Machine learning is a branch of computing that studies the design of algorithms with the ability to “learn.” A subfield would be deep learning, which is a series of techniques that make use of deep artificial neural networks, that is, with more than one hidden layer, to computationally imitate the structure and functioning of the human organ and related diseases. The analysis of health interest images with deep learning is not limited to clinical diagnostic use. It can also, for example, facilitate surveillance of disease-carrying objects. There are other examples of recent efforts to use deep learning as a tool for diagnostic use. Chest X-rays are one approach to identify tuberculosis; by analysing the X-ray, you can spot any abnormalities. A method for detecting the presence of tuberculosis in medical X-ray imaging is provided in this paper. Three different classification methods were used to evaluate the method: support vector machines, logistic regression, and nearest neighbors. Cross-validation and the formation of training and test sets were the two classification scenarios used. The acquired results allow us to assess the method’s practicality.

Hybrid synchronization is one of the most significant aspects of a dynamic system. We achieve nonlinear control unit results to synchronize two comparable 7D structures in this study. Many dynamic systems are directly connected to health care and directly enhance health. We employed linearization and Lyapunov as analytical methods, and since the linearization method does not need updating the Lyapunov function, it is more successful in achieving synchronization phenomena with better outcomes than the Lyapunov method. The two methods were combined, and the result was a striking resemblance to the dynamic system’s mistake. The mathematical system with control and error of the dynamic system was subjected to digital emulation. The digital good outcomes were comparable to the two methods previously stated. We compared the outcomes of three hybrid synchronizations based on Lyapunov and linearization methods. Finally, we used the existing system, presenting it in a new attractor and comparing the findings to those of other similar systems.

This study aims to investigate the effect of metaphorical content on e-participation in healthcare. With this objective, the study assesses the awareness and capability of e-participants to navigate through healthcare metaphors during their participation. Healthcare-related e-participation data were collected from the Twitter platform. Data analysis includes (i) awareness measurements by topic modelling and sentiment analysis and (ii) participation abilities by problem-based learning models. Findings show that a lack of effort to validate metaphors harms e-participation levels and awareness, resulting in a problematic health environment. Exploring metaphors in these intricate forums has the potential to enhance service delivery. Improving web service delivery requires valuable input from stakeholders on the application of metaphors in the health domain.

In this paper, our work aims in fair queuing with priority to consider scheduling of various requests received at the RSU; an important issue for desired data access in vehicular ad-hoc networks. As thousands of requests may arrive at the data center, so scheduling these requests in a proper and efficient way is a challenge. Many scheduling algorithms have been proposed in the literature. In this paper, we are summarizing schemes that categorize requests based on the priority associated with it. The high priority request is served in comparison to the other requests. Data scheduling through roadside units (RSUs) has been done at the application layer by considering the message priority. However, without consideration of such priorities, emergency messages can be treated as general and can be delayed, blocked and dropped by lower-priority messages, which are not urgent and can wait for later transmission. The proposed work aims in fair queuing with priority.

Children initiates the usage of Internet during young age and spend more time online. Apart from the benefits like improved education, entertainment, news and gaming, Internet poses severe threats to the children online. Ensuring online safety is a global challenge. The purpose of this paper is to examine online social media responses and awareness posts on children online safety. In this relation, Twitter social media responses after freeing the accusers of children sexual harassment and Facebook pages of some prominent personalities in India for online safety are analyzed. The results reveal that though the people are angry and fearful, they believe judiciary and police system and expecting safety from the same. The analysis of Facebook posts depicts that the concerned authorities are active towards child online safety and providing awareness through their representatives. People demand legal actions against the perpetrators of the crime to punish them. The necessary actions should be taken for cyber-crime awareness information to reach all social media users.

People like listening music primarily due to the emotion it evokes. Any activity or work that a person performs also generates emotions. Considering the above two statements it can assume that people tend to associate music with certain activity if it induces emotions that are in sync with it. In today's world of infinite storage, the number of songs that a user has is ever increasing. With the increased number of songs, listening music in a way one likes has become difficult. Playlist provide us a way to better organize our music library. These can be created manually or by using various smart playlist creators that generates them by analyzing various components of music files used by the user. In the current scenario the playlist created has a specific number of tracks which are played over and over again, with the only variation occurring being in the order in which songs are played. Also the songs that are not part of any playlist are left redundant, occupying useful memory. Our work focuses on the reducing the number of redundant songs in the music library while creating playlist. We have worked on grouping songs with similar emotional effect together in a segment, and then creating a dynamic playlist every time the user plays music. The results show that as the system is provided with required input, we get a robust playlist for each segment, which consist of diverse mix of songs having similar emotional perception. The playlist created is a non monotonous collection of music which is emotionally supportive to the listener towards the work he is doing.