An integrated machine learning and fractional calculus approach to predicting diabetes risk in women by David Amilo, Khadijeh Sadri, Evren Hincal, Muhammad Farman, Kottakkaran Sooppy Nisar, Mohamed Hafez

“…We employ seven machine learning algorithms: Decision Tree, Logistic Regression, Support Vector Machine (SVM), Random Forest, Bagged Trees, Naive Bayes, and XGBoost, to identify key risk factors, with XGBoost demonstrating higher performance. …”
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Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification by Haniyeh Rafiepoor, Mohammad M. Banoei, Alireza Ghorbankhanloo, Ahad Muhammadnejad, Amirhossein Razavirad, Saeed Soleymanjahi, Saeid Amanpour

“…Correlation analysis revealed different patterns of prognostic markers in the non-survivor and survivor cohorts and different GC subtypes. …”
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Transplantation of Patients with Hepatocellular Carcinoma Through Increased Utilization of Machine Perfusion Technology by Lauren E. Matevish, MD, Jason Guo, BS, Andrew D. Shubin, MD, PhD, Malcolm MacConmara, MD, Christine S. Hwang, MD, Nathanael Raschzok, MD, Nicole E. Rich, MD, MSCS, Arjmand R. Mufti, MD, Amit G. Singal, MD, MS, Parsia A. Vagefi, MD, Madhukar S. Patel, MD, MBA, ScM

“…However, the adoption of machine perfusion (MP) technologies has shown promise in improving deceased donor graft yield and utilization. …”
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Application of machine learning in early childhood development research: a scoping review by Akbar K Waljee, Amina Abubakar, Patrick N Mwangala, Faith Neema Benson, Daisy Chelangat, Willie Brink, Cheryl A Moyer

“…Artificial intelligence techniques, particularly machine learning (ML), offer an innovative approach by analysing complex datasets to detect subtle developmental patterns.Objective To map the existing literature on the use of ML in ECD research, including its geographical distribution, to identify research gaps and inform future directions. …”
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Technical parameters analyses of different types of impact-vibration soil compacting machines by I. S. Tyuremnov

“…This is a common pattern: as the mass of the machine increases, the excitation force increases, but the relative excitation force and frequency of oscillation decrease. …”
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In‐Situ Rheology Measurements via Machine‐Learning Enhanced Direct‐Ink‐Writing by Robert D. Weeks, Jennifer M. Ruddock, J. Daniel Berrigan, Jennifer A. Lewis, James. O. Hardin

“…However, an iterative approach, using random selection or constant expert guidance, is still used to create printable inks and optimize printing parameters by expending significant amounts of time, materials, and effort. Herein, a machine learning (ML) model that estimates ink rheology in‐situ from a simple printed test pattern is reported. …”
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Image Reconstruction Algorithm Based on Extreme Learning Machine for Electrical Capacitance Tomography by SU Ziheng, CHEN Deyun, WANG Lili

“…Aiming at the problem that the traditional ECT is not accurate in complex situations, this paper proposes a depth learning based inversion method Through the improvement and optimization of the traditional extreme learning machine, the image feature information obtained by the reconstructed image method is used as the training data, and the result obtained by inputting the data into the predictive model is used as the prior information The cost function is used to encapsulate the prior knowledge and domain expertise, and spatial regularizers and time regularizers are introduced to enhance sparsity The separated Bregman (SB) algorithm and the iterative shrinkage threshold (FIST) method are used to solve the specified cost function The final imaging result is obtained The simulation results show that the image reconstructed by this method has less than 10% error compared with the original flow pattern, and reduces artifacts and distortion, which improves the reconstructed image quality…”
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Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm by Kuan-Cheng Lin, Sih-Yang Chen, Jason C. Hung

