Titlebook: Data Science and Artificial Intelligence; First International Chutiporn Anutariya,Marcello M. Bonsangue Conference proceedings 2023 The Ed

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書目名稱Data Science and Artificial Intelligence影響因子(影響力)




書目名稱Data Science and Artificial Intelligence影響因子(影響力)學(xué)科排名




書目名稱Data Science and Artificial Intelligence網(wǎng)絡(luò)公開度




書目名稱Data Science and Artificial Intelligence網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Science and Artificial Intelligence被引頻次




書目名稱Data Science and Artificial Intelligence被引頻次學(xué)科排名




書目名稱Data Science and Artificial Intelligence年度引用




書目名稱Data Science and Artificial Intelligence年度引用學(xué)科排名




書目名稱Data Science and Artificial Intelligence讀者反饋




書目名稱Data Science and Artificial Intelligence讀者反饋學(xué)科排名

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A Modified Hybrid RBF-BP Network Classifier for?Nonlinear Estimation/Classification and?Its Applicat[., .] is proposed. The modified hybrid RBF-BP network is formulated as an adaptive incremental learning algorithm for a single-layer RBF hidden neuron layer. The algorithm uses a density clustering approach to determine the number of RBF hidden neurons and it maintains the self-learning process of

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Exploration of?the?Feasibility and?Applicability of?Domain Adaptation in?Machine Learning-Based Codee to limited choices of the publicly available datasets, most of the machine learning-based classifiers were trained by the earlier versions of open-source projects that no longer represent the characteristics and properties of modern programming languages. Our experiments exhibit the feasibility an

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Web Usage Mining for?Determining a?Website’s Usage Pattern: A Case Study of?Government Websiteyze a website’s usage. This study examined web usage mining to discover online users’ usage patterns and used the results to redesign and improve the government website. This study aims to help online customers obtain a better experience. A dataset was collected from the Metropolitan Electricity Aut

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Deep-Learning-Based LSTM Model for Predicting a Tidal River’s Water Levels: A Case Study of the Kapuing method to forecast the water level dynamics of the Kapuas Kecil River and determine the optimal window size for precise predictions. Our results reveal an optimal window size of 336 h (equivalent to 14?days) for water level prediction using LSTM in this coastal region. Using this optimal window

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Data Augmentation for?EEG Motor Imagery Classification Using Diffusion Model brain-computer interfaces (BCIs). However, due to the limited amount of available data, overfitting is a common problem, especially when using a deep-learning classifier. One way to address this is by performing data augmentation. In this paper, we investigate the efficacy of the diffusion model as

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