Search

Facial expression recognition (FER) plays a vital role in image processing according to the widespread development of human interactive applications. In the past few years, various researchers have focused on FER for implementing it in different applications. The existing system suffers from various complexities such as low accuracy, computational cost, and poor recognition performances. In this article, we proposed a novel concept to recognize facial expressions. The proposed work comprises three sections that are pre-processing, feature extraction, and classification. The pre-processing techniques remove the unwanted data from the original image and enhance the crucial details for further processing. The Convolutional Neural Network (CNN) is used for feature extraction. But, it yields lower performance in terms of feature extraction due to the shortage of hyperparameter tuning. Hence, the Multi-strategy Gravitational Search Algorithm (M-GSA) is utilized to extract the facial expression features from the eyebrow movement, nose, chin, and lip corner of the facial images. The facial expressions are classified via the Support Vector Machine (SVM) classifier. In this work, the top five facial expressions such as surprise, sad, happy, fear, and angry with three facial expression datasets such as FER-2013 dataset, CK+dataset, and JAFFE dataset. Ultimately, the proposed method demonstrates better classification accuracy and recognition rates than different kinds of state-of-art methods.

In MANET, node within its transmission range can be communicated directly and the nodes that are out of its transmission range may be communicated via other intermediate nodes. The existing popular reactive AOMDV protocol in MANET, suffers routing overhead due to flooding of RREQ and RREP packets. In many cases reactive routing protocol in MANET uses AOMDV for route discovery. A node consumes some energy level for receiving and forwarding data packets. A path loss may happen due to draining of node energy level or node moves out of coverage area. To overcome this drawback we propose a modified AOMDV algorithm, enhanced energy and channel aware load balancing (EECALB-AOMDV) routing protocols that reduces routing overhead by multicasting RREQ packet only to selected neighbour nodes. These neighbour nodes are selected by examining the channel, node energy level and existing load analysis. The proposed algorithm will not only minimise routing overhead, but also establish stable path, that over comes the path loss and also distribute load for multipath, and for efficient transmission between sources to destination.

Sickle cell disease (SCD) is a heritable blood disorder resulting in deformed, rigid red blood cells, rendering them more prone to vaso-occlusion. Ocular complications are known to affect multiple organs through the body's vasculature. Here, we evaluated the incidence of eye complications in patients with SCD at King Abdulaziz Medical City in Jeddah, Saudi Arabia.