An optimization based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance by Abhijeet Das

“…In addition, the study area's hydro-chemical facies were examined, and machine learning models’ hyperparameters such as Random Forest (RF), Borda Scoring Algorithm (BSA), Decision Tree (DT), Multilayer Perception (MLP), and Naïve Bayes (NB), were executed before, to training and testing the samples of surface water. …”
Get full text

Evaluation of Machine Learning Models for Enhancing Sustainability in Additive Manufacturing by Waqar Shehbaz, Qingjin Peng

Get full text

Dynamic Capacitated Arc Routing Problem in E-Bike Sharing System: A Monte Carlo Tree Search Approach by Shiqi Tan, Zhiheng Li, Na Xie

Get full text

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

Get full text

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

Get full text

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

Get full text

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

Get full text

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. …”
Get full text

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). …”
Get full text

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”. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

Clinical Applications of Artificial Intelligence in Teleorthodontics: A Scoping Review by Alessandro Polizzi, Sara Serra, Rosalia Leonardi, Gaetano Isola

“…One study explored an alternative system, StrojCHECK™, based on a decision tree algorithm, showing improved compliance with personalized feedback. …”
Get full text

HIV epidemic appraisals for assisting in the design of effective prevention programmes: shifting the paradigm back to basics. by Sharmistha Mishra, Sema K Sgaier, Laura H Thompson, Stephen Moses, B M Ramesh, Michel Alary, David Wilson, James F Blanchard

“…<h4>Methods and findings</h4>We compared outputs of the traditional methods in five countries for which results were published, and applied the numeric and MOT model to India and six districts within India. We discovered three limitations of the current methods for epidemic appraisal: (1) their results failed to identify the key behaviours that drive the epidemic; (2) they were difficult to apply to local epidemics with heterogeneity across district-level administrative units; and (3) the MOT model was highly sensitive to input parameters, many of which required extraction from non-regional sources. …”
Get full text

Comparative techno-economic analysis of grid-connected solar PV-battery and PV-fuel cell systems for educational institutions sustainable academic laboratories by Takele Ferede Agajie, Fuseini Seidu Ibrahim, Isaac Amoussou, Elsabet Ferede Agajie, Eriisa Yiga Paddy, Yayehyirad Ayalew Awoke, Wirnkar Basil Nsanyuy, Mohit Bajaj

“…Total net present cost (TNPC) was used for economic analysis, and mathematical modeling was created in order to apply metaheuristic optimization for system sizing. Techno-economic decision criteria were implemented using MATLAB. Three algorithms were used: Teaching–Learning-Based Optimization (TLBO), Water Cycle Algorithm (WCA), and Improved Grey Wolf Optimization (I-GWO). …”
Get full text

An in-depth exploration of supervised and semi-supervised learning on face recognition by Purnawansyah, Wibawa Aji Prasetya, Widiyaningtyas Triyanna, Haviluddin, Darwis Herdianti, Azis Huzain

“…This study aims to assess the effectiveness of various algorithms in the realms of supervised and semi-supervised learning applied to three multiclass facial image datasets: JAFFE, Georgia tech, and Yale. …”
Get full text

Development and validation of a machine learning model for prediction of cephalic dystocia by Yumei Huang, Xuerong Ran, Xueyan Wang, Defang Wu, Zheng Yao, Jinguo Zhai

“…The least absolute shrinkage and selection operator (LASSO) algorithm was used to select predictive factors, followed by the development of logistic regression, decision tree, and random forest machine learning models. …”
Get full text

Adaptive AI-enhanced computation offloading with machine learning for QoE optimization and energy-efficient mobile edge systems by Dinesh Kumar Nishad, Vandna Rani Verma, Pushkar Rajput, Sandeep Gupta, Anurag Dwivedi, Dharti Raj Shah

“…This paper introduces a novel Adaptive AI-enhanced offloading (AAEO) framework that uniquely integrates three complementary AI approaches: deep reinforcement learning for real-time decision-making, evolutionary algorithms for global optimization, and federated learning for distributed knowledge sharing. …”
Get full text

Applications of Raspberry Pi for Precision Agriculture—A Systematic Review by Astina Joice, Talha Tufaique, Humeera Tazeen, C. Igathinathane, Zhao Zhang, Craig Whippo, John Hendrickson, David Archer

“…This review consolidates details on Raspberry Pi versions, sensors, devices, algorithm deployment, and PA applications. A systematic literature review of three academic databases (Scopus, Web of Science, IEEE <i>Xplore</i>) yielded 84 (as of 22 November 2024) articles based on four research questions and screening criteria (exclusion and inclusion). …”
Get full text