The Algorithm for Preparing a Set of Data for Teaching Neural Networks on the Example of the Task to Analyze the Radiological Images of Lungs by A. A. Kosareva

“…The influence of the stages (marking of images, normalization of data, determining the dynamic image range, the composition of the training sample) of the algorithm for the learning result is evaluated. …”
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A Novel PNDA-MMNet Model for Evaluating Dynamic Changes in the Brain State of Patients with PTSD During Neurofeedback Training by Peng Ding, Lei Zhao, Anmin Gong, Wenya Nan, Yunfa Fu

“…Background: Monitoring and evaluating dynamic changes in brain states during electroencephalography (EEG) neurofeedback training (NFT) for post-traumatic stress disorder (PTSD) patients remains challenging when using traditional methods. …”
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How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms by Sophie G. Zaloumis, Megha Rajasekhar, Julie A. Simpson

“…This tutorial demonstrates how to generate and interpret learning curves for malaria prediction models developed using machine learning algorithms. Methods To illustrate the approach, training dataset sizes were evaluated to inform the design of a “mock” prediction modelling study aimed to predict the artemisinin resistance status of Plasmodium falciparum malaria isolates from gene expression data. …”
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Modeling peak ground acceleration for earthquake hazard safety evaluation by Fatima Khalid, Milad Razbin

“…Following the model assessment, a genetic algorithm (GA) was integrated with the ANN model to enhance its predictive capabilities. …”
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Preliminary study on objective evaluation algorithm of human infrared thermogram seriality and its clinical application in population with metabolic syndrome by Yu Chen, Jia-Yang Guo, Yan-Hong An, Xian-Hui Zhang, Jia-Min Niu, Xiao-Ran Li, Hui-Zhong Xue, Yi-Meng Yang, Lu-Qi Cai, Yu-Chen Xia, Quan-Yi Chen, Bing-Yang Cai, Wen-Zheng Zhang, Yong-Hua Xiao

“…Objectives To develop an objective evaluation algorithm for assessing the seriality of infrared thermograms for the auxiliary diagnosis of diseases, and to internally validate the algorithm using metabolic syndrome (MS) as a case example.Methods A total of 266 healthy participants (133 of each sex) and 180 patients with MS (133 males and 47 females) were retrospectively enrolled. …”
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Mean Field Initialization of the Annealed Importance Sampling Algorithm for an Efficient Evaluation of the Partition Function Using Restricted Boltzmann Machines by Arnau Prat Pou, Enrique Romero, Jordi Martí, Ferran Mazzanti

“…Probabilistic models in physics often require the evaluation of normalized Boltzmann factors, which in turn implies the computation of the partition function <i>Z</i>. …”
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Evaluating machine learning algorithms for predicting HIV status among young Thai men who have sex with men by Krittaka Soha, Sadiporn Phuthomdee, Thanapat Srichai, Lanchakorn Kittiratanawasin, Win Min Han, Sirinya Teeraananchai

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Evaluation Modeling of Electric Bus Interior Sound Quality Based on Two Improved XGBoost Algorithms Using GS and PSO by Enlai ZHANG, Yi CHEN, Liang SU, Ruoyu ZHONGLIAN, Xianyi CHEN, Shangfeng JIANG

“…Aiming at the practical application requirements of high-precision modeling of acoustic comfort in vehicles, this paper presented two improved extreme gradient boosting (XGBoost) algorithms based on grid search (GS) method and particle swarm optimization (PSO), respectively, with objective parameters and acoustic comfort as input and output variables, and established three regression models of standard XGBoost, GS-XGBoost, and PSO-XGBoost through data training. …”
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ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction by Dong Wang, Jieyu Jin, Guqin Shi, Jingxiao Bao, Zheng Wang, Shimeng Li, Peichen Pan, Dan Li, Yu Kang, Tingjun Hou

“…In this study, we conducted an in-depth analysis of the characteristics of an augmented Caco-2 permeability dataset, and evaluated a diverse range of machine learning algorithms in combination with different molecular representations. …”
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Leveraging Artificial Intelligence in Public Health: A Comparative Evaluation of Machine-Learning Algorithms in Predicting COVID-19 Mortality by Eric B. Weiser

“…Objective: This study aimed to evaluate and compare the predictive performance of four ML algorithms – K-Nearest Neighbors (KNN), Random Forest, Extreme Gradient Boosting (XGBoost), and Decision Tree – in estimating daily new COVID-19 deaths. …”
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Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation by Julia Geppert, Peter Auguste, Asra Asgharzadeh, Hesam Ghiasvand, Mubarak Patel, Anna Brown, Surangi Jayakody, Emma Helm, Dan Todkill, Jason Madan, Chris Stinton, Daniel Gallacher, Sian Taylor-Phillips, Yen-Fu Chen

