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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

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Research on military target detection method based on YOLO method 3

Research on military target detection method based on YOLO m...

引用

3rd IEEE international conference on Information Technology, Big Data and Artificial intelligence, ICIBA 2023

作者： Wang, Huibai Han, Ji North China University of Technology Information Institute Beijing China

ISBN: (纸本)9781665490788

In the field of military target recognition, in order to solve the problems of easy loss of small target detection results and slow detection speed, this paper proposes a target recognition detection algorithm YOLO-G2S, which reduces the parameters and reduces the model size by sacrificing a little accuracy. In the original model of YOLO v5, the 2nd, 3rd and 4th C3 modules in the backbone were replaced by C3G2 modules, thus reducing the model size and improving the detection speed. Then the accuracy is improved by changing the RELU activation function in C3G2 module. The experimental results show that the average accuracy of YOLO-G2S reaches 95%, the number of parameters is reduced by 7.3%, and the amount of calculation is reduced by 17.8%. Therefore, the improved algorithm in this paper is more suitable for deployment in the field of military target recognition. © 2023 IEEE.

关键词： C3G2 Identification Military objectives Reducing the model size YOLO v5

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StraightLine: An End-to-End Resource-Aware Scheduler for machine Learning Application Requests 3

StraightLine: An End-to-End Resource-Aware Scheduler for Mac...

引用

3rd international conference on Artificial intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2024

作者： Ching, Cheng-Wei Guan, Boyuan Xu, Hailu Hu, Liting University of California Santa Cruz Department of Computer Science and Engineering Santa CruzCA United States Center of Florida International University MiamiFL United States California State University Department of Computer Engineering and Computer Science Long BeachCA United States

ISBN: (纸本)9798331530280

The life cycle of machine learning (ML) applications consists of two stages: model development and model deployment. However, traditional ML systems (e.g., training-specific or inference-specific systems) focus on one particular stage or phase of the life cycle of ML applications. These systems often aim at optimizing model training or accelerating model inference, and they frequently assume homogeneous infrastructure, which may not always reflect real-world scenarios that include cloud data centers, local servers, containers, and serverless platforms. We present StraightLine, an end-to-end resource-aware scheduler that schedules the optimal resources (e.g., container, virtual machine, or serverless) for different ML application requests in a hybrid infrastructure. The key innovation is an empirical dynamic placing algorithm that intelligently places requests based on their unique characteristics (e.g., request frequency, input data size, and data distribution). In contrast to existing ML systems, StraightLine offers end-to-end resource-aware placement, thereby it can significantly reduce response time and failure rate for model deployment when facing different computing resources in the hybrid infrastructure. © 2024 IEEE.

关键词： Resource allocation

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Research on Named Entity recognition in Chinese Resumes Based on ResNEZHA-BiLSTM-Attention-CRF Model 3

Research on Named Entity Recognition in Chinese Resumes Base...

引用

3rd IEEE international conference on Social Sciences and intelligence Management, SSIM 2023

作者： Wu, Xiaoqin Yu, Conggen Zhang, Chi Yu, Zhiwei School of Artificial Intelligence and Big Data Hefei University Anhui Wuwei China School of Artificial Intelligence and Big Data Hefei University Anhui Mingguang China School of Artificial Intelligence and Big Data Hefei University Woyang Anhui China School of Artificial Intelligence and Big Data Hefei University Henan Xinyang China

ISBN: (纸本)9798350327786

To extract meaningful entity information from a substantial volume of Chinese resume data, we introduced a ResNEZHA-BiLSTM-Attention-CRF model based on multi-task pre-training. The model enhanced the accuracy and generalization ability of the ResNEZHA model by incorporating a multi-task pre-training mechanism. The model facilitated Chinese word segmentation and entity sequence extraction. Subsequently, the attention mechanism in the BiLSTM layer refines the extraction of local features. The CRF was then employed to assign labels. The model exhibited outstanding performance in the experiment, achieving accuracy, recall, and F1 values of 97.47, 97.79, and 97.73%, respectively. Research findings suggested that, compared to alternative models, the proposed model demonstrated superior performance in the task of named entity recognition in Chinese resumes. © 2023 IEEE.

