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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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Implementing machine learning for Early Detection and Prognostic Modeling of Chronic Diseases

Implementing Machine Learning for Early Detection and Progno...

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2025 International conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Iqbal, J. L. Mazher Gurrapu, Omprakash Saritha, P.S. Rajesh Kanna, R. Jayanthi, R. Tejaskumar, M.B. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi India Volvo Trucks NC United States Dhanalakshmi Srinivasan College of Engineering Department of Artificial intelligence and data science Coimbatore India Christ University Department of Computer Science Bangalore India Dayananda Sagar College of Engineering Department of Master of Computer Applications Bengaluru India Don Bosco Institute of Technology Bangalore India

ISBN: (纸本)9798331538538

The employ of deep learning methods for the diagnosis and prognosis model of chronic diseases is an important discovery to change the healthcare service. Some of the chronic diseases which prevalence and incidence rates remain high globally include diabetes, cardiovascular diseases, chronic kidney diseases, and cancers. There is nothing more critical than early diagnosis and accurate prediction of the patients' condition and the best course of action that has to be taken. This paper aims at examining the possibility of utilizing ANN, Random Forest, XGBoost, and CNN to forecast the occurrence of the. Due to integration of big and varied data which involve clinical characteristics, biochemical parameters and medical images among others, ML models have the ability recognize complex relations not easily recognizable by conventional diagnostic procedures. These illustrations prove that deep learning models or more specifically the convolutional neural networks for image diagnosis outperform other traditional methods in performance and prognosis. Nevertheless, some issues, such as data quality, model's interpretability, and its implementation into clinical practice, are still present. The challenges appeared in this paper are key to understanding the future of ML in healthcare as they can pave the way to the integration of such models into practice, therefore leading to early detection, better prognosis, and effective management of chronic diseases. This paper aims at exploring on how ML can be of significance in transformation of the health care sector and orderly improve patients care. © 2025 IEEE.

关键词： Diseases

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Enhanced Satellite image Classification Using Squeeze-and-Excitation Blocks and Deep learning

Enhanced Satellite Image Classification Using Squeeze-and-Ex...

引用

International conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Zarin Tasnim Aditi Roy Zakia Sultana Md Saiful Islam Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Satellite image classification is a vital tool in environmental monitoring, urban planning, and disaster management. This research focuses on addressing key challenges, including data imbalance and variations in image quality, by leveraging machine learning techniques, specifically convolutional neural networks (CNNs). The methodology incorporates a deep learning approach for classifying satellite images into four categories: Cloudy, Desert, Green Area, and Water using the RSI-CB256 dataset. The model is based on a Convolutional neural Network (CNN) integrated with Squeeze-and-Excitation (SE) blocks, which enhance the feature extraction process by adaptively reweighting channel-wise features. The dataset consists of satellite images that are preprocessed through augmentation techniques, such as rotation, zoom, flipping, and rescaling, to improve model robustness. The model was trained using the Adam optimizer with categorical cross-entropy loss and achieved an impressive accuracy of 92.5% on the test dataset. This performance highlights the effectiveness of the proposed approach in handling complex satellite image classification tasks and demonstrates the potential of using SE blocks for enhancing model accuracy in real-world applications.

关键词： Deep learning Adaptation models Accuracy Urban planning Computer architecture Feature extraction Transformers Data models Satellite images Convolutional neural networks

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ML-Based image Recognition Platform for Detecting Forbidden Items in Airports

ML-Based Image Recognition Platform for Detecting Forbidden ...

引用

Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Alaa Atef Abdeljalil Naser Mahmoud Mohamed Mariam Safwat Menna Tulla Ayman Mohamed Mostafa Salma Hesham Khaled Salah Department of Electronics and Communications Faculty of Engineering Ain Shams University Cairo Egypt Siemens Digital Industries Software Fremont USA

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

A significant challenge faced at airports and similar venues is the detection of dangerous items, such as sharp weapons and firearms, within travelers' luggage. This study focuses on analyzing X-ray images of luggage to identify the presence of prohibited items, which include guns, knives, wrenches, pliers, and scissors. Our approach leverages machine learning principles and neural networks to create and implement our model. The Mobile App boasts a response time of 0.13 seconds, while the Desktop App has a response time of 0.54 seconds. The Mobile App achieves an accuracy rate of 79%, and the Desktop App reaches 91%.

关键词： Accuracy image recognition Weapons neural networks machine learning Airports Public security Mobile applications Time factors X-ray imaging

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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 ...

引用

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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From classical techniques to convolution-based models: A review of object detection algorithms

From classical techniques to convolution-based models: A rev...

引用

International image processing, applications and Systems conference (IPAS)

作者： Fnu Neha Deepshikha Bhati Deepak Kumar Shukla Md Amiruzzaman Dept. of Computer Science Kent State University Kent OH USA Rutgers Business School Rutgers University Newark New Jersey USA Dept. of Computer Science West Chester University West Chester PA USA

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Object detection is a fundamental task in computer vision and image understanding, with the goal of identifying and localizing objects of interest within an image while assigning them corresponding class labels. Traditional methods, which relied on handcrafted features and shallow models, struggled with complex visual data and showed limited performance. These methods combined low-level features with contextual information and lacked the ability to capture high-level semantics. Deep learning, especially Convolutional neural networks (CNNs), addressed these limitations by automatically learning rich, hierarchical features directly from data. These features include both semantic and high-level representations essential for accurate object detection. This paper reviews object detection frameworks, starting with classical computer vision methods. We categorize object detection approaches into two groups: (1) classical computer vision techniques and (2) CNN-based detectors. We compare major CNN models, discussing their strengths and limitations. In conclusion, this review highlights the significant advancements in object detection through deep learning and identifies key areas for further research to improve performance.

关键词： Deep learning Computer vision Visualization Reviews Computational modeling Semantics Object detection Feature extraction Convolutional neural networks Object recognition

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neural Network Deep Supervised learning Algorithm Based on Multimodal Data

Neural Network Deep Supervised Learning Algorithm Based on M...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International conference on

作者： Yunxia Fu College of Humanities and Teachers Heilongjiang University of Technology Jixi Heilongjiang China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In response to the problems of difficulty in information fusion, complex feature learning and difficulty in deep network training in multimodal data, a deep supervised learning algorithm is introduced. Through a multi-level supervision mechanism, it aims to improve the performance and generalization ability of neural networks in multimodal data processing. First, a deep supervised learning framework based on multi-modal information is proposed to jointly learn and fuse features of different modalities. Then, a hierarchical supervision mechanism is designed to introduce supervision signals at different levels of the network to enhance the model's understanding and utilization of the characteristics of each modality. Next, the improved Convolutional neural Network (CNN) and LSTM (Long Short-Term Memory) structures are used to process image and text modal data. Finally, in order to avoid information loss, adversarial training is adopted to ensure that the fused features have good robustness and optimize the gradient flow during the training process. The multi-modal + text combination solution that introduces text information has significantly improved the model accuracy. The accuracy of some samples is as high as 0.95, far exceeding the effect of using images or multi-modal images alone. The multi-modal data processing framework based on deep supervised learning proposed in this study effectively solves the difficulties in information fusion and feature learning. Especially when processing heterogeneous data such as images and text, the generalization ability and robustness of neural networks are significantly improved. This method provides new ideas for the promotion of multimodal learning in practical applications.

关键词： Training Representation learning Accuracy Supervised learning neural networks Reliability theory Data models Robustness Noise measurement Long short term memory

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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...

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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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