Statistical Data-Generative Machine Learning-Based Credit Card Fraud Detection Systems by Xiaomei Feng, Song-Kyoo Kim

“…This study addresses the challenges of data imbalance and missing values in credit card transaction datasets by employing mode-based imputation and various machine learning models. …”
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Deep Aramaic: Towards a synthetic data paradigm enabling machine learning in epigraphy. by Andrei C Aioanei, Regine R Hunziker-Rodewald, Konstantin M Klein, Dominik L Michels

“…Epigraphy is witnessing a growing integration of artificial intelligence, notably through its subfield of machine learning (ML), especially in tasks like extracting insights from ancient inscriptions. …”
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Big data-driven machine learning: transforming multi-omics lung cancer research by Yanqi Zhang, Mingyu Liu, Jinhua Luo, Zhongqing Xu

“…Results Our machine learning approaches effectively distinguished cancer patients from healthy controls. …”
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Machine Learning Approaches for Data-Driven Self-Diagnosis and Fault Detection in Spacecraft Systems by Enrico Crotti, Andrea Colagrossi

“…Traditional approaches often rely on precise, model-based methods executed onboard. This study explores data-driven alternatives for self-diagnosis and fault detection using Machine Learning techniques, focusing on spacecraft Guidance, Navigation, and Control (GNC) subsystems. …”
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Ionospheric Electron Density and Temperature Profiles Using Ionosonde-like Data and Machine Learning by Jean de Dieu Nibigira, Richard Marchand

“…This paper presents a novel way of inferring ionospheric electron density profiles and electron temperature profiles using machine learning. The analysis is based on the Nearest Neighbour (NNB) and Radial Basis Function (RBF) regression models. …”
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Review on the Application of Remote Sensing Data and Machine Learning to the Estimation of Anthropogenic Heat Emissions by Lingyun Feng, Danyang Ma, Min Xie, Mengzhu Xi

“…Based on big data and machine learning techniques, the research on feature engineering and model fusion will bring about major changes in data analysis and modeling of anthropogenic heat. …”
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Planning and layout of tourism and leisure facilities based on POI big data and machine learning. by Shifeng Wu, Jiangyun Wang, Yinuo Jia, Jintian Yang, Jixiu Li

“…Drawing on POI and demographic data, and considering the distribution patterns of existing tourism and leisure facilities, this research applies machine learning to quantitatively simulate the optimal siting of such amenities. …”
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A machine learning approach for multimodal data fusion for survival prediction in cancer patients by Nikolaos Nikolaou, Domingo Salazar, Harish RaviPrakash, Miguel Gonçalves, Rob Mulla, Nikolay Burlutskiy, Natasha Markuzon, Etai Jacob

“…Abstract Technological advancements of the past decade have transformed cancer research, improving patient survival predictions through genotyping and multimodal data analysis. However, there is no comprehensive machine-learning pipeline for comparing methods to enhance these predictions. …”
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Leveraging machine learning and open accessed remote sensing data for precise rainfall forecasting by Bambang Kun Cahyono, Muhammad Hidayatul Ummah, Ruli Andaru, Neil Andika, Adjie Pamungkas, Hepi Hapsari Handayani, Paramita Atmodiwirjo, Rory Nathan

“…Machine learning methods, including Support Vector Regression, Gradient Boosting Regression, Random Forest, and Deep Neural Networks, were applied. …”
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A Novel Forecasting System with Data Preprocessing and Machine Learning for Containerized Freight Market by Yonghui Duan, Xiaotong Zhang, Xiang Wang, Yingying Fan, Kaige Liu

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Data-Driven Modeling of Electric Vehicle Charging Sessions Based on Machine Learning Techniques by Raymond O. Kene, Thomas O. Olwal

“…To address this issue, this study presents data-driven modeling of EV charging sessions based on machine learning (ML) techniques. …”
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Modelling the Temperature of a Data Centre Cooling System Using Machine Learning Methods by Adam Kula, Daniel Dąbrowski, Marcin Blachnik, Maciej Sajkowski, Albert Smalcerz, Zygmunt Kamiński

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Porosity prediction of tight reservoir rock using well logging data and machine learning by Yawen He, Hongjun Zhang, Zhiyu Wu, Hongbo Zhang, Xin Zhang, Xiaojing Zhuo, Xiaoli Song, Sha Dai, Wei Dang

“…To address these issues, we apply advanced machine learning algorithms—gradient boosting decision tree (GBDT), random forest, XGBoost, and multilayer perceptron—using well logging data, including acoustic time (AC), well logging (CAL), compensating neutrons (CNL), density (DEN), natural gamma (GR), resistivity (RT), and spontaneous potential (SP). …”
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Data Driven Classification of Opioid Patients Using Machine Learning–An Investigation by Lisan Al Amin, Md. Saddam Hossain Mukta, Md. Sezan Mahmud Saikat, Md. Ismail Hossain, Md. Adnanul Islam, Mohiuddin Ahmed, Sami Azam

“…This paper investigates the opioid classification problem by using machine learning and deep learning based techniques. …”
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When Machine Learning Meets Geospatial Data: A Comprehensive GeoAI Review by Anasse Boutayeb, Iyad Lahsen-Cherif, Ahmed El Khadimi

“…This article offers a comprehensive review of GeoAI as a synergistic concept applying artificial intelligence (AI) models, specifically those of machine learning (ML), to geospatial data. A preliminary study is carried out, identifying the methodology of the work, the research motivations, the issues, and the directions to be tracked, followed by exploring how GeoAI can be used in various interesting fields of application, such as precision agriculture, environmental monitoring, disaster management, and urban planning. …”
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Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook by Pengcheng Xue, Rui Qiu, Chuchuan Peng, Zehang Peng, Kui Ding, Rui Long, Liang Ma, Qifeng Zheng

“…Furthermore, other possible strategies for addressing data quality such as database management techniques and data analysis methodologies are also emphasized. …”
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Scalable and robust machine learning framework for HIV classification using clinical and laboratory data by Qian Sui, Gaoxu Li, Yaqi Peng, Jiasheng Zhang, Yibo Zhang, Riyang Zhao

“…Moreover, these outcomes underscore the potential of combining machine learning techniques with critical clinical data to enhance the accuracy of HIV infection classification, ultimately contributing to improved patient management and treatment strategies. …”
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Hybrid machine learning for flood prediction: comparing CHIRPS satellite and ground station data by Agustina Rachmawardani, Budhy Kurniawan, Sastra Kusuma Wijaya, Ardhasena Sopaheluwakan, Marzuki Sinambela

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Industrial data-driven machine learning soft sensing for optimal operation of etching tools by Feiyang Ou, Henrik Wang, Chao Zhang, Matthew Tom, Sthitie Bom, James F. Davis, Panagiotis D. Christofides

“…One industry that has a high volume of labor-intensive, time-consuming, and expensive processes is the semiconductor industry. AI machine learning methods can meet these challenges by taking in operational data and extracting process-specific information needed to meet the high product specifications of the industry. …”
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Landslide data sample augmentation and landslide susceptibility analysis in Nyingchi City based on the MCMC model by Lianchen Liu, Peiqing Wang, Libin Su, Feng Li

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