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3rd IEEE International Conference on Electronic Engineering, ICEEM 2023

作者： Sherif, Khaled Azmy, Mohamed Alakkari, Khder Abotaleb, Mostafa El-Kenawy, El-Sayed M. Delta Higher Institute Department of Communications Mansoura Egypt Delta Higher Institute Department of Civil Engineering Mansoura Egypt University of Tishreen Department of Statistics and Programming Faculty of Economics Tartous Syria South Ural State University Department of System Programming Chelyabinsk454080 Russia

ISBN: (纸本)9798350323511

This study presents a novel application of a Long Short-Term Memory (LSTM) deep learning model for time-series analysis of the Normalized Difference Vegetation Index (NDVI) from January 1, 1984, to April 21, 2023. As remote sensing technologies generate substantial environmental data, advanced analytics like LSTM provide essential tools for precise interpretation and forecasting. Through grid search optimization, hyperparameters were fine-tuned for optimal LSTM performance. The NDVI mean value over the study period is 0.332, indicative of a moderate vegetation presence. The data series' sta-tionarity, confirmed through the Dickey-Fuller test, contributes to accurate prediction outcomes. The LSTM model demonstrates superior predictive performance, evidenced by the Root Mean Squared Error (RMSE) values of 0.000764 and 0.000900 for the training and testing datasets respectively. The high R-squared and correlation values further substantiate its efficacy. This study paves the way for leveraging LSTM models in large-scale NDVI data analysis, contributing to environmental monitoring, climate change tracking, and vegetation health assessments. Future work can extend this model to other remote sensing indices and explore various deep learning architectures for enhanced predictive accuracy. The main objective is to identify the optimal LSTM hyperparameters for NDVI prediction using grid search optimization. Our results are expected to provide valuable insights into how LSTM models can be effectively tuned for improved NDVI prediction, potentially benefiting environmental monitoring and decision-making processes. © 2023 IEEE.

关键词： Forecasting

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Forecasting Global Monkeypox Infections Using LSTM: A Non-Stationary Time Series Analysis 3

Forecasting Global Monkeypox Infections Using LSTM: A Non-St...

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3rd IEEE International Conference on Electronic Engineering, ICEEM 2023

作者： Osama, Omnia M. Alakkari, Khder Abotaleb, Mostafa El-Kenawy, El-Sayed M. Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura Egypt University of Tishreen Faculty of Economics Department of Statistics and Programming Tartous Syria South Ural State University Department of System Programming Chelyabinsk454080 Russia Delta Higher Institute Department of Communications Mansoura Egypt

ISBN: (纸本)9798350323511

This study leverages the capabilities of Long Short-Term Memory (LSTM) models in forecasting global Monkeypox infections, thereby demonstrating the significant potential of advanced machine learning techniques in epidemiological forecasting. Our LSTM model effectively navigates the challenges posed by non-stationary time-series data, a common issue in epidemiological studies. It successfully captures the underlying patterns in the data, producing reliable forecasts. The model's performance was evaluated using several metrics, including RMSE, MSE, MAE, and R2, all of which pointed to its robust and satisfactory predictive capabilities. Our findings underscore the significant role LSTM models can play in informing the development of timely and effective disease control and prevention strategies. They thereby contribute to enhancing public health responses to emerging infectious diseases such as Monkeypox. However, despite the promising results, the study highlights the ongoing challenge of enhancing the interpretability of LSTM models, an area that warrants further research. As a future direction, efforts should focus on refining LSTM models to bolster their interpretability, ensuring their broader adoption and utility in public health practice. © 2023 IEEE.

关键词： Long short-term memory

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Deep Learning in IoT: An LSTM Approach for NDVI Forecasting

Deep Learning in IoT: An LSTM Approach for NDVI Forecasting

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Electronic Engineering (ICEEM), International Conference on

作者： Khaled Sherif Mohamed Azmy Khder Alakkari Mostafa Abotaleb El-Sayed M. El-Kenawy Department of Communications Delta Higher Institute Mansoura Egypt Department of Civil Engineering Delta Higher Institute Mansoura Egypt Department of Statistics and Programming Faculty of Economics University of Tishreen Tartous Syria Department of System Programming South Ural State University Chelyabinsk Russia

This study presents a novel application of a Long Short-Term Memory (LSTM) deep learning model for time-series analysis of the Normalized Difference Vegetation Index (NDVI) from January 1, 1984, to April 21, 2023. As remote sensing technologies generate substantial environmental data, advanced analytics like LSTM provide essential tools for precise interpretation and forecasting. Through grid search optimization, hyperparameters were fine-tuned for optimal LSTM performance. The NDVI mean value over the study period is 0.332, indicative of a moderate vegetation presence. The data series’ sta-tionarity, confirmed through the Dickey-Fuller test, contributes to accurate prediction outcomes. The LSTM model demonstrates superior predictive performance, evidenced by the Root Mean Squared Error (RMSE) values of 0.000764 and 0.000900 for the training and testing datasets respectively. The high R-squared and correlation values further substantiate its efficacy. This study paves the way for leveraging LSTM models in large-scale NDVI data analysis, contributing to environmental monitoring, climate change tracking, and vegetation health assessments. Future work can extend this model to other remote sensing indices and explore various deep learning architectures for enhanced predictive accuracy. The main objective is to identify the optimal LSTM hyperparameters for NDVI prediction using grid search optimization. Our results are expected to provide valuable insights into how LSTM models can be effectively tuned for improved NDVI prediction, potentially benefiting environmental monitoring and decision-making processes.

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Forecasting Global Monkeypox Infections Using LSTM: A Non-Stationary Time Series Analysis

Forecasting Global Monkeypox Infections Using LSTM: A Non-St...

