A review of machine learning and internet-of-things on the water quality assessment: Methods, applications and future trends by Gangani Dharmarathne, A.M.S.R. Abekoon, Madhusha Bogahawaththa, Janaka Alawatugoda, D.P.P. Meddage

“…Explainable AI (XAI) which can explain the decision making process of ML, is underutilised, appearing in only a few recent studies. …”
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SVR-Optimized ANN Model for Predicting Earthquake Risk in Electrical Substations Based on Disaster Datasets in the Aceh Region, Indonesia by Elvy Sahnur Nasution, Yuwaldi Away, Syahrial, Ira Devi Sara, Andri Novandri

“…This paper proposes a predictive model based on an Artificial Neural Network (ANN) optimized with Support Vector Regression (SVR) to predict seismic parameters as part of earthquake risk mitigation for substations. …”
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Genomic selection in pig breeding: comparative analysis of machine learning algorithms by Ruilin Su, Jingbo Lv, Yahui Xue, Sheng Jiang, Lei Zhou, Li Jiang, Junyan Tan, Zhencai Shen, Ping Zhong, Jianfeng Liu

“…Machine learning (ML) methods are usually used to predict phenotypic values since their advantages in processing high dimensional data. While, the existing researches have not indicated which ML methods are suitable for most pig genomic prediction. …”
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Emotion-Aware Ensemble Learning (EAEL): Revolutionizing Mental Health Diagnosis of Corporate Professionals via Intelligent Integration of Multi-Modal Data Sources and Ensemble Tech... by Gaurav Yadav, Mohammad Ubaidullah Bokhari, Saleh I. Alzahrani, Shadab Alam, Mohammed Shuaib

“…Our investigation methodically trains base classifiers, such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forests (RF), on distinct and combined datasets derived from facial expressions and typing patterns. …”
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Sustainable Polyurethane-Based Polymer Concrete: Mechanical and Non-destructive Properties with Machine Learning Technique by S. I. Haruna, Han Zhu, Yasser E. Ibrahim, Jian Yang, AIB Farouk, Jianwen Shao, Musa Adamu, Omar Shabbir Ahmed

“…The experimental datasets from mechanical and NDT tests were utilized to train machine learning (ML) models, including multilinear regression (MLR), artificial neural network (ANN), support vector machine (SVM), Gaussian regression process (GPR), and stepwise regression (SWR) models for estimating the f c. …”
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Evaluation of oxidative stress status and antioxidant defense status of athletes after inclusion of a special product for sportive nutrition in their food ration by R. Rakhmanov, T. Blinova, L. Strakhova, S. Kolesov, V. Troshin, R. Hyairov

“…The sportsmen of experimental group additionally received the «product for sportive nutrition» in their food ration. The training process was the same in both groups; it was divided into three stages. …”
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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. …”
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Development and validation of a machine learning‐based model of ischemic stroke risk in the Chinese elderly hypertensive population by Xiaoyue Lyu, Jie Liu, Yingying Gou, Shengli Sun, Jing Hao, Yali Cui

“…The final model, eXtreme gradient boosting, was identified as having superior performance than the other 9 classifers (random forest, Gaussian process, multilayer perceptron, logistic regression, support vector machine, K‐nearest neighbor, decision tree, Gaussian naive bayes, and ensemble model), with area under the receiver‐operating characteristic curves of 0.97 and 0.94 for the test and external validation sets, respectively. …”
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Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy by Nicharee Wisuthiphaet, Huanle Zhang, Xin Liu, Nitin Nitin

“…These ML algorithms, including linear Support Vector Classifier (SVC) and Random Forest (RF), demonstrate high accuracy (>0.85) for detecting E. coli at 102 CFU/ml concentration within 6 h. …”
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Automated Parkinson’s Disease Diagnosis Using Decomposition Techniques and Deep Learning for Accurate Gait Analysis by S. Jeba Priya, C. Anand Deva Durai, M. S. P. Subathra, S. Thomas George, Andrew Jeyabose

“…The successful application of these methods highlights the importance of advanced signal processing techniques in improving the detection and management of neurological disorders.…”
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CNN-Based Optimization for Fish Species Classification: Tackling Environmental Variability, Class Imbalance, and Real-Time Constraints by Amirhosein Mohammadisabet, Raza Hasan, Vishal Dattana, Salman Mahmood, Saqib Hussain

