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One of the challenging factor in computer vision is Moving Object Detection under Dynamic condition, dynamic condition involves changes in the background like illumination changes, shadows, slowly moving background and the object, occlusion, noise in the video or image, motion of the camera. In order to overcome the problems of dynamic back ground, and detect the moving object correctly many algorithms have been proposed in the literature survey. In this paper an attempt has been made to study two algorithms for segmenting the moving objects from a video. Firstly the color and motion cues based segmentation is performed. In this method frames are extracted from the video and the motion information is considered and the color information is extracted using the color histogram method. The color and the motion information is combined using Markov Random Field (MRF) to segment the object from the back ground. The second algorithm is based on Spatio-Temporal method of segmentation. In this method spatial and temporal information of the frames are extracted separately. These features are combined to form Information Saliency Map(ISM) and from ISM the foreground is segmented from the back ground. The comparative study is performed on both the algorithms for segmentation. Analysis is performed on these methods and a little variation is found.

Image Processing, Segmentation, Histogram, Moving Object Detection, Markov Random Field Information Saliency Map

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Kala Chandrashekhar L, Manasa B S, "A Study on Segmentation of Moving Objects Under Dynamic Conditions," International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.173-179, 2016.

Data mining is the one of the most important research area in the field of Computer Science. By using Data mining techniques we can extract the hidden patterns from large amount of data. The Data mining is the process of categorizing valid, novel, potentially useful and understandable patterns in data. Data mining plays a major role in several application areas like business organizations, Educational institutions, Government sectors, Health care industry, scientific and Engineering. In Data mining techniques classification is one of the most important techniques. The classification technique has several algorithms like ID3,C4.5,Navie Bayes etc. In this paper we analyze the different data mining algorithms. By using these algorithms we can classify our data. Classification is used in every field of real life. The data mining classification technique is used to classify each item in a set of data into one of predefined set of classes or groups. The classification technique is used to predict group membership for data instances. In this Data mining the classification technique include several techniques such as Decision tree, Bayesian classification, Classification by Back propagation, Association Rue mining.

Data mining, Classification, Decision tree, ID3,C4.5, Bayesian classification, Naive Bayes classification

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S. Nagaparameshwara Chary, B.Ram, "Analysis of Classification Technique Algorithms in Data mining- A Review," International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.180-185, 2016.

Cloud Computing is making inroads into other areas of research. Advanced research area like Deep Learning is now not limited to just huge organizations. Cloud Computing is making it easy for even small researchers to have a go at Deep Learning. One needs to just harness the unlimited Computing power the Cloud offers. This paper presents a review of Deep Learning, opportunities & Challenges of Deep Learning .The paper also throws light on the role of Cloud Computing in Deep Learning.

Deep Learning, Machine Learning, Cloud Computing, Deep Clouds

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Srinivas Jagirdar, K. Venkata Subba Reddy and Ahmed Abdul Moiz Qyser, "Cloud Powered Deep Learning-Emerging Trends," International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.186-190, 2016.

For the past decades, an extensive variety of cryptographic protocols have been suggested to resolve secure communication problems even in the occurrence of challenger. The assortment of this work varies from developing fundamental security primitives providing confidentiality and authenticity to solving more difficult, application-specific problems. With rapid developments in perimeters and potential of communications and information broadcasts, there is a rising require of authentication protocol. However, when these protocols are deployed in practice, a significant challenge is to ensure not just security but also privacy throughout these protocols’s lifetime. As computer-based devices are more extensively used and the Internet is more globally accessible, new types of applications and new types of privacy threats are being introduced to password privacy in the context of authenticated key exchange (AKE). Especially, we show that AKE protocols provably meeting the existing formal definitions do not accomplish the anticipated level of password privacy when organized in the real world.

Authenticated Key exchanges (AKE), Authentication, AMEA, minimum space complexity, Symmetric Key, attacks

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Stuti Nathaniel, Syed Imran Ali, Sujeet Singh, "A Review of Authenticated Key Exchange Protocol Using Random Key Selection with Minimum Space Complexity," International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.191-196, 2016.