Search engines are the entrance for today's web. Probably most of us get through the information's with the help of it. Seeking and rendering information from web through the search engine gives more pressure to retain the credibility and reliability of the search engine. Spam was one that creates a severe pain for the reliability of the search engines. Spam's occurrence in the search engine deceives the ranking algorithm to attain higher ranks rather for commercial purposes or to seek attention. Crawlers are the programs which facilitate the search engine's work by following the hyperlinks in Web pages to automatically download a partial snapshot of the Web. Crawling is the initial and also the most important step during the web searching procedure.............

Keywords: Crawler, Mining, Topical crawler, Search Engine, Link Spam Trapper

[1]. Tan Su Tung, Nor Adnan Yahaya, " Multi-level Link Structure Analysis Technique for Detecting Link Farm Spam Pages", Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
[2]. Li Wei-jiang,Ru Hua-suo, Zhao Tie-jun,Zang Wen-mao ," A New Algorithm of Topical Crawler", in the proc 2009 Second International Workshop on Computer Science and Engineering
[3]. X. Ni, G.-R. Xue, X. Ling, Y. Yu, and Q. Yang, "Exploring in the weblog space by detecting informative and affective articles," in WWW '07: Proceedings of the 16th international conference on World Wide Web.
[4]. Y. Cheng, G. Qiu, J. Bu, K. Liu, Y. Han, C. Wang, and C. Chen, "Model bloggers' interests based on forgetting mechanism," in WWW '08: Proceeding of the 17th international conference on World Wide Web.
[5]. K. Ishida, "Extracting spam blogs with co-citation clusters," in WWW '08: Proceeding of the 17th international conference on World Wide Web.

A rating scale is precisely defined format which focuses on the conversation between the respondent and the questionnaire on the relevant areas. All respondents are motivated to communicate with their opinions in the shared language of the specified option choices given in the prescribed format. This paper deals about users choices and expectations in multiple domains and how it varies from user to user and will deals their opinions in various occasions through the CF algorithms and their expectations in various aspects will be evaluated through the proposed algorithm MDURA(Multiple Domain User Rating Algorithm).This algorithm finally evaluate all the products in different aspects associated with different users. Moreover this algorithm will help the users evaluate not only products but also evaluates products concurrently in different aspects.

Keywords: Collaborative Filtering, MDURA, Likert, NPRS, MPRS

[1]. A Collaborative Filtering Recommender System in Primary Care: Towards a Trusting Patient-Doctor Relationship Qiwei Han; Íñigo Martínez de Rituerto de Troya; Mengxin Ji; Manas Gaur; Leid Zejnilovic 2018 IEEE International Conference on Healthcare Informatics (ICHI) Collaborative Filtering Under a Sybil Attack: Similarity Metrics do Matter!
[2]. Antoine Boutet; Florestant De Moor; Davide Frey; Rachid Guerraoui; Anne-Marie Kermarrec; Antoine Rault 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN).
[3]. Collaborative Filtering-based Electricity Plan Recommender System Yuan Zhang; Ke Meng; Weicong Kong; Zhao Yang Ge Dong IEEE Transactions on Industrial Informatics,2018.
[4]. Hybrid recommender system for learning material using content-based filtering and collaborative filtering with good learners' rating Rudolf Turnip; Dade Nurjanah; Dana Sulistyo Kusumo 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e).

Data mining model has been adopted in various fields including agriculture. Data mining has been used to interpret specific details and used as process to predict the crop patterns in a clustering form in order to get more precise and accurate information. Crop productivity depends upon the various factors like climate, Type of soil, precipitation and groundwater level in the specific region. Many clustering algorithm has been derived in existing to group the amount of data related to the agriculture. Nowadays the due to employment of information gathering sensor, data about the agriculture is exploiting, in order explore to these data finds to critical using traditional data model. Hence it leads to importance of the big data solution. In this paper, we propose a novel hybrid clustering technique.............

Keywords: Agricultural data, Clustering, Prediction, Forecasting, Partition around Medoids, Clustering for large Applications.

