Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

Solving fuzzy linear programming problem using symmetric fuzzy number approximation

Abstract: In this paper, we discussed the fully fuzzy linear programming (FFLP) problems in which all the parameters and variables are triangular fuzzy numbers. We proposed a new approach of solving FFLP problem using the concept of nearest symmetric triangular fuzzy number approximation with preserve expected interval. Using this technique, FFLP problems model turned into two crisp linear problems: first problem is designed in which the centre objective value will be calculated. Second is designed to obtain the margin from the objective of the principal problem. Finally, the fuzzy approximate solution of the FFLP problem is obtained from the solution of two linear programming problems.

Optimization of Evolutionary Algorithm Using Machine Learning Techniques for Pattern Mining in Transactional Database

Abstract: In recent years, the data analysts are facing many challenges in high utility itemset (HUI) mining from given transactional database using existing traditional techniques. The challenges in utility mining algorithms are exponentially growing search space and the minimum utility threshold appropriate to the given database. To overcome these challenges, evolutionary algorithm-based techniques can be used to mine the HUI from transactional database. However, testing each of the supporting functions in the optimization problem is very inefficient and it increases the time complexity of the algorithm. To overcome this drawback, reinforcement learning-based approach is proposed for improving the efficiency of the algorithm, and the most appropriate fitness function for evaluation can be selected automatically during execution of an algorithm. Furthermore, during the optimization process when distinct functions are skillful, dynamic selection of current optimal function is done. Optimization of Evolutionary Algorithm Using Machine Learning Techniques for Pattern Mining in Transactional Database

Seizure Detection Based on EEG Signals Using Asymmetrical Back Propagation Neural Network Method

Abstract: Abnormal activity in the human brain is a symptom of epilepsy. Electroencephalogram (EEG) is a standard tool that has been widely used to detect seizures. A number of automated seizure detection systems based on EEG signal classification have been employed in present days, which includes a mixture of approaches but most of them rely on time signal features, time intervals or time frequency domains. Therefore, in this research, deep learning-based automated mechanism is introduced to improve the seizure detection accuracy from EEG signal using the Asymmetrical Back Propagation Neural Network (ABPN) method. The ABPN system includes four levels of repetitive training with weight adjustment, feed forward initialization, error and update weight and bias back-propagation. The proposed ABPN-based seizure detection system is validated using Physionet EEG dataset with matlab simulation, and the effectiveness of proposed seizure system is confirmed through simulation results. As compared with Deep Convolutional Neural Network (CNN) and Support Vector Machine–Particle Swarm Optimization (SVM-PSO)-based seizure detection system, the proposed ABPN system gives the best performance against various parameters. The sensitivity, specificity and accuracy are 96.32%, 95.12% and 98.36%, respectively.