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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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Tennis Momentum: An Analytical Study of Tennis Performance Based on the CNN-LSTM-RF Model

Tennis Momentum: An Analytical Study of Tennis Performance B...

引用

international conference on Communications, Information System and Computer engineering (CISCE)

作者： Wei Weng Zixuan Lin School of Internet Economics and Business Fujian University of Technology Fuzhou China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

In this study, we introduce an innovative CNN-LSTM-RF model to analyze and predict momentum shifts in tennis, using the 2023 Wimbledon Men's Singles Final as a case study. Our approach processes match data to identify key factors affecting momentum, such as serve effectiveness, player psychology, and match dynamics, employing factor analysis and the AHP-TOPSIS method for a comprehensive performance evaluation. Findings indicate a significant correlation between momentum and winning streaks, highlighting momentum's importance in influencing match outcomes. Despite its predictive success, we recognize the model's limitations and the complexity of accurately forecasting momentum shifts. this concise analysis not only demonstrates the model's utility in tennis strategy but also suggests areas for future research and model enhancement.

关键词： Training Performance evaluation Analytical models Correlation Accuracy Machine learning algorithms Psychology

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Comparative Analysis of Deep Learning Techniques for Prediction of Protein Structure 6th

Comparative Analysis of Deep Learning Techniques for Predict...

引用

6th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

作者： Mishra, Sunit Kumar Tripathi, Sudhakar Gupta, C.L.P. AKTU Uttar Pradesh Lucknow India Rajkiya Engineering College Ambedkar Nagar Uttar Pradesh Akbarpur India Bansal Institute of Engineering and Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9783031813412

Protein structure prediction is essential for comprehending protein function and developing therapeutic interventions. this paper focuses on review of various papers related to the protein structure prediction by using Deep Learning (DL) techniques and also performs a comparison of DL models employed in protein structure prediction, utilizing a dataset sourced from PDB. the paper covers DL principles, ensemble strategies and performs the comparative study of various DL techniques on the basis of various parameters such as accuracy, precision, recall, and F1 score. the findings provide nuanced insights into the strengths and limitations of each technique, contributing to the refinement of computational methods for the prediction of protein structure. this research aims to advance the field, offering a valuable foundation for the development of more accurate and scalable predictive models. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Determining Component and Parameter Weighting Factors using Analytic Hierarchy Process for thermal Generator Health Index Model

Determining Component and Parameter Weighting Factors using ...

引用

international conference on Power engineering and Renewable Energy (ICPERE)

作者： Verdiaz Refikhanata Esa Awal Gumilar Bryan Denov Umar Khayam School of Electrical Engineering and Informatics Institut Teknologi Bandung PT. PLN (Persero) Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798331504694

ISBN: (纸本)9798331504700

Generators play a crucial role in power generation, ensuring energy production and distribution in power systems. However, traditional diagnostic practices, which typically focus on offline specific component tests, do not provide a holistic view of generator health. this often leads to challenges in early detection of systemic issues, potentially resulting in significant operational disruptions and economic losses. Addressing this gap, this research introduces a comprehensive approach to assess generator health by identifying crucial components and parameters that significantly influence overall generator functionality. Utilizing the Analytic Hierarchy Process (AHP), this study quantitatively determines the weighting factors of each component and parameter. these factors are then integrated into a novel Generator Health Index (GHI) model, which combines scoring guidelines based on extensive literature review and international standards. the developed GHI model offers a more integrated and predictive framework for generator health assessment, providing critical insights into the overall condition of generators based on available data. the findings demonstrate the model's effectiveness in offering a detailed and informative health profile of generators, suggesting its potential as a standard tool in the energy sector for achieving optimal operational efficiency.

