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A Novel Process Monitoring Method of Heat Treatment Furnace based on ReliefF-SFA 3

A Novel Process Monitoring Method of Heat Treatment Furnace ...

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3rd international conference on Machine learning and Computer Application, ICMLCA 2022

作者： Zhang, Yue Wang, Sen Leng, Yueyan Shan, Zhiyong Jin, Aobo Guo, Zhicheng Lin, Chenzao Zhang, Jiawei Zhang, Shiyi Shenyang Institue of Engineering Shenyang110136 China

ISBN: (纸本)9781510664814

A novel process monitoring method based on ReliefF-slow feature analysis (ReliefF-SFA) is proposed, to solve the problem of the large variable dimensions and feature selection. First, the data is mapped into a new space, and the slow characteristics of the data is obtained. The new obtained can not only extract the slowest changing components, but represent the inherent characteristics of the system. Second, the feature selection algorithm ReliefF is used to select the more weighted features, to achieve dimensionality reduction. The main subspace is established based on the obtained features, and the remaining features are put into the residual space. The corresponding statistics and control limits are calculated based on the two subspace. Finally, the process monitoring using the proposed algorithm is performed by testing the actual heat treatment furnace process data, and the results show the effectiveness of the proposed method. © 2023 SPIE.

关键词： Process monitoring

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Machine-learning-Based Analysis for Problem Detection in Electric Vehicles 3rd

Machine-Learning-Based Analysis for Problem Detection in Ele...

引用

3rd international conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Priya, R. Karpaga Guru, V. Raj, T. Kirthick Department of EEE Saveetha Engineering College Tamil Nadu Chennai602105 India

ISBN: (纸本)9789819737444

Electric vehicles (EVs) are gaining popularity because of their energy efficiency and positive environmental impacts. However, challenges arise from the intricacy of EV systems and the need for reliable, safe, and efficient functioning. This study explores the use of machine learning algorithms to get around these challenges and enhance the upkeep and performance of EVs. This project’s primary objective is to use machine learning techniques to monitor the condition of EV components, identify issues, anticipate maintenance needs, and improve various aspects of EV operation. Through the application of machine learning algorithms to a variety of data obtained from sensors and vehicle systems, abnormalities and possible problems may be identified in advance. This proactive strategy aims to prolong the lifespan of important components, especially motors and batteries, and avoid breakdowns. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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VEL@LT-EDI-2023: Detecting Homophobia and Transphobia in Code-Mixed Spanish Social Media Comments 3

VEL@LT-EDI-2023: Detecting Homophobia and Transphobia in Cod...

引用

3rd Workshop on Language Technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Kumaresan, Prasanna Kumar Ponnusamy, Kishore Kumar Shanmugavadivel, Kogilavani Navaneethakrishnan, Subalalitha Chinnaudayar Priyadharshini, Ruba Chakravarthi, Bharathi Raja Insight SFI Research Centre for Data Analytics University of Galway Ireland Guru Nanak College Tamil Nadu Chennai India Kongu Engineering College Tamil Nadu India Department of Computer Science & Engineering SRM Institute of Science and Technology Tamil Nadu India Gandhigram Rural Institute-Deemed to be University Tamil Nadu India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Ireland

ISBN: (纸本)9789544520847

Our research aims to address the task of detecting homophobia and transphobia in social media code-mixed comments written in Spanish. Code-mixed text in social media often violates strict grammar rules and incorporates non-native scripts, posing challenges for identification. To tackle this problem, we perform pre-processing by removing unnecessary content and establishing a baseline for detecting homophobia and transphobia. Furthermore, we explore the effectiveness of various traditional machine-learning models with feature extraction and pre-trained transformer model techniques. Our best configurations achieve weighted F1 scores of 0.86 on the test set and 0.86 on the development set for Spanish, demonstrating promising results in detecting instances of homophobia and transphobia in code-mixed comments. © 2023 LTEDI 2023 - 3rd Workshop on Language Technology for Equality, Diversity and Inclusion, associated with the 14th international conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Social networking (online)

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A Latent Dirichlet Allocation Technique for Opinion Mining of Online Reviews of Global Chain Hotels 3

A Latent Dirichlet Allocation Technique for Opinion Mining o...

