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Efficient Deep Learning Model for Analyzing Muscle Activity Patterns in Biomechanical Simulations

SN computer science 2025年第2期6卷 1-13页

作者： Dangi, Dharmendra Dixit, Dheeraj Kumar Bhagat, Amit Rao, Durgesh Gupta, Jeetendra Kumar Department of Information Technology IIIT Madhya Pradesh Bhopal India Department of Computer Science and Engineering MITS Madhya Pradesh Gwalior India Department of Computer Applications MANIT Madhya Pradesh Bhopal India Department of Computer Science & Engineering LNCT University Madhya Pradesh Bhopal India Department of Computer Science & Applications Atal Bihari Vajpayee Vishwavidyalaya Chhattisgarh Bilaspur India

Human motion detection is a critical application of biomechanics, leveraging computer vision to analyse and interpret human activities. This technology is essential across various fields, including sports training, where it aids in monitoring and optimizing athlete performance, and security, where it plays a role in tracking movements in sensitive areas. In this study, we employ two distinct datasets: the Movi dataset and the MoCap CMU dataset. To address the challenge of limited data, we utilized a Deep Convolutional Generative Adversarial Network (DCGAN) for data augmentation, generating new, synthetic images to enhance the diversity and quantity of the training data. Subsequent analysis was conducted using multiple deep learning models, including Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), VGG16, AlexNet, and AlexNet with K-Fold Cross Validation. The application of these models yielded promising results, with accuracies of 87.5%, 89.9%, 90.2%, 91.2%, and 98.3%, respectively. The findings demonstrate the effectiveness of DCGAN in improving model performance through data augmentation, as well as the potential of these deep learning models to accurately detect and classify human motion. This research contributes to advancements in biomechanics and its applications in sports science and security domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biomechanics DCGAN Deep learning Human motion detection K-Fold cross validation Transfer learning

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MAUN:Memory-Augmented Deep Unfolding Network for Hyperspectral Image Reconstruction

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1139-1150页

作者： Qian Hu Jiayi Ma Yuan Gao Junjun Jiang Yixuan Yuan Electronic Information School Wuhan UniversityWuhan 430072China School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Department of Electronic Engineering Chinese University of Hong KongHong Kong 999077China

Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D *** algorithm for restoring the original 3D hyperspectral images(HSIs)from compressive measurements is pivotal in the imaging *** approaches painstakingly designed networks to directly map compressive measurements to HSIs,resulting in the lack of interpretability without exploiting the imaging *** some recent works have introduced the deep unfolding framework for explainable reconstruction,the performance of these methods is still limited by the weak information transmission between iterative *** this paper,we propose a Memory-Augmented deep Unfolding Network,termed MAUN,for explainable and accurate HSI ***,MAUN implements a novel CNN scheme to facilitate a better extrapolation step of the fast iterative shrinkage-thresholding algorithm,introducing an extra momentum incorporation step for each iteration to alleviate the information ***,to exploit the high correlation of intermediate images from neighboring iterations,we customize a cross-stage transformer(CSFormer)as the deep denoiser to simultaneously capture self-similarity from both in-stage and cross-stage features,which is the first attempt to model the long-distance dependencies between iteration *** experiments demonstrate that the proposed MAUN is superior to other state-of-the-art methods both visually and *** code is publicly available at https://***/HuQ1an/MAUN.

关键词： Deep folding iteration

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Sentiment Analysis for Stock Prediction Using Mass Media Sources 7th

Sentiment Analysis for Stock Prediction Using Mass Media Sou...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Billade, Kunal Kishor Patel, Jeel Karthik, N. Vani, V. Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal609609 India

ISBN: (纸本)9783031699818

"Sentiment Analysis for Stock Prediction Using Mass Media Sources" introduces a groundbreaking approach to forecasting stock movements by harnessing sentiment analysis applied to economic news gathered from a diverse range of mass media sources. The project encompasses the creation of a comprehensive system that systematically scrapes economic news websites to gather information relevant to specific companies. This data is then meticulously analyzed to discern the sentiments expressed in the news articles. Subsequently, cutting-edge Machine learning techniques are put to use to anticipate potential fluctuations in the stock prices of the target companies. This holistic approach capitalizes on the amalgamation of Natural language processing which combined with machine learning techniques, and real-time news data, delivering invaluable insights for both investors and traders. © IFIP International Federation for Information Processing 2024.

关键词： Prediction models

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Document classification using deep neural network with different word embedding techniques

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International Journal of Web engineering and technology 2022年第2期17卷 203-222页

作者： Kathiria, Preeti Patel, Usha Kansara, Nishant Department of Computer Science and Engineering Institute of Technology Nirma University India

Document classification has played a major role in many fields like information retrieval, data mining, etc. where machine learning and deep learning models can be applied. But, before applying any model for classification, textual data must be converted into a numerical measure, where word embedding can help. The selection of appropriate word embedding techniques plays a vital role in classification. So, we analysed the classification performance by widely used deep learning models long short-term memory (LSTM) and convolution neural network (CNN) with various word embedding techniques on five benchmark datasets. The pre-processed dataset is converted into vector representation using a word embedding techniques TF-IDF, Word2Vec, and Doc2Vec. The output is given to the LSTM and CNN classifier and documents are classified as per their context. The CNN classifier with Doc2Vec word embedding technique achieves almost 12% more accuracy as compared to other word embedding techniques on all the datasets. © 2022 Inderscience Enterprises Ltd.

