Incremental Weak Subgradient Methods for Non-Smooth Non-Convex Optimization Problems by Narges Araboljadidi, Valentina De Simone

“…Non-smooth, non-convex optimization problems frequently arise in modern machine learning applications, yet solving them efficiently remains a challenge. …”
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Predicting survival in malignant glioma using artificial intelligence by Wireko Andrew Awuah, Adam Ben-Jaafar, Subham Roy, Princess Afia Nkrumah-Boateng, Joecelyn Kirani Tan, Toufik Abdul-Rahman, Oday Atallah

“…Recently, advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), have enabled significant improvements in survival prediction for glioma patients by integrating multimodal data such as imaging, clinical parameters and molecular biomarkers. …”
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ZenLDA: Large-Scale Topic Model Training on Distributed Data-Parallel Platform by Bo Zhao, Hucheng Zhou, Guoqiang Li, Yihua Huang

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ISOA‐DBN: A New Data‐Driven Method for Studying the Operating Characteristics of Air Conditioners by Mengran Zhou, Qiqi Zhang, Feng Hu, Ling Wang, Guangyao Zhou, Weile Kong, Changzhen Wu, Enhan Cui

“…Secondly, the Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) are used to study the operating characteristics of air conditioning. …”
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Development of a fundamental model for pelleting efficiency of an innovative hybrid fish feed processing system by Daniel C., Nnadi, John Chijioke, Edeh, Offiong Alexander, Aniekan, Aniekan, Offiong

“…The system was designed for simplicity, quality, and precision in fish feed production. Machine parameters, derived from comprehensive design and parametric analysis, were used to establish input variables for the pelleting efficiency model, including feed rate and number of orifices. …”
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Enhanced predictability and interpretability of COVID-19 severity based on SARS-CoV-2 genomic diversity: a comprehensive study encompassing four years of data by Miao Miao, Yonghong Ma, Jiao Tan, Renjuan Chen, Ke Men

“…By extracting SARS-CoV-2 genomic features, optimizing model parameters, and integrating models, this study improved the prediction accuracy. …”
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Integration of single‐cell and bulk RNA‐sequencing data reveals the prognostic potential of epithelial gene markers for prostate cancer by Zhuofan Mou, Lorna W. Harries

“…Subsequently, we applied 97 advanced machine‐learning algorithms across five PCa cohorts and developed an 11‐gene epithelial expression signature. …”
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Artificial intelligence as a potential tool for oxidative stress estimation in medicine by Yan Kazakov, Alexander Halperin, Khiena Brainina

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Design and Performance Evaluation of a Self-Propelled Mugwort Harvester for Hilly and Mountainous Regions by Yi Li, Yongsheng He, Kai Zhang, Siqi Wang, Xinyu Hu, Junnan Chen

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Data-Driven Revolution in Academic Support for Mathematics Underachievers through Random Forest Individual and Hybrid Model by Asadi Srinivasulu, Vanithamani Palanisamy

“…Educational Data Mining, leveraging machine learning and data mining techniques, aims to predict student performance using available datasets. …”
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Bio-Impedance Analysis of Human Upper Limbs Based on Transient Simulation Using the Finite Element Method by Enver Salkim, Tayfun Abut

“…<b>Introduction:</b> Upper-limb loss results in significant functional impairment and a reduced quality of life. A human–machine interface (HMI) using surface electromyography (sEMG) establishes a link between the user and a hand prosthesis to recognize hand gestures and motions. …”
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Algorithm for Recognition of Small Air Targets by Trajectory Features in Passive Bistatic Radar by Dao Van Luc, A. A. Konovalov, Le Minh Hoang

“…A comparative analysis of the six most common recognition methods based on machine learning (Naïve Bayes, decision trees, k-nearest neighbors, neural network recognition algorithm, support vector machine, random forests) was carried out, which showed that, under the conditions of this problem, the most effective are k-nearest neighbor method and support vector machine.   …”
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Inversion of citrus SPAD value and leaf water content by combining feature selection and ensemble learning algorithm using UAV remote sensing images by Quanshan Liu, Fei Chen, Ningbo Cui, Zongjun Wu, Xiuliang Jin, Shidan Zhu, Shouzheng Jiang, Daozhi Gong, Shunsheng Zheng, Lu Zhao, Zhihui Wang

“…Soil and Plant Analyzer Development (SPAD) value and leaf water content (LWC) are critical physiological parameters for agricultural irrigation and growth monitoring in late-maturing citrus. …”
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Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation by Adil Khan, Majid Khan, Waseem Akhtar Khan, Muhammad Ali Afridi, Khawaja Atif Naseem, Ayesha Noreen

“…The ten most optimal parameters were selected as inputs. To ensure robustness and accurate evaluation, the collected dataset was partitioned into three distinct subsets: the training set (70%), the testing set (15%), and the validation set (15%). …”
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Optimized prediction of diabetes complications using ensemble learning with Bayesian optimization: a cost-efficient laboratory-based approach by Dapeng Yan, Xiaohan Li, Yifan Wang, Zhikuang Cai

“…Various machine learning classifiers, including Random Forest, XGBoost, Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained on this dataset to evaluate their predictive performance. …”
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Comparative analysis of different algorithms for VAS station land cover classification with limited training points by D. García-Rodríguez, A. Pérez-Hoyos, B. Martínez, Pablo Catret Ruber, J. Javier Samper-Zapater, E. López-Baeza, J.J. Martínez Durá

“…., monthly, seasonal), iii) the performance of six Machine Learning algorithms (i.e., CART, GTB, k-NN, NB, RF, and SVM, alongside three deep learning models (FC-NN, MLP-ED, and ResCNN) and iv) the optimization of classifier tuning parameters. …”
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Hybrid Convolutional Neural Network-Based Intrusion Detection System for Secure IoT Networks by Sami Qawasmeh, Ahmad Habboush, Bassam Elzaghmouri, Qasem Kharma, Da'ad Albalawneh

“…The proposed method outperforms the traditional machine learning and deep learning models in identifying IoT network attacks. …”
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Genetic algorithm optimization of ensemble learning approach for improved land cover and land use mapping: Application to Talassemtane National Park by Ali Azedou, Aouatif Amine, Isaya Kisekka, Said Lahssini

“…Using Sentinel-2 satellite imagery processed in Google Earth Engine (GEE), six spectral features and six vegetation indices were extracted. Multiple Machine Learning (ML) classifiers including Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), Classification and Regression Tree (CART), Minimum Distance (MinD), and Gradient Tree Boost (GTB), and a Grid Search (GS)-optimized ensemble-were evaluated. …”
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Enhancing Heart Attack Prediction: Feature Identification from Multiparametric Cardiac Data Using Explainable AI by Muhammad Waqar, Muhammad Bilal Shahnawaz, Sajid Saleem, Hassan Dawood, Usman Muhammad, Hussain Dawood

“…The proposed study enhances heart attack prediction using the University of California, Irvine (UCI) dataset, which includes various heart analysis parameters collected through electrocardiogram (ECG) sensors, blood pressure monitors, and biochemical analyzers. …”
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Predictive modeling and interpretative analysis of risks of instability in patients with Myasthenia Gravis requiring intensive care unit admission by Chao-Yang Kuo, Emily Chia-Yu Su, Hsu-Ling Yeh, Jiann-Horng Yeh, Hou-Chang Chiu, Chen-Chih Chung

“…Methods: In this retrospective analysis of 314 MG patients hospitalized between 2015 and 2018, we implemented four machine learning algorithms, including logistic regression, support vector machine, extreme gradient boosting (XGBoost), and random forest, to predict ICU admission risk. …”
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