Privacy-Aware Table Data Generation by Adversarial Gradient Boosting Decision Tree by Shuai Jiang, Naoto Iwata, Sayaka Kamei, Kazi Md. Rokibul Alam, Yasuhiko Morimoto

“…Synthetic table data generation has emerged as a promising solution to enable data analysis while mitigating privacy risks. …”
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Influence of Explanatory Variable Distributions on the Behavior of the Impurity Measures Used in Classification Tree Learning by Krzysztof Gajowniczek, Marcin Dudziński

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Enhancing Sentiment and Emotion Classification with LSTM-Based Semi-Supervised Learning by Rochmat Husaini, Nur Heri Cahyana, Wisnalmawati Wisnalmawati, Tri Mardiana, Yuli Fauziah

“…The evolution of sentiment analysis has increasingly relied on semi-supervised learning (SSL) models, particularly due to their efficiency in utilizing large amounts of unlabeled data. …”
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Probabilistic Deep Learning for Highly Multivariate Spatio-Temporal Log-Gaussian Cox Processes by Achmad Choiruddin, Ekky Rino Fajar Sakti, Tintrim Dwi Ary Widhianingsih, Jorge Mateu, Kartika Fithriasari

“…Despite limited applications in multivariate point pattern analysis, this study aims to introduce deep learning as a tool for parameter estimation of the multivariate spatio-temporal log-Gaussian Cox Process (LGCP) model. …”
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Review and prospect of floating car data research in transportation by Chi Zhang, Yuming Zhou, Min Zhang, Bo Wang, Yuhan Nie

“…The results show an overall parabolic increase in publication volume, with research hotspots mainly focusing on mountainous cities, cluster analysis, machine learning, and deep learning. The three major research clusters include traffic flow state, traffic safety, and route planning. …”
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Methods and reliability study of moral education assessment in universities: A machine learning-based approach by Ting Jin

“…The research aims to assess the effectiveness of machine learning (ML) techniques in evaluating moral education programs at university institutions. …”
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Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI by Lei Guo, Peisi Xie, Xionghui Shen, Thomas Ka Yam Lam, Lingli Deng, Chengyi Xie, Xiangnan Xu, Chris Kong Chu Wong, Jingjing Xu, Jiacheng Fang, Xiaoxiao Wang, Zhuang Xiong, Shangyi Luo, Jianing Wang, Jiyang Dong, Zongwei Cai

“…To address these challenges, a novel deep learning‐based approach is proposed, GraphMSI, which integrates metabolic profiles with spatial information to enhance MSI data analysis. …”
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University Student’s Emotional States during Virtual Learning by Eija Henritius, Markku Hannula, Panu Erästö, Visajaani Salonen, Erika Löfström

“…The sample consisted of 85 students. Data were collected at nine measurement points during a half semester foundation course in statistics. …”
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Satellite Image Price Prediction Based on Machine Learning by Linhan Yang, Zugang Chen, Guoqing Li

“…This study develops a comprehensive, data-driven framework for predicting satellite imagery prices using four state-of-the-art ensemble learning algorithms: XGBoost, LightGBM, AdaBoost, and CatBoost. …”
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Sinh-Cosh Optimization-Based Efficient Clustering for Big Data Applications by Lahbib Khrissi, Mohammed Es-Sabry, Nabil El Akkad, Hassan Satori, Saad Aldosary, Walid El-Shafai

“…Data clustering is a pivotal aspect of data mining, attracting significant attention in the exploration of cluster analysis through optimization algorithms. …”
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Variational autoencoders for at-source data reduction and anomaly detection in high energy particle detectors by Alexander Yue, Haoyi Jia, Julia Gonski

“…To address these challenges, machine learning in readout electronics can be leveraged for smart detector designs, enabling intelligent inference and data reduction at-source. …”
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Double-Weighted Bayesian Model Combination for Metabolomics Data Description and Prediction by Jacopo Troisi, Martina Lombardi, Alessio Trotta, Vera Abenante, Andrea Ingenito, Nicole Palmieri, Sean M. Richards, Steven J. K. Symes, Pierpaolo Cavallo

“…Background/Objectives: This study presents a novel double-weighted Bayesian Ensemble Machine Learning (DW-EML) model aimed at improving the classification and prediction of metabolomics data. …”
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Explainable Offline‐Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave‐QBO Testbed in the Small‐Data Regime by Hamid A. Pahlavan, Pedram Hassanzadeh, M. Joan Alexander

“…Fourier analysis of these three NNs' kernels suggests why/how re‐training works and reveals that these NNs primarily learn low‐pass, high‐pass, and a combination of band‐pass filters, potentially related to the local and non‐local dynamics in GW propagation and dissipation. …”
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Bayesian deconvolution for computational cognitive modeling of fMRI data by Jonathon R. Howlett, Katia M. Harlé, Alan N. Simmons, Charles T. Taylor

“…A central goal of cognitive neuroscience is to make inferences about underlying cognitive processes from observable data. However, current fMRI analysis tools cannot directly estimate latent parameters in computational cognitive models from blood-oxygen-level-dependent (BOLD) signal. …”
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Enhancing Real Estate Data Analytics Education with Digital Badges by Jeffrey G. Robert, Marc R. Zaldivar, Helen Ajao, Zhenhuan Yang

“…Real Estate statistical courses provide foundational content for students in business programs to be proficient in data analytics and management science skills. Not only are these skills essential for future real estate finance and investment analysis courses, but also they are highly demanded by real estate employers. …”
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Data-Driven Carbon Emission Dynamics Under Ship In-Port Congestion by Weiyu Liu, Bowei Xu, Junjun Li

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A WGAN-GP Approach for Data Imputation in Photovoltaic Power Prediction by Zhu Liu, Lingfeng Xuan, Dehuang Gong, Xinlin Xie, Zhongwen Liang, Dongguo Zhou

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Harnessing Novel Data‐Driven Techniques for Precise Rainfall–Runoff Modeling by Saad Sh. Sammen, Reza Mohammadpour, Karam AlSafadi, Ali Mokhtar, Shamsuddin Shahid

“…Developing an accurate model to capture the dynamic connection between rainfall and runoff remains problematic and challenging in water resource management due to the nonstationary characteristics of hydrologic processes and the effects of noise. In this study, data‐driven techniques, such as the group method of data handling (GMDH), extreme learning machine (ELM), and two hybrids of artificial neural network (ANN) with Cuckoo search algorithm (ANN + Cuckoo) and genetic algorithm (ANN + GA) were used to model the rainfall–runoff relationship. …”
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Artificial Neural Network for the Clustering of Vibration Signals for Condition Monitoring of Rotating Machines by Eyere Emagbetere, Samson Uwatse, Omonzokpia Goerge Okoidigun

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Blended Ensemble Learning for Robust Normal Behavior Modeling of Wind Turbines by Jianghao Zhu, Tingting Pei, Le Su, Bin Lan, Wei Chen

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