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Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

Intelligent Automation & Soft Computing 2023年第3期35卷 2893-2907页

作者： C.Santhosh Kumar K.Vishnu Kumar Department of Computer Science and Engineering Priyadarshini Engineering College635751TamilnaduIndia Department of Computer Science and Engineering KPR Institute of Engineering&TechnologyCoimbatore641407India

Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare *** patient data pro-cessing from remote places may lead to severe privacy ***,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to ofﬂoading of live patient data to remote cloud *** the privacy *** proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption *** can help maintain the privacy preservation and conﬁdentiality of patients’medical data during diagnosis of Parkinson’s *** addition,the energy and delay aware computational ofﬂoading scheme is proposed to minimize the uncertainty and energy consumption of end-user *** proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.

关键词： Peer-to-peer computing energy and delay aware ofﬂoading edge-cloud enabled healthcare system parkinson’s disease prediction

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3D Anisotropic Asymmetric Pyramid (AAP-Net) Network Model for Brain Metastases Segmentation on Multi-Sequence MRI

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IEEE Access 2025年 13卷 86983-86997页

作者： Farghaly, Omar Deshpande, Priya Electrical and Computer Engineering Department Maquette University MilwaukeeWI53233 United States

Brain metastases (BM) present a formidable challenge in clinical oncology, demanding precise segmentation and prediction to optimize patient care. Definite detection of BM is crucial for effective treatment planning and prognosis, particularly in radiation therapy. Manual detection of BMs is laborious and prone to error due to their small sizes, irregular shapes, and low contrast in the imaging. The emergence of deep learning technologies has shown promise in automating BM detection, thereby addressing the limitations of manual methods. However, most existing approaches utilize 2D images, overlooking spatial information and employing uniform isotropic images, which deviates from real-life conditions where anisotropic images are more common. This paper presents a novel 3D anisotropic aysmmetric pyramid (AAP-Net) model that integrates advanced deep learning techniques to accurately detect and segment BMs in 3D magnetic resonance imaging (MRI) volumes, capturing the intricate details and spatial relationships crucial for precise diagnosis. By preserving the anisotropic nature of medical images, our model better reflects real-life scenarios, enhancing its efficacy in clinical settings. Through extensive evaluation, we demonstrate the superior performance of our 3D model compared to previous state-of-the-art models. This superiority is evidenced by achieving a mean Dice Similarity Coefficient (DSC) score across K-fold of 0.88 ± 0.01 , Intersection Over Union (IOU) score of 0.80 ± 0.02 , recall and precision values of 0.91 ± 0.01 and 0.93 ± 0.01 respectively using T1 post-contrast MRI images, underscoring the model’s enhanced accuracy and efficacy in BM detection and segmentation. © 2013 IEEE.

关键词： Radiotherapy

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An Equivalent Voltage Index to Quantify the Impact of Harmonics on Shunt Capacitors

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2023年 21卷 63-68页

作者： Yu, Tian Yong, Jing Xu, Wilsun Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

Harmonic distortions are known to affect the normal operation and life of power equipment. Shunt capacitor is one piece of equipment that is very sensitive to harmonics. Although limits have been established to limit the harmonic distortions experienced by a capacitor, methods to quantify the harmonic impact with easy-to-understand indices are still at large. This paper aims to address the above gap by proposing an equivalent voltage index. This index is to represent the impact of harmonic voltages and their features (such as peak value) into an equivalent increase in the fundamental frequency voltage otherwise experienced by the capacitor. The main advantage of such an equivalence is that PQ and non-PQ engineers can easily relate the impact to the general capacitor loading concept. The index is especially helpful to understand the impact of harmonics on capacitor and thus to justify harmonic mitigation projects. © 2023, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Harmonic distortion

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KidneyMultiNet: A Web-Based Automatic System for Kidney Disease Detection Using Hybrid Machine Learning Model From CT Scan Images

Biomedical Materials and Devices

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Biomedical Materials and Devices 2024年第2期3卷 885-896页

作者： Rana, Sahab Uddin Nur-A-Alam, Md. Akter, Sadeka Likhon, Md. Nur Hosain Department of Computer Science and Engineering Dhaka University of Engineering & amp Technology Gazipur Dhaka Bangladesh Department of Computer Science and Engineering Sunamgonj Science and Technology University Sylhet Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka Bangladesh

