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
Boriana Vrusho, Alma Golgota, Klodian Dhoska, and Mostafa Abotaleb
Lembaga Penelitian dan Pengabdian masyarakat Universitas Jambi
Reducing the energy demand of residential buildings is crucial for mitigating climate change, lowering energy costs, reducing health risks associated with fuel poverty, and improving the overall residential environment. Given the global significance of these challenges, this research aims to explore the impact of energy-saving measures in residential buildings, focusing on façade renovation systems in Tirana, Albania. The methodology employed in this research work involved a comprehensive approach combining field assessments, energy performance analysis of completed projects, and case studies of residential buildings in Tirana. The research specifically focused on the implementation of façade renovation systems and evaluated their impact on reducing energy consumption. The results demonstrate significant improvements in energy performance following the renovation of building façades. Enhanced insulation, upgraded materials, and the addition of energy-efficient windows led to reduced heating and cooling demands, contributing to a more stable indoor climate and lower energy consumption. The energy simulations confirmed that facade renovations resulted in a notable reduction in overall energy use, particularly during the colder months. The findings suggest that facade renovation systems are an effective strategy for reducing the energy demand of residential buildings in Tirana. These improvements not only help to mitigate the effects of climate change by lowering carbon emissions but also offer a cost-effective solution for improving the quality of life for residents. This study offers a novel contribution by focusing specifically on the impact of facade renovation systems in the context of residential buildings an area with limited previous research on energy efficiency improvements.
Malik Sallam, Maad M. Mijwil, Mostafa Abotaleb, and Ali S. Abed Al Sailawi
IGI Global
The utilization of the wearable devices (WDs) that are enhanced by artificial intelligence (AI) can have a notable potential in healthcare. This chapter aimed to provide an overview of the applications of AI-driven WDs in enhancing the early detection and management of virus infections. First, we presented examples to highlight the capabilities of WDs in very early monitoring of virus infections such as COVID-19. In addition, we provided an overview on the utility of machine learning algorithms to analyze large data for the detection of early signs of virus infections. We also overviewed the AI-driven WDs potential to enable real-time surveillance for effective virus outbreak management. We showed how this AI-driven WDs surveillance can be achieved via the collection and analysis of diverse real-time WDs' data across various populations. Finally, this chapter discussed the challenges and ethical issues that comes with AI-driven WDs in virology diagnostics, including concerns about data privacy and security as well as the issue of equitable access.
Winfred Sila, Fredrick Kayusi, Shillah Atuheire, Petros Chavula, Maad M. Mijwil, Mostafa Abotaleb, Kevin Okoth Ouko, and Benson Turyasingura
IGI Global
The integration of Artificial Intelligence (AI) into livestock management in Sub-Saharan Africa (SSA) offers a promising solution for improving food security amid climate change challenges. AI technologies have the potential to optimize agricultural practices, enhance supply chain management, and address animal health concerns. However, barriers to AI adoption, such as inadequate data processing capabilities, remain a challenge, especially for smallholder farmers. Food insecurity is a major issue in SSA, driven by climate change, rapid population growth, overreliance on foreign aid, and weak policies. Livestock supports 1.3 billion global livelihoods and plays a crucial role in SSA's food systems. Smallholders rely on livestock as a pathway out of poverty. By 2030, demand for animal-source food is expected to triple due to population growth and shifting consumption patterns. Despite this, there is a gap in policies supporting sustainable livestock production, essential for meeting demand and ensuring long-term food security. This review explores the links between livestock and food security and policy opportunities for a sustainable livestock system.