[1]. Shweta Taneja, Rashmi Arora, Savneet Kaur, "Mining of Soil Data Using Unsupervised Learning Technique", International Journal of Applied Engineering Research, Vol. 7 No.11, 2012. [2]. D Ramesh , B Vishnu Vardhan, "Data mining technique and applications to agriculture yield data", International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 9, September 2013 .
[3]. Chandrakanth Biradar, Chatura S Nigudgi,"Statistical Based Agriculture Data Analysis", in International Journal of Emerging Technology and Advanced Engineering, 2012.
[4]. M. Divya, T.N. Manjunath, R.S. Hegadi, "A Study on Developing Analytical Model for Groundnut Pest Management using Data Mining Techniques", In IEEE International Conference on Computational Intelligence and Communication Networks (pp. 691-696).,2014
[5]. A. Raorane, R. Kulkarni, "Data Mining: An effective tool for yield estimation in the agricultural sector", In International Journal of Emerging Trends and Technology in Computer Science, vol.1, issue 2, pp. 75-79, 2012.

Among brain tumours, gliomas are the most common and aggressive, leading to a very short life. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess these tumours, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required. However, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. The proposed system is an automatic segmentation method based on Convolution Neural Networks (CNN), exploring small 3×3 kernels. Our proposal was validated using BRATS database.

Keywords: - Brain tumor, Convolution Neural Network (CNN), Magnetic Resonance Imaging (MRI), Segmentation.

[1]. Balkrishna B Yeole - Trends in the Brain Cancer Incidence in India.
[2]. Deepthi Murthy T.S, G.Sadashivappa - Brain Tumor Segmentation Using Thresholding, Morphological Operations and Extraction of Features of Tumor.
[3]. Neethu Ouseph C - A reliable method for brain tumor detection using CNN techniques.
[4]. Huajin Tang Kay Chen Tan - Neural Networks: Computational Models and Applications.
[5]. Raunaq Rewari, Stanford University - Automatic Tumor Segmentation from MRI scans.
[6]. S.Rajasekaran, G.A. Vijayalakshmi pai - Neural networks, Fuzzy Logic, and Genetic Algorithms Synthesis and Applications.

Remote sensor systems (WSN), now and again called remote sensor and actuator systems (WSAN), are spatially dispersed independent sensors to screen physical or ecological conditions, for example, temperature, sound, weight, and so on and to agreeably go their information through the system to a primary area. WSN are assuming an awesome job in the controlling and overseeing conditions in various circumstances and has turned out to be essential piece of research territory. WSN inquire typically ordered into three classes i.e. equipment and programming of the sensors nodes, application zone, and correspondence and security. Reducing vitality utilization in remote interchanges has pulled in expanding consideration as of late. The programming sensor systems.............

Keywords : Biometric, Energy efficiency, Security, Wireless Sensor Network.

[1]. Raja Waseem Anwar, Majid Bakhtiari, Anazidazainal, Abdul Hanan Abdullah And KashifNaseerQureshi,(2015): Enhanced Trust Aware Routing Against Wormhole Attacks In Wireless Sensor Networks, International Conference On Smart Sensors And Application, IEEE.
[2]. R. W. Anwar, M. Bakhtiari, A. Zainal, A. H.Abdullah, and K. N. Qureshi,(2014):Security Issues and Attacks in Wireless Sensor Network, World Applied Sciences Journal, vol. 30, pp. 1224-1227,.
[3]. H. Ehsan and F. A. Khan,(2012): Malicious AODV: implementation and analysis of routing attacks in MANETs, in Trust, Security and Privacy in Computing and Communications (Trust Com), 2012 IEEE 11th International Conference on,, pp. 1181-1187.
[4]. M. Medadian, A. Mebadi, and E. Shahri, (2009,): Combat with Black Hole attack in AODV routing protocol," in Communications (MICC), IEEE9th Malaysia International Conference on, pp. 530-535.
[5]. Blackert, W.J., Gregg, D.M., Castner, A.K., Kyle, E.M., Hom, R.L., and Jokerst, R.M., "Analyzing interaction between distributed denial of service attacks and mitigation technologies", Proc. DARPA Information Survivability Conference and Exposition, Volume 1, 22-24 April, 2003, pp. 26 – 36.

