Similarity between Euclidean and cosine angle distance for nearest neighbor queries

neighbor querie

Privacy Control In Social Networks By Trust Aware Link Prediction

Social networks are exceedingly common in today’s society. A social network site is an online platform where people build social relations with others and share information. For the last two decades, rapid growth in the number of users and applications with these social networking sites, make the security as the most challenging issue. In this virtual environment, some greedy people intentionally perform illegal activities by accessing others’ private information. This paper proposes a novel approach to detect the illegal access of a particular’s information by using trustaware link prediction. The facebook dataset is used for experiments and the results justify the robustness andtrustworthiness of the proposed model

Eigenface Based Recognition of Emotion Variant Faces

In present, the automatic machine based face recognition has become significant due to its urgency in potential application and current scientific challenges of industries. However, most of the existing systems designed up to now can only effectively distinguish the faces when source images are collected under numerous constrained conditions. The success rate or positive impact of face recognition systems depend on a variety of information imposed in images of human faces. Pose of face, facial expression, angle, occlusion and state of structural components are some of those. Emotions can be expressed in different ways that can be seen such as facial expression, speech, written text and gestures. This model propose an efficient approach for the recognition of expression or emotion variant faces since there are very few emotion recognition software tools to handle such problems and there is a significant importance to this research area in the field of face recognition. Especially an approach proposed here to face recognition where the facial expression in the training image set and in the testing image set diverge and only one sample image per class is existing in the system. The input to the system is a frontal neutral expression oriented face image with unique background. In this image the hair is tied away from the face and facial hair should be removed. Principal Component Analysis approach was used as a primary mechanism in the proposed model. This approach has been applied purely on a set of face images in order to extract a set of eigenface images as the output. Here weights of the representation or image are used for recognition of emotions. One of the distance metric approaches Euclidean Distance used to discover the distance with the weight vectors which was associated with each of the training images for the existence of classification task. Keywords: Face Recognition, Emotion-variant faces, Image Processing, Principal Component Analysis, Euclidean Distanc

Similarity Measures for Automatic Defect Detection on Patterned Textures

Similarity measures are widely used in various applications such as information retrieval, image and object recognition, text retrieval, and web data search. In this paper, we propose similarity-based methods for defect detection on patterned textures using five different similarity measures, viz., Normalized Histogram Intersection Coefficient, Bhattacharyya Coefficient, Pearson Product-moment Correlation Coefficient, Jaccard Coefficient and Cosine-angle Coefficient. Periodic blocks are extracted from each input defective image and similarity matrix is obtained based on the similarity coefficient of histogram of each periodic block with respect to itself and other all periodic blocks. Each similarity matrix is transformed into dissimilarity matrix containing true-distance metrics and Ward’s hierarchical clustering is performed to discern between defective and defect-free blocks. Performance of the proposed method is evaluated for each similarity measure based on precision, recall and accuracy for various real fabric images with defects such as broken end, hole, thin bar, thick bar, netting multiple, knot, and missing pick

Learning Tversky Similarity

In this paper, we advocate Tversky's ratio model as an appropriate basis for computational approaches to semantic similarity, that is, the comparison of objects such as images in a semantically meaningful way. We consider the problem of learning Tversky similarity measures from suitable training data indicating whether two objects tend to be similar or dissimilar. Experimentally, we evaluate our approach to similarity learning on two image datasets, showing that is performs very well compared to existing methods

Advanced Design Architecture for Network Intrusion Detection using Data Mining and Network Performance Exploration

The primary goal of an Intrusion Detection System (IDS) is to identify intruders and differentiate anomalous network activity from normal one. Intrusion detection has become a significant component of network security administration due to the enormous number of attacks persistently threaten our computer networks and systems. Traditional Network IDS are limited and do not provide a comprehensive solution for these serious problems which are causing the many types security breaches and IT service impacts. They search for potential malicious abnormal activities on the network traffics; they sometimes succeed to find true network attacks and anomalies (true positive). However, in many cases, systems fail to detect malicious network behaviors (false negative) or they fire alarms when nothing wrong in the network (false positive). In accumulation, they also require extensive and meticulous manual processing and interference. Hence applying Data Mining (DM) techniques on the network traffic data is a potential solution that helps in design and develops better efficient intrusion detection systems. Data mining methods have been used build automatic intrusion detection systems. The central idea is to utilize auditing programs to extract set of features that describe each network connection or session, and apply data mining programs to learn that capture intrusive and non-intrusive behavior. In addition, Network Performance Analysis (NPA) is also an effective methodology to be applied for intrusion detection. In this research paper, we discuss DM and NPA Techniques for network intrusion detection and propose that an integration of both approaches have the potential to detect intrusions in networks more effectively and increases accuracy