Mostafa Abotaleb
@susu.ru
System of programming department
Research Engineer, System of programming department, South Ural State University, Chelyabinsk, Russia.
RESEARCH INTERESTS
Computer Science
Mathematics
Agricultural and Biological Sciences
Decision Sciences
Biochemistry, Genetics and Molecular Biology
Physics and Astronomy
Environmental Science
Earth and Planetary Sciences
Immunology and Microbiology
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Pradeep Mishra, Amel Ali Alhussan, Doaa Sami Khafaga, Priyanka Lal, Soumik Ray, Mostafa Abotaleb, Khder Alakkari, Marwa M. Eid, and El-Sayed M. El-kenawy
Springer Science and Business Media LLC
Tatiana Makarovskikh, Anatoly Panyukov, Mostafa Abotaleb, Valentina Maksimova, Olga Dernova, and Eugeny Raschupkin
Springer Nature Switzerland
H. K. Al-Mahdawi, Zainalabideen Albadran, Hussein Alkattan, Mostafa Abotaleb, Khder Alakkari, and Ali J. Ramadhan
AIP Publishing
Mostafa Abotaleb, Tatiana Makarovskikh, Zainalabideen Albadran, and Ali J. Ramadhan
AIP Publishing
Tatiana Makarovskikh, Mostafa Abotaleb, Zainalabideen Albadran, and Ali J. Ramadhan
AIP Publishing
Mustafa Kamal, Husam Eldin Sadig, Aned Al Mutairi, Ibrahim Alkhairy, Fatma Masoud A. Zaghdoun, M. Yusuf, Eslam Hussam, Mostafa Abotaleb, Manahil SidAhmed Mustafa, and Anas Faiz Alsaedy
Elsevier BV
Fehaid Alqahtani, Mostafa Abotaleb, Alhumaima Ali Subhi, El-Sayed M. El-Kenawy, Abdelaziz A. Abdelhamid, Khder Alakkari, Amr Badr, H. K. Al-Mahdawi, Abdelhameed Ibrahim, and Ammar Kadi
Springer Science and Business Media LLC
Pradeep Mishra, Abdullah Mohammad Ghazi Al Khatib, Priyanka Lal, Ayesha Anwar, Korakot Nganvongpanit, Mostafa Abotaleb, Soumik Ray, and Veerasak Punyapornwithaya
Springer Science and Business Media LLC
Xia Liu, Baraa Abd Alreda, Jalil Manafian, Baharak Eslami, Mehdi Fazli Aghdaei, Mostafa Abotaleb, and Ammar Kadi
Elsevier BV
Uday Bagale, Ammar Kadi, Mostafa Abotaleb, Irina Potoroko, and Shirish Hari Sonawane
MDPI AG
The aim of this paper was to determine the effect of stabilized curcumin nanoemulsions (CUNE) as a food additive capable of directionally acting to inhibit molecules involved in dairy products’ quality and digestibility, especially cheese. The objects were cheeses made from the milk of higher grades with addition of a CUNE and a control sample. The cheeses were studied using a scanning electron microscope (SEM) in terms of organoleptic properties, such as appearance, taste, and aroma. The results show that the addition of CUNEs improved the organoleptic properties compared to the control cheese by 150% and improved its shelf life. The SEM study shows that formulation with CUNE promotes the uniform distribution of porosity. The CUNE-based cheese shows a better sensory evaluation compared to the emulsion without curcumin. CUNE-processed cheese provided better antioxidant and antimicrobial analysis than the control sample and offers added value to the dairy sector.
Yongyi Gu, Syed Maqsood Zia, Mubeen Isam, Jalil Manafian, Afandiyeva Hajar, and Mostafa Abotaleb
Elsevier BV
Omnia M. Osama, Khder Alakkari, Mostafa Abotaleb, and El-Sayed M. El-Kenawy
IEEE
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.
Basant Sameh, Menna Atef, Tatiana Makarovskikh, Mostafa Abotaleb, Valentina Maksimova, Olga Dernova, and El-Sayed M. El-Kenawy
IEEE
In this paper, we discuss the design of a system for crop forecasting. Despite the presence of a fairly large number of software products for precision farming on the market, many of the existing developments, on the one hand, are not intended to provide data for further analysis by specialists who are not employees of the developer company, and on the other hand, do not ensure food security of the Russian Federation. We have designed a system for the acquisition and subsequent analysis of aerial photographic data. The paper describes the method of presenting data, the process of calculating vegetation indices for a certain date, and provides approaches to modeling the vegetation process using high-quality determinate mathematical models and predicting the yield of a field in the next season, and the cost of this crop. We discuss the opportunities of our system and show the ways of further increasing its quality.
Khaled Sherif, Mohamed Azmy, Khder Alakkari, Mostafa Abotaleb, and El-Sayed M. El-Kenawy
IEEE
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.
Tatiana Makarovskikh, Anatoly Panyukov, and Mostafa Abotaleb
Springer Nature Switzerland
Tatiana Makarovskikh, Anatoly Panyukov, and Mostafa Abotaleb
Springer Nature Switzerland
Khder .., , , , , , , , , Alhumaima Ali Subhi,et al.