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Electronic Engineering (ICEEM), International Conference on

作者： Omnia M. Osama Khder Alakkari Mostafa Abotaleb El-Sayed M. El-Kenawy Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Department of Statistics and Programming Faculty of Economics University of Tishreen Tartous Syria Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications Delta Higher Institute Mansoura Egypt

This study leverages the capabilities of Long Short-Term Memory (LSTM) models in forecasting global Monkeypox infections, thereby demonstrating the significant potential of advanced machine learning techniques in epidemiological forecasting. Our LSTM model effectively navigates the challenges posed by non-stationary time-series data, a common issue in epidemiological studies. It successfully captures the underlying patterns in the data, producing reliable forecasts. The model’s performance was evaluated using several metrics, including RMSE, MSE, MAE, and R 2 , all of which pointed to its robust and satisfactory predictive capabilities. Our findings underscore the significant role LSTM models can play in informing the development of timely and effective disease control and prevention strategies. They thereby contribute to enhancing public health responses to emerging infectious diseases such as Monkeypox. However, despite the promising results, the study highlights the ongoing challenge of enhancing the interpretability of LSTM models, an area that warrants further research. As a future direction, efforts should focus on refining LSTM models to bolster their interpretability, ensuring their broader adoption and utility in public health practice.

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Modeling and forecasting of wheat of South Asian region countries and role in food security

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Advances in Computational Intelligence 2021年第6期1卷 1-8页

作者： Yonar, Aynur Yonar, Harun Mishra, Pradeep Kumari, Binita Abotaleb, Mostafa Badr, Amr Department of Statistics Faculty of Science Selçuk University Konya Turkey Department of Biostatistics Faculty of Veterinary Medicine Selçuk University Konya Turkey College of Agriculture J.N.K.V.V. Powarkheda India Department of Agricultural Economics Rashtriya Kisan (PG) College (affiliated to Chaudhary Charan Singh University Meerut) Shamli India Department of System Programming South Ural State University Chelyabinsk Russia Faculty of Science School of Science and Technology University of New England Armidale Australia

Wheat is the most important source of food on earth and vital for food safety. It contains 75–80% carbohydrates, 9–18% protein, fiber, many vitamins (especially B vitamins), calcium, iron, and many macronutrients and micronutrients. According to data from the International Grain Council (IGC), wheat has continued to be the most important food grain source for humans in the world. Therefore, determining wheat production behavior has a very important role in food security. In this study, we have modeled and forecasted the production of wheat for 6 years from 2020 to 2025 using ARIMA and Holt’s linear trend models in Afghanistan, Bangladesh, Bhutan, China, India, Nepal, and Pakistan, which are all countries in the South Asian region. Since there is an expectation of a decrease in wheat production in some of these countries, this study can provide these countries with the information they need to take appropriate decisions to prevent the occurrence of food problems in the future and to help deal with food security. Moreover, this projection helps with policy implications and planning.

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Evaluating carbon cap and trade policy effects on a multi-period bi-objective closed-loop supply chain in retail management under mixed uncertainty: Towards greener horizons

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Expert Systems with Applications 2024年 250卷

作者： Bhunia, Satyajit Das, Soumen Kumar Jablonsky, Josef Roy, Sankar Kumar Department of Pure and Applied Science Midnapore City College Kuturia West Bengal Bhadutala721129 India Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Department of Econometrics Faculty of Informatics and Statistics Prague University of Economics and Business Praha 3 Prague Czech Republic Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University West Bengal Midnapore721102 India

In response to the growing imperative of addressing environmental concerns and aligning with governmental regulations in supply chain management, this study navigates the optimization landscape of closed-loop supply chains through the lens of carbon cap and trade policy. The optimization model aims to account for the impact of consumer demands, distribution networks, and retail operations on the overall closed-loop supply chain dynamics. The innovative approach introduces an optimization model designed to grapple with the challenges posed by imprecise information within an intuitionistic type-2 fuzzy framework. The bi-objective mixed-integer model integrates pivotal objectives, including carbon emissions reduction, total cost minimization, and trade policy compliance. For a nuanced comprehension of the proposed model, we present three numerical examples, offering clarity on its complexities. Following this, a sensitivity analysis visually depicts the impact of fluctuations in demand and capacity on the defined objective functions. Furthermore, these conclusions unveil promising avenues for future research and exploration within the ambit of this study. © 2024 Elsevier Ltd

关键词： Multiobjective optimization

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Using the inverse Cauchy problem of the Laplace equation for wave propagation to implement a numerical regularization homotopy method

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AIP Conference Proceedings 2023年第1期2977卷

作者： H. K. Al-Mahdawi Zainalabideen Albadran Hussein Alkattan Mostafa Abotaleb Khder Alakkari Ali J. Ramadhan Electronic Computer Centre University of Diyala Diyala Iraq Department of Computer Techniques Engineering College of Technical Engineering University of Alkafeel Najaf Iraq Department of System Programming South Ural State University South Ural State Russia Department of Statistics and Programming Faculty of Economics University of Tishreen Latakia Syria

In this paper, we introduce an approach via regularization and Homotopy way for resolving the inverse Cauchy problem of the Laplace of system partial differential equation which appears in the wave propagation for communication networks. We considered the method of Homotopy Perturbation Metheod (HPM) for solving the integral equations of the first kind named Fredholm. In order to formulate the Laplace equation into the first-kind integral equation (Fredholm) the Fourier series used. Then the discretization method used to reduce the integral equation into a linear operator equation for the first kind. It is clear that this kind of problem is callsified as an ill-posed and the direct way to solve it unacceptably. Tikhonov’s regularization method with Homotopy Perturbation algorithm used for obtaing the approximation solution for the Laplace differential equation. Finally, the numerical example is proposed.

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