Smart sentiment analysis system for pain detection using cutting edge techniques in a smart healthcare framework.

A sentiment analysis system has been proposed in this paper for pain detection using cutting edge techniques in a smart healthcare framework. This proposed system may be eligible for detecting pain sentiments by analyzing facial expressions on the human face. The implementation of the proposed system has been divided into four components. The first component is about detecting the face region from the input image using a tree-structured part model. Statistical and deep learning-based feature analysis has been performed in the second component to extract more valuable and distinctive patterns from the extracted facial region. In the third component, the prediction models based on statistical and deep feature analysis derive scores for the pain intensities (no-pain, low-pain, and high-pain) on the facial region. The scores due to the statistical and deep feature analysis are fused to enhance the performance of the proposed method in the fourth component. We have employed two benchmark facial pain expression databases during experimentation, such as UNBC-McMaster shoulder pain and 2D Face-set database with Pain-expression. The performance concerning these databases has been compared with some existing state-of-the-art methods. These comparisons show the superiority of the proposed system.

Efficient Obstacle Detection and Tracking Using RGB-D Sensor Data in Dynamic Environments for Robotic Applications.

Obstacle detection is an essential task for the autonomous navigation by robots. The task becomes more complex in a dynamic and cluttered environment. In this context, the RGB-D camera sensor is one of the most common devices that provides a quick and reasonable estimation of the environment in the form of RGB and depth images. This work proposes an efficient obstacle detection and tracking method using depth images to facilitate quick dynamic obstacle detection. To achieve early detection of dynamic obstacles and stable estimation of their states, as in previous methods, we applied a u-depth map for obstacle detection. Unlike existing methods, the present method provides dynamic thresholding facilities on the u-depth map to detect obstacles more accurately. Here, we propose a restricted v-depth map technique, using post-processing after the u-depth map processing to obtain a better prediction of the obstacle dimension. We also propose a new algorithm to track obstacles until they are within the field of view (FOV). We evaluate the performance of the proposed system on different kinds of data sets. The proposed method outperformed the vision-based state-of-the-art (SoA) methods in terms of state estimation of dynamic obstacles and execution time.

Person identification using fusion of iris and periocular deep features.

A novel method for person identification based on the fusion of iris and periocular biometrics has been proposed in this paper. The challenges for image acquisition for Near-Infrared or Visual Wavelength lights under constrained and unconstrained environments have been considered here. The proposed system is divided into image preprocessing data augmentation followed by feature learning for classification components. In image preprocessing an annular iris, the portion is segmented out from an eyeball image and then transformed into a fixed-sized image region. The parameters of iris localization have been used to extract the local periocular region. Due to different imaging environments, the images suffer from various noise artifacts which create data insufficiency and complicate the recognition task. To overcome this situation, a novel method for data augmentation technique has been introduced here. For features extraction and classification tasks well-known, VGG16, ResNet50, and Inception-v3 CNN architectures have been employed. The performance due to iris and periocular are fused together to increase the performance of the recognition system. The extensive experimental results have been demonstrated in four benchmark iris databases namely: MMU1, UPOL, CASIA-Iris-distance, and UBIRIS.v2. The comparison with the state-of-the-art methods with respect to these databases shows the robustness and effectiveness of the proposed approach.

A hierarchical approach for speech-instrumental-song classification.

Audio classification acts as the fundamental step for lots of applications like content based audio retrieval and audio indexing. In this work, we have presented a novel scheme for classifying audio signal into three categories namely, speech, music without voice (instrumental) and music with voice (song). A hierarchical approach has been adopted to classify the signals. At the first stage, signals are categorized as speech and music using audio texture derived from simple features like ZCR and STE. Proposed audio texture captures contextual information and summarizes the frame level features. At the second stage, music is further classified as instrumental/song based on Mel frequency cepstral co-efficient (MFCC). A classifier based on Random Sample and Consensus (RANSAC), capable of handling wide variety of data has been utilized. Experimental result indicates the effectiveness of the proposed scheme.

Comparative Analysis of Cluster Validity Indices in Identifying Some Possible Genes Mediating Certain Cancers.

In this article, we compare the performance of 19 cluster validity indices, in identifying some possible genes mediating certain cancers, based on gene expression data. For the purpose of this comparison, we have developed a method. The proposed method involves cluster generation, selection of the best k-value or c-values, cluster identification, identifying the altered gene cluster, scoring an altered gene cluster and determining the best k-value or c-value exploring through biological repositories. The effectiveness of the method has been demonstrated on three gene expression data sets dealing with human lung cancer, colon cancer, and leukemia. Here, we have used three clustering algorithms, i.e., k-means, PAM and fuzzy c-means. We have used biochemical pathways related to these cancers and p-value statistics for validating the study.