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检索条件"任意字段=International Conference on Learning and Teaching in Computing and Engineering"

共 108002 条记录，以下是4401-4410 订阅

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Associative Memory Design Based on Region Attracivity Neural Networks and Reinforcement learning

Associative Memory Design Based on Region Attracivity Neural...

引用

2023 international conference on Neuromorphic computing, ICNC 2023

作者： Bao, Gang Song, Zhenyan Zhou, Ranran China Three Gorges University Hubei Key Laboratory of Cascaded Hydropower Stations Operation & Control Electrical Engineering & New Energy Yichang443002 China

ISBN: (纸本)9798350316889

This paper introduces a novel associative memory design method based on neural networks which are region attractivity. For the given memory patterns, we can take any value for networks connection weights and then compute network external inputs based on reinforcement learning algoritms. One numerical experiment is given for validify our method. © 2023 IEEE.

关键词： Reinforcement learning

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Enhancing Heart Disease Prediction with GANs in Clinical Decision Support Systems 6

Enhancing Heart Disease Prediction with GANs in Clinical Dec...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Periya Nayaki, A. VishwaShree, S.V. Mithra, N. Priyadharshini, C. Thanabal, M.S. Penyameen, K. Department of Information Technology Velammal College of Engineering and Technology Madurai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331522667

Heart disease remains a leading global cause of mortality, emphasizing the need for innovative diagnostic solutions. Traditional Clinical Decision Support Systems (CDSS) often struggle with limited datasets and imbalances, impacting prediction accuracy. This study proposes a novel approach by integrating Conditional Generative Adversarial Networks (CGANs) into a CDSS framework, enhancing its ability to generate high-quality synthetic data. The augmented dataset is utilized to train a Gradient Boosting model capable of identifying subtle patterns in patient data. This methodology significantly improves prediction precision and addresses limitations of traditional techniques. The results demonstrate the model's superiority inaccurately identifying heart disease risks, making it a valuable tool for early diagnosis and clinical decision-making. By combining advanced machine learning techniques with domain- specific innovations, this research sets a new benchmark in the application of AI for healthcare. © 2025 IEEE.

关键词： Generative adversarial networks

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Design and Development of Extended Reality Environment for 3-Dimensional Model Experience 4

Design and Development of Extended Reality Environment for 3...

引用

4th international conference on Educational Technology, ICET 2024

作者： Lee, Kah Yong Ooi, Pei Cheng University of Nottingham Malaysia Faculty of Science and Engineering Department of Electrical and Electronic Engineering Selangor Malaysia

ISBN: (纸本)9798350376944

This paper explores the potential of Extended Reality (XR) in enhancing engineering education through the integration of advanced 3D model interaction and visualization technologies. Utilizing SolidWorks and Blender for model creation and Unity for XR application development, this project provides an immersive learning platform that enables users to interact with complex 3D models using the PICO 4 VR headset. The project demonstrates significant advancements in educational methodologies by enabling a hands-on, interactive learning experience that improves comprehension of spatial and mechanical relationships. Key achievements include the successful integration of detailed 3D models from SolidWorks into an interactive XR environment in Unity, enhancing the educational process and potentially reducing reliance on physical resources, thus aligning with sustainable educational practices. This approach not only enriches users' learning experiences but also prepares them for modern engineering challenges, fostering innovation in teaching methodologies across disciplines. © 2024 IEEE.

关键词： Digital elevation model

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Parametric Analysis of a Cricketer's Performance using Machine learning Approach 7

Parametric Analysis of a Cricketer's Performance using Machi...

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Vestly, Doddamani John Hariharan, Shanmugasundaram Kukreja, Vinay Prasad, Andraju Bhanu Swaraj, Kondawar Gopichand, Devarakonda Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350397253

This paper aimed to suggest the appropriate way to analyze the performance related to cricket players based on some predominant parameters such as batting, bowling, fielding, and overall contribution to the team success. In order to fetch the good result and success in cricket match, it is important to select the best players for the match. This is considered to be one of the crucial decision successful match outcomes. Eventually right actions and right decisions leads to success. These actions and decisions can be made through perfect selection of the machine learning algorithms with correct data sets and accurate attributes as well. This study makes recommendations for the best data source, approximate properties, and machine learning techniques. This paper aims to identify the factors that contribute to good performance and to provide insights that can be used by coaches and selectors in identifying and developing talented players. © 2023 IEEE.

