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Al-based Traffic System: A Novel Approach 24

Al-based Traffic System: A Novel Approach

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24th international Middle East Power System conference, MEPCON 2023

作者： Sakr, Hesham. A. Yasser, A. Elzimity, E. Hazim, M. Ahmed, M. Gammaz, Y. Eid, Y. Reda, H. Nasser, S. Nasser, A. Bdelnasser, A. Meshref, M. Hazem, M. El-Afifi, Magda I. Nile Higher Institute for Engineering and Technology Dept. of Electronics and Communication Engineering Mansoura Egypt

ISBN: (纸本)9798350358469

the notion of smart cities has recently gained popularity. We are experiencing traffic congestion as the number of automobiles in metropolitan areas grows. the development of an automated intelligent traffic system is critical. the recommended method is to digitalize roadways and create innovative systems to overcome the difficulties above. As part of this plan, the creation of an intelligent robot equipped with artificial intelligence (AI) and cutting-edge technologies is proposed. the robot would perform traffic management tasks such as verifying drivers' personal licenses, checking vehicle registration and licenses, identifying wanted individuals, monitoring seat belts and cell phone usage while driving, and enabling electronic payment of highway fees. AI algorithms, image processing techniques, and other tools and procedures would be employed. Image recognition technologies, data analysis, and mobile application development are all areas of expertise. © 2023 IEEE.

关键词： Traffic congestion

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automated clustering and knowledge acquisition support for beginners 24

Automated clustering and knowledge acquisition support for b...

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24th KES international conference on Knowledge-Based and intelligent Information and engineering Systems, KES 2020

作者： Kamoshida, Ryota Ishikawa, Fuyuki Research and Development Group Hitachi Ltd. Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan Department of Informatics University of Electro-Communications Tokyo Japan

Although automated machine learning (AutoML) is receiving attention in the field of data science, most AutoML open source software focuses on supervised learning tasks, and little attention has been given to unsupervised learning tasks. Moreover, AutoML has a disadvantage in that it tends to deprive users of a chance to acquire knowledge about machine learning and data science because it usually works as a black box. the purpose of this study is to help inexperienced data scientists and machine learning engineers conduct clustering data analysis, which is an unsupervised learning task, while simultaneously enabling them to acquire knowledge about clustering data analysis by extending our existing AutoML software, the machine learning support system (MALSS). the MALSS helps with clustering data analysis by automatically determining the optimal number of clusters, which is one of the main purposes of clustering data analysis. Furthermore, the MALSS helps users to acquire knowledge about clustering data analysis by generating a report after automated clustering data analysis is conducted. We validated the effectiveness of our approach by using open datasets and by running an experiment on a crowdsourcing platform. © 2020 the Authors. Published by Elsevier B.V.

关键词： Unsupervised learning

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A Machine learning Approach to Predict Cyclists’ Functional threshold Power 24th

A Machine Learning Approach to Predict Cyclists’ Functional...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Stockwell, Ronald Andrew Corradini, Andrea Oakville ON Canada MCI Universitätsstraße 15 Innsbruck6020 Austria

ISBN: (纸本)9783031482311

the estimated Functional threshold Power (eFTP) obtained from a cyclists best 20-min effort, expressed in watts, and multiplied by 95% is sometimes overstated, especially for non-elite level cyclists or cyclists just starting out. Since Functional threshold Power (FTP) is used for training prescription, in terms of creating power zones to train in, it is of utmost importance that the correct FTP is used. If FTP is overstated, a cyclist will overtrain at a level that is unrealistic to achieve. this research project aims at validating eFTP using a large, big data dataset, as well as to build out a supervised machine learning model, in the form of a weighted logistic regression, that can be used to predict FTP (pFTP), to create predictor of FTP that’s better than the currently accepted eFTP formula. Based on the data provided, our approach predicts FTP with far greater accuracy than eFTP, which turned out to overestimate cyclists’ FTP. the model outcome for each athlete, pFTP, was evaluated versus eFTP, by comparing each of these outcomes to the actual FTP (aFTP) observed in the data. To do this, Mean Squared Error and Mean Absolute Error were computed with pFTP producing values of 314.87 and 10.38 respectively when compared to aFTP, and eFTP producing 6417.32 and 61.16. With more accurate FTP estimations, cyclists are more likely to train in the correct power zones. It also validates that a laboratory environment is not always required when trying to validate eFTP and that a large, big data dataset can act as a valid substitute. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Mean square error

