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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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Knee Osteoarthritis detection and classification using a customized CenterNet with DenseNet201 3

Knee Osteoarthritis detection and classification using a cus...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Shirisha, N. Shilpa, K. Meghana, C.H. Rahman, Mohammed Abdul MLR Institute of Technology Dept. of Computer Science and Engineering Hyderabad India MLR Institute of Technology Dept. of Computer Science and Engineering Nalgonda India

ISBN: (纸本)9798331523893

Knee osteoarthritis (KOA) is a prevalent joint disorder diagnosed using imaging modalities like MRI, CT scans, and X-rays, with X-rays being the most cost-effective. Early detection is crucial for effective management. This study presents an automated deep learning approach to detect and classify KOA severity based on the Kellgren-Lawrence (KL) grading system using single posteroanterior standing knee X-ray images. Utilizing the Osteoarthritis Initiative dataset, we employed transfer learning to fine-tune DenseNet-201, enhancing model performance. Additionally, knowledge distillation was applied to reduce computational complexity while maintaining accuracy. Our model achieved over 95% accuracy on both testing and cross-validation datasets, outperforming existing methods. This approach offers a reliable tool for early KOA diagnosis and grading, potentially aiding clinical decision-making © 2025 IEEE.

关键词： deep learning DenseNet-201 Kellgren-Lawrence grading Knee osteoarthritis knowledge distillation.

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Multi-head Attention Fusion Network for Mpox Screening Using Skin Lesion Image Analysis

Multi-head Attention Fusion Network for Mpox Screening Using...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Suparna Sarker Jahid Hasan Rony Ashifur Rahman Momotaz Begum Dept. of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Monkeypox also known as Mpox is an infectious disease where screening is a vital task for early detection and effective management, particularly during recent outbreaks. Screening using skin lesions is a quick and convenient method. However, it can be challenging to differentiate Mpox from other diseases like chickenpox and measles in the early stages because their skin lesions can appear similar. This study introduces a multi-head Attention Based Fusion Network (MAFNet) to analyze skin lesion images for Mpox detection. The model achieves an accuracy of 89.81%, surpassing traditional architectures like DenseNet, VGG16, and ResNet. Using the multiple head attention mechanisms the proposed model enhances its focus on critical features within skin lesions and more accurately differentiates among various dermatological conditions. Besides that the model is trained not only on Mpox and healthy skin but also along with four other classes of similar diseases, enhancing its ability to generalize effectively. By the identification of Mpox without explicit clinical diagnosis and easy to use web app, this model helps early detection of Mpox patients. Such early identification is particularly valuable in high-traffic areas such as airports, where rapid decision-making is crucial. This approach aims to help prevent future pandemics similar to Covid-19, by promoting timely public health interventions.

关键词： Deep learning Image analysis Attention mechanisms Pandemics Measles Skin Lesions Public healthcare Standards Medical diagnostic imaging

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YOLOv8 for Robust Traffic Object Instance Segmentation using Image Quality Assessment

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Procedia computer Science 2025年 260卷 1060-1070页

作者： Deepthi shree A.M. M. Brindha Dept. of Computer Science & Engg National Institute of Technology Thiruchirappalli Tamilnadu India-620015

Detecting objects in traffic plays a vital role across different domains, including autonomous driving, surveillance of traffic, and managing smart cities. The proposed work investigates the potential of deep learning models, specifically yolov8, for robust multi-spectral traffic object instance segmentation with a novel approach to image quality assessment. This study seeks to enhance object detection accuracy and performance in difficult traffic scenarios, including low light conditions, motion blur, and high noise levels. The results demonstrate the effectiveness of the hybrid image quality assessment approach in achieving robust object detection, showcasing a notable improvement in performance compared to baseline models and yielding promising results in comparison to state-of-the-art methods. The proposed framework contributes to the advancement of autonomous vehicle perception systems by offering improved accuracy and robustness in complex traffic environments.

