Algoritam planiranja operacija "flow shop" u cilju smanjivanja vremena izvršenja kod problema n-poslova i m-strojeva

In multi stage job problems, simple priority dispatching rules such as shortest processing time (SPT) and earliest due date (EDD) can be used to obtain solutions of minimum total processing time, but may not sometimes give sequences as expected that are close to optimal. The Johnson\u27s algorithm is especially popular among analytical approaches that are used for solving n-jobs, 2-machines sequence problem. In this paper the presented algorithm is based on converting an m-machine problem to a 2-machine problem. Based on testing and comparison with other relevant methods, the proposed algorithm is offered as a competitive alternative for practical application when solving n-jobs and m-machines problems.U problemima posla s više faza, mogu se koristiti jednostavna prioritetna dispečerska pravila kao što su najkraće vrijeme obrade (PT) i najraniji datum dospijeća (EDD) za dobivanje rješenja najmanjega ukupnog vremena obrade. Međutim, ona ponekad ne daju slijed za koji se očekuje da je blizu optimalnom. Johnsonov algoritam je posebno popularan među analitičkim pristupima koji se koriste za rješavanje problema slijeda n-poslova i 2-stroja. Algoritam prikazan u ovom radu se temelji na pretvaranju problema m-strojeva u problem 2-stroja. Na temelju ispitivanja i usporedbe s drugim relevantnim metodama, predloženi algoritam se nudi kao konkurentna alternativa za praktičnu primjenu pri rješavanju problema n-poslova i m-strojeva

A Novel SVM and K-NN Classifier Based Machine Learning Technique for Epileptic Seizure Detection

An EEG signal is used for capturing the signals from the brain, which helps in localization of epileptogenic region, thereby which plays a vital role for a successful surgery. The focal and non-focal signals are obtained from the epileptogenic region and normal region respectively. The localization of epileptic seizure with the help of focal signal is necessary while detecting seizures. Hence, the present article provides detailed analysis of EEG signals. The Focal and Non-focal signals are decomposed using EMD-DWT. A combination of EMD-DWT decomposition method in accordance with log-energy entropy gives an efficient accuracy in comparison to other entropy in differentiating the Focal from Non-focal signals. The extracted features are subjected to SVM and KNN classifiers whose performance will be calculated and verified with respect to accuracy, sensitivity and specificity. At the end, it will be shown that KNN produces the highest accuracy when compared to SVM classifier