关键词： Extraction

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Computational intelligence to Progress in WMN 3rd

Computational Intelligence to Progress in WMN

引用

3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Revathy, G. Senthilkumar, C. Rajesh, A. Selvi, C. P. Thamil Department of CSE Srinivasa Ramanujam Centre SASTRA Deemed University Tamilnadu Kumbakonam India Department of CSE Erode Sengunthar Engineering College Erode India Associate Professor Department of Artificial Intelligence & Data Science Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9789819783540

Wireless mesh networks are been prominently developing in the past two decades and have received a lot of accomplished tasks in various applications. machine learning is an art of computational intelligence that enhances development day by day in a rapid manner. The planned work deals with the combination of machine learning techniques to increase the Eminence of Amenities in wireless mesh networks. The prominent ideas of machine learning are to enhance the performance in Wireless Mesh Networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wireless mesh networks (WMN)

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Prediction of gender from structural MRI images using Multiscale ShuffleNet Extreme Learning machine 3

Prediction of gender from structural MRI images using Multis...

引用

3rd international conference on Artificial intelligence For Internet of Things, AIIoT 2024

作者： Stanley, Berakhah.F. Jeen Retna Kumar, R. Gnanaprakash, V. Bini Palas, P. Patturose, J Gold Beaulah Joel Devadass Daniel, D.J. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SRM Easwari Engineering College Department of Electronics and Communication Engineering Chennai India St. Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350372120

In recent years, the application of machine learning techniques to medical imaging data has shown promising results in various clinical tasks. One such task is the prediction of gender from structural MRI (magnetic resonance imaging) images, which holds potential implications for personalized medicine and understanding neurobiological differences between genders. In this study, we propose a novel approach utilizing Multiscale ShuffleNet Extreme Learning machine (MSEL), a fusion of Multiscale feature extraction and ShuffleNet architecture with Extreme Learning machine classifier. We demonstrate the effectiveness of our method on a large dataset of structural MRI images, employing state-of-the-art preprocessing techniques and feature extraction methods. Our results indicate significant accuracy and robustness in gender prediction, outperforming existing methodologies. Furthermore, we conduct comprehensive analyses to investigate the contribution of different components in our proposed framework, shedding light on the underlying mechanisms of gender-related brain structural differences. Overall, our study presents a promising avenue for utilizing advanced machine learning techniques in neuroimaging research, with potential applications in clinical diagnostics and personalized healthcare. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Enhancing Gastrointestinal Disease Detection Through ResNet50 and Comparative Analysis of Deep Learning Architectures for Medical Image Classification 3

Enhancing Gastrointestinal Disease Detection Through ResNet5...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Vardhini, Harsha Sophia Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India

ISBN: (纸本)9798331509675

Accurate and timely detection of gastrointestinal diseases is critical to an effective treatment and the best outcomes for patients. Manual examination of endoscopic images during an endoscopy by trained specialists still constitutes the traditional and most reliable method of diagnostic of diseases in their early stages. This study presents a deep learning-based approach of automating the detection of gastrointestinal diseases using the ResNet50 convolutional neural network (CNN) architecture. This research evaluates the performance of ResNet50 on the task of classifying gastrointestinal conditions from medical images against other established models, VGG16 and MobileNetv2, by means of a comparative analysis. The results of experiments show that ResNet50 has better accuracy, precision, recall and computational efficiency compared the other models. The results suggest the application of ResNet50 in improving diagnostic accuracy as well as a shortening of the detection process, hence paving the way for clinical practices. © 2025 IEEE.

关键词： Endoscopy

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Enhanced Tuberculosis Detection using Deep Learning and GAN Data Augmentation Techniques for Chest X-ray Analysis 3

Enhanced Tuberculosis Detection using Deep Learning and GAN ...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chaykam, Varun Reddy Koduru, Avinash Reddy Karthick, S. Arun, C. Department of Computational Intelligence SRM Institute of Science and Technology KTR campus Chennai India

ISBN: (纸本)9798331527549

With around 1.6 million deaths yearly, primarily in low-resource environments, abstract-tuberculosis (TB) is a serious worldwide health issue. While human analysis can be error-prone and labor-intensive, early discovery using chest X-rays is vital. We used advanced machine learning methods including a mix of Histogram of Oriented Gradients (HOG) and Convolutional Neural Networks (CNN), alongside Deep Convolutional Generative Adversarial Networks (DCGANs) to create synthetic TB images, so helping to balance the dataset and reduce class imbalance. We applied explainable artificial intelligence (XAI) methods including LIME and Grad-CAM for greater interpretability in order to improve model transparency. The outcomes showed notable progress;our hybrid model obtained a stunning 99.5% diagnosis accuracy. With respective accuracy of 96.87% and 94.67%, models such as Xception and MobileNetV2 also fared rather well. Especially in resource-limited healthcare settings, this study provides a consistent and interpretable strategy for tuberculosis screening. © 2025 IEEE.

