Emotion recognition is a significant research topic for interactive intelligence system with the wide range of applications in different tasks, like education, social media analysis, and customer service. It is the process of perceiving user's emotional response automatically to the multimedia information by means of implicit explanation. With initiation of speech recognition and the computer vision, research on emotion recognition with speech and facial expression modality has gained more popularity in recent decades. Due to non-linear polarity of signals, emotion recognition results a challenging task. To achieve facial emotion recognition using multimodal signals, an effective Bat Rider Optimization Algorithm (BROA)-based deep learning method is proposed in this research. However, the proposed optimization algorithm named BROA is derived by integrating Bat Algorithm (BA) with Rider Optimization Algorithm (ROA), respectively. Here, the multimodal signals include face image, EEG signals, and physiological signals such that the features extracted from these modalities are employed for the process of emotion recognition. The proposed method achieves better performance against exiting methods by acquiring maximum accuracy of 0.8794, and minimum FAR and minimum FRR of 0.1757 and 0.1806.

In the biomedicine field, blood cell analysis is the first step for diagnosis of many of the disease. The first test that is requested by a doctor is the CBC (Complete Blood cell Count). Microscopic image of blood stream contains three types of blood cells: Red Blood Cells (RBCs), White Blood Cells (WBCs) and platelets. Earlier counting of blood cell was done manually which was inaccurate and depends on operator's skill. Counting of blood cells using image processing provides cost effective and accurate result than manual counting. During the counting process, the splitting of clumped cell is the most challenging issue. This paper represents segmentation and counting of RBCs and WBCs from microscopic blood sample images. Segmentation is done using Otsu's thresholding and morphological operations. Counting of cells is done using geometric features of cells. RBCs contain clumped cells which make the task of counting of cells accurately very challenging. For counting of RBCs, two different methods are used: 1) Watershed segmentation 2) Circular Hough Transform. Comparison of both this method is shown for randomly selected images. The performance of counting methods is also analyzed by comparing it with results obtained by manual counts.