A Human mind is the most significant making of God. The man is savvy in light of the mind. "Blue Brain" is the name of the world's first virtual cerebrum. That implies a machine can work as human mind. With the progression in innovation, human, a definitive wellspring of data and revelation ought to additionally be saved. As such, human is does not live for a large number of years yet the data in his psyche could be spared what's more, utilized for a few a great many years. Today researchers are in research to make a fake cerebrum that can think, reaction, take choice, and keep anything in memory. The primary point is to transfer human mind into machine. With the goal that man can think, take choice with no exertion. After the passing of the body, the virtual mind will go about as the man. So, even after the passing of an individual we won't lose the learning, insight, identities, emotions and recollections of that man that can be utilized for the advancement of the human culture.

Keyword : Blue brain, Brain, Neurons, Virtual brain, Nan bots.

[1]. Mannu Kumari,Rizwan Khan, "Review paper on Blue Brain Technology", International Journal of Computer Applications (0975 – 8887) Volume 180 – No.48, June 2018.
[2]. Kavya Priya G.V and Monika Sruthi.J, "A Review on Blue Brain Technology", International Journal of Trend in Research and Development, Volume 3(6), ISSN: 2394-9333 www.ijtrd.com.
[3]. Priyanka Ingle, M. U. Karande, "The Blue Brain - The Survey of Paper Blue Brain", International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056, Volume: 05 Issue: 05 | May-2018.
[4]. Kodi Balasriram, "The Man-Machine-Man(MMM) Interfacing with the Blue Brain Technology", International Journal of Current Trends in Engineering & Research (IJCTER) e-ISSN 2455–1392 Volume 3 Issue 7, July 2017 pp. 7 – 12.
[5]. Swapnil S. Bachatel, Prof. Vamshi Krishna, "A Survey Paper on Blue Brain Technology", International Journal of Advance Research in Computer Science and Management Studies Volume 3, Issue 7, July 2015 pg. 137-144.

Fuzzy clustering is a one of the most fashionable problems in the data mining because of it is an NP hard problem. However, the conventional fuzzy clustering algorithms have many drawbacks, including the poor convergence rate, least classification accuracy. Hence, to solve the drawbacks of conventional methods, many research works have been developed using optimization techniques. This paper discussed the wide-range of studies focused on the fuzzy clustering using optimization algorithms and it helps the researcher how to enhance the efficiency of the clustering accuracy in the futures.

Keywords: Data mining, Fuzzy Clustering, Optimization Techniques.

[1]. P. Melin and O. J. A. s. c. Castillo, "A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition," vol. 21, pp. 568-577, 2014.
[2]. A. A. Esmin, R. A. Coelho, and S. J. A. I. R. Matwin, "A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data," vol. 44, pp. 23-45, 2015.
[3]. J. C. Bezdek, R. Ehrlich, W. J. C. Full, and Geosciences, "FCM: The fuzzy c-means clustering algorithm," vol. 10, pp. 191-203, 1984.
[4]. J. C. Bezdek, R. Ehrlich, and W. Full, "FCM: The fuzzy c-means clustering algorithm," Computers & Geosciences, vol. 10, pp. 191-203, 1984.
[5]. C. Li, J. Zhou, P. Kou, and J. J. N. Xiao, "A novel chaotic particle swarm optimization based fuzzy clustering algorithm," vol. 83, pp. 98-109, 2012

The Big Data is customary information preparing which is taking care of an extremely tremendous sort of information in this advanced world. It deals with the organized and unstructured volume of information. The Big Data is essential on this advanced world to developing the information the board, information investigation, business, promoting, and so on, to build up the speed and savvy information supplier. The fundamental goal of this paper is to investigate big data and its difficulties, its applications in different fields and the tools and programming used to process the huge information. Here featured big data like a review. Since the huge information not specifically handle, it has different fields and every day the big data make a development progressively life and web period. This will address the different data has a place with the big data to improves training, business, web based business, medicinal services, and so on., and its procedure to make another future to new age individuals for give new advancements on the information field.

