TOPS-speed complex-valued convolutional accelerator for feature extraction and inference by Yunping Bai, Yifu Xu, Shifan Chen, Xiaotian Zhu, Shuai Wang, Sirui Huang, Yuhang Song, Yixuan Zheng, Zhihui Liu, Sim Tan, Roberto Morandotti, Sai T. Chu, Brent E. Little, David J. Moss, Xingyuan Xu, Kun Xu

“…Abstract Complex-valued neural networks process both amplitude and phase information, in contrast to conventional artificial neural networks, achieving additive capabilities in recognizing phase-sensitive data inherent in wave-related phenomena. …”
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Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches by Manjunath Patel Gowdru Chandrashekarappa, Prasad Krishna, Mahesh B. Parappagoudar

“…The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. …”
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Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) by Parisa Shafiee, Bogdan Dorneanu, Harvey Arellano-Garcia

“…Moreover, various machine-learning models (Random Forest (RF), Gradient Boosted, CatBoost, and artificial neural networks (ANN)) were evaluated to predict CO conversion and C8-C16 selectivity. …”
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Modelling and Optimization of Fluid Frictional Torque in a Single Stage Centrifugal Pump with a Vaned Diffuser Based on RSM, ANN and Desirability Function by K. Singh, A. Singh, D. K. Singh

“…Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs) are utilized to capture complex parameter interactions, with optimization performed using a Desirability Function (DF). …”
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Predictive Modeling of Total Real and Reactive Power Losses in Contingency Systems Using Function-Fitting Neural Networks with Graphical User Interface by Alfredo Bonini Neto, Alexandre de Queiroz, Giovana Gonçalves da Silva, André Gifalli, André Nunes de Souza, Enio Garbelini

“…This study introduces an innovative approach using Artificial Neural Networks (ANN) combined with the graphical interface to predict complete curves of real and reactive power losses in power systems under various contingencies. …”
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Social exclusion as a side effect of machine learning mechanisms by A. G. Tertyshnikova, U. O. Pavlova, M. V. Cimbal

“…The conclusion about the sources of social exclusion and stigmatization in society is made due to the similarity between natural and artificial neural networks functioning. The authors suggest that it is the principles of neurotraining in a “natural” society that lead not only to discrimination at the macro level, but also cause vivid negative reactions towards representatives of the exclusive groups, for example, interethnic hatred, homophobia, sexism, etc. …”
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Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm by El Assri Nasima, Ennejjar Mohammed, Jallal Mohammed Ali, Chabaa Samira, Zeroual Abdelouhab

“…The primary focus is on evaluating the performance of two prominent and widely-used machine learning algorithms: Artificial Neural Networks (ANN) and Random Forest (RF). The results indicate a promising predictive capacity of both models, showcasing their effectiveness in capturing intricate patterns within the dataset. …”
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A New GLLD Operator for Mass Detection in Digital Mammograms by N. Gargouri, A. Dammak Masmoudi, D. Sellami Masmoudi, R. Abid

“…We propose in this paper a new local pattern model named gray level and local difference (GLLD) where we take into consideration absolute gray level values as well as local difference as local binary features. Artificial neural networks (ANNs), support vector machine (SVM), and k-nearest neighbors (kNNs) are, then, used for classifying masses from nonmasses, illustrating better performance of ANN classifier. …”
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Double weighted combat data quality evaluation method based on CVF optimized FAHP by Jianwei Wang, Chengsheng Pan, Qing Zhang

“…Analysis of the experimental results indicates that the proposed method reduces the mean squared error to 5.35 when compared to results obtained using FAHP, interval intuitionistic fuzzy methods, and artificial neural networks, bringing it closer to actual standard values. …”
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Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning by Xiaoqing Liu, Miaoran Wang, Rui Wen, Haoyue Zhu, Ying Xiao, Qian He, Yangdi Shi, Zhe Hong, Bing Xu

