The automatic measurement of pain intensity from facial expressions, mainly from face images describes the patient’s health. Hence, a robust technique, named Water Cycle Henry Gas Solubility Optimization-based Deep Neuro Fuzzy Network (WCHGSO-DNFN) is designed for compound FER and pain intensity measurement. However, the proposed WCHGSO is the incorporation of Water Cycle Algorithm (WCA) with Henry Gas Solubility Optimization (HGSO). Here, Compound Facial Expressions of Emotion Database (dataset-2) is made to perform compound FER, whereas the input image from UNBC pain intensity dataset (dataset-1) is utilized to measure the pain intensity, and the processes are performed separately. The developed technique achieved better performance with respect to testing accuracy, sensitivity, and specificity with the highest values of 0.814, 0.819, and 0.806 using dataset-1, whereas maximum values of 0.815, 0.758 and 0.848 is achieved using dataset-2.
Chip multiprocessor (CMP) systems have become inevitable to meet high computing demands. In such systems sharing of resources is imperative for better resource utilisation. The challenge arises when various application programs running on neighbouring cores compete for these resources concurrently and introduce contention. We aim to present in a simple, lucid and captivating manner a review of previous work on contention in multicores due to various shared resources like shared caches, main memory, memory bus bandwidth, prefetchers etc. The work investigates key ideas proposed by the research community to alleviate resource contention due to these various resources, under a single umbrella. The prime objective of the study is to throw light upon the fact that, alone a single shared component is not a dominant reason for performance degradation in CMPs, rather all elements in the memory hierarchy introduce resource contention thereby affecting performance cumulatively. The work presented would assist novice readers, researchers and academicians to further serve to propose optimal policies to address contention in designing multicore applications, considering the overall impact of these resources on the performance of multicore systems.
Human facial expressions are an indication of true emotions. To recognize facial expressions accurately is useful in the field of Artificial Intelligence, Computing, Medical, e-Education, and many more. The facial expression recognition (FER) system detects emotion through facial expression. But, it is challenging to detect facial emotions accurately. However, recent advancements in technology, research, and availability of facial expression datasets have led to the development of many FER systems which can accurately detect facial emotions. Past research in the field of FER indicates With Convolutional Neural Networks (CNNs), deep learning techniques are the most advanced presently. Custom CNN Architecture is used to implement basic facial emotion recognition in static images in this paper. A K-fold cross-validation method was used to train them using FER13, CK+, and the JAFFE data set. On the seven classes of fundamental emotions, including anger, disgust, fear, happiness, neutrality, sorrow, and surprise, the FER13, CK+, and JAFFE datasets had an accuracy rate of 91.58 percent. Given the difficulty of developing unique CNN architecture, this study’s accurate findings contrast well with those of previous studies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.