Engine Optimization Model for Accurate Prediction of Friction Model in Marine Dual-Fuel Engine by Mina Tadros

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Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks by Zakaria Elbayed, Abdelmjid Qadi EI Idrissi

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Prediction of the Cause of Fundus-Obscuring Vitreous Hemorrhage Using Machine Learning by Jinsoo Kim, Bo Sook Han, Joo Eun Ha, Min Seon Park, Soonil Kwon, Bum-Joo Cho

“…<b>Conclusions</b>: The unknown etiology of FOVH could be predicted preoperatively with considerable accuracy by ML algorithms. …”
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DLTM: a deep learning method for tearing mode simulation and prediction by Zhipeng Chang, Baofeng Gao, Ruijie Yin, Xiaofei Zhao

“…Machine learning, particularly deep learning algorithms, has shown significant potential in various plasma applications, including disruption prediction and tokamak control. …”
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Predicting soybean seed germination using the tetrazolium test and computer intelligence by Marcio Alves Fernandes, Izabela Cristina de Oliveira, Marcio Dias Pereira, Breno Zaratin Alves, Alan Mario Zuffo, Charline Zaratin Alves

“…Therefore, the use of machine learning can provide an efficient approach for predicting germination. The aim of this work was to investigate algorithms that, together with tetrazolium test data, lead to efficient prediction of soybean seed germination. …”
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Machine learning based risk analysis and predictive modeling of structure fire related casualties by Andres Schmidt, Eric Gemmil, Russ Hoskins

“…The network model achieves a prediction accuracy of 92.5 % for the classification of structural fire-related casualty severities. …”
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Prediction Analysis of Pre-Camber for Continuous Girder Bridge Cantilever Casting Construction Based on DBO-CNN-BiLSTM-Attention Neural Network by Jinyang Zhang, Haiqing Liu, Xiangen Gong, Ming Lei, Zimu Chen

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A Solution for Predicting the Timespan Needed for Grinding Roller Bearing Rings by Cezarina Chivu, Mitica Afteni, Gabriel Radu Frumusanu, Florin Susac

“…In this paper, the HOM is presented as a solution for predicting the timespan needed for grinding roller bearing rings. …”
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Machine learning for predicting Chagas disease infection in rural areas of Brazil. by Fabio De Rose Ghilardi, Gabriel Silva, Thallyta Maria Vieira, Ariela Mota, Ana Luiza Bierrenbach, Renata Fiuza Damasceno, Lea Campos de Oliveira, Alexandre Dias Porto Chiavegatto Filho, Ester Sabino

“…In recent years, machine learning algorithms have emerged as powerful tools for disease prediction and diagnosis.…”
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Dynamic ensemble-based machine learning models for predicting pest populations by Ankit Kumar Singh, Md Yeasin, Ranjit Kumar Paul, A. K. Paul, Anita Sarkar

“…Error metrics include the root mean square log error (RMSLE), root relative square error (RRSE), and median absolute error (MDAE), along with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm. This study concluded that the proposed dynamic ensemble algorithm demonstrated better predictive accuracy in forecasting YSB infestation in rice crops.…”
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Predicting Diabetic Retinopathy and Nephropathy Complications Using Machine Learning Techniques by D. R. Manjunath, J. J. Lohith, S. Selva Kumar, Abhijit Das

“…Diabetes and its complications, especially Diabetic Retinopathy (DR) and Diabetic Nephropathy (DN) is a big challenge to the global healthcare system and needs accurate predictive models to help in early diagnosis and intervention. …”
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An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes by Juan Lu, Xiaoping Liao, Steven Li, Haibin Ouyang, Kai Chen, Bing Huang

“…To improve the prediction accuracy and reduce parameter adjustment time of SVM model, artificial bee colony algorithm (ABC) is employed to optimize internal parameters of SVM model. …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…This method makes full use of the diversity characteristics of base learners, leverages their classification ability, optimizes the selection method for ensemble learning, and enhances the predictive performance of the ensemble model. The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), na&#x00EF;ve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Predicting Prognosis of Early-Stage Mycosis Fungoides with Utilization of Machine Learning by Banu İsmail Mendi, Hatice Şanlı, Mert Akın Insel, Beliz Bayındır Aydemir, Mehmet Fatih Atak

“…The results suggest that ML algorithms may be useful in predicting prognosis in early-stage MF patients.…”
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Using topological data analysis and machine learning to predict customer churn by Marcel Sagming, Reolyn Heymann, Maria Vivien Visaya

“…An effective way to further improve churn prediction capability of different ML algorithms is through the employment of topological data analysis (TDA). …”
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Presenting a prediction model for HELLP syndrome through data mining by Boshra Farajollahi, Mohammadjavad Sayadi, Mostafa Langarizadeh, Ladan Ajori

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An Efficient Prediction System for Diabetes Disease Based on Deep Neural Network by Tawfik Beghriche, Mohamed Djerioui, Youcef Brik, Bilal Attallah, Samir Brahim Belhaouari

“…Such algorithms are state-of-the-art in computer vision, language processing, and image analysis, and when applied in healthcare for prediction and diagnosis purposes, these algorithms can produce highly accurate results. …”
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Short-Term Prediction of Traffic Flow Based on the Comprehensive Cloud Model by Jianhua Dong

“…These algorithms are designed to address the short-term traffic flow prediction problem. …”
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An explainable AI-based approach for predicting undergraduate students academic performance by Fatema Tuz Johora, Md Nahid Hasan, Aditya Rajbongshi, Md Ashrafuzzaman, Farzana Akter

“…Two eXplainable Artificial Intelligence (XAI) algorithms, namely SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), were integrated to provide a comprehensible prediction of the best model and determine the significant factors. …”
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Using artificial intelligence techniques and econometrics model for crypto-price prediction by Abhidha Verma, Jeewesh Jha

“…The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). …”
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