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The inclusion of multimodal inputs improves the accuracy and dependability of smart healthcare systems. A user satisfaction monitoring system that uses multimodal inputs composed of users' facial images and speech is proposed in this paper. This smart healthcare system then sends multimodal inputs to the cloud. The inputs are processed and classified as fully satisfied, partly satisfied, or unsatisfied, and the results are sent to various stakeholders in the smart healthcare environment. Multiple image and speech features are extracted during cloud processing. Moreover, directional derivatives and a weber local descriptor is used for speech and image features, respectively. The features are then combined to form a multimodal signal, which is supplied to a classifier by support vector machine. Our proposed system achieves 93% accuracy for satisfaction detection.

Digital image processing techniques are very useful in abnormality detection in digital mammogram images. Nowadays, texture-based image segmentation of digital mammogram images is very popular due to its better accuracy and precision. Local binary pattern (LBP) descriptor has attracted many researchers working in the field of texture analysis of digital images. Because of its success, many texture descriptors have been introduced as variants of LBP. In this work, we propose a novel texture descriptor called generic weighted cubicle pattern (GWCP) and we analyzed the proposed operator for texture image classification. We also performed abnormality detection through mammogram image segmentation using k-Nearest Neighbors (KNN) algorithm and compared the performance of the proposed texture descriptor with LBP and other variants of LBP namely local ternary pattern (LTPT), extended local texture pattern (ELTP) and local texture pattern (LTPS). For evaluation, we used the performance metrics such as accuracy, error rate, sensitivity, specificity, under estimation fraction and over estimation fraction. The results prove that the proposed method outperforms other descriptors in terms of abnormality detection in mammogram images. [ABSTRACT FROM AUTHOR]