Outsourcing information in the direction of an outsider administrative control, as is done in Cloud Computing (CC), offers leads in the direction of security issues. The information trade off might happens since of attacks by different clients and nodes inside the CC. In this manner, high safety efforts are required to secure information inside the cloud. In any case, the utilized security methodology should likewise regard as the advancement of the information and automatic updating of cloud information inside recovery time. This work introduced a novel Adaptive Particle Swarm Division and Replication of Data Optimization (APSDRDO) for increasing security of cloud data and automatic............

Keyword : Centrality, cloud security, fragmentation, Adaptive Particle Swarm Division and Replication of Data Optimization (APSDRDO) , optimization, cloud computing , and replication.

[1]. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I. and Zaharia, M., 2010. A view of cloud computing. Communications of the ACM, 53(4), pp.50-58.
[2]. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J. and Brandic, I., 2009. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation computer systems, 25(6), pp.599-616.
[3]. Zhang, Q., Cheng, L. and Boutaba, R., 2010. Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications, 1(1), pp.7-18.
[4]. Hayes, B., 2008. Cloud computing. Communications of the ACM, 51(7), pp.9-11.
[5]. B. Grobauer, T.Walloschek, and E. Stocker, "Understanding cloud computing vulnerabilities," IEEE Security and Privacy, Vol. 9, No. 2, 2011, pp. 50-57.

The demand of information stockpiling limit is expanding drastically. As a result of more demands of capacity, the PC society is pulling in toward distributed storage. Security of information and cost factors are crucial challenges in distributed storage. A copy document not simply squander the capacity, it moreover extends the entrance time. So the detection and evacuation of copy information is a basic undertaking. Information de-duplication, a capable approach to deal with information diminishment, has expanded expanding consideration and notoriety in largescale capacity systems .This paper demonstrates our approach to deal with tending to the scalability and throughput issues of de-duplication-based open cloud fortification administrations..............

Keywords: Cluster, De-duplication, Load Balancing, Scalability

[1]. Q. Sean. and S. Dorward, "Venti: A new approach to archival storage," Bell Labs, Lucent Technologies.
[2]. C. Dubnicki, L. Gryz, L. Heldt, M. Kaczmarczyk, W. Kilian, P. Strzelczak, J. Szczepkowski, C. Ungureanu and M. Welnicki, "HYDRAstor: A Scalable Secondary Storage," in 7th USENIX Conference on File and Storage Technologies (FAST 09), SAN FRANCISCO, California, 2009.
[3]. D. Bhagwat, K. Eshghi, D. D. E. Long and M. Lillibridge, "Extreme Binning: Scalable, parallel deduplication for chunk-based file backup," in IEEE International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems, LONDON, 2009.

In this paper, we present an overview of our research in real time Neutrosophic logic based intrusion detection systems (IDSs). We focus on issues related to deploying a data mining-based IDS in a real time environment Information security has become a critical issue with the rapid development of business and other transaction systems over the internet. One of the toughest disputes in IDS is uncertainty handling. IDS offer a new challenge in handling uncertainty when normal and the abnormal behaviors in networked computers are hard to predict as the boundaries cannot be well defined

Keywords: Indeterministic, uncertainty, Neutrosophic, intrusion, genetic algorithm.

[1]. John E. Canavan , Fundamentals of network security, British Library Cataloguing in Publication Data, ISBN 1-58053-176-8, 2001, ARTECH HOUSE, INC.
[2]. D. E. Denning, "An intrusion detection model," IEEE Transactions on Software Engineering, vol. 13, no. 2, pp. 222– 232, 1987.
[3]. J. P. Anderson, "Computer security threat monitoring and surveillance," Tech. Rep., James P. Anderson Co., Fort, Washington, PA USA, 198
[4]. J. Cannady, "Artificial neural networks for misuse detection," in Proceedings of the 1998 National Information Systems Security Conference, pp. 443–456, Arlington, VA, USA, 1998.
[5]. A. A. Aburomman and M. B. I. Reaz, "A novel weighted support vector machines multiclass classifier based on differential evolution for intrusion detection systems," Information Sciences, vol. 414, pp. 225–246, 2017.

