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检索条件"任意字段=5th International Conference on Machine Learning and Computer Application, ICMLCA 2024"

共 2900 条记录，以下是61-70 订阅

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IoT-Based Convolutional Neural Networks in a Farm Pest Detection Using Transfer learning 5th

IoT-Based Convolutional Neural Networks in a Farm Pest Detec...

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5th international conference on Computing Science, Communication and Security

作者： Jani, Keyurbhai A. Chaubey, Nirbhay Kumar Panchal, Esan Tripathi, Pramod Yagnik, Shruti Gujarat Technol Univ Comp IT Engn Ahmadabad 382424 Gujarat India Ganpat Univ Comp Sci Mahesana 384012 Gujarat India Govt Polytech Informat Technol Gandhinagar 382027 Gujarat India Indus Univ Comp Engn Ahmadabad 382115 Gujarat India

ISBN: (纸本)9783031751691;9783031751707

In this study explores agriculture pest detection using transfer learning with IoT devices, evaluating VGG16, VGG19, Inception, and Xception CNN architectures with the agripest dataset. VGG16 and VGG19 show effective learning with consistent accuracy improvements. Inception V3 exhibits strong training but with variability in validation metrics, while Xception demonstrates robust performance and strong generalization to new data. the integrated system utilizes cameras, sensors, and drones for real-time image processing through a gateway and cloud server with a customized agripest dataset. Transfer learning generates a deployable.h5 file for pest identification. the generated custom model deployed on a gateway or server to classifies pests, alerting farmers through SMS, dashboard, or mobile app notifications. this synergy between machine learning and IoT offers rapid and precise pest detection in agriculture.

关键词： IoT deep learning CNN pest detection cloud farm gateway

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A Reinforcement learning Algorithm for Dynamic Job Shop Scheduling 5th

A Reinforcement Learning Algorithm for Dynamic Job Shop Sche...

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5th international conference on Innovative Intelligent Industrial Production and Logistics

作者： Alcamo, Laura Bruno, Giulia Giovenali, Niccolo Politecn Torino I-10129 Turin Italy

ISBN: (纸本)9783031807596;9783031807602

the job shop scheduling problem, a notable NP-hard problem, requires scheduling jobs with multiple operations on specific machines in a predetermined order. A strong assumption is that all the information of the manufacturing environment is known in advance and there is no modification during the scheduling process. However, the real-world environment is significantly affected by uncertainties. the dynamic job shop scheduling is a variant of the job shop scheduling problem in which the scheduling environment is subject to changes over time including variations in job arrival times, processing times, machine breakdowns, resource availability and job priority. To address this issue, this paper presents a single-agent reinforcement learning algorithm, which implements a proximal policy optimization that uses masking to reduce the search space and improve efficiency. the algorithm was tested in both deterministic and dynamic environments and compared to traditional scheduling methods. the results demonstrate that the proposed approach is comparable to traditional methods in deterministic cases and outperforms them in dynamic environments. these findings emphasize the potential of reinforcement learning in addressing and optimizing complex scheduling challenges.

关键词： Job shop scheduling Dynamic job shop scheduling Industry 4.0 Combinatorial optimization Reinforcement learning Artificial intelligence

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CSGNet: A network with attention mechanism for automatic modulation recognition 5

CSGNet: A network with attention mechanism for automatic mod...

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5th international conference on Signal Processing and computer Science, SPCS 2024

作者： Li, Xianyi School of Control Science and Engineering Shandong University Shandong Jinan250002 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In this paper, a new network called CSGNet is introduced for automatic modulation recognition, designed to improve recognition accuracy in complex electromagnetic environments. this innovative network leverages the strengths of both Convolutional Neural Networks (CNN) and Gate Recurrent Units (GRU) while mitigating their inherent weaknesses. Additionally, it incorporates a Self-Attention mechanism to further enhance its perceptual abilities. the primary advantage of this method is its superior recognition accuracy under low SNR conditions compared to traditional methods. the process starts by extracting basic I/Q data from the RadioML 2018.01a dataset as input. Subsequently, the CSGNet model performs an end-to-end modulation recognition task. the results demonstrate that this method excels at multiple SNR levels, highlighting the significant potential of deep learning in solving automatic modulation recognition challenges. this study underscores the effectiveness of integrating CNN, GRU, and Self-Attention mechanisms in enhancing the robustness and accuracy of modulation recognition systems. Furthermore, it demonstrates how deep learning techniques can be effectively applied to complex signal processing tasks. the findings suggest that CSGNet could be a valuable tool for improving communication systems, especially in challenging environments with low SNR. © 2025 SPIE.

