Analysis and Prediction of Energy Consumption Data, Using Data Mining Software by KAZOLIS Dimitrios, FANTIDIS Jacob, FOTAKIS Christos Dionyshs, TRAMANTZAS Kostantinos, TSIANTOS Vassilios

“…This paper explores the ability of prediction in electricity consumption using data mining techniques and emphasizes the integration of weather conditions and previous consumption trends. …”
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Improved phase prediction of high-entropy alloys assisted by imbalance learning by Libin Zhang, Chang-Seok Oh, Yoon Suk Choi

“…Herein, machine learning and imbalance learning algorithms were combined together to improve the phase prediction of HEAs. …”
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Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data by A. Gaspar-Cunha, G. Recio, L. Costa, C. Estébanez

“…In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. …”
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Pattern mining and prediction techniques for user behavioral trajectories in e-commerce. by Xin Wang, Dong-Feng Liu

“…Second, it introduces a frequent sub-trajectory mining algorithm that emphasizes the order of user visits for trajectory analysis and prediction. …”
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NEXT-GENERATION SEQUENCING IN CANCER: GENETIC DIAGNOSIS, RISK PREDICTION AND CLASSIFICATION by Özkan Özdemir

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Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction by Tian Sun, Hefei Hu, Sirui Zhang

“…Therefore, we propose the Resource Predictive Load Balancing SFC Migration (RP-LBM) algorithm in this paper. …”
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Industrial-scale prediction of cement clinker phases using machine learning by Sheikh Junaid Fayaz, Néstor Montiel-Bohórquez, Shashank Bishnoi, Matteo Romano, Manuele Gatti, N. M. Anoop Krishnan

“…Traditional models, limited to steady-state conditions, lack predictive accuracy for clinker mineralogical phases. …”
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Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches by Chunhua Gao, Hui Wang

“…Further ablation experiments also show that the designed prediction model has certain robustness and can effectively predict stroke diseases.…”
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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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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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