Liquid Biopsy Based Bladder Cancer Diagnostic by Machine Learning by Ērika Bitiņa-Barlote, Dmitrijs Bļizņuks, Sanda Siliņa, Mihails Šatcs, Egils Vjaters, Vilnis Lietuvietis, Miki Nakazawa-Miklaševiča, Juris Plonis, Edvīns Miklaševičs, Zanda Daneberga, Jānis Gardovskis

“…<b>Methods</b>: This study combined molecular biology methods for liquid biopsy, routine clinical data, and application of machine learning approach for the acquired data analysis. …”
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Learning analytics in immersive virtual learning environments: a systematic literature review by Lei Tao, Mutlu Cukurova, Yanjie Song

“…However, these areas remain unevenly explored; (2) research has predominantly examined desktop-based immersive VLEs, while fewer studies have explored immersive virtual reality settings such as head-mounted displaysand cave automatic virtual environments; (3) higher education students have been the most frequently studied participants, with fewer studies involving K-12 students and adult learners; (4) most studies have employed data-driven approaches to identify behavioural patterns, but explicit theoretical frameworks have been used less frequently to guide analysis and interpretation; (5) behaviour data remains the most commonly used data type; (6) statistical methods such as regression and ANOVA dominate the analytical approaches, with machine learning and deep learning techniques remaining underutilised; and (7) challenges including technical complexity, data interpretability, privacy concerns, and adoption barriers impact the effectiveness and scalability of LA applications in immersive VLEs. …”
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Artificial intelligence in electroencephalography analysis for epilepsy diagnosis and management by Chenxi Wang, Chenxi Wang, Xinyue Yuan, Wei Jing

“…Traditional EEG interpretation relies on manual analysis, which suffers from high misdiagnosis rates and inefficiency.MethodsThis review systematically evaluates the integration of artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML), into EEG analysis for epilepsy management. …”
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Preface to Special Issue on Scientific Computing and Learning Analytics for Smart Healthcare Systems (Part II) by Chinmay Chakraborty, Sayonara Barbosa, Lalit Garg

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Machine learning models and methods for solving optimization and forecasting problems of the work of seaports by M. N. Lukashevich, M. Y. Kovalyov

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Compositional Data Analysis (CoDA) of Clinopyroxene From Abyssal Peridotites by I. Nishio, K. Itano, P. Waterton, A. Tamura, K. Szilas, T. Morishita

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Analysis of HIV high-risk population characteristics with Baidu Tieba data by Shiyao XIAO, Wei LYU, Saran CHEN, Shuo QIN, Ge HUANG, Mengsi CAI, Yuejin TAN, Xu TAN, Xin LU

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Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical Review by Maria Gerakari, Anastasios Katsileros, Konstantina Kleftogianni, Eleni Tani, Penelope J. Bebeli, Vasileios Papasotiropoulos

“…This review discusses the potential of artificial intelligence (AI), particularly machine learning (ML) and its subset, deep learning (DL), in advancing the genetic improvement of Solanaceous crops. …”
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A Data-Driven Monitoring System for a Prescriptive Maintenance Approach: Supporting Reinforcement Learning Strategies by Joaquín Ordieres-Meré, Antonio Sánchez-Herguedas, Ángel Mena-Nieto

“…The aim of this study was to evaluate machine learning algorithms’ capacity to improve prescriptive maintenance. …”
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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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Big Data Meets Social Networks: A Survey of Analytical Strategies and Research Challenges by Shashank Sheshar Singh, Shashank Singh, Kuldeep Singh, Vishal Srivastava, Harish Kumar Shakya

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Identifying Behaviours Indicative of Illegal Fishing Activities in Automatic Identification System Data by Yifan Zhou, Richard Davies, James Wright, Stephen Ablett, Simon Maskell

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RETRACTED: Challenges and opportunities of big data analytics in healthcare by Priyanshi Goyal, Rishabha Malviya

“…Abstract Data science is an interdisciplinary discipline that employs big data, machine learning algorithms, data mining techniques, and scientific methodologies to extract insights and information from massive amounts of structured and unstructured data. …”
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Cloud-Driven Data Analytics for Growing Plants Indoor by Nezha Kharraz, István Szabó

“…Machine learning models, including SVM (Support Vector Machine), Gradient Boosting, and DNN (Deep Neural Networks), analyzed 12 weeks of sensor data to predict growth trends and optimize thresholds. …”
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Predicting Sentiments in Spotify Comments: A Comparative Analysis of Machine Learning Models by Filipe Augusto Felix de Queiroz, Igor Barbosa Negreiros, Giovana de Souza, Débora Cordeiro de Sousa, Sílvio Fernando Alves Xavier Júnior

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Breath Detection from a Microphone Using Machine Learning by Tomasz Sankowski, Piotr Sulewski, Jan Walczak, Aleksandra Bruska

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The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review by Norah Hamad Alhumaidi, Doni Dermawan, Hanin Farhana Kamaruzaman, Nasser Alotaiq

“… BackgroundMachine learning (ML) and big data analytics are rapidly transforming health care, particularly disease prediction, management, and personalized care. …”
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Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble by Tossapon Boongoen, Natthakan Iam-On, James Mullaney

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P-V-L Deep: A Big Data Analytics Solution for Now-casting in Monetary Policy by Maryam Hajipour Sarduie, Mohammadali Afshar Kazemi, Mahmood Alborzi, Adel Azar, Ali Kermanshah

“…So, it is necessary to consider the highly data-driven technologies and to use new methods of analysis, like machine learning and visualization tools, with the ability of interaction and connection to different data resources with varieties of data regarding the type of big data aimed at reducing the risks of policy-making institution’s investment in the field of IT. …”
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Machine learning in big data: A performance benchmarking study of Flink-ML and Spark MLlib by Messaoud MEZATI, Ines AOURIA

“… Machine learning (ML) in big data frameworks plays a critical role in real-time analytics, decision making, and predictive modeling. …”
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