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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Liang, Huifang Wuhan Guanggu Vocational College Wuhan China

ISBN: (纸本)9781510689053

In recent years, under the context of rapid development of information technology, the demand for personalized teaching in higher vocational computer courses has become increasingly high, and the shortcomings of traditional teaching methods have become prominent, making it impossible to meet the diverse learning needs brought by modern information technology development. This study mainly uses machine learning and advanced technologies to create a personalized tutoring and optimization system to enhance students' learning outcomes. The research primarily applies the Random Forest and Collaborative Filtering algorithms, using these two algorithms to analyze students' learning data and formulate personalized learning paths for users. In this paper, the method is analyzed based on 5000 pieces of student data. By using the Random Forest to classify students' knowledge mastery and combining it with the Collaborative Filtering algorithm to recommend personalized resources, the system operation logic is formed. The experiment shows that, compared to the control group's improvement rate of 8.26%, the average improvement rate of students in the experimental group reached 11.29%, with the best result improving by 15.38%. This fully demonstrates the effectiveness of the system in improving learning efficiency and provides a new technical solution for personalized teaching in higher vocational education. © 2025 SPIE.

关键词： Optimization algorithms

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Harnessing the Power of AI with Deep learning: Innovations and Implications

Harnessing the Power of AI with Deep Learning: Innovations a...

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

作者： Upender, Talluri Sathiyamoorthy, C. Udayakumar, K. Kirubakaran, D. Purushothaman, V. Raja, Kiruba Thangam CMR College of Engineering and Technology Department of CSE Medchal Telangana Hyderabad India Department of Commerce Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India VIT University Department of Software and Systems Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331512965

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues. © 2025 IEEE.

关键词： Convolutional neural networks

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Resource allocation and task scheduling for computing power aware networks

Resource allocation and task scheduling for computing power ...

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2024 international conference on Physics, Photonics, and Optical engineering, ICPPOE 2024

作者： Peng, Guozheng Zhao, Chuanqi Chen, Lingying Wang, Lanruo Beijing100089 China State Grid Shandong Electric Power Research Institute Ji’nan250003 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

As an important cornerstone of future information and communication technology, the computing power network cleverly integrates cloud resources, third-party computing devices, and various heterogeneous computing resources scattered at the edge of the network through seamless fusion of widely covered network connections. This innovative architecture aims to break the boundaries of traditional computing models and achieve precise scheduling and global optimization of computing resources by building a highly collaborative computing network resource management system. This article proposes a resource allocation and task scheduling algorithm based on deep reinforcement learning (DRL), which simulates the human decision-making process and enables the system to autonomously learn and optimize strategies in a constantly changing computing network environment. This algorithm can not only effectively cope with large-scale and high dynamic computing power demands, but also accurately predict future resource trends, pre allocate resources in advance, and ensure smooth and efficient task execution. The experimental results show that the algorithm performs well in improving the utilization of computing power resources, reducing task execution latency, enhancing system stability and flexibility, and laying a solid foundation for the further development and application promotion of computing power networks. © 2025 SPIE.

关键词： computing power

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Machine learning Methods to Predict Resilient Moduli Behavior of Subgrade Soils 5th

Machine Learning Methods to Predict Resilient Moduli Behavio...

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5th international conference on Transportation Geotechnics

作者： Chou, Sopharith Biswas, Nripojyoti Puppala, Anand J. Texas A&M Univ College Stn TX 77843 USA Univ Massachusetts Lowell Lowell MA 01854 USA

ISBN: (纸本)9789819782154;9789819782130;9789819782123

Due to limited budgets and complex testing procedures to determine resilient modulus (MR), engineers often rely on correlations between modulus and other engineering properties such as unconfined compressive strength (UCS). However, a majority of such correlations are based on linear regression, which can often lead to under or over-prediction of the correlated values. With the advancement in computing techniques, it has become convenient to understand and predict the behavior of engineering materials using optimization techniques. In this study, two machine learning (ML) techniques, including Artificial Neural Network (ANN) and Random Forest (RF), were used to predict the MR values from UCS data. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R) were computed to assess the effectiveness of the models. The statistical analysis of the testing sets indicated that the RF model achieved a goodness of fit value of 0.97, an MAE of 12.7 MPa, and an RMSE of 18.5 MPa. Alternatively, the ANN model indicated the corresponding goodness of fit value of 0.71 with an MAE of 44.3 MPa and an RMSE of 60.9 MPa. Furthermore, among various contributing factors, UCS was identified as the primary factor in predicting MR values for both models. Based on these findings, the RF model outperformed the ANN model in predicting unknown data within the examined parameter ranges and provides fitting parameters depending on the nature of the datasets, which avoids the overfitting effect. Therefore, this study demonstrates a progressive understanding of the potential use of the advanced computing tool to obtain more accurate resilient modulus values from the strength data.

