检索结果-内蒙古大学图书馆

Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： G. Ramkumar N. Janardhana Rao V. Nanammal SK. Shaphiya J Giri Ahmad Abdelhafiz Ali Samhan Dept of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of MBA QIS College of Engineering and Technology Ongole Andhra Pradesh Department of Electronics and Communication Engineering Jeppiaar Engineering College Chennai Department of S&H QIS College of Engineering and Technology Ongole AndhraPradesh Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of Software Engineering Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

This study presents an empirical assessment of identifying human blood groups using image processing assisted by deep learning principles, specifically employing a cascaded Convolutional Neural Network (CNN) and Light Gradient Boosting Machine (LightGBM). Traditional methods of blood group identification are time-consuming and prone to errors, prompting the need for automated, more efficient systems. In this work, a CNN model was initially used to extract deep features from blood sample images, followed by a LightGBM classifier for final blood group classification. A comprehensive dataset, representing all major blood groups (A, B, AB, O, and Rh types), was collected and preprocessed for training and testing. The cascaded CNN-LightGBM approach achieved an accuracy of 92.4%, significantly outperforming baseline models, including standalone CNN (88.2%) and Random Forest (83.5%). The model also demonstrated high precision (92.1%), recall (92.0%), and F1-score (92.1%). real-world testing was conducted to validate its robustness in clinical settings. The results indicate that the proposed model is well-suited for accurate and efficient blood group identification, with low inference time, making it ideal for real-time applications. This approach has the potential to transform blood diagnostics by providing an automated, scalable, and accurate solution.

关键词： deep learning Training Accuracy Transforms Feature extraction Robustness real-time systems Convolutional neural networks Blood Testing

来源：评论

学校读者我要写书评

暂无评论

deep learning-Driven Smart Signal Systems for Advanced image and Video processing in Urban Infrastructure

Deep Learning-Driven Smart Signal Systems for Advanced Image...

引用

Smart Technologies, Communication and Robotics (STCR)

作者： K. S. Bharaty Praveen SR Konduri Department of EEE School of Engineering Mohan Babu University Tirupati Andhra Pradesh Department of Data Science School of Computing Mohan Babu University Tirupati Andhra Pradesh

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

The increasing integration of intelligent systems in metropolitan environments emphasizes the important role effective image and video processing performs in applications including the traffic management, the monitoring of safety, and the urban planning. When handling dynamic scenarios, that is, changes in traffic conditions, occlusions, and the need for real-time analysis, conventional signal processing methods sometimes fail. Strong and adaptable solutions able to manage both these complexity as well as others are thus much needed. This work intends to introduce a deep learning-based smart signal system designed to process real-time video feeds for uses connected to urban traffic and safety. The proposed system uses recurrent neural networks (RNNs) for temporal sequence comprehension; conversely, convolutional neural networks (CNNs) are used for spatial data analysis. Two approaches used in order to improve the performance of the model are data augmentation and transfer learning, which increase the accuracy of decision-making using multi-modal data sources, video streams, sensor inputs, environmental parameters. This helps one to get beyond the challenge of small datasets. System performance is discovered using benchmark datasets and video from actual traffic events. Better than the methods now in use, the results show object detection has 95% accuracy and anomaly detection has 92% accuracy. Promising for use in smart cities, the proposed framework exhibits scalability and flexibility over a wide range of events. Its characteristics, traffic optimization, safety monitoring, and data-driven urban planning, which comprise traffic control, safety monitoring, and data-driven urban planning, have the ability to drastically change urban transportation infrastructure management.