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24th international conference on Artificial Intelligence in Education , AIED 2023

24th International Conference on Artificial Intelligence in ...

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24th international conference on Artificial Intelligence in Education , AIED 2023

ISBN: (纸本)9783031363351

the proceedings contain 123 papers. the special focus in this conference is on Artificial Intelligence in Education. the topics include: Simulating learning from Language and Examples;learner Perception of Pedagogical Agents;how Useful Are Educational Questions Generated by Large Language Models?;"A Fresh Squeeze on data": Exploring Gender Differences in Self-efficacy and Career Interest in Computing Science and Artificial Intelligence Among Elementary Students;preserving Privacy of Face and Facial Expression in Computer Vision data Collected in learning Environments;towards Extracting Adaptation Rules from Neural Networks;predicting Student Scores Using Browsing data and Content Information of learning Materials;item Difficulty Constrained Uniform Adaptive Testing;a Support System to Help Teachers Design Course Plans Conforming to National Curriculum Guidelines;virtual Agent Approach for Teaching the Collaborative Problem Solving Skill of Negotiation;Generative AI for learning: Investigating the Potential of learning Videos with Synthetic Virtual Instructors;evaluating the Rater Bias in Response Scoring in Digital learning Platform: Analysis of Student Writing Styles;equity, Diversity, and Inclusion in Educational Technology Research and Development;it’s Good to Explore: Investigating Silver Pathways and the Role of Frustration During Game-Based learning;evaluating Language learning Apps for Behaviour Change Using the Behaviour Change Scale;Ghost in the Machine: AVATAR, a Prototype for Supporting Student Authorial Voice;AI Education for K-12: A Survey;annotating Educational Dialog Act with data Augmentation in Online One-on-One Tutoring;utilizing Natural Language Processing for automated Assessment of Classroom Discussion;using Large Language Models to Provide Formative Feedback in intelligent Textbooks;improving Code Comprehension through Scaffolded Self-explanations;tutorial: Educational Recommender Systems;AI Cognitive - Based Systems Supporting learning Process

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Investigation of different machine learning approaches for fault identification in microgrid 24

Investigation of different machine learning approaches for f...

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24th international Middle East Power System conference, MEPCON 2023

作者： Elsaeed, Mohamed A. Menaem, Amir Abdel Mansi, Ibrahim I. Badran, Ebrahim A. Mansoura University Electrical Engineering Department Mansoura Egypt Ural Federal University Department of Automated Electrical Systems Yekaterinburg Russia

ISBN: (纸本)9798350358469

the growing adoption of microgrids in modern energy systems has underscored the critical need for robust protection mechanisms to ensure their reliability and stability. With the dynamic nature of microgrids resulting from the large integration of renewable energy sources, conventional fault prediction schemes do not provide reliable protection. this paper investigates different protection models based on machine learning approaches: support vector machine (SVM), decision tree (DT), and random forest (RF), which accurately detect the variability in the microgrid operation under normal and fault conditions. A MATLAB Simulink environment is employed to model a comprehensive microgrid system, incorporating PV systems, diesel generators, and diverse loads. Subsequently, RMS values of three-phase voltage and current measurements are seamlessly integrated into a Python codebase as data inputs to train and test the protection models. By employing simulation data, the models based on the three techniques (SVM, DT, and RF) learn to accurately identify the mode of operation, the variations in PV generation, and the occurrence of a fault and its type in the microgrid. the practicability of the proposed schemes is tested on a five-bus AC microgrid. Comparisons among approaches in terms of accuracy and speed of operation results are highlighted. the results show the improved accuracy and adaptability of the machine learning-based protection system compared to conventional methods. © 2023 IEEE.