关键词： Analytical models thermal factors Windings Analytic hierarchy process predictive models Generators thermal analysis Indexes Standards Systematic literature review

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Mix of Experts Language Model for Named Entity Recognition

Mix of Experts Language Model for Named Entity Recognition

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international conference on Communications, Information System and Computer engineering (CISCE)

作者： Xinwei Chen Kun Li Tianyou Song Jiangjian Guo Department of Electronical and Computer Engineering University of Illinois at Urbana Champaign Urbana IL USA Department of Computer Science University of Illinois at Urbana Champaign Urbana IL USA Department of Computer Science Columbia University New York USA Department of Computer Science and Engineering University of California San Diego La Jolla US

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Named Entity Recognition (NER) is an essential steppingstone in the field of natural language processing. Although promising performance has been achieved by various distantly supervised models, we argue that distant supervision inevitably introduces incomplete and noisy annotations, which may mislead the model training process. To address this issue, we propose a robust NER model named BOND-MoE based on Mixture of Experts (MoE). Instead of relying on a single model for NER prediction, multiple models are trained and ensembled under the Expectation-Maximization (EM) framework, so that noisy supervision can be dramatically alleviated. In addition, we introduce a fair assignment module to balance the document-model assignment process. Extensive experiments on real-world datasets show that the proposed method achieves state-of-the-art performance compared with other distantly supervised NER.

关键词： Training Annotations Computational modeling Named entity recognition predictive models Noise measurement Information systems

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Robust Multi-Object Tracking with CV AE Motion Model for Maritime Vessels

Robust Multi-Object Tracking with CV AE Motion Model for Mar...

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6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Yuxuan Peng Wenhui Liu Yulong Qiao College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

In the maritime background, Multi-Object Tracking (MOT) for ships has great research value. However, many existing multi-object tracking methods depend heavily on the appearance features of objects, and the utilization of motion cues are not sufficient, resulting in low robustness of the MOT algorithm, including the ID Switch of the trajectory, and the jitter and low accuracy of the bounding box. In the complex maritime background, these problems are even more serious. To break this situation, we propose a robust multi-object tracking algorithm with Conditional Variational AutoEncoder (CVAE) motion model, which better learns the complex motion of the object. Based on this model, we propose motion prediction and motion optimization, which enable us to reduce trajectory ID switch and bounding box jitter and enhance bounding box accuracy. Extensive experiments demonstrate the competitive performance of our method against many state-of-the-art methods.

关键词： Accuracy Target tracking Signal processing algorithms Switches Jitter predictive models Signal processing Robustness Trajectory Optimization

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EEG Technology Interface: Synchronous Online Education Assistance System

EEG Technology Interface: Synchronous Online Education Assis...

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international conference on Communications, Information System and Computer engineering (CISCE)

作者： Ruiqin Wang Yancheng Hu Shuaichen Ge Dept of Information Management Beijing Jiaotong University Weihai China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Online Education is widely used in schools and institutions due to its convenience. However, during online classes, students may lack access to instant feedback that solves their confusion, and their instructors may face difficulty in judging students' learning status to determine their level of comprehension. To realize remote real-time detection of student's learning status and improve study efficiency, this paper proposes an online education assistance system based on single-channel EEG. In this system, recorded EEG data will be upheld into a prediction model based on the Optimizable Ensemble Model, and the predicted results will be transformed into the MYSQL database. this database will provide real-time data on a developed web page. On the web page, instructors can identify students' confusion status and attention level to provide instant learning assistance, which solves students' confusion.

关键词： Databases Education Web pages Training data predictive models Brain modeling Electroencephalography

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Gap and Glitch Simulation for Taiji Data Challenge

Gap and Glitch Simulation for Taiji Data Challenge

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IEEE international conference on Information Communication and Signal Processing (ICICSP)

作者： Zhang Jiafeng Peng Xiaodong Zhao Mengyuan Xu Peng Wu Pengzhan Gao Chen Ma Xiaoshan Key Laboratory of Electronics and Information Technology for Space System National Space Science Center Chinese Academy of Sciences Beijing China Institute of Mechanics Chinese Academy of Sciences Beijing China Institute of Engineering Thermophysics Chinese Academy of Sciences Beijing China

Simulating the detection process of a space-based gravitational wave (GW) measurement system is crucial for the task demonstration, design, operation and data processing. Gaps and glitches, which are common types of abnormal events in the detection process, are an important part of data simulation. this paper explores two gap models and three glitch models, provides a comprehensive overview of these models. the implementation of these models in Taiji Data, the simulation data of the Taiji program, is designed. Dedicated software modules were developed to incorporate the gap and glitch models into the simulation process. the impact of adding gap and glitch to the measurement is illustrated and analyzed. Finally, the time-series simulation detection data of space GWs that integrates gap and glitch models has been obtained, providing support for promoting the improvement of the detection data processing capabilities.