“…The proposed method is a classified model in which an artificial fish swarm algorithm and a support vector machine are combined. A LAN environment with several computers which has infected by the botnet virus was simulated for testing this model; the packet data of network flow was also collected. …”
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Connecting metal-organic framework synthesis to applications using multimodal machine learning by Sartaaj Takrim Khan, Seyed Mohamad Moosavi

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Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies by Qi Liu, Wanjing Ma

“…The results indicate that performance degradation follows a diminishing-return pattern, well modeled by an exponential function. …”
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Detecting expert’s eye using a multiple-kernel Relevance Vector Machine by Giuseppe Boccignone, Mario Ferraro, Sofia Crespi, Carlo Robino, Claudio de’Sperati

“…Decoding mental states from the pattern of neural activity or overt behavior is an intensely pursued goal. …”
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The Synergy of Minds and Machines: Rethinking the AI-HI Relationship through Dialectical Reconstruction by Ridwan Ishola MOGAJI, Adewale Oluwaseun MOTADEGBE

“…Artificial intelligence on the other hand is the simulation of human cognitive functions by machines, especially in tasks such as problem-solving, pattern recognition, and decision-making, which often operates based on algorithms and data, both of which are unique and important in themselves. …”
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Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map by Satoshi Suzuki, Fumio Harashima

“…We present an intention estimator algorithm that can deal with dynamic change of the environment in a man-machine system and will be able to be utilized for an autarkical human-assisting system. …”
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Machine learning and spatio-temporal analysis of meteorological factors on waterborne diseases in Bangladesh. by Arman Hossain Chowdhury, Md Siddikur Rahman

“…Exploratory spatial analysis, spatial regression and tree-based machine learning models were utilized to analyze the data.…”
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Remote sensing with machine learning for multi-decadal surface water monitoring in Ethiopia by Mathias Tesfaye, Lutz Breuer

“…We assess Gradient Tree Boosting (GTB), Support Vector Machines (SVM), and Random Forest (RF) running on the Google Earth Engine (GEE) using Landsat for surface water monitoring at four sites in Ethiopia from 1986 to 2023. …”
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Interpretable Machine Learning for Population Spatialization and Optimal Grid Scale Selection in Shanghai by Yuan Cao, Hefeng Wang, Lanxuan Guo, Anbing Zhang, Xiaohu Wu

“…Fine-scale population distribution information is crucial for applications in urban public safety, planning, and management. However, when using machine learning methods for population spatialization, issues such as data overfitting and limited interpretability need to be addressed. …”
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Applying Causal Machine Learning to Spatiotemporal Data Analysis: An Investigation of Opportunities and Challenges by Christian M. Mulomba, Vogel M. Kiketa, David M. Kutangila, Pescie H. K. Mampuya, Junior N. Mukenze, Landry M. Kasunzi, Kyandoghere Kyamakya, Tasho Tashev, Selain K. Kasereka

“…To bridge this gap, we review causal machine learning (CML) techniques for spatiotemporal data, aiming to provide robust insights into their unique advantages. …”
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Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review by Florin-Stefan Zamfir, Madalina Carbureanu, Sanda Florentina Mihalache

“…This research studies how machine learning (ML) with a focus on deep learning (DL) techniques can be applied to optimize the treatment processes of WWTPs, highlighting those case studies that propose ML and DL methods that directly address this issue. …”
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Forecasting Delivery Time of Goods in Supply Chains Using Machine Learning Methods by V. K. Rezvanov, O. M. Romakina, E. V. Zaytseva

“…The research objective is to describe a pattern of appropriate selection of the least resource-intensive delivery forecasting model based on the analysis of machine learning algorithms.Materials and Methods. …”
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Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning by Paul A. Constable, Javier O. Pinzon-Arenas, Luis Roberto Mercado Diaz, Irene O. Lee, Fernando Marmolejo-Ramos, Lynne Loh, Aleksei Zhdanov, Mikhail Kulyabin, Marek Brabec, David H. Skuse, Dorothy A. Thompson, Hugo Posada-Quintero

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