Implementation of Big Data Analytics for Simulating, Predicting &amp; Optimizing the Solar Energy Production

Yousif, Jabar H.

doi:10.52098/acj.202140

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…Neural Network design refers to the different approaches and strategies to create effective neural network models for solving specific problems. In each of these cases, the neural network model design methodology involves selecting appropriate architectures, hyperparameters, and optimization algorithms to achieve optimal performance [40,41]. Critical analysis points could be considered when evaluating current neural network model design.…”

Section: Neural Network Designmentioning

confidence: 99%

Critical Review of Neural Network Generations and Models Design

Yousif,

Yousif

2023

Preprint

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In recent years, Neural networks are increasingly deployed in various fields to learn complex patterns and make accurate predictions. However, designing an effective neural network model is a challenging task that requires careful consideration of various factors, including architecture, optimization method, and regularization technique. This paper aims to comprehensively overview the state-of-the-art artificial neural network (ANN) generation and highlight key challenges and opportunities in machine learning applications. It provides a critical analysis of current neural network model design methodologies, focusing on the strengths and weaknesses of different approaches. Also, it explores the use of different learning approaches, including convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN) in image recognition, natural language processing, and time series analysis. Besides, it discusses the benefits of choosing the ideal values for the different components of ANN, such as the number of Input/output layers, hidden layers number, activation function type, epochs number, and model type selection, which help improve the model performance and generalization. Furthermore, it identifies some common pitfalls and limitations of existing design methodologies, such as overfitting, lack of interpretability, and computational complexity. Finally, it proposes some directions for future research, such as developing more efficient and interpretable neural network architectures, improving the scalability of training algorithms, and exploring the potential of new paradigms, such as Spiking Neural Networks, quantum neural networks, and neuromorphic computing.

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Section: Neural Network Designmentioning

confidence: 99%

Critical Review of Neural Network Generations and Models Design

Yousif,

Yousif

2023

Preprint

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“…The fuel that burns by ships is RFO (Residual Fuel Oil) which contains sulfur content. The emission of polluted gas will increase because of burning RFO (Wang et al, 2019), so we should use renewable energy to reduce the negative impact of burning RFO (Kazem et al, 2022;Yousif et al, 2022;Yousif J., 2021). However, the number of deaths because of air population was reduced from 214.02 to 129.44 death per 100.000 individuals in 2019, according to Statista.com.…”

Section: Introductionmentioning

confidence: 99%

Deep learning Feedforward Neural Network in predicting model of Environmental risk factors in the Sohar region

KHAMIS¹,

Yousif²

2023

airdj

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AQI (Air Quality Index) is the standard degree that guides us to measure air pollution levels such as PM2.5, O3, NO2, and SO2 to show the state of air quality. Polluted gas causes much damage and problems to people, plants, and the environment because of its negative impact. Data mining successfully examines an enormous cluster of data to recognize associations, determine relations between variables, and predict future values. In this paper, an experimental study was performed on analyzing the previous dataset of (PM2.5 and O3) for accurately predicting AQI using deep learning Feedforward Neural network techniques. The deep learning (Feedforward Neural Network (FFNN) predicting models are employed to evaluate based on R, R², MSE, MAE, and RMSE criteria using historical data from (the Ministry of Environment-Oman). Different epochs and a different number of hidden layers are deployed to improve and boost performance. In FFNN, the epochs number increase by 50,100 and 500 while the hidden layer utilized to 1,5 and 10. This optimization technique exceeds the performance from R=0.892 to R=0.992 in predicting the level of (PM2.5) and the (O3) from R=0.864 to R=0.999. The results show that the Sohar Region in a safe level of AQI.

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