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Feature Extraction From Single-Channel EEG Using Tsfresh and Stacked Ensemble Approach for Sleep Stage Classification

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International Journal of e-Collaboration 2023年第3期19卷 1-20页

作者： Radhakrishnan, B.L. Ezra, Kirubakaran Jebadurai, Immanuel Johnraja Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering GRACE College of Engineering Thoothukudi India

The smart world under Industry 4.0 is witnessing a notable spurt in sleep disorders and sleep-related issues in patients. Artificial intelligence and IoT are taking a giant leap in connecting sleep patients remotely with healthcare providers. The contemporary single-channel-based monitoring devices play a tremendous role in predicting sleep quality and related issues. Handcrafted feature extraction is a time-consuming job in machine learning-based automatic sleep classification. The proposed single-channel work uses Tsfresh to extract features from both the EEG channels (Pz-oz and Fpz-Cz) of the SEDFEx database individually to realise a single-channel EEG. The adopted mRMR feature selection approach selected 55 features from the extracted 787 features. A stacking ensemble classifier achieved 95%, 94%, 91%, and 88% accuracy using stratified 5-fold validation in 2, 3, 4, and 5 class classification employing healthy subjects data. The outcome of the experiments indicates that Tsfresh is an excellent tool to extract standard features from EEG signals. © 2023 IGI Global. All rights reserved.

关键词： Extraction

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An efficient integrity based multi-user blockchain framework for heterogeneous supply chain management applications

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International Journal of computers and Applications 2023年第4期45卷 337-351页

作者： Karumanchi, Mani Deep Sheeba, J.I. Devaneyan, S. Pradeep Department of Computer Science and Engineering Bapatla Engineering College Bapatla India Department of Computer Science and Engineering Puducherry Technological University Puducherry India Department of Mechanical Engineering Sri Venkateshwaraa College of Engineering and Technology Puducherry India

Most of the traditional cloud-based applications are insecure and difficult to compute the data integrity with variable hash size on heterogeneous supply chain datasets. Also, cloud storage systems are independent of integrity computational and data security due to structured data and computational memory. As the size of the cloud data files is increasing in the public and private cloud servers, it is difficult to provide strong data security due to file format and high dimensionality. The computational time and storage space of the conventional attribute-based encryption and decryption models are high during the data integrity verification in the traditional blockchain frameworks. This paper implements a hybrid variable size data integrity algorithm on the heterogeneous cloud supply chain data files for the strong data encryption and decryption process. This work implements an optimized blockchain framework using the advanced heterogeneous integrity computation approach and integrity policy-based attribute encryption and decryption approach for better cloud data security. Experimental results proved that the proposed integrity-based encryption model has better efficiency than the traditional integrity-based encryption frameworks on cloud heterogeneous data types. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Blockchain

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A High-Speed Compression and Secure Authentication Framework for Data Sharing Cloud-Enabled Big Data Environments

A High-Speed Compression and Secure Authentication Framework...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Abirami, I. Selvi, S. Lalitha, R. Julian, Anitha Rmk Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509828

With rapidly expanding cloud-enabled big data environments, there is an imperative need for efficient data-sharing mechanisms that are multidimensional and balance both speed and security. In this connection, high-speed compression and secure authentication come forth playing a very crucial role. The two significant challenges addressed in this framework are data compression for efficient storage and transmission and robust authentication for secure access and integrity of shared data. This way, the framework compresses data size significantly without losing its quality, leading to efficient, faster data transfers and reduced considerably storage costs on cloud infrastructures. At the same time, secure authentication mechanisms [1] such as cryptography techniques and multi-factor authentication ensure that only authorized users access sensitive data, thus protected from potential unauthorized access or cyber-attacks. Integration of both capabilities speed compression and secure authentication-functionalizes big data environments to their peak performance and security. This framework is highly suitable for high-scale data-oriented industries like healthcare, finance, and IoT, which necessitate high-speed, secure, and reliable data sharing. Ultimately, this approach improves scalability and security in cloud-enabled big data systems by enabling organizations to keep pace with growing needs for highly efficient and secure data sharing in a highly dynamic environment. © 2025 IEEE.

