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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection 31

MedEx: Enhancing Medical Question-Answering with First-Order...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zafar, Aizan Mishra, Kshitij Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761964

In medical question-answering, traditional knowledge triples often fail due to superfluous data and their inability to capture complex relationships between symptoms and treatments across diseases. This limits models' ability to provide accurate, contextually relevant responses. To overcome this, we introduce MedEx, which employs First-Order Logic (FOL)-based reasoning to model intricate relationships between diseases and treatments. We construct FOL-based triplets that encode the interplay of symptoms, diseases, and treatments, capturing not only surface-level data but also the logical constraints of the medical domain. MedEx encodes the discourse (questions and context) using a transformer-based unit, enhancing context comprehension. These encodings are processed by a Knowledge Injection Cell that integrates knowledge graph triples via a Graph Attention Network. The Logic Fusion Cell then combines medical-specific logical rule triples (e.g., co-occurrence, causation, diagnosis) with knowledge triples and extracts answers through a feed-forward layer. Our analysis demonstrates MedEx's effectiveness and generalization across medical question-answering tasks. By merging logical reasoning with knowledge, MedEx provides precise medical answers and adapts its logical rules based on training data nuances. © 2025 Association for Computational Linguistics.

关键词： Question answering

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Skin Cancer Classification Using a Quad Ensemble 2

Skin Cancer Classification Using a Quad Ensemble

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

作者： Blaise, Don Bosco Manoj Kumar, G. Jeswanth, S. Bhuvaneswari, G. Rajesh, C. Bharathi, A. Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Skin cancer is a highly prevalent form of cancer that frequently arises from the proliferation of abnormal cells on the skin. Early detection is crucial for effective treatment and better prognosis. Traditional diagnostic methods face challenges in accuracy and timeliness, making automated techniques a valuable tool in disease identification. This article proposes a deep learning framework based on an ensemble, aimed at enhancing the detection and classification of various types of cancer on the skin. The approach integrates four optimized Convolutional Neural Networks (CNNs) using a weighted average ensemble technique. This model addresses common challenges in data preparation, model selection, and overfitting through comprehensive preprocessing and hyperparameter tuning, resulting in improved accuracy and robustness compared to existing systems. © 2025 IEEE.

关键词： Convolutional neural networks

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AI-Powered Mental Health Screening and Support for Homeless Children

AI-Powered Mental Health Screening and Support for Homeless ...

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2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Raga Madhuri, C.H. Bandaru, Jaya Sankar Krishna Srinu, Medisetti Vardhan, Gangadhari Midhun Anand Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Department of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798331536336

Mental health illness is a significant global public health threat exacerbated by the lack of effective early identification and intervention measures. This project aims to address these challenges by focusing on mental health recognition among homeless children. Individuals who are particularly vulnerable due to the loss of their parents. We created a model that screens mental health status by analyzing the responses to screening pediatric symptoms checklist (PSC) questions using an SVM algorithm. This model achieved 90% accuracy in predicting the mental health status of children. This approach makes it more accessible and effective for caretakers in orphan homes. The idea of providing some clear guidelines from the obtained results would make sure these children are well taken care of at the right time, which ultimately improves the mental health status of this group of people. © 2025 IEEE.

关键词： Pediatrics

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From Efficiency to Innovation: LLM 5.0 and the Future of Industry

From Efficiency to Innovation: LLM 5.0 and the Future of Ind...

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

作者： Gantayat, Pradosh Kumar Kaur, Tejinder Majhi, Madhusmita Jena, Uttam Kumar Das, Swatismita Soni Department of Artificial Intelligence and Data Science Telangana Hyderabad501203 India Department of Mmictbm Mullana Haryana Ambala133207 India Department of Computer Science and Engineering Telangana Hyderabad501203 India Raajdhani Engineering College Biju Patnaik University of Technology Department of Computer Science and Engineering Odisha Bhubaneswar India Raajdhani Engineering College Bhubaneswar India

ISBN: (纸本)9798331509828

Industry 5.0 is a new way of thinking that is consistent with the ideas of Industry 4.0 but places greater emphasis on sustainability, sustainability, and human-centricity. Unlike Industry 4.0, which emphasizes the efficient integration of advanced technologies such as the Internet, AI, robots, and automation, Industry 5.0 seeks to balance human-machine collaboration to create more inclusive, meaningful, and sustainable systems. In Industry 5.0, LLM has a bigger role than automating similar activities. LLMs make sectors more resilient, adaptable and aligned with social values by fostering innovation, improving human-centred processes and promoting sustainable practices. Industry 5.0 will create a revolutionary force in the global industrial landscape thanks to a combination of emerging technologies that will further enhance the synergies between human creativity and machine intelligence. © 2025 IEEE.

关键词： Human-AI Collaboration Industrial Efficiency Industry 5.0 Innovation LLM 5.0

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Evolutionary Paths to Systematically Unlocking the Frequency-Selective Potential of 3D Meta-Atoms 6

Evolutionary Paths to Systematically Unlocking the Frequency...

