Mapping fruit tree dynamics using phenological metrics from optimal Sentinel-2 data and Deep Neural Network by Yingisani Chabalala, Elhadi Adam, Mahlatse Kganyago

“…The models were optimized to determine the best hyperparameters to achieve the best classification results. …”
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Multi-Agent Deep Reinforcement Learning for Integrated Demand Forecasting and Inventory Optimization in Sensor-Enabled Retail Supply Chains by Yongbin Yang, Mengdie Wang, Jiyuan Wang, Pan Li, Mengjie Zhou

“…While existing approaches employ statistical and machine learning methods for demand forecasting, they often fail to capture complex temporal dependencies and lack the ability to simultaneously optimize inventory decisions. …”
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AI-Driven Handover Management and Load Balancing Optimization in Ultra-Dense 5G/6G Cellular Networks by Chaima Chabira, Ibraheem Shayea, Gulsaya Nurzhaubayeva, Laura Aldasheva, Didar Yedilkhan, Saule Amanzholova

“…This paper presents a comprehensive review of handover management and load balancing optimization (LBO) in ultra-dense 5G and emerging 6G cellular networks. …”
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Early Warning of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Multi-Omics Signature: A Machine Learning-Based Retrospective Study by Ke Z, Shen L, Shao J

“…The AUC of GLRM was 0.818 (95% CI: 0.757~0.879), significantly lower than that of RFM’s AUC 0.893 (95% CI: 0.836~0.950).Conclusion: The prediction models based on machine learning (ML) algorithms and multiomics have shown good performance in predicting ALN metastasis, and RFM shows greater advantages compared to traditional GLRM. …”
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Forecasting Megaelectron‐Volt Electrons Inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms by Rafael Pires de Lima, Yue Chen, Youzuo Lin

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Exploring the best fit: A comparative analysis of AFINN, Textblob, VADER, and Pattern on Arabic reviews for optimal dictionary extraction by Shakeel Ahmad, Sheikh Muhammad Saqib, Asif Hassan Syed, Nashwan Alromema, Ali Kararay

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Optimizing demulsifier selection for crude oil dehydration: a fuzzy TOPSIS-based multi-criteria decision-making approach by Jianyong Yu, Merwa Alhadrawi, Farag M. A. Altalbawy, Ahmed Rasol Hasson, M. Mehdi Shafieezadeh

“…Future research should focus on incorporating real-time operational data, expanding the evaluation to emerging eco-friendly demulsifiers, and integrating predictive machine learning models to enhance the accuracy of the selection process.…”
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Multimodal machine learning-based marker enables the link between obesity-related indices and future stroke: a prospective cohort studyResearch in context by Beilei Hu, Xuan Chen, Tingyang Chen, Tong Xu, Yungang Cao, Jing Sun, Xuanyu Chen, Songfang Chen, Keyang Chen

“…Stacked ML models with optimal obesity indices to detect the risk of stroke were constructed. …”
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Nonlinear Multivariate Calibration of Shelf Life of Preserved Eggs (Pidan) by Near Infrared Spectroscopy: Stacked Least Squares Support Vector Machine with Ensemble Preprocessing by Lu Xu, Si-Min Yan, Chen-Bo Cai, Xiao-Ping Yu, Jian-Hui Jiang, Hai-Long Wu, Ru-Qin Yu

“…To reduce the negative influence of unwanted spectral variations, stacked least squares support vector machine (LS-SVM) with an ensemble of 62 commonly used preprocessing methods is proposed to automatically optimize data preprocessing and develop the nonlinear model. …”
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Predicting Rolling Element Bearings’ Deterioration Vibration Trend based on Limited Historical Data for a Desired Confidence Level using Machine Learning Algorithms by Mohammad Reza Seif, Somaye Mohammadi, Parham Rahimi, Mehdi Behzad

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Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3–5: a multicentre study using the machine learning approach by Usman Iqbal, Tzu-Hao Chang, Chu-Lin Chou, Mai-Szu Wu, Yung-Ho Hsu, Chih-Wei Huang, Anh Trung Hoang, Phung-Anh Nguyen, Thanh Phuc Phan, Gia Tuyen Do, Huu Dung Nguyen, I-Jen Chiu, Yu-Chen Ko, Chia-Te Liao

