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
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
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.
Ali J. Ramadhan, Bhukya Arun Kumar, Indu Bala, Maad M. Mijwil, Mostafa Abotaleb, Hussein Alkattan, and Zainalabideen Albadran
EDP Sciences
Through the use of smart sensors to monitor and regulate plant conditions, smart home gardening management systems can maximize resource utilisation and minimize human intervention. This study offers a new system that remotely controls the water supply to ensure optimal plant growth without the need for personal presence. The system uses the Blynk IoT platform to monitor soil moisture and water levels. A Raspberry Pi is used in conjunction with several sensors, such as a soil moisture sensor and a DHT11 sensor for temperature and humidity readings. The technology activates a motor to provide water to the plants automatically when the soil moisture level falls below a certain threshold. Users can remotely monitor and manage the system from their cell phones thanks to integration with the Blynk platform. The suggested method is an affordable and effective way to garden in your home, and it’s simply customizable to fit the requirements of different users.
Sahar Yousif Mohammed, Mohammad Aljanabi, Maad M. Mijwil, Ali J. Ramadhan, Mostafa Abotaleb, Hussein Alkattan, and Zainalabideen Albadran
EDP Sciences
The goal of phishing assaults is to trick users into giving up personal information by making them believe they need to act quickly on critical information. The creation of efficient solutions, such as phishing attack detection systems backed by AI, is essential for the safety of users. This research suggests a two-stage hybrid strategy that uses both URL and content analysis to identify phishing assaults. In the first step of the suggested method, URL analysis is used to determine the legitimacy of suspected phishing assaults. If the site is still live, the second check uses content analysis to determine how serious the attack is. Both analysis' findings are taken into account in the decision-making procedure. As can be seen from the experiments, the hybrid system obtains an astounding 99.06% accuracy rate. This research adds to the existing body of knowledge by providing a massive dataset of over 14 million data samples that includes both legal and phishing URLs. Furthermore, when content analysis is required for phishing URL detection, the two-stage hybrid technique significantly outperforms URL analysis alone by 70.23 %. The proposed method provides better defense against phishing attempts and is practical enough for widespread use.
Ali J. Ramadhan, S. R. Krishna Priya, N. Naranammal, S. Pavishya, K. Naveena, Soumik Ray, P. Mishra, Mostafa Abotaleb, Hussein Alkattan, and Zainalabideen Albadran
EDP Sciences
Sugarcane is the largest crop in the world in terms of production. We use sugarcane and its byproducts more and more frequently in our daily lives, which elevates it to the status of a unique crop. As a result, the assessment of sugarcane production is critical since it has a direct impact on a wide range of lives. The yield of sugarcane is predicted using ARIMA and ANN models in this study. The models are based on sugarcane yield data collected over a period of 56 years (1951-2017). Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) have been used to analyze and compare the performance of different models to obtain the best-fit model. The results show that the RMSE and MAPE values of the ANN model are lower than those of the ARIMA model and that the ANN model matches best to this data set.
Ali J. Ramadhan, Diksha Bohra, Supriya, Aditya Bhooshan Srivastava, Prateek Kumar, Sandeep Gautam, Suman, Priyanka Lal, Mostafa Abotaleb, and Hussein Alkattan
EDP Sciences
The paper attempts to analyze fish production and the direction of trade. Data for the analysis was taken from a period of 10 years (2011- 2021) from the Ministry of Commerce & Industry and FAO. To examine the type and extent of increase in the fish area, production, and productivity throughout the course of the year for several countries, including China, Vietnam, the United States, Norway, and India, descriptive statistics and the sustainability index were utilized. Markov chain analysis employing linear programming was then applied to determine transition probabilities in fish trade. The fish export markets were the USA, China, Japan, Thailand, Taiwan, Kuwait, Hong Kong, and others. The fish export markets were categorized as stable markets (China, USA, Taiwan, Thailand, and Hong Kong) and unstable markets (Japan and Kuwait) based on the magnitude of transition probabilities. Though the country has a good potential for export of fish. India must therefore give rising output more consideration, supported by measures that encourage exports. In addition, initiatives must be made to develop a new market and broaden the trade area to include other significant, global markets.
