Introduction: Oral tori and exostosis are non-pathological bony protuberances seen on the alveolar surfaces of the jaw bones. These are commonly seen on the palatal surfaces of the maxilla [torus palatinus (TP)] and around the premolars in the lingual surface of the mandible [torus mandibularis (TM)]. The aim of this cross-sectional study was to determine the prevalence of tori/exostosis in the Malaysian population. Methodology: A total of 2666 patients were examined for the presence of tori and exostosis in the maxilla and mandible and were categorized into TP, TM, and exostosis (facial/labial). Collected data was analysed for obtaining descriptive statistics. Results: 882 subjects were noticed with oral tori/exostosis among the population studied with a prevalence rate of 33%. TP was seen more in females (35%), compared to males (20%), and this difference was statistically significant (p value < 0.001). Highest prevalence of TP was seen in Malays (43%), followed by Chinese (31%) and Indians (21%), which was statistically significant (p value < 0.00). Discussion: High prevalence of tori and exostosis was seen in the population studied. Though harmless, in certain circumstances, their presence necessitates changes in the denture design during fabrication of prosthesis, which the dentist should be mindful.

Rice is amongst the majorly cultivated crops in India and its leaf diseases can have a substantial impact on output and quality. The most important component is identifying rice leaf diseases, which have a direct impact on the economy and food security. Brown spot, Leaf Blast, Hispa are the most frequently occurring rice leaf diseases. To resolve this issue, we have studied various machine learning and deep learning approaches for detecting the diseases on their leaves by calculating their accuracy, recall, and precision to measure the performance. This study helps the farmers by detecting the diseases in rice leaves in order to get a healthy crop yield. The deep learning models perform well when compared with the machine learning methods. After analyzing all of the deep learning models, we found that the 5-layer convolution model had the best accuracy of 78.2 %, while others, such as VGG16, had a lower accuracy of 58.4%.

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In Chapter 10.1007/978-1-4842-6405-8_6, we explored the concepts of Spark and Azure Databricks’ implementation of the platform. In this chapter, we will be doing a hands-on exploration of these concepts in Azure Databricks.

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