关键词： Machine learning

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Research on the Application of Decision Tree Algorithm in the teaching System 13th

Research on the Application of Decision Tree Algorithm in th...

引用

13th international conference on Frontier computing, FC 2023

作者： Yang, Lei Tianjin Academy of Fine Arts Tianjin300020 China

ISBN: (纸本)9789819992980

The role of teaching management in teaching management is very important, but there is a problem of low management level. The teaching management system cannot solve the problem of multi-type lesson plan management in the teaching management system, and the matching is low. Therefore, this decision tree a teaching management. First, the teaching plan is classified according to the teaching plan and content, and the teaching plan is selected according to the teaching plan to realize the standardized processing of teaching information. Then, according to the classification of teaching content, a collection of teaching information is formed, and the teaching content is iteratively analyzed. MATLAB simulation shows that the decision tree the standard management method in optimizing the teaching management system in terms of optimization degree and optimization time under the condition that the teaching plan remains unchanged. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Decision trees

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Improving Iris Recognition Systems with Transfer learning and Pretrained CNN Models 1

Improving Iris Recognition Systems with Transfer Learning an...

引用

1st international conference on Advanced computing and Emerging Technologies, ACET 2024

作者： Lalitha, S. Padmavathy, B. Chaithra, S. Gomalavalli, R. Rajlakshmi, P.V. Dhanakodi, V. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Chennai India Sri Venkateshwara College of Engineering Department of Cse Karnataka Bengaluru India Sri Sairam Institute of Technology Department of Ece Tamilnadu Chennai India Kongu Engineering College Department of English Tamilnadu Erode India Mahendra College of Engineering Department of Cse Tamilnadu Mallasamudram India

ISBN: (纸本)9798350367720

Conventional iris identification systems have failed to manage environmental variables and changes in iris patterns, sometimes depending on handmade characteristics that may lack the delicate details required for reliable identification. The paper offers a revolutionary iris identification system based on deep learning, especially pre-trained Convolutional Neural Networks (CNNs) such as ResNet and VGG, which are fine-tuned utilizing transfer learning methods. The system is trained and verified using a variety of iris datasets, taking into account illumination fluctuations, occlusions, and other environmental conditions. Using these pre-trained CNN models, the proposed system intends to dramatically improve the accuracy and reliability of iris detection, even under difficult settings. The proposed system attains significant gains in recognition accuracy, even under challenging circumstances, thorough preprocessing, transfer learning, fine-tuning, and data augmentation. Testing and validation show how effective the proposed system is;it has better performance metrics than existing systems, with 98.5% accuracy, 97.8% precision, 98.9% recall, and 98.3% F1-score, which are all higher than those of existing systems. The methodology also guarantees scalability and computing efficiency, confirming the proposed system's potential for accurate and dependable iris detection in practical settings. The reliability and efficiency of iris recognition technology have been significantly improved by these advances. © 2024 IEEE.

关键词： Convolutional neural networks

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Vegetable Leaf Disease Detection Using Machine learning 3

Vegetable Leaf Disease Detection Using Machine Learning

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Gupta, Kapil Kumar Goyal, Harshit Dhanda, Namrata Amity University Computer Science & Engineering Department UttarPradesh Lucknow India

ISBN: (纸本)9798331515911

For maximum crop productivity, leaf disease classification and early identification are essential. Conventional methods for identifying illness need a lot of time and money. Promising results have been observed in the detection and classification of leaf diseases using Machine learning (ML) approaches. This research presents a robust machine learning approach for the identification and classification of leaf diseases. A convolutional neural network (CNN) is used in the suggested method to categorize leaf diseases into various classifications. The suggested method's overall effectiveness is assessed using a leaf photo dataset that is available to the public. The results show that the suggested method achieves 98.92% accuracy. India is a developing nation where agriculture is important. Approximately 58% of rural India's livelihood is influenced by agricultural intervention. Tomatoes are one of the most common crops utilized to make agricultural products. To prevent a significant loss in tomato yield and quantity, it is imperative to identify and classify any illnesses that a tomato plant may have. These issues are addressed by the newest and most advanced technologies, like image processing, which use a range of techniques and algorithms. A particular type of disease initially affects the leaves of a tomato plant. © 2025 IEEE.