关键词： Deep neural networks

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Investigation on Enhanced Detection of Parkinson's Disease in Brain Imaging Using Ensemble Learning Approaches 3

Investigation on Enhanced Detection of Parkinson's Disease i...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sathishkumar, S. Santhoshkumar, S.P. Kumar, Satyam Banupriya, N. Hariharasudhan, S. Gajalakshmi, S. Hindustan Institute of Technology Department of Computer Science and Engineering Coimbatore India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi India School of Cs and It Mahatma Gandhi Central University Motihari India Sri Eshwar College of Engineering Department of Computer and Communication Engineering Coimbatore India Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331508456

Masses of people around the world are affected by Parkinson's disease (PD), a progressive neurological disorder. Timely and precise diagnosis is critical for effective management. Yet conventional diagnostic methods often lack compassion and specificity. Advances in machine learning and cerebral imaging offer promising approaches to improve PD diagnosis. This investigation investigates an ensemble machine learning move toward to enhance the identification of Parkinson's disease using cerebral imaging data. By integrating models such as decision trees (DT), support vector machines (SVM), and neural networks, the ensemble method combines their strengths to create a reliable diagnostic system. Feature extraction and preprocessing further enhance interpretability and performance. Compared to individual models, the ensemble approach significantly improves diagnostic accuracy, sensitivity, and specificity. Brain imaging aids differential diagnosis in Parkinsonism, but no Cerebral imaging methods is currently recommended for routine clinical practice despite their proven utility. Larger trial are needed to validate the medical value and long-standing cost-effectiveness of Cerebral imaging for managing patients within healthcare systems like the NHS. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Review on Image Processing for MRI-Focused Brain Tumor Identification Using Machine Learning 7

Review on Image Processing for MRI-Focused Brain Tumor Ident...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Agha, Dur-E-Shawar Qazi, Farheen Rasheed, Rohail Ali Khan, M. Sadiq Naseem, Muhammad Mubeen, Tauseef Sir Syed University of Engineering & Technology Software Engineering Department Karachi Pakistan Computer Science Department Karachi Pakistan

ISBN: (纸本)9798331534271

Today, image processing plays a significant role in early disease detection, allowing doctors to treat conditions like breast, lung, and brain malignancies. A brain tumor is a serious condition that can be fatal. Brain tumor diagnosis is the most important task for early therapy, and automatic visual classification might be extremely important. It is essential to classify brain cancers in order to assess the tumor's and determine the best course of treatment for each class. To find brain tumors, numerous imaging modalities are used. However, MRI is frequently utilized since it produces superior images and uses no ionizing radiation. This research aims to provide a review of different Machine Learning (ML) techniques for detection and classification of brain tumor. Also, different feature extraction techniques are analyzed and their benefits are discussed. This review is conducted by different research papers which helps new researchers to start immediately a literature review and knowing different machine learning techniques their advantages and disadvantages to classify brain tumor through MRI. Also, they know the accuracy of each algorithm which leads them to directly target the problem not implementing the existing techniques. © 2024 IEEE.

关键词： Lung cancer

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Secure Data Sharing with Confidentiality,Integrity and Access Control in Cloud Environment

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computer Systems science & engineering 2022年第2期40卷 779-793页

作者： V.Rajkumar M.Prakash V.Vennila Department of Computer Science and Engineering Krishnasamy College of Engineering and Technology(Affiliated to Anna UniversityChennai)Cuddalore607109India Department of Computer Science and Engineering School of ComputingSRM Institute of Science&TechnologyKattankulathur603203India Department of Computer Science and Engineering K.S.R.College of Engineering(Affiliated to Anna UniversityChennai)Tiruchengode637215India

Cloud storage is an incipient technology in today’s *** of security in cloud environment is one of the primary challenges faced these *** scenario poses new security issues and it forms the crux of the current *** current study proposes Secure Interactional Proof System(SIPS)to address this *** methodology has a few key essential components listed herewith to strengthen the security such as authentication,confidentiality,access control,integrity and the group of components such as AVK Scheme(Access List,Verifier and Key Generator).It is challenging for every user to prove their identity to the verifier who maintains the access *** is conducted by following Gulliou-Quisquater protocol which determines the security level of the user in multi-step authentication ***,RSA algorithm performs the key generation process while the proposed methodology provides data integrity as well as confidentiality using asymmetric *** methodological operations such as time consumption have been used as performance evaluators in the proposed SIPS *** proposed solution provides a secure system for firm data sharing in cloud environment with confidentiality,authentication and access *** Timed Petri(STPN)Net evaluation tool was used to verify and prove the formal analysis of SIPS *** evidence established the effectiveness of the proposed methodology in secure data sharing in cloud environment.

