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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Velusamy, Dhanalakshmi Harish, U. Lingesh, P. Kaviyarasan, P. Cyril Poornaraj, J. Prem, D. Sharan, K. Jeppiaar Institute of Technology Department of Computer Science and Engineering Chennai India Jeppiaar Institute of Technology Bachelor of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368109

Around the world, kidney diseases are a major open prosperity concern. Environmental components, tall salt levels, and additional minutes are all risk factors. Kidney dissatisfaction can be a critical prosperity issue, and it has gotten to be more transcendent in afterward a long time, with horribleness and mortality extending by 100% over the ultimate decade. Several kidney sicknesses, tallying those caused by renal stones and rankles, can be easily recognized through CT imaging. In separate, manual examination of a CT picture is time-consuming and requires a portion of effort. Deep learning methods, checking 3D CNNs, can be utilized to recognize plans of hurt and make assurance easier. By planning these calculations on clarified datasets, they can distinguish kidney stones and rankles with tall accuracy. Despite its potential to assist inside the early recognizing verification and treatment of ailments, modified revelation has to be utilized with caution and not as an elective to restorative information. © 2024 IEEE.

关键词： Deep learning

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On the simulation of hypervisor instructions for accurate timing simulation of virtualized systems

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International Journal of Information technology (Singapore) 2024年 1-10页

作者： Mhatre, Swapneel Chandran, Priya Department of Computer Science and Engineering National Institute of Technology Calicut Kerala India

Architectural simulators help in better understanding the behaviour of existing architectures and the design of new architectures. Virtualization has regained importance and this has put a pressing demand for the simulation of virtualized systems. Existing full-system simulators for virtualized systems simulate the application program instructions and the operating system instructions but abstract the hypercalls or traps to the hypervisor. This leads to inaccuracy in the simulation. This paper proposes an approach to simulate hypervisor instructions in addition to operating system instructions for accurate timing simulation of virtualized systems. The proposed approach is demonstrated by simulating RISC-V binary instructions. The simulator is an execution-driven, functional-first, software-based simulator coded in Python. Three programs—searching (linear search), sorting (bubble sort) and ***_ir workload from SPEC CPU2017 benchmarks are simulated using the developed simulator. It is seen that the number of instructions executed when the simulator is configured for a virtualized system are higher than the number of instructions executed when the simulator is configured for a non-virtualized system. This is due to the hypervisor instructions that get executed in a virtualized system. The paper concludes that the proposed approach leads to accurate timing simulation of virtualized systems. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Architecture Hypercall Hypervisor Operating system Simulation Virtualization

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Bio-Inspired Optimization Algorithm in Machine Learning and Practical Applications

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SN computer Science 2024年第8期5卷 1-18页

作者： Juneja, Shallu Taneja, Harsh Patel, Ashish Jadhav, Yogesh Saroj, Anita Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi India Department of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Engineering SVM Institute of Technology Bharuch India ATLAS SkillTech University UGDX School of Technology Mumbai India

Software fault prediction is critical to ensuring system reliability and user satisfaction while minimizing financial losses. This research paper presents a novel and comprehensive technique for predicting software faults by integrating a Genetic Algorithm (GA) with an ensemble KNN and XG Boost algorithms classifier. The proposed approach is evaluated on 24 datasets, including 12 open-source Java projects and 12 NASA Metrics Data Program datasets, with the NASA Metrics Data Program datasets serving as macroscale datasets and open-source Java projects datasets serving as microscale datasets. By optimizing the selection and combination of fault-related metrics, the integrated GA on the ensemble classifier demonstrates enhanced accuracy in fault prediction compared to existing methods. The experimental results highlight the effectiveness of the proposed approach, establishing its superiority in predictive accuracy. This study significantly contributes to software engineering, offering an efficient technique for identifying and mitigating potential software faults, thereby elevating software reliability and quality assurance processes. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Genetic Algorithm JAVA open-source projects Machine learning NASA MDP PROMISE Stacking ensemble model of k-nearest neighbors (KNN) SVM XG boost

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Fuzzy-GEC an Energy-Aware Hybrid Task Scheduling on the Cloud 2nd

Fuzzy-GEC an Energy-Aware Hybrid Task Scheduling on the Clou...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Lalitha Devi, K. Deepa Thilak, K. Shanmuganathan, C. Kalaiselvi, K. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9789819995172