关键词： Resilient modulus Ground Improvement Machine learning Random forest model Artificial neural network model

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Tennis teaching assistance model based on double chain shared unsupervised action recognition algorithm

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international Journal of Cognitive computing in engineering 2025年第1期6卷 21-31页

作者： Bai, Hongzhou Department of Physical Education Luoyang Institute of Science and Technology Luoyang471023 China

The traditional self-supervised methods based on skeleton data frequently categorize the various enhancements of a given sample as positive examples, while the remaining samples are designated as negative examples. This approach results in a significant imbalance in the ratio of positive to negative samples, which in turn constrains the efficacy of samples with identical semantic information. Therefore, to further improve the training quality in tennis teaching and enhance the accuracy of action recognition, a dual chain sharing unsupervised action recognition algorithm has been proposed. This study first designs a skeleton topology data augmentation method based on the physical connections of human joints to obtain advanced semantic embeddings. Then, an improved positive sample expansion strategy is utilized to enhance the diversity and quality of training data. Next, an unsupervised learning mechanism is employed to autonomously learn and recognize complex patterns of tennis movements. To measure the performance of the model, tests were conducted on its accuracy, F1 score, recognition time, and fitting degree. The experimental results showed that the proposed algorithm could reach its optimal state after 23 iterations of training, and its F1 value reached 0.918, with an average accuracy of 92.9 % and an average recognition time of 7.4 s. The research algorithms were superior to the multi-input branch graph convolutional network action recognition algorithms used for comparison, pull-push contrastive loss action recognition algorithms, and multi-granularity anchor contrastive representation learning action recognition algorithms. Its accuracy was leading by 8.6 %-23.5 %, and the recognition time has been reduced by 2.3s-7.4 s. In addition, in terms of the fitting degree of action recognition, compared with other methods, the proposed method had a fitting degree of up to 95.7 %, which was at least 8.1 % higher. This research method can improve the timeliness of feed

关键词： Contrastive learning

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Regression analysis based on driving schools in Haikou 24

Regression analysis based on driving schools in Haikou

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8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Yang, Darun Yang, Daqing Yang, Dashuai Yang, Dacai College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China Longjie Town Tianqiao Primary School Guizhou Bijie China College of Humanities and Technology Guizhou University for Nationalities Guizhou Guiyang China Guiyang University of Information Technology Guizhou Guiyang China

ISBN: (纸本)9798400710094

With the development of society and the growth of population in Haikou city, the driving school market in Haikou faces a series of challenges such as unreasonable teaching methods, cumbersome registration processes, non-standard training sites, overly long learning periods, delayed handling of student complaints, and lack of supervision. Using univariate regression and ridge regression to analyze the relationship between the number of exam takers and the number of passers, predicting the number of passers with a certain number of exam takers, providing reference for Haikou local undergraduate students and social individuals when registering for driving schools. Also, it aims to provide scientific decision-making basis for driving school managers, decision-makers, and practitioners, and predict future trends. © 2024 Copyright held by the owner/author(s).

关键词： Students

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DemoCraft: Using In-Context learning to Improve Code Generation in Large Language Models 3

DemoCraft: Using In-Context Learning to Improve Code Generat...

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

作者： Joshua, Kapu Nirmal Sreejith, Mihit Indian Institute of Technology Department of Electrical Engineering Kanpur India Indian Institute of Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331515911

Producing executable code from natural-language directives via Large Language Models (LLMs) involves obstacles like semantic uncertainty and the requirement for task-focused context interpretation. To resolve these difficulties, we introduce DemoCraft, a framework that refines code generation through in-context learning and demonstration selection, in tandem with latent concept learning. Latent concept learning appends extra concept tokens, which act as trainable embeddings encapsulating task-oriented insights. We evaluate our approach on two promi- nent datasets: MBPP and Humaneval. Our experimental findings reveal that this method attains roughly a 2x boost in the pass@k metric compared to baseline models. Moreover, we propose two new evaluation measurements: correctness@k and similarity@k. Empirical analyses indicate that our system achieves nearly a 3x advancement in these measures as well. © 2025 IEEE.

