Papers by Keyword: Grid Search

Abstract: Software that can damage an information system asset is considered a malware, such information systems have been rendered to several destructive attacks mainly due to the emergence of the Internet. Conventional Antimalware software is not effective at eliminating malware due to its many evasion techniques such as polymorphism and code obfuscation. Antimalware software is ineffectual and defenseless against zero-day attacks as it can only eliminate malware for which it has signatures. K Nearest Neighbor, Decision Tree and Support Vector Machine are some of the leading classifiers that has successfully detect and classify Malware but optimal accuracy of detection has not been achieved, in addition, false positives and false negatives persists because the hyperparameters of these classifiers were not optimized and noise was not filtered out of the datasets using feature selection technique. The aim of this research is to develop an optimized malware detection and classification framework employing Principal Components Analysis to mitigate the curse of dimensionality while utilizing optimal hyperparameters of chosen classifiers to boost accuracy of malware detection and classification as well as reduction of false positives and false negatives. This research employed K Nearest Neighbor, Decision Tree, and Support Vector Machine to detect and classify malware with CICMalmem dataset to train the model. Grid search optimization was combined with K-fold cross-validation to optimize the hyperparameters of the selected classifiers in order to boost the model's performance and achieve high detection accuracy as well as low false positives and low false negatives. Machine learning performance metrics such as the F1 Score, Precision, Recall, and Confusion Matrix were used to evaluate the Research Model. K Nearest Neighbor generated Zero False Positives while KNN, Decision Tree and Support Vector Machine achieved Accuracy of 99%, 98.64, and 100% respectively.

Abstract: The maintenance efficiency of Chinese railway turnout is closely related to the accuracy of its fault diagnosis method. A proper method will provide great help to railway staff in maintaining turnouts. The research introduced in this paper built a model based on Support Vector Machine (SVM) and Grid Search and later than tested its effect with the data from experiments. Result of that test shows that the method can achieve a diagnosis accuracy as high as 98.33%.

Abstract: SVM (Support Vector Machine) is a powerful data mining algorithm, and is mainly used to finish classification or regression tasks. In this literature, SVM is used to conduct disease prediction. We focus on integrating with stratified sample and grid search technology to improve the classification accuracy of SVM, thus, we propose an improved algorithm named SGSVM: Stratified sample and Grid search based SVM. To testify the performance of SGSVM, heart-disease data from UCI are used in our experiment, and the results show SGSVM has obvious improvement in classification accuracy, and this is very valuable especially in disease prediction.

Abstract: Two new methodologies are established based on existing frameworks. The Bayesian model take ages, intellectual capability, transportation into consideration to find the offender’s anchor point. The grid search model investigation start with the highest score grid cell first for investigation and proceed down the list of grid cells sorted by score, which implement searching according to simulated annealing program . Making use of two different models we generate an integrated model and test their reliability according to historical serial crime samples.