In an infrastructure cloud environment, task scheduling should focus on optimizing execution time and saving energy. The data center consumes a large amount of energy during the execution of the task. Energy-saving techniques reduce the amount of energy consumed based on proper task scheduling approaches. The existing fuzzy-based hybrid genetic algorithm considers job length to optimize the makespan. However this fuzzy genetic encoded chromosome (fuzzy-GEC) algorithm considers both makespan and energy consumption to optimize the calculation of fitness value for assigning the task to the virtual machine (VM) by considering the characteristics of the task and VM. According to these characteristics, a fuzzy genetic rule-based encoding scheme is developed to schedule the tasks onto the virtual machines to minimize makespan and energy consumption. The effectiveness of the new technique is evaluated against FCFS and conventional genetic scheduling algorithm using Google Cloud trace workload. The results show the efficiency of the developed approach for makespan and energy consumption. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Virtual machine

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Quantum-Inspired Differential Evolution Based Efficient IoT Service Placement in Edge Networks 15

Quantum-Inspired Differential Evolution Based Efficient IoT ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bey, Marlom Kuila, Pratyay Naik, Banavath Balaji Department of Computer Science and Engineering National Institute of Technology Sikkim 737139 India Department of Computer Science and Engineering National Institute of Technology Patna800005 India

ISBN: (纸本)9798350370249

The rise in Internet of Things (IoT) devices has led to the creation of sophisticated applications that demand various resources in real time to support a wide range of IoT services. Leveraging edge computing (EC) infrastructure, these services can be effectively placed on edge nodes (ENs). However, due to the limited computational resources of ENs, it becomes challenging to manage a large number of services while maintaining the system's quality of service (QoS) and quality of experience (QoE). This paper introduces a quantum-inspired differential evolution method (QIDE-IoTSP) designed to optimize the placement of IoT services within EC networks. The primary objectives of QIDE-IoTSP are to maximize throughput, ensure optimal load balance, and minimize computation time. A quantum vector (QV) is utilized to develop a robust solution for the optimal deployment of IoT services in EC networks to achieve this. The effectiveness of each solution is evaluated using a formulated fitness function. Simulation results demonstrate that QIDE-IoTSP surpasses other metaheuristic techniques in terms of throughput, computation latency, and load balancing. © 2024 IEEE.

关键词： Quantum computers

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Parkinson’s disease diagnosis using deep learning model by analyzing the channels of electroencephalography signals from substansia niagra and ventral tegmental area regions of human brain

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International Journal of Information technology (Singapore) 2025年第4期17卷 2129-2152页

作者： Kumar, Kaushal Ghosh, Rajib Department of Computer Science and Engineering National Institute of Technology Patna Patna 800005 India

Parkinson’s disease (PD) is a progressive neurological disorder primarily affecting motor functions and mobility. Early diagnosis is crucial for reducing disease progression and treatment costs. This paper proposes a bi-directional long short-term memory (BLSTM)-based system for PD diagnosis using electroencephalography (EEG) signals collected exclusively from the substantia nigra (SN) and ventral tegmental area (VTA), key brain regions associated with dopamine production. Mel-frequency cepstral coefficients (MFCC) features are extracted from the EEG data, and the BLSTM classifier is employed to differentiate PD subjects from healthy individuals. The performance of the proposed system is evaluated on three public datasets, namely, the University of Iowa (UI) dataset, Parkinson’s Disease Gait (PDG) dataset, and the UC San Diego Resting State (USDRS) dataset, demonstrating superior classification accuracy compared to existing methods. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Electroencephalography signal Mel-frequency cepstral coefficients features Parkinson’s disease Recurrent neural network Substansia niagra and ventral tegmental area

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Patient-centric information management in blockchain and interplanetary storage

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Journal of Ambient Intelligence and Humanized Computing 2025年第1期16卷 85-96页

作者： Dewangan, Narendra Chandrakar, Preeti Department of Computer Science and Engineering National Institute of Technology Raipur G.E. Road Chhattisgarh Raipur492010 India

In the healthcare sector, the protection of patient information is an essential factor in terms of secrecy and privacy. This information is very useful for industrial and research purposes. Electronic medical records (EMR) hold data related to patients and their treatments. According to GDPR (general data protection regulation), patient information should be anonymous. There should not be any disclosure of patients private information during data transfer. For hiding patients private information, encryption is required. For developing countries, the treatment cost is also a matter for health services. Blockchain technology provides facilities like distributed and immutable data storage and peer-to-peer data transfer. We need cheaper blockchain-based solutions for the healthcare sector. IPFS provides a secure way to store the content of the files in a decentralized manner by using the Merkle tree. By using IPFS with blockchain, we can counter the use of fake medical records or the forge of EMR. In this paper, the interplanetary file system (IPFS) is used to store EMR in compliance with GDPR. We work on the treatment cycle of patients with a blockchain-maintained hospital and doctor. Our proposed system enhances the security and transparency of patients’ treatment information. We compared our proposed system with previously developed systems and provided analytical results in terms of security, privacy, and cost. We implemented our proposed system on a Python-customized blockchain with a customized consensus algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Electronic health record

