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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 6052 条记录，以下是71-80 订阅

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Research on GSEm-Net detection method of embroidery pattern lightweight operator for digital protection of Gansu intangible cultural heritage Long Embroidery 2

Research on GSEm-Net detection method of embroidery pattern ...

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2nd International conference on Big Data, Computational Intelligence, and applications, BDCIA 2024

作者： Ba, Yuanxiao Lanzhou Vocational Technical College Gansu Lanzhou China

ISBN: (纸本)9781510689053

The digital image of embroidery has complex edge structure and high repetition rate texture features. Post-processing such as region detection is a challenging task. A long embroidery image data set for learning and training is constructed. Based on the deep network and multi-scale detection structure, the lightweight detection network model GSEm-Net is introduced to detect the long embroidery pattern in real time. Experiments show that compared with yoloV8, the detection accuracy of this method is equivalent, and the detection speed is 1.5 times that of yoloV8, but the generated model is smaller, and it is easier to deploy to low-computing edge devices, which effectively improves the target detection efficiency. © 2025 SPIE.

关键词： Convolutional neural networks

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Investigating the Use of Hierarchical Recurrent neural networks for Medical image Segmentation 5th

Investigating the Use of Hierarchical Recurrent Neural Netwo...

引用

5th International conference on Data Science, machine learning and applications, ICDSMLA 2023

作者： Suneetha, K. Dobriyal, Rohit Elangovan, Vetrimani Savita Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper investigates the usage of hierarchical recurrent neural networks (HRNNs) for clinical photo segmentation. HRNNs are a neural network structure combining more than one layer of recurrent cells and layers of convolution cells. The intention of this paper changed to evaluate the effectiveness of HRNNs for medical picture segmentation and how well it can be applied for this reason. The paper evaluates a present HRNN implementation for medical picture segmentation and shows its accuracy primarily based on the dice coefficient (DC). The authors additionally proposed a changed HRNN architecture to improve the segmentation performance: the architecture integrated residual connections, multi-scale inputs, multi-resolution outputs, and an utterly convolution structure. The proposed DT-SL loss was potent in improving the segmentation accuracy. Typically, the outcomes reveal that HRNNs are promising devices for clinical photograph segmentation tasks and can effectively facilitate faster and extra correct segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Leveraging Artificial neural networks for Breast Cancer Detection and Prognosis

Leveraging Artificial Neural Networks for Breast Cancer Dete...

引用

2025 IEEE International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Goel, Aadya Mishra, Pallavi Bhatia, Rachna School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Advanced Sciences Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331512965

Breast cancer remains one of the leading causes of mortality among women worldwide, making early detection and accurate classification critical for improving patient out-comes. This study explores the application of Artificial neural networks (ANN) for breast cancer multiclass classification and prediction using the CBIS-DDSM: Breast Cancer image Dataset. The model was trained to classify mammographic images into three categories: benign, malignant, and benign without callback, achieving an impressive accuracy of 99.6%. The choice of ANN was driven by its proven capability to model complex, nonlinear relationships within data, offering a significant improvement over traditional machine learning technique. Despite the challenges posed by the limited size and heterogeneity of the dataset, the ANN demonstrated robust performance, highlighting its potential for clinical applications. The high accuracy of the model underscores its value as a reliable tool for aiding radiologists in the early detection and prognosis of breast cancer, which could lead to more effective treatment planning and better patient outcomes. This study lays the foundation for integrating advanced AI technologies into clinical settings, offering promise for more precise, timely breast cancer diagnoses. © 2025 IEEE.

关键词： Lung cancer

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CNN-based Plant Leaf Disease Identification and Classification for Sustainable Agriculture 6

CNN-based Plant Leaf Disease Identification and Classificati...

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6th International conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Srilatha, A. Praveen, P. School of Computer Science & Artificial Intelligence SR University Warangal506371 India

ISBN: (纸本)9798331522667

The rapid spread of leaf-diseases among the large-scale crops or plants will pose a great risk to the agricultural output and crop yield. Earlier detection and proper analysis of foliar disorders are essential for minimizing yield losses as well as forwarding continuous farming trials. This research presents a novel way to find and analyze leaf diseases using various deep learning algorithms. The proposed approach integrates Convolutional neural networks (CNN) in feature extraction for automatically identifying disease patterns from leaf-based images with high accuracy to exploit manual inspection of plant leaves. To achieve higher accuracy, this model is train and tested on a wide variety from diseased & healthy leaf images. This approach surpasses traditional image processing methods, exhibiting high precision and recall in identifying various disease types across multiple crops. The deep learning-based solution offers a scalable, efficient, and reliable tool for farmers and agricultural experts, potentially revolutionizing plant disease management strategies. In addition, it can expand the dataset and enhance real-time detection ability which probably could be applied into user-friendly mobile applications in future work. © 2025 IEEE.