关键词： Decision trees

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Using Deep learning Models to Predict the Electrical Conductivity of the Influent in a Wastewater Treatment Plant 24th

Using Deep Learning Models to Predict the Electrical Condu...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Pereira, João Oliveira, Pedro Duarte, M. Salomé Martins, Gilberto Novais, Paulo LASI/ALGORITMI Centre University of Minho Braga Portugal CEB - Centre of Biological Engineering University of Minho Campus de Gualtar Braga4710-057 Portugal LABBELS - Associate Laboratory Braga/Guimarães Portugal

ISBN: (纸本)9783031482311

Nowadays, human population face increasing water pollution problems, so treating and managing this resource is crucial. Wastewater Treatment Plants (WWTPs) provide essential services for human life since they treat wastewater and monitor its parameters to preserve water quality standards. One of these parameters is electrical conductivity, essential in quantifying water salinity levels. therefore, this paper aims to forecast the influent conductivity in a WWTP for the next two timesteps. Hence, several experiments were conducted, considering the use of Transformers and Long Short-Term Memory (LSTMs) candidate models that were developed, tuned, and evaluated, utilising a recursive multi-step forecasting approach. the best candidate model was based on a Transformer architecture with encoding and obtained a RMSE of 155.2 μ S/cm. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Water pollution

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Conditioned Generative Model via Latent Semantic Controlling for learning Deep Representation of data 20th

Conditioned Generative Model via Latent Semantic Controlling...

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20th international conference on intelligent data engineering and automated learning (ideal)

作者： Kim, Jin-Young Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9783030336073;9783030336066

learning representations of data is an important issue in machine learning. though generative adversarial network has led to significant improvements in the data representations, it still has several problems such as unstable training, hidden manifold of data, and huge computational overhead. Moreover, most of GAN's have a large size of manifold, resulting in poor scalability. In this paper, we propose a novel GAN to control the latent semantic representation, called LSC-GAN, which allows us to produce desired data and learns a representation of the data efficiently. Unlike the conventional GAN models with hidden distribution of latent space, we define the distributions explicitly in advance that are trained to generate the data based on the corresponding features by inputting the latent variables, which follow the distribution, into the generative model. As the larger scale of latent space caused by deploying various distributions makes training unstable, we need to separate the process of defining the distributions explicitly and operation of generation. We prove that a variational auto-encoder is proper for the former and modify a loss function of VAE to map the data into the corresponding pre-defined latent space. the decoder, which generates the data from the associated latent space, is used as the generator of the LSC-GAN. Several experiments on the CelebA dataset are conducted to verify the usefulness of the proposed method. Besides, our model achieves a high compression ratio that can hold about 24 pixels of information in each dimension of latent space.

关键词： Generative model data representation Latent space Variational autoencoder Generative adversarial nets

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Comparing Ranking learning Algorithms for Information Retrieval Systems 24th

Comparing Ranking Learning Algorithms for Information Retri...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Zilles, J. Borges, E.N. Lucca, G. Marco-Detchart, C. Berri, Rafael A. Dimuro, G.P. Centro de Ciências Computacionais Universidade Federal do Rio Grande Av. Itália km 08 Campus Carreiros Rio Grande96201-900 Brazil Programa de Pós-Graduação em Engenharia Eletrônica e Computação Universidade Católica de Pelotas Gonçalves Chaves Pelotas96015-560 Brazil Camino de Vera s/n Valencia46022 Spain

