Agriculture is defined as the science and art of cultivating the flora and fauna. Farming in India is ranked as second around the globe and occupies 60.45% of Indian land. The Indian economy, dominatingly, depends upon farming along with agro-industry things. The soil ingredients (like Nitrogen, Phosphorous, Potassium), crop rotation, soil clamminess, atmospheric and surface temperature, precipitation, etc, play an efficient role in cultivation. The present evidence related to this field includes a model which is incorporated with ML algorithms (Random Forest, Decision Tree, Artificial Neural Network) to determine best crop. In this paper, the proposed model is enhanced by applying deep learning techniques and along with the prediction of crop, a clear information is achieved regarding the amounts of soil ingredients needed with their expenses separately. It provides a better accuracy than the existing model. It analyzes the given data and help the farmers in predicting a crop which in return help in gaining profits. The climatic and soil conditions of land are taken into consideration to predict a proper yield. The objective is to present a python based system that uses strategies smartly to anticipate the most productive reap in given conditions with less expenses. In this paper, SVM is executed as Machine Learning algorithm while LSTM and RNN are used as Deep Learning algorithms.
Having a secure and sound system is the most important need of the end-user. Confidential and authentic information about the system must be available to the genuine user when required. This paper presents a generalized Keystroke Dynamics Technique for identification of genuine users. The method works for the authentication of the user while user is entering the password to using the system. It is a three-layer approach which first check the typing pattern while entering the password then it also monitors the system while user is using the system. Different users have different typing pattern which could be used to recognize a user. For identification of the user a time-based tool is used to collect data pertaining to the typing time of each user for words of different lengths. This is a very easy and cost-effective way of collecting data for differentiating between a genuine user and imposter. At first layer elbow method is used to know unknown targets depending upon different word combinations. For the second layer principal component analysis (PCA) is used to find suitable factors where user typing pattern is making users indistinguishable. For the third step Long-Short Term Memory (LSTM) technique is used to forecast whether the user is a genuine user or an imposter.
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