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6th Australian Microwave Symposium, AMS 2025

作者： Lv, Xiaojing Luo, Zhen Tyo, J. Scott Yang, Yang University of Technology School of Electrical and Data Engineering Sydney Australia University of Technology School of Mechanical and Mechatronic Engineering Sydney Australia Monash University Department of Electrical and Computer Systems Engineering Australia

ISBN: (纸本)9798350379969

This paper highlights the benefits of advanced multi-material 3D printing technology in enhancing the electromagnetic performance of metasurfaces. By systematically evolving geometric structures, the proposed bandpass filtering meta-Atom outperforms existing designs in key metrics, including bandwidth, out-of-band suppression, in-band insertion loss, and transition band steepness, underscoring the significant potential of 3D unit cells. Through the integration of qualitative approach and semi-Analytical design algorithm, the geometric topology is fully optimized without imposing substantial computational demands. © 2025 IEEE.

关键词： Wiener filtering

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Enhancing Real-World Active Speaker Detection With Multi-Modal Extraction Pre-Training

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IEEE Transactions on Multimedia 2025年 27卷 2362-2373页

作者： Tao, Ruijie Qian, Xinyuan Das, Rohan Kumar Gao, Xiaoxue Wang, Jiadong Li, Haizhou National University of Singapore Department of Electrical and Computer Engineering Singapore119077 Singapore University of Science and Technology Beijing Department of Computer Science and Technology Beijing100083 China Fortemedia Singapore138637 Singapore Institute for Infocomm Research Singapore138632 Singapore The Chinese University of Hon g Kong Shenzhen Research Institute of Big data School of Data Science Guangdong Shenzhen518172 China University of Bremen Bremen28359 Germany

Audio-visual active speaker detection (AV-ASD) aims to identify which visible face is speaking in a scene with one or more persons. Most existing AV-ASD methods prioritize capturing speech-lip correspondence. However, there is a noticeable gap in addressing the challenges from real-world AV-ASD scenarios. Due to the presence of low-quality noisy videos in such cases, AV-ASD systems without a selective listening ability are short of effectively filtering out disruptive voice components from mixed audio inputs. In this paper, we propose a Multi-modal Speech Extraction-to-Detection framework named ‘MuSED’, which is pre-trained with audio-visual target speech extraction to learn the denoising ability, then it is fine-tuned with the AV-ASD task. Meanwhile, to better capture the multi-modal information and deal with real-world problems such as missing modality, MuSED is modelled on the time domain directly and integrates the multi-modal plus-and-minus augmentation strategy. Our experiments demonstrate that MuSED substantially outperforms the state-of-the-art AV-ASD methods and achieves 95.6% mAP on the AVA-ActiveSpeaker dataset, 98.3% AP on the ASW dataset, and 97.9% F1 on the Columbia AV-ASD dataset, respectively. We will publicly release the code in due course. © 1999-2012 IEEE.

关键词： Time domain analysis

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Dynamic Resource Allocation in Edge Computing via Deep Deterministic Policy Gradient Reinforcement Learning 3

Dynamic Resource Allocation in Edge Computing via Deep Deter...

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

作者： Gopal, Vidhya Surendar, U. Bajulunisha, A. Tamilselvi, M. Sathiyapriya, V. Saranya, N. Sona College of Technology Department of Computer Science and Engineering Salem India Srm institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur India Roever Engineering College Department of Computer Science and Engineering Perambalur India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Karpagam College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798331508456

The difficulty with dynamic and heterogeneous natured edge computing environments is resource provisioning. Reinforcement Learning (RL) can be promising to solve the problems of resource allocation under conditions of dynamics and complexity. This research work presents a novel DDPG-based (Deep Deterministic Policy Gradient) approach for optimizing the dynamics of resource allocation in edge computing environments by exploiting the CRAWDAD KAIST-WIBRO dataset for the proposed model. This approach implements a neural network that learns the optimal policies of allocations with dynamism towards new changes in workloads and resource availabilities. In order to test the performance of this method, conducted several simulations over real-world workload and resource data, mapping the parameters like rewards and variation in episodes. The simulation results indicate that proposed approach performs superior compared to traditional heuristic-based approaches as well as other RL algorithms for resource usage, response time, and energy efficiency. This proposed approach permits optimized resource utilization along with quality of service, of paramount importance in executing complex and demanding applications at the edge computing environment. © 2025 IEEE.

关键词： Reinforcement learning

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Predictive Cardiovascular Health Assessment with Flask and Machine Learning Models

Predictive Cardiovascular Health Assessment with Flask and M...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Mahesh, Kurma Aishwarya, Paladi Nithin, Sabavath Hariharan, Shanmugasundaram Vaishnavi, Bandar Vasantha, S.V. Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331527495

Cardiovascular Health Forecaster Employing Flask is a cutting-edge online application that combines Python for its backend and Flask for its frontend. It anticipates heart disease risk with a smooth integration of Flask for data input and Python for machine learning, making it perfect for both users and healthcare professionals. As heart attacks take one life every minute, prediction automation becomes essential. Information science handles large amounts of data in healthcare, tackling the difficulty of predicting coronary heart disease. making use of a dataset on cardiac illness from the UCI AI repository. The AI algorithms-Light GBM, AdaBoost, Gradient Boosting, XG Boost, and Random Forests-are used in this study to predict and classify the risk of cardiovascular illness. The study shows Light GBM's remarkable 90% accuracy, making it a relative report on algorithm performance. © 2025 IEEE.

关键词： Cardiology

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TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

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computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

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