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Enhancing Healthcare Data Security in Cloud Computing Systems through Advanced Homomorphic Encryption Techniques Integrated with Cuckoo Search Optimization Algorithm

Enhancing Healthcare Data Security in Cloud Computing System...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Sudha, I. Ramesh, P.S. Ponmalar, A. Jagannathan, J. Pandiarajan, Rajkumar Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering 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 Tamilnadu Chennai India R.M.K. Engineering College Tamilnadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Kalasalingam Academy of Research and Education School of Computing Department of Information and Technology Krishnankoil Virudhunagar India

ISBN: (纸本)9798350388602

The studies in this research are focused on the mix of homomorphic encryption mechanisms and the Cuckoo Search Optimization Algorithm to promote the security of the healthcare data in the cloud computing system. I have observed that cloud solutions are now at the center of the contemporary health care systems, and security of patient's data remains a top priority. Earlier used encryption techniques, though secure if implemented, have problems in the way that data is to be processed first it has to be decrypted and then made ready for use - it is in this phase that data can be easily hacked into. Homomorphic encryption provides solution to this issue since it enables one to perform computations on encrypted data while maintaining its confidentiality. However, it takes a lot of computation which presents a lot of problems even in terms of simple performance. The Cuckoo Search Algorithm, a new nature-inspired metaheuristic, is incorporated into the encryption process to provide further optimization, whilst preserving the security benefits of the algorithm. The use of the proposed methodology takes place employing Python and its deployment at the cloud platform with elaborated dataset containing anonymized records extracted from electronic health records. The outcome proves that there are great enhancements in encryption and decryption time, computational complexity, and capacity, and thereby making the system appropriate for real-world healthcare solutions. This research offers a strong base for protection of healthcare data in cloud and is useful for the enhancement of adaptive, comprehensive and secure cloud computing systems. © 2024 IEEE.

关键词： Electronic health record

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Optimized Task Scheduling in Diverse Distributed Systems with a Hybrid Genetic Algorithm Approach 3rd

Optimized Task Scheduling in Diverse Distributed Systems wit...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Muthusamy, Ramesh Kumar Kadu, Swati Ayyasamy, Karthikeyan Pichuka Veera Venkata Satya, Srinivas Arumugam, Ramachandran Ammasai, Viswanathan Sengan, Sudhakar Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of AI&DS AISSMS IOIT Pune411001 India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai602105 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627012 India

ISBN: (纸本)9789819783540

In Heterogeneous Distributed Systems (HDS), efficient task scheduling (TS) across diverse resources remains a pressing challenge. This paper introduces a Hybrid Genetic Algorithm (HGA) as a solution, blending heuristic techniques with the evolutionary strengths of Genetic Algorithms (GAs) to optimize Task Scheduling (TS). Traditional algorithms often fall short in balancing computational speed with solution quality, especially in large-scale environments. This proposed HGA addresses this by leveraging domain-specific heuristics to provide a promising starting point for the GA's optimization process. The TS dilemma has been successfully defined by an all-encompassing logical system, which ensures the accuracy of the design of algorithms. In comparison tests, the HGA frequently comes out on top, demonstrating less makespan and a more excellent distribution of tasks among the resources available. Also, resources with more efficient processing capabilities will be provided priority in this recommended heuristic method, which focuses on tasks with higher computational demands. The findings of the present investigation demonstrate that the HGA may transform TS approaches by providing high-quality solutions while increasing performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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Advanced Multi-Spectral Image Processing Techniques for Enhanced Remote Sensing and Comprehensive Environmental Monitoring in Diverse Ecosystems

Advanced Multi-Spectral Image Processing Techniques for Enha...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Jagannathan, J. Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering 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 Tamilnadu Chennai India

ISBN: (纸本)9798350388602

The research is aimed at improvement of multi-spectral image analysis for remote sensing and monitoring the environment at various ecosystems. A combined spectral imagery encompasses several spectrums at a time compared to single spectral imagery that takes only one spectrum at an instance or at a given area to cover all these spectrums. The study employs digital high resolution satellite images and ground based data to quantify the vegetation vigour, water quality, soil characteristics and changes in land cover. Features, indexes, and classification routines are employed to process the data that is collected with the aid of avionics and other peripherals. This provincial level modelling highlights important gains in environmental status, which includes the general improvement in vegetation condition, water quality as well as physical properties of the soil in the province. This study focuses on the complementary possibilities of integration of multi-spectral imaging with other remote sensing technologies and the high performance of data processing. These techniques play significant roles if used to give current and comprehensive information in the management and conservation of the environment as highlighted in the study. © 2024 IEEE.

关键词： data analysis environmental monitoring image processing techniques machine learning Multi-spectral imaging remote sensing soil properties vegetation health water quality

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Integrating Deep Q-Networks and YOLO with Autonomous Robots for Efficient Warehouse Management 5

Integrating Deep Q-Networks and YOLO with Autonomous Robots ...

