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The enactment of automatic medial image taxonomy using customary methods of machine learning and data mining mostly depend upon option of significant descriptive characteristics obtained from the medical images. Reorganization of those skins obliges domain-specific skillful awareness moreover not a forthright process. Here in this paper we are going to propose a deep learning based cnn's named as deep cnn architecture. Which is a generic architecture and it accepts input as medical image data and produces the class or type of the decease. And we made comparison with the classical models like svm and elm. RÉSUMÉ. L'adoption d'une taxonomie automatique des images médianes à l'aide des méthodes habituelles d'apprentissage automatique et d'exploration de donné es dé pend principalement de l'option de caractéristiques descriptives significatives obtenues à partir des images mé dicales. La ré organisation de ces skins né cessite en outre une connaissance approfondie du domaine spé cifique, et non un processus direct. Dans cet article, nous allons proposer un CNN's basée sur l'apprentissage profond appelée architecture profonde de CNN qui est une architecture gé né rique. Elle accepte les entré es en tant que donné es d'images mé dicales et produit la classe ou le type de dé cè s. Et nous avons comparé les modè les classiques comme SVM et ELM.

The research activity considered in this paper concerns about efficient approach for modeling and prediction of air quality. Poor air quality is an environmental hazard that has become a great challenge across the globe. Therefore, ambient air quality assessment and prediction has become a significant area of study. In general, air quality refers to quantification of pollution free air in a particular location. It is determined by measuring different types of pollution indicators in the atmosphere. Traditional approaches depend on numerical methods to estimate the air pollutant concentration and require lots of computing power. Moreover, these methods cannot draw insights from the abundant data available. To address this issue, the proposed study puts forward a deep learning approach for quantification and prediction of ambient air quality. Recurrent neural networks (RNN) based framework with special structured memory cells known as Long Short Term Memory (LSTM) is proposed to capture the dependencies in various pollutants and to perform air quality prediction. Real time dataset of the city Visakhapatnam having a record of 12 pollutants was considered for the study. Modeling of temporal sequence data of each pollutant was performed for forecasting hourly based concentrations. Experimental results show that proposed RNN-LSTM frame work attained higher accuracy in estimating hourly based air ambience. Further, this model may be enhanced by adopting bidirectional mechanism in recurrent layer.Index Terms-Air quality, air pollution, prediction, environment, deep learning, recurrent neural networks, long short term memory.

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In this paper, a new robust and imperceptible digital image watermarking scheme that can overcome the limitation of traditional wavelet-based image watermarking schemes is proposed using hybrid transforms viz. Lifting wavelet transform (LWT), discrete cosine transform (DCT) and singular value decomposition (SVD). The scheme uses canny edge detector to select blocks with higher edge pixels. Two reference sub-images, which are used as the point of reference for watermark embedding and extraction, have been formed from selected blocks based on the number of edges. To achieve a better trade-off between imperceptibility and robustness, multiple scaling factors (MSF) have been employed to modulate different ranges of singular value coefficients during watermark embedding process. Particle swarm optimization (PSO) algorithm has been adopted to obtain optimized MSF. The performance of the proposed scheme has been assessed under different conditions and the experimental results, which are obtained from computer simulation, verifies that the proposed scheme achieves enhanced robustness against various attacks performed. Moreover, the performance of the proposed scheme is compared with the other existing schemes and the results of comparison confirm that our proposed scheme outperforms previous existing schemes in terms of robustness and imperceptibility.

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