K-Nearest Neighbors for Anomaly Detection and Predictive Maintenance in Water Pumping Systems by João Pablo Santos da Silva, André Laurindo Maitelli

“…The input parameters used for a model are the sensor parameters, and the model analyzes the correlation between the input parameters (sensors) and determines which parameters are the most important, deciding on the output of the model, and thereby building the simplest model, which requires the least input parameters and gives the most accurate prediction results. In this project, using on the input signal from the sensors, the k-nearest neighbors machine learning algorithm was used to correlate/predict whether the pump was shut down (broken) for a certain period of time.…”
Get full text

Multi-objective Predictive Control of Gas Turbine System Based on T-S Fuzzy Model by Guolian HOU, Xiaoyan DAI, Linjuan GONG, Haixin XU, Jianhua ZHANG

“…In this paper, a T-S fuzzy model-based multi-objective predictive controller is proposed to enhance both the tracking performance and economic performance together. …”
Get full text

Disturbance Rejection and Uncertainty Analysis in Wind Turbines Using Model Predictive Control by Alok Kumar, Atul Kelkar

“…In our work, we propose a Model Predictive Control (MPC) framework with quadratic cost functions, incorporating control input and state constraints to mitigate the challenge of disturbance rejection and uncertainty analysis for the wind turbine operation. …”
Get full text

Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures by Min-Ying Li, Kang-Di Lu, Yu-Xing Dai, Guo-Qiang Zeng

“…The comprehensive simulation results demonstrate the performance of the proposed control method by comparing with a recently developed predictive functional control, genetic algorithm, and particle swarm optimization-based versions in terms of four performance indices.…”
Get full text

Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side by Ali Sharida, Abdullah Berkay Bayindir, Sertac Bayhan, Haitham Abu-Rub

“…Then, an adaptive estimation strategy employing a recursive least square algorithm is proposed for online dynamic model estimation, which is then used by the IMPC for optimal switching states prediction. …”
Get full text

Beyond purchase patterns: harnessing predictive analytics to anticipate unarticulated consumer needs by Khaled Alshaketheep, Hind Al-Ahmed, Ahmad Mansour

“…The results underscore the importance of integrating human-centered design with algorithmic precision to translate predictive insights into actionable business value.…”
Get full text

Optimizing public health management with predictive analytics: leveraging the power of random forest by Hongman Wang, Yifan Song, Yifan Song, Hua Bi

“…This study employs a Random Forest Algorithm (RFA) to address this limitation and enhance the predictive modeling of community health outcomes. …”
Get full text

Traffic flow prediction based on improved deep extreme learning machine by Xiujuan Tian, Shuaihu Wu, Xue Xing, Huanying Liu, Heyao Gao, Chun Chen

“…Abstract A new hybrid prediction model is proposed for short-term traffic flow, which is based on Deep Extreme Learning Machine improved by Sparrow Search Algorithm (SSA-DELM). …”
Get full text

Predictive density profile control with discrete pellets, applied to integrated simulations of ITER by C.A. Orrico, M. van Berkel, T.O.S.J. Bosman, L. Ceelen, W.P.M.H. Heemels, F. Koechl, D. Krishnamoorthy

“…The model predictive control (MPC) scheme we deploy combines the offset-free method to correct prediction model inaccuracies and our novel modified penalty term homotopy algorithm for real-time MPC (PTH-MPC). …”
Get full text

Sialyltransferase-related genes as predictive factors for therapeutic response and prognosis in cervical cancer by Jia Shao, Can Zhang, Yaonan Tang, Aiqin He, Xiangyan Cheng

“…“MCPcounter” R package, ESTIMATE algorithm and TIMER online tools were used to depict the tumor immune microenvironment in CC. …”
Get full text

Predicting students’ academic procrastination tendencies using online learning trajectories by Gisu Sanem Öztaş, Gökhan Akçapınar

“…The study compared the performance of different machine learning algorithms in predicting students’ academic procrastination tendencies, analysed the important features of prediction models, and examined whether there is a difference between the academic performance of low and high academic procrastinators. …”
Get full text

Machine-learning synergy in high-entropy alloys: A review by Sally Elkatatny, Walaa Abd-Elaziem, Tamer A. Sebaey, Moustafa A. Darwish, Atef Hamada

Get full text

A Comparative Study on DG Placement Using Marine Predator and Osprey Algorithms to Enhance Loss Reduction Index in the Distribution System by Syazwan Ahmad Sabri, Siti Rafidah Abdul Rahim, Azralmukmin Azmi, Syahrul Ashikin Azmi, Muhamad Hatta Hussain, Ismail Musirin

Subjects: Get full text

Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction by Yanli Xu, Yalan Shi

“…Therefore, this paper proposes a communication coverage model based on mobile user route prediction and a UAV dynamic deployment algorithm (RUDD). …”
Get full text

Rolling Force Prediction of Hot Rolling Based on GA-MELM by Jingyi Liu, Xinxin Liu, Ba Tuan Le

“…In this paper, a rolling force prediction method based on genetic algorithm (GA), particle swarm optimization algorithm (PSO), and multiple hidden layer extreme learning machine (MELM) is proposed, namely, PSO-GA-MELM algorithm, which takes MELM as the basic model for rolling force prediction. …”
Get full text

Load prediction based elastic resource scheduling strategy in Flink by Ziyang LI, Jiong YU, Yuefei WANG, Chen BIAN, Yonglin PU, Yitian ZHANG, Yu LIU

“…In order to solve the problem that the load of big data stream computing platform fluctuates drastically while the cluster was suffering from the performance bottleneck due to the shortage of computing resources,the load prediction based elastic resource scheduling strategy in Flink (LPERS-Flink) was proposed.Firstly,the load prediction model was set up as the foundation to propose the load prediction algorithm and predict the variation tendency of the processing load.Secondly,the resource judgment model was set up to identify the performance bottleneck and resource redundancy of the cluster while the resource scheduling algorithm was proposed to draw up the resource rescheduling plan.Finally,the online load migration algorithm was proposed to execute the resource rescheduling plan and migrate processing load among nodes efficiently.The experimental results show that the strategy provides better performance promotion in the application with drastically fluctuating processing load.The scale and resource configuration of the cluster responded to the variation of processing load in time and the communication overhead of the load migration was reduced effectively.…”
Get full text

Protein homodimers structure prediction based on deep neural network by A. Y. Hadarovich, A. A. Kalinouski, A. V. Tuzikov

“…Recently Deep Neural Networks allowed to predict structures of individual proteins greatly prevailing in accuracy over other algorithmic approaches. …”
Get full text

Road Accident Prediction Model for the Roads of National Significance of Lithuania by Vilma Jasiūnienė, Donatas Čygas

“…Chapter 4 describes the testing and analysis of software intended for the implementation of accident prediction algorithm.…”
Get full text

Construction and evaluation of yeast expression networks by database-guided predictions by Katharina Papsdorf, Siyuan Sima, Gerhard Richter, Klaus Richter

“…We then show the ability of our networks to accurately predict further differentially expressed genes. Including these predicted genes into the networks improves the network quality and allows quantifying the predictive strength of the networks based on a newly implemented scoring method. …”
Get full text

Research on COP Prediction Model of Chiller Based on PSO-SVR by Zhou Xuan, Cai Panpan, Lian Sizhen, Yan Junwei

“…The results shows that the prediction accuracy of SVR model based on PSO optimization algorithm is higher than that of BP neural network and the relative error is within 3%. …”
Get full text