Routing algorithm issue is one of the real issues to be settled in wireless sensor networks research. In this paper, the study is about some critical various levelled hierarchical routing protocols for Wireless Sensor Networks. This paper initially examines the activities of these protocols to put it plainly, and after that, we will feature the advantages and disadvantages of every last one of them. In particular, we will think about these conventions as far as energy utilization, and system lifetime.

Keywords: Cluster Head, Genetic Algorithm, Base Station, Routing Protocol, Wireless Sensor Network.

[1]. Chang, Ni-Bin, Ho-Wen Chen, and Shu-Kuang Ning. "Identification of river water quality using the fuzzy synthetic evaluation approach." Journal of environmental management63.3 (2001): 293-305
[2]. Heinzelman, Wendi Rabiner, Anantha Chandrakasan, and Hari Balakrishnan. "Energy-efficient communication protocol for wireless microsensor networks." System Sciences, 2000. Proceedings of the 33rd annual Hawaii international conference on. IEEE, 2000.
[3]. Hussain, Sajid, Abdul W. Matin, and Obidul Islam. "Genetic algorithm for energy efficient clusters in wireless sensor networks." Information Technology, 2007. ITNG'07. Fourth International Conference on. IEEE, 2007.
[4]. Kim, Teasung, et al. "REACH: An Efficient MAC Protocol for RF Energy Harvesting in Wireless Sensor Network." Wireless Communications and Mobile Computing 2017 (2017).
[5]. Liu, Shuangyin, et al. "Fault Diagnosis of Water Quality Monitoring Devices Based on Multiclass Support Vector Machines and Rule-Based Decision Trees." IEEE Access 6 (2018): 22184-22195.

Data Mining is the process of finding new patterns from large data set and converting them into meaningful information which can be used for any business logic. This paper focuses on the survey of Data Mining techniques used and application of new techniques for the better crop yield production. Application of Data Mining techniques in agricultural fields can revolutionize the crop yield production and its analysis. Data mining techniques such as K-Mean and K nearest neighbor are used in this paper. The application of Data Mining techniques enables us to automate the prediction and analysis of various problems and helps farmers to make critical farming decisions based on the conditions, soil fertility, crop duration, disease detection and other crucial factors that can result in low yield production. The hidden pattern in these data helps us to explore new possibilities in maximizing the yield production.

Keywords - agriculture, data mining techniques.

[1]. V. Ramesh and K. Ramr Classification of agricultural land soils: A data mining approach International Journal on Computer Science and Engineering (IJCSE) ISSN : 0975-3397 Vol. 3 No. 1 Jan 2011379.
[2]. Rainfall variability analysis and its impact on crop productivity Indian agriculture research journal 2002 29,33.,8) SPRS Archives XXXVI-8/W48 Workshop proceedings: Remote sensing support to crop yield forecast and area estimates GENERALIZED SOFTWARE TOOLS FOR CROP AREA ESTIMATES AND YIELD FORECAST by Roberto Benedetti A, Remo Catenaro A, Federica Piersimoni B.
[3]. S.Veenadhari, Dr. Bharat Misra, Dr. CD Singh, ―Data mining Techniques for Predicting Crop Productivity – A review article‖, International Journal of Computer Science and technology, march 2011.
[4]. D.Rajesh, ―Application of Spatial Data mining in Agriculture International Journal of Computer Applications, Volume 15, February 2011.