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Heart Disease Evaluation with Deep Learning and Machine Learning

Heart Disease Evaluation with Deep Learning and Machine Lear...

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international conference on Information Systems and Computer Networks (ISCON)

作者： Sachin Upadhyay Sanjiv Kumar Singh Jayati Krishna Goswami Shiv Shanker Singh Department of Computer Engineering & Application GLA University Mathura

All over the world is affected by the disease which is named as heart disease. the main reason behind the heart disease is our busy life, as the person is affected not only by office work but also by personal problems. this mortality rate is too high. We can predict this disease with the help of Machine Learning (ML) and Deep Learning (DL) prediction models. In this paper, to reach the accuracy we worked on three ML & DL models. For this paper we use ML models named as: SVM (Support Vector Machine), LR (Logistic Regression) & Naïve Bayes. DL models named as: CNN (Convolutional Neural Network), RNN (Recurrent Neural Network) & LSTM (Long Short Term Memory). the accuracy obtained in this study is made up of the 85% accuracy of Logistic Regression, the 89% accuracy of SVM, and the 85% accuracy of Naive Bayes. LSTM has an accuracy of 83%, RNN has an accuracy of 91%, and CNN has an accuracy of 83%. the study’s findings indicate that the RNN model is the most accurate, coming in at 90%, and from this we can say that it is the best at predicting heart disease.

关键词： Heart Support vector machines Deep learning Recurrent neural networks Machine learning algorithms Computational modeling predictive models

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Dynamic Language Refinement for Graceful Degradation in Scene Text Recognition

Dynamic Language Refinement for Graceful Degradation in Scen...

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Pattern Recognition and Artificial Intelligence (PRAI), international conference on

作者： Shaocong Tian Rize Jin Joon-Young Paik Chen Fu School of Software Tiangong University Tianjin China School of Computer Science &Technology Tiangong University Tianjin China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen China

Scene text recognition is a mature technology which can get a near-perfect result in regular scenes. However, there are still troubles in recognizing scene texts with image blur, characters missing, and occlusion. Recent end-to-end frameworks have investigated the importance of linguistic knowledge, utilizing either implicit or explicit language models. However, joint training of language and vision models tends to interfere the tunings of each other. In this paper, we present a novel gradient isolation method to train language and vision models separately, yet in an end-to-end architecture. In addition, we propose a dynamic iterative training procedure for graceful refining the language model. Extensive experiments confirm that the proposed method has superiority on regular and irregular scene text images and achieves state-of-the-art results.

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RFID-Based Toll Plaza System For Developing Countries

RFID-Based Toll Plaza System For Developing Countries

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international conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Md Abu Talha Tanbir Ibne Anowar Shuvra Mondal Department of Electrical and Electronic Engineering American International University - Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

this study presents an RFID-based toll collection system specifically designed for developing countries like Bangladesh to reduce traffic congestion and improve transparency in toll operations. Combining RFID technology with ultrasonic sensors, an Arduino microcontroller is used to identify and process vehicles at the toll stations automatically. through intensive testing, the system has demonstrated its ability to accurately track toll transactions and efficiently generate reports, meeting its goals of streamlining the toll collection process, reducing traffic congestion, and ensuring proper record-keeping. this innovative combination of RFID technology and ultrasonic sensors marks a quantum leap in toll collection, providing users with a smooth and cashless toll experience. Not only does this research provide a viable industry-standard solution, but it also addresses the social, economic, and environmental issues associated with traditional toll methods. Its findings lay the foundation for future improvements and research in innovative toll collection methodologies.

关键词： Biological system modeling Scalability Developing countries predictive models Acoustics Smart transportation Security

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