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In this study, a multi-linear regression model for potential fishing zone (PFZ) mapping along the Saudi Arabian Red Sea coasts of Yanbu’ al Bahr and Jeddah was developed, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived parameters, such as sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll-a (Chl-a). MODIS data was also used to validate the model. The model expanded on previous models by taking seasonal variances in PFZs into account, examining the impact of the summer, winter, monsoon, and inter-monsoon season on the selected oceanographic parameters in order to gain a deeper understanding of fish aggregation patterns. MODIS images were used to effectively extract SSS, SST, and Chl-a data for PFZ mapping. MODIS data were then used to perform multiple linear regression analysis in order to generate SSS, SST, and Chl-a estimates, with the estimates validated against in-situ data obtained from field visits completed at the time of the satellite passes. The proposed model demonstrates high potential for use in the Red Sea region, with a high level of congruence found between mapped PFZ areas and fish catch data (R2 = 0.91). Based on the results of this research, it is suggested that the proposed PFZ model is used to support fisheries in determining high potential fishing zones, allowing large areas of the Red Sea to be utilized over a short period. The proposed PFZ model can contribute significantly to the understanding of seasonal fishing activity and support the efficient, effective, and responsible use of resources within the fishing industry.

This research presents a thorough study of the strategies deployed for information dissemination on social networks. The strategies are modeled for identifying factors proving pivotal in influencing public opinions. The public is categorized into active and passive participants to discuss the study of these strategies for disseminating informational pieces such as images, sketches, text, and so on social networks, based on computer modeling of the social network structure alongside modeling of the behavior of network users. The objective of this research is to benchmark the proposed model on the effectiveness of information dissemination strategies that can be proposed to spread informational influences on social networks. As a rule, "Opinion Leaders" in the purpose network sectors are used to spread information on social media among the largest number of users in the least amount of time. Experiments have proved the effectiveness of the information dissemination model using "Opinion Leaders", as well as the efficiency of the methods of information dissemination for different winning structural positions in a social network. Experiments have also confirmed the high effectiveness in attracting "Opinion Leaders" to the dissemination processes.

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