引用

3rd international conference on intelligent engineering and Management, ICIEM 2022

作者： Afaq, Anam Gaur, Loveleen Singh, Gurmeet Amity University Amity International Business School Noida India The University of the South Pacific School of Business and Management Suva Fiji

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

The hospitality industry has faced unprecedented challenges with the outbreak of Covid-19, which has changed customers' expectations. Therefore, it is essential to identify customers' new perceptions and expectations that lead to positive and negative opinions towards the service providers. Accordingly, this study aims to perform topic modeling and sentiment analysis on 94,200 online reviews of five global chain hotels in South Asia. Topic modeling as a text mining, unsupervised machine learning technique can decipher topics from a corpus such as online reviews, online reports, news covers, etc. In this study, the data is extracted from Trip Advisor through web scraping. Topic modeling is performed using the Latent Dirichlet Approach (LDA) on the extracted data set to analyze the key topics mentioned by the customers in the online reviews. The analysis depicted that cleanliness, food, staff, and service were the main concerns of the hotel guests. Furthermore, the findings represented that the main issues impacting the hotel guests were service delays. However, food and services were the keywords with the maximum word count as depicted by topic modeling. © 2022 IEEE.

关键词： Sentiment analysis

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Analyzing Suicide and Contributing Factors in Thailand Using Classification by Logistic Regression, Decision Tree and Support Vector Machine 3

Analyzing Suicide and Contributing Factors in Thailand Using...

引用

3rd IEEE international conference on Electronic Communications, Internet of Things and Big data, ICEIB 2023

作者： Wongnongtaey, Kanticha Srisomboon, Krissana Sutivong, Daricha Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9798350333862

The World Health Organization (WHO) reports that over 700,000 people die from suicide each year, with the number of attempts far exceeding that. Suicide is also the fourth leading cause of death among teenagers [1]. The suicide rate in Thailand follows a similar trend. This research aims to utilize machine learning techniques to identify cases that lead to suicide deaths and to study the contributing factors, both internal and external. Specifically, this paper proposes using classification methods to create a prediction model from people's demography, behaviors, and other factors in order to analyze features that influence the risk of suicide deaths. Classification techniques employed include Logistic Regression, Decision Tree, and Support Vector Machine. We have analyzed the data gathered by Thailand National Suicide Prevention Department from January 2018 to September 2021, which includes 49,417 suicidal cases, 30% of which resulted in fatalities. Two models are proposed: a general model to identify significant factors leading to suicide deaths and a practical model to predict suicide deaths given only available factors in practice. © 2023 IEEE.

关键词： Decision trees

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Maximum Entropy Inverse Reinforcement learning based Trajectory Planning for Autonomous Driving 3

Maximum Entropy Inverse Reinforcement Learning based Traject...

引用

3rd international conference on High Performance Computing and Communication engineering, HPCCE 2023

作者： Zhang, Puhao Xie, Shumin Lu, Xiaoya Zhong, Zuodong Li, Qing School of Automation and Electrical Engineering Beijing University of Science and Technology 30 Xueyuan Road Haidian District Beijing100083 China School of Mechanics and Civil Engineering and Architecture Northwestern Polytechnical University 127 Youyi West Road Xi'an710072 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education 30 Xueyuan Road Haidian District Beijing100083 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

With the rapid advancement of autonomous driving technology, effective trajectory planning has become crucial for ensuring road safety and driving efficiency. Traditional trajectory planning methods often rely on preset rules and models, making them ill-suited for the complex and dynamic traffic environment. To address this, a Maximum Entropy Inverse Reinforcement learning (MaxEnt IRL) based trajectory planning method is proposed in this paper, aiming at learning from expert driving behaviors to infer an efficient reward function, which in turn guides decision-making and path planning for autonomous vehicles. This study begins by analyzing expert driving data to extract key state and action features. Then, the MaxEnt IRL algorithm is applied to learn the reward function underlying these features, reflecting the decision-making logic of expert drivers. The learned reward function is subsequently used to guide the trajectory planning of the autonomous driving system, generating safe and efficient driving paths. A series of experiments conducted in a simulated environment demonstrate that the MaxEnt IRL-based method proposed in this paper exhibits higher adaptability and efficiency in handling complex traffic scenarios compared to traditional trajectory planning methods. © 2024 SPIE.

关键词： Decision making

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Predicting Liver Disease from MRI with Machine learning-Based Feature Extraction and Classification Algorithms 3rd

Predicting Liver Disease from MRI with Machine Learning-Base...

引用

3rd international conference on Machine learning, Internet of Things and Big data, ICMIB 2023

作者： Laddha, Snehal V. Yadav, Manish Dube, Dhaval Dhone, Mahansa Sharma, Madhav Ochawar, Rohini S. Department of Electronics Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9789819939312

Globally, liver disease is the leading cause of death for a huge number of people. Inflammation of the liver is caused by a number of factors. Diagnosing liver infection early is essential for more effective treatment. In the current scenario, sensors are employed to identify liver diseases. Precise classification methods are necessary for the automatic diagnosis of illness samples. The cost of diagnosing this illness is high and complicated. The purpose of this study is to decrease the high cost of chronic liver disease diagnosis through prediction. This paper reviews the emerging techniques of data pre-processing, feature extraction, and classification on liver MRI. The primary goal of the current work is to use clinical data to predict the presence or absence of liver disease from MRI by applying various Machine learning methods. In this paper, we have performed feature extraction from liver MRI using the HOG method followed by the Random Forest algorithm for the classification of images. With our approach, the accuracy achieved is 91.67%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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intelligent Distribution Grid Reliability Simplification Algorithm and Online Computing Implementation 3

Intelligent Distribution Grid Reliability Simplification Alg...

