International Journal of Engineering Research in Africa Vol. 60

Abstract: Using mobile cloud technology, local application resources can move into the cloud computing resource pool in the form of web services. The web services discovery and execution processes in the mobile environment are considered as a very difficult challenge. Moreover, these processes may degrade network performance due to the mobile environment. To overcome these issues, we present a new approach for better discovery, selection, execution, and negotiation of OWL-S services by utilizing a multi-agent system in mobile cloud computing. By using the Multi-Agent System (mobile agent and fixed agent), these processes are made with the minimum utilization of resources because the mobile agents migrate to targeted services and interact locally with them. Moreover, the use of mobile agent overcomes the problems related to the mobile environment and wireless devices. Besides, it automatizes the discovery, selection, execution, and negotiation of OWL-S services. The obtained results demonstrate the effectiveness of the proposed system. Moreover, we conclude that the mobile agent is a good solution to eliminate the problem of wireless devices and the mobile environment, and reduced the execution time of service discovery and invocation. Also, the finding indicates that cloud computing is a good solution to eliminate the problem of storage and execution in mobile devices. Besides, the context-aware provides a more accurate service selection.

Abstract: The accuracy of demand forecasting has a significant impact on the supply chain system's performance, which in turn has a major effect on company performance. Accurate forecasting will allow the organization to make the best use of its resources. The synchronization of customer orders to support production is critical for on-time order fulfillment. However, In fact many organizations report that their forecasting method is not working as effectively as they had hoped because orders regularly alter due to client demands. The purpose of this paper is to present an Internet of Things (IoT)-based inventory management system (IMS) that combines a causal method of multiple linear regressions (MLR) with genetic algorithms (GA) to improve the accuracy of demand forecasting in the future period by the customer as closely as feasible and enable smart inventory for Industry 4.0. Based on the data gathered from a semiconductor company that specializes in low-volume, high-mix contract manufacturing equipment and services integration, the suggested IoT-based IMS indicates that inventory productivity and efficiency could be enhanced, and it is resilient to order fluctuation.

Abstract: Agricultural mechanization is an essential factor influencing agricultural output and the profitability of farming activities. The influence of agricultural mechanization on agricultural production in Lagos State, where the majority of the farmers use modern technologies for their farming operations, was investigated. The investigative research approach method was employed to retrieve information from farmers through a structured questionnaire. A five rating scale questionnaire was utilized for the respondents to show their level of agreement or disagreement. The percentage was used to analyze the respondents' bio-data. At the same time, the mean was employed to answer the research questions. The null hypotheses were tested using Chi-square statistics at 0.05 significant levels. The results revealed that agricultural mechanization increased the cultivated land, crop yields, and farmers’ income with cumulative means of 2.34, 1.07, and 1.44, respectively. Socioeconomic characteristics, available technology, and government policies influenced agricultural mechanization with cumulative means of 1.93, 1.24, and 1.79, respectively. The entire six hypotheses were rejected based on the results of the Chi-square statistics with the calculated X² values of 8,989.09, 473.59, 3,977.42, 2,192.63, 226.07 and, 1,878.05; and critical X² values of 46.19, 46.19, 36.42, 31.41, 21.03, and 31.41, for the significant effect on the size of land cultivated, crop yield, farmer’s income, socioeconomic characteristics, available technology, and government policies respectively. The study showed that agricultural mechanization had a significant influence on crop production and farmers’ income. Therefore, there is a need to improve the available technologies and formulate and implement policies to make agricultural mechanization accessible and sustainable.