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Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan Nouf Abdullah Almujally Taerang Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer *** have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far *** recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access *** systems require a complex combination of technical,operational,and definitional *** employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked *** proposes a novel deep learning-based framework for human gait classification in video *** framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing *** proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer ***,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)***,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization *** algorithm chooses the best features,combined in a novel correlation-based fusion ***,the fused best features are categorized using medium,bi-layer,and tri-layered neural *** the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was *** achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this wor

关键词： Gait recognition covariant factors biometric deep learning fusion feature selection

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A novel deep dual self-attention and Bi-LSTM fusion framework for Parkinson’s disease prediction using freezing of gait: a biometric application

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Multimedia Tools and Applications 2024年第33期83卷 80179-80200页

作者： Habib, Zeeshan Mughal, Muhammad Ali Khan, Muhammad Attique Hamza, Ameer Alturki, Nazik Jamel, Leila Department of Electrical Engineering HITEC University Taxila Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia

Parkinson’s disease (PD) disorder is caused by the imbalance of inhibitory dopamine and excitatory acetylcholine neurotransmitters, which causes hindrance in locomotion. Freezing of gait (FOG), tremors, and bradykinesia are the most debilitating and disconcerting symptoms of Parkinson’s Disease (PD). FOG episodes occur due to neurological control disorder and motorized impairments, which sternly hinder voluntary movement and forward locomotion. The computational intelligence techniques can effectively predict PD from the sensor data. To detect the FOG episodes often experienced by PD patients, this paper utilizes a 5G spectrum that operates at 4.8 GHz. The main idea of the proposed system is to extract the wireless channel characteristics information (containing amplitude variances) that can be integrated into a 5G communication system. This system collects data through wireless devices such as dipole antennas, network interface cards, and RF signal generators. Five human activities were performed: fast walking, slow walking, sitting on a chair, FOG episodes, and voluntary stopping. We proposed a novel deep dual self-attention and Bi-LSTM fusion architecture for PD prediction to train the collected dataset. Two architectures have been proposed, namely Deep Dual Attention Neural Network (D2ANN) and BiLSTM Neural Network (BNN), for the classification of PD using Chanel State Information (CSI) data. After that, the most prominent information is extracted using self-attention activation. A newly informed weighted fusion approach is proposed, and tree growth optimization is employed for the best feature selection. Finally, the selected features are classified using a multilayer full SoftMax neural network. The experimental process was conducted on FOG wireless sensor network data and obtained the highest accuracy of 98.66%. Compared to the results with state-of-the-art techniques, the proposed framework shows improved accuracy, recall, and precision rates. © The Author(s

关键词： Network architecture

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Latent Variable and Classification Performance Analysis of Bird-Drone Spectrograms with Elementary Autoencoder

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar systems 2024年 3卷 115-123页

作者： White, Daniel Jahangir, Mohammed Mishra, Amit Kumar Baker, Chris J. Antoniou, Michail University Of Birmingham School Of Electrical Electronic And Systems Engineering United Kingdom Aberystwyth University Department Of Computer Science United Kingdom

Deep learning with convolutional neural networks has been widely utilised in radar research concerning automatic target recognition. Maximising numerical metrics to gauge the performance of such algorithms does not necessarily correspond to model robustness against untested targets, nor does it lead to improved model interpretability. Approaches designed to explain the mechanisms behind the operation of a classifier on radar data are proliferating, but bring with them a significant computational and analysis overhead. This work uses an elementary unsupervised convolutional autoencoder (CAE) to learn a compressed representation of a challenging dataset of urban bird and drone targets and subsequently if apparent quality of the representation via preservation of class labels leads to better classification performance after a separate supervised training stage. It is shown that a CAE that reduces the features output after each layer of the encoder gives rise to the best drone vs bird classifier. A clear connection between unsupervised evaluation via label preservation in the latent space and subsequent classification accuracy after supervised fine-tuning is shown, supporting further efforts to optimise radar data latent representations to enable optimal performance and model interpretability. © 2023 IEEE.

