount of duplicate information that users must transfer. The implementation of blockchain technology is done using Hyperledger Composer and Hyperledger Fabric. Burrows-Abadi–Needham (BAN) logic has been used in the formal security verification of the suggested system to provide mutual authentication among the participants. Furthermore, the suggested model performs noticeably better than the other current systems by providing thirteen distinct security characteristics, requiring less processing time (about 8 ms) and communication overhead (around 2832 bits). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mobile telecommunication systems

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Human Face Mask Detection using RNN and Fast RNN 15

Human Face Mask Detection using RNN and Fast RNN

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Neware, Shubhangi Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9798331300579

Facemasks are an essential tool for reducing the spread of respiratory viruses, including the COVID-19 *** the wake of the COVID-19 pandemic, the use of facemasks has become an important measure to prevent the spread of respiratory *** monitor compliance with facemask-wearing regulations in public spaces, computer vision techniques can be used to detect whether individuals in images or videos are wearing *** this paper, we proposed facemask detection using R-CNN and Fast R-CNN *** evaluate the performance of each technique using publicly available datasets and compare their accuracy and *** results show that object detection using RNN and Fast RNN performs well as compared to the other techniques considering accuracy and ***, our study demonstrates the potential of computer vision techniques for facemask detection and required further research in this area to increase accuracy and robustness. © Grenze Scientific Society, 2024.

关键词： COVID-19

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Enhancing Fish Species Detection in Murky Waters with Yolov5 15

Enhancing Fish Species Detection in Murky Waters with Yolov5

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kalyani, Kanak Damdoo, Rina Agrawal, Sujal Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9798331300579

Aquatic life plays an essential role in the food chain and it is very important to monitor life underwater especially in the current times with increasing global warming. Several existing approaches are designed and implemented to aid marine biologists and scientists in keeping track of marine life, specifically fishes, but these approaches either give results with poor accuracy or fail to detect fishes in the dark or murky underwater environment. In this research, we present the YOLO model for fish detection along with fish identification under various environmental scenarios such as rocky shores, coral reefs, and murky or unclear water conditions to aid Marine biologists and conservationists in easily identifying the fishes in any given region. © Grenze Scientific Society, 2024.

关键词： Fish

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Clinical deep model to analyse medical multivariate time-series data for health diagnosis

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Cyber-Physical Systems 2025年第2期11卷 139-164页

作者： Almahadeen, Layth Vijay, Richa Shabaz, Mohammad Soni, Mukesh Singh, Pavitar Parkash Patel, Pavan Byeon, Haewon Department of Financial and Administrative Sciences Al- Balqa’Applied University Al-Salt Jordan Department of computer science Amity University UP Noida India Department of Computer Science Engineering Model Institute of Engineering and Technology Jammu Jammu and Kashmir India Department of Computer Science Engineering University Centre for Research & Development Chandigarh University Mohali India Department of Management Lovely Professional University Phagwara India Department of Computer Science Engineering Ahmedabad Institute of Technology Ahmedabad India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy;and provide a basis for prediction conclusions. In this context, a clinical deep model proposed an interpretable assessment method of patient health status based on contextual learning of medical features, encoding the time-series features of each variable separately, and using a multi-head de-coordination self-attention mechanism for learning Relationships between different features;feature skip-connection encoding based on a compressed excitation mechanism is proposed to improve the sensitivity of the model. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Decision making

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Relationship Extraction from Electronic Health Records & Generating Knowledge Graph 4

Relationship Extraction from Electronic Health Records & Gen...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Tirpude, Shubhangi C. Shahu, Vedant R. Neema, Meghal G. Chatterjee, Rishika K. Wanve, Piyush H. Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350349856