关键词： Secure interactional proof system access control multi-step authentication Gulliou-Quisquater protocol

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An upgraded variant of backtracking search algorithm and its application to COVID-19 CT images segmentation

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Multimedia Tools and Applications 2025年 1-57页

作者： Debnath, Sanjib Nama, Sukanta Chakraborty, Sanjoy Saha, Apu Kumar Mirjalili, Seyedali Department of Computer Science & Engineering National Institute of Technology Agartala Tripura Agartala799046 India Department of Science & Humanities Gomati District Polytechnic Fulkumari Tripura Udaipur799013 India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane Australia Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Ostrava Czech Republic University Research and Innovation Center Obuda University Budapest Hungary Department of Computer Science & Engineering The ICFAI University Tripura Tripura 799210 India

To improve the performance of the original backtracking search algorithm (BSA), this work combines BSA with a centralized population initialization and an elitism-based local escape operator and proposes a new, improved variant of BSA called CiBSA. BSA's performance is improved by centralized population initialization regarding search capability, convergence speed, and capacity to jump out of the local optimum. The suggested algorithm's elitism-based local escape operator method prevents BSA searching slowness and enhances convergence rates and local search efficiency. The performance of the proposed algorithm has been tested on a set of 54 test suites. An extensive comparison with other recent meta-heuristics algorithms (MA) demonstrates that both methodologies substantially assist BSA in boosting the quality of its solutions and speeding up the convergence rate. The proposed CiBSA is then applied to a real-world problem: image segmentation using Kapur's entropy function. The proposed CiBSA is successfully applied to the segmentation of COVID-19 CT images using Kapur's entropy for grey-scale images. Considering the performance matrices Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Intersection over Union (IoU), the suggested CiBSA outperformed conventional MA algorithms. In COVID-19 CT image segmentation trials, CiBSA has superior segmentation capability and adaptability at varied threshold levels than other approaches. As a result, CiBSA has a lot of potential for enhancing COVID-19 diagnosis accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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A Robust Approach for Analysis and Visualization of CO2and Greenhouse Gas Emission and Its Effect 10

A Robust Approach for Analysis and Visualization of CO2and G...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Gupta, Tanushree Pandit, Gaurav Kumar Sharan, Bhagwati Mishra, Himanshu Singh, Shikha Dewan, Ritu Apex Institute of Technology Chandigarh University Department of Computer Science and Engineering Gharuan India Galgotias College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9789380544472

Carbon Dioxide and Greenhouse Gases are the main cause of the change in the climate. It is recognized worldwide that need to reduce the emissions of greenhouse gases to avoid their worst impact on the change of the climate. Greenhouse effect is the effect in which the greenhouse gases in our environment traps the heat which leads to the rise in the temperature of earth. Greenhouse gases include carbon dioxide, methane, nitrous oxides, and water vapor. In coming years this will harm agriculture, biosphere, and many living organisms. In this research, we have analyzed different countries and their level of emission of harmful gases. And want to tell countries to take steps towards the reduction in the production of these gases. In this Analysis, we worked on 'Who is responsible for climate change?' 'What are the main sources?', 'Who is producing it at a very high rate? We have used the dashboard for analyzing it in all possible ways. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Greenhouse gases

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Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for Homogeneous MPSoCs

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computers, Materials & Continua 2023年第1期74卷 965-981页

作者： Hamayun Khan Irfan Ud din Arshad Ali Sami Alshmrany Department of Electrical Engineering Faculty of Engineering&Technology Superior University Lahore54000Pakistan Department of Computer Science Faculty of Computer Science&IT Superior University Lahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahAl Madinah Al Munawarah42351Saudi Arabia

Increasing the life span and efficiency of Multiprocessor System on Chip(MPSoC)by reducing power and energy utilization has become a critical chip design challenge for multiprocessor *** the advancement of technology,the performance management of central processing unit(CPU)is *** densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip *** energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor(CMOS)circuits and reduces the speed by 10%–15%because excessive on-chip temperature shortens the chip’s life *** this paper,we address the scheduling&energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling(EA-EDF)based technique formultiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor systemon-chip while lowering energy and power *** selection of core andmigration of tasks prevents the system from reaching itsmaximumenergy utilization while effectively using the dynamic power management(DPM)*** in the execution of tasks the temperature and utilization factor(u_(i))on-chip increases that dissipate more *** proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple *** performance of the EA-EDF algorithm was evaluated by an extensive set of experiments,where excellent results were reported when compared to other current techniques,the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%–4.7%on a utilization of 6%,36%&46%at 520&624 MHz operating frequency when particularly in comparison to other energy-aware methods for *** are running and accurately scheduled to make an energy-efficient

关键词： Dynamic power management dynamic voltage&frequency scaling dynamic thermal management multiprocessor system on chip complementary metal oxide semiconductor reliability

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