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Prediction of student performance at early stage in higher education is important for academic society so that strategic decisions can be made before students are placed to keep them from dropping out of the course. Due to India's massive student population and extremely ancient educational system, there are significant difficulties in measuring and forecasting students' performance. Every institution in India has its own unique set of criteria for measuring student achievement, and there is no formal process for keeping track of and evaluating a student's progress and improvement. Over the last decade, researchers in the education domain have presented numerous types of machine learning techniques. However, there are significant obstacles to dealing with imbalanced datasets in order to predict the performance of students. In this paper, the first phase of traditional classification algorithms has been applied to the dataset, which contains the progress of 4424 students. In the second phase, novel hybrid machine learning (ML) algorithms were used to get better predictions. The outcome of the proposed model makes it easier to predict how well students will do so that early decisions can be made about the growth of higher education institutions.

Objectives: To analyze the issue of cold-start (user cold-start and item cold-start) in Collaborative Filtering Recommender System (CFRS) and to compare its solution with various approaches are summarized in this paper. Methods/Statistical Analysis: The manuscript discussed about the cold-start issue in which the recommender system cannot recommend items to the new user because no ratings made by the new user (user cold-start) as well as for the newly added items, the system cannot be able to provide recommendations to the user because the system has no ratings for the newly added item (item cold-start). The solutions for cold-start issue are analyzed based on the model based approach, demographic data, ask-to-rate technique, and Social Network Analysis (SNA). Findings: The comparative review of the aforementioned approaches provides the detail about how to implement the model based approach, how to collect the demographic data from the new user, how to apply the ask-to-rate technique and how to make use of the SNA concept to solve the cold-start issue in CF recommender system. Application: The recommender system on Amazon helps the user to purchase books, Compact Disks (CDs), Netflix helps the user to choose CDs to purchase/rent and Epinions, helps the users to decide to purchase based on user reviews. Keywords: Ask-To-Rate Technique, Cold-Start, Collaborative Filtering Recommender System, Demographic Data, Model Based Approach, Social Network Analysis

Recommender system (RS) has become a very important factor in many eCommerce sites. In our daily life, we rely on the recommendation from other persons either by word of mouth, recommendation letters, movie, item and book reviews printed in newspapers, etc. The typical Recommender Systems are software tools and techniques that provide support to people by identifying interesting products and services in online store. It also provides a recommendation for certain users who search for the recommendations. The most important open challenge in Collaborative filtering recommender system is the cold start problem. If the adequate or sufficient information is not available for a new item or users, the recommender system runs into the cold start problem. To increase the usefulness of collaborative recommender systems, it could be desirable to eliminate the challenge such as cold start problem. Revealing the community structures is crucial to understand and more important with the increasing popularity of online social networks. The community detection is a key issue in social network analysis in which nodes of the communities are tightly connected each other and loosely connected between other communities. Many algorithms like Givan-Newman algorithm, modularity maximization, leading eigenvector, walk trap, etc., are used to detect the communities in the networks. To test the community division is meaningful we define a quality function called modularity. Modularity is that the links within a community are higher than the expected links in those communities. In this paper, we try to give a solution to the cold-start problem based on community detection algorithm that extracts the community from the social networks and identifies the similar users on that network. Hence, within the proposed work several intrinsic details are taken as a rule of thumb to boost the results higher. Moreover, the simulation experiment was taken to solve the cold start problem.

In a given geometric objects, set of objects are retrieved as results for the given query. The problem is defined as geometric query intersection problem. The problem defines that, for the given user query, all the relevant results which are intersected by the query are showed as output. Because of the large number results, it occupies the huge memory space. For that here we propose a solution that we show the top-k results as the output where k is the integer. This paper gives a general technique to solve any top-k GIQ problem efficiently. The technique relies only on the availability of an efficient solution for the underlying (non-top-k) GIQ problem, which is often the case. Using this, asymptotically efficient solutions are derived for several top-k GIQ problems, including top-k orthogonal and circular range search, point enclosure search, half space range search, etc. Implementations of some of these solutions, using practical data structures, show that they are quite efficient in practice. This paper also does a formal investigation of the hardness of the top-k GIQ problem, which reveals interesting connections between the top-k GIQ problem and the underlying (non-top-k) GIQ problem. In our proposed system we are going to perform multiple query processing. Because in the existing approach it can able to process only one query at the time. But in our proposed work we process multiple queries at the time.