Density-based trajectory outlier detection algorithm

With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.

Improved insensitive to input parameters trajectory clustering algorithm

The existing trajectory clustering （TRACLUS） is sensitive to the input parameters c and MinLns. The parameter value is changed a little, but cluster results are entirely different. Aiming at this vulnerability, a shielding parameters sensitivity trajectory cluster （SPSTC） algorithm is proposed which is insensitive to the input parameters. Firstly, some definitions about the core distance and reachable distance of line segment are presented, and then the algorithm generates cluster sorting according to the core dis- tance and reachable distance. Secondly, the reachable plots of line segment sets are constructed according to the cluster sorting and reachable distance. Thirdly, a parameterized sequence is extracted according to the reachable plot, and then the final trajectory cluster based on the parameterized sequence is acquired. The parameterized sequence represents the inner cluster structure of trajectory data. Experiments on real data sets and test data sets show that the SPSTC algorithm effectively reduces the sensitivity to the input parameters, meanwhile it can obtain the better quality of the trajectory cluster.

Ensemble kernel method：SVM classification based on game theory

With the development of the support vector machine（SVM）,the kernel function has become one of the cores of the research on SVM.To a large extent,the kernel function determines the generalization ability of the classifier,but there is still no general theory to guide the choice and structure of the kernel function.An ensemble kernel function model based on the game theory is proposed,which is used for the SVM classification algorithm.The model can effectively integrate the advantages of the local kernel and the global kernel to get a better classification result,and can provide a feasible way for structuring the kernel function.By making experiments on some standard datasets,it is verified that the new method can significantly improve the accuracy of classification.

Data Clustering Algorithm Based on Artificial Immune Network

For the problem that the termination condition of artificial immune network algorithm aiNet is difficult to determine, an intelligent artificial immune network algorithm S-aiNet is proposed. The S-aiNet determines whether the network is saturated by monitoring the change trend of new generation population in the iterative process according to the affinity of the new generation of network cells and existing cells. The algorithm improves the adaptability of aiNet and reduces the number of parameters. For the problem that the network of aiNet updates slowly, a regional search optimization algorithm AS-aiNet is proposed. The AS-aiNet equally divides the antibody space where the network cells and antigen located, and only searches the antibody cells located in the same region as antigens in the immune response. The AS-aiNet reduces the workload of search in the process of immune response and effectively enhances the time efficiency of algorithm operation. Adopting public data set, experiments show that the time efficiency of AS-aiNet is 10% better than that of aiNet.

piEnPred:a bi-layered discriminative model for enhancers and their subtypes via novel cascade multi-level subset feature selection algorithm

Enhancers are short DNA cis-elements that can be bound by proteins(activators)to increase the possibility that transcription of a particular gene will occur.The Enhancers perform a significant role in the formation of proteins and regulating the gene transcription process.Human diseases such as cancer,inflammatory bowel disease,Parkinson’s,addiction,and schizophrenia are due to genetic variation in enhancers.In the current study,we havemade an effort by building,amore robust and novel computational a bi-layered model.The representative feature vector was constructed over a linear combination of six features.The optimum Hybrid feature vector was obtained via the Novel Cascade Multi-Level Subset Feature selection(CMSFS)algorithm.The first layer predicts the enhancer,and the secondary layer carries the prediction of their subtypes.The baseline model obtained 87.88%of accuracy,95.29%of sensitivity,80.47%of specificity,0.766 of MCC,and 0.9603 of a roc value on Layer-1.Similarly,the model obtained 68.24%,65.54%,70.95%,0.3654,and 0.7568 as an Accuracy,sensitivity,specificity,MCC,and ROC values on layer-2 respectively.Over an independent dataset on layer-1,the piEnPred secured 80.4%accuracy,82.5%of sensitivity,78.4%of specificity,and 0.6099 as MCC,respectively.Subsequently,the proposed predictor obtained 72.5%of accuracy,70.0%of sensitivity,75%of specificity,and 0.4506 of MCC on layer-2,respectively.The proposed method remarkably performed in contrast to other state-of-the-art predictors.For the convenience of most experimental scientists,a user-friendly and publicly freely accessible web server@/bienhancer dot pythonanywhere dot com has been developed.