关键词： Autonomous vehicles Image quality assessment traffic object detection YOLO V8

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A CNN Approach to Automated Detection and Classification of Brain Tumors

A CNN Approach to Automated Detection and Classification of ...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Md. Zahid Hasan Abdullah Tamim D.M. Asadujjaman Md. Mahfujur Rahman Md. Abu Ahnaf Mollick Nosin Anjum Dristi Abdullah Al Noman Dept. of Computer Science & Engineering University of Rajshahi Bangladesh Dept. of Computer Science & Engineering Varendra University Rajshahi Bangladesh Dept. of Computer Science & Engineering Khulna University of Engineering & Technology Khulna Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

For a timely diagnosis and successful treatment, brain tumors need to be carefully evaluated. Tumor inspection is complicated by morphological factors like size, location, texture, and varying appearance. Additionally, medical imaging poses challenges such as noise and incomplete data. This research article presents a methodology for processing Magnetic Resonance Imaging (MRI) data, encompassing techniques for image classification and denoising. The effective use of MRI images allows medical professionals to detect brain disorders, including tumors. This study uses MRI data analysis to classify brain tumors and healthy brain tissue. MRI is the best imaging technique for studying brain tumors because it offers a more thorough image of internal anatomical features than other imaging techniques like Computed Tomography (CT). Initially, an anisotropic diffusion filter is used to denoise the MRI picture. The models are based on an open-access, clinically validated MRI dataset for brain tumor classification, containing 3,264 brain MRI scans. SMOTE (Synthetic Minority Over-sampling Technique) is a widely used approach for tackling class imbalance in datasets by generating synthetic samples to augment and balance the data. It is utilized for dataset augmentation and balancing. Convolutional Neural Networks (CNNs), including ResNet152V2, VGG, ViT, and EfficientNet, were utilized for the classification task. Among them, EfficientNet attained an accuracy of 98%, the highest recorded.

关键词： Accuracy Anisotropic magnetoresistance Magnetic resonance imaging Biological system modeling Computational modeling Computed tomography Brain tumors Brain modeling Feature extraction Convolutional neural networks

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A CNN Approach to Automated Detection and Classification of Brain Tumors

arXiv

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arXiv 2025年

作者： Hasan, Md. Zahid Tamim, Abdullah Asadujjaman, D.M. Rahman, Md. Mahfujur Mollick, Md. Abu Ahnaf Dristi, Nosin Anjum Al-Noman, Abdullah Dept. of Computer Science & Engineering University of Rajshahi Bangladesh Dept. of Computer Science & Engineering Khulna University of Engineering & Technology Khulna Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of Computer Science & Engineering Varendra University Rajshahi Bangladesh

Brain tumors require an assessment to ensure timely diagnosis and effective patient treatment. Morphological factors such as size, location, texture, and variable appearance complicate tumor inspection. Medical imaging presents challenges, including noise and incomplete images. This research article presents a methodology for processing Magnetic Resonance Imaging (MRI) data, encompassing techniques for image classification and denoising. The effective use of MRI images allows medical professionals to detect brain disorders, including tumors. This research aims to categorize healthy brain tissue and brain tumors by analyzing the provided MRI data. Unlike alternative methods like Computed Tomography (CT), MRI technology offers a more detailed representation of internal anatomical components, making it a suitable option for studying data related to brain tumors. The MRI picture is first subjected to a denoising technique utilizing an Anisotropic diffusion filter. The dataset utilized for the model’s creation is a publicly accessible and validated Brain Tumour Classification (MRI) database, comprising 3,264 brain MRI scans. SMOTE was employed for data augmentation and dataset balancing. Convolutional Neural Networks(CNN) such as ResNet152V2, VGG, ViT, and EfficientNet were employed for the classification procedure. EfficientNet attained an accuracy of 98%, the highest *** Codes 68T07 © 2025, CC BY-NC-SA.

关键词： computerized tomography

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Knee Osteoarthritis detection and classification using a customized CenterNet with DenseNet201

Knee Osteoarthritis detection and classification using a cus...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： N. Shirisha K. Shilpa CH. Meghana Mohammed Abdul Rahman Dept. of Computer Science and Engineering MLR Institute of Technology Hyderabad India Dept. of Computer Science and Engineering MLR Institute of Technology Nalgonda India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

关键词： Training Deep learning Accuracy Computational modeling Magnetic resonance imaging Computed tomography X-rays Feature extraction Osteoarthritis Testing

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AlertCam-An Effective Anomaly Detection System using computer Vision

