The Role of Machine Learning in Cognitive Impairment in Parkinson Disease: Systematic Review and Meta-Analysis by Yanyun Wu, Yangfan Cheng, Yi Xiao, Huifang Shang, Ruwei Ou

“…The utilization of machine learning (ML) has recently shown promise in identifying cognitive impairment in patients with PD. …”
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Predicting the infecting dengue serotype from antibody titre data using machine learning. by Bethan Cracknell Daniels, Darunee Buddhari, Taweewun Hunsawong, Sopon Iamsirithaworn, Aaron R Farmer, Derek A T Cummings, Kathryn B Anderson, Ilaria Dorigatti

“…We applied four machine learning classifiers and multinomial logistic regression to the titre data to predict the infecting serotype. …”
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Implementing an undergraduate learning assistant model to foster engagement and professional development in microbiology courses by Katriana A. Popichak, Paige E. Gruber, Erica L. Suchman, Jennifer L. McLean

“…This manuscript presents a field-tested framework for implementing and sustaining a structured ULA program in a General Microbiology course, offering a replicable model for educators seeking to enhance student learning and ULA professional development (PD). …”
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Explainable brain age prediction: a comparative evaluation of morphometric and deep learning pipelines by Maria Luigia Natalia De Bonis, Giuseppe Fasano, Angela Lombardi, Carmelo Ardito, Antonio Ferrara, Eugenio Di Sciascio, Tommaso Di Noia

“…In this study, we present a comparative evaluation of two pipelines: one using morphometric features from FreeSurfer and the other employing 3D convolutional neural networks (CNNs). …”
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Assessing acoustic receiver detection efficiency using autocorrelation adjusted machine learning models by Devon A. Smith, James A. Crossman, Eduardo G. Martins

“…Restricted data availability in the mountainous stream and short rapid environmental changes in both systems presented challenges for model accuracy. Accounting for detection efficiency is an important component of describe animal movement using acoustic telemetry and our findings demonstrate machine learning models as an approach to predicting detection efficiency in acoustic receiver arrays across riverine environments with diverse hydrological and geomorphological characteristics.…”
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FArSide Trained Active Region Recognition (FASTARR): A Machine Learning Approach by Amr Hamada, Mitchell Creelman, Kiran Jain, Charles Lindsey

“…Based on well-developed machine learning networks, we present a convolutional far-side active region recognition algorithm that aims to enhance active region identification in far-side helioseismic maps. …”
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A machine learning approach to predict phyllosphere resistome abundance across urbanization gradients by Rui-Ao Ma, Yi-Hui Ding, Shifa Zhong, Ting-Ting Jing, Xuechu Chen, Si-Yu Zhang

“…Moreover, the phyllosphere presented more ARG-MGE (mobile gene element) pairs in metagenome-assembled genomes than soil, suggesting greater transmission potential than soil ARGs. …”
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Architecture for pavement pothole evaluation using deep learning, machine vision, and fuzzy logic by Mario Roman-Garay, Hector Rodriguez-Rangel, Carlos Beltran Hernandez-Beltran, Peter Lepej, José Eleazar Arreygue-Rocha, Luis Alberto Morales-Rosales

“…The architecture utilizes transfer learning with a Segformer network, achieving high detection performance with a Recall of 90.87 %, Precision of 90.01 %, Accuracy of 86.8 % F1 Score of 90.433 %, and a loss of 0.0431. …”
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Voice pathology detection using machine learning algorithms based on different voice databases by Nurul Mu'azzah Abdul Latiff, Fahad Taha Al-Dhief, Nurul Fariesya Suhaila Md Sazihan, Marina Mat Baki, Nik Noordini Nik Abd. Malik, Musatafa Abbas Abbood Albadr, Ali Hashim Abbas

“…Unlike traditional approaches that focus solely on single-database training and testing, this study presents a cross-database evaluation strategy to assess the robustness and generalizability of machine learning algorithms for voice pathology detection. …”
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Deep learning-based prediction of mortality using brain midline shift and clinical information by An-Rong Wu, Sun-Yuan Hsieh, Hsin-Hung Chou, Cheng-Shih Lai, Jo-Ying Hung, Bow Wang, Yi-Shan Tsai