关键词： Accuracy Recurrent neural networks Smart cities Urban planning Object detection Streaming media Traffic control real-time systems Safety Monitoring

来源：评论

学校读者我要写书评

暂无评论

deep learning, Machine learning - Digital Signal and image processing: From Theory to Application

arXiv

引用

arXiv 2024年

作者： Hsieh, Weiche Bi, Ziqian Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Xu, Jiawei Wang, Jinlang Chen, Keyu Yin, Caitlyn Heqi Feng, Pohsun Wen, Yizhu Wang, Tianyang Li, Ming Ren, Jintao Niu, Qian Chen, Silin Liu, Ming National Tsing Hua University Taiwan Indiana University United States Kyoto University Japan AppCubic Rutgers University United States University of Wisconsin-Madison United States Purdue University United States Georgia Institute of Technology United States National Taiwan Normal University Taiwan University of Hawaii United States Xi’an Jiaotong-Liverpool University China Aarhus University Denmark Zhejiang University China

Digital Signal processing (DSP) and Digital image processing (DIP) with Machine learning (ML) and deep learning (DL) are popular research areas in Computer Vision and related fields. We highlight transformative applications in image enhancement, filtering techniques, and pattern recognition. By integrating frameworks like the Discrete Fourier Transform (DFT), Z-Transform, and Fourier Transform methods, we enable robust data manipulation and feature extraction essential for AI-driven tasks. Using Python, we implement algorithms that optimize real-time data processing, forming a foundation for scalable, high-performance solutions in computer vision. This work illustrates the potential of ML and DL to advance DSP and DIP methodologies, contributing to artificial intelligence, automated feature extraction, and applications across diverse domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Discrete Fourier transforms

来源：评论

学校读者我要写书评

暂无评论

image processing and deep learning Based Road Object Detection System for Safe Transportation 15

Image Processing and Deep Learning Based Road Object Detecti...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Prito, Rizvee Hassan Hossain, Md. Shafayat Islam, Md. Shajibul Meem, Mehzabin Ali, Md. Nawab Yousuf East West University Computer Science and Engineering Dept Dhaka Bangladesh

ISBN: (纸本)9798350370249

Road object detection, a pivotal task in computer vision and artificial intelligence, is dedicated to the identification and precise localization of a diverse array of elements on roadways, including vehicles, pedestrians, road signs, traffic lights, and potential obstacles. The essence of this task lies in its ability to provide real-time and precise object detection, ultimately serving as a crucial safeguard to prevent accidents and ensure the safety of drivers, passengers, and pedestrians. It also lays the foundation for advanced warning systems and aids in collision avoidance. Several popular models were implemented, encompassing YOLOv7, YOLOv7-Modified, YOLOv7-Tiny, YOLOv7-E6E, Faster R-CNN, and SSD. Among these, YOLOv7 achieved an impressive mean average precision (mAP) of 83.6%, with an inference speed of 15.1 ms, while YOLOv7E6E achieved the highest mAP of 86.2%, but with the cost of a slower inference speed of 30.3 ms. The modified version of YOLOv7 has produced 2.2% higher average precision accuracy (89.1%) than the main version of YOLOv7 due to its double RepVGG layers, skip connection and concatenation layers in the head architecture. To make this research accessible to a wider audience, a user-friendly web application is developed with an intuitive interface. © 2024 IEEE.

关键词： Traffic signs

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of image processing and deep learning Techniques for License Plate Recognition in Difficult Environments

A Comparative Analysis of Image Processing and Deep Learning...

引用

IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Tejaswi S Tina Babu Tejaswi K Department of Computer Science Seshadripuram Degree College Mysuru India Department of Computer Science and Engineering School of Engineering Alliance University Bengaluru India Department of Computer Applications JSS Science and Technology University Mysuru India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

License plate recognition is one of the challenging tasks and it belongs to ITS, due to backgrounds, variation of illumination, occlusion the recognition is became the challenging task. These challenges enabled a comparison between the conventional image enhancement and the deep learning scheme for license plate recognition. We analyze and compare multiple techniques that we consider significant, including, the Edge detection, Morphological operations and template matching and also deep learning models like CNNs and R-CNNs. In this framework of experiment scenarios, we scale out a comprehensive evaluation of the methods given different datasets and compare their accuracy, speed as well as stability in conditions that may be hostile. In the experiments, we see that Naive Bayes and SVM outperform in low-resource conditions, but deep learning methods are significantly more accurate and more flexible in accommodating complicated cases and trends, which makes deep learning methods the method of choice for contemporary applications. This work provides an understanding of the strengths and weaknesses of both approaches to serve as a guideline in choosing the most effective detection techniques in relation to certain environmental conditions.

