Vortex gust mitigation from onboard measurements using deep reinforcement learning by Brice Martin, Thierry Jardin, Emmanuel Rachelson, Michael Bauerheim

“…The controller is modeled as an artificial neural network, and it is trained to minimize using deep reinforcement learning (DRL). …”
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A study comparing energy consumption and environmental emissions in ostrich meat and egg production by Behrooz Behboodi, Mohammad Gholami Parashkoohi, Davood Mohammad Zamani, Saeed Firouzi

“…This study delves into the impact of egg and meat production on human health, revealing a slight difference of 0.23 disability adjusted life years (DALY), hinting that egg production could potentially have marginally more negative health effects than meat production. Artificial neural network (ANN) analysis indicates that optimizing machinery, diesel fuel, and energy usage can enhance the productivity of meat production. …”
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Predicting the urban water demand by equipping intelligent-based methods with discrete wavelet transform function by Mohammad Reza Alikhani, Ramtin Moeini

“…For this purpose, in this research, artificial intelligence and data mining methods, including genetic programming (GP), gene expression programming (GEP), artificial neural network (ANN), and discrete wavelet transform function, are used to predict the daily drinking water consumption values of WDN. …”
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Applications of the neuro-evolutionary approach to the parabolic type partial differential equations by Waseem, Asad Ullah, Emad A.A. Ismail, Fuad A. Awwad

“…This work aims to investigate the Cuckoo search-active set algorithm (CS-ASA), which is based on the artificial neural network (ANN) approach to the nonlinear partial differential equations (PDEs). …”
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Modeling Evapotranspiration Response to Climatic Forcings Using Data-Driven Techniques in Grassland Ecosystems by Xianming Dou, Yongguo Yang

“…These models were compared with the extensively utilized data-driven models, including artificial neural network, generalized regression neural network, and support vector machine (SVM). …”
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On the Analysis and Assessment of First-Order Group Contribution Models for the Calculation of Normal Boiling Point and Critical Properties of Pure Compounds by Vanessa Villazón-León, Adrián Bonilla-Petriciolet, Juan Carlos Tapia-Picazo

“…The performance of these models was characterized and compared for several compound families using a standardized approach to determine their group contributions and parameters. An artificial neural network model was also applied and assessed to improve the estimations obtained with the best group contribution models. …”
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Investigation of ANN Architecture for Predicting Load-Carrying Capacity of Castellated Steel Beams by Thuy-Anh Nguyen, Hai-Bang Ly, Van Quan Tran

“…This paper aims to propose an artificial neural network (ANN) model with optimal architecture to predict the load-carrying capacity of CSB with a scheme of the simple beam bearing load located at the center of the beam. …”
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Predictive performance of count regression models versus machine learning techniques: A comparative analysis using an automobile insurance claims frequency dataset. by Gadir Alomair

“…The research involved a comparative evaluation of several models, including Poisson, NB, zero-inflated Poisson (ZIP), hurdle Poisson, zero-inflated negative binomial (ZINB), hurdle negative binomial, random forest (RF), support vector machine (SVM), and artificial neural network (ANN) on an insurance dataset. The performance of these models was assessed using mean absolute error. …”
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A generic self-learning emotional framework for machines by Alberto Hernández-Marcos, Eduardo Ros

“…Applied in a case study, an artificial neural network trained on unlabeled agent’s experiences successfully learned and identified eight basic emotional patterns that are situationally coherent and reproduce natural emotional dynamics. …”
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Modeling the Liquid-Phase Adsorption of Cephalexin onto Coated Iron Nanoparticles Using Response Surface and Molecular Modeling by Shabnam Ahmadi, Soumya Ghosh, Alhadji Malloum, Charné Bornman, Christian Osagie, Leili Mohammadi, Chinenye Adaobi Igwegbe

“…In addition, the data was used to test and fit an artificial neural network (ANN) model. Molecular-level DFT calculations on the CEX molecule were carried out. …”
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Bone Mineral Density Prediction from CT Image: A Novel Approach using ANN by S. L. Resmi, V. Hashim, Jesna Mohammed, P. N. Dileep

