A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device by Debajyoti Pal, Vajirasak Vanijja

“…We also use an Artificial Neural Network approach for building the model and compare its performance with the regressive approach.…”
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Improving Electron Density Predictions in the Topside of the Ionosphere Using Machine Learning on In Situ Satellite Data by S. Dutta, M. B. Cohen

“…This research focuses on predicting the electron density in the topside of the ionosphere using satellite data, in particular from the Defense Meteorological Satellite Program, a collection of 19 satellites that have been polar orbiting the Earth for various lengths of times, fully covering 1982 to the present. An artificial neural network was developed and trained on two solar cycles worth of data (113 satellite‐years), along with global drivers and indices such as F10.7, interplanetary magnetic field, and Kp to generate an electron density prediction. …”
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Optimization of dried garlic physicochemical properties using a self-organizing map and the development of an artificial intelligence prediction model by Hany S. El-Mesery, Mohamed Qenawy, Mona Ali, Merit Rostom, Ahmed Elbeltagi, Ali Salem, Abdallah Elshawadfy Elwakeel

“…The relationships between the input process factors and response factors’ physicochemical properties of dried garlic were optimized by a self-organizing map (SOM), and the model was developed using machine learning. Artificial Neural Network (ANN) with 99% predicting accuracy and Self-Organizing Maps (SOM) with 97% clustering accuracy were used to determine the quality characteristics of garlic. …”
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Advanced neural network modeling with Levenberg–Marquardt algorithm for optimizing tri-hybrid nanofluid dynamics in solar HVAC systems by A. Aziz, S.A.H. Shah, H.M.S. Bahaidarah, T. Zamir, T. Aziz

“…To address this, we propose a nanofluid-based thermal cooling model and develop an advanced computational solver using an Artificial Neural Network (ANN) trained with the Levenberg–Marquardt algorithm (LMA-TNN). …”
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Distribution network fault comprehensive identification method based on voltage–ampere curves and deep ensemble learning by Jian Wang, Bo Zhang, Dong Yin, Jinxin Ouyang

“…Moreover, the proposed method has significant advantages over the impedance method and artificial neural network method for fault section identification and fault distance estimation. …”
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Design, Sensing and Control of a Robotic Prosthetic Eye for Natural Eye Movement by J. J. Gu, M. Meng, A. Cook, P. X. Liu

“…Theoretical issues on sensor failure detection and recovery, and signal processing techniques used in sensor data fusion, are studied using statistical methods and artificial neural network based techniques. In addition, practical control system design and implementation using micro-controllers are studied and implemented to carry out the natural eye movement detection and artificial robotic eye control tasks. …”
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Design and Optimization of Iron Cow Stem with Flaps by Finite Element Method and Genetic Algorithm by Khaled Kamal Oude, Ali Adelkhani

“…Then, using an artificial neural network, a model was presented to estimate the von Mises tension based on the information related to the cross-sectional area and stem curvature, and this model was able to estimate the maximum von Mises tension with an accuracy of 99%. …”
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Enhancing heat exchanger design using autoencoder model for predicting efficiency and cost in chemical processing by Manimegalai T, Anitha Gopalan, Vanmathi Murugesan, Jayant Giri, Praveen Barmavatu, Praveenkumar T R, Dinesh Mavaluru, Rafath Samrin

“…The autoencoder model, a type of artificial neural network, is trained on a comprehensive dataset encompassing various operating conditions, geometric configurations, and material properties of heat exchangers. …”
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Review on operation control of cold thermal energy storage in cooling systems by Huan Wang, Baoshan Xie, Chuanchang Li

“…Two types of cold load predictions, parametric regression and artificial neural network method, are introduced. Three aspects of economic costs are summarized in terms of initial equipment investment cost, operational cost, and life-cycle cost are summarized. …”
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Study of Microstructure and Wear Resistance of AA5052/B4C Nanocomposites as a Function of Volume Fraction Reinforcement to Particle Size Ratio by ANN by D. Dinesh Kumar, A. Balamurugan, K. C. Suresh, R. Suresh Kumar, N. Jayanthi, T. Ramakrishnan, S. K. Hasane Ahammad, S. Mayakannan, S. Venkatesa Prabhu

“…This research examines a model function developed from an artificial neural network (ANN). AA5052/B4C composites bent using a powder metallurgy technique to hardness and ball-on-disc wear testing. …”
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Monitoring and Simulation of Dynamic Spatiotemporal Land Use/Cover Changes by Andong Guo, Yuqing Zhang, Qing Hao

“…In this study, we preprocessed multiperiod land use and socioeconomic data, combined with spatial zoning, multilayer perception (MLP) artificial neural network, and Markov chain (MC), to construct a cellular automaton model of spatial zoning. …”
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A Novel Mathematical Approach to Define the Genes/SNPs Conferring Risk or Protection in Sporadic Amyotrophic Lateral Sclerosis Based on Auto Contractive Map Neural Networks and Gra... by Massimo Buscema, Silvana Penco, Enzo Grossi

“…The Auto Contractive Map (AutoCM), belonging to the Artificial Neural Network (ANN) architecture, “spatializes” the correlation among variables by constructing a suitable embedding space where a visually transparent and cognitively natural notion such as “closeness” among variables reflects accurately their associations. …”
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Optimization of Mix Proportions for Novel Dry Stack Interlocking Concrete Blocks Using ANN by Krishna Prakash A, Jane Helena H, Paul Oluwaseun Awoyera

“…This paper proposes novel concrete interlocking blocks made of fly ash and GGBS which are an alternative for the conventional concrete blocks. The artificial neural network (ANN) technique is used to estimate the mechanical strength of interlocking blocks and is verified with experimental investigation. …”
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Children Tracking System Based on ZigBee Wireless Network and Neural Network by Nadia Ahmed, Sadik Kamel Gharghan, Ammar Hussein Mutlag, M. G. M. Abdolrasol

“…This paper proposes an indoor localization system based on ZigBee wireless sensor network (WSN) and Backpropagation Artificial Neural Network (BP-ANN) to locate the child in an indoor environment. …”
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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

“…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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A Pervasive Approach to EEG-Based Depression Detection by Hanshu Cai, Jiashuo Han, Yunfei Chen, Xiaocong Sha, Ziyang Wang, Bin Hu, Jing Yang, Lei Feng, Zhijie Ding, Yiqiang Chen, Jürg Gutknecht

“…Four classification methods (Support Vector Machine, K-Nearest Neighbor, Classification Trees, and Artificial Neural Network) distinguished the depressed participants from normal controls. …”
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A Comparison Study on Rule Extraction from Neural Network Ensembles, Boosted Shallow Trees, and SVMs by Guido Bologna, Yoichi Hayashi

“…One way to make the knowledge stored in an artificial neural network more intelligible is to extract symbolic rules. …”
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Machine learning to identify environmental drivers of phytoplankton blooms in the Southern Baltic Sea by Maximilian Berthold, Pascal Nieters, Rahel Vortmeyer-Kley

“…We employed generalized additive mixed models to characterize similar blooming patterns and trained an artificial neural network within the Universal Differential Equation framework to learn a differential equation representation of these pattern. …”
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The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models by Marcin Wrona, Andrej Prša

“…To address this, we present a fully connected feedforward artificial neural network (ANN) trained on a data set of over one million synthetic light curves generated with PHOEBE. …”
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A Machine Learning-Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel by Ngoc-Long Tran, Trong-Ha Nguyen, Van-Tien Phan, Duy-Duan Nguyen

“…The purpose of this study is to develop a practical artificial neural network (ANN) model for predicting the atmospheric corrosion rate of carbon steel. …”
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