Academic progress monitoring through neural network by Ramri Shukla, Bardia Khalilian, Sara Partouvi

“…Students are classified using artificial neural networks and random forests in this article. …”
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The classification of concentration of mixture of analytes using total principal component regression by Romas Baronas, Feliksas Ivanauskas, Robertas Paulauskas, Pranas Vaitkus

“…The results are compared with the results obtained using artificial neural networks. …”
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Reforming Real Estate Valuation for Financial Auditors With AI: An In-Depth Exploration of Current Methods and Future Directions by Silviu-Ionut BABTAN

“…This article examines several AI methods – Regression Models, Decision Trees, Random Forests, Artificial Neural Networks, and XGBoost – and explores their applications for improving property valuation accuracy and efficiency, with implications for other professions involved, e.g. audit. …”
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The application of expert system to exploration of the robot motion by Olegas Ramašauskas, Artūras Bielskis

“…The coherency using XPS principles with artificial neural networking methods also discussed. The software implementation in Visual LISP environment examples and knowledge base files are presented. …”
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Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning by Dongbao Jia, Weixiang Xu, Dengzhi Liu, Zhongxun Xu, Zhaoman Zhong, Xinxin Ban

“…Artificial neural networks have achieved a great success in simulating the information processing mechanism and process of neuron supervised learning, such as classification. …”
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Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems by Zachary Kral, Walter Horn, James Steck

“…A novel method of implementing artificial neural networks and acoustic emission sensors to form a structural health monitoring (SHM) system for aerospace inspection routines was the focus of this research. …”
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Forecasting Models for Time and Cost Performance Predicting of Infrastructural Projects by Aldhamad Saja Hadi Raheem, Maya Rana, Alazawy Suha Falih Mahdi, Alzwainy Faiq M.

“…The efficacy of residential property investment projects will be assessed through the implementation of models that incorporate Artificial Neural Networks and Multiple Linear Regression. Historical information of thirteen boundaries for twenty finished Private Property Venture Tasks were separated from the records of the Directorate of Lodging, then four models were created by utilizing Multiple Linear Regression strategy and Artificial Neural Networks method. …”
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Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks by Shougi S. Abosuliman, Saleem Abdullah, Nawab Ali

“…Abstract Artificial neural networks, a major element of machine learning, focus additional attention on the decision-making process. …”
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Neural Networks in Mechanical System Simulation, Identification, and Assessment by Thomas L. Paez

“…Artificial neural networks (ANNs) have been used in the solution of a variety of mechanical system design, analysis, and control problems. …”
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Identification of discrete chaotic maps with singular points by P. G. Akishin, P. Akritas, I. Antoniou, V. V. Ivanov

“…We investigate the ability of artificial neural networks to reconstruct discrete chaotic maps with singular points. …”
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The Control Data Method: A New Method of Modeling in Population Dynamics by Lin-Fei Nie, Zhi-Dong Teng

“…Using a the approximation property and the machine learning approach of artificial neural networks, a tuning algorithm of unknown parameters is obtained and the factual data of predator-prey can be asymptotically stabilized using a neural network controller. …”
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Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics by F. Cadini, E. Zio, N. Pedroni

“…Artificial neural networks are powerful algorithms for constructing nonlinear empirical models from operational data. …”
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Efficient Prediction of Network Traffic for Real-Time Applications by Muhammad Faisal Iqbal, Muhammad Zahid, Durdana Habib, Lizy Kurian John

“…Many predictors from three different classes, including classic time series, artificial neural networks, and wavelet transform-based predictors, are compared. …”
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Use of Arduino for Monitoring the Air Quality of Indoor Environments by Felipe Macedo Freitas Siqueira, Lizandro de Sousa Santos

“…Another approach, based on artificial neural networks, was formulated in which the parameters were used as input arguments of the network and the TCI was used as a target parameter. …”
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Consilience of Reductionism and Complexity Theory in Language Research: Adaptive Weight Model by Chao Zhang

“…This paper starts by discussing the adaptability of complex dynamic systems and combines cognitive processing model and artificial neural networks to construct and verify an adaptive weight model, showing that the study of reductionism is induction of high-weight elements and the study of complexity theory is a discussion of system complexity from adaptability, meaning that there is a good fit between the two frameworks. …”
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Spatial analysis of hyperspectral images for detecting adulteration levels in bon-sorkh (Allium jesdianum L.) seeds: Application of voting classifiers by Golshid Fathi, Seyed Ahmad Mireei, Mehrnoosh Jafari, Morteza Sadeghi, Hassan Karimmojeni, Majid Nazeri

“…After image preprocessing using median blur and bilateral filters, pixel-wise classification models were developed using artificial neural networks, random forest, and voting classifiers to detect pure bon-sorkh and shallot seeds. …”
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Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment by Stefano Curcio, Alessandra Saraceno, Vincenza Calabrò, Gabriele Iorio

“…The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. …”
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A novel prediction model of grounding resistance based on long short-term memory by Xinghai Pu, Jing Zhang, Fei Wang, Shuai Xue

“…Furthermore, the study benchmarks the LSTM model’s performance against traditional Artificial Neural Networks, confirming the LSTM’s superior predictive accuracy regarding time-dependent changes in grounding resistance. …”
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Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles by Yu Sun, Shuhuai Qin, Yingli Li, Naimul Hasan, Yan Vivian Li, Jiangguo Liu

“…Experimental data collected from about 50 papers are analyzed by machine learning algorithms including linear regression, principal component analysis, Gaussian process regression, and artificial neural networks. The focus is to understand the effect of drug solubility, drug molecular weight, particle size, and pH-value of the release matrix/environment on drug release profiles. …”
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Detection and diagnosis of fault bearing using wavelet packet transform and neural network by Djaballah Said, Meftah Kamel, Khelil Khaled, Tedjini Mohsein, Sedira Lakhdar

“…This work is part of the diagnosis and classification of bearing defects by vibration analysis of signals from defective bearings using time domain and frequency analysis and wavelet packet transformations (Wavelet Packet Transform WPT) with Artificial Neural Networks (ANN). WPT is used for extracting defect indicators to train the neural classifier. …”
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