With advancement of Automation technology, life is getting simpler and easier in all aspects. In today's world Automatic systems are being preferred over manual system. With the rapid increase in the number of users of internet over the past decade has made Internet a part and parcel of life, and IoT is the latest and emerging internet technology. Internet of things is a growing network of everyday object-from industrial machine to consumer goods that can share information and complete tasks while you are busy with other activities. Wireless Home Automation system(WHAS) using IoT is a system that uses computers or mobile devices to control basic home functions and features automatically through internet from anywhere around the world, an automated home is sometimes called a smart home.............

Keywords - Home automation System (HAS), Internet of Things (IoT), Cloud networking, Wi-Fi network, Intel Galileo Microcontroller..

[1]. Sirsath N. S, Dhole P. S, Mohire N. P, Naik S. C & Ratnaparkhi N.S Department of Computer Engineering, 44, Vidyanagari, Parvati, Pune-411009, India University of Pune, "Home Automation using Cloud Network and Mobile Devices".
[2]. Deepali Javale, Mohd. Mohsin, Shreerang Nandanwar "Home Automation and Security System Using Android ADK" in International Journal of Electronics Communication and Computer Technology (IJECCT) Volume 3 Issue 2 (March 2013).
[3]. Charith Perera, Student Member, IEEE, Arkady Zaslavsky, Member, IEEE, Peter Christen,and Dimitrios Georgakopoulos, Member, IEEE "Context Aware Computing for The Internet of Things: A Survey". IEEE COMMUNICATIONS SURVEYS & TUTORIAL.
[4]. Charith Perera_y, Arkady Zaslavskyy, Peter Christen_ and Dimitrios Georgakopoulosy Research School of Computer Science, The Australian National University, Canberra, ACT 0200, Australia yCSIRO ICT Center, Canberra, ACT 2601, Australia " CA4IOT: Context Awareness for Internet of Things".
[5]. Bill N. Schilit, Norman Adams, and Roy Want, "Context-Aware Computing Applications".

Cloud computing is architecture for providing computing services through the internet on demand and pay-per-use access to a pool of shared resources namely networks, storage, servers and applications without physically acquiring them. So it saves managing cost and time for organizations. Developing an application in the cloud enables users to get their product to market quickly. Many industries, such as banking, healthcare and education are moving towards the cloud due to the efficiency of services provided by the pay-per-use pattern based on resources such as processing power used, transactions carried out, bandwidth consumed, data transferred, storage space occupied etc. Cloud computing architecture comprises of many cloud components, which are loosely coupled..............

Keywords - Cloud architecture, components and issues.

[1]. T. Dillon, C. Wu, and E. Chang, "Cloud Computing: Issues and Challenges," 2010 24th IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 27-33, DOI= 20-23 April 2010.
Proceedings Papers:
[2]. http://www.ijfcc.org/papers/95-F0048.pdf
[3]. http://www.worldscientificnews.com/wp-content/uploads/2017/08/WSN-863-2017-253-264-1.pdf
[4]. https://pdfs.semanticscholar.org/ca5d/86b602c4fe2625ca80ac4da6704c18f6a279.pdf

In recent days the cloud computing is a new business model and it is a recent and advance topic. It is an online file storage center. The cloud computing users are exploring new ways to share the business related documents, files through the network anywhere at any time .The customers and industries to utilize the apps via the internet. In cloud computing has four models they are private cloud, public cloud, community cloud and hybrid cloud. The private is a particular model of cloud computing that involves a distinct and secure cloud based environment in which only the specified client can operate. The public cloud is based on the standard cloud computing model, in which a service provider makes resources, such as virtual machine, applications or storage , available to the general public over the internet.............

[1]. AWS for dummies by Bernard Golden.
[2]. Cloud computing: Concepts, Technology & Architecture by Thomas Erl
[3]. B. Peng ,B. Cui and X.Li," Implementation issues of a cloud computing platform," IEEE Data Eng.
[4]. Nimbus: Open Source Infrastructure-as-a-Service cloud computing software, 2009.
[5]. Kuyoro,S.O., Ibikunle,F. and Awodele, O.(2011) Cloud computing Challenges. International Journal of computer networks.
[6]. www.spinger.com/engineering/Mechanical+eng/journal.

