Automatic animal sound classification and retrieval is very helpful for bioacoustic and audio retrieval applications. In this paper we propose a system to define and extract a set of acoustic features from all archived wild animal sound recordings that is used in subsequent feature selection, classification and retrieval tasks. The database consisted of sounds of six wild animals. The Fractal Dimension analysis based segmentation was selected due to its ability to select the right portion of signal for extracting the features. The feature vectors of the proposed algorithm consist of spectral, temporal and perceptual features of the animal vocalizations. The minimal Redundancy, Maximal Relevance (mRMR) feature selection analysis was exploited to increase the classification accuracy at a compact set of features. These features were used as the inputs of two neural networks, the k-Nearest Neighbor (kNN), the Multi-Layer Perceptron (MLP) and its fusion. The proposed system provides quite robust approach for classification and retrieval purposes, especially for the wild animal sounds.

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Abstract-Automatic animal sound classification and retrieval is very helpful for bioacoustic and audio retrieval applications. In this paper we propose a system to define and extract a set of acoustic features from all archived wild animal sound recordings that is used in subsequent feature selection, classification and retrieval tasks. The database consisted of sounds of six wild animals. The Fractal Dimension analysis based segmentation was selected due to its ability to select the right portion of signal for extracting the features. The feature vectors of the proposed algorithm consist of spectral, temporal and perceptual features of the animal vocalizations. The minimal Redundancy, Maximal Relevance (mRMR) feature selection analysis was exploited to increase the classification accuracy at a compact set of features. These features were used as the inputs of two neural networks, the k-Nearest Neighbor (kNN), the Multi-Layer Perceptron (MLP) and its fusion. The proposed system provides quite robust approach for classification and retrieval purposes, especially for the wild animal sounds.

Nowadays people using the internet for shopping, banking, mailing etc. Phishing is one of the major attacks on the website which people are facing in their day to day life. A phishing attack is one of cybercrime because it is the illegal attempt that gets sensitive information of the user such as username, password, and credit card detail. Too aware of such phishing attacks taken online so in this paper have to detect phishing Uniform Resource Locator (URL), that is, we loading the URL data from the Kaggle open source website which is an online community of data scientists and machine learning, owned by Google Limited Liability Company( LLC). In most of the phishing website, the attackers use a malicious URL which will display to the user like an authorized URL. Different algorithms like Naive Bayes, Random Forest, K nearest neighbor are performed in detection of the URL, by using algorithm their accuracy level will be different. So in this paper can adopt the best classification machine learning algorithm with SVM (Support Vector Machine), this predicts the phishing or non-phishing status of the given URL and it is the best algorithm in classification (based on the features of given data) and regression (is the continuous prediction of uniform data) from which we have to improve our accuracy level.