Nowadays Internet is an excellent priority base source for everyone for sales and distribution and everything for digital assets, but because of this world wide sharing of digital information now there is an issue of copyright compliance and content management can be a challenge for us. Now a digital information can be used everywhere very easy way with or without using consent. Digital image Watermarking is an ultimate solutions that can add an extra security layer of protection for a digital images. In a digital image watermarking technique an image or an object embedded with some information carrying watermark, this object may be audio or video. These watermarking embedded techniques based on DWT -Twin Encoding technique drive the benefits from random sequence generated by Arnold and Chaos transformations. The proposed system before sending the multimedia data it apply the composite partition algorithm, watermark optimization techniques and the resulting file is Encrypted using RSA algorithm with public key and send it to the client side. Simulation result shows the performance of watermarking of image against different attacks. In the end, the performance of the proposed technique will be measured on the basis of PSNR, and NCC.

Machine learning applications employ FFNN (Feed Forward Neural Network) in their discipline enormously. But, it has been observed that the FFNN requisite speed is not up the mark. The fundamental causes of this problem are: 1) for training neural networks, slow gradient descent methods are broadly used and 2) for such methods, there is a need for iteratively tuning hidden layer parameters including biases and weights. To resolve these problems, a new emanant machine learning algorithm, which is a substitution of the feed-forward neural network, entitled as Extreme Learning Machine (ELM) introduced in this paper. ELM also come up with a general learning scheme for the immense diversity of different networks (SLFNs and multilayer networks). According to ELM originators, the learning capacity of networks trained using backpropagation is a thousand times slower than the networks trained using ELM, along with this, ELM models exhibit good generalization performance. ELM is more efficient in contradiction of Least Square Support Vector Machine (LS-SVM), Support Vector Machine (SVM), and rest of the precocious approaches. ELM’s eccentric outline has three main targets: 1) high learning accuracy 2) less human intervention 3) fast learning speed. ELM consider as a greater capacity to achieve global optimum. The distribution of application of ELM incorporates: feature learning, clustering, regression, compression, and classification. With this paper, our goal is to familiarize various ELM variants, their applications, ELM strengths, ELM researches and comparison with other learning algorithms, and many more concepts related to ELM.

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There are various approaches for the noise detection in the audio files. Human ear can detect the sound intensity change of 34 milliseconds. In this paper propose novel method for noisy node detection in the wave file. The wave file is divided in chunks depending on Vivaldi concept. Iterative k-means clusters are created. The silhouette noisy node is detected, by comparing silhouette nodes in all iteration for each chunk.

Machine learning applications employ FFNN (Feed Forward Neural Network) in their discipline enormously. But, it has been observed that the FFNN requisite speed is not up the mark. The fundamental causes of this problem are: 1) for training neural networks, slow gradient descent methods are broadly used and 2) for such methods, there is a need for iteratively tuning hidden layer parameters including biases and weights. To resolve these problems, a new emanant machine learning algorithm, which is a substitution of the feed-forward neural network, entitled as Extreme Learning Machine (ELM) introduced in this paper. ELM also come up with a general learning scheme for the immense diversity of different networks (SLFNs and multilayer networks). According to ELM originators, the learning capacity of networks trained using backpropagation is a thousand times slower than the networks trained using ELM, along with this, ELM models exhibit good generalization performance. ELM is more efficient in contradiction of Least Square Support Vector Machine (LS-SVM), Support Vector Machine (SVM), and rest of the precocious approaches. ELM’s eccentric outline has three main targets: 1) high learning accuracy 2) less human intervention 3) fast learning speed. ELM consider as a greater capacity to achieve global optimum. The distribution of application of ELM incorporates: feature learning, clustering, regression, compression, and classification. With this paper, our goal is to familiarize various ELM variants, their applications, ELM strengths, ELM researches and comparison with other learning algorithms, and many more concepts related to ELM.