<strong>Hybrid neural network with genetic algorithms for predicting distribution pattern of <em>Tetranychus urticae</em> (Acari: Tetranychidae) in cucumbers field of Ramhormoz, Ir... by Alireza Shabaninejad, Bahram Tafaghodinia, Nooshin Zandi Sohani

“…Results showed that in training and test phases of neural network combined genetic algorithm, there was no significant difference between variance and statistical distribution of actual values and predicted values, but distribution was no significant. …”
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Integration of intratumoral and peritumoral CT radiomic features with machine learning algorithms for predicting induction therapy response in locally advanced non-small cell lung... by FangHao Cai, Zhengjun Guo, GuoYu Wang, FuPing Luo, Yang Yang, Min Lv, JiMin He, ZhiGang Xiu, Dan Tang, XiaoHui Bao, XiaoYue Zhang, ZhenZhou Yang, Zhi Chen

“…Three machine learning algorithms—Support Vector Machine (SVM), XGBoost, and Gradient Boosting—were employed to construct radiomic models for each region. …”
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Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm by Raji Krishna, Hemamalini S

“…In the first phase of this paper, uncertainty parameters like day‐ahead power from renewable energy sources (RES) and load demand (LD) are forecasted using the long short‐term memory (LSTM) deep learning algorithm. The LSTM outperforms the artificial neural network (ANN) model in terms of mean square error (MSE) and prediction accuracy (R2) for both training and testing datasets. …”
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Usability of machine learning algorithms based on electronic health records for the prediction of acute kidney injury and transition to acute kidney disease: A proof of concept stu... by Lorenzo Ruinelli, Pietro Cippà, Chantal Sieber, Clelia Di Serio, Paolo Ferrari, Antonio Bellasi

“…The database was divided into training and validation sets. Machine Learning (ML) algorithms were developed with 10-fold cross-validation, and diagnostic accuracy was evaluated.…”
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Usage of the optimal fuzzy system models on chloride diffusion of concrete by Shuai Sun

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MALDI-TOF mass spectrometry combined with machine learning algorithms to identify protein profiles related to malaria infection in human sera from Côte d’Ivoire by Fateneba Kone, Lucie Conrad, Jean T. Coulibaly, Kigbafori D. Silué, Sören L. Becker, Brama Kone, Issa Sy

“…MALDI-TOF MS analysis was carried out by generating protein spectra profiles from 131 Plasmodium-positive and 94 Plasmodium-negative sera as the training set. Machine learning (ML) algorithms were employed for distinguishing P. falciparum-positive from P. falciparum-negative samples. …”
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Prediction of peripheral lymph node metastasis (LNM) in thyroid cancer using delta radiomics derived from enhanced CT combined with multiple machine learning algorithms by Wenzhi Wang, Feng Jin, Lina Song, Jinfang Yang, Yingjian Ye, Junjie Liu, Lei Xu, Peng An

“…During model training, cross-validation was used to evaluate model performance, and the optimal model was selected. …”
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Support vector machine based fast Monte Carlo reliability evaluation method for composite power system by Yuxiao LEI, Gengfeng LI, Yuxiong HUANG, Zhaohong BIE

“…A fast Monte Carlo reliability evaluation method for composite power system based on support vector machine (SVM) was proposesd.Firstly,sample data for training the SVM model was obtained by enumerating component failures and calculating the corresponding minimum load shedding.Then,the SVM algorithm was used to mine the nonlinear mapping relationship between component failures and minimum load shedding,and the minimum load shedding estimation model was trained.Finally,the model was combined with the Monte Carlo simulation.By randomly sampling component states,for each state,the estimation model obtained by the training directly gave the minimum load shedding,thereby achieving a rapid assessment of the reliability of the composite power system.The proposed method is applied to the IEEE RTS 79 system,which verifies its effectiveness.…”
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Revolutionizing Nursing and Midwifery Informatics Curriculum Evaluation in Ghana: A Data-Driven Machine Learning Approach by Iven Aabaah, Japheth Kodua Wiredu, Bakaweri Emmanuel Batowise, Nelson Abuba Seidu

