Predictive maintenance based on anomaly detection in photovoltaic system using SCADA data and machine learning by Agussalim Syamsuddin, Andrew Cahyo Adhi, Amie Kusumawardhani, Toni Prahasto, Achmad Widodo

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A guide to neural ordinary differential equations: Machine learning for data-driven digital engineering by Joseph M. Worsham, Jugal K. Kalita

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Enhancing Mobile App Recommendations With Crowdsourced Educational Data Using Machine Learning and Deep Learning by Naadiya Mirbahar, Kamlesh Kumar, Asif Ali Laghari

“…In the rapidly evolving digital landscape, personalized recommendations have become essential for enhancing user experience. Machine learning models analyze user behavior patterns to suggest relevant entertainment, education, or e-commerce content. …”
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Comprehensive machine learning analysis of PANoptosis signatures in multiple myeloma identifies prognostic and immunotherapy biomarkers by Yashu Feng, Shuoting Wang, Jingwen Zhang, Chengcheng Liu, Ling Zhang, Jiajun Liu

“…Quantitative PCR (qPCR) further validated differential expression patterns of the seven core PRGs between MM patients and healthy controls. …”
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Multimodal machine learning for analysing multifactorial causes of disease—The case of childhood overweight and obesity in Mexico by Rosario Silva Sepulveda, Magnus Boman, Magnus Boman

“…Public health interventions have shown only moderate success, possibly from relying on knowledge extracted using limited types of statistical data analysis methods.PurposeTo explore if multimodal machine learning can enhance identifying predictive features from obesogenic environments and investigating complex disease or social patterns, using the Mexican National Health and Nutrition Survey.MethodsWe grouped features into five data modalities corresponding to paediatric population exogenous factors, in two multimodal machine learning pipelines, against a unimodal early fusion baseline. …”
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Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study by Maryam Abbasi, Marco V. Bernardo, Paulo Váz, José Silva, Pedro Martins

“…This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. …”
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The future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure by Nima Kianfar, Benn Sartorius, Colleen L. Lau, Robert Bergquist, Behzad Kiani

“…Research in spatial epidemiology relies on both conventional approaches and Machine- Learning (ML) algorithms to explore geographic patterns of diseases and identify influential factors (Pfeiffer & Stevens, 2015). …”
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Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit by Hyungjun Kim, Hyungjun Kim, Min Kim, Da Young Kim, Dong Gi Seo, Ji Man Hong, Ji Man Hong, Dukyong Yoon, Dukyong Yoon

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Machine Learning for the Photonic Evaluation of Cranial and Extracranial Sites in Healthy Individuals and in Patients with Multiple Sclerosis by Antonio Currà, Riccardo Gasbarrone, Davide Gattabria, Nicola Luigi Bragazzi, Giuseppe Bonifazi, Silvia Serranti, Paolo Missori, Francesco Fattapposta, Carlotta Manfredi, Andrea Maffucci, Luca Puce, Lucio Marinelli, Carlo Trompetto

“…We sought to identify the diagnostic accuracy of wavelength-specific patterns in distinguishing MS from normal controls and spectral markers associated with disability (e.g., Expanded Disability Status Scale scores). …”
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Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation by Han Chen, Han Chen, Enguang Chen, Enguang Chen, Ting Cao, Feifan Feng, Min Lin, Xuan Wang, Yu Xu

“…Validation confirmed the diagnostic efficiency and expression patterns of the PANoptosis-related hub genes, supported by in vitro and the GSE60436 dataset analysis. …”
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On Training Spiking Neural Networks by Means of a Novel Quantum Inspired Machine Learning Method by Jean Michel Sellier, Alexandre Martini

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Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano by C. X. Ren, A. Peltier, V. Ferrazzini, B. Rouet‐Leduc, P. A. Johnson, F. Brenguier

“…In this study we leverage machine learning techniques using 6 years of continuous seismic data from the Piton de la Fournaise volcano (La Réunion island) to describe specific patterns of seismic signals recorded during eruptions. …”
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Integrating Dimensional Analysis and Machine Learning for Predictive Maintenance of Francis Turbines in Sediment-Laden Flow by Álvaro Ospina, Ever Herrera Ríos, Jaime Jaramillo, Camilo A. Franco, Esteban A. Taborda, Farid B. Cortes

“…Key variables, including power output, water flow rate, and operational time, were extracted and analyzed to identify patterns and correlations affecting turbine performance. …”
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Novel application of unsupervised machine learning for characterization of subsurface seismicity, tectonic dynamics and stress distribution by Mohammad Salam, Muhammad Tahir Iqbal, Raja Adnan Habib, Amna Tahir, Aamir Sultan, Talat Iqbal

“…Our study pioneers an innovative use of unsupervised machine learning, a powerful tool for navigating unclassified data, to unravel the complexities of subsurface seismic activities and extract meaningful patterns. …”
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Dynamic demand response strategies for load management using machine learning across consumer segments by Ravi Kumar Goli, Nazeer Shaik, Manju Sree Yalamanchili

“…Demand Response (DR) programs, which provide financial incentives to promote participation, aim to modify customer energy usage patterns, especially during periods of peak demand. …”
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Predicting the Energy Consumption in Chillers: A Comparative Study of Supervised Machine Learning Regression Models by Mohamed Salah Benkhalfallah, Sofia Kouah, Saad Harous

“…This paper examines the application of artificial intelligence and supervised machine learning techniques to modeling and predicting the energy consumption patterns in the smart grid sector of a commercial building located in Singapore. …”
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Bayesian Unsupervised Machine Learning Approach to Segment Arctic Sea Ice Using SMOS Data by Christoph Herbert, Adriano Camps, Florian Wellmann, Mercedes Vall‐llossera

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Machine learning and multi-omics analysis reveal key regulators of proneural–mesenchymal transition in glioblastoma by Can Xu, Jin Yang, Huan Xiong, Xiaoteng Cui, Yuhao Zhang, Mingjun Gao, Lei He, Qiuyue Fang, Changxi Han, Wei Liu, Yangyang Wang, Jin Zhang, Ying Yuan, Zhaomu Zeng, Ruxiang Xu

“…PMTRG expression patterns in patient tissues and different cell subsets were examined by proteomics and single-cell transcriptome data analysis. …”
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Machine learning phase control of filled-aperture coherent beam combining: principle and numerical demonstration by Hongbing Zhou, Rumao Tao, Xi Feng, Haoyu Zhang, Min Li, Xiong Xin, Yuyang Peng, Honghuan Lin, Jianjun Wang, Lixin Yan, Feng Jing

“…Machine learning has already shown promising potential in tiled-aperture coherent beam combining (CBC) to achieve versatile advanced applications. …”
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Unveiling the complementariness of robotic tablet dispensing machines for elderly care: A bibliometric data analysis by Sunday Adewale Olaleye, Olaleye Esther Olubunmi, Berhanemeskel Weldegerima Atsbeha, Mulugeta Negash Wodaje

“…Data analysis was performed using the Biblioshiny App through RStudio, examining publication trends, authorship patterns, citation networks, and other bibliometric indicators. …”
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