The Blue Eyes technology aims in establishing the computational machines which has perceptual and sensory ability like human beings. The basic idea behind Blue Eyes Technology is that to give computer the human power. It operates on non-obtrusive sensing method and employs most modern video cameras and microphones which identifies the user's actions through the usage of imparted sensory ability. The machine comprehends the need of user, where he is looking at, and even realizes his physical or emotional states. The development of Blue Eyes Technology is to intend a complex solution.........

Keywords: Bluetooth connection, Computational machine, CSU, DAU, Wearable device.

[1]. Chandani Suryawanshi, T. Raju, "Blue Eyes Technology", IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 01, page- 639, 2014.
[2]. Hardik Anil Patil, Shripad Amol Laddha, Nachiket Milind Patwardhan, "A Study on Blue Eyes Technology", International Journa of Innovative Research in Computer and Communication Engineering Vol. 5, Issue 3,page-5596, march 2017.
[3]. Priti Kumari, Loveleen Kumar, "A Review On Blue Eyes Technology", International Journal of Advanced Engineering Research and Science Vol-2, Issue-2, page-49, Feb 2015.
[4]. R.Jothi1 , A.Kasthuri2 , K. Jasmine, "Blue Eyes Technology", International Journal of science technology and management vol-05, Issue-01, page-411, Jan 2016.
[5]. Swati, "Blue Eyes Technology", International Journal of Advance Research In Science And Engineering IJARSE Vol-4, Special Issue (01), page-(234-235) April 2015.

Data mining is the process of investigating large datas to identify patterns and established relationship to solve problems. This paper studies the survey of data mining with image processing algorithm and recent technology in plant disease, soil contamination and under water image with numerical and categorical data. The study involved with Unmanned Aerial Vehicle (UAV) which is an aircraft that flies without a human pilot onboard, controlled remotely or flown autonomously via pre-programmed flight plans or other automated guidance systems.

Keywords : Unmanned Aerial Vehicle (UAV), soil, plant disease, underwater image

[1]. Adriano Mancini, Jack Dyson, Emanuele Frontoni, Primo Zingaretti," Soil / Crop Segmentation from Remotely Sensed Data Acquired byUnmanned Aerial System", 2017 International Conference on Unmanned Aircraft Systems (ICUAS) June 13-16, 2017, Miami, FL, USA
[2]. Arya M S, Anjali K, Mrs.Divya Unni ," DETECTION OF UNHEALTHY PLANT LEAVES USING IMAGE PROCESSING AND GENETIC ALGORITHM WITH ARDUINO".
[3]. M. F. Yahya, M. R. Arshad," Image-Based Visual Servoing for Docking of an Autonomous Underwater Vehicle".
[4]. Savita N. Ghaiwat, Parul Arora "Detection and Classification of Plant Leaf Diseases Using Image processing Techniques: A
Review", International Journal of Recent Advances in Engineering & Technology, ISSN (Online): 2347 - 2812, Volume-2, Issue - 3, 2014
[5]. I. Colomina and P. Molina, "Unmanned aerial systemsfor photogrammetry and remote sensing: A review," fISPRSg Journal of Photogrammetry and Remote Sensing, vol. 92, pp. 79 – 97, 2014. [Online]. Available: http://www.sciencedirect.com/ science/article/pii/ S0924271614000501

A huge repository of terabytes of data is generated each day from modern information systems and digital technologies such as Internet of Things and cloud computing. Analysis of these massive data requires a lot of efforts at multiple levels to extract knowledge for decision making. Therefore, big data analysis is a current area of research and development. Machine learning is the essence of artificial intelligence. Machine Learning learns from past experiences to improve the performances of intelligent programs. Machine learning system builds the learning model that effectively "learns" how to estimate from training data of given example. In this new era, Machine learning is mostly in use to demonstrate..........