American Scientific Publishing Group
The COVID-19 epidemic has in fact placed the whole community in a dire predicament that has led to numerous tragedies, including an economic downturn, political unrest, and job losses. Forecasting and identifying COVID-19 infection cases is crucial for the government at all levels because the pandemic grows exponentially and results in fatalities. Hence, by giving information about the spread of the epidemic, the government can move quickly at multiple levels to establish new policies and modalities in order to minimize the trajectory of the COVID-19 pandemic's effects on both public health and the economic sectors. Forecasting models for COVID-19 infection cases in the Ural region in Russia were developed using two deep Long Short-Term Memory (LSTM) learning-based approaches namely Encoder–Decoder LSTM and Attention LSTM algorithms. The models were evaluated based on five standard performance evaluation metrics which include Mean Square Error (MSE), Mean Absolute Error (MAE), Root MSE (RMSE), Relative RMSE (RRMSE), and coefficient of determination (R2). However, the Encoder–Decoder LSTM deep learning-based forecasting model achieved the best performance results (MSE=32794.09, MAE=168.56, RMSE=181.09, RRMSE=13.46, and R2=0.87) compared to the model developed with Attention LSTM models.
El-Sayed M. El-kenawy, Abdelaziz A. Abdelhamid, Fadwa Alrowais, Mostafa Abotaleb, Abdelhameed Ibrahim, and Doaa Sami Khafaga
Computers, Materials and Continua (Tech Science Press)
El-Sayed M. El-kenawy, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Mostafa Abotaleb, Tatiana Makarovskikh, Amal H. Alharbi, and Doaa Sami Khafaga
Computers, Materials and Continua (Tech Science Press)
Several instances of pneumonia with no clear etiology were recorded in Wuhan, China, on December 31, 2019. The world health organization (WHO) called it COVID-19 that stands for "Coronavirus Disease 2019," which is the second version of the previously known severe acute respiratory syndrome (SARS) Coronavirus and identified in short as (SARSCoV-2). There have been regular restrictions to avoid the infection spread in all countries, including Saudi Arabia. The prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus spread. Methodology: Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory (LSTM). The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius (BER) algorithm. Results: To evaluate the effectiveness of the proposed methodology, a dataset is collected based on the recorded cases in Saudi Arabia between March 7th, 2020 and July 13th, 2022. In addition, six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed approach. The achieved results show that the proposed approach could reduce the mean square error (MSE), mean absolute error (MAE), and R2 by 5.92%, 3.66%, and 39.44%, respectively, when compared with the six base models. On the other hand, a statistical analysis is performed to measure the significance of the proposed approach. Conclusions: The achieved results confirm the effectiveness, superiority, and significance of the proposed approach in predicting the infection cases of COVID-19. © 2023 CRL Publishing. All rights reserved.
Bashar Talib Al-Nuaimi, H.K. Al-Mahdawi, Zainalabideen Albadran, Hussein Alkattan, Mostafa Abotaleb, and El-Sayed M. El-kenawy
MDPI AG
The boundary value problem, BVP, for the PDE heat equation is studied and explained in this article. The problem declaration comprises two intervals; the (0, T) is the first interval and labels the heating of the inside burning chamber, and the second (T, ∞) interval defines the normal cooling of the chamber wall when the chamber temperature concurs with the ambient temperature. It is necessary to prove the boundary function of this problem has its place in the space H10,∞ in order to successfully apply the Fourier transform method. The applicability of the Fourier transform for time to this problem is verified. The method of projection regularization is used to solve the inverse boundary value problem for the heat equation and to obtain an evaluation for the error between the approximate and the real solution. These results are new and of practical interest as shown in the numerical case study.
Doaa Sami Khafaga, El-Sayed M. El-kenawy, Faten Khalid Karim, Mostafa Abotaleb, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid, and D. L. Elsheweikh
Computers, Materials and Continua (Tech Science Press)
Reem Alkanhel, El-Sayed M. El-kenawy, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Manal Abdullah Alohali, Mostafa Abotaleb, and Doaa Sami Khafaga
Computers, Materials and Continua (Tech Science Press)
Reem Alkanhel, Doaa Sami Khafaga, El-Sayed M. El-kenawy, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Rashid Amin, Mostafa Abotaleb, and B. M. El-den
Computers, Materials and Continua (Tech Science Press)
Reem Alkanhel, El-Sayed M. El-kenawy, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Mostafa Abotaleb, and Doaa Sami Khafaga
Computers, Materials and Continua (Tech Science Press)
Pradeep Mishra, Khder Mohammed Alakkari, Achal Lama, Soumik Ray, Monika Singh, Claris Shoko, Mostafa Abotaleb, Abdullah Mohammad Ghazi Al khatib, and Kadir Karakaya
King Mongkut's Institute of Technology Ladkrabang
Sugarcane industry is of crucial importance to the South Asian countries. These countries depend heavily on agriculture and the sugarcane industry has immense potential to contribute towards its economic development. Hence, the precise and timely forecast of sugarcane production is of concern for farmers, policy makers and other stakeholders. In this manuscript, we strived to forecast the production and growth rate of this important commodity using standard statistical approaches. The ARIMA (Auto Regressive Integrated Moving Average) and ETS (Exponential Smoothing) models were applied and compared on the basis of their forecasting efficiency for South Asia countries. This study also investigated the trends in sugarcane production in the region and studies the causes of the decline in production of sugarcane in Sri Lanka and Bangladesh. Furthermore, the expected production for following 7 years was computed using both models. In addition, we also calculated the projected growth rates of sugarcane production of South Asian countries over the years 2020-2027.