METHODS OF TEXT INFORMATION CLASSIFICATION ON THE BASIS OF ARTIFICIAL NEURAL AND SEMANTIC NETWORKS by L. V. Serebryanaya, V. V. Potaraev

“…The article covers the use of perseptron, Hopfild artificial neural network and semantic network for classification of text information. Network training algorithms are studied. An algorithm of inverse mistake spreading for perceptron network and convergence algorithm for Hopfild network are implemented. …”
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On Training Of Feed Forward Neural Networks by Baghdad Science Journal

“…In this paper we describe several different training algorithms for feed forward neural networks(FFNN). …”
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Assessing Similarity Between Datasets Using Vector Representations by А. А. Usatoff, A. M. Nedzved, Guo Jiran

“…The article considers an approach to determining the similarity of datasets for training algorithms using datasets with human faces as an example. …”
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Prediction of the discharge coefficient of steeply crested inclined weirs using different neural network techniques by Adnan A. Ismael, Abdulnaser A. Ahmed, Raid Rafi Omar Al-Nima, Mohammed Khaire Hussain

“… The main objective of this work is to accurately predict in irrigation and hydraulic systems the discharge coefficient of the used sharp-crested inclined dams. Training algorithms on radial basis function RBF and multilayer perceptron MLP, and input variables such as weir height, length, inclination, and flow rates. …”
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Predicting the availability of power line communication nodes using semi-supervised learning algorithms by Kareem Moussa, Khaled Mostafa Elsayed, M. Saeed Darweesh, Abdelmoniem Elbaz, Ahmed Soltan

“…The more the machine learning models learn, the more accurate they become, as the model becomes always updated with the node’s continuous availability status, so self-training algorithms have been used. A dataset of 2000 instances of a node of a 500-node implemented PLC network has been collected. …”
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Investigating factors affecting the quality of water resources by multivariate analysis and soft computing approaches by Bing Cheng, Xinyu Liu, Keke Guo, Ahmad Rastegarnia

“…Support vector machine (SVM) with various kernel functions, multilayer perceptron artificial neural network (MLP-ANN) with various training algorithms, random forest algorithm (RFA), Gaussian process regression (GPR), and statistical analysis methods were used for modeling. …”
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Device Specifications for Neural Network Training with Analog Resistive Cross‐Point Arrays Using Tiki‐Taka Algorithms by Jinho Byun, Seungkun Kim, Doyoon Kim, Jimin Lee, Wonjae Ji, Seyoung Kim

“…Recently, specialized training algorithms for analog cross‐point array‐based neural network accelerators have been introduced to counteract device non‐idealities such as update asymmetry and cycle‐to‐cycle variation, achieving software‐level performance in neural network training. …”
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Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks by Chamaka Karunanayake, Miyuru B. Gunathilake, Upaka Rathnayake

“…Results revealed that the LM training algorithm outperforms the other tests algorithm in developing the prediction model. …”
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Stability Analysis of Neural Networks-Based System Identification by Talel Korkobi, Mohamed Djemel, Mohamed Chtourou

“…The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. …”
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The Method for Calculating the Contact Ｒesistance Used Fuzzy System Optimized by Ｒecursive Least Square by WANG Gang, WANG Zhi-jun, ZHAO Li-li, LIU Ya-pei, WANG Xu, HAO Xiang-yu

“…Using training data，fuzzy system is trained，the model of contact resistance is set up，and training algorithm is recursive least square combined tabu search. …”
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Oral Cancer Screening by Artificial Intelligence-Oriented Interpretation of Optical Coherence Tomography Images by Kousar Ramezani, Maryam Tofangchiha

“…Since the interpretation of OCT images requires professional training and OCT images contain information that cannot be inferred visually, artificial intelligence (AI) with trained algorithms has the ability to quantify visually undetectable variations, thus overcoming the barriers that have postponed the involvement of OCT in the process of screening of oral neoplastic lesions. …”
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A Revised Counter-Propagation Network Model Integrating Rough Set for Structural Damage Detection by Shao-Fei Jiang, Chun Fu, Chun-Ming Zhang, Zhao-Qi Wu

“…Firstly, rough set is used in the model to deal with a large volume of data; secondly, a revised training algorithm is developed to improve the capabilities of the CPN model; and lastly, the least input vectors are input to the revised CPN (RCPN) model, hence the rough set-based RCPN is proposed in the paper. …”
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Training hybrid neural networks with multimode optical nonlinearities using digital twins by Oguz Ilker, Suter Louis J.E., Hsieh Jih-Liang, Yildirim Mustafa, Dinc Niyazi Ulas, Moser Christophe, Psaltis Demetri

“…Training the hybrid architecture is achieved through a neural model that differentiably approximates the optical system. The training algorithm updates the neural simulator and backpropagates the error signal over this proxy to optimize layers preceding the optical one. …”
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Dataset for training neural networks in concrete crack detection: laboratory-classified beam and column imagesRepositorio Institucional – Universidad de Lima by Alexandre Almeida Del Savio, Ana Luna Torres, Daniel Cárdenas-Salas, Mónica Vergara Olivera, Gianella Urday Ibarra

“…However, obtaining high-quality datasets to train algorithms for detecting concrete cracks in structural components remains challenging, as such cracks normally develop over an extended period under real-world conditions. …”
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Optimization of Bayesian Neural Networks using hybrid PSO and fuzzy logic approach for time series forecasting by Farideh Sobhanifard

“…Providing flexible frameworks for the Neural Network training algorithm is one of the topics that has focused on many issues of the real world. …”
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Classification of mammograms: Comparing a graphical to a geometrical approach by Anirban Ghosh, Priya Ranjan, Kumar Dron Shrivastav, Rajiv Janardhanan

“…In this scenario, the large-scale deployment of computer-aided diagnosis using well-trained algorithms could significantly reduce the morbidity and mortality associated with this carcinoma. …”
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Current application of artificial intelligence in laparoscopic cholecystectomy by S.M. Chooklin, S.S. Chuklin

“…Advances in AI have made it possible to train algorithms that identify anatomy and interpret the surgical field. …”
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Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics by Priyanka Dhurve, Ayon Tarafdar, Vinkel Kumar Arora

“…A feedforward backpropagation ANN model was trained by the Levenberg–Marquardt training algorithm using a TANSIGMOID activation function with 2-10-2 topology. …”
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Rheological behavior of MWCNT-SnO2/SAE50 hybrid nanolubricant: Experimental evaluation and viscosity prediction using optimized machine learning model by Mojtaba Sepehrnia, Hamid Maleki, Alireza Hamidi, Ali Davoodi, Hamidreza Golmohammadi

“…The proposed MLPNN optimization strategy, through the optimal selection of parameters such as the number of neurons of hidden layers (HLs), transfer functions of HLs, the transfer function of the output layer, and the training algorithm, provided significant efficiency in developing single HL (R2 = 0.99979) and double HLs (R2 = 0.99996) MLPNN models.…”
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Temperature Compensation Method for MEMS Ring Gyroscope Based on PSO-TVFEMD-SE-TFPF and FTTA-LSTM by Hongqiao Huang, Wen Ye, Li Liu, Wenjing Wang, Yan Wang, Huiliang Cao

“…For compensation, the football team training algorithm (FTTA) is used to optimize the parameters of the long short-term memory (LSTM) neural network, forming a novel FTTA-LSTM architecture. …”
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