Keywords: - Ada Boost, Big Data, Classification, Hadoop, Machine Learning,

[1]. M. K.Kakhani, S. Kakhani and S. R.Biradar, Research issues in bigdata analytics, International Journal of Application or Innovation inEngineering & Management, 2(8) (2015), pp.228-232.
[2]. Gandomi and M. Haider, Beyond the hype: Big data concepts, methods,and analytics, International Journal ofInformation Management,35(2) (2015), pp.137-144.
[3]. McKinsey Global Institute (MGI), Big Data: The next frontier for innovation, competition, and productivity, Report, June, 2012.
[4]. X. Jin, B. W.Wah, X. Cheng and Y. Wang, Significance and challengesof big data research, Big Data Research, 2(2) (2015), pp.59- 64.
[5]. D. P. Acharjya and Kauser Ahmed P, International Journal of Advanced Computer Science and Applications, 7(2) (2016).

Human retinal image plays a vital role in detection and diagnosis of various eye diseases for ophthalmologist. Automated blood vessel segmentation diagnoses many eye diseases like diabetic retinopathy, hypertension retinopathy, retinopathy of prematurity or glaucoma based on the feature extraction. Automated image analysis tool based on machine learning algorithms are the key point to improve the quality of image analysis. Deep learning (DL) is a subset of machine learning which is completely based on artificial neural network. It helps a machine to analyze the data efficiently. Deep learning is one extensively applied techniques that provides state of the art accuracy. Different types of neural network and platform used for DL also discussed. This paper reviews the different DL approaches for blood vessels segmentation. It concludes that the deep learning methods produces high level of accuracy in disease identification.

Keywords - Diabetic Retinopathy, Deep Learning, Segmentation, Neural Network.

[1]. (2017).Deep Learning for Java:Open-Source, Distributed, Deep Learning Library for the JVM. [Online]. Available: https:// deep learning4j.org/
[2]. M.Nielsen.(2017).NeuralNetworksandDeepLearning.[Online].Available: http://neuralnetworksanddeeplearning.com/
[3]. Steven W. Smith, "The Scientist and Engineering guide to Signal Processing" Book chapter 26.

The are various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. A popular source of data is microarrays, a biological platform for gathering gene expressions. analysing microarrays can be difficult due to the size of the data they provide. This paper presents some of the most popular methods for selecting significant features.

Keywords- Big Data, Feature selection, Filters, Wrappers.

[1]. Y. Saeys, I. Inza, P. Larranaga, A review of feature selection techniques in bioinformatics, Bioinformatics 23 (2007) 2507–2517.
[2]. S. Mitra, R. Das, Y. Hayashi, Genetic networks and soft computing, IEEE/ACM Trans. Comput. Biol. Bioinf. 8 (1) (2011) 94–107, http://dx.doi.org/10.1109/ TCBB.2009.39.
[3]. J.H. Phan, C.F. Quo, M.D. Wang, Cardiovascular genomics: a biomarker identification pipeline, IEEE Trans. Inf. Technol. Biomed. 16 (5) (2012) 809– 822, http://dx.doi.org/10.1109/TITB.2012.2199570.
[4]. C.C.M. Chen, H. Schwender, J. Keith, R. Nunkesser, K. Mengersen, P. Macrossan, Methods for identifying SNP interactions: a review on variations of logic regression, random forest and bayesian logistic regression, IEEE/ACM Trans. Comput. Biol. Bioinf. 8
(6) (2011) 1580–1591, http://dx.doi.org/10.1109/ TCBB.2011.46.
[5]. K. Kourou, T.P. Exarchos, K.P. Exarchos, M.V. Karamouzis, D.I. Fotiadis, Machine learning applications in cancer prognosis and prediction, Comput. Struct. Biotechnol. J. 13 (2015) 8–17, http://dx.doi.org/10.1016/j. csbj.2014.11.005

Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale.