“…An 80/20 train-test split was implemented for model development and validation, employing various machine learning classifiers, including artificial neural networks (ANN), random forest (RF), XGBoost (XGB), and LASSO regression. …”
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Design of Morlet Wavelet Neural Networks for Solving the Nonlinear Van der Pol–Mathieu–Duffing Oscillator Model by Ali Hasan Ali, Muhammad Amir, Jamshaid Ul Rahman, Ali Raza, Ghassan Ezzulddin Arif

“…The proposed technique utilizes artificial neural networks to model equations and optimize error functions using global search with a genetic algorithm (GA) and fast local convergence with an interior-point algorithm (IPA). …”
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BIM-Based Machine Learning Application for Parametric Assessment of Building Energy Performance by Panagiotis Tsikas, Athanasios Chassiakos, Vasileios Papadimitropoulos, Antonios Papamanolis

“…They include statistical regression modeling (SRM), decision trees (DTs), random forests (RFs), and artificial neural networks (ANNs). The analysis reveals the contribution of each factor and highlights the ANN as the best performing model. …”
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Shannon Entropy Computations in Navier–Stokes Flow Problems Using the Stochastic Finite Volume Method by Marcin Kamiński, Rafał Leszek Ossowski

“…Further numerical extension of this technique is seen in an application of the artificial neural networks, where polynomial approximation may be replaced automatically by some optimal, and not necessarily polynomial, bases.…”
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Prediction of the Impact of Bank Failure Risk on Micro-Credit in Iran: An Artificial Intelligence Approach by Reza Taheri Haftasiabi, Yusef Mohammadzadeh, Ameneh Naderi

“…Machine learning tools, including artificial neural networks (ANN) and support vector machine (SVM), were used to analyze macroeconomic indicators such as GDP, inflation, exchange rate, interest rate, and financial variables of banks such as investment volume, amount of loans granted, total deposits, and bankruptcy risk indicators. …”
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From Baseline to Best Practice: An Advanced Feature Selection, Feature Resampling and Grid Search Techniques to Improve Injury Severity Prediction by Soukaina EL Ferouali, Zouhair Elamrani Abou Elassad, Sara Qassimi, Abdelmounaîm Abdali

“…Fourth predictive systems are employed to investigate the intricate problem of predicting the severity of injuries sustained in traffic crashes using different regression algorithms, such as Random Forest, Decision Trees, XGBoost, and Artificial Neural Networks. Compared to comparable systems without feature selection, feature resampling, and optimization methods, the results demonstrate that employing optimized XGBoost along with grid search in conjunction with SelectKBest and SMOTE strategy has resulted in greater performance, with an 89% R2 score. …”
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Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression by Ali Ghorbani, Mostafa Firouzi Niavol

“…Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. …”
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Development of Comprehensive Predictive Models for Evaluating Böhme Abrasion Value (BAV) of Dimension Stones Using Non-Destructive Testing Methods by Ekin Köken

“…Three predictive models were established using multivariate adaptive regression spline (MARS), M5P, and artificial neural networks (ANN) methodologies. The performance of the models was assessed through scatter plots and statistical indicators, showing that the ANN-based model outperforms those based on M5P and MARS. …”
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Influence of Caprock Morphology on Solubility Trapping during CO2 Geological Sequestration by Pradeep Reddy Punnam, Balaji Krishnamurthy, Vikranth Kumar Surasani

“…In the future, the simulation data using Artificial Neural Networks can be applied to predict the structural and solubility trapping of geological formations. …”
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Mathematical and computational modeling for organic and insect frass fertilizer production: A systematic review. by Malontema Katchali, Edward Richard, Henri E Z Tonnang, Chrysantus M Tanga, Dennis Beesigamukama, Kennedy Senagi

“…Mathematical models such as simulation, regression, dynamics, and kinetics have been applied while computational data driven machine learning models such as random forest, support vector machines, gradient boosting, and artificial neural networks have also been applied as well. These models have been used in quantifying nutrients concentration/release, effects of nutrients in agro-production, and fertilizer treatment. …”
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Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing by Müge Sinem Çağlayan, Aslı Aksoy

“…The research employs six machine learning (ML) algorithms—logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)—to develop a multi-class classification model for material feeding system selection. …”
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