ISBN: (纸本)9783031482311

the growth of machine learning applications in various fields has enabled the advancement of Information Retrieval systems. As a result of this evolution, it has become possible to solve the well-known document classification problem. In the beginning, document positions within a result were given from a score, where each document receives an assigned value based on the terms used in the input query. the use of machine learning in this field is known as learning to Rank, which allows the classification of documents to better meet user search requirements, taking into account aspects such as document preference, importance, and relevance. this paper presents a comparison of different algorithms for ranking documents using machine learning. It is observed that RankSVM presents relatively satisfactory results in smaller datasets, while algorithms that use Gradient Boosting obtain better results for larger datasets. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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A Machine learning Framework for Analyzing the Causes of Traffic Accident-Induced Congestion 24

A Machine Learning Framework for Analyzing the Causes of Tra...

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24th COTA international conference of Transportation Professionals: Resilient, intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Xu, Pengfei Zhang, Buhao Zeng, Jintao Song, Li Wuhan Univ. of Technology Wuhan China Zhejiang Intelligent Transportation Engineering Technology Research Center Enjoyor Technology Co. Ltd. Hangzhou China Co. Ltd. Foshan China School of Transportation and Logistics Engineering Wuhan Univ. of Technology Wuhan China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

To explore the factors of traffic accident-induced congestion in depth and analyze the influence degree of each factor precisely, this study proposes a framework for analyzing the causes of interrelated traffic accidents and congestion. An evaluation of the influencing factors is conducted by comparing six algorithms, including logistic regression, support vector machines, decision trees, random forests, neural networks, and naive Bayes methods. the traffic accident data in the United States and the traffic collision data in California are selected and combined into a traffic accident-induced congestion data set with a total of 30,911 observations in California from 2017 to 2020. the influencing factors are identified and analyzed based on the feature importance. the results indicate that the random forest model yields the highest prediction accuracy, and the cause analysis framework is proposed based on this model. theoretical suggestions for congestion mitigation and measures for preventing and resolving accident-related congestion are discussed. © ASCE.

关键词： Traffic congestion

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24th international conference on engineering Applications of Neural Networks, EANN 2023

24th International Conference on Engineering Applications of...

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24th international conference on engineering Applications of Neural Networks, EANN 2023

ISBN: (纸本)9783031342035

the proceedings contain 49 papers. the special focus in this conference is on engineering Applications of Neural Networks. the topics include: Towards Improved User Experience for Artificial Intelligence Systems;forecasting Functional Time Series Using Federated learning;group-Personalized Federated learning for Human Activity Recognition through Cluster Eccentricity Analysis;modeling Others as a Player in Non-cooperative Game for Multi-agent Coordination;neural Network Bootstrap Forecast Distributions with Extreme learning Machines;subsampled dataset Challenges and Machine learning Techniques in Table Tennis;towards Explaining Shortcut learning through Attention Visualization and Adversarial Attacks;a Novel Neural Network-Based Recommender System for Drug Recommendation;DACL: A Domain-Adapted Contrastive learning Approach to Low Resource Language Representations for Document Clustering Tasks;evaluating the Extraction of Toxicological Properties with Extractive Question Answering;semi-automated System for Pothole Detection and Location;a Critical Analysis of Classifier Selection in Learned Bloom Filters: the Essentials;balancing High-Dimensional datasets with Complex Layers;botDroid: Permission-Based Android Botnet Detection Using Neural Networks;classification of Time Signals Using Machine learning Techniques;conductivity Classification Using Machine learning Algorithms in the "Bramianon" Dam;preface;a Machine learning Approach for Seismic Vulnerability Ranking;generation of Bases for Classification in the Bio-inspired Layered Networks;load-Shedding Management in a Smart Grid Architecture through Smart Metering;MiniAnDE: A Reduced AnDE Ensemble to Deal with Microarray data;multi-view Semi-supervised learning Using Privileged Information;a Multi-relationship Language Acquisition Model for Predicting Child Vocabulary Growth;knowledge Graph of Urban Firefighting with Rule-Based Entity Extraction;optimal Traffic Flow Distributions on Dynamic Networks;Streamlined Traini

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