Keywords: Big data, Big data analytics, Healthcare, Big data tools, Hadoop.

 

[1]. Kuchipudi Sravanthi, Tatireddy Subba Reddy, "Applications of Big data in Various Fields" Kuchipudi Sravanthi et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (5), 2015, 4629-4632, ISSN 097 -9646.
[2]. Adam Belloum, "Introduction to Big data", https://hpc.uva.nl/uploaded_files/inlineitem/Lecture_6_BigData.pdf
[3]. https://dataconomy.com/2017/07/6-ways-use-big-data-ecommerce
[4]. Leena Das, Siddharth Swarup Rautaray and Manjusha Pandey, "Big Data Analytics for Medical Applications" Published: 08 February 2018 Article in International Journal of Modern Education and Computer Science·DOI: 10.5815/ijmecs.2018.02.04
[5]. https://medium.com/the-research-nest/real-world-applications-of-big-data-in-healthcare-5c84696fd3d4

The large damage after effect of a natural tragedy is not a simple task and in fact it is a inconvenient process to scope the real consequences with accuracy. This paper deals with estimating the disastrous impact regarding the loss of the vegetation. The random forest and decision tree classifiers are used to extract the green areas before and after the cyclone to determine the unite loss. The random forest approach is compared with the decision tree algorithms and from the experimental results the random forest algorithm is performed and produced high accuracy.

Keywords: Classifier, segmentation, classification, accuracy.

[1]. BREIMAN, L. (2001). Random Forests. Machine Learning, 45, 5–32.
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[3]. Genuer.R, Poggi, and Tuleau- variable selection using random forst, pattern recognition letters, 31, pp,2225-2236.
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[5]. TRIMBLE (2010A). eCognition® Developer 8.64.0 reference book. Available at:. http://www.definiens.com/ .

Man-made consciousness in this day and age is advancing quickly with new propelled advancements day in day out. The present PC frameworks are intended to perform little undertakings, for example, facial acknowledgment, vehicle driving, and execution of other minor obligations. Notwithstanding, the essential objective of computerized reasoning is to create progressed furthermore, increasingly complex frameworks that would outflank people at the way. This incorporates the execution of progressively convoluted undertakings like playing chess and unraveling conditions. Accordingly, the future objective of AI is to consummate all human exercises and give preferred answers for issues over the human can do. In the long haul, a mechanized framework that does all the human capacities from controlling vehicles to automated business frameworks will represent a few challenges............

[1]. Abdalla, A. M. B., Mustafa, M. A. M., Yousif, A. A. A., & Osman, M. A. A. A. (2016). Line Following Robotic Vehicle (Doctoral dissertation, Sudan University of Science and Technology).
[2]. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.
[3]. Cunha, F., Villas, L., Boukerche, A., Maia, G., Viana, A., Mini, R. A., & Loureiro, A. A. (2016). Data communication in VANETs: Protocols, applications, and challenges. Ad Hoc Networks, 44, 90-103.
[4]. Frey, C. B., & Osborne, M. A. (2017). The future of employment: how susceptible are jobs to computerization?. Technological Forecasting and Social Change, 114, 254-280.
[5]. Frey, C. B., & Osborne, M. A. (2017). The future of employment: how susceptible are jobs to computerization?. Technological Forecasting and Social Change, 114, 254-280.

Human Brain Projectis developed to integrate a strategy for understanding the human brain by gathering all existing data and knowledge that can be acquired about the structure and function of the brain and use it to develop a virtual brain structureby using latesttechnologies. Thishelps in understanding new fundamentals of the human brain and its reactions to various diseases and drugs, as well as brain-like computing technologies. This project has incredibly unlimited applications such as, medical, drugs designing, computer technologies, etc.

Keywords- Blue Brain, Super Computer, Simulation, Brain Model, NEURON, PyNN, Blue Gene.