The research delves into the process of extracting relationships from biomedical literature and building a knowledge graph using those relationships. Information retrieval, knowledge discovery, and data analysis are just a few of the uses for named entity extraction in biomedical research, which includes important entities such as Adverse Drug Effects (ADE), doses, drug names, and more. Using BERT's word contextual comprehension, the report investigates the use of the BioBERT model for named entity recognition and relationship extraction. When calibrated on Electronic Health Records (EHR) data, BioBERT - a biological text-specific model - helps with clinical decision-making and research by recognizing links between elements such as diseases, symptoms, and therapies. The use of Neo4j for knowledge graph generation, which permits a thorough representation of biomedical concepts and their relationships, is also highlighted in the paper. Information retrieval and analysis are made easier by this organized knowledge. This strategy has the potential to revolutionize biomedical research and healthcare by providing fresh perspectives and better results, despite ongoing challenges. More developments in this field are anticipated as a result of ongoing research and development. © 2024 IEEE.

关键词： Knowledge graph

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Enhancing Accessibility for Visually Impaired using an RFID and Face Recognition System with Audio Feedback 15

Enhancing Accessibility for Visually Impaired using an RFID ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Borade, Prachi Vinod Neware, Shubhangi Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9798331300579

This document aspires to pioneer an innovative system designed to support individuals facing blindness or visual impairment by integrating Radio Frequency, Identification (RFID) technology with objects and face recognition via image processing. The key objective of the proposed system is enhancing independence and maintaining security of blind person as they navigate their environment. Household utensils and various items within a residence or building are equipped with passive RFID tags. Each of these RFID tags is associated with a different audio file, individually recorded for every object. These audio files are stored on a centralized server. Furthermore, the system maintains a dataset encompassing unidentified faces, assist blind individuals to recognize unknown person through audio feedback. The data is sent to the server by the RFID reader, which reads the tags. This server conducts the database search for the particular ID and plays the audio file. Additionally, the system captures and stores the facial data of unknown persons in the database. When the person reappears in front of blind person the system employs the facial recognition by cross-referencing the information within the database to facilitate face identification. © Grenze Scientific Society, 2024.

关键词： Image enhancement

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Hybrid Quantum-Classical Neural Networks and its Hyperparameter Optimization-A Study

Hybrid Quantum-Classical Neural Networks and its Hyperparame...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Adithya, R. Dsa, Joyline G Adarsh Rag, S. Bhyratae, Suhas A Sahyadri College of Engineering and Management Adyar Department of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331517892

In recent years, the field of machine learning has witnessed a paradigm shift with the emergence of hybrid quantum-classical neural networks. These networks combine the power of classical neural networks with the computational advantages offered by quantum variational circuits. This study presents a comprehensive survey of hybrid quantum-classical machine learning algorithms, focusing on their architecture, performance, and hyperparameter optimization techniques. Introducing the concept of hybrid quantum-classical machine learning and its significance in contemporary image classification tasks. It explores the theoretical foundations of quantum variational circuits and their integration with classical neural network architectures. Special attention is given to prominent algorithms such as Quantum Convolutional Neural Networks, Quanvolutional Neural Networks, and Quantum ResNet, each offering unique advantages in image classification tasks. A major aspect covered in this study is the hyperparameter optimization techniques employed in hybrid quantum-classical machine learning. The chapter discusses quantum-inspired optimization methods tailored for efficiently tuning the hyperparameters of complex neural network models. Through comparative analysis, the effectiveness of these optimization techniques is demonstrated in reducing computational overhead and improving model performance. Moreover, the study also delves into experimental results obtained from benchmarking hybrid quantum-classical models against traditional classical approaches. It highlights the accuracy variations observed across different quantum architectures and explores the impact of architectural permutations on model convergence. Insights gained from these experiments provide valuable guidance for researchers and practitioners seeking to leverage hybrid quantum-classical neural networks in real-world applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Sign Language to Text Conversion Using Deep Learning Techniques 2

Sign Language to Text Conversion Using Deep Learning Techniq...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Sonsare, Pravin Gupta, Diksha Sayyed, Juhie Nandha, Piyush Laira, Swyam Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350387933

