The expansion of the Internet of Things has resulted in new creative solutions, such as smart cities, that have made our lives more productive, convenient, and intelligent. The core of smart cities is the Intelligent Transportation System (ITS) which has been integrated into several smart city applications that improve transportation and mobility. ITS aims to resolve many traffic issues, such as traffic congestion issues. Recently, new traffic flow prediction models and frameworks have been rapidly developed in tandem with the introduction of artificial intelligence approaches to improve the accuracy of traffic flow prediction. Traffic forecasting is a crucial duty in the transportation industry. It can significantly affect the design of road constructions and projects in addition to its importance for route planning and traffic rules. Furthermore, traffic congestion is a critical issue in urban areas and overcrowded cities. Therefore, it must be accurately evaluated and forecasted. Hence, a reliable and efficient method for predicting traffic is essential. The main objectives of this study are: First, present a comprehensive review of the most popular machine learning and deep learning techniques applied in traffic prediction. Second, identifying inherent obstacles to applying machine learning and deep learning in the domain of traffic prediction.

The Egyptian economy relies heavily on the agricultural sector. As the population grows, arable land will diminish in the next decades. This makes food supply a priority. Big data could help the agriculture sector to address food security, especially in Egypt. In this paper, we examined the role of big data in agriculture in response to three questions: (1) What are the trend in peer-reviewed papers in the field of business development modeling and management? (2) What approaches were widely used especially in underdeveloped countries? (3) What is the current gap in terms of data sources, modeling, and analytic methods? As a result, 242 peer-reviewed articles have been studied. The contribution and findings of this study are summarized as. (1) A briefing on popular approaches which used frameworks was provided. (2) Publications based on the Internet of Things (IoT) in agriculture have increased dramatically by about 27%, 40%, and 44% in the years 2017, 2018, and 2019, respectively. (3) Around 37% of publications used Landsat and Sentinel-2 satellite images to build popular vegetation indices and land cover maps. (4) The challenges were identified as well as substantial opportunities that might serve as a roadmap for future growth. Therefore, by performing a comparative study in big data from this perspective, we explored the design principles using artificial intelligence and discussed a converged architecture to address the above-mentioned challenges.

The expansion of the Internet of Things has resulted in new creative solutions, such as smart cities, that have made our lives more productive, convenient, and intelligent. The core of smart cities is the Intelligent Transportation System (ITS) which has been integrated into several smart city applications that improve transportation and mobility. ITS aims to resolve traffic issues, especially traffic congestion. Recently, new traffic flow prediction models and frameworks have been rapidly developed in tandem with the introduction of Artificial Intelligence (AI) approaches to improve the accuracy of traffic flow prediction. Traffic forecasting is a crucial duty in the transportation industry. It can significantly affect the design of road constructions and projects in addition to its importance for route planning and traffic rules. Furthermore, traffic congestion is a critical issue in urban areas and overcrowded cities. Therefore, it must be accurately evaluated and forecasted. Hence, a reliable and efficient method for predicting traffic is essential. The main objectives of this study are: First, present a comprehensive review of the most popular machine learning and deep learning techniques applied in traffic prediction. Second, identifying inherent obstacles to applying machine learning and deep learning in the domain of traffic prediction.

Multiple-Criteria Decision Analysis (MCDA) is concerned with structuring and taking decisions involving multiple criteria. Integrated with Geographic Information System (GIS) technology, MCDA can be used to select the best locations for any investment project. However, not all investors/clients have the required skills to use these specialised methods and programmes. Also, there may be incompatibility between the consultant and the end user in the end results. Therefore, this research aims to program a web-based Decision Support Engine (DSE) to facilitate the decision-making process and involve the end user in such process in an easy interactive manner. The proposed DSE was applied on E-marketing applied study in Cairo and Giza governorates. Data were collected by electronic questionnaire (928 Questionnaires) that was processed by SPSS, to create the geodatabase used by Django web framework to build the DSE. The end result of DSE is producing an Interactive Map to support clients decision-making in an easy manner.