关键词： Semantics

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Skin Melanoma Classification from Dermoscopy Images: A Review 6

Skin Melanoma Classification from Dermoscopy Images: A Revie...

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

作者： Mistry, Jeet Barve, Amit Farooqui, Yassir Computer Engineering Department PIET Parul University Gujarat Vadodara India

ISBN: (纸本)9798331522667

Skin melanoma is one of the most dangerous forms of skin cancer and it was discovered that accurate and early screening significantly enhances patients’ prognosis. Since Dermoscopy and computational studies have enhanced, the identification and classification of melanoma is improved. This review systematically discusses recent developments and approaches usually employed in melanoma detection from Dermoscopy images with a focus on Machine learning (ML), Deep learning (DL) and Transfer learning (TL) methods. Given solutions interact with feature extraction techniques, image preprocessing techniques, and classification techniques and their efficacy are analyzed. The discussion focuses on the existing and the latest contributions to this field of study, new issues, and future directions in this subject area. In detail, it highlights efforts to enhance classification accuracy, cope with differing data sets and extend applicability of the algorithms. The findings are expected to fill research gaps and provide evidence-based recommendations for improving the automated melanoma detection systems hence alleviating the problem of early diagnosis and poor prognosis. © 2025 IEEE.

关键词： Contrastive learning

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Deep learning - Based Prediction Models for Colon and Rectum Cancer Disease: Enhancing the Precision Diagnosis

Deep Learning - Based Prediction Models for Colon and Rectum...

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2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Suman, Sunil Kumar Kumaraswamy, D. Sahu, Sunil Kumar Akella, Prasanna Lakshmi Santhoshkumar, S.P. Karthika, R. Supaul College of Engineering Department of Computer Science and Engineering Supaul India Ellenki College of Engineering and Technology Telangana Sangareddy India Hyderabad India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computing Department of Information Technology Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331538538

Colon and rectal cancer are among the leading causes of cancer-related illness and death worldwide. The detection and treatment of colon cancer are viewed as social and economic issues due to the high fatality rates. Every year, around half a million people worldwide develop cancer, including colon cancer. Although improving patient outcomes requires an accurate and timely diagnosis, traditional diagnostic methods usually suffer from subjectivity and variability problems. An extensive summary of colon cancer diagnosis is given here. Deep learning, a branch of artificial intelligence, has become a powerful tool for increasing cancer detection accuracy by utilizing large datasets and complex algorithms. This study combines genomic data, medical imaging, histopathological analysis, and clinical decision-making tools to better comprehend the creation and application of deep learning-based prediction models in the diagnosis of colon and rectal cancer. Through the use of Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and hybrid models, deep learning can improve the accuracy of cancer detection, anticipate the course of a disease, and assist with customized treatment planning. By automating and enhancing diagnostic precision, these models have the potential to revolutionize healthcare workflows, reduce diagnostic errors, and ultimately increase patient survival rates. This research also discusses the challenges and possible advantages of using deep learning to clinical practice for colon and rectal cancer. Last but not least, the problems that exist today and possible directions for future research are examined. © 2025 IEEE.

关键词： Diseases

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Deep learning for Disaster Detection: A Framework for Automated Multimodal Data Classification 6

Deep Learning for Disaster Detection: A Framework for Automa...

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

作者： Nandhakumar, C. MarkElfric, J. Maruthanayagam, S. Naveenkumar, G. Sudharson, T. Department of Computer Science and Engineering M.Kumarasamy College of Engineering Thalavapalayam Tamilnadu Karur India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

Natural disasters such as earthquakes, floods are becoming more frequent and severe due to climate change and urban expansion. The ability to quickly detect and assess the severity of such events is necessary requirements for timely disaster response and mitigation. Traditionally, disaster detection relied on isolated data sources like weather reports, seismic sensors, and ground reports. However, the availability of multi-modal data has opened up opportunities for more holistic monitoring systems in disasters. Traditional disaster detection methods remain limited by sheer volumes and complexity, making automated systems a necessity for real-time analysis. This project will explore the concepts of deep learning frameworks for automating classification and analysis of multimodal data for disaster scenarios. DL, deep learning, with powerful tools automatically examines and classifies such enormous heterogeneity in a vast data field. Therefore, from the perspective of DL algorithms and architectures, extraction of meaningful patterns from multimodal data becomes feasible towards rapid and correct disaster detection. © 2025 IEEE.

关键词： Deep learning

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