Maad M. Mijwil, Mohammad Aljanabi, Mostafa Abotaleb, Ban Salman Shukur, Ali S. Abed Al Sailawi, Indu Bala, Kamal Kant Hiran, Ruchi Doshi, and Klodian Dhoska
Mesopotamian Academic Press
Blockchain technology is a type of distributed ledger that provides secure and efficient storage, management, and transmission of data over a decentralized network. With its ability to ensure transparency and immutability, blockchain is increasingly adopted across various sectors ranging from finance, healthcare, and logistics to education. In healthcare, blockchain technology is attracting attention because of its potential to fundamentally transform health ecosystems. The healthcare sector has significantly benefited from blockchain technology by enhancing data security and interoperability and reducing medical errors. In this context, a set of studies highlighted the importance of blockchain in the field of healthcare, enhancing trust and security in the exchange of data and preventing unauthorized access. The article also studies the meaning, structure, function, types, and areas of use of blockchain technology and discusses the distribution of medical products in supply chain management. This article concludes that blockchain technology is highly important for storing health records, enhancing patient privacy, protecting patient data, and allowing the secure sharing of these data with physicians and healthcare workers.
Bushra Ali, Khder Alakkari, Mostafa Abotaleb, Maad M Mijwil, and Klodian Dhoska
Mesopotamian Academic Press
In the realm of stock market prediction and classification, the use of machine learning algorithms has gained significant attention. In this study, we explore the application of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithms in predicting and classifying stock prices, specifically amidst economic policy uncertainty. Stock market fluctuations are greatly influenced by economic policies implemented by governments and central banks. These policies can create uncertainty and volatility, which in turn makes accurate predictions and classifications of stock prices more challenging. By leveraging MLP and RBF algorithms, we aim to develop models that can effectively navigate these uncertainties and provide valuable insights to investors and financial analysts. The MLP algorithm, based on artificial neural networks, is able to learn complex patterns and relationships within financial data. The RBF algorithm, on the other hand, utilizes radial basis functions to capture non-linear relationships and identify hidden patterns within the data. By combining these algorithms, we aim to enhance the accuracy of stock price prediction and classification models. The results showed that both MLB and RBF predicted stock prices well for a group of countries using an index reflecting the impact of news on economic policy and expectations, where the MLB algorithm proved its ability to predict chain data. Countries were also classified according to stock price data and uncertainty in economic policy, allowing us to determine the best country to invest in according to the data. The uncertainty surrounding economic policy is what makes stock price forecasting so crucial. Investors must consider the degree of economic policy uncertainty and how it affects asset prices when deciding how to allocate their assets.
Khder Alakkari, Bushra Ali, Mostafa Abotaleb, Rana Ali Abttan, and Pushan Kumar Dutta
Mesopotamian Academic Press
This study addresses the challenge of nowcasting Gross Domestic Product (GDP) in data-scarce environments, with a focus on Syria, a country facing significant economic and political instability. Utilizing a dataset from 2010 to 2022, three machine learning algorithms Elastic Net, Ridge, and Lasso were applied to model GDP dynamics based on macroeconomic indicators, commodity prices, and high-frequency internet search data from Google Trends. Among these, the Lasso regression model, noted for its variable selection and sparsity promotion, proved most effective in capturing Syria's complex economic realities, achieving the lowest Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). This accuracy highlights the Lasso model's capability to identify robust economic relationships despite limited data, thereby reducing overfitting and improving forecast generalizability. The study underscores the significant impact of non-traditional indicators, such as Google Trends Agriculture (GTA) and Google Trends Consumption (GTC), on GDP growth, offering valuable insights for policymakers and analysts in data-scarce environments. The findings support the use of machine learning techniques, particularly Lasso regression, as powerful tools for economic forecasting, enhancing informed decision-making in challenging settings.
Mostafa Abotaleb and Tatiana Makarovskikh
Springer Science and Business Media LLC
Moussa Belletreche, Nadjem Bailek, Mostafa Abotaleb, Kada Bouchouicha, Bilel Zerouali, Mawloud Guermoui, Alban Kuriqi, Amal H. Alharbi, Doaa Sami Khafaga, Mohamed EL-Shimy,et al.
