Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review by Suhila Sawesi, Arya Jadhav, Bushra Rashrash

“… Abstract BackgroundLeptospirosis, a zoonotic disease caused by Leptospira ObjectiveThis systematic review aimed to evaluate the application of machine learning (ML) and deep learning (DL) techniques in predicting and diagnosing leptospirosis, focusing on the most used algorithms, validation methods, data types, and performance metrics. …”
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AI-Driven Belt Failure Prediction and Prescriptive Maintenance with Motor Current Signature Analysis by João Paulo Costa, José Torres Farinha, Mateus Mendes, Jorge O. Estima

“…Through the integration of motor current signature analysis (MCSA) and machine learning algorithms, particularly long short-term memory (LSTM) networks, this study aims to predict and detect belt degradation in real time. …”
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TCDE: Differential Evolution for Topographical Contour-Based Prediction to Solve Multimodal Optimization Problems by Zhen Cheng, Yun Zhang, Caifu Fan, Xingwei Gao, Haohan Jia, Lei Jiang

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Machine Learning-Based Prediction Performance Comparison of Marshall Stability and Flow in Asphalt Mixtures by Muhammad Farhan Zahoor, Arshad Hussain, Afaq Khattak

“…The potential of various machine learning (ML) algorithms to predict Marshall Stability (MS) and Marshall Flow (MF) was investigated in this work. …”
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Pathological omics prediction of early and advanced colon cancer based on artificial intelligence model by Zhe Wang, Yang Wu, Yingjie Li, Qingwen Wang, Hongfei Yi, Hongyan Shi, Xinyue Sun, Chengxiang Liu, Kuanyu Wang

“…Cellprofiler and CLAM tools were used to extract pathological features, and machine learning algorithms and deep learning algorithms were used to construct prediction models. …”
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Analysis of disease severity and mortality prediction using machine learning during COVID-19 by Hodjat (Hojatollah) Hamidi, Mostafa Moradi

“…This paper focuses on how machine learning (ML) algorithms and applications have been used to analyze disease severity and mortality prediction in COVID-19 research. …”
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Back Propagation Neural Network model for analysis of hyperspectral images to predict apple firmness by Shuiping Li, Yueyue Chen, Xiaobo Zhang, Junbo Wang, Xuanxiang Gao, Yunhong Jiang, Zhaojun Ban, Cunkun Chen

“…This research provides a reference point for the non-destructive detection of apple in the selection of preprocessing, feature selection algorithms, and predicting firmness model.…”
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MRI-based radiomic and machine learning for prediction of lymphovascular invasion status in breast cancer by Cici Zhang, Minzhi Zhong, Zhiping Liang, Jing Zhou, Kejian Wang, Jun Bu

“…This study aimed to investigate the value of eight machine learning models based on MRI radiomic features for the preoperative prediction of LVI status in BC. Methods A total of 454 patients with BC with known LVI status who underwent breast MRI were enrolled and randomly assigned to the training and validation sets at a ratio of 7:3. …”
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Evaluation and use of in-silico structure-based epitope prediction with foot-and-mouth disease virus. by Daryl W Borley, Mana Mahapatra, David J Paton, Robert M Esnouf, David I Stuart, Elizabeth E Fry

“…Therefore we have extended several existing structural prediction algorithms to build a method for identifying epitopes on the appropriate outer surface of intact virus capsids (which are structurally different from globular proteins in both shape and arrangement of multiple repeated elements) and applied it here as a proof of principle concept to the capsid of foot-and-mouth disease virus (FMDV). …”
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Prediction method of gas emission in working face based on feature selection and BO-GBDT by MA Wenwei

“…The wrapping method was identified as the most effective feature selection algorithm. Based on field conditions, 8 optimal features were selected for prediction. …”
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Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms by Ayesha Siddika, Momotaz Begum, Fahmid Al Farid, Jia Uddin, Hezerul Abdul Karim

