KCG College of Technology

About: KCG College of Technology is a based out in . It is known for research contribution in the topics: Adsorption & Diesel fuel. The organization has 427 authors who have published 381 publications receiving 2193 citations.

Spam Detection System Using Supervised ML

Abstract: Spam is one of the major problems around the world today. To resolve this problem, different spam filtering techniques and approaches were used. in our proposed system, we are using a Naive Bayesian classifier, a content-based technique for classifying the spam from the consistent messages or emails using a Supervised Machine learning algorithm. Naive Bayesian is one of the familiar methods of spam classification because of its efficiency and simplicity. This application will detect the message as Spam or not spam and convey the result to the user.

Evolutionary Fuzzy-based gravitational search algorithm for query optimization in crowdsourcing system to minimize cost and latency

Abstract: Crowdsourcing is an environment where a group of users collaborates together to exchange information and to find answers for complex problems (queries). Query optimization is the task of selecting the best query strategy with less cost associated with it. The crowdsourcing cost can be determined by selecting the best plan from the set of options available and the best plan considerably reduce the cost for the inquiry configuration. As one of the center tasks in information recovery, the investigation of top‐k queries with crowdsourcing, to be specific group empowered top k inquiries is depicted. This issue is defined with three key variables, latency, money related expense, and nature of answers. The fundamental point is to plan a novel system that limits financial cost when the latency is compelled. In this article, we used a heuristic search algorithm named as Evolutionary Fuzzy‐based Gravitational Search algorithm (EFGSA) that produces an optimal query feature selection results with minimizing cost and latency. EFGSA‐based crowdsourcing framework gives a better balance between latency and cost while generating query plans. The performance analysis of proposed EFSGA for optimal query plan is evaluated in terms of running time, accuracy, monetary cost, and so on. From the experimental results, the proposed method achieved better results than other methods in our cost and latency model.

Effective prediction of heart disease using hybrid ensemble deep learning and tunicate swarm algorithm.

Abstract: Heart disease (HD) is the major reason for the rampant cause of death around the world. It is deemed as a crucial illness among the middle and old age people which tends to high mortality rates. Re...