引用

3rd international conference on New Energy and Power engineering, ICNEPE 2023

作者： Wen, Hongjun Chen, Bowei Li, Zuxin Chen, Jiqiao Nie, Zhi Wu, Qian State Grid Zhejiang Electric Power Co. Ltd. Lishui Company Power Grid Development Department Lishui China Technology Co. Ltd Beijing China

ISBN: (纸本)9798350393224

At present, reliability calculation is mostly focused on the calculation of current years, and the calculation of planned years is often carried out using fuzzy estimation. The calculation methods are complex and diverse, which is not conducive to the modular implementation of software. This article deeply analyzes the essence of reliability calculation based on the topology simplification model of power flow, and summarizes the parts related to the grid structure in its classic calculation formula as the grid coefficient. The parts related to the equipment are collected as the equipment coefficient, and the calculation is simplified based on practical applications. Then, based on the power grid resource business center and data center, a centralized (microservices/micro applications) + collaborative computing framework construction model is adopted, integrating basic data such as reliability facility ledger, topology, and graph model, and obtaining important parameters such as fault rate and average recovery time of distribution grid equipment components. Based on simplified calculation formulas, a power supply reliability online calculation micro application module is developed, The calculation results can provide convenient data support for reliability management and decision-making of distribution grids. This article also provides the specific implementation of software architecture design and interface display effects. © 2023 IEEE.

关键词： business architecture data architecture equipment coefficient grid coefficient power flow calculation topology power supply reliability technical architecture

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Design and Construction of MT SPOC intelligent Teaching Service Platform 3

Design and Construction of MT SPOC Intelligent Teaching Serv...

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3rd international conference on Big data and Artificial Intelligence and Software engineering, ICBASE 2022

作者： Zhou, Xiaojian Kan, Jicheng Zhang, Zhen Yuan, Hua Shi, Jinmei College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou571126 China

With the development of MOOCs and intelligent teaching technology, the integration of them is a research trend to optimize teaching and improve learning effects. Aiming at the problems existing in OER and MS SPOC platform, this paper analyzes the requirements of interdisciplinary research and teaching optimization, and puts forward the design of physical architecture, software structure and technical scheme of MT SPOC intelligent teaching service platform. MT SPOC platform introduces EDM and LA technologies into its construction by learning data collecting, sharing and analysis to obtain a comprehensive profile of students, and provide intelligent teaching support for teachers to optimize their teaching resource designing and strategies making. Finally, with the help of MT SPOC platform, the appropriate long term development goals for education will be made. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0)

关键词： data mining

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An Approach to Ensemble Simplified ResNet Models-Using Egyptian Hieroglyphs data Set 3

An Approach to Ensemble Simplified ResNet Models-Using Egypt...

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3rd international conference on Electronic Information engineering and Computer Science, EIECS 2023

作者： Yan, Zixiang Jin, Kecheng Hou, Jingkun Zhang, Pu Nanjing Tech University Nanjing211816 China Nanjing Hankai Academy Nanjing210031 China School of Inteligence Science and Technology University of Science and Technology Beijing Beijing100083 China Austin Friars St Monica's School Etterby Scaur Cumbria CarlisleCA3 9PB United Kingdom

ISBN: (纸本)9798350316674

Since the inception of computer vision, recognizing text, including both printed and handwritten characters, has posed a significant challenge for traditional machine learning models. However, only a handful of these models have focused on ideographical scripts. Similarly, while mathematical solutions to common neural network problems such as degradation and computational complexity have been studied, possible shortcuts are often overlooked. This paper presents an innovative approach by introducing a novel ensemble network called "SparrowNet"and compares it to ResNet18 and GoogLeNet V2. Using an open-source Egyptian hieroglyph dataset, the paper focuses on evaluating the efficiency and accuracy of different networks using various performance metrics. Additionally, the paper discusses the advantages of the two existing models and explains the reasons behind the modifications made to SparrowNet. Through this research, it has been demonstrated that the use of 1x1 convolutions can significantly improve speed and reduce computational complexity, while a voting mechanism can be a novel solution to prevent degradation, and the modified network is capable to solve this particular character recognition task. © 2023 IEEE.

关键词： Deep learning Egyptian Hieroglyphs Ensemble learning GoogLeNet ResNet

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