Artificial System for Prediction of Studentas Academic Success from Tertiary Level in Bangladesh

Every year a large scale of students in Bangladesh enrol in different Universities in order to pursue higher studies With the aim to build up a prosperous career these students begin their academic phase at the University with great expectation and enthusiasm However among all these enthusiastic and hopeful bright students many seem to become successful in their academic career and found to pursue the higher education beyond the undergraduate level The main purpose of this research is to develop a dynamic academic success prediction model for universities institutes and colleges In this work we first apply chi square test to separate factors such as gender financial condition and dropping year to classify the successful from unsuccessful students The main purpose of applying it is feature selection to data Degree of freedom is used to P-value Probability value for best predicators of dependent variable Then we have classify the data using the latest data mining technique Support Vector Machines SVM SVM helped the data set to be properly design and manipulated After being processed data we used the MATH LAB for depiction of resultant data into figure After being separation of factors we have had examined by using data mining techniques Classification and Regression Tree CART and Bayes theorem using knowledge base Proposition logic is used for designing knowledge base Bayes theorem will perform the prediction by collecting the information from knowledge Base Here we have considered most important factors to classify the successful students over unsuccessful students are gender financial condition and dropping year We also consider the sociodemographic variables such as age gender ethnicity education work status and disability and study environment that may inflounce persistence or academic success of students at university level We have collected real data from Chittagong University Bangladesh from numerous students Finally by mining th

Uncertainty Analysis for Spatial Image Extractions in the context of Ontology and Fuzzy C-Means Algorithm

This paper emphasis on spatial feature extractions and selection techniques adopted in content based image retrieval that uses the visual content of a still image to search for similar images in large scale image databases, according to a user2019;s interest. The content based image retrieval problem is motivated by the need to search the exponentially increasing space of image databases efficiently and effectively. It is also possible to classify the remotely sensed image to represent the specific feature of the target images. In this research we first imposed the Fuzzy C-means algorithm to our sample image and observed its value. After getting the experimental result from Fuzzy C-means we have had designed Ontological Matching algorithm which aftereffect better than the previous one. We have had espy that our Ontological Matching algorithm is twenty (20%) percent better than Fuzzy C-means algorithm