关键词： Modulation Signal to noise ratio Feature extraction Deep learning Matrices Data modeling machine learning Data processing Neural networks Quadrature amplitude modulation

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Addressing Geographical Disparities in Education: An Integrated E-learning Approach for Empowering Students in Diverse Regions of India 5th

Addressing Geographical Disparities in Education: An Integra...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Chanusha, Chappidi Lakshmi Naidu, K. TNarasimha Indira, Mandadapu Hussain, SMahaboob Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031770807

A comprehensive e-learning application aims to transform the educational landscape by seamlessly integrating study materials, job opportunities, research and development initiatives, communication tools, mentor access, and information about grants and loans. In response to the growing importance of education in the 21st century, particularly in the wake of the global pandemic, this e-learning application aims to provide a holistic and inclusive learning environment. the platform offers a user-friendly interface accessible from anywhere, with resources available in multiple languages to help all kinds of learners. the application provides a learning Management System (LMS) to deliver a structured and interactive learning experience. As technology continues to reshape the educational landscape, this application stands out as a pioneering initiative, embracing innovation to create a platform that not only meets the current demands of education but also anticipates and adapts to future challenges. this research paper explores the innovative features of this application, examines its potential impact on education, and highlights its role in promoting holistic learning and career development in the evolving landscape of online education. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Collaborative learning

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Research on the application of deep neural networks in predicting English language proficiency 5

Research on the application of deep neural networks in predi...

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5th international conference on Telecommunications, Optics, and computer Science, TOCS 2024

作者： Zhang, Jing Wuhan Institute of Commerce Hubei Wuhan 430070 China

ISBN: (纸本)9781510691667

this study explores the integration of Deep Neural Networks (DNNs) and Improved Support Vector machines (ISVMs) to predict students' English language proficiency within a cloud-based educational platform. Utilizing a hybrid ISVM-DNN model, the research leverages DNNs' capability for automatic feature extraction and handling complex nonlinear relationships, alongside SVMs' robust classification performance in high-dimensional spaces. Data were collected from an online English learning platform, undergoing thorough preprocessing to ensure quality and consistency. the hybrid model demonstrated superior prediction accuracy compared to traditional machine learning methods, effectively capturing intricate patterns in students' learning behaviors and linguistic data. Performance evaluations highlighted significant reductions in system latency through Dynamic Resource Scheduling (DRS), ensuring scalability and responsiveness under varying user loads. Additionally, a questionnaire-based survey revealed high levels of user satisfaction, indicating the model's practical applicability in personalized education. this study underscores the potential of hybrid machine learning models in advancing personalized and data-driven educational assessments. © 2025 SPIE.

关键词： Deep Neural Networks educational data mining English proficiency prediction hybrid machine learning models support vector machines

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Production Classification in E-Commerce Based on Product Descriptions with Natural Language Processing (NLP) and machine learning Models 5th

Production Classification in E-Commerce Based on Product Des...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Bist, Yuvraj Gurbaxani, Paramesh Gupta, Neetu Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9783031770746

the rapid growth of the internet has increased people's reliance on it for expressing opinions on products and stores. Text sentiment analysis is now a key research area. Deep learning methods are commonly used for text classification, but they suffer from issues like information loss and weak context. this paper enhances the existing models to simplify the process, educe training time, and improve overall recall and accuracy in text sentiment classification. With the rapid development of artificial intelligence, traditional manual techniques are fading, and AI algorithms are driving the swift progress of text sentiment classification. Product categorization is critical in e-commerce since it influences search, recommendations, inventory, and consumer experience. NLP approaches and models such as TF-IDF and Word2Vec are effective for categorizing products based on their textual descriptions. this literature study investigates the research evolution, problems, and upcoming trends in using TF-IDF and Word2Vec for product classification in e-commerce. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Adversarial machine learning

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Research on the application of Multi-Channel Display Technology in Virtual Scene Image Generation 5

Research on the Application of Multi-Channel Display Technol...