“…This research addresses this need by combining advanced techniques (ensemble techniques) with seventeen machine learning algorithms for predicting software defects, categorised into three types: semi-supervised, self-supervised, and supervised. …”
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Spatial differences in predicted Phalaris arundinacea (reed canarygrass) occurrence in floodplain forest understories by John T. Delaney, M. Van Appledorn, N. R. De Jager, K. L. Bouska, J. J. Rohweder

“…We used an ensemble of species distribution models including Bayesian additive regression trees, boosted trees, and random forest algorithms to predict habitat suitability for reed canarygrass in forest understories across the Upper Mississippi River floodplain (~41,000 ha). …”
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Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

“…This research investigation optimizes Feature Selection (FS) and prediction results for PV energy prediction by applying Bayesian Density Estimation (BDE) with Elastic Net (ELNET) regression analysis. …”
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Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma by Tianzhi Tang, Tianyu Guo, Bo Zhu, Qihui Tian, Yang Wu, Yefu Liu

“…Variable selection was performed using the least absolute shrinkage and selection operator regression in conjunction with random forest and recursive feature elimination (RF-RFE) algorithms. Subsequently, 12 distinct ML algorithms were employed to identify the optimal prediction model. …”
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Development and clinical application of an automated machine learning-based delirium risk prediction model for emergency polytrauma patients by Zhenyi Liu, Yihao Huang, Long Li, Yisha Xu, Peng Wu, Zhigang Zhang, Tingyong Han, Liangjie Zhang, Ming Zhang

“…ObjectiveTo address the limitations of conventional delirium prediction models in emergency polytrauma care, this study developed an interpretable machine learning (ML) framework incorporating trauma-specific biomarkers and advanced optimization algorithms for risk stratification of delirium in emergency polytrauma patients.MethodsThis multi-center retrospective observational cohort study was conducted across six hospitals in the Ya’an region. …”
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Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study by Jiani Liu, Xin Zhang, Wei Li, Francis Manyori Bigambo, Dandan Wang, Xu Wang, Beibei Teng

“…This study revealed that parental height, caregiver education, and children’s weight significantly influenced the prediction of normal-variant short stature risk, and both the random forest model and gradient boosting machine model exhibited the best discriminatory ability among the 9 machine learning models. …”
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The outcome prediction method of football matches by the quantum neural network based on deep learning by Yang Sun, Hongyang Chu

“…During the model training phase, gradient descent is used to optimize weight parameters, and quantum algorithms are integrated to continuously adjust network weights to minimize prediction errors. …”
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Impact of atmospheric corrections on satellite imagery for corn yield prediction using machine learning by Octávio Pereira da Costa, Franklin Daniel Inácio, Jéssica Elaine da Silva, Thiago Orlando Costa Barboza, Wender Henrique Batista da Silva, Lorena Nunes Lacerda, Adão Felipe dos Santos

“…However, the performance of the models differed for corn yield estimation. The SVM algorithm showed the lowest performance during the main crop season (R² = 0.36), while both RF and kNN yielded prediction results with an accuracy of over 55 %, with RF providing the highest R² values and the lowest errors (RMSE = 0.3 t ha−1). …”
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Predicting the interfacial tension of CO2 and NaCl aqueous solution with machine learning by Kashif Liaqat, Daniel J. Preston, Laura Schaefer

“…Our findings indicate a notable enhancement in prediction accuracy over previous ML studies in this area. …”
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Predicting mother and newborn skin-to-skin contact using a machine learning approach by Sanaz Safarzadeh, Nastaran Safavi Ardabili, Mohammadsadegh Vahidi Farashah, Nasibeh Roozbeh, Fatemeh Darsareh

“…Results Of 8031 eligible mothers, 3759 (46.8%) experienced SSC. The algorithms created by deep learning (AUROC: 0.81, accuracy: 0.75, precision: 0.67, recall: 0.77, and F_1 Score: 0.73) and linear regression (AUROC: 0.80, accuracy: 0.75, precision: 0.66, recall: 0.75, and F_1 Score: 0.71) had the highest performance in predicting SSC. …”
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