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Agrarian sector in India is facing rigorous problem to maximize the crop productivity. More than 60 percent of the crop still depends on monsoon rainfall. Recent developments in Information Technology for agriculture field has become an interesting research area to predict the crop yield. The problem of yield prediction is a major problem that remains to be solved based on available data. Data Mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. This paper presents a brief analysis of crop yield prediction using Multiple Linear Regression (MLR) technique and Density based clustering technique for the selected region i.e. East Godavari district of Andhra Pradesh in India.

Abstract:With the huge development of the usage of computer over network and advancement in applications running on various platforms captures the attention towards network security. The Intrusion Detection System (IDS) plays a vital role in detecting anomalies and attacks in the network. Earlier approaches of IDS relied on Machine Learning (M L) techniques. Due to some limitations, a better approach is needed. A combination of Machine Learning techniques and data preprocessing is an effective approach for IDS. In this work, a new dimensionality reduction technique combined with the Multi-class SVM (Support Vector Machine) is proposed for intrusion detection. In the proposed model, Multi-Linear Dimensionality Reduction (ML-DR) is proposed as a feature extraction technique to reduce the dimension in order to shorten the training time. A Multi-class SVM (M-SVM) is used to detect whether the action is an attack or not. Here the Multi-class SVM is adopted to perform multi-attack classification in a layered fashion. Radial Basis Function Kernel is used as a SVM kernel. NSL-KDD data set is used for the performance evaluation of the proposed approach. The performance metrics such as classification accuracy, false alarm rate and the correlation coefficient are evaluated to measure its efficiency. In comparison with other detection approaches, the experimental results show that the proposed model outperforms the higher classification accuracy.

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