Data-Driven Optimization of CNC Manufacturing Using Simulation and DOE Techniques by Vijay Sevella, Ahad Ali, Abdelhakim Abdelhadi, Ahmad Alkhaleefah

“…The goal of this research is to use Arena simulation software to model a CNC production system to assess and improve system performance. …”
Get full text

RARE: right algorithm for the right errand; a multi-model machine learning-based approach for tourism routes and spots recommendation by Ling Luo

“…The framework employs long short-term memory (LSTM) for spot relevance prediction, support vector machine (SVM) for spot name classification, and depth first search (DFS) for optimal route generation. …”
Get full text

Enhancing Agricultural Sustainability Through Intelligent Irrigation Using PVT Energy Applications: Implementing Hybrid Machine and Deep Learning Models by Youness El Mghouchi, Mihaela Tinca Udristioiu

“…Through this process, 301 optimal combinations of predictors and best-performing linear models for ETo prediction were identified. …”
Get full text

Machine learning-based prediction of 6-month functional recovery in hypertensive cerebral hemorrhage: insights from XGBoost and SHAP analysis by Menghui He, Zhongsheng Lu, Yiwei Lv, Zihai Cheng, Qiang Zhang, Xiaoqing Jin, Pei Han

“…Accurate early prediction of 6-month functional outcomes is critical for optimizing therapeutic strategies. This study compared the predictive efficacy of multiple machine learning models to identify the optimal model for forecasting long-term prognosis in HICH patients.MethodsWe conducted a retrospective analysis of clinical data from 807 HICH patients admitted to Qinghai Provincial People's Hospital's Neurosurgery Department between June 2020 and June 2024. …”
Get full text

XAIHO: explainable AI leveraging hybrid optimized framework for liver cirrhosis detection by Prashant Kumar Mishra, Brijesh Kumar Chaurasia, Man Mohan Shukla

“…To bridge this gap, the research initially explored machine learning (ML) models and then integrated XAI techniques to improve model explainability. …”
Get full text

Establishment and assessment of an early screening model for cervical cancer based on single-cell Raman spectroscopy combined with machine learning algorithms by MA Dongmei, ZHAO Wenjie, LIU Shihai, XU Haicang, CAI Duo, JI Yuetong, XU Jian, GUO Cancan, MA Bo, PAN Huazheng

“…Based on the SCRS data of the training set, 7 machine learning algorithms (KNN, PLS, LDA, RF, SVM, SVMRBF, and Stack) were used to establish an early screening model for cervical cancer, which was applied in the test set to identify the optimal model. …”
Get full text

Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting Pneumonia by Kriengsak Yothapakdee, Yosawaj Pugtao, Sarawoot Charoenkhun, Tanunchai Boonnuk, Kreangsak Tamee

“…This study focused on algorithm performance and training/testing time, evaluating the most suitable chest X-ray image size for machine learning models to predict pneumonia infection. …”
Get full text

Distinct morphometric features of cardiomyocytes isolated from mouse hypertrophy models: An ImageJ analysis combined with machine learning algorithms by Hoang Duc Minh Pham, Marie‐Thérèse Daher, Onnik Agbulut, Zhenlin Li, Ara Parlakian

“…Abstract This study aims at defining a standardized workflow based on a customized ImageJ macro combined with a machine‐learning algorithm to analyze morphometric features of isolated cardiomyocytes using high‐resolution/high‐content photomicrographs and to identify key and specific morphological features of cardiomyocytes isolated from various murine cardiac hypertrophy models. …”
Get full text

Development of a prediction model for chemotherapy and immunotherapy response in esophageal squamous cell carcinoma patients using machine learning algorithms by CHEN Jincheng, ZHANG Xiaoqin, LIU Jie

“…Objective‍　‍To develop models for predicting response to chemotherapy combined with immunotherapy in patients with esophageal squamous carcinoma with various machine learning algorithms, and then select the optimal model. …”
Get full text

Forest cover and canopy health mapping in Australian subalpine landscape: supervised machine learning models for Sentinel-2 and Landsat images by Weerach Charerntantanakul, Marta Yebra, Hilary Rose Dawson, Adrienne Beth Nicotra, Saul Alan Cunningham, Matthew Theodore Brookhouse

“…In conclusion, constraining the region of interest and optimizing model configurations can improve forest cover mapping. …”
Get full text

