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检索条件"任意字段=Conference on Applications of Neural Networks and Machine Learning in Image Processing IX"

共 6109 条记录，以下是171-180 订阅

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Fusion Grad-CAM: A Methodology for Generating Unified Attention Maps in Majority Voting Classifiers

Fusion Grad-CAM: A Methodology for Generating Unified Attent...

引用

International image processing, applications and Systems conference (IPAS)

作者： Nikolaos Ntampakis Konstantinos Diamantaras Vasileios Argyriou Panagiotis Sarigianndis International Hellenic University Greece MetaMind Innovations Kozani Greece Kingston University London London UK University of Western Macedonia Kozani Greece

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

In this work, we introduce a novel methodology called Fusion Grad-CAM for generating attention maps in ensemble classification systems that utilize majority voting. Currently, there is no established technique for producing a consolidated attention map in such systems. Traditional approaches require inspecting individual attention maps from each contributing model, which is inefficient. Our proposed method integrates Grad-CAM produced attention maps by averaging or weighted averaging them based on both the predicted class and the confidence level of each model’s decision. This approach offers a holistic representation of the ensemble’s decision-making process. We validate our methodology using various combinations of well-known convolutional neural networks (CNNs) pretrained on imageNet, demonstrating the effectiveness and clarity of the resulting attention maps.

关键词： Heating systems Visualization image processing Decision making Predictive models Inspection Convolutional neural networks Ensemble learning

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Transfer and Bayesian learning Approaches for Land Use and Land Cover Classification

Transfer and Bayesian Learning Approaches for Land Use and L...

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Zainab Malik Aisha Sir Elkhatem Gokhan Bilgin Dept. of Computer Engineering Yildiz Technical University Istanbul Turkey Dept. of Control and Automation Engineering Yildiz Technical University Istanbul Turkey

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Land use and land cover (LULC) classification is essential for understanding the impact of both human activities and natural processes on the Earth’s surface. This classification plays a critical role in deforestation, urban planning, and damage assessment. However, traditional machine learning techniques often face challenges in achieving consistent accuracy in diverse datasets. In this study, we implement transfer learning and Bayesian learning approaches to enhance the accuracy and robustness of LULC classification. Transfer learning leverages knowledge from previous classification problems, while Bayesian learning addresses uncertainties in the classification process. Using the Remote Sensing image Classification Benchmark (RSI-CB128) dataset, the study evaluates the performance of Bayesian Convolutional neural networks (CNN), Mobile neural networks (MobileNet), Inception neural networks (InceptionNet), Densely Connected Convolutional networks (DenseNet) and Efficient neural networks (EfficientNet). The study also investigates the impact of varying the Bayesian layers on model performance, finding that a medium number of layers optimally captures model uncertainty. The results indicate that the transfer learning models, particularly MobileNet, achieved the highest accuracy of 97. 48% compared to 90. 33% for Bayesian CNN. These findings suggest that transfer learning techniques are highly effective for LULC classification, providing a reliable method for practical applications in remote sensing.

关键词： Deforestation Accuracy Uncertainty Computational modeling Transfer learning neural networks Urban planning Land surface Bayes methods Convolutional neural networks

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Low-Data Classification of Historical Music Manuscripts: A Few-Shot learning Approach

Low-Data Classification of Historical Music Manuscripts: A F...

引用

International image processing, applications and Systems conference (IPAS)

作者： Elona Shatri Daniel Raymond György Fazekas Centre for Digital Music Queen Mary University of London London UK Big Data Science Queen Mary University of London London UK

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

In this paper, we explore the intersection of technology and cultural preservation by developing a self-supervised learning framework for the classification of musical symbols in historical manuscripts. Optical Music Recognition (OMR) plays a vital role in digitising and preserving musical heritage, but historical documents often lack the labelled data required by traditional methods. We overcome this challenge by training a neural-based feature extractor on unlabelled data, enabling effective classification with minimal samples. Key contributions include optimising crop preprocessing for a self-supervised Convolutional neural Network and evaluating classification methods, including SVM, multilayer perceptrons, and prototypical networks. Our experiments yield an accuracy of 87.66%, showcasing the potential of AI-driven methods to ensure the survival of historical music for future generations through advanced digital archiving techniques.

关键词： Training Support vector machines Scalability Symbols Music Self-supervised learning Multilayer perceptrons Feature extraction Cultural differences Few shot learning

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Indian Sign Language Alphanumeric Hand Gestures Recognition Using Deep learning Techniques 5

Indian Sign Language Alphanumeric Hand Gestures Recognition ...