Springer Science and Business Media LLC
Amel Ali Alhussan, Doaa Sami Khafaga, Mostafa Abotaleb, Pradeep Mishra, and El-Sayed M. El-Kenawy
Springer Science and Business Media LLC
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
Mostafa Abotaleb and Pushan Kumar Dutta
De Gruyter
Mostafa Abotaleb and Pushan Kumar Dutta
De Gruyter
Pushan Kumar Dutta, Debosree Ghosh, and Mostafa Abotaleb
De Gruyter
Maad M. Mijwil, Mostafa Abotaleb, and Pushan Kumar Dutta
De Gruyter
Mostafa Abotaleb and Pushan Kumar Dutta
De Gruyter
Mostafa Abotaleb and Pushan Kumar Dutta
De Gruyter
Omega John Unogwu, Ruchi Doshi, Kamal Kant Hiran, Maad M. Mijwil, Ankar Tersoo Catherine, and Mostafa Abotaleb
IGI Global
In this chapter, the effects of cutting-edge artificial intelligence (AI) technologies at edge computing are examined in higher education. Edge computing offers a decentralized method of computing in which processing is done near the data source. Due to less network traffic, response times can be quicker. AI technology can be implemented at the edge to offer instructors and students intelligent and individualized services. The chapter addresses the advantages of edge computing and AI in higher education, including enhanced student involvement, better learning results, and simplified administrative procedures. It also looks at the difficulties of implementing AI at the edge, such as data privacy and security issues. To fully fulfill the potential of AI, the article's conclusion emphasizes the necessity for additional study in this field.
Shikha Yadav, Nadjem Bailek, Prity Kumari, Alina Cristina Nuţă, Aynur Yonar, Thomas Plocoste, Soumik Ray, Binita Kumari, Mostafa Abotaleb, Amal H. Alharbi,et al.
AIP Publishing
In the literature, it is well known that there is a bidirectional causality between economic growth and energy consumption. This is why it is crucial to forecast energy consumption. In this study, four deep learning models, i.e., Long Short-Term Memory (LSTM), stacked LSTM, bidirectional LSTM, and Gated Recurrent Unit (GRU), were used to forecast energy consumption in Brazil, Canada, and France. After a training test period, the performance evaluation criterion, i.e., R2, mean square error, root mean square error, mean absolute error, and mean absolute percentage error, was performed for the performance measure. It showed that GRU is the best model for Canada and France, while LSTM is the best model for Brazil. Therefore, the energy consumption prediction was made for the 12 months of the year 2017 using LSTM for Brazil and GRU for Canada and France. Based on the selected model, it was projected that the energy consumption in Brazil was 38 597.14–38 092.88, 63 900–4 800 000 GWh in Canada, and 50 999.72–32 747.01 GWh in France in 2017. The projected consumption in Canada was very high due to the country’s higher industrialization. The results obtained in this study confirmed that the nature of energy production will impact the complexity of the deep learning model.
Maad M. Mijwil, Indu Bala, Ali Guma, Mohammad Aljanabi, Mostafa Abotaleb, Ruchi Doshi, Kamal Kant Hiran, and El-Sayed M. El-Kenawy
IGI Global
Internet of things solutions have brought about a significant revolution in the development of healthcare by providing remote monitoring capabilities and providing doctors with reports on patients in real-time, which leads to developing the care of patients with type 2 diabetes and enhancing their health condition. Through several sensors, IoT devices can collect patients' health data, such as glucose level, blood pressure, heart rate, and physical activity, so that healthcare workers can assess patients' health status and disease development within the body. These devices contribute to saving patients' lives by providing continuous monitoring of vital signs and disease management by physicians and healthcare workers. In this context, this article contributes to reviewing the development of IoT solutions in providing information and mechanisms adopted in monitoring patients with type 2 diabetes, data security issues, privacy concerns, and interoperability.
