Monkeypox Detection Through Watershed Segmentation and Appending 2D CNN Based Auto Encoder: Monkeypox Detection Through CNN-Auto Encoder

Monkeypox, a viral zoonosis, may spread from animals to people. Fever, rashes, and swollen lymph nodes might create medical complications. Its symptoms resemble smallpox. To prevent monkey pox sickness, you must be prepared and treat it immediately. Public health systems should be aware of effective monkeypox mitigation methods because to its global health impacts. Watershed segmentation using CNN-based auto encoder detected monkeypox. Monkeypox may be distinguished from other skin infections. Watershed segmentation, elevation map utilisingsobel, and region-based feature extraction function well on impacted skin photos. Segmenting Monekypox images is tough due to similarities and variations across classes and the difficulties of focusing on skin lesions. Unsupervised learning models like the convolutional autoencoder duplicate the input image in the output layer. Encoders, ConvNets that produce low-dimensional images, process images passed via them

Open Issues, Research Challenges, and Survey on Education Sector in India and Exploring Machine Learning Algorithm to Mitigate These Challenges

The nation's core sector is education. But dealing with problems in educational institutions, particularly in higher education, is a challenging task. The growth of education and technology has led to a number of research challenges that have attracted significant attention as well as a notable increase in the amount of data available in academic databases. Higher education institutions today are worried about outcome-based education and various techniques to assess a student's knowledge level or capacity for learning. In general, there are more contributors in the academic field than there are authors. Research is being done in this field to determine the best algorithm and features that are crucial for predicting the future outcomes. This survey can help educational institutions assess themselves and find any gaps that need to be filled in order to fulfil their purpose and vision. Machine Learning (ML) approaches have been explored to solve the issues as higher education systems have grown in size

Analysis of Statistical and Structural Properties of Complex networks with Random Networks

Random graphs are extensive, in addition, it is used in several functional areas of research, particularly in the field of complex networks. The study of complex networks is a useful and active research areas in science, such as electrical power grids and telecommunication networks, collaboration and citation networks of scientists,protein interaction networks, World-Wide Web and Internet Social networks, etc. A social network is a graph in which n vertices and m edges are selected at random, the vertices represent people and the edges represent relationships between them. In network analysis, the number of properties is defined and studied in the literature to identify the important vertex in a network. Recent studies have focused on statistical and structural properties such as diameter, small world effect, clustering coefficient, centrality measure, modularity, community structure in social networks like Facebook, YouTube, Twitter, etc. In this paper, we first provide a brief introduction to the complex network properties. We then discuss the complex network properties with values expected for random graphs