The development of a sign language text translation system using deep learning and YOLO NAS (Neural Architecture Search) is an innovative application that bridges the communication gap. By integrating YOLO NAS for real-time sign language recognition, this model can accurately localize and identify sign language gestures. The deep learning component uses recurrent neural networks (RNNs) or transformer models to convert recognized characters into text. This innovation provides a comprehensive solution for the deaf and hard of hearing, allowing them to seamlessly communicate with the hearing world. Although this approach leverages the power of modern deep learning, existing methods have limitations. YOLO NAS is used to create a more efficient and accurate sign language translation system. Sign-to-Text powered by Mediapipeline is an innovative, research-based technology that makes communication easier for people who are hearing impaired. Combines advanced computer vision and natural language processing to convert sign language gestures into written or spoken words in real time. This research focuses on improving recognition accuracy and expanding vocabulary coverage, as well as improving accessibility and inclusiveness across different communication channels. We tested both models on American, Indian, and Japanese sign language datasets. Our model showed excellent accuracy of 97.8%, 96.67%, and 91.3% in American Sign Language, Indian Sign Language, and Japanese Sign Language, respectively. © 2024 IEEE.

关键词： Audition

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Wavelet-Infused Convolution-Transformer for Efficient Segmentation in Medical Images

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3326-3337页

作者： Dutta, Pallabi Mitra, Sushmita Roy, Swalpa K. Indian Statistical Institute Machine Intelligence Unit Kolkata700108 India Alipurduar Government Engineering and Management College Department of Computer Science and Engineering Alipurduar736206 India

Recent medical image segmentation methods extract the characteristics of anatomical structures only from the spatial domain, ignoring the distinctive patterns present in the spectral representation. This study aims to develop a novel segmentation architecture that leverages both spatial and spectral characteristics for better segmentation outcomes. This research introduces the wavelet-infused convolutional Transformer (WaveCoformer), a computationally effective framework to fuse information from both spatial and spectral domains of medical images. Fine-grained textural features are captured from the wavelet components by the convolution module. A transformer block identifies the relevant activation maps within the volumes, followed by self-attention to effectively learn long-range dependencies to capture the global context of the target regions. A cross-attention mechanism effectively combines the distinctive features acquired by both modules to produce a comprehensive and robust representation of the input data. WaveCoformer outperforms related state-of-the-art networks in publicly available Synapse and Adrenal tumor segmentation datasets, with a mean Dice score of 83.86% and 79%, respectively. The model is feasible for deployment in resource-constrained environments with rapid medical image analysis due to its computationally efficient nature and improved segmentation performance. © 2013 IEEE.

关键词： Convolutional neural networks

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Cost Optimization of Cloud Services through Automated Analytics and Resource Allocation 15

Cost Optimization of Cloud Services through Automated Analyt...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Shende, Mukesh Sevak Chandak, Manoj B. Department of Computer Science & Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9798331300579

Cloud computing offers scalable and cost-effective access to computing resources, necessitating meticulous cost optimization. This project introduces an automated system for cloud cost optimization, amalgamating pricing analysis, service metrics, and resource utilization assessment. It encompasses an administrative portal for provider analysis and resource monitoring, alongside analytics modules tracking availability, latency, scalability, and pricing competitiveness. Leveraging REST APIs, machine learning, and tools like Terraform and Ansible, the system proposes cost-effective provider and instance allocations based on real-time and forecasted needs. Administrators can define allocation policies and scaling thresholds for automated adjustments. The system culminates in an intelligent software agent adept at efficiently administering cloud resources, minimizing costs while meeting application demands. It exemplifies the application of analytics, automation, and AI to continually optimize dynamic cloud infrastructure, surpassing manual or reactive approaches. © Grenze Scientific Society, 2024.

关键词： Cloud analytics

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