Ali J. Ramadhan, S. R. Krishna Priya, V. Pavithra, Pradeep Mishra, Abhiram Dash, Mostafa Abotaleb, Hussein Alkattan, and Zainalabideen Albadran
EDP Sciences
Weather has a profound influence on crop growth, development and yield. The present study deals with the use of weather parameters for sugarcane yield forecasting. Machine learning techniques like K- Nearest Neighbors (KNN) and Random Forest model have been used for sugarcane yield forecasting. Weather parameters namely maximum temperature and minimum temperature, rainfall, relative humidity in the morning and evening, sunshine hours, evaporation along with sugarcane yield have been used as inputs variables. The performance metrics like R2, Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) have been used to select the best model for predicting the yield of the crop. Among the models, Random Forest algorithm is selected as the best fit based on the high R2 and minimum error values. The results indicate that among the weather variables, rainfall and relative humidity in the evening have significant influence on sugarcane yield.
Ali J. Ramadhan, Soumik Ray, Mostafa Abotaleb, Hussein Alkattan, Garima Tiwari, Deepa Rawat, Pradeep Mishra, Shikha Yadav, Pushpika Tiwari, Adelaja Oluwaseun Adebayo,et al.
EDP Sciences
To model and forecast complex time series data, machine learning has become a major field. This machine learning study examined Moscow rainfall data's future performance. The dataset is split into 65% training and 35% test sets to build and validate the model. We compared these deep learning models using the Root Mean Square Error (RMSE) statistic. The LSTM model outperforms the BILSTM and GRU models in this data series. These three models forecast similarly. This information could aid the creation of a complete Moscow weather forecast book. This material would benefit policymakers and scholars. We also believe this study can be used to apply machine learning to complex time series data, transcending statistical approaches.
Ali J. Ramadhan, S. R. Krishna Priya, Noor Razzaq Abbas, N. Kausalya, Shikha Yadav, Pradeep Mishra, Mostafa Abotaleb, and Hussein Alkattan
EDP Sciences
Sugarcane is the primary agricultural industry that sustains and promotes economic growth in India. In 2018, the majority of India's sugarcane production, specifically 79.9%, was allocated for the manufacturing of white sugar. A smaller portion, 11.29%, was used to produce jaggery, while 8.80% was utilized as seed and feed components. A total of 840.16 million metric tonnes of cane sugar was shipped in the year 2019. The primary objective of this research is to determine the most suitable forecasting model for predicting the monthly export price of sugarcane in India. The input consists of a time series with 240 monthly observations of the export price of sugarcane in India, spanning from January 1993 to December 2013. The SARIMA approach was employed to predict the monthly export price of sugarcane and it is concluded that the SARIMA (0, 1, 1), (0, 0, 0)12 model is the best-fitted one by the expert modeler method. As a result, the fitted model appears to be adequate. The RMSE and MAPE statistics are used to analyze the precision of the model.
Ali J. Ramadhan, Shikha Yadav, Subhash Anand, Aditya Pratap Singh, Kousik Atta, Mostafa Abotaleb, Hussein Alkattan, and Zainalabideen Albadran
EDP Sciences
Delhi's Yamuna River serves as a notable illustration of an ecologically compromised system that has undergone a transition into a conduit for sewage due to pervasive pollution and escalating anthropogenic influences. Delhi, being the primary contributor to pollution, is responsible for over 70% of the total pollutant load in the Yamuna. The city's drainage systems discharge a substantial Biological Oxygen Demand load into the river daily, resulting in severe pollution. This research utilizes pre-existing data to examine diverse factors, evaluating the quality of water at distinct observation locations along the Yamuna. The utilization of correlation analysis aids in recognizing connections among elements influencing the pollution of river water. The outcomes of the correlation analysis disclose a notable link between COD-BOD factors, whereas the connections among alternative factors like BOD-DO, BOD-pH, COD-DO, COD-pH, and DOpH range from moderate to negligible. The majority of observed parameters exceed hazardous levels deemed acceptable for river water utilization. The evaluation of Sewage Treatment Plants highlights the imperative to augment capacity in terms of treatment, storage, reactivation of closed plants, and efficient operation to meet the growing demand for fresh water. Additionally, there is a pressing need to generate demand for wastewater in diverse urban sectors.