“…This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3–5.Methods Retrospective electronic health record data from the Taipei Medical University clinical research database were used. …”
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Prediction and validation of mechanical properties of self-compacting geopolymer concrete using combined machine learning methods a comparative and suitability assessment of the be... by Kennedy C. Onyelowe, Ahmed M. Ebid, Paul Awoyera, Viroon Kamchoom, Evelin Rosero, María Albuja, Carlos Mancheno

“…Second, it has become important to reduce laboratory and equipment costs by establishing intelligent models through the application of these supplementary cements and optimized for optimal performance of concrete materials. …”
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A Retrospective Machine Learning Analysis to Predict 3-Month Nonunion of Unstable Distal Clavicle Fracture Patients Treated with Open Reduction and Internal Fixation by Ma C, Lu W, Liang L, Huang K, Zou J

“…Our results suggest that ML, particularly the CatBoost model, can be integrated into clinical workflows to aid surgeons in optimizing intraoperative techniques and postoperative management to reduce nonunion rates.Keywords: distal clavicle fracture, machine learning, prediction, nonunion…”
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Enhancing Security in CPS Industry 5.0 using Lightweight MobileNetV3 with Adaptive Optimization Technique by Mohammed A. Aleisa

“…Computational efficiency is maximized through the implementation of MobileNetV3, a thin convolutional neural network optimized for mobile and edge devices. The accuracy of the model is further improved by applying a Chaotic Tent-based Puma Optimization (CTPOA) technique. …”
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Conflict evidence combination rule based on multi-dimensional weighted evidence optimization method and its applications in pattern recognition by Fuhai Xi, Mengli Mei, Shi Yang, Hang Guo, Min Yu

“…Furthermore, this paper constructs a decision-level multi-source information fusion model. The model integrates the outputs of three basic classifiers—K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forests (RF)—as multi-source information, and applies the proposed conflict evidence combination rule for fusion. …”
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Impact of the number of dissected lymph nodes on machine learning-based prediction of postoperative lung cancer recurrence: a single-hospital retrospective cohort study by Kyoichi Okishio, Shinji Atagi, Kensuke Kojima, Hironobu Samejima, Toshiteru Tokunaga, Hyungeun Yoon

“…Five machine learning models were trained using clinicopathological variables to predict postoperative recurrence. …”
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A hybrid statistical-machine learning approach for experimental analysis of biogas production in a waste to energy plant using a vacuum evaporator systems by Vakkar Ali, Praveen Pachauri, Azhar Equbal, Osama Khan, Mohd Parvez, Haidar Howari, Taufique Ahamad, Ashok Kumar Yadav, Brahmdeo Yadav

“…This study addresses a critical research gap by investigating the influence of key operating parameters as pressure (P), temperature (T), and flow rate (FR) on cavitation phenomena within vacuum evaporators, which significantly impact system durability during large-scale digestate treatment. Optimizing these parameters while mitigating cavitation effects improves energy efficiency, prolonged equipment lifespan, and reliable operation of vacuum evaporators in large-scale biomass digestate treatment systems. k-means machine learning clustering validated by statistical modelling combined methodology is used for this analysis. …”
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An Intelligent LoRaWAN-Based IoT Device for Monitoring and Control Solutions in Smart Farming Through Anomaly Detection Integrated With Unsupervised Machine Learning by Maram Fahaad Almufareh, Mamoona Humayun, Zulfiqar Ahmad, Asfandyar Khan

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Enhanced Blade Fault Diagnosis Using Hybrid Deep Learning: A Comparative Analysis of Traditional Machine Learning and 1D Convolutional Transformer Architecture by Syed Asad Imam, Meng Hee Lim, Ahmed Mohammed Abdelrhman, Iftikhar Ahmad, Mohd Salman Leong

“…This research investigates blade FD, comparing traditional machine learning approaches with a novel hybrid deep learning fused model based on a one‐dimensional (1D) convolutional transformer. …”
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Clinical and economic effectiveness of Schroth therapy in adolescent idiopathic scoliosis: insights from a machine learning- and active learning-based real-world study by Erdal Ayvaz, Merve Uca, Ednan Ayvaz, Zafer Yıldız

“…Recent advancements in active learning (AL) and machine learning (ML) techniques offer the potential to optimize treatment protocols by uncovering hidden predictors and enhancing model efficiency. …”
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