Prediction of EV Charging Behavior Using Machine Learning by Shahriar, Sakib

“…Therefore, in this paper we propose the usage of historical charging data in conjunction with weather, traffic, and events data to predict EV session duration and energy consumption using popular machine learning algorithms including random forest, SVM, XGBoost and deep neural networks. …”
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Cross entropy error function in neural networks by Nasr, G.E.

“…This paper applies artificial neural networks to forecast gasoline consumption. …”
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Prediction of biogas production from chemically treated co-digested agricultural waste using artificial neural network by Fares Almomani (12585685)

“…<p dir="ltr">The present study evaluates the effect of co-digestion of agricultural solid wastes (ASWs), cow manure (CM), and the application of chemical pre-treatment with NaHCO₃ on the performance of anaerobic digestion (AD) process. An Artificial neural network (ANN) algorithm was developed to model and optimize the cumulative methane production (CMP) from ASWs, CM, and their mixture under mesophilic and thermophilic conditions. …”

Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition by Dabeeruddin Syed (16864260)

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Developing a UAE-Based Disputes Prediction Model using Machine Learning by Abu Laila, Ibrahim Wasef Subhi

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On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network by Nasru Minallah (9427338)

Mobile Robot Trajectory Tracking Using Neural Networks by Mohareri, Omid

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Cortical EEG Source Localization of Focal Epilepsy by Siyam, Wisal Elfatih Mohamed

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YOLO-DefXpert: An Advanced Defect Detection on PCB Surfaces Using Improved YOLOv11 Algorithm by Prabu Selvam (22330264)

Enhanced PSO-Based NN for Failures Detection in Uncertain Wind Energy Systems by Khaled Dhibi (16891524)

“…Finally, PSO and RPSO-based interval centers and ranges and upper and lower bounds techniques are developed to deal with model uncertainties in WES. The last retained features from the proposed PSO-based methods are fed to the neural network (NN) classifier. …”

“Adaptive Control Technique Using Multilayer Feedforward Neural Networks” by Al-Duwaish, H.

“…An adaptation algorithm to adjust the connection weights of the neural network has been derived. …”
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A method for data path synthesis using neural networks by Harmanani, H.

“…The method is based on the modified Hopfield neural network model of computation and the McCulloch-Pitts binary neuron model. …”
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Soft Sensor for NOx Emission using Dynamical Neural Network by Shakil, M.

“…The soft sensor is based on a dynamical neural network model. A simplified structure of the dynamical neural network model is achieved by grouping the input variables using basic knowledge of the system. …”
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Estimating Bridge Deterioration Age Using Artificial Neural Networks by AL HUSSEIN, ASEEL

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DAP: A dataset-agnostic predictor of neural network performance by Sui Paul Ang (18460605)

“…This task often must be repeated many times, especially when developing a new deep learning algorithm or performing a neural architecture search. …”

Correlation Clustering with Overlaps by Fakhereldine, Amin

“…Moreover, we allow the new vertex splitting operation, which allows the resulting clusters to overlap. In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …”
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Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment by Farhat Mahmood (15468854)

“…First, an analytical model based on mass and energy balance and a data-driven model based on an artificial neural network is developed, and their prediction performance is compared. …”

One-Hour-Ahead Wind Power Forecast Using Hybrid Grey Models by Osman, Ahmed

“…These techniques are combined with ARMA models and nonlinear autoregressive neural network (NARnet) models, respectively. …”
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FAILURE RATE ANALYSIS OF BOEING 737 BRAKES EMPLOYING NEURAL NETWORK by Al-Garni, Ahmed Z.

“…., Boeing 737, is analyzed using the artificial neural network and Weibull regression models. One-layered feed-forward back-propagation algorithm for artificial neural network whereas three parameters model for Weibull are used for the analysis. …”
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Deep Neural Networks for Electromagnetic Inverse Scattering Problems in Microwave Imaging by Maricar, Mohammed Farook

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