关键词： Data integrity

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Real Time 3D Gesture and Traffic Sign Language Recognition Using Multi-Layer Perceptron with Mediapipe 2

Real Time 3D Gesture and Traffic Sign Language Recognition U...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Swapna, D. Shanmuga Sundari, M. Sreeja, K. Keerthi, Nsml Shriya, D Sai BVRIT HYDERABAD College of Engineering for Women Computer Science and Engineering Hyderabad India BVRIT HYDERABAD College of Engineering for Women Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331505745

In today's rapidly evolving technological landscape, natural gestures provide a seamless mode of interaction between humans and machines. This paper presents a 'Real-Time 3D Gesture and Traffic Sign Language Recognition Module' capable of interpreting gestures using advanced machine learning techniques. The system leverages MediaPipe for 3D hand landmark tracking and a Multi-Layer Perceptron (MLP) for classification. Key applications include traffic management, autonomous vehicles, robotics, and human-computer interaction. Experimental results demonstrate high accuracy, robustness, and flexibility in various real-world scenarios. © 2025 IEEE.

关键词： Gesture recognition

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A comprehensive systematic review of machine learning in the retail industry: classifications, limitations, opportunities, and challenges

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Neural Computing and Applications 2024年第4期37卷 2035-2070页

作者： Hassan, Dler O. Hassan, Bryar A. Department of Computer Science College of Science Charmo University Kurdistan Region Chamchamal Sulaimani46023 Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Erbil Iraq

Machine learning has profoundly transformed various industries, notably revolutionizing the retail sector through diverse applications that significantly enhance operational efficiency and performance. This comprehensive review examines the state-of-the-art machine learning applications in the retail sector from 2019 to 2024, focusing on supervised learning, unsupervised learning, and ensemble methods. It aims to identify and categorize recent machine learning applications in retail, evaluate the performance of machine learning algorithms, and determine the most suitable algorithms for specific retail use cases. This review article examines 56 studies and identifies 20 unique machine learning applications within the retail sector. This review also discusses the challenges and opportunities of implementing machine learning in retail, offering valuable insights to guide future research and enhance retail performance and customer satisfaction. The findings highlight the strengths and limitations of different machine learning methods, providing insights into their practical applications and future potential. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Ensemble methods Machine learning Retail Supervised learning Unsupervised learning

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Early-stage stroke prediction based on Parkinson and wrinkles using deep learning

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Neural Computing and Applications 2024年第30期36卷 18781-18805页

作者： Haritha, T. Babu, A. V. Santhosh Department of Information Technology Vivekanandha College of Engineering for Women Tamilnadu Tiruchengode India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tamilnadu Tiruchengode India

The application of noninvasive methods to enhance healthcare systems has been facilitated by the development of new technology. Among the four major cardiovascular diseases, stroke is one of the deadliest and potentially fatal, but, if detected early enough, a patient's life may be spared. Most stroke research has centered on MRI and CT scans for uncomplicated categorization. This medical approach (imaging) is costly, time-consuming and needs the utilization of complex technology. To make up for these shortcomings, however, there has been a lot of interest in adopting noninvasive, measurable EEGs. Nevertheless, the raw data should be classified before the proper characteristics can be formed, both the forecasting algorithms and the analytical techniques demand time. As a result, this work proposes a deep learning-based model that aims to predict the chance of stroke at an early stage utilizing Parkinson's disease and wrinkles as markers. A patient may have a stroke disease if they are diagnosed with both Parkinson's disease and wrinkles. To the best of our knowledge, this research is the first to use these biomarkers to predict the risk of having a stroke. The proposed model achieves a higher accuracy of 94.7% on the considered dataset. Additionally, the recommended model was evaluated and tested in terms of loss, training time, accuracy, recall, and F1-score versus the other existing models. With less price and pain than present testing approaches, these discoveries are predicted to result in major enhancements in the early detection of strokes. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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Illumination-Free Clustering Using Improved Slime Mould Algorithm for Acute Lymphoblastic Leukemia Image Segmentation

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Journal of Bionic engineering 2023年第6期20卷 2916-2934页

作者： Krishna Gopal Dhal Swarnajit Ray Sudip Barik Arunita Das Department of Computer Science and Application Midnapore College(Autonomous)Paschim MedinipurMidnaporeWest BengalIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataWest BengalIndia Department of Computer Science and Engineering Kalyani Government Engineering CollegeKalyaniWest BengalIndia

Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image ***,because their initial cluster centers are randomly determined,it is often seen that certain clusters converge to local *** addition to that,pathology image segmentation is also problematic due to uneven lighting,stain,and camera settings during the microscopic image capturing ***,this study proposes an Improved Slime Mould Algorithm(ISMA)based on opposition based learning and differential evolution’s mutation strategy to perform illumination-free White Blood Cell(WBC)*** ISMA helps to overcome the local optima trapping problem of the partitional clustering techniques to some *** paper also performs a depth analysis by considering only color components of many well-known color spaces for clustering to find the effect of illumination over color pathology image *** and visual results encourage the utilization of illumination-free or color component-based clustering approaches for image ***-KM and“ab”color channels of CIELab color space provide best results with above-99%accuracy for only nucleus ***,for entire WBC segmentation,ISMA-KM and the“CbCr”color component of YCbCr color space provide the best results with an accuracy of above 99%.Furthermore,ISMA-KM and ISMA-RKM have the lowest and highest execution times,*** the other hand,ISMA provides competitive outcomes over CEC2019 benchmark test functions compared to recent well-established and efficient Nature-Inspired Optimization Algorithms(NIOAs).

