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The present research work was carried out to understand the influence of basin morphometric parameters on runoff potential in an ungauged basin using satellite images, topographical maps, and rainfall data combined with geospatial techniques. The upper Gosthani river basin is an ungauged basin which is located in the Eastern Ghats of Visakhapatnam District, Andhra Pradesh State, Southern India. The river Gosthani and its tributaries are draining through the basin area covering about 321.1 km 2. The quantitative analysis of basin morphometry reveals that the area is under influenced by steep ground slopes, with moderate to less permeable rocks, leading to high runoff. The basin is elongated in shape resulting to flatter peak of flow for longer duration. The daily rainfall data during 2008-2016 were used in the estimation of runoff potential with the help of the Soil Conservation Service-Curve Number (SCS-CN) model. The weighted curve number was determined by the integration of land use and land cover, antecedent moisture condition, and hydrological soil groups. It was observed from the analysis that the overall increase in runoff corresponding to the rainfall. The area receives a good amount of rainfall, but most of it lost as surface runoff (nearly 40% of total rainfall) due to rapid overland flow and impermeable rocks. Analysis of morphometric parameters combined with SCS-CN-based approaches can be explored as an alternative for simulating the hydrological response of the basins.

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Email Spam has become a vital issue currently, with high-speed growth of internet users. Some people are using them for illegal conducts, phishing and fraud. Sending malicious link through spam emails which can harm our system and may also they will seek into our system. The need of email spam detection is to prevent spam messages from lagging into user’s inbox so it’ll improve user experience. This project will identify those spam emails by using machine learning approach. Machine learning is one amongst the applications of Artificial Intelligence that allow systems to read and improve from experience without being specific programmed. This paper will discuss the machine learning algorithm which is Naïve Bayes. It is a probabilistic classifier, which means it predicts on the idea of the probability of an object and it is selected for the email spam detection having best precision and accuracy.

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