The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jersey/Bib number that may present in different regions of their outfit captured in cluttered envi...
详细信息
software institutions recently implement more than one software process model in order to achieve a high value in their products and services. The challenges of implementing several quality models are increasing the r...
详细信息
Parameter reduction has a significant role in making precision decisions. Several decisions making researches mine Boolean soft set with defined operations such as AND, OR, union and intersection to utilize their thin...
详细信息
This paper overcome the false parameters from soft set which focuses on original decision partitions order whereas in some cases the decision partition order not induced original set extensions or the reductions of or...
详细信息
The outcome of the reduction of soft data is dependent on the quality and discount evidence that increases with optimization analysis. There is a set of techniques that can be used to reduce the data, but the differen...
详细信息
Voluminous data are available in soft sets, which makes it difficult to recognize the soft set decisions of the data. The large increase in the volume of these soft set has made it necessary to enlarge the size of the...
详细信息
Recently, Wireless Sensor Network (WSN) are an important research area because of its real-time response, accurate, improved node capability, low in cost and simple infrastructure. Because of the huge number of sensor...
详细信息
Finding the right employee in the employee recruitment process in an organization is a challenging tasks. For example, enormous candidates participated in the selection, various parameters to be examined from the cand...
Finding the right employee in the employee recruitment process in an organization is a challenging tasks. For example, enormous candidates participated in the selection, various parameters to be examined from the candidates. Furthermore, the selection process done manually could take long time during the selection process. In addition, there is a case that sometimes the position of the employee's work is not suitable with the strengths and weaknesses of the candidates. Moreover, in some cases using Curriculum Vitae (CV), Interview, and Assessment Test could not give the optimal selection results. There is a need to overcome this problems to create more comprehensive and could suggest the employee placement based on their characteristics. In this study, Profile Matching is used to comprehensively assess the candidates. In addition, Naïve Bayes is used in the next step to determine or predict the employee placement based on their characteristics. Validation is conducted by performing bivariate correlation to evaluate the Profile Matching and accuracy is used to evaluate the suitability of the employee placement. The results shows that there is a positive correlation with the value of 0.93. Meanwhile, the prediction using Naïve Bayes model shows a 100% accuracy to predict employee placement.
The use of appropriate requirements prioritization techniques is crucial to the success of a software development project. There are many techniques offered with all the advantages and disadvantages of each. The quest...
详细信息
Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber criminals are executing malicious actions, such as tracking user activities, stealing personal data, an...
详细信息
Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber criminals are executing malicious actions, such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as too many people use Android for their daily routines, including important communications. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. This study used static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis requires a minimum number of features to efficiently classify malware. Therefore, we used genetic search(GS), which is a search based on a genetic algorithm(GA), to select the features among 106 strings. To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Na?ve Bayes(NB), functional trees(FT), J48, random forest(RF), and multilayer perceptron(MLP). Among these classifiers, FT gave the highest accuracy(95%) and true positive rate(TPR)(96.7%) with the use of only six features.
暂无评论