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5th IEEE international conference on Power, Electronics and computer applications, ICPECA 2025

作者： Liu, Yuanlin Wang, Lingling Haikou University of Economics Haikou571127 China

ISBN: (纸本)9798331533694

this study proposes an innovative algorithm based on DCNN and multi-channel image fusion, aiming to improve the quality and efficiency of virtual scene image generation. the algorithm extracts depth information and texture features in virtual scenes through DCNN, and combines multi-channel image fusion technology to optimize and fuse the information of different image channels (such as color, depth, normal, etc.), thereby significantly improving the image rendering effect. through adaptive weight adjustment and image fusion strategy, the image detail performance is optimized, especially in scenes with complex lighting and rich textures, the rendering quality is significantly improved. Experimental results show that the image generation system based on this algorithm improves image quality by 15%, rendering speed by 25%, and computing resource consumption by 20%. When processing highly complex virtual scenes, the system can achieve real-time rendering and meet the performance requirements of VR and AR applications. this study provides a new and efficient solution for image generation in virtual reality and augmented reality, and demonstrates the great potential of combining multi-channel display technology with deep learning in improving rendering effects. © 2025 IEEE.

关键词： Virtual environments

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Sanda posture recognition using feature extraction algorithms 5

Sanda posture recognition using feature extraction algorithm...

引用

5th international conference on Telecommunications, Optics, and computer Science, TOCS 2024

作者： Zhang, Qilong Han, Tingting Harbin Huade University Harbin China Harbin Institute of Petroleum Harbin China

ISBN: (纸本)9781510691667

computer vision and machine learning techniques play a pivotal role in advancing the recognition and analysis of dynamic human postures, particularly in sports such as Sanda, a form of Chinese martial arts. this paper explores the application of feature extraction algorithms for the efficient recognition of Sanda postures. By utilizing sensor data, including motion trajectories, joint angles, and body part coordinates, we develop a robust algorithmic framework to identify and classify various stances and movements in real-time. the research integrates traditional image processing methods with modern machine learning approaches, such as deep learning and support vector machines (SVM), to enhance the accuracy and efficiency of posture recognition. Experimental evaluations using video sequences and motion-capture data show that deep learning models, particularly convolutional neural networks (CNNs), outperform traditional techniques in both speed and precision. the findings highlight the potential of feature extraction algorithms in the development of intelligent systems for automating posture recognition in martial arts, offering significant improvements in training, performance monitoring, and real-time feedback. this work paves the way for data-driven sports methodologies, improving the effectiveness of martial arts training through advanced computational techniques. © 2025 SPIE.

关键词： feature extraction algorithms gesture recognition machine learning martial arts technology motion detection posture recognition Sanda sports training

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Domain Transfer learning for Medical Image Segmentation Tasks 5th

Domain Transfer Learning for Medical Image Segmentation Task...

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

作者： Kharbas, Vikas Kumar Srivastava, Pulkit Bargavi, Manju Pandeya, Megha Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Mechanical Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Domain transfer learning is a powerful device for clinical image Segmentation duties. It involves transferring information from a source domain to a goal area so that related tasks on those domains can be advantageous from every difference. this era allows improving and robust clinical photo segmentation models by exploiting the prevailing area information and facts from a source area. this approach has been used to enhance the performance of sure segmentation responsibilities in medical imaging together with the segmentation of pathologies from imaging, segmentation of anatomic structures, and system getting to know-based total segmentation. the most critical domain version strategies encompass domain-adversarial neural networks, generative fashions, and meta-getting to know. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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application of Optimized Artificial Neural Networks to Enhance Medical Data Interpolation 5

Application of Optimized Artificial Neural Networks to Enhan...

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5th international conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Mohanty, Suneeta Dey, Sainath Goswami, Mitul Pattnaik, Prasant Kumar Kiit Deemed to be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798331522100

this study implements a K- Nearest Neighbors Model using the Heart Disease Prediction Dataset, and then intentionally introduces missing values by replacing around 300 original cholesterol (chol) values with NaN (Not a Number) values. Further, an Artificial Neural Network (ANN) model has been deployed upon the dataset to predict these missing chol values based on other features using linear interpolation techniques. this prediction is subsequently compared against the original chol values that were initially present in the dataset. Various visualizations and comparisons have been carried out through scatter plots, which allow for analyzing the proximity of the predicted chol values to the original ones, considering the interplay between other dataset features and the remaining chol values. this study contributes to the field of medical sciences and machine learning, showing the importance of accurate interpolation of data values in case of missing or inadequate data points in medical datasets. © 2025 IEEE.

关键词： k-nearest neighbors

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