Chronic renal disease is the term used to describe kidney function that gradually declines. The kidneys’ final byproduct of eliminating waste and surplus fluid from the bloodstream is urine. Abnormal accumulations of fluid, electrolytes, and waste products can occur in the body when chronic renal disease progresses to a critical degree. Nonetheless, kidney disease has been predicted using various artificial machine learning algorithm approaches. In this study, authors present the “KidneyMultiNet” framework, which combines two models of convolutional neural networks Densenet201 and Xception, which are based on the idea of transfer learning and can predict kidney disease based on kidney CT scan datasets. The “KidneyMultiNet” design that has been suggested makes a thorough and reasonable diagnosis of kidney disease in the kidney conceivable. The authors used a publicly available kidney CT scan image dataset to train and test the proposed model. To determine the efficacy of the output from suggested advanced machine learning CNN methods, the author fed them picture data using a train generator. Then, the author used a rate of learning to decrease the datasets within the proposed model. After completing various learning procedures with many kidney pictures, the proposed model could accurately predict kidney disease, reaching a maximum performance of 99.92%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural network Image processing Kidney disease Machine learning

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Face Mask and Social Distance Monitoring via computer Vision and Deployable System Architecture

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Intelligent Automation & Soft Computing 2023年第3期35卷 3641-3658页

作者： Meherab Mamun Ratul Kazi Ayesha Rahman Javeria Fazal Naimur Rahman Abanto Riasat Khan Department of Electrical and Computer Engineering North South UniversityDhakaBangladesh

The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the *** awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at *** though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained ***ﬁght the spread of this virus,technologically developed systems have become very ***,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efﬁciently deployed on an embedded device still has not become prevalent in the mass *** paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modiﬁed version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in *** Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social *** efﬁciency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS *** results show that the implemented model can efﬁciently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.

关键词： Artiﬁcial intelligence COVID-19 deep learning technique face mask detection social distance monitor you only look once

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Task scheduling and data replication in cloud with improved correlation strategy

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International Journal of computers and Applications 2023年第11期45卷 697-708页

作者： Rambabu, D. Govardhan, A. Department of Computer Science and Engineering Sreenidhi Institute of Science and Technology Hyderabad India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Hyderabad Hyderabad India

Cloud providers frequently utilize two tightly coupled resource management strategies like task scheduling & data replication to boost the performance of the system generally, guaranteeing service level agreement (SLA) compliance, as well as protecting their own financial gain. An Improved Correlation strategy-based Task Scheduling and Data Replication in Cloud (ICTSDC) is what this work aims to give. The suggested system's primary phases are as follows: Management of replication and task scheduling. Initial job scheduling will be optimization-based and take into account goals such bottleneck value, migration cost, VM load, enhanced correlation, and replication, respectively. For this, a brand-new extended DMO algorithm called Self-adaptive Dwarf Mongoose Optimization (SADMO) is presented. In the replication management stage, the potential copies must first be identified based on the prior objective. The suggested SADMO model implements the optimization technique for replica placement throughout the replication management process. The outcomes of the ICTSDC technique are evaluated to other methods using a variety of metrics, like bottleneck value, migration cost, Virtual Machine (VM) load, improved correlation, as well as replication efficiency. A lower mean value of 0.324 is gained with the ICTSDC scheme for fitness. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Virtual machine

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Label Consistent Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning 6

Label Consistent Generalized Adaptive Weighted Recursive Lea...

引用

6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Yousefi, Mohadeseh Naderahmadian, Yashar Beheshti, Soosan University of Guilan Department of Electrical and Computer Engineering Rasht Iran Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9798331506520

The Generalized Adaptive Weighted Recursive Least Squares (GAWRLS) dictionary learning method has shown potential for unsupervised dictionary learning. This paper advances GAWRLS by incorporating classification error as an additional cost to enable supervised learning tasks and introduces the Label Consistency for online supervised dictionary learning in classification tasks. The new method is denoted as Label Consistent Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning (LC-GAWRLS). By incorporating both sparse representation error and classification error into the cost function, LC-GAWRLS enables simultaneous learning of the dictionary and classifier parameters. Particularly, to ensure label consistency, the proposed algorithm introduces a correction weight to adaptively regulate the impact of each training data during the model update, enhancing robustness against variations in training data compared to previous dictionary learning methods. Simulation results on real datasets demonstrate that LC-GAWRLS achieves higher classification accuracy compared to existing state-of-the-art supervised dictionary learning methods, particularly in scenarios with limited training samples per class. © 2025 IEEE.