关键词： Generative adversarial networks

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Implementation of Parallel Evolutionary Convolutional Neural Network for Classification in Human Activity and Image recognition 22nd

Implementation of Parallel Evolutionary Convolutional Neural...

引用

22nd Mexican international conference on Artificial intelligence (MICAI)

作者： Villegas-Cortez, Juan Roman-Alonso, Graciela Fernandez De Vega, Francisco Flores-Morales, Yafte Aaron Cordero-Sanchez, Salomon Univ Autonoma Metropolitana Dept Sistemas Azapotzalco Av Sn Pablo 180 Mexico City 02200 DF Mexico Univ Autonoma Metropolitana Dept Ingn Elect Unidad Iztapalapa Mexico City DF Mexico Univ Extremadura Dept Comp Sci C Santa Teresa Jornet38 Merida 06800 Spain Univ Autonoma Metropolitana Unidad Iztapalapa Dept Chem Mexico City DF Mexico

ISBN: (纸本)9783031477645;9783031477652

pattern recognition has been evolving to include problems posed by new sceneries containing a high number of pattern components. Processing this volume of information allows a more exact classification in wider types of applications;however, some of the difficulties of this scheme is the maintenance of numerical precision and mainly the reduction of the execution time. During the last 15 years, several machine Learning solutions have been implemented to reduce the number of pattern components to be analyzed, such as artificial neural networks. Deep learning is an appropriate tool to accomplish this task. In this paper, a convolutional neural network is implemented for recognition and classification of human activity signals and digital images. It is achieved by automatically adjusting the parameters of the neural network through genetic algorithms using a multiprocessor and GPU platform. The results obtained show the reduction of computational costs and the possibility of better understanding of the solutions provided by Deep Learning.

关键词： Deep Learning pattern recognition HAR Image recognition Parallel Genetic Algorithms

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Construction of a Smart Classroom for Image Processing Courses in Colleges and Universities Based on Artificial intelligence: Taking Fundamentals of Photoshop as an Example 3

Construction of a Smart Classroom for Image Processing Cours...

引用

3rd IEEE international conference on Social Sciences and intelligence Management, SSIM 2023

作者： Zhang, Yanyan Jilin Agricultural Science and Technology University Jilin China

ISBN: (纸本)9798350327786

Accompanied by the continuous progress of social science and technology, the hybrid teaching model and the intelligent classroom based on artificial intelligence (AI) technology in higher education institutions have become an innovative trend that has attracted much attention from the education sector. The purpose of this study was to delve into how to integrate hybrid teaching and AI technology in university education and create a smarter and more personalized classroom atmosphere. This study was carried out to build a smart classroom for a university image processing course with AI technology, using the book 'Photoshop Basics' as an example. By integrating advanced technology in AI, the quality of classroom instruction was improved to help students learn image processing as a fundamental concept. The Smart Classroom program used adaptive learning systems, image recognition technology, and real-time feedback mechanisms to provide a personalized learning experience. By blending the foundational concepts of traditional Photoshop with the latest AI technologies, students understood the core concepts of image processing, thereby enhancing their creative and practical skills. AI-based blended teaching models in higher education continue to evolve as a powerful means of fostering students with innovative thinking and practical skills. © 2023 IEEE.

关键词： Students

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Analysis of Intelligent Research and Development System of Power Communication Equipment Based on Artificial intelligence Technology 3

Analysis of Intelligent Research and Development System of P...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Qin, Weiwei Luo, Pengyuan Wang, Kaixing Liu, Yulong Luo, Jing Qin, Hao Nanning Power Supply Bureau Guangxi Nanning China

ISBN: (纸本)9798350317022

The research and development of electric power communication equipment is a heavy task. Traditional research and development systems have problems such as inconsistent communication protocols and interface standards between devices and poor compatibility. This paper studies the intelligent research and development system of power communication equipment based on artificial intelligence, and tests and evaluates its performance. In this paper, the response time of the system is tested by simulating the load in real scenarios. Secondly, the throughput of the system is evaluated by sending a large number of requests and recording the number of requests processed by the system. At the same time, we conducted a concurrency capability test to simulate concurrent requests and observe the response time and throughput of the system. In addition, stability tests were performed to evaluate how the system would perform over long periods of time and under sustained load. The research results show that the artificial intelligence-based intelligent R&D system for power communication equipment shows obvious advantages in terms of throughput, stability and response time. The system can efficiently process large-scale data and requests to improve throughput. At the same time, through learning and pattern recognition, the system can improve stability, prevent and repair potential anomalies. © 2023 IEEE.

关键词： Artificial intelligence Communication Equipment Electric Power Project Intelligent Research and Development System

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