Probabilistic Routing Based on Two-Hop Information in Delay/Disruption Tolerant Networks by Xu Wang, Rongxi He, Bin Lin, Ying Wang

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A Hybridization of Modified Rough Bipolar Soft Sets and TOPSIS for MCGDM by Saba Ayub, Muhammad Shabir, Rizwan Gul

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Identifying Influential Nodes in Complex Networks via Transformer with Multi-Scale Feature Fusion by Tingshuai Jiang, Yirun Ruan, Tianyuan Yu, Liang Bai, Yifei Yuan

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Providing a Robust Dynamic Pricing Model and Comparing It with Static Pricing in Multi-level Supply Chains Using a Game Theory Approach by Sara Mehrjoo, Hanan Amoozad Mahdirji, Jalil Heidary Dahoei, Seyyed Hossein Razavi Haji Agha, Mahnaz Hosseinzadeh

“…For data analysis, a genetic algorithm, particle accumulation optimization, and MATLAB software were employed. …”
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Prediction of Train Arrival Delay Using Hybrid ELM-PSO Approach by Xu Bao, Yanqiu Li, Jianmin Li, Rui Shi, Xin Ding

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Robust Switching Control and Subspace Identification for Flutter of Flexible Wing by Yizhe Wang, Zhiwei Xu, Wei Li

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Efficient and Stable Learning for Distribution Network Operation: A Model-Based Reinforcement Learning Approach by Dong Yan, Zhan Shi, Xinying Wang, Yiying Gao, Tianjiao Pu, Jiye Wang

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Trajectory Optimization Approach for Adaptive Multiple-Pass Aeroassisted Orbital Maneuver by Rui Teng, Hongwei Han, Jilin Chen, Bo Pang

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Exploring New Boundaries to Mitigate Structural Vibrations of Bridges in Seismic Regions: A Smart Passive Strategy by Giuseppe Maddaloni, Nicola Caterino, Gianluca Nestovito, Antonio Occhiuzzi

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Prediction of water inrush from coal seam floor based on machine learning with small sample data by Chenxi LI, Haifeng LU

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Modelling Enterprise’s Coordinated Development Strategy with a Soft Fuzzy Rough Set by Tongtong Wang, Jianing Cui

“…Furthermore, we use training methods and soft fuzzy rough sets’ learning algorithm to calculate, and the evaluating indicator rough set is constructed with a three-tier model structure. …”
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Predictive value of the stone-free rate after percutaneous nephrolithotomy based on multiple machine learning models by Zhao Rong Liu, Zhao Rong Liu, Zhan Jiang Yu, Jie Zhou, Jian Biao Huang

“…PurposeThis study aimed to develop three types of machine learning (ML) models based on gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) to explore their predictive value for the stone-free rate after percutaneous nephrolithotomy (PCNL).Patients and methodsA retrospective analysis was conducted on 160 patients who underwent PCNL. …”
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Conjecture Interaction Optimization Model for Intelligent Transportation Systems in Smart Cities Using Reciprocated Multi-Instance Learning for Road Traffic Management by Abdullah Faiz Al Asmari, Ahmed Almutairi, Fayez Alanazi, Tariq Alqubaysi, Ammar Armghan

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Evaluation of hydraulic fracturing using machine learning by Ali Akbari, Ali Karami, Yousef Kazemzadeh, Ali Ranjbar

“…This study presents a comprehensive machine learning (ML)-based framework to address this challenge by predicting HF efficiency using three widely used algorithms: Random Forest (RF), Support Vector Machine (SVM), and Neural Networks (NN). …”
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Methodology For Extracting Poplar Planted Fields From Very High-Resolution Imagery Using Object-Based Image Analysis and Feature Selection Strategy by E. O. Yilmaz, T. Kavzoglu, I. Colkesen, H. Tonbul, A. Teke

“…The objective of this research is to create a map of poplar plantations in the Sakarya region of Turkey utilizing Worldview-3 satellite imagery. Object-based image analysis (OBIA) through the application of the multi-resolution segmentation method (MRS) was employed to generate image segments, and then three prevailing machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF) and Rotation Forest (RotFor) were implemented to produce LULC maps of the study area including 11 landscape features. …”
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A multi-objective improved horse herd optimizer based on convex lens imaging for stochastic optimization of wind energy resources in distribution networks considering reliability a... by Fude Duan, Ali Basem, Dheyaa J. Jasim, Mahdiyeh Eslami, Mustafa Okati

“…The objective function aims to minimize power loss, improve reliability, and reduce the costs associated with wind turbines (WTs), presenting these goals as a three-dimensional function. The Multi-Objective Improved Horse Herd Optimizer (MOIHHO) is derived from an enhanced version of the traditional horse herd optimizer. …”
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Trends and Challenges in Plant Cryopreservation Research: A Meta-Analysis of Cryoprotective Agent Development and Research Focus by Pilsung Kang, Sung Jin Kim, Ha Ju Park, Se Jong Han, Il-Chan Kim, Hyoungseok Lee, Joung Han Yim

“…In order to confirm the declining trend, we performed text-mining analysis using the Latent Dirichlet Allocation (LDA) algorithm, which identifies underlying topics across diverse documents to aid decision-making and classified the abstracts into three distinct topics: Topic 1, “Seed bank”; Topic 2, “Physiology”; and Topic 3, “Cryopreservation protocol”. …”
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TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion by Yong Huang, Xiewen Hu, Shilong Pang, Wei Fu, Shuaipeng Chang, Bin Gao, Weihua Hua

“…Firstly, a multi-source dataset is established by systematically integrating TBM tunnelling parameters, horizontal acoustic profile (HSP) detection data and three-dimensional geological spatial information. …”
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Investigating lightweight and interpretable machine learning models for efficient and explainable stress detection by Debasish Ghose, Ayan Chatterjee, Indika A. M. Balapuwaduge, Yuan Lin, Soumya P. Dash

“…Promisingly, among the developed models, the k-nearest neighbors (k-NN) algorithm has emerged as the best-performing model, achieving an accuracy score of 99.3% using only three selected features. …”
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Additive value of computed tomography severity scores to predict lengths of stay in hospital and ICU for COVID-19 patients: a machine learning study by Seyed Salman Zakariaee, Mikaeil Molazadeh, Hossein Salmanipour, Negar Naderi

“…This algorithm with 95.3% sensitivity, 86.2% specificity, 90.8% accuracy, 87.3% precision, 91.2% F-Measure, and an AUC of 95.8% had also the best performance for predicting ICU LOS of the patients. …”
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