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5th IEEE International Conference on Recent Trends in computer Science and technology, ICRTCST 2024

作者： Vivek, N. Suriya, A. Kamala, V. Chinnasamy, N.V. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak SRM Institute of Science and Technology Department of Computational Intelligence Chennai India KGiSL Institute of Technology Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India SRM Institute of Science and Technology Department of Networking and Communications Chennai India KGiSL Institute of Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350351378

Autonomous robotic cars' rapid, accurate, and efficient product handling and navigation are changing warehouse inventory management. These 24/7 smart vehicles save labor costs, human error, and operational inefficiencies. Time-consuming and incorrect manual scanning and recording is used for inventory management. Traditional autonomous car Q-learning algorithms may suffer in dynamic inventory circumstances and lack the complexity needed for real-time decision-making. Our research creates novel algorithms for autonomous robotic cars. The Deep Q-Network (DQN) we utilize for navigation adapts better to changing circumstances than ordinary Q-learning. YOLO object detection replaces slower QR code recognition for inventory monitoring. We use Decision Trees to find QR code data insights for decision-making. Our research results are promising. A greater cumulative reward curve over 100 simulated episodes gives DQN a 40% learning efficiency advantage over Q-learning. With 95% accuracy, YOLO outperformed Haar Cascade classifiers in QR code detection by 40% speed and 15% accuracy. Decision Trees identified QR code data 5.9% better than Rule-Based Systems, indicating inventory dependability. Autonomous robots can simplify inventory management in warehouses. Algorithmic technology allows these ambulances to drive intelligently, detect warehouse things faster and more accurately, and make decisions based on complicated data. These advanced artificial intelligence algorithms may make robotic systems more responsive and customized, changing inventory management subfields. These algorithms must be updated so autonomous cars can drive to meet logistical needs. © 2024 IEEE.

关键词： Object detection

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Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

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High-Confidence Computing 2024年第2期4卷 50-61页

作者： Huan Wang Yunlong Tang Yan Wang Ning Wei Junyi Deng Zhiyan Bin Weilong Li School of Computer Science and Technology Guangxi University of Science and TechnologyLiuzhou 545000China Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Liuzhou 545000China Guangxi Education System Network Security Monitoring Center Liuzhou 545000China Center of Graphics and Information Guangxi ECO-Engineering Vocational&Technical CollegeLiuzhou 545004China

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense ***,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense *** designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

关键词： Active defense decision-making Cloud boundary network security Intelligent agent reinforcement learning Offensive and defensive game

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Mental Health Disorder Prediction Using Recurrent Neural Network Algorithm

Mental Health Disorder Prediction Using Recurrent Neural Net...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Mariappan, Umasree Balakrishnan, D. Merline, G. Sandhia, M. Reddy, Dubba Saitej Gagan Teja, Sattineni P.S.R. Engineering College Department of Information Technology Tamil Nadu Sivakasi626140 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil626126 India Sri Vidya College of Engineering & Technology Department of Electronics and Communication Engineering Tamil Nadu Virudhunagar626005 India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore641114 India

ISBN: (纸本)9798350361537

Mental health disorders pose a significant challenge to healthcare professionals due to their complex and multifactorial nature. Early identification and accurate prediction of mental health disorders play a crucial role in timely intervention and effective treatment planning. The objective of this study is to explore the potential of deep learning algorithms in predicting mental health disorders, specifically focusing on depression, anxiety, and schizophrenia. The study utilizes a dataset containing clinical and demographic information of patients diagnosed with these disorders. The dataset was pre-processed using feature scaling algorithm, and features dimensionality reduction is done using t-Distributed Stochastic Neighbor Embedding (t-SNE) to train and evaluate deep learning models. These models were trained on large-scale datasets, allowing for the identification of complex patterns and relationships within the data. The training on dataset is done by using the Recurrent Neural Networks. The performance of the models was evaluated using standard evaluation metrics, such as accuracy, precision, recall, and F1-score. The results indicate that deep learning algorithms have shown promising results in predicting mental health disorders. The models achieved high accuracy rates, with some models surpassing the performance of traditional machine learning algorithms. Performance evaluation of the model is conducted using relevant metrics such as precision, recall, and F1-score, on a separate validation or test dataset from which it is proved that the proposed methodology attains 6% performance improvement than the existing methodology. © 2024 IEEE.

关键词： Recurrent neural networks

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Enhancing Fintech Fraud Detection through Graph Convolutional Neural Networks 1

Enhancing Fintech Fraud Detection through Graph Convolutiona...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Pavan, V. Omprakash, Omprakash Bugge, Bhagya Prasad Thangamani, S. Perada, Anvesh Suneetha, Sampathirao Manipal Academy of Higher Education Manipal Law School Manipal India Jain University School of Law Bengaluru India S.R.K.R Engineering College Department of Electronics and Communication Engineering Bhimavaram India Nandha Engineering College Department of Information Technology Erode India Drexel University Department of Electrical and Computer Engineering PhiladelphiaPA United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798331529246

The swift advancement of fintech has transformed financial services, offering users seamless digital experiences. Nonetheless, this expansion has also presented intricate cybersecurity difficulties, especially in fraud detection. This research investigates novel methods for addressing fintech fraud through the utilization of GCNN and the ResNet101-C model. GCNN adeptly addresses dynamic fraud trends via graph-based feature aggregation, whereas ResNet101-C, augmented with dropout and fully connected layers, attains enhanced classification performance. Preprocessing procedures, such as imputation, noise reduction, and feature standardization, enhance data quality, whereas feature extraction emphasizes essential transaction variables like frequency and value. Assessment of synthetic datasets reveals that ResNet101-C achieves an accuracy of 93.46%, surpassing leading models. These results underscore the transformational capability of GCNN and ResNet101-C in tackling the dynamic nature of fraud, providing scalable, precise, and privacy-preserving solutions for the financial sector. © 2025 IEEE.

