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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Brain Tumor Localization and Multimodal Segmentation in MRI Images by Automated Process

Brain Tumor Localization and Multimodal Segmentation in MRI ...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Kaviya, S. Ricky, A.Sam Gowtham, S. Nath, Srigitha.S. Bharathi, R.Jennie Praveena, N.G. Saveetha Engineering College Department of Electronics and Communication Engineering Chennai India Nmv Institute of Science and Technology Department of Artificial Intelligence and Data Science Chennai India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350359688

In neuro-oncology, precise MRI tumor segmentation and localization are essential for efficient diagnosis and therapy planning. Although automated segmentation methods have advanced, there are still difficulties in reaching high efficiency and accuracy. A new ML framework for automated multimodal brain tumor localization and segmentation in MRI imaging is proposed. The model incorporates various MRI modalities to improve segmentation accuracy and localization precision by using CNNs and DL architectures. The quality of MRI datasets is standardized and enhanced by data preprocessing approaches, and model training optimizes validation measures and loss functions to assess performance. The suggested model yields significant gains in tumor segmentation accuracy and localization precision compared to earlier methods. Evaluation measures show that the model performs better than earlier approaches on various MRI datasets, including Sensitivity, Specificity, and Dice Similarity Coefficient. The results highlight the effectiveness of the suggested machine learning framework in automating the segmentation and localization of brain tumors, providing a dependable tool to support physicians in the practice of neuro-oncology. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Recent Advances in AI-Powered Drug Discovery: Leveraging Machine Learning for Mechanism of Action Prediction 1st

Recent Advances in AI-Powered Drug Discovery: Leveraging Mac...

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1st International Conference on Recent Advancements in Materials science and technology, ICRAMST 2024

作者： Sathya, K. Kannimuthu, S. Department of Artificial Intelligence and Data Science Karpagam College of Engineering Tamil Nadu Coimbatore India Department of Information Technology Karpagam College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9783031699658

In an era where unprecedented speed and precision are required for drug discovery, artificial intelligence (AI) has emerged as a game changer. This chapter gives a clear summary of how AI-powered strategies are transforming the pharmaceutical sector. The investigation focuses on how AI models predict the efficacy of new therapeutic molecules and reveal their interactions with target proteins. Indeed, these breakthroughs play a critical role in lowering the time and resources required for traditional drug discovery, allowing for the rapid development of life-saving drugs. Furthermore, the impact of AI on improving clinical trials and streamlining the path from laboratory testing to patient treatment is investigated and explored. As the trip progresses through this revolutionary landscape, the chapter emphasizes major obstacles and future directions in AI-powered drug discovery, such as Mechanism of Action (MoA) prediction. It provides an introduction to the dynamic world of AI in drug development, making this cutting-edge field accessible to anyone. The study focuses on the use of machine learning methods such as Support Vector Machine (SVM) and Random Forest on a large dataset, with the goal of furthering our understanding and application of AI in drug development. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Drug discovery

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A Review on the Applications of Natural Language Processing in Healthcare 4

A Review on the Applications of Natural Language Processing ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Warbhe, Mohan K. Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Design Sawangi Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331529635

In healthcare, Natural Language Processing (NLP) is a technique to improve clinical decision support, patient care, and medical research. By automating clinical recording, NLP enhances the calibre of medical records and professional communication. It makes Electronic Health Records (EHRs) easier to use by facilitating quick and easy data entry, retrieval, and access to vital patient data that helps with precise diagnosis and efficient treatment planning. Through the analysis of patient-specific data, NLP supports clinical decision support by delivering prompt recommendations. Chatbots and virtual assistants managing repetitive duties like booking appointments also improve patient connection. NLP makes it easier to analyse vast amounts of literature for medical research, facilitating the extraction and summary of important discoveries. Healthcare data processing requires text preparation, sentiment analysis, Named Entity Recognition (NER), semantic parsing, and Natural Language Generation (NLG). However, there are obstacles to overcome, like bias, ethical concerns, system integration, privacy, and data quality. The healthcare industry can use NLP to enhance patient outcomes, promote personalized medication, and streamline healthcare delivery by guaranteeing interoperability and setting standards. © 2024 IEEE.