关键词： Drones

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Deploying On-Device AIGC Inference Services in 6G via Optimal MEC-Device Offloading

IEEE Networking Letters

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IEEE Networking Letters 2024年第4期6卷 232-236页

作者： Zhou, Changshi Liu, Weiqi Han, Tao Ansari, Nirwan New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States

From AI-assisted art creation to large language model (LLM)-powered ChatGPT, AI-generated contents and services are becoming a transforming force. It calls for the telecom industry to embrace the prospects of AIGC services and face the unique challenges posed by incorporating generative model services into the AI-native 6G wireless network paradigm. We propose enabling AIGC inference services on mobile devices by optimizing MEC-device computing offloading, through which AIGC task latency is minimized by reinforcement learning based policy agent in a computing resource constrained and bandwidth limited wireless environment. Simulation results are presented to demonstrate the performance advantage. © 2019 IEEE.

关键词： Reinforcement learning

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Stress Prediction using Machine Learning Algorithms 15

Stress Prediction using Machine Learning Algorithms

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sivaiah, B.V. Gayatri, Tangi Setti Umadevi, Thati Reddy, Rajula Partha Siva Sravya, Modiboina Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Many people nowadays are dealing with psychological trauma. referred to as stress. Stress is defined as "a state of tension and pressure brought on by any thought or incident that makes you feel anxious, agitated, or frustrated". Many people have experienced stress as a result the current situation, particularly youth and working professionals. These days, a rise in stress can cause a number of issues, including heart attacks, depression, and suicide. Many academics have turned their attention to stress identification as mounting data indicates that stress related health illnesses associated with the demanding modern lifestyle are becoming more and more common. Consequently, this study's main objective is to predict stress levels, using user-retrieved data that is then confirmed using a subjective stress scale. In this case, a ML algorithm should be trained on the data to forecast the stress level. With the help of our stress prediction, you can quickly determine your current stress level and steer clear of any unfavorable circumstances. With the help of this approach, people may proactively handle future problems and avoid unfavorable consequences by having correct insights into their stress levels. The project's ultimate goal is to improve mental health by providing a tool for efficiently determining and controlling stress levels and encouraging a better way of living. © Grenze Scientific Society, 2024.

关键词： Diseases

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Tag Prediction of Stack Overflow Questions using NLP and ML 15

Tag Prediction of Stack Overflow Questions using NLP and ML

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Prasad, P. Yogendra Priyanka, Mylavarapu Amreen, Shaik Reddy, N. Desai Sreenivas Teja, Muppala Bhanu Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Technology is growing in great leaps and bounds and the number of innovations is increasing every day with major contributor being the advancement in the software development environment. Programming environment includes plethora of tools and support platforms that gives a complete assistance to the programming designers. These platforms can be question- answer based or coding based or tutorial based. Stack Overflow is a very popular online question- answer platform in the current generation and is growing to be the greatest question-answer gathering for programmers and software developers. The amount of information in this forum is so huge that one of the main functionalities of this platform would be tagging i.e., identifying the label or class to which the piece of information belongs to. Tagging helps in easy retrieval and organization of data. We propose a multi label classification system that labels all the questions on the forums with their appropriate tags autonomously. This auto-labelling framework makes it very easy for the users to find answers for the inquiries posted, given significant tags for the same and also helps the site in organizing their questions in a better way. © Grenze Scientific Society, 2024.

关键词： Software design

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Advanced Auditing Solutions Through the Blockchain Cutting Edge Technology 10

Advanced Auditing Solutions Through the Blockchain Cutting E...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Siva, K.P. Sudhakar, S. Sahaya Gurus Raja, M. Kanishka, G. Niranjan, V. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

In the modern business landscape, organizations seek technology-driven auditing solutions. This Abstract empowers auditors by reducing their workload and providing precise questionnaires to extract detailed financial information for a comprehensive examination process, ensuring accuracy and efficiency. The scourge of manual data entry is relegated to history, as this system seamlessly connects to bank accounts for effortless data import or accommodates the upload of data in universally accepted document formats. At its core, an advanced algorithm works tirelessly to analyze extensive transaction data, swiftly pinpointing potential fraud or suspicious activities and expediting the investigative process. After analysis, this system meticulously compiles transaction data, associates them with the relevant parties, and diligently flags anomalies, facilitating the identification of stolen assets and the methodologies employed in the transgressions. Security is paramount through the implementation of blockchain technology, ensuring data integrity, while the project fosters collaboration by facilitating discussions with senior auditors and aligning with established audit standards. The culmination of these features streamlines the creation of auditable, court-ready reports, simplifying the transformation of findings into comprehensive documents fit for legal *** represents a revolutionary paradigm shift, poised to redefine auditing by harmonizing technology, efficiency, security, and adherence to professional principles. Overall, your abstract is comprehensive and well-structured, conveying the significance of your auditing initiative effectively. © 2024 IEEE.

