Prediction of Heart Disease using Major Risk Factors based on ABC Optimized Neural Network
Abstract: This paper is particularly interested in the classification of data. The classification allows obtaining a prediction model from training data and test data. These data are screened by a classification algorithm that through the combination of mathematical tools and computer methods produces a new model capable of classified data possibly having the same classes of data. The analysis of data in the field of medicine is becoming more frequent in order to clarify the diagnoses, to refine the research methods and to envisage appropriate supplies of equipment according to the importance of the pathologies that appear. To analyze the present data in order to predict optimal results. This paper aims to implement a framework for prediction of heart disease using major risk factors based on the Artificial Bee Colony (ABC) optimized Neural Network. Performance of proposed research work is evaluated using the true positive rate, false positive rate, accuracy, and specificity.

Authors: Priyanka Mandot, Ajay Singh Dhabariya

File Name: Priyanka_70300-18-101.pdf
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Maximizing Response Time for Load Balancing in Cloud Computing Environment using Fusion Algorithm
Abstract: Cloud computing world has many challenges and one of the most important challenges in this world is minimizing the response time and cost in instruction, in order to stabilize the load and increase the performance of the business along with customer satisfaction. Decreasing the cost is not only proficient but also be the most import reason for satisfying the customers. Transferring huge quantity of data using a balanced method with low cost is highly beneficial in the Cloud computing environment. By setting the number of processors for each VM, a technique is proposed by us based on fusion Swam Optimization, to determine the optimal solution for allocating our resources, which in turn gives increased distribution map. The response time of our proposed technique is highly efficient when compared to the other algorithms. To determine the Efficient Fusion Algorithm (based on partial swam optimization along with genetic algorithm) and it is related with ESCE, round robin and throttled scheduling to determine the response and processing time, which affects the cost.

Authors: Ankit Adaniya, Komal Paliwal

File Name: Ankit_70300-18-102.pdf
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