In cloud environment, cloud user store information and accesses the same when required from the cloud server. Though several methods and mechanisms were introduced to address the security aspect, still full proof system related to security during data storage and access remains unsolved. In order to improve security during data storage and access, a novel steganography method, called, Mean Pixel Embedding based Image Steganography (MPE-IS) is designed. Three stages are involved in the design of MPE-IS. They are data preparation, embedding and extraction. In data preparation stage, gray scale image is considered as the cover image. The gray scale cover image is split into number of grids where each grid comprises two consecutive non-overlapping pixels represented in the form of matrix...............

Keywords - Baum sweet sequence, Hiding Capacity, Image Steganography, Mean Pixel Embedding, Three-state Automaton.

[1]. Pauline Puteaux and William Puech, "An Efficient MSB Prediction-Based Method for High-Capacity Reversible Data Hiding in Encrypted Images", IEEE Transactions on Information Forensics and Security, Vol. 13, Issue 7, July 2018, Pages 1670 - 1681
[2]. Weiming Zhang, Hui Wang, Dongdong Hou and Nenghai Yu, "Reversible Data Hiding in Encrypted Images by Reversible Image Transformation", IEEE Transactions on Multimedia, Vol. 18, Issue 8, August 2016, Pages 1469 – 1479
[3]. Priyanka Singh and Balasubramanian Raman, "Reversible data hiding based on Shamir‟s secret sharing for color images over cloud", Information Sciences, Elsevier, Vol. 422, 2018, Pages 77–97
[4]. G. Swain, S.K. Lenka, "Classification of spatial domain image steganography techniques: a study," International Journal of Computer Science & Engineering Technology, Vol. 5, no.3, pp.219-232, 2014
[5]. Sreeja Cherillath Sukumaran, Misbahuddin Mohammed, "PCR and Bio-signature for data confidentiality and integrity in mobile cloud computing", Journal of King Saud University – Computer and Information Sciences, Elsevier, Aug 2018

As of late, a multi-million dollar painting by Andy Warhol was tokenized and sold on blockchain in what was the first transaction happened of its kind. So plainly these kinds of exchanges are no longer outside the real world of possibility. The current systems have at last democratized the craftsmanship market. This methodology makes availability for non-conventional art collectors and empowers art dealers to enter new markets on a worldwide scale. This code design includes an application that instantiates a local blockchain system to store fine art pictures in digital format on the ledger.

Keywords: - Block chain, Crypto currency, Hyperledger, VerisArt, Vooglue

[1]. Peters, Gareth W., and Efstathios Panayi. "Understanding modern banking ledgers through blockchain technologies: Future of transaction processing and smart contracts on the internet of money." Banking Beyond Banks and Money. Springer, Cham, 2016. 239-278.
[2]. "Ekblaw, Ariel, et al. "A Case Study for Blockchain in Healthcare:"MedRec" prototype for electronic health records and medical research data." Proceedings of IEEE Open & Big Data Conference. 2016."
[3]. Samaniego, Mayra, and Ralph Deters. "Using blockchain to push software-defined IoT components onto edge hosts." Proceedings of the International Conference on Big Data and Advanced Wireless Technologies. ACM, 2016.
[4]. Atzori, Marcella. "Blockchain technology and decentralized governance: Is the state still necessary?." (2015).

Opinion Mining is a type of natural language processing for tracking the mood of the people about any particular domain or product by review. The opinions are reviews from customers; comments are collected from web sites and user groups. The collected opinions are manipulated by various techniques, methods, algorithms and software tools to extract the polarity information. In this work, Support Vector Machine presents an approach for mining online user reviews to generate feature-based sentiment analysis on car domain that can guide a user in making an online purchase............

Keywords- Opinion Mining, Sentiment Analysis, Feature-Based Opinion Mining, Summarization.