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5th International conference on Advanced Research in Computing, ICARC 2025

作者： Mahendran, Shakana Mahendran, Ramya Vavuniya Campus of the University of Jaffna Department of Physical Science Fas Vavuniya Sri Lanka EUS. Information Technology Center Trincomalee Campus Trincomalee Sri Lanka

ISBN: (纸本)9798331530983

Alphanumeric sign language recognition plays a vital role in enabling effective communication between individuals who are deaf and the general population. This research focuses on classifying alphanumeric hand gestures in Indian Sign Language using real-time image processing. The study collects diverse hand shape images captured under varying backgrounds and lighting conditions, making it suitable for real-world applications. In the preprocessing stage, images are cropped and resized to a standardized 128×128 pixel format to ensure uniformity in the dataset. A two-pronged approach is adopted to classify the collected images. Sequential and parallel CNNs are employed to capture spatial and channel information effectively. Additionally, a channel attention module enhances the model's ability to identify crucial channel information. The Deep Convolutional neural Network (DCNN) employed in this study achieved remarkable accuracy, reaching 99.976%. This outcome demonstrates the superior performance of this approach compared to other classification methods, whether utilizing real-time or non-laboratory experimental data. This research contributes to developing an efficient, accurate, and cost-effective system for classifying alphanumeric signs in Indian Sign Language. It facilitates easy and fast communication between individuals who are deaf and those who are not, promoting inclusive interactions. © 2025 IEEE.

关键词： Convolutional neural networks

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SmartPosture: Real-Time Posture Detection and Feedback System Integrating IoT and machine learning

SmartPosture: Real-Time Posture Detection and Feedback Syste...

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AIML-applications for Engineering & Technology (ICAET), International conference on

作者： Milind Rane Shrihari Mohole Yash Pahade Gokul Kurandale Aditya Munjale E&TC VIT Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Traditional posture monitoring methods often rely on physical correction, lacking advanced technologies for real-time detection and feedback, which can result in delayed interventions. This research presents a machine learning framework designed to address this deficiency by giving instantaneous posture detection and feedback. The framework categorizes user posture into “Good” or “Bad” classifications, achieving an accuracy rate of 75.52%, by the use of Convolutional neural networks (CNNs) along with TensorFlow. Trained on a 2,000-image Yoga Poses Dataset and fine-tuned over 50 epochs, the model achieved optimal performance. The system integrates IoT and machine learning, providing recommendations for posture -related advice through a medical chatbot powered by techniques of NLP like Langchain and Hugging Face embeddings. Key hardware components include the flexible sensor, LCD display, vibration motor, and ESP8266 Wi-Fi module, which offer real-time alerts and monitoring. The web application, built using Flask and Streamlit, allows users to track posture data and receive corrective prompts. This innovative approach not only enhances posture monitoring but also advances smart healthcare, offering a continuously learning and adaptable solution relevant to real-world scenarios.

关键词： Vibrations machine learning Medical services Chatbots Motors Real-time systems Liquid crystal displays Convolutional neural networks Monitoring Wireless fidelity

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Advancing Peer Review Integrity: Automated Reviewer Assignment Techniques with a Focus on Deep learning applications 1st

Advancing Peer Review Integrity: Automated Reviewer Assignme...

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1st International conference on Computation of Artificial Intelligence and machine learning, ICCAIML 2024

作者： Bhaisare, Bhumika Bharati, Rajesh Department of Computer Engineering Dr. D. Y. Patil Institute of Technology Pune Pimpri India

ISBN: (纸本)9783031714801

The reviewer assignment process in academic research papers is crucial for ensuring peer review integrity and fairness, and it is often referred to as the reviewer assignment problem (RAP). This initial step lays the foundation for the entire review process. To address the limitations of manual reviewer assignment, there is a growing trend among journal and conference editors to explore automated solutions. For quite some time, machine learning techniques have found application in this domain, recent attention has shifted toward the application of Deep learning in the education sector. Deep learning, rooted in neural networks with diverse processing components, is gaining prominence in fields like natural language processing (NLP) and image recognition. In this comprehensive review, we aim to evaluate the key research achievements in the realm of reviewer assignment algorithms. It commences by presenting background information and discussing the necessity for automated reviewer assignment. The paper then systematically examines the existing research in this domain, with a particular focus on recent advancements in Deep learning techniques. The review provides an unbiased assessment of the current algorithms’ strengths and weaknesses. Overall, this analysis emphasizes the growing significance of RAP research and the demand for further innovation and framework development in this field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Attentional Mechanisms for Enhancement in the Convolutional neural Network Task of Recognizing Handwritten Chinese Character Styles

Attentional Mechanisms for Enhancement in the Convolutional ...

引用

Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe conference on

作者： Yuwei Peng School of Computer Science and Technology Tongji University Shanghai China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

This paper investigates the enhancement of attention mechanism in the task of recognizing handwritten Chinese character styles by convolutional neural networks (CNNs). With the wide application of deep learning in image recognition, convolutional neural network has become a mainstream method for processing image tasks due to its excellent feature extraction capability. However, in complex handwritten Chinese character recognition, the performance of traditional CNN models is still somewhat limited, especially in the task of recognizing different styles of calligraphic glyphs. For this reason, this paper proposes an attention mechanism, that can improve the model's ability to pay attention to important information, thus improving the accuracy of the model in recognizing different handwritten Chinese character patterns. By conducting experiments on the Chinese Calligraphy Styles by Calligraphers dataset, the paper shows that adding an attention mechanism significantly improves the functionality of CNN models on the task of recognizing different fonts. The result demonstrates that the attention mechanism can effectively mitigate the recognition difficulties due to the variation of calligraphic fonts, and provides new ideas for future applications in more complex scenarios.