关键词： Fruits

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Music Genre Classification Using LSTM and CNN 3

Music Genre Classification Using LSTM and CNN

引用

3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Samuel Wycliffe, J. Ponnaiah Karthik, R.M. Praveen Kanth, K. Prasanna, J. Sri Krishna College of Engineering and Technology Dept. of Computer Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350322842

In this comparative study of various machine learning techniques for music genre classification. Music genre classification is a challenging task due to the subjective nature of music and the lack of clear boundaries between genres. This study intends to design and develop a novel deep learning model based on a combination of different deep learning algorithms such as LSTM and CNN and train them on a dataset of audio features extracted from a large collection of music tracks. We also explored the impact of various traditional features and devised a new variety rich feature-set and analyzed their impact on the classification accuracy. Here, the performance is measured by using GTZAN dataset © 2023 IEEE.

关键词： Long short-term memory

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OptNet: Innovative Model for Early Lung Cancer Diagnosis integrating TabNet and Optuna

OptNet: Innovative Model for Early Lung Cancer Diagnosis int...

引用

2024 IEEE international conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Bhuvanya, R. Kujani, T. Manoj Kumaran, S. Lokesh Kumar, N. Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Engineering and Technology Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331522988

Lung cancer remains a leading cause of cancer-related deaths worldwide, emphasizing the critical need for early and accurate prediction to improve patient outcomes. This study introduces OptNet, an advanced framework that integrates the TabNet classifier with hyperparameter optimization using Optuna, specifically designed for lung cancer prediction on tabular patient data. TabNet, a deep learning architecture optimized for structured datasets, leverages sequential attention mechanisms to capture complex feature interactions and dependencies. The model was trained and validated on a comprehensive dataset comprising a wide range of clinical and radiological features. Experimental results consistently demonstrated that OptNet surpassed conventional machine learning models, achieving an exceptional accuracy of 95%, along with enhanced precision and recall. These results highlight OptNet's potential as a cutting-edge solution for early lung cancer detection, facilitating timely interventions and significantly improving patient survival prospects. © 2024 IEEE.

关键词： Lung cancer

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Research on crop remote sensing image segmentation method integrating CNN and transformer

Research on crop remote sensing image segmentation method in...

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2024 international conference on Cloud computing, Performance computing, and Deep learning, CCPCDL 2024

作者： Guo, Wenjing Jin, Yuan Cheng, Xiaodong College of Electronic Information Engineering Inner Mongolia University Hohhot010000 China

ISBN: (纸本)9781510683044

Currently, when performing pixel-level semantic segmentation of crop planting in high-resolution images, it is difficult for deep convolutional neural networks to simultaneously capture global features and local detailed features at multiple scales in space. This can lead to blurred boundary contours between different farmland plots, as well as lower integrity within similar farmland areas. In view of the above research content, a model that integrates Transformer and CNN is proposed to classify and identify crops in the study area, using drone remote sensing images from a competition as the data source. (1) The model adopts a multi-level skip-connected encoder-decoder network architecture. The encoding part of the model uses an improved MobileNetV2 as the front-end feature extractor to extract local detail features, and then inputs the extracted features into Vision Transformer for global feature capture and further processing, thereby capturing details while retaining global context information. (2) The decoding part adopts the design of UNet, extracting features from different levels of the encoding part and directly transferring them to the corresponding levels of the decoding part through skip connections to ensure the retention of detail information, and using upsampling layers to gradually restore the spatial resolution of the image. In an experiment on a public competition dataset, the experimental results show that the MIoU of the network proposed in this paper reaches 85.96%, the PA reaches 92.30%, and the Dice value reaches 0.922, which has the highest segmentation accuracy compared with the comparison network. © 2024 SPIE.

关键词： Semantic Segmentation

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