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Wireless communication systems must be able to support the high demands of new networks and devices in order to meet the rapidly expanding economic and cultural needs of human society. 5G/Beyond 5G systems must connect with a high number of devices, have significantly lower latency than 3G/4G wireless networks, and meet strict quality-of-service (QoS) requirements. The application of artificial intelligence (AI) and machine learning (ML) to wireless applications will be one of the primary and essential elements of 5G systems. These methods are used in all parts and building blocks of wireless systems at the physical, network, and application layers. Researchers from all over the world are very interested in the 5G criteria and these technologies. A 5G network needs AI technologies to be completely functional and effective. On the other hand, ML is a promising technology that may be applied to data processing. Existing generations broadcast using Internet Protocol (IP) broadband access, which is inefficient. Hence, in order to function 5G networks they must be proactive and predictive to ML and AI. In this regard, this paper highlights the role and significance of AI and ML in 5G communications and also provide potential future research directions. [ABSTRACT FROM AUTHOR]

We made acquainted an submerged concept augmentation model from the idea of patterning and the teleost fish retina's role. We want to provide answers to the concerns about underwater face deconstruction that have been raised by both clouding and decreased contrast. The variable colour bias is adjusted using the reaction from color-impressionable level containers to cones and a red channel adjustment. The edges and contrasts of the product depiction are probably accentuated by the center surround aspirant strategy of the vacillating containers and the reaction from amacrine containers to interplexi form containers before reaching level containers. The center of activity containers accompanying color-rival means are secondhand for color augmentation and color discipline. The enhanced figure is reorganized using a fusion process that combines the angle retina's ON and OFF pathway outputs. Extensive methods and mathematical evaluations on differing submerged settings certify the hostile depiction of our method. This model again considerably develops the veracity of broadcast plan guess and local feature point equal utilizing the submerged countenance. [ABSTRACT FROM AUTHOR]

Now a days we see more people are dying because of heart attacks and cardiac arrest. The Electrocardiogram is mostly used for identifying the heart diseases and also, we can find the cardiovascular state of the person. The deep learning is an approach to study about the abnormalities present in an ECG cycle. In this project we discuss about two methods reduction of noise and detection of abnormalities. In this approach we use Convolutional neural networks which gives the accuracy between 75 to 85% and also this method can identify the minute changes in the given signal. This process is done using MIT-BIH database. [ABSTRACT FROM AUTHOR]

Satellite images mainly utilized in the events of a natural disaster management, identifying geographical information, viz land cover classes namely, buildings, roads, vegetation, water, agriculture land, crop types, plants, bare ground, cities, atmosphere conditions. Machine Learning (ML) approaches have been utilized effectively to develop a model for classification, detection, and segmentation tasks. Therefore, Satellite image processing and analysis purpose, ML techniques plays vital role and remotely sensed data become essential while training the model. The aim of this study is to investigate the various of ML techniques in satellite image analysis. However, to predict the various events in advance across the globe, it is necessary to focus more on remote sensed data and data processing techniques for accurate classification. Even though remote sensing quality has been increased and artificial intelligence solutions are equally increased. This paper addressed various types of advanced ML techniques utilized in the classification and assessment of satellite images and used to track the earthquakes, faulting, landslides, floodings, wildfire, and hazards associated with the stated activities. Still there is a gap and interference in the approaches and it is important to fill the gap by thorough review of recent classification approaches. In this connection it is necessary to look in depth to the state-of-the-art ML techniques of satellite image processing. [ABSTRACT FROM AUTHOR]