A scientometric review of the relationship between learning agility and work engagement in modern management context by Farira Nareswari, Rini Juni Astuti

“…Machine learning, artificial neural networks, and predictive analytics can improve learning agility and work engagement. …”
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Three-dimensional design, simulation and optimization of a centrifugal compressor impeller with double-splitter blades by Mohammadjavad Tasharrofi, Mojtaba Heidarian Shahri, Ali Madadi

“…Since the optimization process only using genetic algorithms is very time-consuming and has high computational costs, artificial neural networks were used to reduce costs. The objective function in this optimization process was to increase efficiency while maintaining the flow rate and pressure ratio at the design point. …”
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Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction by Yuehjen E. Shao, Jun-Ting Dai

“…Other than the ARIMA, the components of the proposed integrated forecasting models include artificial neural networks (ANNs), support vector regression (SVR), and multivariate adaptive regression splines (MARS). …”
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Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks by Wilson Castillo-Rojas, Juan Bekios-Calfa, César Hernández

“…For the implementation of these models, a hybrid architecture based on recurrent neural networks (RNN) with long short-term memory (LSTM) or gated recurrent units (GRU) structure, combined with shallow artificial neural networks (ANN) with multilayer perceptron (MLP) structure, is established. …”
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Automated CATS system for distance learning by Yu. B. Popova

“…As mathematical methods, it is proposed to use the analysis of expert systems, as well as artificial neural networks. These mathematical methods made it possible to develop adaptability algorithms, their software implementation and testing in the educational process. …”
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An Overview of Pavement Degradation Prediction Models by Amir Shtayat, Sara Moridpour, Berthold Best, Shahriar Rumi

“…The findings show that most previous studies preferred machine learning approaches and artificial neural networks forecasting and estimating the road pavement conditions because of their ability to deal with massive data, their higher accuracy, and them being worthwhile in solving time-series problems.…”
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Automatic Adaptive Algorithm for Delineation of Cerebral-Spinal Fluid Regions for Non-contrast Magnetic Resonance Imaging Volumetry and Cisternography in Mice by Ryszard Gomolka

“…Despite the increasing use of artificial neural networks in image analysis, this analytical approach provides robustness, especially when the dataset is insufficiently small and limited for training the network. …”
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A Step Towards Neuroplasticity: Capsule Networks with Self-Building Skip Connections by Nikolai A. K. Steur, Friedhelm Schwenker

“…<b>Background:</b> Integrating nonlinear behavior into the architecture of artificial neural networks is regarded as essential requirement to constitute their effectual learning capacity for solving complex tasks. …”
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Enhancing Fault Detection and Classification in MMC-HVDC Systems: Integrating Harris Hawks Optimization Algorithm with Machine Learning Methods by Omar Hazim Hameed Hameed, Uğurhan Kutbay, Javad Rahebi, Fırat Hardalaç, Ibrahim Mahariq

“…Leveraging machine learning (ML) and artificial neural networks (ANN), this technique demonstrates its effectiveness in generating a fault locator with exceptional accuracy. …”
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Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities by Sarat Kumar Chettri, Rup Kumar Deka, Manob Jyoti Saikia

“…It finds that the existing studies mostly used conventional machine learning (ML) algorithms and artificial neural networks (ANNs) for a variety of tasks, such as drug discovery, disease surveillance systems, early disease detection and diagnostic accuracy, and management of healthcare resources in India. …”
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Data-Driven Approach to Evaluate the Level of Service (LOS) of Demand-Responsive Transport for the Disabled (DRTD) with an ANFIS Algorithm by Seohyeon Park, Sooyeon Park, Hosik Choi, Do-Gyeong Kim

“…The model was estimated using an Adaptive Neuro-Fuzzy Inference System (ANFIS), which is known to have an excellent predictive performance by combining the advantages of both artificial neural networks and fuzzy inference systems. Four variables, including the number of calls (or requests), the number of vacant vehicles, Medical Infrastructure Concentration Index (MICI), and Disabled Population Concentration Index (DPCI), were used as input variables for the ANFIS-based model. …”
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Intelligent Early Warning System for Construction Safety of Excavations Adjacent to Existing Metro Tunnels by Wei Tian, Jiang Meng, Xing-Ju Zhong, Xiao Tan

“…However, the trial application of artificial neural networks (ANNs) and building information modelling (BIM) for engineering projects provides a new method for solving such problems. …”
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Robust neural network filtering in the tasks of building intelligent interfaces by A. V. Vasiliev, A. O. Melnikov, S. A. Lesko

“…The possibility of using artificial neural networks to identify and suppress individual human characteristics in biological signals is demonstrated. …”
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The Real-Time Prediction of Cracks and Wrinkles in Sheet Metal Forming According to Changes in Shape and Position of Drawbeads Based on a Digital Twin by Sarang Yi, Daeil Hyun, Seokmoo Hong

“…A digital twin was developed to predict the sheet metal forming process using Support Vector Machine, Random Forest, Gradient Boosting Machine, and Artificial Neural Networks. The machine learning models were trained using finite element analysis data corresponding to the position and bead force of drawbeads, enabling the real-time prediction of wrinkles and crack occurrences. …”
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Lung Diseases Diagnosis-Based Deep Learning Methods: A Review by Shahad A. Salih, Sadik Kamel Gharghan, Jinan F. Mahdi, Inas Jawad Kadhim

“…DL methods, which utilize artificial neural networks to extract features from medical images automatically, have shown great promise in improving the accuracy and efficiency of lung disease diagnosis. …”
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First-principles based data-driven strain engineering for ferroelectrics via active machine learning: A nonlinear piezoelectric constitutive equation by Susumu MINAMI, Yasuaki MARUYAMA, Yoshimasa ABE, Tomohiro NAKAYAMA, Takahiro SHIMADA

“…Here, we developed a technical framework that enables efficient exploration of physical properties in the vast strain space based on machine learning (i.e., artificial neural networks), active learning, and high-throughput first-principles calculation. …”
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Exploring the Potential of Neural Networks to Predict Statistics of Solar Wind Turbulence by Daniel Wrench, Tulasi N. Parashar, Ritesh K. Singh, Marcus Frean, Ramesh Rayudu

“…Here, we study the utility of artificial neural networks (ANNs) to predict statistics of sparse time series. …”
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A comparative performance analysis of machine learning models for compressive strength prediction in fly ash-based geopolymers concrete using reference data by Muhammad Kashif Anwar, Muhammad Ahmed Qurashi, Xingyi Zhu, Syyed Adnan Raheel Shah, Muhammad Usman Siddiq

“…Seven models such as multiple linear regression (MLR), artificial neural networks (ANNs), support vector machines (SVMs), K-nearest neighbor (KNNs), decision trees (DT), and ensemble methods combining DT with boosting and bootstrapping were employed. …”
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Enhancing Indoor mmWave Communication With ML-Based Propagation Models by Gustavo Adulfo Lopez-Ramirez, Alejandro Aragon-Zavala

“…We employ various ML models, including Artificial Neural Networks (ANNs), hybrid models integrating linear regression, ANNs, and Gaussian Processes, and Extreme Gradient Boosting (XGBoost), to predict and analyze the propagation loss in a controlled indoor setting. …”
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A holistic research based on RSM and ANN for improving drilling outcomes in Al–Si–Cu–Mg (C355) alloy by Şenol Bayraktar, Cem Alparslan, Nurten Salihoğlu, Murat Sarıkaya

“…Statistical analyses of the effects of V and f on thrust force (Fz), surface roughness (Ra), and torque (Mz) were performed using Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Analysis of Variance (ANOVA). …”
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