关键词： Deep learning Training Handwriting recognition Adaptation models Attention mechanisms image recognition Accuracy Generative adversarial networks Character recognition Convolutional neural networks

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Hybrid Deep learning for Automatic Gauge Reading with CNN-Bidirectional GRU-LSTM

Hybrid Deep Learning for Automatic Gauge Reading with CNN-Bi...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Hitesh Ninama Jagdish Raikwal Institute of Engineering and Technology Devi Ahilya University Indore India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Automatic gauge reading plays a vital role in industrial automation, enabling efficient and accurate monitoring of parameters such as pressure, temperature, and flow. Traditional image processing struggles with diverse gauge designs, inconsistent lighting, and environmental factors. This study introduces a hybrid deep learning approach, combining Convolutional neural networks (CNNs) with Recurrent neural networks (RNNs) like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, to enhance accuracy and robustness. Five hybrid models, including CNN-LSTM, CNN-GRU, and their bidirectional versions, were evaluated using synthetic and real-world *** best performing model, CNN with SpatialDropout2D and Bidirectional GRU-LSTM (Model E), achieved a Mean Absolute Error (MAE) of 0.030, Root Mean Squared Error (RMSE) of 0.041, and an R 2 score of 0.991. It maintained high accuracy in various conditions, with an average inference time of 25 milliseconds per image, demonstrating suitability for real-time applications.

关键词： Deep learning Training Technological innovation Accuracy Temperature Robustness Real-time systems Convolutional neural networks Monitoring Long short term memory

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processing and Classification Algorithms of Medical images

Processing and Classification Algorithms of Medical Images

引用

International image processing, applications and Systems conference (IPAS)

作者： Oltiana Seitaj Department of Industrial Engineering Electronics Mechanics University of Roma Tre Rome Italy

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This paper evaluates the impact of hybrid deep learning approaches on lung tumor segmentation by combining traditional image processing techniques with advanced AI-driven models. The study integrates Convolutional neural networks (CNNs) with preprocessing methods such as noise reduction, adaptive thresholding, and contrast enhancement to address challenges associated with complex anatomical structures and variability in medical image quality. A novel hybrid framework is proposed, leveraging traditional methods to preprocess data and improve input quality for deep learning models, ultimately enhancing segmentation accuracy and *** effectiveness of the proposed approach is assessed using quantitative performance metrics, including Dice Similarity Coefficient (DSC), Hausdorff Distance, Jaccard Index, Precision, and Recall. Preliminary results indicate significant improvements in tumor boundary detection and reduced false-positive rates compared to existing methods. By streamlining segmentation workflows and enabling near-realtime applications in clinical settings, this research offers a pathway to improved diagnostic accuracy, treatment planning, and workflow *** implications include the potential for integration into clinical imaging pipelines, fostering advancements in computer-assisted diagnosis and personalized treatment strategies. This study underscores the value of hybrid methodologies in addressing current limitations and paving the way for more precise and efficient medical image segmentation.

关键词： Deep learning image segmentation Adaptation models Accuracy Thresholding (Imaging) Streaming media Reliability MATLAB Biomedical imaging Tumors

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Enhanced Surveillance Through YOLOv3-Based on Deep learning for Real-Time Weapon Detection

Enhanced Surveillance Through YOLOv3-Based on Deep Learning ...

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AIML-applications for Engineering & Technology (ICAET), International conference on

作者： A Shalini Satya Charan V Khyrul Mateen Khan Nithin K Vaishnavi Shantagiri B Rohan Srivatsav Department of Information Science and Engineering New Horizon College of Engineering Affiliated to VTU Bengaluru India Department of Electronics and Communication Engineering New Horizon College of Engineering Affiliated to VTU Bengaluru India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

For public safety requirements, the use of machine learning in developing weapon detection in surveillance footage is essential. We apply YOLO v3, which is based on convolutional neural networks (CNN) for image and video analysis that helps in distinguishing weapons from non-weapon objects. We train our models on a large variety of datasets to increase their accuracy, and we utilize transfer learning strategies to lower the number of false positives and negatives. According to the early findings, machine learning holds great promise for improving real-time weapon detection. This work presents a low- cost system design in case study form, demonstrating applicability to large security projects for diverse environments like airports or public events where different types of threats have to be addressed with limited resource allocation but still effective enough towards ensuring safety and security within those areas.

关键词： Deep learning YOLO Accuracy Weapons Surveillance Transfer learning Real-time systems Public security Security Convolutional neural networks

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