Abstract: Outliers are an important conception in data analysis and are being researched in diverse fields. Outliers have a value that is numerically different from the rest of the data when viewed as patterns. It is subjective exercise in various knowledge disciplines. Applications can range from data cleaning to fraud and intrusion detection. Businesses are susceptible to fraud which be detected using Data mining tools. Offenders may display random and/or occasional dishonest behaviour when there is opportunity. The key challenge in outlier detection is representing normal behaviour and then exploring an unknown domain, since different domains impose different requirements or constraints giving rise to different formulations in outlier detection. This paper proposes and attempts a Novel Outlier Detection Algorithm (N-SOD) based on select fields of chosen object characteristics that are interesting to analyze and demonstrated with examples.

Abstract: Supply chain management (SCM) is concerned with the efficient integration of suppliers, factories, distributors, warehouses, and stores so that merchandise is produced and distributed in the right quantities to the right locations at the right time in order to minimize total system cost and satisfy customer service requirements. In SCM, what is required is how to improve the performance. Supply chains, in an attempt to be more competitive, are adopting new management paradigms. Among these paradigms, there are five that deserve particular mention because of their importance to better SC performance: lean, agile, resilient, green and talentship (LARGT) paradigms. Performance measurement is crucial to better SCM. The lack of appropriate metrics for these measurements could be the main reason responsible for the following failure breakdowns in the supply chains: (1) inability to meet customer satisfaction; (2) suboptimization of firms’ performance; (3) loss of opportunities to outperform the competition; and (4) creation of conflicts within the supply chain. The simultaneous integration of LARGT paradigms in SCM may help supply chains to become more efficient and streamlined, and also more sustainable. In this paper an extensive literature review was discussed and an extensive overview on supply chain practices, and competitive capability with detailed LARGT practices is discussed to improve supply chain performance. The detailed overview provides a foundation for further research directions.

Abstract: In Round-Robin Scheduling, the time quantum is fixed and processes are scheduled such that no process uses CPU time more than one time quantum in one go. If time quantum is too large, the response time of the processes will not be tolerated in an interactive environment. If the time quantum is too small, unnecessary frequent context switch may occur. Consequently, overheads result in fewer throughputs. Round Robin scheduling algorithm is the most suitable choice for time shared system but not for soft real time systems due to a large turnaround time, large waiting time and high number of context switches. The choice of the quantum time in RR is the optimal solution for the problem of large turnaround and waiting time with RR. In this study, we propose a priority algorithm with dynamic quantum time (PDQT), to improve the work of RR by improving the concept of Improved Round Robin with varying time quantum (IRRVQ). The proposed algorithm gave results better than RR and IRRVQ in terms of minimizing the number of context switches, average waiting time, average turnaround time, design and analysis. The simple Round-Robin algorithm has been improved by about 40%. By controlling quantum time according to the priorities and burst times of the processes, we experience fewer context switches and shorter waiting and turnaround times, thereby obtaining higher throughput.

Abstract: with increasing competitive global world markets, companies are under intense pressure to find ways to cut production and material costs to survive and sustain their competitive position in their respective markets. Since a qualified supplier is a key element and a good resource for a buyer in reducing such costs, evaluation and selection of the potential suppliers has become an important component to improve supply chain performance. This paper presents a hybrid model using analytic hierarchy process (AHP), artificial neural networks (ANNs), and Relative Reliability Risk Index (R3I) to assess supplier performance. The model consists of three modules: Module1 applies AHP using pair wise comparison of criteria for all suppliers. Module2 utilizes the results of AHP into NNs model for selecting the supplier. Three activation functions namely; the sigmoid activation function, the tanh activation function and the arctangent activation function are used to compare the results. Module3 utilizes the results of AHP into R3I model for selecting the supplier. The results is promising and it gives up an appropriate score to obtain and compare the performance of each supplier and choosing the best one.