The main objective of this study was to evaluate and compare the performances of rainfall-runoff models that were developed by using support vector machines (SVMs). Rainfall and runoff data of Haripura and Baur dams were adopted on daily basis from Irrigation Division Rudrapur in Uttarakhand. In this study, radial kernel function was used. As the values of Cost function (C),  and  varies, performances of the models can be altered. So, at optimum values of these variables, there exists a best correlation between rainfall and runoff. It can be inferred from the study that SVM models provide satisfactory results for both dams. These results can be used for runoff prediction for various purpose such as irrigation etc.

A study was conducted to determine whether groundwater is suitable for drinking or not in the part of Tons River Basin, which is located at Dehradun in Uttarakhand. Groundwater samples from sixteen locations were collected in May 2012 and May 2019 by Central Ground Water Board at Dehradun. Fifteen physico-chemical parameters were evaluated for these groundwater samples using standard methods of groundwater quality analysis. In this study the parameters responsible for the pollution of groundwater at the sampling site were investigated using factor analysis. Also, the groundwater quality for drinking and irrigation purposes were assessed using water quality index, seven irrigation water quality parameters and criteria given by Richard, Wilcox, Wescot & Ayers.

One of the leading causes of mortality for women worldwide is breast cancer. The likelihood of breast cancer-related mortality can be decreased by early identification and rapid treatment. Machine learning-based predictive technologies provide ways to detect breast cancer earlier. Several analytical techniques, such as breast MRI, X-ray, thermography, mammography, ultrasound, etc., may be used to find it. Accuracy metrics are the most extensively used approach for performance evaluation, and the Tropical Convolutional Neural Networks (TCNNs) model for breast cancer detection is the most precise and popular model. The proposed approach was examined using the Kaggle Breast Cancer Datasets (KBCD). The data set is partitioned into training and testing. We suggest a new class of CNNs called Tropical Convolutional Neural Networks (TCNNs), which are based on tropical convolutions and replace the multiplications and additions in traditional convolutional layers with additions and min/max operations, respectively, in order to reduce the number of multiplications. The results of the review demonstrated that the Tropical Convolutional Neural Networks (TCNNs) is the most successful and popular model for detecting breast cancer, and that accuracy metrics is the most popular approach for evaluating performance. It is amazing how deep learning is being used to so many different real-world problems. Additionally, because tropical convolution operators are basically nonlinear operators, we anticipate that TCNNs will be better at nonlinear fitting than traditional CNNs. The Kaggle Breast Cancer Datasets (KBCD) findings demonstrate that TCNN can reach more expressive power than regular convolutional layers.

The social media has significantly changed how we communicate and exchange information throughout time. Along with it comes the issue of fake news' quick spread, which may have detrimental effects on both people and society. Fake news has been surfacing often and in enormous quantities online for a variety of political and economic goals. To increase the appeal of their publications, fake news publishers employ a number of stylistic strategies, one of which is stirring up readers' emotions. To increase the appeal of their publications, fake news publishers employ a number of stylistic strategies, one of which is stirring up the feelings of readers. As an outcome, it is now extremely difficult to analyses bogus news so that the creators may verify it through data processing channels without misleading the public. It is necessary to implement a system for fact-checking claims, especially those that receive thousands of views and likes before being disputed and disproved by reliable sources. Numerous machine learning algorithms have been applied to accurately identify and categories bogus news. A ML classifier was used in this investigation to determine if news was phony or authentic. On the dataset, the proposed model and other benchmark methods are assessed using the best characteristics. Results from the classification show that our suggested model (CNNs) performs better than the current models with a precision of 98.13%.