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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Securing federated learning: a defense strategy against targeted data poisoning attack by Ansam Khraisat, Ammar Alazab, Moutaz Alazab, Tony Jan, Sarabjot Singh, Md. Ashraf Uddin

“…This paper investigates targeted data poisoning attacks in FL systems, where a small fraction of malicious participants corrupt the global model through mislabeled data updates. …”
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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

“…Further, data processing was performed by means of the Google Earth Engine (GEE) platform. …”
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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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Sample selection using multi-task autoencoders in federated learning with non-IID data by Emre Ardıç, Yakup Genç

“…Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while ensuring data privacy. …”
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Geological object recognition in legacy maps through data augmentation and transfer learning techniques by Wenjia Li, Weilin Chen, Jiyin Zhang, Chenhao Li, Xiaogang Ma

“…This study addresses these challenges by proposing an innovative approach that leverages legend data for data augmentation and employs transfer learning techniques to improve the quality of object recognition. …”
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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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Using traffic data to identify land-use characteristics based on ensemble learning approaches by Jiahui Zhao, Zhibin Li, Pan Liu

“…In this context, a novel land-use identification framework is proposed to quantify land-use characteristics using traffic-flow and traffic-events data. Regarding the identification models, two widely used Ensemble learning methods: Random Forest and Adaboost, are introduced to classify the land-use type and fit the land-use density. …”
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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. We used structured and unstructured data from the MIMIC-III database to identify intentional and unintentional intake of opioid drugs. …”
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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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Machine learning enables legal risk assessment in internet healthcare using HIPAA data by Shixian Liu, Hailing Liu, Siyu Fan, Leming Song, Zeyu Wang

“…Abstract This study explores how artificial intelligence technologies can enhance the regulatory capacity for legal risks in internet healthcare based on a machine learning (ML) analytical framework and utilizes data from the health insurance portability and accountability act (HIPAA) database. …”
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A Review on Deep Learning for Quality of Life Assessment Through the Use of Wearable Data by Vasileios Skaramagkas, Ioannis Kyprakis, Georgia S. Karanasiou, Dimitris I. Fotiadis, Manolis Tsiknakis

“…This paper presents a comprehensive review of the integration of Deep Learning (DL) techniques in QoL assessment, focusing on the analysis of wearable data. …”
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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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