A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning by Dennis Zanutto, Christos Michalopoulos, Georgios-Alexandros Chatzistefanou, Lydia Vamvakeridou-Lyroudia, Lydia Tsiami, Konstantinos Glynis, Panagiotis Samartzis, Luca Hermes, Fabian Hinder, Jonas Vaquet, Valerie Vaquet, Demetrios Eliades, Marios Polycarpou, Phoebe Koundouri, Barbara Hammer, Dragan Savić

“…The framework integrates an ensemble of machine learning forecasting models into a deterministic outcome consistent with the competition formulation. …”
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Artificial Intelligence: Basic Concepts by Mihail Kozhuharov

“…The specifics of deep machine learning are discussed, with an emphasis on their capacity to process complex data and extract patterns. …”
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Batik Lasem motifs dataset: Gunung Ringgit, Kricak, Latohan, Nyuk Pitu, SeritanMendeley Data by Vera Jenny Basiroen, Ida Bagus Kerthyayana Manuaba

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Economic Viability and Environmental Benefits of Integrating Solar Photovoltaics in Public Community Buildings by Mohannad Alhazmi, Abdullah Alfadda, Abdullah Alfakhri

“…This study evaluates the environmental and financial impacts of integrating solar PV systems in public buildings, specifically mosques and schools, in the central region of Saudi Arabia. Using machine learning-based forecasting, we analyzed power consumption and solar generation patterns. …”
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Forecasting Insurance Company Commitments with Long Short-Term Memory Models by Negar Tehraniyazdi, Reza Vaezi, Saeed Setayeshi, Iman Raeesi Vanani

“…It can identify hidden patterns and non-linear, complex relationships within claims data. …”
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Artificial intelligence in personalized cardiology treatment by Abbas Mohammadi, Sheida Shokohyar

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Causal estimation of the relationship between reproductive performance and the fecal bacteriome in cattle by Yutaka Taguchi, Haruki Yamano, Yudai Inabu, Hirokuni Miyamoto, Koki Hayasaki, Noriyuki Maeda, Yoshiro Kanmera, Seiji Yamasaki, Noboru Ota, Kenji Mukawa, Atsushi Kurotani, Shigeharu Moriya, Teruno Nakaguma, Chitose Ishii, Makiko Matsuura, Tetsuji Etoh, Yuji Shiotsuka, Ryoichi Fujino, Motoaki Udagawa, Satoshi Wada, Jun Kikuchi, Hiroshi Ohno, Hideyuki Takahashi

“…Subsequently, correlation analysis evaluated the interrelationships between bacteriomes, which demonstrated that the patterns exhibited distinct characteristics. Therefore, four machine-learning algorithms were employed to identify the distinctive factors between the two groups. …”
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Functional Connectivity Changes in Primary Motor Cortex Subregions of Patients With Obstructive Sleep Apnea by Lifeng Li, Qimeng Shi, Bowen Fang, Yuting Liu, Xiang Liu, Yongqiang Shu, Yingke Deng, Yumeng Liu, Haijun Li, Junjie Zhou, Dechang Peng

“…Additionally, we employed three machine learning algorithms—support vector machine (SVM), random forest (RF), and logistic regression (LR)—to distinguish patients with OSA from HC based on FC features. …”
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DETECTION AND RECOGNITION OF IRAQI LICENSE PLATES USING CONVOLUTIONAL NEURAL NETWORKS by Mohammed Hayder Abbas, Zeina Mueen Mohammed

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A Hybrid Methodology of Data Science and Decision Making Techniques: Lessons from COVID-19 Pandemic Management by mehdi dadehbeigi, ali taherinezhad, alireza alinezhad

“…With the spread of COVID-19 and the significant financial, economic, and human losses it caused, data mining has once again played a role in improving outcomes, predicting trends, and reducing these losses by identifying patterns in the data. This paper aims to assess and predict the efficiency of countries in preventing and treating COVID-19 by combining DEA and MCDM models with machine learning models. …”
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Harnessing AI-Powered Genomic Research for Sustainable Crop Improvement by Elżbieta Wójcik-Gront, Bartłomiej Zieniuk, Magdalena Pawełkowicz

“…AI systems integrate machine learning (ML) and deep learning (DL) with big data to identify complex patterns and relationships by analyzing vast genomic, phenotypic, and environmental datasets. …”
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A Global Probability‐Of‐Fire (PoF) Forecast by J. R. McNorton, F. Di Giuseppe, E. Pinnington, M. Chantry, C. Barnard

“…Here, we use a vegetation characteristic model, weather forecasts and a data‐driven machine learning approach to construct a global daily ∼9 km resolution Probability of Fire (PoF) model operating at multiple lead times. …”
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PREDICT LEARNERS’ PERFORMANCE USING AN ONTOLOGICAL-BASED MODEL ON AN E-LEARNING PLATFORM by Safa Ridha Albo Abdullah

“…By embedding decision tree rules that differentiate between pass and fail outcomes within an ontology, superior portability, and adaptability are achieved across courses with similar usage patterns. Applied to the Open University Learning Design (OULD) science courses dataset, advanced machine learning techniques were utilized to derive predictive rules, achieving an exceptional prediction accuracy of 97%. …”
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Big Data in traumatic brain injury; promise and challenges by Denes V Agoston, Dianne Langford

“…Big data analyses require specialized big data analytics applications, machine learning and artificial intelligence platforms to reveal associations, trends, correlations and patterns not otherwise realized by current analytical approaches. …”
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Adoption of Data-Driven Automation Techniques to Create Smart Key Performance Indicators for Business Optimization by Michael Sishi, Arnesh Telukdarie

“…The model then employs exploratory Factor Analysis (FA) techniques to identify correlations and patterns, prioritize KPIs, and automatically generate smart KPIs for business optimization. …”
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Using Computational Methods to Explore Law in Sermons by Markus M. Totzeck, Valentin Fuchs

“…The process integrates manual coding via MaxQDA with machine learning techniques, notably contextual embeddings derived from Transformer architectures such as <i>SBERT</i>, enabling us to detect patterns across large corpora. …”
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Reinforcement Learning in Energy Finance: A Comprehensive Review by Spyros Giannelos

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Technology acceptance model for online education: identifying interdisciplinary topics and their evolution based on BERTopic model by Songyu Jiang, Hao Li, Du Gan

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XRGuard: A Model-Agnostic Approach to Ransomware Detection Using Dynamic Analysis and Explainable AI by M. Adnan Alvi, Zunera Jalil

“…This paper introduces XRGuard, a novel ransomware detection framework that utilizes machine learning techniques to analyze Event Tracing for Windows (ETW) logs, identifying critical file I/O patterns indicative of ransomware attacks. …”
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The Prediction of Soybean Price in China Based on a Mixed Data Sampling–Support Vector Regression Model by Xing Liu, Wenhuan Zhou, Zhihang Gao, Dongqing Zhang, Kaiping Ma

“…Given the complexity and nonlinearity of soybean price fluctuations, machine learning techniques were adopted. Therefore, a MIDAS-SVR model (combining the MIDAS model and support vector regression) is proposed in this paper, which can capture the nonlinear and non-stationary patterns of soybean prices. …”
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