Ali J. Ramadhan, Tufleuddin Biswas, Soumik Ray, S. R. Anjanawe, Deepa Rawat, Binita Kumari, Shikha Yadav, Pradeep Mishra, Mostafa Abotaleb, Hussein Alkattan,et al.
EDP Sciences
The study aimed to compare ARIMA and Holt's models for predicting coconut metrics in Kerala. The coconut data series was collected from the period 1957 to 2019. Of this, 80% of the data (from 1957 to 2007) is treated as training data, and the rest (20% from 2008 to 2019) is treated as testing data. Ideal models were selected based on lower AIC and BIC values. Their accuracy was evaluated through error estimation on testing data, revealing Holt's exponential, linear, and ARIMA (0,1,0) models as the bestfit choices for predicting coconut area, production, and productivity respectively. After using the testing data, we tried for the forecasting for 2020-2024 using these models, and the DM test confirmed their significant forecasting accuracy. This comprehensive analysis provides valuable insights into effective prediction models for coconut-related metrics, offering a foundation for informed decision-making and future projections.
Ali J. Ramadhan, Ankit Kumar Tiwari, Birendra Kumar, Supriya, Harshit Mishra, Sandeep Gautam, Rajani Gautam, Mostafa Abotaleb, and Hussein Alkattan
EDP Sciences
This study offers a detailed comparative analysis of maize crop cultivation in the kharif and rabi seasons within the agricultural landscape of Gonda District. 50 respondents were carefully selected from various villages in the block, with proportional representation for Marginal, Small, and Medium-sized farmers. The research delves into the economics of maize cultivation, emphasizing factors such as the cost of cultivation, input expenses, income generation, and input-output ratios. In the kharif season, it was distinguished that the cost of cultivation of maize with the farm's size. Marginal farms spent an average of ₹ 48125.93 per hectare, small farms incurred ₹ 51002.89, and large farms invested ₹ 54295.17. Similarly, during the rabi season, the cost of cultivation increased with farm size, with marginal farms investing an average of ₹ 52397.57, small farms spending ₹ 55444.93, and large farms allocating ₹ 58604.68 per hectare. Crucially, the study found that input-output ratios remained consistent across farm sizes in both seasons, reflecting uniform agricultural practices. The findings underscore the importance of efficient management, the adoption of advanced agricultural techniques, the use of high-quality seeds, and the timely application of irrigation and plant protection practices in enhancing net income, particularly on marginal farms.
Ali J. Ramadhan, S. R. Krishna Priya, R. Keerti Balambiga, Ali J. Othman, Shikha Yadav, Pradeep Mishra, Mostafa Abotaleb, Hussein Alkattan, and Zainalabideen Albadran
EDP Sciences
The present study aims to develop yield forecast models for the Sugarcane crop of the Coimbatore district in Tamilnadu using two different techniques namely Variables and Months in Discriminant function analysis. For this, the Sugarcane yield data for 57 years along with the monthly data on seven weather variables have been taken. For applying discriminant analysis, the yield data of sugarcane has been divided into two categories namely two groups and three groups. The discriminant scores from the two and three-group discriminant functions were employed as independent variables in the development of yield forecast models. The yield forecast models for both strategies were created utilizing scores and trend values as independent variables. The first 52 years of yield data (1960-2012) were used to create the model, and the last five years of data (2012-2016) were used for validation. The comparison has been made between two and three groups for both techniques. The results indicate the technique using the variable-wise method gives better results based on goodness of fit. Among the two categories in the variable-wise method, three groups performed better.
Ali J. Ramadhan, S. R. Krishna Priya, N. Naranammal, Suman, Priyanka Lal, Pradeep Mishra, Mostafa Abotaleb, and Hussein Alkattan
EDP Sciences
Agriculture is the backbone of Indian Economy. Proper forecast of food crops and cash crops are necessary for the government in policy making decisions. The present paper aims to forecast Wheat and Sugarcane yield using Random Forest Regression. For the development of Random Forest models, Yield has been taken as dependent variable and variables like Gross Cropped Area, Maximum Temperature, Minimum Temperature, Rainfall, Nitrogen, Phosphorous Oxide, Potassium Oxide, Minimum Support Price and Area under Irrigation are taken as independent variables for both Wheat and Sugarcane crop. Values of R2 for Wheat and Sugarcane is 0.995 and 0.981 which indicates that the model is a good fit and other performance measures are calculated and results are satisfactory.