“…Nine of them allowed comparison with stand-alone AI software without human input (‘stand-alone AI’). One study evaluated readers with concurrent AI only (vs. a reference standard); five studies evaluated stand-alone AI only; and one further study compared stand-alone AI with unaided readers. …”
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Algorithmic Generation of Parameterized Geometric Models of the Aortic Valve and Left Ventricle by Nikita Pil, Alex G. Kuchumov

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Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms by Jaturong Som-ard, Savittri Ratanopad Suwanlee, Dusadee Pinasu, Surasak Keawsomsee, Kemin Kasa, Nattawut Seesanhao, Sarawut Ninsawat, Enrico Borgogno-Mondino, Filippo Sarvia

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Ecological and Statistical Evaluation of Genetic Algorithm (GARP), Maximum Entropy Method, and Logistic Regression in Predicting Spatial Distribution of Astragalus sp. by Amir Ghahremanian, Abbas Ahmadi, Hamid Toranjzar, Javad Varvani, Nourollah Abdi

“…This study aims to evaluate the potential habitat of Astragalus sp. using three different species distribution modeling methods: the maximum entropy (MaxEnt) model, the Genetic Algorithm for Rule-Set Production (GARP), and logistic regression. …”
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Performance evaluation of STATCOM placement in real-world power system through DOA-NVSP algorithm for congestion management and stability improvement by Tayo Uthman Badrudeen, Funso K. Ariyo, Nnamdi Nwulu

“…The Levenberg–Marquardt (LM) and trust-region (TR) algorithm were considered for the training of the NLS. …”
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Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera by Ashish Sharma, Taraprasad Das, Bryan Ong, Florian Mickael Savoy, Divya Parthasarathy Rao, Jun Kai Toh, Anand Sivaraman

“…Automated analysis of retinal images captured via smartphone presents a potential solution; however, to our knowledge, such an artificial intelligence (AI) system has not been evaluated. The study aimed to assess the performance of an AI algorithm in detecting referable AMD on images captured on a portable fundus camera.Design, setting A retrospective image database from the Age-Related Eye Disease Study (AREDS) and target device was used.Participants The algorithm was trained on two distinct data sets with macula-centric images: initially on 108,251 images (55% referable AMD) from AREDS and then fine-tuned on 1108 images (33% referable AMD) captured on Asian eyes using the target device. …”
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Petrophysical evaluation of clastic formations in boreholes with incomplete well log dataset by using joint inversion technique and machine learning algorithms by Felipe Santana-Román, Ambrosio Aquino López, Manuel Romero Salcedo (+), Raúl del Valle García, Oscar Campos Enriquez

“…To determine petrophysical parameters (i.e., volumes of laminar, structural and disperse shale) in clastic rocks from incomplete well log data we followed three approaches which are based on a hierarchical model, and on a joint inversion technique: 1) Available well log data (excluding the incomplete well log) are used to train machine learning algorithms to generate a predictive model; 2) the first step of the second approach machine learning algorithms are used to generate the missing data which are subsequently included a joint inversion; 3) in the third approach, machine learning process is used to estimate the missing data which are subsequently included in the prediction of the petrophysical properties. …”
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Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-po... by Kaimin Hu, Yinan Wu, Yajing Huang, Meiqi Zhou, Yanyan Wang, Xingru Huang

“…To confirm the presence of morphological discrepancies in tumor tissues of ER+ breast cancer classified as epithelial- and mesenchymal-phenotypes according to EMT-related transcriptional features, we trained deep learning algorithms based on EfficientNetV2 architecture to assign the phenotypic status for each patient utilizing hematoxylin & eosin (H&E)-stained slides from The Cancer Genome Atlas database. …”
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Development of Anthro-Fitness Model for Evaluating Firefighter Recruits’ Performance Readiness Using Machine Learning by Mohamed Borhanudin Mohd Yusof, Musa Rabiu Muazu, Nazarudin Mohamad Nizam, Abdul Majeed Anwar P. P., Raj Naresh Bhaskar, Razmaan Mohd Azraai Mohd

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Salp Navigation and Competitive based Parrot Optimizer (SNCPO) for efficient extreme learning machine training and global numerical optimization by Oluwatayomi Rereloluwa Adegboye, Afi Kekeli Feda, Ghanshyam G. Tejani, Aseel Smerat, Pankaj Kumar, Ephraim Bonah Agyekum

“…To validate the efficacy of SNCPO, rigorous experimental evaluations were conducted on CEC2015 and CEC2020 benchmark functions, four engineering design optimization problems, and Extreme Learning Machine (ELM) training tasks across 14 datasets. …”
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