关键词： deep learning Support vector machines YOLO Accuracy Lighting Computer architecture real-time systems Vectors License plate recognition Meteorology

来源：评论

学校读者我要写书评

暂无评论

Cotton Plant Disease Detection and Prevention Using deep learning and image processing

Cotton Plant Disease Detection and Prevention Using Deep Lea...

引用

International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Ruthvija Kesani Jaya Prakash Sunkavalli Mahamamad Rafi Shaik Kusuma Yadala Dept. of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

One of the most important aspects of the agricultural economy is the production of cotton, which is threatened by diseases that lower crop quality and yield. Conventional techniques for diagnosing diseases are frequently subjective and labor-intensive. This paper presents a novel method for the automatic detection and prevention of cotton plant diseases that makes use of deep learning techniques. A convolutional neural network (CNN) model is trained using a dataset that includes various photos of both healthy and sick cotton plants. The suggested model offers a dependable and time-efficient solution by exhibiting high accuracy in differentiating between different diseases. Moreover, proactive disease detection is made possible by the integration of real-time monitoring systems, such as drones fitted with high-resolution cameras. Early detection lessens the need for broadspectrum antibiotics by enabling the prompt application of preventive measures, such as targeted therapies. Finally, we conduct a comprehensive computational analysis of eight cutting-edge object detection algorithms on the cotton plant dataset to identify diseases on the leaves and seven cutting-edge classification algorithms on the cotton plant datasets to determine if a leaf has a disease or not. computed results indicate that it has a high degree of object detection accuracy.

关键词： deep learning Plant diseases image processing Plants (biology) Transfer learning Object detection Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

A Novel Human Face Expression Recognition Based on image processing Assisted Convoluted deep learning Methodology

A Novel Human Face Expression Recognition Based on Image Pro...

引用

Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： M. Tamilselvi Venkata Siva Prasad Ch M. Varuna Kumari R. Sai Lavanya Nandurkar Y Mohammad Rasmi Al-Mousa Dept of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of ECE Jeppiaar Engineering College Rajiv Gandhi salai Chennai Department of S&H QIS College of Engineering and Technology Ongole Prakasam District Andhra Pradesh India Department of MCA QIS College of Engineering and Technology Ongole Prakasam District Andhra Pradesh India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Cyber Security College of Information Technology Zarqa University zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Facial expression recognition has become a critical component in applications involving human-computer interaction, security systems, and behavioral analysis. This paper presents a novel approach to human face expression recognition using a convoluted deep learning methodology supported by image processing techniques. The system is designed to accurately classify seven fundamental emotions—Anger, Disgust, Fear, Happy, Sadness, Surprise, and Neutral—using the FER2013 dataset. The methodology includes preprocessing steps such as noise reduction, grayscale conversion, and face registration. Feature extraction is performed using Gabor filters and Local Binary Patterns (LBP), while Convolutional Neural Networks (CNNs) are employed to learn deep hierarchical features. The system was optimized through hyperparameter tuning, achieving an overall accuracy of 96.29% on the test set. Precision, recall, and F1-scores for each emotion also exceeded 95%, with “Happy” and “Surprise” emotions showing the best performance. The results indicate that the model is effective for real-time applications requiring reliable emotion detection. The paper concludes with discussions on model performance, challenges in classifying similar emotions, and recommendations for future improvements.

关键词： deep learning Emotion recognition image recognition Face recognition Computational modeling Feature extraction real-time systems Security Reliability Tuning

来源：评论

学校读者我要写书评

暂无评论

Experimental Analysis of Malarial Parasites Infection by using deep learning based on Medical image processing Logic 3

Experimental Analysis of Malarial Parasites Infection by usi...