[1]. https://en.wikipedia.org/wiki/Henry_Markram
[2]. https://www.humanbrainproject.eu
[3]. https://en.wikipedia.org/wiki/Blue_Brain_Project
[4]. Iyer Mili Venkat, Sachin Gala, Ruchik Thakkar, Manan Mehta, Yash Bhawre. (2016). Blue Brain: Bringing a Virtual Brain to Life.International Journal on Recent and Innovation Trends in Computing and Communication, 110-112.
[5]. Kavya Priya G.V and Monika Sruthi.J. (2016). A Review on Blue Brain Technology.International Journal of Trend in Research and Development, 796–798.

In this paper an automatic technique for flower identification that's strong to uncontrolled environments and applicable to completely different flower species. In existing methodology depends on Associate in nursing finish-to- end residual convolutional neural network (CNN) that represents the progressive in linguistics segmentation. to reinforce its sensitivity to flowers, we tend to fine-tune this network employing a single dataset of apple flower pictures. In projected methodology refinement methodology to higher distinguish between individual flower instances. with none preprocessing or dataset-specific coaching, experimental results on pictures of apple, peach.............

[1]. Xianghan Zheng , Zhipeng Zeng , Zheyi Chen, Yuan long Yu ,Chumming Rong, [2015], "Detecting spammers on social networks", Neurocomputing 159 (www.elsevier.com/locate/neucom), PP 27-34.
[2]. Guang-Bin Huang, Lei Chen, and Chee-Kheong Siew, [2006],"Universal approximation using Incremental Constructive Feedforward Networks with random hidden nodes", IEEE Transactions on Neural Networks, Vol. 17, No. 4.
[3]. Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew, [2006], "Extreme learning machine: Theory and applications", Neuro computing 70 (www.elsevier.com/ locate/neucom), PP 489–501
[4]. Hythem Hashim, A.Ahmed, Ali Satty and A. Samani,[2015] "Data mining methodologies to study student's academic performance using the C4.5 algorithm", International Journal on Computational Sciences & Applications (IJCSA) Vol.5, No.2.
[5]. Yong Xu, Yi Zhou, Kai Chen, [2013] "Observation on Spammers in Sina Weibo", Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering.

Remote systems administration is a technique by which homes, media communications systems and business establishments maintain a strategic distance from the exorbitant procedure of bringing links into a building, or as an association between different gear locations. Remote broadcast communications systems are for the most part actualized and controlled utilizing radio correspondence. This execution happens at the physical level (layer) of the OSI show arrange structure. We demonstrate that keenly blending bundles expands organize throughput. And also my paper we display our work towards conveying a group remote system with specially appointed correspondence what's more, steering between its components. We portray our organize model and execution of remote switches, while rousing choices and calling attention to open issues.And also we explain the 802.11 wireless networks in this section.

Keywords : Wifi, Cisco, 802.11an and etc.

[1]. Wi-Fi Alliance Marketing Requirements Document for Interoperability Testing of Approved VHT5G Products, Version 0.71
[2]. IEEE 802.11ad D8.0 May 2012
[3]. http://www.isuppli.com/mobile-and-wirelesscommunications/marketwatch/pages4.http://www.abiresearch.com/research/1008n
[4]. J. Walker, ―Unsafe at Any Key Size: An Analysis of the WEP Encapsulation,‖ IEEE 802.11 Task Group E, 2000.
[5]. S. Fluhrer, A. Shamir, and I. Mantin, ―Weaknesses in theKey Scheduling Algorithm of RC4,‖ Sel. Areas of Cryptography, Toronto, Canada, 2001.
[6]. W. A. AR Baugh, ―An Inductive Chosen Plaintext Attackagainst WEP and WEP2. 2001,‖ IEEE 802.11 Working Group, Task Group I (Security), 2002.

With the quick advancement in sight and sound organizations and the huge offers of video substance in online casual associations, customers encounter issues in getting their interests. Thusly, unique altered recommendation structures have been proposed. Likewise, none of them has considered both the security of customers' particular circumstances (e.g., monetary prosperity, ages and side interests) and video advantage merchants' vaults, which are to an incredible degree unstable and of basic business regard. In our venture we proposed the new improvement system for suggestion. The video suggestion depends on client's conduct (client's advantage) and furthermore utilizing the example digging for video label look proposal. We have look alternative as sub class seeks and worldwide inquiry in our application............