关键词： Accuracy CNN Deep learning image Classification leaf disease detection

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A Safe and Secured Deep Learnıng Cnn Approach for Robust Intellıgent Malware Detectıon Usıng Artıfıcıal Intellıgence 5th

A Safe and Secured Deep Learnıng Cnn Approach for Robust In...

引用

5th International conference on Data Science, machine learning and applications, ICDSMLA 2023

作者： Murali, Kanthi Maneiah, D. Kiran Kumar, Adepu Skandha Sanagala, Siva Suhasini, R. Archana, B. CMR Technical Campus Telangana Hyderabad India Department of Mechanical CMR Technical Campus Telangana Hyderabad India Department of CSE CMR Technical Campus Telangana Hyderabad India Department of CSE CMR College of Engineering and Technology Telangana Hyderabad501401 India Department of CSC CMR College of Engineering and Technology Telangana Hyderabad501401 India

ISBN: (纸本)9789819780426

Malware attacks continue to pose an important threat for individuals who use computers, enterprises, and authorities in the digital age as they multiply constantly. Existing malware detection systems take a long time and are ineffective at discovering unknown malware since they analyze the signatures of malware and behavior patterns both statically and dynamically. Recent malware quickly changes its behavior and generates a lot of malware using polymorphic, metamorphic, and other types of evasive techniques. As new malware is frequently an updated version of an existing infection, machine learning algorithms (MLAs) have been utilized to analyze malware successfully. This requires significant feature technology, learning of features, and feature representation. The feature engineering stage can be entirely skipped by employing the more sophisticated MLAs, like deep learning. Although there are several current research papers in this area, the algorithms’ performance is skewed by the training data. To develop new, improved approaches for efficient zero-day malware detection, bias must be reduced and these methods must be evaluated independently. This paper examines traditional MLAs and deep learning architectures for detecting malware, categorization, and classification with both public and private datasets to address the vacuum in the literature. Private and public data sets utilized in the experimental study have test and training splits that are not connected to one another and were gathered at various dates. In addition, we suggest a brand-new image processing method with ideal settings for Convolutional deep learning neural networks (DLCNN) architectures. Overall, this work suggests an extensible and a hybrid deep learning system that allows actual time deployment for efficient malware visual detection. Illustrations and deep learning framework for hybrid dynamic, static, and graphical processing-based methods are used as a novel enhanced way for effective zero-d

关键词： Adversarial machine learning

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Land Cover Segmentation for Agricultural Monitoring Using Synthetic Aperture Radar (SAR) imagery with Convolutional neural networks and Attention-Based Transformer Models

Land Cover Segmentation for Agricultural Monitoring Using Sy...

引用

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

作者： Prakash, E.P. Sathiya, K.G. Manoranjitham, S. Sekar, G. Munaf, S. Santhia, R.K. Dr. N.G.P. Institute of Technology Department of Computer Science and Engineering Coimbatore641048 India Arunai Engineering College Department of Electronics and Communication Engineering Tiruvannamalai606604 India Agni college of Technology Department of Information Technology Chennai600130 India Vsb College Of Engineering Department of Electronics and Communication Engineering Technical Campus Coimbatore642109 India Sri Ramakrishna Institute of Technology Department of Electronics and Communication Engineering Coimbatore641010 India Pondicherry University Department of Computer Science and Engineering 605014 India

ISBN: (纸本)9798331509828

Automated classification of land cover types is one of the most crucial aspects within many remote sensing applications, including the monitoring of agricultural land and territories. This work proposes a novel approach for division of land covers using a hybrid of CNNs and Attention-based Transformer networks. CNNs are adopted to extract the spatial features from different levels of the SAR image, while Transformer Model Arch applies self-attention functions to explore the long-range dependency and contextual relation of the entire image. The model learning process is performed end-to-end from multi-temporal SAR data to differentiate different types of land covers, including crops, vegetation, water, and urban surfaces. In this case, we apply the proposed approach to different agricultural areas to show that it outperforms traditional methods, such as thresholding and other machine learning approaches. The results highlight the viability of this mixed form to solve the noise and changeable nature of SAR imagery, in terms of enhancing the use of the agricultural monitoring application. © 2025 IEEE.

关键词： Convolutional neural networks

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Improving Medical image Segmentation Through Knowledge Transfer and Deep learning 5th

Improving Medical Image Segmentation Through Knowledge Trans...

引用

5th International conference on Data Science, machine learning and applications, ICDSMLA 2023

作者： Pandeya, Megha Yadav, Surjeet Batra, Raman Gobi, N. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Mechanical Engineering Vivekananda Global University Jaipur India Department of Mechanical Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Clinical image segmentation is vital to offering higher medical analysis and remedy. But, due to the complex nature of clinical images, conventional segmentation algorithms frequently fail to phase the anatomy safely. With the advances in deep learning, it has become feasible to apply the expertise switch and deep gain knowledge of strategies to enhance the overall performance of medical photo segmentation. Information transfer, additionally known as switch mastering, applies know-how obtained from one set of supply statistics to any other set of target facts. This technique can be applied to medical picture segmentation to transfer expertise from the source facts set to the target statistics. Expertise switch entails the switch of characteristic representations among the source and goal facts and can contain modifying the parameters of the target community. Deep gaining knowledge of techniques, such as convolution neural networks (CNNs), was used to improve overall performance in scientific image segmentation. These networks can examine the information, considering higher segmentation results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： datasets Medical performance segmentation techniques

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A CNN-Random Forest Hybrid Model for Network Traffic Classification 5

A CNN-Random Forest Hybrid Model for Network Traffic Classif...

