Seaports play a crucial role in the global economy, and researchers have sought to understand their significance through various studies. In this paper, we aim to explore the common characteristics shared by important ports by analyzing the network of connections formed by vessel movement among them. To accomplish this task, we adopt a bottom-up network construction approach that combines three years' worth of AIS (Automatic Identification System) data from around the world, constructing a Ports Network that represents the connections between different ports. Through such representation, we use machine learning to measure the relative significance of different port features. Our model examined such features and revealed that geographical characteristics and the depth of the port are indicators of a port's significance to the Ports Network. Accordingly, this study employs a data-driven approach and utilizes machine learning to provide a comprehensive understanding of the factors contributing to ports' importance. The outcomes of our work are aimed to inform decision-making processes related to port development, resource allocation, and infrastructure planning in the industry.

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed significant health challenges worldwide. While respiratory symptoms have been the primary focus, emerging evidence has highlighted the impact of COVID-19 on various organ systems, including the gastrointestinal (GI) tract. This study, based on data from 913 post-COVID-19 patients in Iraq collected during 2022 and 2023, investigates the prevalence and patterns of GI symptoms in individuals recovering from COVID-19 and leverages machine learning algorithms to identify predictive factors for these symptoms. The research findings reveal that a notable percentage of post-COVID-19 patients experience GI symptoms during their recovery phase. Diarrhea emerged as the most frequently reported symptom, followed by abdominal pain and nausea. Machine learning analysis uncovered significant predictive factors for GI symptoms, including age, gender, disease severity, comorbidities, and the duration of COVID-19 illness. These findings underscore the importance of monitoring and addressing GI symptoms in post-COVID-19 care, with machine learning offering valuable tools for early identification and personalized intervention. This study contributes to the understanding of the long-term consequences of COVID-19 on GI health and emphasizes the potential benefits of utilizing machine learning-driven analysis in predicting and managing these symptoms. Further research is warranted to delve into the mechanisms underlying GI symptoms in COVID-19 survivors and to develop targeted interventions for symptom management. Keywords: COVID-19, gastrointestinal symptoms, machine learning, predictive factors, post-COVID-19 care, long COVID.