Oral cancer is caused by the mutation of the cells in the lips or in the mouth. The incidence rate and prevalence rate of oral cancer are increasing worldwide. Recently, the Machine Learning (ML) approaches play a vital role in medical image diagnosis. They provide accurate and rapid evaluation of the analysis of histopathological images using supervised learning. In this study, three different modules are developed namely preprocessing, feature extraction and classification module. Initially, the raw histopathological image is given to the median filter for the removal of background noise in the preprocessing module. In the next module, the temporal features such as energy, entropy etc., are extracted from the color components of the filtered images. Finally, the classification is done by employing the Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) to classify histopathological images as normal or abnormal. Results show that the SVM classifier is better than KNN for the classification of oral cancer. The classification accuracy on 1224 histopathological images has been improved to 98% by using SVM classifier as compared with the KNN results of 83%.

Application of internet of things has been frequently used in smart cities. It has been observed that people at different localities are showing interest in IoT applications for smart cities. Their interest, awareness, and significant consideration for IoT application varies according to their qualification, locality, and standard of living. But it is essential to know the interest; the awareness of public regarding such systems. Present research is focused of IoT automation in case of smart cities and considered IoT applications for commercial and healthcare. Present work is considering awareness, interest, and significance of consideration among males and females. Research is focusing whether there is awareness in people in IoT automation for healthcare and commercial applications. The interest of people for an IoT automation system of a smart city is also taken in account.

The protection of wireless sensor networks (WSN) against malware attacks is crucial. The paper discusses the issue of malware attacks in WSN, which are commonly used for monitoring and surveillance in various applications. Due to resource constraints, sensor nodes in WSN are vulnerable to malware attacks, which can spread rapidly and paralyze the network. The development of new technologies such as IoT, Industry 4.0 has increased the importance of WSN, and it has become essential to address the challenges posed by the resource‐constrained nature of sensor nodes and security concerns. In this paper, a model is considered with two exposed states to investigate the behaviour of malware spreading in WSN, and a SE1E2IR (Susceptible—Exposed State 1 ‐ Exposed State 2 ‐ Infectious—Recovered) model is proposed. The model is formulated as a system of differential equations, and its equilibrium and stability are examined. The basic reproduction number (R0) is also calculated as a key parameter that characterizes the spread of malware in the network. This parameter helps to identify the conditions under which the network will remain malware‐free or when it will experience an outbreak of malware. The proposed model provides a mechanism for the earlier detection of malware occurrences in WSN, and also discusses the effect of connectivity and coverages on the propagation of malware in the network. The paper also includes a comparative study of the proposed model with existing models; extensive theoretical study and computation analysis are performed to validate the proposed model.

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