“…In this approach, the BMD is predicted using clinical CT scan images taken for other indications based on image processing and artificial neural network (ANN). The network used in this study is a standard backpropagation neural network having five input neurons with one hidden layer having 40 neurons with a tan-sigmoidal activation function. …”
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Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization by Rajeev Goel, Sandhya Bansal, Kavita Gupta

“…A handcrafted artificial neural network serves as the classifier within this integrated framework, denoted as AEGWO-Net. …”
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An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities by Mohamad Reda A. Refaai, Vinjamuri S. N. C. H. Dattu, H. S. Niranjana Murthy, P. Pramod Kumar, B. Kannadasan, Abdi Diriba

“…Depending on many relevant criteria, an artificial neural network (ANN)-centered precise and effective method is provided to forecast the signal strength from such drones. …”
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Empirical modeling potential transfer of land cover change pa city with neural network algorithms by fatemeh mohammadyary, hamidreza pourkhabbaz, hossin aghdar, morteza Tavakoly

“…The transmission potential modeling was performed by using the multi-layer perceptron artificial neural network algorithm using six independent variables and the distribution of changes in user usage were calculated by Markov chain method. …”
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Comparison of Different Machine Learning Methodologies for Predicting the Non‐Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials by Roberto Gomeni, Françoise Bressolle‐Gomeni

“…At this purpose, six machine learning methodologies (gradient boosting machine, lasso regression, logistic regression, support vector machines, k‐nearest neighbors, and random forests) were compared to the multilayer perceptrons artificial neural network (ANN) methodology for predicting the probability of individual non‐specific treatment response. …”
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Waste heat recovery cycles integration into a net-Zero emission solar-thermal multi-generation system; Techno-economic analysis and ANN-MOPSO optimization by Pradeep Kumar Singh, Ali Basem, Rebwar Nasir Dara, Mohamed Shaban, Sarminah Samad, Raymond Ghandour, Ahmad Almadhor, Samah G. Babiker, Iskandar Shernazarov, Ibrahim A. Alsayer

“…To optimize the system's performance, an artificial neural network is integrated with a multi-objective particle swarm optimization algorithm to reduce computational time from approximately 16 h to 4 min. …”
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Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations by Badriah Alkalifah, Muhammad Tariq Shaheen, Johrah Alotibi, Tahani Alsubait, Hosam Alhakami

“…To support this an Artificial Neural Network (ANN), Binary Decision Tree (BDT), Linear Regression (LR), Boosting Regression Tree Ensemble (BSTE), Linear Regression with Stochastic Gradient Descent (LRSGD), Stepwise (SW), Support Vector Machine (SVM), and Gaussian process regression (GPR) were investigated. …”
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Load frequency control in renewable based micro grid with Deep Neural Network based controller by Prasantini Samal, Niranjan Nayak, Anshuman Satapathy, Sujit Kumar Bhuyan

“…This paper introduces a novel control strategy to optimise the load frequency model in a microgrid (MG) with vehicle-to-grid interactions using Particle Swarm Optimisation - deep Artificial Neural Network (PSO-DNN). The performance of the suggested controller is evaluated against traditional techniques, including dynamic EV charging and discharging, renewable energy integration, and fluctuating generation, using the proportional integral derivative (PID) controller and the PSO-PID controller. …”
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An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance by Gunapriya Balan, Singaravelan Arumugam, Suresh Muthusamy, Hitesh Panchal, Hossam Kotb, Mohit Bajaj, Sherif S. M. Ghoneim, null Kitmo

“…The proposed model (RF-DNN) achieved 97.05% of accuracy and the PCA-DNN model achieved 95.55% of accuracy, whereas the artificial neural network as ANN with PCA and RF achieved nearly 92% of accuracy.…”
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Exploration of Arrhenius activation energy and thermal radiation on MHD double-diffusive convection of ternary hybrid nanofluid flow over a vertical annulus with discrete heating by Shilpa B, V. Leela, Irfan Anjum Badruddin, Sarfaraz Kamangar, P. Ganesan, Abdul Azeem Khan

“…Also, the heat and mass transfer characteristics are forecasted and analyzed by considering the Levenberg–Marquardt backpropagating artificial neural network technique.…”
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