Keywords- Big data, black box data, social media data, stock exchange data, power grid data, transport data, search engine data

[1]. Big Data: A Revolution That Will Transform How We Live, Work, and ThinkAuthor: Viktor Mayer-Schonberger and Kenneth Cukier
[2]. The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost ProfitsI. Author: Russel Glass & Sean Calahan
[3]. The Human Face of Big Data Author: Rick Smolan

The advent of digital era and technological advancements leads to generation of voluminous datasets which increase the need for secured transmission of the data. Expansive research on developing strategies for data security during transmission resulted in two efficient strategies called cryptography and
steganography.The study of Steganography applications in chaotic system are exponentially increasing within the recent years. Depending on the sensitivity to initial conditions, chaotic systems are unit characterised, similarity to continuous broad-band power spectrum and random behavior. The chaotic system is high sensitive to the initial condition and could be a high complicated nonlinear dynamic system. The chaotic sequence is unpredictable and extreme sensitivity to initial conditions.............

Keywords- Chaotic encryption, DCT, LSB, image encryption,quantization.

[1]. Yen. J,C and Guo, J.I., "A New Chaotic Key Based Design for Image Encryption and Decryption, Proceedings of the IEEE International Symposium Circuits and Systems,2000,4-52 vol 4.
[2]. S.A. Halim and M.F.A Sani. " Embedding using spread spectrum image steganography with GIF," in proceedings of the IMT-GTICMSA ,2010 pp.659-666, 2010.
[3]. Fridrich.J., Symmetric Ciphers Based on Two Dimensional Chaotic Maps,Int J.Bifurication and chaos .1998 8(6)
[4]. Sapna Saidharan , Deepu Sleeba Philip," A Fast Partial Image Encryption sechme with wavelet transform and rc4", The college of Information Sciences and Technology 2015 The Pennsylvania State University
[5]. MatuJokayand Tom Moravik,"Image based JPEG steganography", Tatra Mountains Mathematical Publications ,DOI 10.2478/v10127-010-0006-9,2010.

Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups or some similar to the other data points in the similar group than those in other groups. In this paper four clustering algorithm are described Expectation–maximization (EM) algorithm, Hierarchical algorithm, Density based algorithm, simple k-means algorithm. These algorithms were implemented in two different dataset. The data set were divide the entire dataset under different labels with similar data points into one cluster. The dataming algorithms are compared in the clustering data using weka tool

Keywords - Data mining, clustering algorithm, simple K-means algorithms, Hierarchical clustering, Density based algorithm.

[1]. R. C. Tryon, Cluster analysis: correlation profile and orthometric (factor) analysis for the isolation of unities in mind and personality. Edwards brother, Incorporated, lithoprinters and publishers, 1939.
[2]. Y. Yang, X. Guan, and J. You, "CLOPE: a fast and effective clustering algorithm for transactional data," in Proceedings of th e eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 2002, pp. 682–687.
[3]. Aastha Joshi and Rajneet Kaur .: "A Review: Comparative Study of Various Clustering Techniques in Data Mining" IJARCSSE, 2013
[4]. Swasti Singal and Monika Jena: "A Study on WEKA Tool for Data Preprocessing, Classification and lustering", IJITEE, 2013.
[5]. Andrew Moore: "K-means and Hierarchical Clustering - Tutorial Slides" http://www2.cs.cmu.edu/~awm/tutorials/kmeans.html.

Wireless Sensor Networks (WSN) is a trending technology now-a-days and has a wide range of applications such as battlefield surveillance, traffic surveillance, forest fire detection, flood detection etc. The many researchers have conducted different detection techniques and algorithms to proposed different types of detection schemes. But wireless sensor networks are susceptible to a variety of potential attacks which obstructs the normal operation of the network. The blackhole nodes will launch black hole attack to conserve its resource or to perform attacks that reduce the network. In this paper, survey the existing solutions and discuss the stateof- the-art routing methods. In this paper analysis the different type black hole attacks detection techniques in addition conceive the open issues and future trends of black hole detection and prevention in WSN based on the survey results of this paper.

