A Review of Artificial Intelligence Applications in Predicting Faults in Electrical Machines by Mathew Habyarimana, Abayomi A. Adebiyi

“…Traditional human-dependent diagnostic techniques are changing as a result of advances in artificial intelligence (AI), opening the door to automated and predictive maintenance plans. This paper provides a detailed examination of artificial intelligence (AI) applications in the prediction of electrical device failures, with a focus on techniques such as fuzzy systems, expert systems, artificial neural networks (ANNs), and complex machine-learning algorithms. …”
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Random Forest–Based Coal Mine Roof Displacement Prediction and Application by Hongxia Li, Rong Wu, Jianan Gao

“…Based on Random Forest (RF), XGBoost, and Gradient Boosting Decision Tree (GBDT) algorithms, three kinds of roof displacement prediction models are established respectively. …”
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Fast Multimodal Trajectory Prediction for Vehicles Based on Multimodal Information Fusion by Likun Ge, Shuting Wang, Guangqi Wang

“…Trajectory prediction plays a crucial role in level autonomous driving systems, as real-time and accurate trajectory predictions can significantly enhance the safety of autonomous vehicles and the robustness of the autonomous driving system. …”
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Predicting the Performance of Rural Banks in Ghana Using Machine Learning Approach by Emmanuel Awoin, Peter Appiahene, Frank Gyasi, Abdulai Sabtiwu

“…This study used three decision tree algorithms, namely, C5.0, C4.5, and CART, to build the various decision tree predictive models. …”
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Short-term prediction of trimaran load based on data driven technology by Haoyun Tang, Rui Zhu, Qian Wan, Deyuan Ren

“…To highlight the trimaran high-frequency load fluctuation and improve the prediction accuracy, the LSTM neural network combines with different signal decomposition algorithms, such as Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and Variational Mode Decomposition (VMD). …”
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Addressing data imbalance in collision risk prediction with active generative oversampling by Li Li, Xiaoliang Zhang

“…Abstract Data imbalance is a critical factor affecting the predictive accuracy in collision risk assessment. This study proposes an advanced active generative oversampling method based on Query by Committee (QBC) and Auxiliary Classifier Generative Adversarial Network (ACGAN), integrated with the Wasserstein Generative Adversarial Network (WGAN) framework. …”
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GLAD: Global–Local Approach; Disentanglement Learning for Financial Market Prediction by Humam M. Abdulsahib, Foad Ghaderi

“…Accurate prediction of financial market trends can have a great impact on maximizing profits and avoiding risks. …”
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Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting Concrete by AGRAWAL Achal, CHANDAK Narayan

“…The present study utilizes advanced numerical evaluation techniques like Artificial Intelligence (AI), including Support Vector Machines (SVM), Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems with Genetic Algorithms (ANFIS-GA), Gene Expression Programming (GEP), and Multiple Linear Regression (MLR) to develop and compare the predictive models for determination of compressive and tensile strength. …”
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Predicting Leadership Status Through Trait Emotional Intelligence and Cognitive Ability by Bogdan S. Zadorozhny, K. V. Petrides, Yongtian Cheng, Stephen Cuppello, Dimitri van der Linden

“…The results demonstrated the superior predictive strength of trait EI over cognitive ability, especially of its sociability factor, and supported the predictive utility of the random forest (RF) algorithm in this context. …”
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Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning by Sangwoo Park, Osvaldo Simeone

“…The approach, which builds on probabilistic conformal prediction (CP), turns an arbitrary, possibly small, number of shots from a pretrained quantum model into a set prediction, e.g., an interval, that <italic>provably</italic> contains the true target with any desired coverage level. …”
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Risk Prediction Score for Thermal Mapping of Pharmaceutical Transport Routes in Brazil by Clayton Gerber Mangini, Nilsa Duarte da Silva Lima, Irenilza de Alencar Nääs

“…The classification algorithms (CART-Decision Tree, NB-Naive Bayes, and MP-Multilayer Perceptron) were used to build up a model of rules for predicting risk levels in thermal mapping routes; <i>Results</i>: The MP model presented the best performance, indicating a better application probability. …”
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Fast Task Scheduling With Model Predictive Control Integrating a Priority-Based Heuristic by Francesco Liberati, Manuel Donsante, Chiara Maria Francesca Cirino, Andrea Tortorelli

“…This paper presents a scalable Model Predictive Control (MPC) algorithm for task scheduling and real time re-scheduling. …”
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Improved GOA-based fuzzy PI speed control of PMSM with predictive current regulation. by Taochang Li, Ang Li, Limin Hou

“…Additionally, to address the high torque and current ripple issues commonly associated with traditional PI controllers in the current loop, Model Predictive Control (MPC) is adopted to further improve control performance. …”
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H∞ filtering of uncertain predictive models: Gain computation using LMI and performance evaluation by Eli G. Pale Ramon, Oscar G. Ibarra-Manzano, José A. Andrade-Lucio, Yuriy S. Shmaliy

“…The filter is designed for processes represented in discrete time using the forward Euler method, which allows for predictive modeling. Since the error covariance of a state estimator is a quadratic function of K, a new theorem is proved and a numerical algorithm is developed for computing K using linear matrix inequality (LMI). …”
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Machine Learning Methods for Predicting Cardiovascular Diseases: A Comparative Analysis by Aiym B. Temirbayeva, Arshyn Altybay

“…The primary research question is to identify which algorithm demonstrates the best predictive performance for heart disease diagnosis. …”
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Artificial Intelligence-Based Prediction Model for Maritime Vessel Type Identification by Hrvoje Karna, Maja Braović, Anita Gudelj, Kristian Buličić

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Development and validation of an interpretable risk prediction model for the early classification of thalassemia by Jin-Xin Lai, Jia-Wei Tang, Shan-Shan Gong, Ming-Xiong Qin, Yu-Lu Zhang, Quan-Fa Liang, Li-Yan Li, Zhen Cai, Liang Wang

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Machine learning for predicting resistance spot weld quality in automotive manufacturing by Nuttapong Chuenmee, Nattachai Phothi, Kontorn Chamniprasart, Sorada Khaengkarn, Jiraphon Srisertpol

“…The research highlights that the proposed methodology, particularly leveraging XGBoost, achieves a notable prediction accuracy of 97.1% when applied to unseen data.…”
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Status of research on prediction models for severity in confirmed Covid-19 patients by Maicel Eugenio Monzón-Pérez, Lizet Sánchez-Valdés, Elizabeth Rieche-Gómez, Agustín Lage-Dávila

“…Introduction: Previous knowledge in the scientific literature on clinical prediction models in patients with COVID-19 may be useful for the development of new research. …”
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Predicting cancer risk using machine learning on lifestyle and genetic data by Mohamed Abdelmoaty Ahmed, Ahmed AbdelMoety, Asmaa Mohamed Ahmed Soliman

“…This study investigates the application of Machine Learning (ML) techniques to predict cancer risk based on a combination of genetic and lifestyle factors. …”
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