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
Shatha H. Jafer AL-Khalisy, Wafaa M. Salih Abed, Ghada Al-Kateb, Mohammad Aljanabi, Maad M. Mijwil, Mostafa Abotaleb, and Klodian Dhoska
Akademiai Kiado Zrt.
AbstractThis paper introduces a quantum-inspired ultra-lightweight encryption algorithm tailored for Internet of things devices with limited resources. The proposed algorithm excels in processing speed, memory usage, and energy efficiency, significantly outperforming existing lightweight cryptographic algorithms. With a processing speed of 12.4 ms, memory usage of 3.2 kilobytes, and energy consumption of 0.7 milli-Joules per kilobyte, the proposed algorithm stands out for its robust security and potential to enhance the security of Internet of things devices across various applications. This paper explores the methodology behind the proposed algorithm, comparing its performance metrics with conventional S-box generation approaches, and demonstrates its superiority in both theoretical and practical aspects.
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.
Ghada Al-Kateb, Maad M. Mijwil, Mohammad Aljanabi, Mostafa Abotaleb, S. R. Krishna Priya, and Pradeep Mishra
Springer Science and Business Media LLC
El-Sayed M. El-Kenawy, Amel Ali Alhussan, Doaa Sami Khafaga, Mostafa Abotaleb, Pradeep Mishra, Reham Arnous, and Marwa M. Eid
Springer Science and Business Media LLC
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.
Mostafa Abotaleb and Tatiana Makarovskikh
Springer Nature Switzerland
Nematollah Kadkhoda, Mojtaba Baymani, Maad M. Mijwil, and Mostafa Abotaleb
College of Education - Aliraqia University
Evariste Gatabazi, Maad M. Mijwil, Mostafa Abotaleb, and Saganga Kapaya
IGI Global
This study reviewed a total of 3171 published articles, mainly from 1992-2024. The review was performed using scientifically cited and indexed databases, namely Dimensions, Web Science, Elsevier Scopus, and Google Scholar. This study demonstrates how AI technologies, such as computer vision and system learning, may revolutionize industrial efficiency, productivity, and satisfactory control. Superior algorithms, neural networks, and big data analytics are integrated to optimize manufacturing strategies and enable intelligent decision-making, which is where the innovation lies. Also, it was found that building workforce capacity through collaborations and customized training programs can help close the skills gap, while improving cybersecurity and implementing efficient data management frameworks can help with privacy issues. However, despite the growing body of literature on AI packages, studies specializing in AI embracing on the organizational level stay restrained.
Pradeep Mishra, Mostafa Abotaleb, Binita Kumari, El-Sayed M. El-kenawy, and Shikha Yadav
Springer Science and Business Media LLC
Bhukya Arun Kumar, Ananya Tripathi, Ban Salman Shukur, Indu Bala, Maad M. Mijwil, and Mostafa Abotaleb
Springer Nature Switzerland
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.
Hussein Alkattan, Noor Razzaq Abbas, Oluwaseun A. Adelaja, Mostafa Abotaleb, and Guma Ali
Mesopotamian Academic Press
The reason of this paper is to clarify dynamic clustering, the divisive and agglomerative dynamic clustering techniques. It fundamentally centers on the concept of the divisive different leveled shapes as well known as the top-down approach by creating a workflow appear, dendrograms, clustered data table which accumulated the clusters based the chosen property, and appear the isolated between each cluster with the assistance of an data mining device called Python. The DIANA dynamic approach utilized data tests of the list of laborers in a Data Advancement firm to induce clusters from the position column inside the data test table. In this work, we in addition executed genuine infers by creating barchart that shows up the ages of the chosen agent sets plotted against the positions which are the Engineers, Assistants, Workers and Troughs.
Guma Ali, Maad M. Mijwil, Bosco Apparatus Buruga, Mostafa Abotaleb, and Ioannis Adamopoulos
Mesopotamian Academic Press
Wireless sensor networks and Internet of Things devices are revolutionizing the smart agriculture industry by increasing production, sustainability, and profitability as connectivity becomes increasingly ubiquitous. However, the industry has become a popular target for cyberattacks. This survey investigates the role of artificial intelligence (AI) in improving cybersecurity in smart agriculture (SA). The relevant literature for the study was gathered from Nature, Wiley Online Library, MDPI, ScienceDirect, Frontiers, IEEE Xplore Digital Library, IGI Global, Springer, Taylor & Francis, and Google Scholar. Of the 320 publications that fit the search criteria, 180 research papers were ultimately chosen for this investigation. The review described advancements from conventional agriculture to modern SA, including architecture and emerging technology. It digs into SA’s numerous uses, emphasizing its potential to transform farming efficiency, production, and sustainability. The growing reliance on SA introduces new cyber threats that endanger its integrity and dependability and provide a complete analysis of their possible consequences. Still, the research examined the essential role of AI in combating these threats, focusing on its applications in threat identification, risk management, and real-time response mechanisms. The survey also discusses ethical concerns such as data privacy, the requirement for high-quality information, and the complexities of AI implementation in SA. This study, therefore, intends to provide researchers and practitioners with insights into AI’s capabilities and future directions in the security of smart agricultural infrastructures. This study hopes to assist researchers, policymakers, and practitioners in harnessing AI for robust cybersecurity in SA, assuring a safe and sustainable agricultural future by comprehensively evaluating the existing environment and future trends.
Mostafa Abotaleb and Tatiana Makarovskikh
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
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
Mostafa Abotaleb and Pushan Kumar Dutta
De Gruyter
Mostafa Abotaleb and Pushan Kumar Dutta
De Gruyter
Mostafa Abotaleb and Pushan Kumar Dutta
De Gruyter