关键词： Noise abatement

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STP models of optimal differential and linear trail for S-box based ciphers

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Science China(information Sciences) 2021年第5期64卷 221-223页

作者： Yu LIU Huicong LIANG Muzhou LI Luning HUANG Kai HU Chenhe YANG Meiqin WANG Key Laboratory of Cryptologic Technology and Information Security Ministry of EducationShandong University School of Computer Engineering Weifang University

Dear editor,SAT solvers, based on heuristic algorithms, are used to solve Boolean satisfiability (SAT) problems. Satisfiability modulo theories (SMT) problem is a decision problem concerned with the satisfiability of ... 详细信息

关键词： STP Differential trail Linear trail GIFT-128 DES DESL ICEBERG ARIA SM4

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Chi-Square and Cuckoo Search Based Feature Selection for Heart Disease Prediction

Chi-Square and Cuckoo Search Based Feature Selection for Hea...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Elizabeth, Bolanle Adejumo Muhammad Usman, Ali Hussein, Ahmad MohdAziz Ossai Ebinum, Wallace Maigari, Timothy Umar Usman Abdullahi, Ali Ibrahim, Jalo Abubakar Joda Usman, Mohammed Philips, Jacob Dawaki, Muhammad Department of Computer Sciences Gombe Nigeria Umm Al-Qura University Deanship of E-Learning and Distance Education Makkah21955 Saudi Arabia Kaduna Polytechnic College of Engineering Department of Computer Engineering Kaduna Nigeria Bayero University Faculty of Computer Science and Information Technology Kano Nigeria Gombe State University Department of Computer Sciences Gombe Nigeria

ISBN: (纸本)9798350358155

In many countries across the globe, heart disease is the main root of death. It's only that it has a lot of features that have been validated by numerous sources and incorporate distinct diagnostic techniques. Thus, physicians find it difficult to correctly identify the features that contribute to heart disease for each patient. Besides, most of these features are redundant and irrelevant that create noise towards the description of the target class. Nowadays, machine learning approaches are being used to aid physicians to correctly identify heart disease from a patient. In this study, the chi-square statistic is applied to rank the attributes in the heart disease data set while the cuckoo search optimization algorithm selects the most useful features that contribute significantly to heart disease diagnosis. The selected features are trained using the k-nearest neighbour (KNN) along with support vector machine (SVM). The investigational outcomes displayed that the features are minimized to the lowest level, mainly on the high dimensional data sets. Therefore, contributed positively to the heart disease diagnosis system. SVM outpace KNN in terms of accuracy after the proposed feature selection. On the other hand, KNN performed slightly better than SVM without feature selection on the majority of data sets. The proposed method performed better than other existing approaches especially correlation-based FS with PSO together with fuzzy analytic hierarchy process-based feed-forward neural network, modified differential evolution, on both percentage accuracy and number of chosen features (feature size). © 2024 IEEE.

关键词： Support vector machines

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An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering

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Journal of Safety Science and Resilience 2025年第1期6卷 93-104页

作者： M.Sinthuja S.Pravinthraja B K Dhanalakshmi H L Gururaj Vinayakumar Ravi G Jyothish Lal Department of ISE M.S.Ramaiah Institute of Technology BengaluruVisvesvaraya Technological UniversityBelagaviKarnataka590018India Department of CSE Presidency UniversityBengaluruIndia Department of Computer Science and Engineering BMS institute of Technology&ManagementBengaluruVisvesvaraya Technological UniversityBelagaviKarnataka590018India Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipalIndia Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Center for Computational Engineering and Networking(CEN) Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

The numerous volumes of data generated every day necessitate the deployment of new technologies capable of dealing with massive amounts of data *** is the case with Association Rules,a tool for unsupervised data mining that extracts information in the form of IF-THEN *** various approaches for extracting frequent itemset(prior step before mining association rules)in extremely large databases have been presented,the high computational cost and shortage of memory remain key issues to be addressed while processing enormous *** objective of this research is to discover frequent itemset by using clustering for preprocessing and adopting the linear prefix tree algorithm for mining the maximal frequent *** performance of the proposed CL-LP-MAX-tree was evaluated by comparing it with the existing FP-max *** was performed with the three different standard datasets to record evidence to prove that the proposed CL-LP-MAX-tree algorithm outperform the existing FP-max algorithm in terms of runtime and memory consumption.

关键词： Clustering Data mining Frequent itemset mining Linear prefix tree Maximal frequent itemset mining

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