Saangyong Uhmn

Nurse Scheduling Problem (NSP) is well-known NP-hard problem which may require exhaustive search to obtain optimal solution. Several heuristic algorithms have been already applied to this highly difficult and complicated problem. In this work, we suggested traditional backtrack algorithm to solve nurse scheduling problem. We compared the results from backtrack algorithm and other heuristic algorithms including genetic algorithm and simulated annealing. The experimental results showed the backtracking algorithm generated an optimal solution with small sizes of schedules compared to traditional heuristic algorithms.

Background/Objectives: Recently, storage systems and backup systems are popularly used and the number of duplicated data is increased drastically. To minimize data storage size and efficient use of network bandwidth, we proposed deduplication systems and file similarity measurement schemes with GPGPU scheme. The GPGPUs are applied to file similarity measurement for computation speedup. Methods/Statistical Analysis: To cope with the problem accompanying the parallelization of the measurement, we compare two implementations with shared memory and preprocessing. In addition, we propose an alternative to Rabin fingerprinting algorithm to lessen the computational burden of the algorithm to the GPUs. We compare the performance of the systems in time elapsed for several files. Findings: First, we found through experiments that the preprocessing was slightly faster than the shared memory scheme for the overlapped region of consecutive data segments which were assigned to different cores. This region should be shared by two cores for fingerprinting. By adapting GPGPU parallelization with the preprocessing technique for file similarity measurement, the proposed system outperformed the systems with a multi-core CPU. Also, it gets faster for the bigger file. In addition, we made the system three times faster by adapting an alternative to Rabin fingerprinting algorithm. It eliminates the computational burden of the algorithm and provides comparable results to the system with the latter. Improvements: The procedure will be beneficial to de-duplication system in determining file similarity and finding duplicated regions of two files. We achieved speedup in the measurement of file similarity by parallelization on GP-GPUs with two methods for overlaps of consecutive data segments and an alternative fingerprinting algorithm.

We used several machine learning techniques to predict the susceptibility to chronic hepatitis from single nucleotide polymorphism (SNP) data. These are integrated with several feature selection algorithms to identify a set of SNPs relevant to the disease. In addition, we apply a backtracking technique to a couple of feature selection algorithms, forward selection and backward elimination, and show that it is beneficial to find the better solutions by experiment. The experimental results show that the decision rule is able to distinguish chronic hepatitis from normal with a maximum accuracy of 73.20%, whereas the accuracy of the support vector machine is 67.53% and that of the decision tree is 72.68%. It is also shown that the decision tree and decision rule are potential tools to predict susceptibility to chronic hepatitis from SNP data.