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Secure Access Control of Cloud Data Using DNA Computing 5

Secure Access Control of Cloud Data Using DNA Computing

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

作者： Kumar, Tarun Kumar, Prabhat Namasudra, Suyel National Institute of Technology Department of Computer Science and Engineering Bihar Patna India National Institute of Technology Department of Computer Science and Engineering Tripura Agartala India

ISBN: (纸本)9798350383522

With the exponential development in the usage of cloud data storage across a variety of applications, developing effective access control measures to secure sensitive user data has become critical. This research presents a unique way to use deoxyribonucleic acid (DNA) computing to improve multi-factor authentication enforcement and real-time access controls in cloud storage systems. A synthetic, engineered DNA framework is intended to reliably tie user identification characteristics to randomly produced nucleic acid credentials. After multimodal encrypted biometric authentication, the system provides these DNA access tokens. They may be validated via fast polymerase cycling to enable selective decryption and, eventually, manage allowed actions on stored cloud data based on fine-grained, attributes-based access policies. The method makes revocation happen by using programmable nucleases to quickly remove access rights from DNA structures that are waiting to be used. Rigorous cryptanalysis outperforms current access paradigms in terms of robustness to both brute force and side channel assaults. With significant developments in DNA synthesis and sequencing technologies allowing for scale deployment, bio-cryptographic access control provides a strong method for improving cloud data security via DNA computing. The system greatly increases the obstacles to unwanted data access while protecting user privacy. This study is a pioneering analysis of organizing principles in biologic data processing systems to solve key data security concerns posed by growing cloud use across commercial and public sector enterprises. © 2024 IEEE.

关键词： Access Control Cloud Data Security DNA Computing DNA Cryptography Multi-factor Authentication

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BAHGRF^(3):Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation

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CAAI Transactions on Intelligence technology 2025年第2期10卷 387-401页

作者： Muhammad Abrar Ahmad Khan Muhammad Attique Khan Ateeq Ur Rehman Ahmed Ibrahim Alzahrani Nasser Alalwan Deepak Gupta Saima Ahmed Rahin Yudong Zhang Foundation University Islamabad IslamabadPakistan Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl KhobarSaudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadhSaudi Arabia Department of Computer Science Maharaja Agrasen Institute of TechnologyDelhiIndia United International University DhakaBangladesh School of Informatics University of LeicesterLeicesterUK

Biometric characteristics are playing a vital role in security for the last few *** gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is *** the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and *** models are selected based on the top-5 accuracy and less number of ***,both models are trained through deep transfer learning and extracted deep features fused using a voting *** the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best *** selected features are classified using several supervised learning *** CASIA-B publicly available dataset has been employed for the experimental *** this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.

关键词： deep learning feature fusion feature optimization gait classification indoor environment machine learning

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An Optimal Disassembly Sequence Planning for Complex Products using Enhanced Deep Reinforcement Learning Framework

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SN computer Science 2024年第5期5卷 1-24页

作者： Chand, Mirothali Ravi, Chandrasekar Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

Disassembly Sequence Planning (DSP) is a crucial problem in the field of repair and maintenance. There is a pressing need for an efficient technique to solve the Complete Disassembly Sequence Planning (CDSP) problem for large, highly complex products without compromising time and computational resources. Since exact methods fail to handle complex products, and meta-heuristic approaches often do not produce optimal results, its solution requires a deep reinforcement learning approach. This work proposes a novel Enhanced Deep Reinforcement Learning (EDRL) approach in which the Actor-Critic Networks with an attention mechanism is employed in both networks to assign weightage to important actions, generating optimal sequences based on these actions. To further improve the model and accurately predict the loss value, a hybrid loss function is developed by combining the Categorical Cross-entropy and Log-Cosh loss functions. This work considers DSP attributes like stability, liaison, geometric feasibility, and precedence for experiments on various products. The proposed EDRL-CDSP method outperforms existing techniques in terms of optimality while requiring less time to generate the solution. The optimized disassembly sequence, when followed, consumes less time for the total disassembly of all the parts of the product. These findings suggest that the proposed EDRL-CDSP approach can effectively address the challenges in DSP and provide practical benefits for industrial applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Disassembly Hybrid loss function Optimal sequence planning Optimization

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