关键词： Pathology image Image segmentation Clustering Color space White blood cell Optimization Swarm intelligence Fuzzy clustering Rough clustering

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Optimal feature with modified bi-directional long short-term memory for big data classification in healthcare application

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4441-4450页

作者： Kamble, Subhash Arunalatha, J.S. Venugopal, K.R. Computer Science and Engineering Bangalore University Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bangalore University

Artificial intelligence together with its applications are advancing in all fields, particularly medical science. A considerable quantity of clinical data is available, yet the vast majority of it is wasted. It will be valuable in diagnosing human life early if it is applied effectively. An excellent classification system assists medical practitioners in providing a suitable diagnosis at a younger stage. Medical data typically contain a large number of features, and including all of them in decision making may result in over fitting of the classification model, lowering accuracy. As a result, an effective dimensionality reduction strategy that minimizes the amount of features while also improving classification accuracy is required. Therefore in this study, efficient dimension reduction-based medical data classification is proposed. The proposed approach is made up of three stages namely, pre-processing, feature selection, and classification. Initially, the medical data are collected and pre-processed. Then, to minimize computational complexity and time consumption, optimal features are selected using the adaptive rat swarm optimization (ARWO) algorithm. Then, the selected features are given to the modified bi-directional long short-term memory (MBi-LSTM) classifier to classify data as normal or abnormal. Then, the performance of the model is evaluated using different datasets and the effectiveness of the proposed model is analyzed in terms of different metrics. The proposed model implemented in python. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： ARWO Big data MBi-LSTM Medical data Optimal features

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An Efficient Framework for Utilizing Underloaded Servers in Compute Cloud

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computer Systems science & engineering 2023年第1期44卷 143-156页

作者： M.Hema S.Kanaga Suba Raja Department of Information Technology Easwari Engineering CollegeChennai600089India Department of Computer Science and Engineering Easwari Engineering CollegeChennai600089India

In cloud data centers,the consolidation of workload is one of the phases during which the available hosts are allocated *** phenomenon ensures that the least possible number of hosts is used without compromise in meeting the Service Level Agreement(SLA).To consolidate the workloads,the hosts are segregated into three categories:normal hosts,under-loaded hosts,and over-loaded hosts based on their *** is to be noted that the identification of an extensively used host or underloaded host is challenging to ***-old values were proposed in the literature to detect this *** current study aims to improve the existing methods that choose the underloaded hosts,get rid of Virtual Machines(VMs)from them,andfinally place them in some other *** researcher proposes a Host Resource Utilization Aware(HRUAA)Algorithm to detect those underloaded and place its virtual machines on different hosts in a vibrant Cloud *** mechanism presented in this study is contrasted with existing mechanisms *** results attained from the study estab-lish that numerous hosts can be shut down,while at the same time,the user's workload requirement can also be *** proposed method is energy-efficient in workload consolidation,saves cost and time,and leverages active hosts.

关键词： Workload consolidation energy consumption under load server resource utilization

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Hashing In Blockchain Using Merkle Tree POW Cosensus Algorithms 2

Hashing In Blockchain Using Merkle Tree POW Cosensus Algorit...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Ashok, Kumar K. Anathajothi, K. Kcg College of Engineering Department of Computer Science and Engineering Karapakkam India Rajalakshmil Engineering College Department of Computer Science and Engineering Thandalam India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384277

The preface of Privacy based decentralized application and massive information Analytics has been illustrate the consequence of block chain tools to the industry. Blockchain skill as a policy allows creating a scattered and mock ledger of transactions. Blockchain provide a promise of tamper conflict used for the data generate from various process to assemble enormous datasets. In view of the fact that the data is colossal in mass and grows exponentially by means of point in time, the data management should be proficient to stock up or course of action it. We survey the unusual standards of Merkle Tree Generation works in numerous research instructions for bring the blockchain implementation more rapidly to the variety of domain of database and its operation. in addition, the projected research work also discusses regarding the privacy, challenges, and future research direction by applying Merkle Tree Generation involves Data Slicing and Encryption, Generating Hash value. © 2024 IEEE.

关键词： Differential privacy

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