Keywords- Multimedia services, Private video recommendation, Sub category search and global search.

[1]. Wang Z, Sun L, Zhu W, et al. Joint Social and Content Recommendation for User-Generated Videos in Online Social Network [J]. IEEE Transactions on Multimedia, 2013, 15(3):698-709.
[2]. X. Wang, L. Sun, Z. Wang, and D. Meng, "Group recommendation using external follower for social TV," in Proc. IEEE ICME, 2012.
[3]. D. Kempe, J. Kleinberg, and É. Tardos, "Maximizing the spread of influence through a social network," in Proc. ACM SIGKDD, 2003.
[4]. A Content-Boosted Collaborative Filtering Approach for Movie Recommendation Based on Local and Global Similarity and Missing Data Prediction, Gözde Özbal, Hilal Karaman and Ferda N. Alpaslan, 2010.
[5]. Improving Scalability issues in Collaborative Filtering based on Collaborative tagging using genre interestingness measure Latha Banda and Kamal K. Bharadwaj

Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins. This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this work the unsupervised Gene selection methods and Enhanced Automatic Generation of Merge Factor for ISODATA (EAGMFI) algorithms have been applied for clustering of Gene Expression Data............

Keywords: Clustering, Gene expression data, Gene filter, USRR, EAGMF

[1]. Bashar Al-Shboul and Sung-Hyon Myaeng,(2009) "Initializing K-Means using Genetic Algorithms", World Academy of Science, Engineering and Technology 54.
[2]. Chen Zhang and Shixiong Xia,(2009) " K-Means Clustering Algorithm with Improved Initial center," in Second International Workshop on Knowledge Discovery and Data Mining (WKDD), pp. 790-792.
[3]. Chris Ding and Hanchuna Peng,(2003) "Mininmum Redundancy Feature Selection from Microarray Gene Expression Data", proceedings of the International Bioinformatic Conference, Date on(Aug) 11-14.
[4]. Dongxiao Zhu, Alfred O Hero, Hong Cheng, Ritu Khanna and Anand Swaroop,(2005) "Network constrained clustering for gene microarray Data", doi:10.1093 /bioinformatics / bti 655, Vol. 21 no. 21, pp. 4014 – 4020.
[5]. Doulaye Dembele and Philippe Kastner (2003), "Fuzzy C means method for clustering microarray data", Bioinformatics, vol.19, no.8, pp.973- 980.

The Internet of Things (IoT) ideas are connected to various applications going from home mechanization to mechanical IoT, where interfacing different physical things from anyplace in arrange. The procedures to oversee and use the monstrous volume of information delivered by these items are yet to develop. Customary database administration arrangements are insufficient in fulfilling the advanced application needs of an IoT organize that has a genuinely worldwide scale. Current answers for IoT information administration indicates incomplete parts of the IoT condition with exceptional spotlight on sensor systems. In this paper, we review the information administration lifecycle of the IoT. We at long last propose an information administration system for IoT that mulls over.

Keyword: Internet of Things, Data management, huge data, Real time application, Framework

[1]. Catalin Constantin CERBULESCU, Claudia Monica "Large data management in IoT application", ,2016 IEEE,978-1-4673-8606-7
[2]. Abu-Elkheir, M., "Data management for the Internet of Things: Green directions", Globecom Workshops (GC Wkshps), 2012 IEEE, Anaheim,CA, pp 386 – 390, 3-7 Dec. 2012.
[3]. Yuchao Zhou, Suparna De ; Wei Wang ; Klaus Moessner, "Enabling Query of Frequently Updated Data from Mobile Sensing Sources", Inst. for Commun. Syst., Univ. of Surrey, Guildford, UK, Computational Science and Engineering (CSE), 2014 IEEE 17th International Conference on, 19-21 Dec. 2014, Page(s): 946 – 952, ISBN: 978-1-4799-7980-6
[4]. Mervat Abu-Elkheir, Mohammad Hayajneh and Najah Abu Ali, "Data Management for the Internet of Things: Design Primitives and Solution",Sensors 2013, 13, 15582-15612; doi:10.3390/s131115582 ,14 November 2013 ,ISSN 1424-8220