AlertCam-An Effective Anomaly Detection System using Compute...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Santushti Betgeri Piyush Lodha Aneesh Oak Aryan Parikh Ayush Lodha Dept. Of Computer Engineering Vishwakarma Institute of Technology Pune

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

With the rising incidents of harassment and violence in India, particularly against women, this paper presents a real-time violence detection system using YOLO image detection models (YOLOv7, YOLOv8, and YOLOv9). The models were trained on a carefully curated and manually annotated dataset using Roboflow, ensuring robust detection across diverse scenarios. Transfer learning and hyperparameter tuning were applied to improve accuracy. The system was evaluated using key performance metrics, including mean average precision (mAP), Intersection over Union (IoU), precision, and recall, achieving 80% precision and 50–60% recall. Extensive manual testing on real-world surveillance footage and additional video sources further validated its effectiveness. Results indicate that YOLOv8 outperforms other versions in balancing speed and accuracy. This system has strong potential for deployment in law enforcement, public surveillance, and safety-critical applications.

关键词： YOLO Accuracy Surveillance Scalability Transfer learning Real-time systems Safety Tuning Standards Testing

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Decentralization, Customization, and Emergency Assistance: The Next Generation of Ride-Sharing

Decentralization, Customization, and Emergency Assistance: T...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Avani Acharya Pradeep Kanchan Ashlin Dsouza Chaithra M Dept. of Computer Science and Engineering NMAM Institute of Technology

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Mobile apps for taxi-hailing and carpooling have become very popular to tackle city problems like traffic jams, pollution, and high costs. This review of research aims to spot the main trends in this field and look at different approaches across key areas. We dig into models that don't have a central control using blockchain tech safe options with anonymous IDs and encryption based on traits quick systems to match rides in real-time, ways to manage how well-liked services are to build trust, plans to get more people carpooling, and how ride-sharing affects various groups of users and rules. On top of these current trends, we suggest a new feature of smart contract customization to give ride-sharing platforms more flexibility and the ability to adapt. This customization could allow users and platforms to adjust contracts for specific situations, like changing prices, insurance choices, or extra services. We also support adding real-time ride-matching systems and emergency help options to make users safer and more comfortable. By looking at many studies, we spot promising areas to develop in the future and suggest ways to explore further to improve ride-sharing tech and make it more available to different groups of people.

关键词： Technological innovation Privacy Pollution Shared transport Reviews Urban areas Smart contracts Market research Real-time systems Blockchains

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Decision-Making in Fog Computing: A Comparative Analysis of Pareto-Based Optimization and Weighted Objective Models for Application Placement

Decision-Making in Fog Computing: A Comparative Analysis of ...

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International Conference on computer and Automation Engineering, ICCAE

作者： Zahra Farhadpour Tan Fong Ang Chee Sun Liew dept. Computer System and Technology faculty of Computer Science and Infromation Technology universiti malaya Kuala Lumpur Malaysia

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

The expanding adoption of Internet of Things (IoT) applications has amplified the challenge of identifying optimal solutions for resource allocation while balancing multiple competing objectives. As devices and applications proliferate, the complexity of resource management in fog computing escalates, necessitating advanced optimization techniques capable of adapting to evolving conditions and requirements. This paper presents a comparative analysis of two optimization strategies: Pareto-based multi-objective methods, exemplified by FP-NSGA-II (Fog Placement NSGA-II), a customized version of the Non-dominated Sorting Genetic Algorithm II tailored for task placement in fog environments, and weighted sum objective approaches, represented by Time Cost aware Scheduling (TCaS) and Weighted Sum-Genetic Algorithm (WS-GA). Through simulations and performance evaluations, this study compares these approaches in terms of trade-off management, scalability, and solution quality across varying system workloads. The experimental results highlight the strengths of the Pareto-based method, emphasizing its superior scalability and solution quality compared to weighted objective methods, along with its enhanced ability to balance multiple objectives while maintaining solution quality. The findings indicate that the Pareto-based approach is particularly effective in scenarios with competing objectives, as it provides a diverse set of solutions that can be tailored to specific application requirements.

关键词： Performance evaluation Costs Scalability Decision making Optimization methods Scheduling Resource management Internet of Things Edge computing Sorting

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