引用

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

作者： Meenakshisundaram, N. Sajiv, G. Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331508456

Malaria remains a significant global health challenge, particularly in resource-limited regions, necessitating accurate and rapid diagnostic tools. This study introduces deepMalariaNet, a deep learning model developed for detecting and classifying malarial parasites using the 'Malaria Parasite image - Malaria Species' dataset from Kaggle. The model employs Residual Attention Mechanisms and Parallel Convolutional Stacks (PCS) to improve diagnostic accuracy by focusing on critical image regions and capturing multi-scale features. Experimental results demonstrate that deepMalariaNet achieves 98.5% accuracy for binary classification (infected vs. non-infected) and 95.2% for multiclass classification (species identification). The model's robustness is validated through 10-fold cross-validation and ablation studies, and it outperforms state-of-the-art models such as ResNet-50 and DenseNet-121 in both accuracy and inference time. deepMalariaNet shows significant promise for real-time malaria detection in clinical settings, contributing to early and accurate diagnosis, which is crucial for effective malaria control and treatment. © 2025 IEEE.

关键词： Photointerpretation

来源：评论

学校读者我要写书评

暂无评论

image processing based image to Cartoon Generation: Reducing complexity of large computation arising from deep learning

Image Processing based Image to Cartoon Generation: Reducing...

引用

Computational Intelligence and Sustainable Engineering Solutions (CISES), International Conference on

作者： Shruti Kumari Shrivastava Ruchi Gajjar Electronics and Communication Engineering Department Institute of Technology Nirma University Ahmedabad Gujarat India

This paper proposes an approach to convert real life images into cartoon images using image processing. The cartoon images have sharp edges, reduced colour quantity compared to the original image, and smooth colour regions. With the rapid advancement in artificial intelligence, recently deep learning methods have been developed for image to cartoon generation. Most of these methods perform extremely huge computations and require large datasets and are time consuming, unlike traditional image processing which involves direct manipulation on the input images. In this paper, we have developed an image processing based method for image to cartoon generation. Here, we perform parallel operations of enhancing the edges and quantizing the colour. The edges are extracted and dilated to highlight them in the output colour image. For colour quantization, the colours are assigned based on proposed formulation on separate colour channels. Later, these images are combined and the highlighted edges are added to generate the cartoon image. The generated images are compared with existing image processing approaches and deep learning based methods. From the experimental results, it is evident that the proposed approach generates high quality cartoon images which are visually appealing, have superior contrast and are able to preserve the contextual information at lower comnutational cost.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Terahertz image processing - Resolution Enhancement and Object Segmentationusing W-Net deep learning

SSRN

引用

SSRN 2024年

作者： Mondal, Shyamal Jampani, Kashyap Raj, Akshay R. Sethi, Abhijit Chowdhury, Dibakar Roy Dept. of Applied Physics Defence Institute of Advanced Technology Maharashtra Pune411025 India Dept. of Physics Mahindra Ecole Centrale University Hyderabad500043 India

Terahertz images typically suffer from poor image quality and do not allow traditional machine learning methods to detect and identify objects. In this work, an image segmentation algorithm called W-Net is deployed for the first time, to segment concealed objects from the poor-quality Terahertz image samples. This involves developing an image enhancement and segmentation system for terahertz images. The proposed system comprises of two stages:in the first stage, the resolution of low-resolution THz images has been enhanced by training a U-Net deep learning model. In the latter stage, another U-Net model is trained to segment the enhanced images using corresponding masks to identify regions of interest. The proposed system is evaluated using a dataset of low-resolution terahertzimages with its respective high-resolution images and masks. The system performed well with an accuracy, precision, receiver operating characteristic and F1 scores of 99.1%, 0.9143, 0.9977 and 0.9146, respectively for enhancement. Moreover, values of the same evaluation metrics deduced as 99.9%, 0.9851, 0.9999 and 0.9923, respectively for segmentation. Therefore, the demonstrated scheme is expected to identify and classify objects hidden in real-time terahertz images and improve the quality of the same to enable accurate segmentation for applications like security screening, biomedical applications, quality and health monitoring in food industry, etc. © 2024, The Authors. All rights reserved.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：