Temporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis

In the area of network analysis, centrality metrics play an important role in defining the “most important” actors in a social network. However, nowadays, most types of networks are dynamic, meaning their topology changes over time. The connection weights and the strengths of social links between nodes are an important concept in a social network. The new centrality measures are proposed for weighted networks, which relies on a time-ordered weighted graph model, generalized temporal degree and closeness centrality. Furthermore, two measures—Temporal Degree-Degree and Temporal Closeness-Closeness—are employed to better understand the significance of nodes in weighted dynamic networks. Our study is caried out according to real dynamic weighted networks dataset of a university-based karate club. Through extensive experiments and discussions of the proposed metrics, our analysis proves that there is an effectiveness on the impact of each node throughout social networks

Forward Hand Gesture Spotting and Prediction Using HMM-DNN Model

Automatic key gesture detection and recognition are difficult tasks in Human–Computer Interaction due to the need to spot the start and the end points of the gesture of interest. By integrating Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs), the present research provides an autonomous technique that carries out hand gesture spotting and prediction simultaneously with no time delay. An HMM can be used to extract features, spot the meaning of gestures using a forward spotting mechanism with varying sliding window sizes, and then employ Deep Neural Networks to perform the recognition process. Therefore, a stochastic strategy for creating a non-gesture model using HMMs with no training data is suggested to accurately spot meaningful number gestures (0–9). The non-gesture model provides a confidence measure, which is utilized as an adaptive threshold to determine where meaningful gestures begin and stop in the input video stream. Furthermore, DNNs are extremely efficient and perform exceptionally well when it comes to real-time object detection. According to experimental results, the proposed method can successfully spot and predict significant motions with a reliability of 94.70%

Self co‐articulation detection and trajectory guided recognition for dynamic hand gestures

Hand gestures are a natural way of communication among humans in everyday life. Presence of spatiotemporal variations and unwanted movements within a gesture called self co‐articulation makes the segmentation a challenging task. The study reveals that the self co‐articulation may be used as one of the feature to enhance the performance of hand gesture recognition system. It was detected from the gesture trajectory by addition of speed information along with the pause in the gesture spotting phase. Moreover, a new set of novel features in the feature extraction stage was used such as position of the hand, self co‐articulated features, ratio and distance features. The ANN and SVM were used to develop two independent models using new set of features as input. The models based on CRF and HCRF was used to develop the baseline system for the present study. The experimental results suggest that the proposed new set of features provides improvement in terms of accuracy using ANN (7.48%) and SVM (9.38%) based models as compared with baseline CRF based model. There are also significant improvements in the performances of both ANN (2.08%) and SVM (3.98%) based models as compared with HCRF based model