It has always been an arduous task for the election commission to conduct free and fair polls in our country, the largest democracy in the world. Rupees have been spent in crores on this to make sure that the elections are riot free. But, now- a -days it has become common for some forces to indulge in rigging which may eventually lead to a result contrary to the actual verdict given by the people. In this paper, it has been made a study on different voting technologies that had been incorporated for earlier elections and the future technology that will be made for high secured voting system; it is none other than biometric voting system.

Keyword: Voting system, Types, Electronic voting, biometric based voting.

[1]. Bar-El, H. (2015). Why secure e-voting is so hard to get.
[2]. Schneier, B. (2008). Wayback Machine What's wrong with electronic voting machines?
[3]. Thompson, K. (August 1984). Reflections on Trusting Trust.
[4]. International Journal of Engineering Research –Offline and online E-voting system with embedded security for real time application ,Alaguvel. R, Gnanavel .g.
[5]. RFID based biometric voting machine linked to Aadhaar for safe and secure voting Madan Mohon Reddy,D.Srihari International Journal of science, Engineering and Technology Research (IJSETR).

Vehicular ad hoc network (VANET) is a kind of ad hoc network, where the wireless communication has been established between the moving vehicles.Recently, the clustering scheme is suggested as an effective solution to handle the fast topology changes of vehicular ad hoc networks. However, the stability of the existing clustering approaches shows poor performances due to highly dynamic scenario of VANET. Thus this paper proposes a probabilistic B-Tree based multi-hop clustering scheme for VANET. A probabilistic density function is computed based on the velocity, speed and acceleration of the vehicles in order to select the cluster head (Designated Node (DN)) and the Backup Designated Node (BDN).The clustering will be performed using the direction of vehicles.

Keyword: VANET, multi hop cluster, probabilistic density function, B-Tree, data aggregation

[1]. Amilcare Francesco Santamaria, CesareSottile, and Peppino Fazio, "PAMTree: Partitioned Multicast Tree Protocol for Efficient Data Dissemination in a VANET Environment", International Journal of Distributed Sensor Networks, Article ID 431492, 2015.
[2]. Mohamed Hadded, RachidZagrouba, AnisLaouiti, Paul Muhlethalery and Leila AzouzSaidane, "A Multi-Objectif Genetic Algorithm-Based Adaptive Weighted Clustering Protocol in VANET", Congress on Evolutionary Computation (CEC), IEEE, sendai, pp 994-1002.
[3]. Zaydoun Y Rawashdeh and Syed Masud Mahmud, "A novel algorithm to form stable clusters in vehicular ad hoc networks on highways", EURASIP Journal on Wireless Communications and Networking, vol. 1, no. 15, 2012.
[4]. Daeinabi A, Rahbar AGP, Khademzadeh, "A. VWCA: An efficient clustering algorithm in vehicular ad hoc networks", Journal of Network and Computer Applications, 2011; 34(1):207–222.

Neuroscience deals with the study of nervous system whereas cognitive neuroscience is the branch of neuroscience and it focus on the neural connections of the brain with it's behaviour. Parts of the brain play an important role in this field. The methods include experimental procedures from psychophysics, cognitive psychology, functional neuro imaging, electro physiology, cognitive genomics and behavioural genetics. Studies of patients with cognitive deficits due to brain lesions constitute an important aspect of cognitive neuroscience. The damage in the brain changes the neural circuits and causes it to malfunctioning during basic cognitive processes such as memory or learning. By comparing the neural circuits of the damaged brain we can conclude the affected cognitive processes.

Keyword: Self-reflection, functional MRI, language, gene, sensory experience.

[1]. www.sciencedaily.com
[2]. https://blogs.illionis.edu
[3]. www.oxfordhandbooks.com
[4]. www.mayoclinic.org
[5]. www.ncbi.nlm.nih.gov