<p class="Abstract">Several challenges are associated with online based learning systems, the most important of which is the lack of student motivation in various course materials and for various course activities. Further, it is important to identify student who are at risk of failing to complete the course on time. The existing models applied machine learning approach for solving it. However, these models are not efficient as they are trained using legacy data and also failed to address imbalanced data issues for both training and testing the classification approach. Further, they are not efficient for classifying new courses. For overcoming these research challenges, this work presented a novel design by training the learning model for identifying risk using current courses. Further, we present an XGBoost classification algorithm that can classify risk for new courses. Experiments are conducted to evaluate performance of proposed model. The outcome shows the proposed model attain significant performance over stat-of-art model in terms of ROC, F-measure, Precision and Recall.</p>

Multi-view clustering, which divides objects into multiple clusters with high intra-cluster and low inter- cluster similarity for all perspectives, is of enormous significance for uncovering the mechanisms of systems. Multi-view data effectively model and characterise the underlying complex systems. Because they only consider the shared characteristics of objects or their correlation, current algorithms are criticised for their subpar performance because they ignore the heterogeneity and structural constraints of different views. A brand-new network-based method called Multi-view Clustering with Self-representation and Structural Constraints (MCSSC), which combines matrix factorization with low-rank representation of various perspectives, is presented to address these issues. In particular, a network is built for each view to reduce heterogeneity from multi-view data, converting the multi-view clustering problem into the multi-layer networks clustering problem. The MCSSC factorises network-related matrices by projecting them into a shared space and simultaneously trains an affinity graph for objects in multiple views with self-representation in order to extract the shared properties of multiple views. The structural constraint is applied to the affinity graph, where the clusters are identified, to aid with clustering. Numerous tests show that MCSSC performs noticeably better than the state-of-the-art in terms of accuracy, indicating the superiority of the suggested method

This project pursue to examine how blockchain technology can be appealed in the sector of supply chain management, beyond its common interconnection with cryptocurrencies. While technology is frequently interrelated with finance, it has several favorable applications in non-financial industries such as food and power. By make use of blockchain technology, it is feasible to generate enduring, attainable, and empirical data of products as they proceed through the supply chain. This improves the capability to track the products, guarantee their legitimacy and morality, and do so in a more cost-efficient manner. The possible advantages of using blockchain in agribusiness were also debated, as well as the case for executing a blockchain based small business in the automotive manufacturing industry. This project prefers to outline work of block chain technology in the field of supply chain. As an endeavor to cooperate with the physical one, we support a track of the journey of the supply chain products from producers to consumers. The user can access a complete documentation and confidence that the information is on target and precise. Block chain technology demonstrate to be useful in the supply chain zone in the following ways: diminish mistakes, reduce product retards, delete fraud activities, enhance management, improve consumer or supplier belief