Neural Determinants of Sedentary Lifestyle in Older Adults: A Brain Network Analysis by Mohsen Bahrami, Jonathan H Burdette, Paul J Laurienti, Barbara J Nicklas, W Jack Rejeski, Jason Fanning

“…Method The goal of the current study, using baseline fMRI and accelerometry data from 36 participants and advanced machine learning tools, was to determine if we could identify complex brain circuits underlying variability associated with changes in sitting time and daily steps during the 6‐month intensive phase among participants randomized to the WL + SitLess treatment condition. …”
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Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study by Maria Munoz-Novoa, Morten B. Kristoffersen, Katharina S. Sunnerhagen, Autumn Naber, Max Ortiz-Catalan, Margit Alt Murphy

“…Abstract Background Myoelectric pattern recognition (MPR) combines multiple surface electromyography channels with a machine learning algorithm to decode motor intention with an aim to enhance upper limb function after stroke. …”
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Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. by Hamidreza Moradi, H Timothy Bunnell, Bradley S Price, Maryam Khodaverdi, Michael T Vest, James Z Porterfield, Alfred J Anzalone, Susan L Santangelo, Wesley Kimble, Jeremy Harper, William B Hillegass, Sally L Hodder, National COVID Cohort Collaborative (N3C) Consortium

“…<h4>Conclusions</h4>This machine learning model by accurately predicting the mortality provides insights about the treatment combinations associated with clinical improvement in COVID-19 patients. …”
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Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells by Nida Aslam, Irfan Ullah Khan, Aisha Alansari, Marah Alrammah, Atheer Alghwairy, Rahaf Alqahtani, Razan Alqahtani, Maryam Almushikes, Mohammed AL Hashim

“…Additionally, it can lead to several faulty events that could increase costs and production losses since the engineers tend to focus on the analysis rather than detecting the faulty events. Recently, machine learning (ML) techniques have significantly solved enormous real-time data anomaly problems by decreasing the data engineers’ interaction processes. …”
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Multi-omics analysis reveals the sensitivity of immunotherapy for unresectable non-small cell lung cancer by Rui Wu, Kunchen Wei, Xingshuai Huang, Yinge Zhou, Xiao Feng, Xin Dong, Hao Tang

“…Finally, potential biomarkers were picked out by applying machine learning methods including random forest and stepwise regression and prediction models were constructed by logistic regression.ResultsThe presence of metabolites and proteins in peripheral blood plasma was causally associated with both non-small cell lung cancer and PD-L1/PD-1 expression levels. …”
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Predicting Solar Energetic Particle Events with Time Series Shapelets by Omar Bahri, Peiyu Li, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi

“…Our objective is to mitigate the interpretability challenges inherent to most machine learning models and to show that other methods exist that can not only yield accurate forecasts but also facilitate exploration and insight generation within the data domain. …”
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Efficient diagnosis of diabetes mellitus using an improved ensemble method by Blessing Oluwatobi Olorunfemi, Adewale Opeoluwa Ogunde, Ahmad Almogren, Abidemi Emmanuel Adeniyi, Sunday Adeola Ajagbe, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Asif Mehmood, Habib Hamam

“…Abstract Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. …”
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Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes by Liu Yang, Li Du, Yuanyuan Ge, Muhui Ou, Wanyan Huang, Xianmei Wang

“…Abstract Objective This study aimed to evaluate the predictive performance of inflammatory and nutritional indices for adverse cardiovascular events (ACE) in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI) using a machine learning (ML) algorithm. Methods AMI patients who underwent PCI were recruited and randomly divided into non/ACE groups. …”
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Development and Implementation of an IoT-Based Early Flood Detection and Monitoring System Utilizing Time Series Forecasting for Real-Time Alerts in Resource-Constrained Environmen... by Nik Nor Muhammad Saifudin Nik Mohd Kamal, Ahmad Anwar Zainuddin, Abu Ubaidah Shamsudin, Muhamad Syariff Sapuan, Muhammad Hazim Amin Samsudin, Mohammad Adam Haikal Zulkfli

“…These sensors continually send data to a central processing unit for analysis, and a machine learning model based on Time Series forecasting is used for predictive analysis in the ThingSpeak platform, which is available via an internet dashboard for real-time monitoring. …”
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Prenatal depression level prediction using ensemble based deep learning model by Abinaya Gopalakrishnan, Xujuan Zhou, Revathi Venkataraman, Raj Gururajan, Ka Ching Chan, Guohun Zhu, Niall Higgins

