An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification by Fahdah A Almarshad, Prasanalakshmi Balaji, Liyakathunisa Syed, Eman Aljohani, Santhi Muttipoll Dharmarajlu, Thavavel Vaiyapuri, Nourah Ali AlAseem

“…Gastrointestinal or gastric cancer (GC) classification is a serious field of medical research and healthcare technology, where innovative machine learning (ML) and deep learning (DL) models are employed to categorize and analyze many kinds of GCs like pancreatic, gastric, or colorectal cancer. …”
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Optimizing the Radiative Transfer Model Using Deep Neural Networks for NISAR Soil Moisture Retrieval by Xiaodong Huang, Lorenzo Giuliano Papale, Marco Lavalle, Fabio Del Frate, Heresh Fattahi, Steven K. Chan, Rowena B. Lohman, Xiaolan Xu, Yunjin Kim

“…Semiphysical models, such as the water cloud model, are also extensively used and rely on estimates of vegetation water content or biomass derived from optical vegetation indices, such as LAI and NDVI. …”
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Optimizing zero-shot text-based segmentation of remote sensing imagery using SAM and Grounding DINO by Mohanad Diab, Polychronis Kolokoussis, Maria Antonia Brovelli

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Intelligent irrigation strategy model for farmland using dung beetle optimization-random forest algorithms by Wenwen Hu, Yong Liu, Jun An, Shipu Xu, Zhiwen Zhou, Mingming An, Xiaokun Guo, Xiang Ma, Wenfei Jiang, Yunsheng Wang

“…This study proposes an optimized machine learning prediction model using the Dung Beetle Optimization-Random Forest (DBO-RF) algorithm, thus improving irrigation predictability. …”
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Through the Citizen Scientists’ Eyes: Insights into Using Citizen Science with Machine Learning for Effective Identification of Unknown-Unknowns in Big Data by Kameswara Bharadwaj Mantha, Hayley Roberts, Lucy Fortson, Chris Lintott, Hugh Dickinson, William Keel, Ramanakumar Sankar, Coleman Krawczyk, Brooke Simmons, Mike Walmsley, Izzy Garland, Jason Shingirai Makechemu, Laura Trouille, Clifford Johnson

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Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs by Hatem Gasmi, Azher M. Abed, Ashit Kumar Dutta, Fahad M. Alhomayani, Ibrahim Mahariq, Fahad Alturise, Salem Alkhalaf, Tamim Alkhalifah, Yasser Elmasry, Baseem Khan

“…This study offers an in-depth thermodynamic analysis and optimization of an integrated renewable energy system that merges a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, utilizing machine learning algorithms. …”
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Identification and optimization of relevant factors for chronic kidney disease in abdominal obesity patients by machine learning methods: insights from NHANES 2005–2018 by Xiangling Deng, Lifei Ma, Pin Li, Mengyang He, Ruyue Jin, Yuandong Tao, Hualin Cao, Hengyu Gao, Wenquan Zhou, Kuan Lu, Xiaoye Chen, Wenchao Li, Huixia Zhou

“…Furthermore, an optimal predictive model was developed for CKD using ten machine learning algorithms and enhanced model interpretability with the Shapley Additive Explanations (SHAP) method. …”
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Transforming business management practices through metaverse technologies: A Machine Learning approach by Raghu Raman, Santanu Mandal, Angappa Gunasekaran, Thanos Papadopoulos, Prema Nedungadi

“…Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and machine learning-based BERTopic modeling, the study identifies nine key themes, reflecting the diverse ways augmented reality (AR), virtual reality (VR), extended reality (XR), digital twins, and decentralized finance (DeFi) influence industries. …”
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Optimizing Text Recognition in Mechanical Drawings: A Comprehensive Approach by Javier Villena Toro, Mehdi Tarkian

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CALCULATION OF ACTUAL MODIFICATION OF CYCLOIDAL GEAR BASED ON OPTIMAL APPROXIMATION PRINCIPLE by ZHOU JingYuan, LI TianXing, SU JianXin, WANG Bin, ZHANG RuiYao

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Forecasting water quality indices using generalized ridge model, regularized weighted kernel ridge model, and optimized multivariate variational mode decomposition by Marjan Kordani, Mohsen Bagheritabar, Iman Ahmadianfar, Arvin Samadi-Koucheksaraee

