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Data Mining is taking out of hidden patterns from huge database. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Nowadays traffic accidents are the major causes of death and injuries in this world. Roadway patterns are useful in the development of traffic safety control policy. This paper deals with the some of classification models to predict the severity of injury that occurred during traffic accidents. I have compared Naive Bayes Bayesian classifier, AdaBoostM1 Meta classifier, PART Rule classifier, J48 Decision Tree classifier and Random Forest Tree classifier for classifying the type of injury severity of various traffic accidents. The final result shows that the Random Forest outperforms than other four algorithms.

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SARS-CoV-2 pandemic is having a devastating effect on human lives. Individuals who are symptomatic/asymptomatic or have recovered are reported to have/will have serious health complications in the future, which is going to be huge economic burden globally. Given the wide-spread transmission of SARS-CoV-2 it is almost impossible to test each and every individual for the same and isolate them. Recent reports have shown that sewage can be used as a holistic approach to estimate the epidemiology of the virus. Here we have estimated the spread of SARS-CoV-2 in the city of Hyderabad, India which is populated with nearly 10 million people. The sewage samples were collected from all the major sewage treatment plants (STPs) and were processed for detecting the viral genome using the standard RT-PCR method. Based on the average viral particle shedding per individual, the total number of individuals exposed to SARS-CoV-2 (in a window of 35 days) is about 6.6% of the population, which clearly indicates the rate of community transmission and asymptomatic carriers is higher than the number of reported cases. It is important to note here that the samples collected from the inlet of STPs were positive for SARS-CoV-2, while the outlets were negative indicating the efficient treatment of sewage at STPs. These studies are going to be essential to manage the pandemic better and also to assess the effectiveness of control measure.

Keratocystic odontogenic tumor (KCOT), also known as odontogenic keratocysts, as defined by World Health Organization (WHO), are known for their peculiar behavior, varied origin, debated development, unique tendency to recur, and disputed treatment modalities. We present a case of KCOT involving symphysis menti, right and left halves of the body of mandible in an 11-year-old girl treated with enucleation and open dressing (bismuth, iodoform, paraffin paste) with long-term follow-up.

Precise and fast diagnosis of COVID-19 cases play a vital role in early stage of medical treatment and prevention. Automatic detection of COVID-19 cases using the chest X-Ray images and chest CT-scan images will be helpful to reduce the impact of this pandemic on the human society. We have developed a novel FractalCovNet architecture using Fractal blocks and U-Net for segmentation of chest CT-scan images to localize the lesion region. The same FractalCovNet architecture is also used for classification of chest X-Ray images using transfer learning. We have compared the segmentation results using various model such as U-Net, DenseUNet, Segnet, ResnetUNet, and FCN. We have also compared the classification results with various models like ResNet5-, Xception, InceptionResNetV2, VGG-16 and DenseNet architectures. The proposed FractalCovNet model is able to predict the COVID-19 lesion with high F-measure and precision values compared to the other state-of-the-art methods. Thus the proposed model can accurately predict the COVID-19 cases and discover lesion regions in chest CT without the manual annotations of lesions for every suspected individual. An easily-trained and high-performance deep learning model provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-II-COV.