Keywords: Mobile Ad-hoc networks, Blackhole attacks, machine learning, pre-processing, classification

[1] R. Lakhwani, S. Suhane, and A. Motwani, "Agent based AODV protocol to detect and remove black hole attacks," International Journal of Computer Applications, vol. 59, no. 8, pp. 35-39, 2012.
[2] N. Sharma and A. Sharma, "The black-hole node attack in WSN," in Proceedings of 2nd International Conference on Advanced Computing & Communication Technologies, Rohtak, India, 2012, pp. 546-550.
[3] N. R. Yerneni and A. K. Sarje, "Secure AODV protocol to mitigate black hole attack in mobile ad hoc," in Proceedings of 3rd International Conference on Computing Communication & Networking Technologies (ICCCNT), Coimbatore, India, 2012, pp. 1-5.
[4] R. K. Bar, J. K. Mandal, and M. M. Singh, "QoS of WSN through trust based AODV routing protocol by exclusion of black hole attack," Procedia Technology, vol. 10, pp. 530-537, 2013.
[5] S. Biswas, T. Nag, and S. Neogy, "Trust based energy efficient detection and avoidance of black hole attack to ensure secure routing in WSN," in Proceeding of Applications and Innovations in Mobile Computing (AIMoC), Kolkata, India, 2014, pp. 157- 164.

Data mining is the process of discovering interesting knowledge patterns from large amount of data stored in database. It is an essential process where the intelligent techniques (i.e., machine learning, artificial intelligence, etc ) are used to extract the data patterns (i.e., features). The aim of data mining process is to extract the useful information from dataset and transform it into understandable structure for future use. Feature extraction is the process of extracting the relevant features from large database for dimensionality reduction. Feature extraction is a key process to reduce the dimensionality of medical dataset for efficient disease prediction. The feature extraction technique removes irrelevant features to acquire higher prediction accuracy during disease diagnosis

Keywords- Data Mining, Feature Extraction, Machine Learning, Feature Engineering.

[1]. Bishop, C. M. (2006), Pattern Recognition and Machine Learning, Springer, ISBN 0-387-31073-8
[2]. Daniel Engel, Lars Hüttenberger, Bernd Hamann, "A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and Visualization", LNCS Springer, 2014, pp. 1-16.
[3]. Khalid, Samina, Khalil Tehmina, NasreenShamila, "A Survey of Feature Selection and Feature Extraction Techniques in Machine Learning", IEEE Science and Information Conference, 2014, pp. 372-378.
[4]. "Feature Engineering: How to transform variables and create new ones?". Analytics Vidhya. 2015-03-12. Retrieved 2015-11-12.
[5]. "Discover Feature Engineering, How to Engineer Features and How to Get Good at It - Machine Learning Mastery". Machine Learning Mastery. Retrieved 2015-11-11.

Data stream is an ordered sequence of data objects that can be read only once or a small number of times.The characteristics of data stream are very large, continuous, high dimensional, immeasurable, dynamically high speed and massive amount of data in offline and also in online and there is not sufficient time to rescan the entire database. Data stream are required to store vast amounts of data that are continuously inserted and queried. Due to the above features of data stream, obtaining the fruitful information is a critical process. Hence, analyzing huge data sets and extracting valuable pattern in many applications are interesting for researchers. Moreover, the dynamic high speed of time series data stream is controlled when using..............

