Predictive maintenance based on anomaly detection in photovoltaic system using SCADA data and machine learning by Agussalim Syamsuddin, Andrew Cahyo Adhi, Amie Kusumawardhani, Toni Prahasto, Achmad Widodo

Get full text

SHASI-ML: a machine learning-based approach for immunogenicity prediction in Salmonella vaccine development by Ottavia Spiga, Ottavia Spiga, Ottavia Spiga, Anna Visibelli, Francesco Pettini, Bianca Roncaglia, Annalisa Santucci, Annalisa Santucci, Annalisa Santucci

“…The Extreme Gradient Boosting (XGBoost) algorithm was employed for model development and optimization.ResultsSHASI-ML demonstrated robust performance in identifying bacterial immunogens, achieving 89.3% precision and 91.2% specificity. …”
Get full text

Strategies for Soil Salinity Mapping Using Remote Sensing and Machine Learning in the Yellow River Delta by Junyong Zhang, Xianghe Ge, Xuehui Hou, Lijing Han, Zhuoran Zhang, Wenjie Feng, Zihan Zhou, Xiubin Luo

“…In summary, this research successfully developed a comprehensive, high-resolution soil salinity mapping framework for the Dongying region by integrating multi-source remote sensing data and employing diverse predictive strategies alongside machine learning models. The findings highlight the potential of Vegetation Type Factors to enhance large-scale soil salinity monitoring, providing robust scientific evidence and technical support for sustainable land resource management, agricultural optimization, ecological protection, efficient water resource utilization, and policy formulation.…”
Get full text

Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning by Zipeng Zhao, Yuman Sun, Weiwei Jia, Jinyan Yang, Fan Wang

“…Further optimization using RF as a second-layer model to refine Extreme Trees (ETs) significantly increased R² values to 0.83 and 0.75 for V and V5+, respectively, at this scale. …”
Get full text

Improving prediction of solar radiation using Cheetah Optimizer and Random Forest. by Ibrahim Al-Shourbaji, Pramod H Kachare, Abdoh Jabbari, Raimund Kirner, Digambar Puri, Mostafa Mehanawi, Abdalla Alameen

“…Consequently, this paper introduces an innovative SR prediction model, denoted as the Cheetah Optimizer-Random Forest (CO-RF) model. …”
Get full text

Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures by Tingting Liu, Lifan Zhong, Xizhe Sun, Zhijiang He, Witiao Lv, Liyun Deng, Yanfei Chen

“…The results demonstrated that the proposed system outperformed traditional Machine Learning models, such as Support Vector Machine and Random Forest, in terms of accuracy (98.6%), specificity (0.984), sensitivity (0.979), and F1-score (0.978). …”
Get full text

Machine learning prediction of coal workers’ pneumoconiosis classification based on few-shot clinical data by Jiaqi Jia, Jingying Huang, Yuming Cui, Dekun Zhang, Haiquan Li, Songquan Wang, Wenlu Hang

“…Conclusions The integration of clinical biochemical examination data with ML models, especially the RF-Adaboost and support vector machine-particle swarm optimization models, effectively predicted the staging of CWP. …”
Get full text

DEVELOPMENT OF HEALTH INSURANCE CLAIM PREDICTION METHOD BASED ON SUPPORT VECTOR MACHINE AND BAT ALGORITHM by Syaiful Anam, Abdi Negara Guci, Fery Widhiatmoko, Mila Kurniawaty, Komang Agus Arta Wijaya

“…BA has a faster convergence rate than other algorithms, for example Particle Swarm Optimization (PSO). Based on this situation, this paper offers the new classification model for predicting health insurance claim based on SVM and BA. …”
Get full text

Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review by Álex Paz, Broderick Crawford, Eric Monfroy, José Barrera-García, Álvaro Peña Fritz, Ricardo Soto, Felipe Cisternas-Caneo, Andrés Yáñez

“…It categorizes the use of machine learning algorithms, feature selection methods, and metaheuristic optimization techniques, including genetic algorithms, particle swarm optimization, and biogeography-based optimization. …”
Get full text

An inventory of industrial solid waste in 337 cities of China: Applying machine learning for data completion by Qian Jia, Kunsen Lin, Jiawei Zhuang, Dengyu Yang, Wei Wei, Xiong Xiao, Huanzheng Du, Tao Wang

“…We further developed six machine learning models to complete the dataset across all the 337 cities in China for the period 1990–2022. …”
Get full text

Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour by Yuling Wang, Chen Zhang, Xinhua Li, Longzhu Xing, Mengchao Lv, Hongju He, Leiqing Pan, Xingqi Ou

“…Through a comparative evaluation of five wavelength selection techniques, 25–30 optimal wavelengths were identified, enabling the development of optimized SVR models. …”
Get full text

The Application Status of Radiomics-Based Machine Learning in Intrahepatic Cholangiocarcinoma: Systematic Review and Meta-Analysis by Lan Xu, Zian Chen, Dan Zhu, Yingjun Wang

“…The limited research on deep learning has hindered both further analysis and the development of subgroup analyses across various models. Furthermore, challenges such as data heterogeneity and interpretability caused by segmentation and imaging parameter variations require further optimization and refinement. …”
Get full text

A Comparison of AutoML Hyperparameter Optimization Tools For Tabular Data by Prativa Pokhrel, Alina Lazar

“…Therefore, finding the optimal values of these hyperparameters is integral in improving the prediction accuracy of an ML algorithm and the model selection. …”
Get full text

The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review by Norah Hamad Alhumaidi, Doni Dermawan, Hanin Farhana Kamaruzaman, Nasser Alotaiq

“…For example, random forest models for cardiovascular disease prediction demonstrated an area under the curve of 0.85 (95% CI 0.81-0.89), while support vector machine models for cancer prognosis achieved an accuracy of 83% (P=.04). …”
Get full text

Machine learning-driven design of rare metal doped niobium alloys with enhanced strength and ductility by Zhenqiang Xiong, Zhaokun Song, Jianwei Li, Heran Wang, Xiaoxin Zhang, Bin Liang, Dong Wang

“…A comprehensive database of niobium alloys' properties was analyzed using feature engineering, and a high-accuracy prediction model, Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM), was constructed, achieving R2 values of 0.95 and 0.88 for tensile strength and elongation, respectively. …”
Get full text

Towards arbitrary QUBO optimization: analysis of classical and quantum-activated feedforward neural networks by Chia-Tso Lai, Carsten Blank, Peter Schmelcher, Rick Mukherjee

Get full text

Utilization of Ensemble Techniques in Machine Learning to Predict the Porosity and Hardness of Plasma-Sprayed Ceramic Coating by N. Radhika, M. Sabarinathan, S. Sivaraman

“…Experimental validation confirms the model’s reliability, through minimal error deviation between predicted and actual values for porosity and hardness. …”
Get full text

A reinforcement learning approach to the design of quantum chains for optimal energy and state transfer by S Sgroi, G Zicari, A Imparato, M Paternostro

Get full text

The influence of the harvester manipulator design characteristics on the working area optimal size by Lagerev A.V., Makulina A.V., Lagerev I.A.

“…Optimization of the manipulator’s working area, formed during the operation of a logging machine, is one of the most effective ways to increase its productivity. …”
Get full text

Comparative Analysis of Machine Learning Techniques for Fault Diagnosis of Rolling Element Bearing with Wear Defects by Devendra Sahu, Ritesh Kumar Dewangan, Surendra Pal Singh Matharu

“…This research addresses these challenges by employing advanced signal processing techniques and machine learning algorithms. The study investigates and optimizes fault diagnosis of rolling element bearings using various machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP). …”
Get full text