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5th IEEE Middle East and North Africa COMMunications conference, MENACOMM 2025

作者： Chaiban, Jean Paul Elhajj, Imad H Kayssi, Ayman Tabet, Salam American University of Beirut Department of Electrical and Computer Engineering Beirut Lebanon

ISBN: (纸本)9798331519957

Internet service providers (ISPs) have been using machine learning (ML)-based network traffic classification in recent years primarily to dynamically adjust their networks to the growing needs of their customers. Even though ML models have several advantages in classifying network traffic, especially in their ability to automatically adapt to ever-changing network traffic patterns, our understanding of their accuracy and generalizability remains limited. In this paper, we evaluate the generalizability of the best performing state-of-The-Art machine learning models used for network classification on a Gaming Traffic Dataset and suggest an architecture for enhanced classification of a large number of classes. Initially, we evaluate Random Forest (RF) utilizing pre-engineered variables including packet length, inter-Arrival periods, and other flow data. Additionally, we evaluate another state-of-The-Art model, the Convolutional neural Network (CNN), which receives raw network data that has been transformed into either an RGB or gray-scale image. Furthermore, we propose a classification system that consists of two phases: An unsupervised phase governed by Density-Based Spatial Clustering of applications with Noise (DBSCAN) clustering, followed by a supervised phase that consists of a stacked arrangement of sub CNN base models and a final Random Forest meta estimator. The evaluation of this semi-supervised stacked CNN-RF model shows that it outperforms the state-of-The-Art RF and CNN models, achieving a 7.186% improvement in the testing accuracy in the dataset, and an average 1.7% improvement in the testing accuracy for other datasets. © 2025 IEEE.

关键词： Convolutional neural networks

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Accurate and Efficient Medical image Segmentation with Convolutional neural networks 5th

Accurate and Efficient Medical Image Segmentation with Convo...

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5th International conference on Data Science, machine learning and applications, ICDSMLA 2023

作者： Sahoo, Girija Shankar Sharma, Govind Shay Kapse, Vinod Mansiram Bargavi, Manju Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Mathematics Vivekananda Global University Jaipur India Department of ECE Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Scientific photo segmentation is a crucial challenge in clinical photo evaluation as it is a critical step in growing three-D models from valuable scientific imagery for diagnosis and remedy. Convolution neural networks (CNNs) have been used to phase medical photographs as they proved to obtain excellent effects in phrases with cutting-edge accuracy and velocity compared to traditional techniques. We review the maximum applicable previous work, which includes Unified U-internet, Pixel Net, Multi-Scale Aggregation and attention (MSA), and Multi-Scale attention U-net (MSA-UNet). We speak success packages of those methods, analyzing the architectures followed and datasets used to guide selection-making. In the end, we provide a standard to evaluate modern-day metrics and performance standards used to evaluate the accuracy and efficiency of modern CNN processes for medical photo segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A Rapid Recognition Method for Transformer Partial Discharge Fault Patterns Based on Improved DSCNN 24

A Rapid Recognition Method for Transformer Partial Discharge...

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2nd International conference on Advances in Artificial Intelligence and applications, AAIA 2024

作者： Zhou, Zhengqin Zhan, Hao Cai, Yuru Yang, Xu Zhou, Wen Liu, Mengna Wuhan NARI Limited Liability Company State Grid Electric Power Research Institute Hubei Wuhan China NARI Group Corporation State Grid Electric Power Research Institute Jiangsu Nanjing China

ISBN: (纸本)9798400712883

Pattern recognition of partial discharge (PD) is a key focus in the condition assessment of power equipment. The strong feature extraction capabilities demonstrated by deep learning in the fields of image and signal processing have made it a new trend in PD pattern recognition research. This paper proposes a method for PD pattern recognition based on an improved Deep Separable Convolutional neural Network (DSCNN). The method first introduces an improved model of the DSCNN, secondly, utilizes a PD experimental detection platform to obtain Ultra-High Frequency (UHF) signals from partial discharges, and employs the S-transform to generate the time-frequency spectrum corresponding to the time-domain signals;then, it uses the improved DSCNN model to classify the time-frequency spectrum for the identification of PD types. The results show that the PD pattern recognition model proposed in this paper has a good recognition accuracy;compared to traditional deep learning models, the optimized model proposed in this paper reduces the scale of parameters and inference time, which is conducive to improving fault diagnosis speed. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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