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The key aspect of Outcome-Based Education (OBE) is an assessment of learning outcomes. OBE assessment of the outcomes of the course is the most critical feature required to improve the quality of education. Learning outcomes are concrete, formal statements that state what students are expected to learn in a course. Program Outcomes (POs) are the knowledge, skills, and attitudes that students should have at the end of the course. POs can be measured through Course Outcomes (COs) which are broad statements indicating knowledge, skills acquired at the end of the course. The results of each course are based on COs and POs. An innovative method is needed for assessing the COs and POs. This paper details the CO-PO matrix analysis and CO-PO attainment analysis for Big data analytics course. This study aims to give an effective strategy for evaluating COs and POs, beginning with the formulation of COs using Bloom's Taxonomy. In this methodology by using the students' performance in internalassessment, end exam, assignments, and course exit feedback; calculate the attainment of the course. The proposed method assists in the creation of effective lesson plans, high-quality question papers, and effective rubrics for course evaluation. The outcomebased approach necessitates a paradigm shift in thecurriculum process and how the learner is empowered to achieve outcomes. Keywords: Attainment, Course Outcomes, Outcome Based Education, Program Outcomes, Program Evaluation, Student Evaluation.

In today’s world, the number of companies is increasing day by day that help end users to express opinion i.e. social media management, to watch news, payment applications, retail, ecommerce etc. There are large amount of forms, which take personal information’s like username, password, social security number, credit card, debit card and account information. Thus the applications are vulnerable to security issues like phishing attacks, denial of service attacks, cross-site scripting attack and many more. This paper provides literature review of work done in these areas and their respective mitigations.

This era, in which we currently stand, is an era of public opinion and mass information. People from all around the globe are joined together through various information junctions to create a global community, where one thing from the far east reaches to the people of the far west within seconds. Nothing is hidden, everything and anything can be scrutinized to its core and through these global criticisms and mass discussions of gigantic magnitude, we have reached to the pinnacle of correct decisions and better choices. These pseudo social groups and data junctions have bombarded our society so much that they now hold the forelock of our opinions and sentiments, ergo, we reach out to these groups to achieve a better outcome. But, all this enormous data and all these opinions cannot be researched by a single person, hence, comes the need of sentiment analysis. In this paper we’ll try to accomplish this by creating a system that will enable us to fetch tweets from twitter and use those tweets against a lexical database which will create a training set and then compare it with the pre-fetched tweets. Through this we will be able to assign a polarity to all the tweets by means of which we can address them as negative, positive or neutral and this is the very foundation of sentiment analysis, so subtle yet so magnificent.

In grammatical inference one aims to find underlying grammar or automaton which explains the target language in some way. Context free grammar which represents type 2 grammar in Chomsky hierarchy has many applications in Formal Language Theory, pattern recognition, Speech recognition, Machine learning , Compiler design and Genetic engineering etc. Identification of unknown Context Free grammar of the target language from positive examples is an extensive area in Grammatical Inference/ Grammar induction. In this paper we propose a novel method which finds the equivalent Chomsky Normal form.