[1]. A. Nisha Jebaseeli, Dr. E. Kirubakaran "M-Learning Sentiment Analysis with Data Mining Techniques", International Journal of Computer Science And Telecommunications, Volume 3, Issue. 8, Aug. 2012.
[2]. Arti Buche Dr.M.B. Chandak, Akshay Zadgaonkar, "Opinion Mining And Analysis : A Survey", International Journal on Natural Language Computing (IJNLC), Volume 2, No. 3, June 2013.
[3]. "Data Mining Concepts and Techniques" Jiawei Han, Micheline Hamber Morgan Kaufman Publishers, 2003.
[4]. G. Angulakshmi, Dr. R. Manickachezian, "An Analysis on Opinion Mining : Techniques and Tools", International Journal of Advanced Research in Computer and Communication Engineering, Volume 3, Issue. 7, July 2014.
[5]. Nidhi Mishra, Dr. C.K. Jha, "Classification of Opinion Mining Techniques", International Journal of Computer Applications, Volume 56, No.13, October 2012.

We all know how computers are evolved generation after generation since 1946. Starting with vacuum tubes or transistors science has modernized with evolution of micro processor and so on to make computers even better for computing. Now science is trying to bridge the gap between computing and thinking. This is possible with latest advancement of science under the field of artificial intelligence (AI). This has reduced the efforts of man and also has bridged the gap between computing and manual thinking. Now even more updated versions of AI have introduced the concept of "Neural Networks" which completely make machines to think and respond like human..............

Keywords- Neural networks, computing, artificial intelligence (AI)

[1]. McCulloch, Warren; Walter Pitts (1943). "A Logical Calculus of Ideas Immanent in Nervous Activity". Bulletin of Mathematical Biophysics. 5 (4): 115–133. doi:10.1007/BF02478259.
[2]. Jump up^ Hebb, Donald (1949). The Organization of Behavior. New York: Wiley.
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Home technology started for more than a decade to introduce the concept of networking devices and equipment in the house. In Association the best definition of smart home technology is: the integration of technology, services through home networking for a better quality of living. It used devices connected to the IOT to automate and monitor in home systems. Tools are used in computer systems can also be integrated in Smart Home Systems.

Keywords- Home networking, Internet of things(IOT),smart home.

[1]. Molly Edmonds "How Smart Homes Work - Setting Up a Smart Home" http://home.howstuffworks.com/home-improvement/energy-efficiency/smart-home1.htm
[2]. VictoriaNicks(2009),"Smart Home Appliances", http://artificialintelligence.suite101.com/article.cfm/smart_home_appliances
Books:-
[3]. Li-Der Chou, Chun-Cheng Sheu and Home-Way Chen, (2007), "Design and Prototype Implementation of A Novel Automatic Vehicle Parking System", International Journal of Smart Home, Vol. 1, No. 1, January, 2007
[4]. Tatsuya Yamazaki, (2007), "The Ubiquitous Home", International Journal of Smart Home, Vol. 1, No

In the modern era, abundant data have become available on hand to get voluminous information. Big data points out to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. It can be structured, unstructured or semi-structured, resulting in incapability of conventional data management methods. The rate of data generation is so startling, that it has induced a pressing need to implement easy and cost-effective data storage and retrieval mechanisms. Technologies such as MapReduce & Hadoop are used to essence value from Big Data. Hadoop is wellendorse, standard-based, open source software framework build on the foundation of Google's MapReduce.............

Keywords- Big Data, Hadoop, Hive, Hadoop vs. Hive.

[1]. http://www.ijsrp.org/research-paper-1014/ijsrp- p34125.pdf
[2]. https://pdfs.semanticscholar.org/501d/814f3f83e8ca44afbd3da13792f206a16402.pdf
[3]. http://www.ijcst.com/vol74/1/11-iqbaldeep-kaur.pdf
[4]. https://www.irjet.net/archives/V3/i1/IRJET-V3I1152.pdf
[5]. https://www.infoworld.com/article/2608271/hadoop/hadoop-review-apache-hive-brings-real- time-queries-to-hadoop.html
[6]. https://www.educba.com/hadoop-vs-hive/.