In recent years, the field of research around the congestion problem of 4G and 5G networks has grown, especially those based on artificial intelligence (AI). Although 4G with LTE is seen as a mature technology, there is a continuous improvement in the infrastructure that led to the emergence of 5G networks. As a result of the large services provided in industries, Internet of Things (IoT) applications and smart cities, which have a large amount of exchanged data, a large number of connected devices per area, and high data rates, have brought their own problems and challenges, especially the problem of congestion. In this context, artificial intelligence (AI) models can be considered as one of the main techniques that can be used to solve network congestion problems. Since AI technologies are able to extract relevant features from data and deal with huge amounts of data, the integration of communication networks with AI to solve the congestion problem appears promising, and the research requires exploration. This paper provides a review of how AI technologies can be used to solve the congestion problem in 4G and 5G networks. We examined previous studies addressing the problem of congestion in networks, such as congestion prediction, congestion control, congestion avoidance, and TCP development for congestion control. Finally, we discuss the future vision of using AI technologies in 4G and 5G networks to solve congestion problems and identify research issues that need further study.
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Background: Intralesional injections of vitamin D and bleomycin have gained growing clinical interest in the treatment of recalcitrant plantar warts, with pain as a major limitation associated with their use. Objective: To assess the clinical outcomes of vitamin D and bleomycin in managing treatment-resistant plantar warts. Methods: An interventional comparative study conducted on patients diagnosed with recalcitrant plantar warts over 9 months and not treated for two months. Patients were divided randomly into two groups: group A (24 patients received intralesional vitamin D) and group B (24 patients received intralesional bleomycin). The clearance rate and pain score were estimated by visual analogue scale (VAS). Results: 48 patie
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Gingivitis, the initial stage of periodontal disease, is characterised by inflammation driven by dental biofilm and associated with oxidative stress. Matcha tea, a powdered green tea rich in antioxidants, has shown potential health benefits. This study aimed to investigate the effect of Matcha tea consumption on clinical periodontal parameters and salivary antioxidant levels in patients with gingivitis.
A randomised controlled clinical trial was conducted with 41 participants diagnosed with gingivitis.
This histological study was carried out to compare between the thyroid gland of mice (as a model of the mammals) and the thyroid tissue of fish. Unlike mice, the thyroid gland of fish can't be recognized by naked eye. The present study revealed that the thyroid of mice varied from that of fish by the location and the histological structure. The study classified the physiological state of the thyroid of mice into three states and that of the fish into only two states. Accordingly, the study concluded that the metabolism of thyroid fish was of moderate type.
This paper deals the prediction of the process of random spatial data of two properties, the first is called Primary variables and the second is called secondary variables , the method that were used in the prediction process for this type of data is technique Co-kriging , the method is usually used when the number of primary variables meant to predict for one of its elements is measured in a particular location a few (because of the cost or difficulty of obtaining them) compare with secondary variable which is the number of elements are available and highly correlated with primary variables, as was the&nbs
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The oil and gas industry relies heavily on IT innovations to manage business processes, but the exponential generation of data has led to concerns about processing big data, generating valuable insights, and making timely decisions. Many companies have adopted Big Data Analytics (BDA) solutions to address these challenges. However, determining the adoption of BDA solutions requires a thorough understanding of the contextual factors influencing these decisions. This research explores these factors using a new Technology-Organisation-Environment (TOE) framework, presenting technological, organisational, and environmental factors. The study used a Delphi research method and seven heterogeneous panelists from an Oman oil and gas company
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