“…Eight CNN architectures, including DenseNet121, MobileNetV2, and Xception, were compared alongside traditional classifiers like support vector machines (SVMs) and random forest. DenseNet121 achieved the highest accuracy (90.2%), leveraging its superior feature extraction and generalization capabilities, while MobileNetV2 balanced accuracy (83.57%) with computational efficiency, processing images in 0.07 s, making it ideal for real-time deployment. …”
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Drought monitoring of large lakes in Iraq using remote sensing images and normalized difference water index (NDWI) by Mohammed R. Mahmood, Baydaa Ismail Abrahem, Huda J. Jumaah, Hayder A. Alalwan, Malik M. Mohammed

“…Furthermore, Support Vector Machine (SVM) method was applied on Sentinel-2 images for 2024 to recognize the current changes in the three lakes. …”
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Temperature affects the sorption of trace metals by macro- and microplastics within marine intertidal sediments: insights from a long-term laboratory-based study by Tamara N. Kazmiruk, Juan José Alava, Juan José Alava, Eirikur Palsson, Leah I. Bendell

“…This study enhances our understanding of how temperature can effect trace metals-plastic particle interactions in the marine intertidal sedimentary environment providing insight as to conditions that will create the greatest threat to higher trophic levels by providing an additional vector of Cd, Cu, Pb, and Zn exposure into benthic food webs.…”
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A rolling bearing fault diagnosis method based on GADF-CWT-GCNN by ZHANG Xiaoli, LUO Xin, LI Min, LIANG Wang, WANG Fangzhen

“…The results show that the generalization ability of the data processing method and the model built in this paper in the small-sample environment is much higher than that of other network models such as the small vector machine and the 1D-CNN. …”
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Resolution enhancement of scanning electron micrographs using artificial intelligence by T. Reclik, S. Medghalchi, P. Schumacher, M.A. Wollenweber, T. Al-Samman, S. Korte-Kerzel, U. Kerzel

“…Suitable reference images are selected using vector embeddings and processed by a texture-transformer network. …”
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Missing Data Interpolation of Alzheimer’s Disease Based on Column-by-Column Mixed Mode by Shi-di Miao, Si-qi Li, Xu-yang Zheng, Rui-tao Wang, Jing Li, Si-si Ding, Jun-feng Ma

“…In addition, in the processing of missing data, a combination of deletion method and interpolation method is adopted with reference to expert knowledge. …”
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Optimization of carbon peak path in Fujian Province based on sparrow search algorithm by CAI Huang, LIN Xiaoyu*, CAI Zhiling, ZHONG Yiwen, ZHONG Fenglin

“…To address this, this paper takes Fujian Province as an example, and constructs an SSA-SVR (Sparrow Search Algorithm-Support Vector Regression) model based on the analysis of Fujian′s energy consumption and carbon emission data. …”
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Fusion of microscopic and diffraction images with VGG net for budding yeast recognition in imaging flow cytometry by Yangguang Han, Qifeng Li, Pengpeng Sun, Xiangyun Ma, Yunpeng Yang, Ning Zhang, Jingwen Feng, Yu Sa

“…For comparison, Support Vector Machines (SVM) and Random Forests (RF) based on Grey-Level Co-occurrence Matrix (GLCM) features were employed. …”
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A genetic algorithm to generate maximally orthogonal frames in complex space by Sebastián Roca-Jerat, Juan Román-Roche

“…A frame is a generalization of a basis of a vector space to a redundant overspanning set whose vectors are linearly dependent. …”
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Improving rainfall forecasting using deep learning data fusing model approach for observed and climate change data by Farhan Amir Fardush Sham, Ahmed El-Shafie, Wan Zurina Binti Wan Jaafar, S. Adarsh, Mohsen Sherif, Ali Najah Ahmed

“…The performance of several advanced machine learning models was assessed, with the Efficient Linear Support Vector Machine (ELSVM) showing the highest accuracy in daily rainfall forecasting, yielding an R² value of 0.3868, indicating its ability to effectively capture the variability in rainfall. …”
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