“…Therefore, this study presents a computer-aided deep-learning method for detecting MLS, aiming to predict mortality in a prognosis-predicting cohort using brain MLS and clinical in-formation. …”
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Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete by Ala’a R. Al-Shamasneh, Arsalan Mahmoodzadeh, Faten Khalid Karim, Taoufik Saidani, Abdulaziz Alghamdi, Jasim Alnahas, Mohammed Sulaiman

“…Abstract The accurate prediction of compressive strength (CS) in steel fiber reinforced concrete (SFRC) remains a critical challenge due to the material’s inherent complexity and the nonlinear interactions among its constituents. This study presents a robust machine learning framework to predict the CS of SFRC using a large-scale experimental dataset comprising 600 data points, encompassing key parameters such as fiber characteristics (type, content, length, diameter), water-to-cement (w/c) ratio, aggregate size, curing time, silica fume, and superplasticizer. …”
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Massive shift of schools towards distance learning in the estimates of a local pedagogical community by S. A. Chernyshov

“…In spring 2020, Russia, as all other countries around the world, faced the challenge of massive shift of schools to online learning. This shift revealed the lack of existing empirical data on technological and instructional readiness of the local teaching communities to such learning format. …”
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Impact of the COVID-19 Pandemic and Lockdown on Non-COVID Neurosurgical Patients: Lessons Learned by Rajnish Kumar Arora, Jitendra Shakya, Rajkumar Pannem, Suresh Sharma, Saravanan Sadhasivam, Vikram Singh Rawat, Rajashekhar Rekhapalli, Rakesh Kumar Sihag, Radhey Shyam Mittal

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Machine Learning Beach Attendance Forecast Modelling from Automatic Video-Derived Counting by Bruno Castelle, David Carayon, Jeoffrey Dehez, Sylvain Liquet, Vincent Marieu, Nadia Sénéchal, Sandrine Lyser, Jean-Philippe Savy, Stéphanie Barneix

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Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems by Samir A. Hamad, Mohamed A. Ghalib, Amr Munshi, Majid Alotaibi, Mostafa A. Ebied

“…Abstract This paper presents a machine learning (ML) model designed to track the maximum power point of standalone Photovoltaic (PV) systems. …”
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Reliable Autism Spectrum Disorder Diagnosis for Pediatrics Using Machine Learning and Explainable AI by Insu Jeon, Minjoong Kim, Dayeong So, Eun Young Kim, Yunyoung Nam, Seungsoo Kim, Sehoon Shim, Joungmin Kim, Jihoon Moon

“…<b>Methods:</b> This paper presents a method that combines XAI techniques with a rigorous data-preprocessing pipeline to improve the accuracy and interpretability of ML-based diagnostic tools. …”
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Evaluation of different spectral indices for wheat lodging assessment using machine learning algorithms by Shikha Sharda, Sumit Kumar, Raj Setia, Prince Dhiman, N. R. Patel, Brijendra Pateriya, Ali Salem, Ahmed Elbeltagi

“…This study presented a systematic approach for detecting the wheat lodging occurred during the end of March and April 2023 in the Ludhiana district of Punjab (India) from multi-temporal Sentinel-2 data using the machine learning algorithms. …”
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Predictive modeling of gestational weight gain: a machine learning multiclass classification study by Audêncio Victor, Hellen Geremias dos Santos, Gabriel Ferreira Santos Silva, Fabiano Barcellos Filho, Alexandre de Fátima Cobre, Liania A. Luzia, Patrícia H.C. Rondó, Alexandre Dias Porto Chiavegatto Filho

“…This study aims to develop and evaluate machine learning models to predict GWG categories: below, within, or above recommended guidelines. …”
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A Fundamental Statistics Self-Learning Method with Python Programming for Data Science Implementations by Prismahardi Aji Riyantoko, Nobuo Funabiki, Komang Candra Brata, Mustika Mentari, Aviolla Terza Damaliana, Dwi Arman Prasetya

“…In this paper, we present a self-learning method for fundamental statistics through <i>Python programming</i> for data science studies. …”
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CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland by B. Kraft, M. Schirmer, W. H. Aeberhard, M. Zappa, S. I. Seneviratne, L. Gudmundsson

“…<p>This study presents a data-driven reconstruction of daily runoff that covers the entirety of Switzerland over an extensive period from 1962 to 2023. …”
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