Somenath Chakraborty

The commonly used wireless sensor networks need more efficient routing to preserve the harmony of the performance characteristics and to defend against security assaults. These networks are equipped with sensing, processing, and communication capabilities. Although the used cryptographic techniques were efficient, they required more energy for authentication, encryption, and decryption. As a result, the paper proposes routing that addresses security issues while minimizing energy use, delay, and Prefetching-aware Data Replication (PDR) failures and maximizing network longevity. This multiobjective networking issue for networks of sensors is resolved utilising ant colony networking methods while keeping energy consumption in view and extending the life of sensor networks by taking into account the nodes' remaining power, their distance from one another, and their private data. The suggested routing strategy outperforms existing methods in terms of energy usage, PDR, the longevity...

Cloud computing is the way by which we connect to servers, large systems into a distributed secure manner without worrying about local memory limits. Here this paper we proposed a Novel Distributed Database Architectural Model for Mobile Cloud Computing (NDDAMMCC). Due to the exponential growth of wireless technologies and internet which are following Nielsen's Law of Internet Bandwidth, we are in the new era of cloud computing. In the recent technological era, smart mobile devices play a big role in all sort of day-by-day human needs. The applicability is so huge that the number of apps install on a mobile system becomes a hazard due to local memory limitations for mobile phone users and demands an alternative approach to solve this local memory problems. Mobile Cloud Computing (MCC) is the ultimate solution to this issue and our model presents a promising path in this new kind of cloud computing technology.

With the rapid use of smart phones, digital camera etc we are in a era where it becomes a daily habit to click photos and post it in the internet. The amount of camera capture photos is increasing exponentially and in many photos there is text in it. With the verity of captured images it becomes very difficult to extract text for desired uses. This paper proposes a modified histogram of oriented gradient feature extraction model for detection of text from camera capture images as well as born digital images. Then we use svm-light for classification of the pattern of the text and our model efficiently performs well on both types of images.

In this article we have investigated the possibility of Bose-Einstein Condensation (BEC) in a frame undergoing uniform acceleration or in other wards, in Rindler space associated with the uniformly accelerated frame. We have followed a very simple conventional technique generally used in text book level studies. It has been observed that the critical temperature for BEC increases with the increase in magnitude of acceleration of the frame. Typically the critical temperature in an accelerated frame is of the order of the Unruh temperature. Hence we have concluded that the increase in the magnitude of acceleration of the frame facilitates the formation of condensed phase.

Abstract: In our paper the new algorithm enhanced multi gradient Dilution Preparation (EMDP) is discussed. This new algorithm is reported with a lab on chip or digital Microfluidic biochip to operate multiple operation on a tiny chip. We can use Digital Microfluidic biochip to operate multiple operation on a tiny chip. Samples are very costly which are used in any Biochemical laboratory Protocols. For the case of fast and high throughput application, It is essential to minimize the cost of operations and the time of operations and that is why one of the most challenging and important phase is sample preparation. In our proposed algorithm, we have hide to reduce sample droplets and waste droplets and for this purpose waste recycling is used, when different series of multi gradient targets concentration factors (CFS) are generated. We have compared our proposed algorithm with recent dilution techniques such as MTC, REMIA, and WARA. For the storage of intermediate droplets which, and g...

In our paper the new algorithm enhanced multi gradient Dilution Preparation (EMDP) is discussed. This new algorithm is reported with a lab on chip or digital Microfluidic biochip to operate multiple operation on a tiny chip. We can use Digital Microfluidic biochip to operate multiple operation on a tiny chip. Samples are very costly which are used in any Biochemical laboratory Protocols. For the case of fast and high throughput application, It is essential to minimize the cost of operations and the time of operations and that is why one of the most challenging and important phase is sample preparation. In our proposed algorithm, we have hide to reduce sample droplets and waste droplets and for this purpose waste recycling is used, when different series of multi gradient targets concentration factors (CFS) are generated. We have compared our proposed algorithm with recent dilution techniques such as MTC, REMIA, and WARA. For the storage of intermediate droplets which, and generated d...

The objectives of this paper are to explore ways to analyze breast cancer dataset in the context of unsupervised learning without prior training model. The paper investigates different ways of clustering techniques as well as preprocessing. This in-depth analysis builds the footprint which can further use for designing a most robust and accurate medical prognosis system. This paper also give emphasis on correlations of data points with different standard benchmark

The objectives of this paper are to explore ways to parallelize and distribute deep learning in multi-core and distributed settings. We have heuristically improved the training parameter setting by a Deep Neural Network (DNN) using quad-core CPU and Graphical Processing Unit (GPU) and develop a setting to improve training performances. Along with that, a Parallel Phase Neural network model (PHNNM) has been proposed for the prediction of the long-term survival of liver patients who undergo liver transplantation (LT). We made survival analysis of 13 years in the prediction of liver patients after LT and trained the liver transplantation system to follow up data of 13 years separately using a multilayer perceptron PHNNM model with proper selection of data attributes in conjunction with evaluating the survival probabilities of such data. This paper proved that our prediction model is suitable for the long-term prognosis of survival of patients after LT. The promising results are shown, ...