关键词： Challenges of NLP in Healthcare Future treads of NLP for healthcare Healthcare NLP

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Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GRU 4

Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GR...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Mouthami, K. Yuvaraj, N. Thilaheswaran, K.K. Lokeshvar, K.J. Kpr Institute of Engineering and Technology Dept. of Computer Science Engineering Coimbatore India Kpr Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350300093

Sentiment analysis is a vast subject to explore in natural language processing (NLP) techniques. The film reviews were analyzed and segregated into positive, neutral, and negative reviews. The proposed model examines two distinct datasets, one with multi-class labels and the other with binary-type class labels. The preprocessing is done by using the bag of words and skip-gram word2vec, variety of classifiers, including Two state Gated recurrent units (TS-GRU), was used for binary classification, and the Bidirectional Long Short-Term Memory (Bi-LSTM for the multi-class situation was implemented. Bi-LSTM was utilized in the model, in which symmetric Bi-LSTM replaces the LSTM approaches to get around the high computational cost of training the normal LSTM. The Bi-LSTM has equivalent accuracy to the LSTM while significantly reducing computational costs. Sentiment analysis offers a glimpse of how consumers feel about certain movies. This endeavor is undertaken via the study of natural language processing, in which sentiment analysis is a branch which examines how words are arranged and utilized to extract meaning from user writing. This helps to investigate further the methods for deciphering such data thanks to significant advancements in machine learning techniques;with Keras API and the Internet Movie database (IMDb) dataset, this study compares single and multi-branch CNN with Bidirectional LSTMs with different kernel sizes. GRU achieved 98.24% of accuracy, and bi-LSTM achieved 98.65% of accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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Securing Electronics Gadgets using Blockchain based Framework 2

Securing Electronics Gadgets using Blockchain based Framewor...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Prakash, P. N. Senthil Lanitha, B. Sundaram, Karthic Vellore Institute of Technology School of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India KPR Institute of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798331534950

The usage of electronic gadgets has grown dramatically in the current digital era. Devices like mobile phones and smart watches are unavoidable and has ingrained themselves into every person's daily routine. Almost everyone owns at least one portable electronic device and these devices are usually expensive. Due to this widespread use, there is also an increase in the number of lost or stolen cases reported. In a recent study, it has been revealed that the number of devices lost is mostly because of misplacing in public places like home, office, public transport. Such incidents will not only cause financial loss but it may also lead to data breaches stored in the portable devices. Hence there is a need for a secure eco-system which helps to identify the genuine owners of electronic devices. A blockchain-based framework is deployed on an Ethereum-based network and adopts the Proof of Stake (PoS) mechanism to protecting gadgets. Further, it is designed using the smart contract defined using solidity programming language. The system enables the owner's details to be recorded upon purchase of a device. The complete specification details of the devices along with owner details is contained in a block and then appended to network. Later on, if the owner wants to resale the device, then the transfer of ownership information is stored in a block and added to the network. The proposed system provides an appropriate interface to check the genuine owner. The proposed framework has been implemented using solidity language, Remix IDE and Sepolia testnet. Obtained results highlights the suitability of the method and its performance in terms of gas consumption and execution latency. © 2024 IEEE.

关键词： Ethereum

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Enhanced Thermal Human Detection in Military Applications Using Deep Learning

Enhanced Thermal Human Detection in Military Applications Us...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Elgohary, Rania Mahmoud, Ahmed Khairy Khaled, Omar Salah, Shaimaa Badawi, M.B. College of Information Technology Misr University for Science and Technology Department of Artificial Intelligence Giza Egypt Air Defense College Alexandria University Alexandria Egypt Department of Artificial Intelligence 12566 Egypt Alexandria University Faculty of Engineering Mechanical Engineering Department Egypt

ISBN: (纸本)9798350351408

For monitoring and recognizing objects, thermal detection is crucial, particularly in sectors like the military. Typical imaging techniques employ light;thermal imaging uses heat signatures instead. This makes it possible to see clearly in completely dark, smoke-filled, and low-light conditions. The potential of deep learning, or artificial intelligence, in conjunction with thermal imaging technology, is increased by improving object recognition, tracking, and detection precision. When combined, this makes for a potent weapon that may be used for both offensive and defensive military operations, particularly when there is a possibility of hostage-taking, enemy troops being hidden, and risks being recognized. By utilizing YOLOv8 to enhance person identification in thermal photos, this research seeks to further human detection technology. We aim to create a more robust model for human detection in thermal images by a thorough comparison with conventional traditional human detection algorithms and previous iterations of YOLO. Our research advances security procedures, emergency response systems, and surveillance operations by showcasing the greater mAP achieving 96% mAP, efficiency, and dependability of YOLOv8. It is expected that the results of this research will increase the efficacy of security protocols, facilitate prompt emergency responses, and improve surveillance capabilities in a range of practical applications. © 2024 IEEE.

