The Application of Near-Infrared Spectroscopy Combined with Chemometrics in the Determination of the Nutrient Composition in Chinese <i>Cyperus esculentus</i> L. by Xiaobo Jiao, Dongliang Guo, Xinjun Zhang, Yunpeng Su, Rong Ma, Lewen Chen, Kun Tian, Jingyu Su, Tangnuer Sahati, Xiahenazi Aierkenjiang, Jingjing Xia, Liqiong Xie

“…In addition, to further enhance the prediction performance of the models, various preprocessing and variable selection algorithms were utilized to optimize the prediction models. …”
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Generalized PCA Method and Its Application in Uncertainty Reasoning by Bin Wu, Xiao Yi, Dong Ning Zhao

“…The traditional PCA algorithm is a special case of the generalized PCA algorithm proposed in this paper. …”
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Transformer network for time series prediction via wavelet packet decomposition by Zhichao Wu, Aiye Shi, Yan Ping Tao

“…Although, conventional time series processing methods—such as multi-scale feature extraction or Transformer-based algorithms—produce superior prediction results, when dealing with data that contain morenoise and outliers, the prediction ability of such methods can suffer. …”
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Data-Driven Identification of Gas Turbine Engine Dynamics via Koopman Operator Genetic Algorithm by David Grasev

“…The prediction error was given by a numerical integration of lifted state-space equations as it reflects the usage of the model for real-time prediction in practice. …”
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Modelling the effect of vaccination on the spread of COVID-19 via a novel evolutionary ensemble learning algorithm by Mohammad Hassan Tayarani Najaran

“…This paper proposes a modelling algorithm that takes as input the percentage of the vaccinated population and the policies taken by governments and generates as output a prediction of the number of newly infected cases. …”
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Assessment of Machine Learning Methods for Concrete Compressive Strength Prediction by Oluwafemi Omotayo, Chinwuba Arum, Catherine Ikumapayi

“…The study considered a wide range of literature data and examined the efficiency of boosted algorithms in predicting the strength of ordinary Portland cement concrete. …”
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Analysis and Optimization of the Vibro-acoustic Coupled of Crane Reducer by Dai Jun

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An Analytic Policy Gradient-Based Deep Reinforcement Learning Motion Cueing Algorithm for Driving Simulators by Xiaowei Huang, Xuhua Shi, Peiyao Wang, Hongzan Xu, Xiaojun Tang, Gaoran Zhang

“…At higher prediction horizons, the algorithm achieves performance on par with state-of-the-art MPC-based motion cueing algorithms while exhibiting reduced algorithmic delay. …”
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Machine learning in predicting firm performance: a systematic review by Yaseen Hezam, Hoa Luong, Lilian Anthonysamy

“…It aims to assess the effectiveness of various ML methods and algorithms used in recent research, focusing on the prediction of firm performance across multiple dimensions. …”
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Hybrid Models for Forecasting Allocative Localization Error in Wireless Sensor Networks by Guo Li, Hongyu Sheng

“…The approach utilizes Radial Basis Function (RBF) models enhanced with advanced optimization algorithms, including Coot Optimization Algorithm (COA), Smell Agent Optimization (SAO), and Northern Goshawk Optimization (NGO) to improve ALE prediction accuracy. …”
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From multi-omics to predictive biomarker: AI in tumor microenvironment by Luo Hai, Luo Hai, Ziming Jiang, Haoxuan Zhang, Yingli Sun, Yingli Sun, Yingli Sun

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A network-based approach for predicting missing pathway interactions. by Saket Navlakha, Anthony Gitter, Ziv Bar-Joseph

“…Unlike existing algorithms for predicting general protein interactions, by focusing on proteins involved in specific responses our approach homes-in on pathway-consistent interactions. …”
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Evaluation Method of Total Organic Carbon Content in Shale Based on Stacking Algorithm Ensemble Learning by SONG Yanjie, LIU Yingjie, TANG Xiaomin, ZHANG Zhaoqian

“…The results show that the ensemble learning model based on Stacking algorithm has the highest prediction accuracy for total organic carbon content compared with decision tree model, support vector regression machine model, BP neural network model, and improved ΔlgR method. …”
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Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors by Xinmin Tang, Wenjie Zhao, Shangfeng Gao

“…Simulation results show that the WIMM algorithm can be used efficiently together with an observed small sample of velocity information for target tracking and trajectory prediction. …”
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Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithm by FAN Meng, TONG Bo, GAO Chen, YAO Zhongyuan, ZHANG Yu, HU Bo

“…The results show that the average prediction accuracy of IWOA-BP neural network is above 80%, and the prediction accuracy of depth, length and width is improved respectively by 106.72%, 9.68% and 6.86%.…”
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Early gestational diabetes mellitus risk predictor using neural network with NearMiss by Min Zhao, Xiaojie Su, Lihong Huang

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Drug discovery and mechanism prediction with explainable graph neural networks by Conghao Wang, Gaurav Asok Kumar, Jagath C. Rajapakse

“…Abstract Apprehension of drug action mechanism is paramount for drug response prediction and precision medicine. The unprecedented development of machine learning and deep learning algorithms has expedited the drug response prediction research. …”
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Artificial Flora Algorithm-Based Feature Selection With Support Vector Machine for Cardiovascular Disease Classification by M. M. Asha, G. Ramya

“…This approach utilizes the Cleveland dataset by combining the Artificial Flora Optimization algorithm with the Support Vector Machine. The proposed algorithm functions as a meticulous gardener, selectively identifying the most significant features for heart disease prediction through an objective function. …”
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PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS by Raghda Azad Hasan, Ibrahim Ahmed Saleh

“…The model applies the basic algorithms (Random Forest (RF), Decision Tree (DT), Extra Tree) and the learning model ensemble (Adaboost, xgboost ,Stack, Voting, bagging) and metrics (accuracy, recall, F1 score, accuracy) to measure the prediction performance of the models and a comparison was made between the proposed model algorithms. …”
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Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food by Zhenlong Wang, Wei An, Jiaxue Wang, Hui Tao, Xiumin Wang, Bing Han, Jinquan Wang

“…This study aims to develop a rapid and cost-effective method using an electronic nose (E-nose) and machine learning algorithms to predict whether ZEN levels in pet food exceed the regulatory limits (250 µg/kg), as set by Chinese pet food legislation. …”
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