关键词： Self-supervised learning

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Integrating the Internet of Things to Protect Electric Vehicle Control Systems from Cyber Attacks

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IAENG International Journal of Applied Mathematics 2024年第3期54卷 433-440页

作者： Bhukya, Baddu Naik Venkataiah, V. Kuchibhatla, S. Mani Koteswari, S. Kumari, R.V.S. Lakshmi Raju, Yallapragada Ravi Department of Electrical and Electronics Engineering Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Department of Computer Science Engineering CMR College of Engineering & Technology Telangana Hyderabad India Department of Electrical and Electronics Engineering ACE Engineering College Telangana Hyderabad India Department of Electronics and Communication Engineering Pragati Engineering College Kakinada India Andhra Pradesh Surampalem India Department of Electrical and Electronics Engineering Gayatri Vidya Parishad College of Engineering for Women Andhra Pradesh Visakhapatnam India Department of Computer Science and Technology Madanapalle Institute of Technology and Science Andhra Pradesh India

The Internet of Things (IoT) facilitates the delivery of intelligent services by sensing, gathering, processing, and exchanging data from millions of linked smart devices. The Internet of Things (IoT), which is based on communication infrastructure, provides real-time cyber-physical device monitoring, control, and sensing, such as electric autos. Traditional communication infrastructure's vulnerability to cyberattacks hampered IoT's capacity to investigate these potential applications. This study proposes an algorithm for monitoring and controlling electric vehicles through the Internet of Things communication network to prevent fake data injection attacks. A state-space model depicts a fully autonomous electric car equipped with a vision system. Intelligent sensors and actuators from the Internet of Things are utilized to monitor and change system conditions, compensating for the large distance between the electric car and the control center. Sensing data is delivered from the vehicle to the central command center via an insecure, attack-prone communication path. The mean square error approach is used to derive states in the most effective state estimation system for comprehending and showing autos. A semi-definite programming approach is used to create an optimal control algorithm to manage the vehicle states. The simulation results show how precisely and successfully the proposed algorithms can foresee and regulate a vehicle's state. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Internet of things

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Deep belief bi-directional LSTM network-based intelligent student’s performance prediction model with entropy weighted fuzzy rough set mining

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International Journal of Intelligent Information and Database Systems 2023年第2期16卷 107-142页

作者： Sateesh, Nayani Rao, P. Srinivasa Lakshmi, D. Rajya Department of Computer Science and Engineering Jawaharlal Nehru Technological University Kakinada Kakinada India Department of Computer Science and Engineering MVGR College of Engineering Vizianagaram India Department of Computer Science and Engineering University College of Engineering Vizianagaram Jawaharlal Nehru Technological University Kakinada India

To analyse the student’s academic performance, a new prediction model is developed. This proposed model collects the student’s data from standard online sources. At first, these gathered data are pre-processed by certain methods like outlier removal and filling. Then, the filtered data undergoes feature mining by developing the entropy weighted fuzzy rough set mining (EW-FRSM), where certain parameters are tuned with the proposed hybrid optimisation algorithm with probability-based coyote electric fish optimisation (P-CEFO). The refined features are utilised for performance prediction, where the deep belief-Bi-LSTM (DB-Bi-LSTM) is used for student performance prediction. The parameters in DBN and Bi-LSTM are tuned with the same P-CEFO algorithm to improve the prediction performance. Simulation is performed to examine the efficacy of the designed prediction method with diverse performance metrics by evaluating with diverse baseline approaches to prove the usefulness of the model. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Entropy

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Towards an intrusion detection system for detecting web attacks based on an ensemble of filter feature selection techniques

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Cyber-Physical Systems 2023年第3期9卷 244-259页

作者： Kshirsagar, Deepak Kumar, Sandeep Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India Department of Computer Engineering and IT College of Engineering Pune Pune India

The use of machine learning models in intrusion detection systems (IDSs) takes more time to build the model with many features and degrade the performance. The present paper proposes an ensemble of filter feature selection techniques (EFFST) to obtain a significant feature subset for web attack detection by selecting one-fourth split of the ranked features. The experimentation on the CICIDS 2017 dataset shows that the proposed EFFST method provides a detection rate of 99.9909%, with J48 using 24 features. The system’s performance is compared to the original features and traditional relevant feature selection methods employed in IDSs. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Intrusion detection

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