Pericardial cysts are very rare disorder with an incidence of about 1 in 1, 00,000. Pericardial cyst and diverticulum share similar developmental origin and may appear as an incidental finding in chest x ray in an asymptomatic patient. CT scan is considered as best modality for diagnosis and delineation of surrounding anatomy. Cardiac MRI is another excellent tool in diagnosis and evaluation of compressive effect and diffusion weighted cardiac MRI are very helpful for cases with diagnostic confusion. Echocardiography is best modality for follow up and image guided aspiration of the cyst. Conservative management with regular follow up may be considered if the cyst is small, patient is asymptomatic and probability of subsequent complication is low. Surgical resection should be considered in symptomatic patients, large cysts and with high probability of complications. Percutaneous aspiration and ethanol sclerosis is another attractive option.

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Detecting influential nodes in complex social networks is crucial due to the enormous amount of data and the constantly changing behavior of existing topologies. Centrality-based and machine-learning approaches focus mostly on node topologies or feature values in their evaluation of nodes’ relevance. However, both network topologies and node attributes should be taken into account when determining the influential value of nodes. This research has proposed a deep learning model called Graph Convolutional Networks (GCN) to discover the significant nodes in graph-based large datasets. A deep learning framework for identifying influential nodes with structural centrality via Graph Convolutional Networks called DeepInfNode has been developed. The proposed approach measures up contextual information from Susceptible-Infected-Recovered (SIR) model trials to measure the rate of infection to develop node representations. In the experimental section, acquired experimental results indicate that the suggested model has a higher F1 and Area under the curve (AUC) value. The findings indicate that the strategy is both effective and precise in terms of suggesting new linkages. The proposed DeepInfNode model outperforms state-of-the-art approaches on a variety of publicly available standard graph datasets, achieving an increase in performance of up to 99.1% of accuracy.

Cancer refers to any one of a large number of diseases characterized by the development of abnormal cells that divide uncontrollably and have the ability to infiltrate and destroy normal body tissue.Without treatment, it can cause serious health issues andresult in a loss of life. Breast cancer is the most common cancer among women around the world. Despite enormous medical progress, breast cancer has still remained the second leading cause of death worldwide. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for cancer through Artificial Intelligence (AI) in different ways. Previously Microscopic reviews of tissues on glass slides are used for cancer diagnostics to improve diagnostic accuracy. We can use different techniques such as digital imaging and artificial intelligence algorithm. Cancer care is also advancing thanks to AI’s ability to collect and process data. Due to the nature of processing this information, the task is often a time-consuming and tedious job for doctors. This process may be made much easier, quicker and efficient through the advancement as well as by using modified technologies.

A social network can have many different types of links or margins between nodes. Those, for example, could be social contacts, major contacts, or calls. Link Prediction is the problem of predicting edges that may not be present in a given or present time, but that have not yet been discovered and may occur in the near future. We are developing predictive linking methods based on step-by-step analysis of network nodes. Consider a network of collaborative writing among scientists, e.g. The two scientists closest to the network will have similar colleagues, so they may be working together soon. Our goal is to make this accurate idea more accurate and to understand what steps to take to approach the network that lead to the most accurate prediction of a link. Link prediction algorithms can be divided into three categories: Node Neighborhood Mode, Mode-based Mode, and Meta Mode. The node location method is based on network location features, which focuses mainly on the node structure (i.e., based on the number of common friends shared by two users). Local-based measures are: general neighborhood, Jaccard Coefficient, Adamic/Adar, and preferred attachments. P