An Interpretable Machine Learning Model Based on Inflammatory–Nutritional Biomarkers for Predicting Metachronous Liver Metastases After Colorectal Cancer Surgery by Hao Zhu, Danyang Shen, Xiaojie Gan, Ding Sun

“…Feature selection was performed using Boruta and Lasso algorithms, identifying nine core prognostic factors through variable intersection. Seven machine learning (ML) models were constructed using the training set, with the optimal predictive model selected based on comprehensive evaluation metrics. …”
Get full text

Comparing machine learning models for predicting preoperative DVT incidence in elderly hypertensive patients with hip fractures: a retrospective analysis by Xue Ge, Lan Yao, Yan Liu, Yi Wang, Fang Zhang

“…This study aimed to develop and validate a machine learning-based predictive model to enhance the accuracy of predicting preoperative DVT in elderly patients with hypertension undergoing hip fracture surgery, thereby optimizing preoperative assessment and management. …”
Get full text

Anti-factor Xa during unfractionated heparin therapy in critically ill patients: Development of prediction models using machine learning by Boris Delange, Guillaume Bouzillé, Pauline Guillot, Anaëlle Bichon, Océane Bernard de Lajartre, Isabelle Gouin, Yoann Launey, Alexandre Mansour, Mathieu Lesouhaitier, Jean-Marc Tadié, Arnaud Gacouin, Marc Cuggia, Adel Maamar

“…This study aimed to develop and evaluate machine learning algorithms to predict anti-Xa levels during UFH therapy, helping clinicians optimize dosing. …”
Get full text

Exergy and energy-based sustainability evaluation of diesel-biodiesel-ethanol blends with emission forecasting using advanced machine learning models by Harish Venu, V. Dhana Raju, Jayashri N. Nair, Sameer Algburi, Ali E. Anqi, Ali A. Rajhi, Mohammed Kareemullah

“…The present study of thermodynamic analysis of ternary fuel with advanced Machine learning model provides valuable insights and adds significant outcomes to existing analysis. …”
Get full text

Prognostic Analysis of Elderly Patients with Hepatocellular Carcinoma: an Exploration and Machine Learning Model Prediction Based on Age Stratification and Surgical Approach by Cai C, Zhu H, Li B, Tang C, Ren Y, Guo Y, Li J, Wang L, Li D, Li D

“…Patients were randomly divided into training (70%, n=177) and validation sets (30%, n=75). We evaluated 147 machine learning models to establish the optimal predictive model. …”
Get full text

Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment by Du S, Wu Y, Tao J, Shu L, Yan T, Xiao B, Lv S, Ye M, Gong Y, Zhu X, Hu P, Wu M

“…Univariate and multivariate logistic regressions were performed to find the potential factors, and then nine machine learning models and a stack ensemble model were developed with optimized variables. …”
Get full text

Prediction of barite scale formation and inhibition in hydrocarbon reservoirs using AI modeling: Focus on different optimization algorithms by Ouafa Belkacem, Ahmed Rezrazi, Kamel Aizi, Lokmane Abdelouahed, Maamar Laidi, Abdelhafid Touil, Leila Cherifi, Salah Hanini

“…Innovative intelligent models, including Random Forest (RF), k-nearest Neighbors (KNN), Extreme Learning Machine (ELM), Support Vector Regression (SVR), Decision Trees (DT), and Multilayer Perceptron (MLP), were developed and optimized for this purpose. …”
Get full text

Development and validation of questionnaire-based machine-learning models to predict early natural menopause: a national cross-sectional study by Chunmiao Zhou, Ziwei Xie, Qi Wang, Zhongxuan Wang, Bo Xie, Yehuan Yang, Li Yang, Ting Guo, Ruimin Zheng, Yingying Qin, Dongshan Zhu

“…Abstract This epidemiological survey recruited 18,015 postmenopausal women aged 36–60 in 13 cities across 12 provinces in China. Ten machine learning algorithms were evaluated, with the optimal model was selected by area under the curve (AUC). …”
Get full text

Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance by Leonel Patrício, Leonilde Varela, Zilda Silveira

“…This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. …”
Get full text

Hybrid Model for Novel Attack Detection Using a Cluster-Based Machine Learning Classification Approach for the Internet of Things (IoT) by Naveed Ahmed, Md Asri Ngadi, Abdulaleem Ali Almazroi, Nouf Atiahallah Alghanmi

Get full text