“…The light gradient boosting machine model (LGBM) is also implemented to select the influential input variables. …”
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Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors by Akiyasu Yamamoto, Akinori Yamanaka, Kazumasa Iida, Yusuke Shimada, Satoshi Hata

“…Specifically, we discuss a mechanochemical process involving high-energy milling, in situ observation of microstructural formation using 3D scanning transmission electron microscopy, phase-field modeling coupled with Bayesian data assimilation, nano-orientation analysis via scanning precession electron diffraction, semantic segmentation using neural network models, and the Bayesian-optimization-based process design using BOXVIA software. …”
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CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability by Gregory W. Kyro, Matthew T. Martin, Eric D. Watt, Victor S. Batista

“…Additionally, the discriminative models can also serve independently as effective components of virtual screening pipelines. …”
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Optimization and predictive performance of fly ash-based sustainable concrete using integrated multitask deep learning framework with interpretable machine learning techniques by Bhupesh P. Nandurkar, Jayant M. Raut, Pawan K. Hinge, Boskey V. Bahoria, Tejas R. Patil, Sachin Upadhye, Vikrant S. Vairagade, Sagar D. Shelare

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Machine‐Learning‐Assisted Design and Optimization of Single‐Atom Transition Metal‐Incorporated Carbon Quantum Dot Catalysts for Electrocatalytic Hydrogen Evolution Reaction by Unbeom Baeck, Min‐Cheol Kim, Duong Nguyen Nguyen, Jaekyum Kim, Jaehyoung Lim, Yujin Chae, Namsoo Shin, Heechae Choi, Joon Young Kim, Chan‐Hwa Chung, Woo‐Seok Choe, Ho Seok Park, Uk Sim, Jung Kyu Kim

“…Herein, an effective and facile catalyst design strategy is proposed based on machine learning (ML) and its model verification using electrochemical methods accompanied by density functional theory simulations. …”
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Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy by Xiaote Zhang, Qiaoyi Xie, Ganggang Wu

“…Given the urgent need for improved diagnostic methods and extensive characterization of risk factors for OME in AH children, developing diagnostic models represents an efficient strategy to enhance clinical identification accuracy in practice.ObjectiveThis study aims to develop and validate an optimal machine learning (ML)-based prediction model for OME in AH children by comparing multiple algorithmic approaches, integrating clinical indicators with acoustic measurements into a widely applicable diagnostic tool.MethodsA retrospective analysis was conducted on 847 pediatric patients with AH. …”
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Theoretical analysis of MOFs for pharmaceutical applications by using machine learning models to predict loading capacity and cell viability by Bader Huwaimel, Saad Alqarni

“…Principal Component Analysis (PCA) was applied to reduce dimensionality, and the Water Cycle Algorithm was used to optimize hyperparameters. Evaluation metrics, including R2, Root Mean Squared Error (RMSE), and maximum error, indicated that the QR-MLP model outperformed the other models, achieving test R2 scores of 0.99917 for Drug Loading Capacity and 0.99111 for Cell Viability. …”
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Predictive modeling of Cbr and compressibility in lime stabilized lateritic soil using machine learning and Pchip data augmentation by Hyginus Obinna Ozioko, Emmanuel Ebube Eze

“…Integrating laboratory experiments with machine learning (ML) predictive modeling, the research aimed to support field-scale applications. …”
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An Improved Machine Learning-Based Model for Detecting and Classifying PQDs with High Noise Immunity in Renewable-Integrated Microgrids by Irfan Ali Channa, Dazi Li, Mohsin Ali Koondhar, Fida Hussain Dahri, Ibrahim Mahariq

“…In the optimized-kernel SVM model, computing power is enhanced for classifying multiple PQ events based on the local density and leave-one-out (LOO) algorithm. …”
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Significant adverse prognostic events in patients with urosepsis: a machine learning based model development and validation study by Yiqu Wei, Yiqu Wei, Wanqing Xu, Shuo Yang, Congfeng Zhang, Jia Wang, Xianyao Wan

“…Model development includes seven machine learning methods and ten cross validations. …”
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