关键词： Thermography (imaging)

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Diabetic Retinopathy Detection: A PySpark-Driven Approach with VGG 16 Feature Extraction and MLP Classification 6

Diabetic Retinopathy Detection: A PySpark-Driven Approach wi...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Nabil, Toka Ayman, Sama Ahmed, Samaa Nagy, Mawada Megahed, Muhammad Elsaeed, Abdulmoniem Ashraf, Monier Fouad, Khaled M. Abdelbaky, Ibrahim School of Infromation Technology and Computer Science Nile University Giza Egypt Nile University Giza Egypt New Mansoura University Faculty of Computer Science and Engineering Egypt Benha University Faculty of Computers and Artificial Intelligence Artificial Intelligence Department Banha13518 Egypt

ISBN: (纸本)9798350378511

The current study used cutting-edge techniques to experimentally test the early diagnosis of diabetes via retinal scans. The goal was to enable effective disease prediction and management by facilitating quick and precise medical diagnostics. Three processes were involved in the development of a Diabetic Retinopathy (DR) diagnosis tool: feature extraction, feature reduction, and image classification. The research employed Apache Spark, a distributed computing framework, to manage large datasets and enhance the performance of the multilayer perceptron (MLP) model via hyperparameter tuning and cross validation. Utilizing resources more effectively and achieving faster training times were made possible by Apache Spark. To support data-driven decision-making, the study also emphasized the significance of distributed platforms for analyzing large amounts of real-time diabetic data. To produce discriminative features for classification, the VGG16 architecture was employed for feature extraction. In the last epoch, the MLP model performed remarkably well, with an accuracy of 97%. The study also underlined the value of distributed platforms for data-driven decision-making by analyzing substantial volumes of real-time diabetes data. © 2024 IEEE.

关键词： Big data diabetes Diabetic Retinopathy Image MLP Prediction Spark VGG16

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S5ELBP: Supervised SAR Image Classification using SVM from SSELBP Texture Features

S5ELBP: Supervised SAR Image Classification using SVM from S...

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2023 International Conference on data science, Agents and artificial intelligence, ICDSAAI 2023

作者： Bandi, Sudheer Reddy Anbarasan, M. Merlin Linda, G. Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Swarna Bharathi Institute of Science and Technology Department of Computer Science and Engineering Khammam India

ISBN: (纸本)9798350348910

Synthetic Aperture Radar (SAR) and Optical sensors are very popular depending on their strengths and application needs. The ocean surface will be covered with clouds most of the time and optical sensors cannot capture the information as they cannot operate in dust and cloudy conditions. So, SAR sensors come into existence to capture the atmospheric phenomena in those regions. There is a wide range of texture extraction techniques such as traditional-based wavelet analysis, statistical analysis, local pixel-based approaches, and dimensional reduction approaches. However, these approaches have limitations in boundary extraction, high-dimensional feature extraction, and uniformity representation. In recent times, deep learning (DL) approaches to extract features which varies based on the applications, image interpretation, and so on. At the same time, DL approaches are limited in transparency, computation requirements, and hyperparameter tuning. In contrast, the proposed S5ELBP i.e., Supervised SAR image classification using the integration of Support Vector Machine (SVM) and Scale Selective Extended Local Binary Pattern (SSELBP) extraction techniques shall overcome various challenges of existing traditional and DL approaches such as adaptive thresholding, less computational resources, and noise robustness. Hence, the objective of the proposed methodology is to extract the image features from the Scale Selective Extended Local Binary Pattern (SSELBP). Also, the SSELBP features are integrated with SVM to show the strength of the proposed approach in SAR image classification. Additionally, the performance of the present work is enhanced by comparing the extracted results with various classifiers. Finally, the work is concluded by recommending the scope of future work. © 2023 IEEE.

关键词： Support vector machines

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Analysis of Skin Lesions for Identifying of Skin Cancer Melanoma using Customized CNN Model with Transfer Learning 4

Analysis of Skin Lesions for Identifying of Skin Cancer Mela...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Sinchana, N.S. Jeevitha, K. Kumar Swamy, S. Kodipalli, Ashwini Rao, Trupthi Department of Computer Science and Engineering Global Academy of Technology Karnataka Bangalore India Department of Artificial Intelligence and Data Science and Engineering Global Academy of Technology Karnataka Bangalore India

ISBN: (纸本)9798350305258

Melanoma is a type of cancer known to be the most dangerous and is called the life threatening disease. Every year, there is rise in number of people being diagnosed with melanoma which is more frequent among the males than the females. This type of skin cancer has an uncontrollable growth of cells that can grow really faster, if not treated it can spread all over the body. Hence this disease needs to be detected at the early stages so that the patients can be treated properly. Machine learning approaches were applied to detect skin cancer but the drawbacks were that the performance to identify the cancer was limited. Therefore, we constructed a model by utilizing a Convolution Neural Network of the most advanced deep learning where we conduct investigations for discovering the melanoma skin cancer on the basis of the skin lesion. In this research, using transfer learning techniques ResNet and VGG based models have been brought together for melanoma skin detection. The procedures were applied to train these models using optimizers like RMSprop, Adam, Stochastic Gradient Descent (SGD) and findings were presented with the support of accuracy, confusion matrix, sensitivity, specificity and precision. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Deep Learning Melanoma skin cancer Transfer Learning

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