关键词： Efficiency

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Customer Churn Predictive Analytics: Understanding Customer Segments for Enhanced Business Strategies 10

Customer Churn Predictive Analytics: Understanding Customer ...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Priyanka, G. Sowmiya, M. Vinuja, M. Shree, S.S. Dhanu Shalini, S. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350384369

Getting new clients and keeping current ones are the highest priority goals in modern business world. The subscriber base is growing as each industry's market grows swiftly. As a result, companies increasingly recognize how important it is to retain their current customers. In order to maintain their ability to turn a profit, service providers must now significantly reduce their churn rates. Churn prediction enables the identification of clients who are most likely to shop elsewhere. The purpose of this work is to provide broad guidelines on churn prediction using supervised machine learning techniques as a benchmark in conjunction with widely used data sampling techniques on publicly available datasets. A priori selection of the best sampling technique and classification model is challenging since it heavily depends on the inherent properties of the data. In this research, we investigate the performance of seven sampling methodologies, eleven supervised and semi-supervised learning methods, and sixteen different and openly accessible churn-like datasets. © 2024 IEEE.

关键词： Predictive analytics

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Efficient Text Extraction and Summarization using Easyocr and GPT-3 15

Efficient Text Extraction and Summarization using Easyocr an...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sriharsha, A.V. Bhavana, Mekala Tejaswee, Syamala Ahmed, Mynasaheb Bushra Reddy, Peddi Reddi Jaipal Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Optical character recognition (OCR) extracts text from images while large language models (LLMs) understand and generate human-like text. OCR engines like EasyOCR transform document images into machine-readable text. But this raw extracted text contains artifacts. LLMs like GPT-3 require clean embedding vectors instead of text. We propose an integrated pipeline combining EasyOCR and GPT-3 for enhanced text comprehension from images. EasyOCR optically recognizes text from document images. The extracted text is cleaned and converted into embeddings that are fed to GPT-3. GPT-3's deep language model then provides contextual understanding of the extracted text. This enables interpreting complex concepts, resolving ambiguities, summarizing key ideas, and generating natural language descriptions. Our pipeline establishes a paradigm for augmenting text understanding through synergistic combination of OCR and LLMs. It has diverse applications in document analysis, information retrieval, question answering, and other NLP tasks involving documents. The integrated model outperforms previous approaches on benchmarks for comprehension of extracted text. It demonstrates the benefits of complementing optical text extraction with LLMs' innate language abilities. This paves the way for advanced OCR systems that not just read but also understand text in documents. © Grenze Scientific Society, 2024.

关键词： Deep neural networks

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AI Based Automated Gym Trainer Using Machine Learning 10

AI Based Automated Gym Trainer Using Machine Learning

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Cholaraja, K. Gururaj, M. Swasthika, V. Gnanasambandam, S.R. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The 'Smart Exercise Counter using computer Vision' is a groundbreaking system that blends cutting-edge computer vision technology with exercise monitoring. In an age where fitness and health are paramount, this innovative solution addresses the limitations of conventional fitness tracking methods by offering real-time, accurate feedback and comprehensive data analysis. By utilizing computer vision algorithms, this system tracks and analyzes the movements of individuals during their exercise routines, enhancing exercise counting, form assessment, and overall workout efficacy. Key components of the system include strategically positioned cameras, real-time image processing, machine learning models for exercise recognition, and biomechanically-informed form analysis. The system not only counts repetitions but also evaluates posture and technique, providing immediate feedback and corrections. Users can access their exercise data and progress through an intuitive mobile app or web dashboard, making it accessible and user-friendly. The applications of this technology extend beyond personal fitness, encompassing healthcare, sports training, and gym facilities. Healthcare professionals can employ it for rehabilitation and therapy, while athletes and coaches can refine training regimens. Gyms can enhance member experiences by offering advanced exercise monitoring. The 'Smart Exercise Counter using computer Vision' embodies the future of exercise monitoring, where technology aligns with health and fitness goals, offering a promising path towards more accurate, effective, and enjoyable workouts. This abstract encapsulates the transformative potential of computer vision in shaping the way we approach physical exercise and well-being. © 2024 IEEE.

关键词： Training computer vision Accuracy Tracking Medical treatment Machine learning Real-time systems Mobile applications Monitoring Sports

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