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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A new Differential Evolution (ARDE) algorithm is introduced that automatically adapt a repository of DE strategies and parameters adaptation schemes of the mutation factor and the crossover rate to avoid the problems of stagnation and make DE responds to a wide range of function characteristics at different stages of the evolution. ARDE algorithm makes use of JADE strategy and the MDE_pBX parameters adaptive schemes as frameworks. Then a new adaptive procedure called adaptive repository (AR) has been developed to select the appropriate combinations of the JADE strategies and the parameter control schemes of the MDE_pBX to generate the next population based on their fitness values. Experimental results have been presented to confirm the reli
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The current study aimed to assess the biological efficacy of the triple arbuscular mycorrhiza (AM) mixture of fungi Glomus etunicatum, G. leptotichum and Rhizophagus intraradices, and mix it with organic matter (O) and pathogenic fungi Fusarium oxysporum f.sp.lycopersici by using voyeurism in the plastic house in the growth of the tomato plant after four and eight weeks of cultivation. The results were shown after the treatment of the tomato plant in agriculture with the mixture of mycorrhiza and the pathogenic fungi and organic matter were treated with the mixture of mycorrhiza, organic matter and pathogenic fungi together. The effect of mycorrhiza and organic matter interference on the increase in the percentage of the lignin afte
... Show MoreThe kaizen is considered as one of the most important modern techniques which has been adopted by various economics entities especially manufacturing firms and its beginnings return to the middle of the earlier century that has been used by companies like Toshiba, Matsushita Electric, and Toyota. Which realized that these modern techniques would make a total change in the competitive environment and started qualifying and its staff in such away that enables them to go along with this unique environment. The continuous improvement (Kaizen) depends on the small continuous improvements in the product and the production operations during the production stage. Consequently, the research problem is represented in the improperly of the budg
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The present work aims to study the efficiency of using aluminum refuse, which is available locally (after dissolving it in sodium hydroxide), with different coagulants like alum [Al2 (SO4)3.18H2O], Ferric chloride FeCl3 and polyaluminum chloride (PACl) to improve the quality of water. The results showed that using this coagulant in the flocculation process gave high results in the removal of turbidity as well as improving the quality of water by precipitating a great deal of ions causing hardness. From the experimental results of the Jar test, the optimum alum dosages are (25, 50 and 70 ppm), ferric chloride dosages are (15, 40 and 60 ppm) and polyaluminum chloride dosages were (10, 35 and 55 ppm) for initial water turbidity (100, 500 an
... Show MoreIn this study, ultraviolet (UV), ozone techniques with hydrogen peroxide oxidant were used to treat the wastewater which is produced from South Baghdad Power Station using lab-scale system. From UV-H2O2 experiments, it was shown that the optimum exposure time was 80 min. At this time, the highest removal percentages of oil, COD, and TOC were 84.69 %, 56.33 % and 50 % respectively. Effect of pH on the contaminants removing was studied in the range of (2-12). The best oil, COD, and TOC removal percentages (69.38 %, 70 % and 52 %) using H2O2/UV were at pH=12. H2O2/ozone experiments exhibited better performance compared to
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Materials recycling has a significant economic and environmental impact; as a result, steel, aluminium, plastic, and other recyclable materials have been pushed for use in construction materials. One of these recyclable materials is the crumb rubber, has been considered as a pavement component. The general behaviour of the composite rubber-hot mix asphalt system would be varied from that of the conventional rubber free mix. In this review, desirable characteristics of hot mix asphalt are highlighted first. Also, effect of gradation and the main types of rubber are specified. Afterward, many studies that considered the crumb rubber as a waste product and its associated mixture and modifiers are reviewed. The factors affect the crumb
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The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre
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