[1]. Pantelis n.Karamolegkos, Charalampos Z.Patrikakis Nikolaos D.Doulamis Panagiotis, "An Evaluation Study of Clustering Algorithms in the Scope of user Communities Assessment" Computers $ Mathematics with Applications, Elsevier, Vol No 58, issue no 8, October 2009, Pages 1498 - 1519.
[2]. Man Abdel - Maksoud, Mohammed Elmogy, Rashid Al-Awadi, "Brain Tumor Segmentation Based on a Hybrid Clustering Technique", Egyptian Informatics Journal, Vol No 16, Issue no 1, March 2005, Pages 1 - 81.
[3]. Madjid Khalilian, Norwati Mustapha, Data Stream Clustering: Challenges and Issue, Proceedings of the International Multi conference of Engineers and Computer Scientists 2010 Vol No1, IMECS 2010,March 17-19 2010.
[4]. Maryam Mousavi1 , Azuraliza Abu Bakar, and Mohammadmahdi Vakilian, "Data Stream Clustering Algorithms: A Review", International Journal of Advance Soft Computer Applications Vol o 7, Issue No 3, November 2015, ISSN 2074-8523.
[5]. Jose R. Fernandez," A Framework and Algorithm for Data Stream Cluster Analysis", International Journal of Advanced Computer Science and Applications, Vol No 2, Issue No11, Pages 87, 2011.

The objective of this thesis is to establish automatic content analysis methods and scalable graphbased models for query log analysis. One important aspect of this thesis is therefore to develop a framework to combine the content information and the graph information with the following two purposes: 1) analyzing Web contents with graph structures, more specifically, mining query logs; and 2) identifying high-level information needs, such as expertise retrieval, behind the contents. For the first purpose, a novel entropy-biased framework is proposed for modeling bipartite graphs, which is applied to the click graph for better query representation by treating heterogeneous query-URL pairs differently and diminishing the effect of noisy links............

Keywords- Bipartite graph, Expertise retrieval, novel entropy, Query log analysis, weighted language

[1]. A. Agarwal, S. Chakrabarti, and S. Aggarwal. Learning to rank net-worked entities. In Proceedings of the Twelfth ACM SIGKDD Interna-tional Conference on Knowledge Discovery and Data Mining (KDD), pages 14–23, 2006.
[2]. J. Alpert and N. Hajaj. We knew the web was big... The Official Google Blog, July 25, 2008.
[3]. R. Baeza-Yates, B. Ribeiro-Neto, et al. Modern information retrieval. Addison-Wesley Harlow, England, 1999.
[4]. R. A. Baeza-Yates, C. A. Hurtado, and M. Mendoza. Query recommen-dation using query logs in search engines. In EDBT Workshops, pages 588–596, 2004.
[5]. R. A. Baeza-Yates and A. Tiberi. Extracting semantic relations from query logs. In KDD, pages 76–85, 2007.

Intrusion detection is a surveillance problem of sensible import that is nicely suited to wireless sensor networks. In this paper, we study the latency and power conscious reliable intrusion detection system, secondary cluster head section, modified genetic algorithm, dynamic key generation, secured statistics encryption the use of AES. AES Advanced Encryption Standard contains three block ciphers: AES-128, AES-192 and AES-256. Each cipher encrypts and decrypts data in blocks of 128 bits using cryptographic keys of 128-, 192- and 256-bits, respectively. As the use of pc gadget and network increases, securing information is one of the important in order to obtain tightly closed information transmission barring hacking so two Intrusion detection is the one of the foremost problem in community security..........

Keywords - Resilience, cipher, dynamic key generation ,intrusion detection system, digital security.

[1]. Network Security:The Complete Reference by Mark Rhodes-Ousley, Roberta Bragg and Keith Strassberg
[2]. Introduction to Network Security, Matt Curtin.
[3]. Self-Defending Networks: The Next Generation of Network Security, Duane DeCapite, Cisco Press, Sep. 8, 2006.
[4]. ]Security Threat Mitigation and Response: Understanding CS-MARS, Dale Tesch/Greg Abelar, Cisco Press, Sep. 26, 2006.
[5]. ]Network Infrastructure Security, Angus Wong and Alan Yeung, Springer, 2009..