“…The accuracy of this approach applied to three benchmark datasets produced better results compared to all commonly applied machine learning models, including an Ensemble based Deep Learning model. …”
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Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare by Marina Ramzy Mourid, Hamza Irfan, Malik Olatunde Oduoye

“…Search terms encompassed “pediatric epilepsy,” “artificial intelligence,” “machine learning,” “ethical considerations,” and “data security.” …”
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DualTransAttNet: A Hybrid Model with a Dual Attention Mechanism for Corn Seed Classification by Fei Pan, Dawei He, Pengjun Xiang, Mengdie Hu, Daizhuang Yang, Fang Huang, Changmeng Peng

“…Compared to typical machine learning and deep learning models, the proposed model exhibits superior performance with an overall accuracy, F1-score, and Kappa coefficient of 90.01%, 88.9%, and 88.4%, respectively. …”
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Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn by Josselin Bontemps, Isa Ebtehaj, Gabriel Deslauriers, Alain N. Rousseau, Hossein Bonakdari, Jacynthe Dessureault-Rompré

“…In addition, various soil health indicators, including physical, chemical, and biochemical properties, were monitored to understand their interaction with nitrogen use efficiency. Machine learning techniques, such as augmented extreme learning machine (AELM) and particle swarm optimization (PSO), were employed to optimize nitrogen recommendations by identifying the most relevant features for predicting yield and nitrogen use efficiency (NUE). …”
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Identification of hub biomarkers and immune cell infiltrations participating in the pathogenesis of endometriosis by Kang Li, Jiaxu Wang, Xuyue Liu, Yifei Dang, Kaiting Wang, Manyu Li, Xiaoli Zhang, Yuan Liu

“…The hub genes were screened using machine learning. The qRT-PCR results showed that only CHMP4C and KAT2B differentially expressed in ectopic tissues compared to the normal. …”
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Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients by Dominique J. Monlezun, Alfred T. Samura, Ritesh S. Patel, Tariq E. Thannoun, Prakash Balan

“…We conducted a retrospective cohort analysis of a prospectively collected multicenter dataset of adult patients who sequentially presented to Houston metro area hospitals from 01/01/07-01/01/16. Then AI-based machine learning (backward propagation neural network) augmented multivariable regression and GIS heat mapping were performed. …”
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AI-powered estimation of tree covered area and number of trees over the Mediterranean island of Cyprus by Anna Zenonos, Sizhuo Li, Martin Brandt, Jean Sciare, Philippe Ciais

“…Artificial Intelligence is a powerful tool that can enable the development of tree monitoring systems by applying machine learning models to high-resolution image data. …”
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Integrating testing and modeling methods to examine the feasibility of blended waste materials for the compressive strength of rubberized mortar by Amin Muhammad Nasir, Nassar Roz-Ud-Din, Khan Kaffayatullah, Ul Arifeen Siyab, Khan Mubasher, Qadir Muhammad Tahir

“…Similarly, partial dependence plot analysis suggests that SF, MP, and GP have a comparable effect on the fc′{f}_{\text{c}}^{^{\prime} } of rubberized mortar. The machine learning models demonstrated a significant resemblance to test results. …”
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Hybrid learning strategies: integrating supervised and reinforcement techniques for railway wheel wear management with limited measurement data by Jessada Sresakoolchai, Chayut Ngamkhanong, Sakdirat Kaewunruen

“…The supervised learning model, developed from validated simulations, predicts wear progression, while reinforcement learning improves maintenance decision-making using basic operational data without regular measurements. Various machine-learning techniques are explored and fine-tuned to identify the best models for preventing faulty wheels without the need for frequent inspections. …”
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A patient-based iPSC-derived hepatocyte model of alcohol-associated cirrhosis reveals bioenergetic insights into disease pathogenesis by Bani Mukhopadhyay, Cheryl Marietta, Pei-Hong Shen, Abdul Oiseni, Faridoddin Mirshahi, Maria Mazzu, Colin Hodgkinson, Eli Winkler, Qiaoping Yuan, Daniel Miranda, George Kunos, Arun J. Sanyal, David Goldman

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Recent Developments in Heavy Metals Detection: Modified Electrodes, Pretreatment Methods, Prediction Models and Algorithms by Yujie Shi, Shijie Zhang, Hang Zhou, Yue Dong, Gang Liu, Wenshuai Ye, Renjie He, Guo Zhao

“…To address these issues, two potential solutions have been proposed: the development of advanced algorithms (such as machine learning (ML), back-propagation neural network (BPNN), support vector machines (SVM), random forests (RF), etc.) for signal processing and the use of pretreatment methods (such as Fenton oxidation (FO), ozone oxidation, and photochemical oxidation) to suppress such interferences. …”
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