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 this present study, MWCNT-TiO₂ hybrid nanocomposite synthesized by solvothermal synthesis method with TiCl₄ as a precursor. Then the hybrid nanocomposite based nanofluid produced using base fluid water/Ethylene-glycol (50:50). The microscopic techniques like SEM, TEM and XRD were used to characterize the morphological studies of synthesized MWCNT-TiO₂. The heat-transfer coefficient of synthesized nanofluid inside the Plate Heat Exchanger (PHE) has experimented at selected levels of particle concentrations, inlet temperature and volume flow rate by RSM based Box-Behnken method. The findings revealed a more substantial linear effect at the heat-transfer coefficient of MWCNT-TiO₂ hybrid nanofluid by chosen process parameters. ANOVA results showed that the heat-transfer coefficient of hybrid nanofluid considerably enhanced at a selected higher level of particle concentration and volume flow rate of the MWNCT-TiO₂ hybrid nanofluid. At the same time, the heat-transfer coefficient decreased at a higher level of inlet temperature of MWNCT-TiO₂ hybrid nanofluid. The optimization result compared with the confirmatory experiments, and it yielded less than 1% error which ensures a better agreement with the predicted results.