“…The study employed Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression algorithms, evaluated using standard performance metrics, including accuracy, precision, and recall. …”
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Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam by Tuan Vu Dinh, Nhat-Duc Hoang, Xuan-Linh Tran

“…Classification accuracy rate (CAR) and area under receiver operating characteristic (AUC) were used to evaluate performance of the five models. Significant difference between different algorithms was verified by the Wilcoxon test. …”
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Evaluating end-to-end autonomous driving architectures: a proximal policy optimization approach in simulated environments by Ângelo Morgado, Kaoru Ota, Mianxiong Dong, Nuno Pombo

“…Through a two-phase training regimen, the study evaluates the efficacy of PPO in an end-to-end ADS focused on basic tasks like lane-keeping and waypoint navigation. …”
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Underwater Acoustic Communication Using Multi-Layer Vector Approximate Message Passing Evaluated on the Watermark Benchmark by Julian Winkler, Sabah Badri-Hoeher

“…A pre-trained artificial neural network is used as prior for the channel estimation. …”
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Development of a Clinically Applicable Deep Learning System Based on Sparse Training Data to Accurately Detect Acute Intracranial Hemorrhage from Non-enhanced Head Computed Tomogra... by Huan-Chih WANG, Shao-Chung WANG, Furen XIAO, Ue-Cheung HO, Chiao-Hua LEE, Jiun-Lin YAN, Ya-Fang CHEN, Li-Wei KO

“…This study aimed to develop a deep learning algorithm, referred to as DeepCT, to detect acute intracranial hemorrhage on non-enhanced head computed tomography images and evaluate its clinical applicability. …”
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Comparison of SVM, K-NN, RF, CART, and GNB Algorithms for Water Bodies Detection Using Sentinel-2 Level-2a Imagery in Nakhon Pathom, Thailand by Ni Putu Nita Nathalia, Gede Andra Rizqy Wijaya, Kadek Yota Ernanda Aryanto, Ni Putu Novita Puspa Dewi, Putu Hendra Saputra, Ni Putu Karisma Dewi, Mellisa Damayanti, Kadek Losinanda Prawira

“…The results of these calculations serve as dataset features for training algorithms in the model development and training process. …”
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Investigating Feed-Forward Back-Propagation Neural Network with Different Hyperparameters for Inverse Kinematics of a 2-DoF Robotic Manipulator: A Comparative Study by Rania Bouzid, Jyotindra Narayan, Hassène Gritli

“…Three different training optimizers are considered, namely the Levenberg-Marquardt (LM) algorithm, the Bayesian Regularization (BR) algorithm, and the Scaled Conjugate Gradient (SCG) algorithm. …”
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Planning and Evaluation of Water-Dropping Strategy for Fixed-Wing Fire Extinguisher Based on Multi-Resolution Modeling by Xiyu Wang, Yuanbo Xue, Yongliang Tian, Hu Liu, Zhiyong Cai

“…The formulation of the planning algorithm and the structure of the effectiveness evaluation index system are explained accordingly. …”
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Operation standardization evaluation method based on improved YOLOv8n for ship equipment disassembly and assembly by Zhendong ZHANG, Cong GUAN, Zehui ZHANG, Chao WU, Xuewen DING

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Evaluation of inflammatory serum parameters as a diagnostic tool in patients with endometriosis: a case-control study by Mariz Kasoha, Panagiotis Sklavounos, Istvan Molnar, Meletios P. Nigdelis, Bashar Haj Hamoud, Erich-Franz Solomayer, Gilbert Georg Klamminger

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A Novel Hybrid Deep Learning Framework for Evaluating Field Evapotranspiration Considering the Impact of Soil Salinity by Yao Rong, Weishu Wang, Peijin Wu, Pu Wang, Chenglong Zhang, Chaozi Wang, Zailin Huo

“…Specifically, we integrated physical constraints from process models (Penman‐Monteith or Shuttleworth‐Wallace) and salinity‐induced stomatal stress mechanisms into the DL algorithm, and evaluated its performance by comparing four diverse scenarios. …”
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Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India by Srikanth Bhoopathi, Nitish Kumar, Somesh, Manali Pal

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