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Grid computing is a growing technology, which have hundreds or thousands of computational nodes to execute large applications. The main issues consider in grid computing is Load balancing, Fault tolerant and Fault recovery. In order to utilize the resource efficiently and to satisfy all the requirements of the user, there is need for effective scheduling algorithm. Scheduler schedule job to available resources in the sites and result submit to user. Otherwise if any faults detects by fault detector, In the event of failures, how to execute the job from the processor failure. Since grid is more difficult, complex to implement and manage, so there could be the differences in their performance under diverse experimental conditions. We should modify the algorithm. To achieve high throughput and proper resource utilization we purpose Min-Max fair scheduling algorithm for load balancing. This paper focus on how to increase performance of Grid Environment. The proposed system builds on using Grid Simulation Toolkit (Gridsim).

Load Balancing, Grid Computing, Fault Tolerant & Fault Recovery, Min-Max Fair Scheduling algorithm

Nikolaos D, Doulamis, Anastsios D. Doulamis, Emmanouel A. Varvarigos and Theodora A. Varvarigou, ”Fair scheduling algorithm in grids”, IEEE transactions on parallel and distributed systems, Vol. 18, No.11, November 2007, pp. 1630-1648.
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[6] S.K. Karthik Kumar, M. Udhaya Preethi and P. Chitra “Fair Scheduling Approach for Load Balancing and Fault Tolerant in grid environment”, IEEE International Conference on Emerging Trends in Computing, Communication and Nanotechnology, 2013. pp. 446-451.
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Mahesh Reddy G, Lakshminarayana G, Srinuvasa Reddy K, "Evaluation of Fault Tolerant & Min-Max Load Balancing Algorithm in Grid Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1331-1337, 2018.

A search engine is an online tool for searching any information on the World Wide Web like documents, business services, images, videos and so on. Many tools were there for helping the search engine helping the search engine like page ranking algorithms, voice search, image mining and keywords suggestions. From all this tools keyword suggestions will help online users to retrieve needed information and Express the queries without any background knowledge. Many tools were proposed to enhance the keyword suggestions like click-through, random walk method etc. But those are not satisfied user requirements in the modern world. In this paper proposing a novel structure of Keyword recommendation using location proximity which gives useful suggestion on geo based location. From the baseline algorithms, we are enhancing the partition based algorithm to achieve keyword suggestions. In addition, we enhance instant search keywords by using location proximity. Our proposed algorithm achieves better performance compared with existing algorithms in time ratio.

Baseline algorithm, partition based algorithm, location proximity, keyword suggestions

[1] Shuyao Qi “Location aware keyword query suggestion based on document proximity” IEEE 10.1109/ICDE.2016.7498428
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[10] P. Berkhin, “Bookmark-coloring algorithm for personalized PageRank computing,” Internet Math., vol. 3, pp. 41–62, 2006.
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[12] H. Tong, C. Faloutsos, and J.-Y.Pan, “Fast random walk with restart and its applications,” in Proc. 6th Int. Conf. Data Mining,2006, pp. 613–622.
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[14] R. Li, B. Kao, B. Bi, R. Cheng, and E. Lo, “DQR: A probabilistic approach to diversified query recommendation,” in Proc. 21stACM Conf. Inf. Knowl. Manage., 2012, pp. 16–25.
[15] Y. Song and L.-W.He, “Optimal rare query suggestion with implicit user feedback,” in Proc. 19th Int. Conf. World Wide Web,2010, pp. 901–910.
[16] Q. Mei, D. Zhou, K. Church,“Query suggestion using hittingtime,” In CIKM, 2008, pp. 469–478.

P Sujini, D.N. Vasundhara, "Implementation of Dynamic Keyword Query Suggestions on Geo Location using Document Proximity," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1338-1342, 2018.

In this paper, a fast and robust algorithm for identification of nude images based on the examination of skin color regions and nipple locating and detection is presented. The skin color information fundamentally provides regions of interest. So as to segment human skin area from non-skin areas in view of color, we require a dependable skin color model that is versatile to individuals of various skin hues and to various lighting conditions. We utilized a model of skin color in the YCbCr color space for separate out skin color region.

Skin color segmentation, Object detection, Recognition, Porn image identification

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11. Survey: 70% of boys began watching porn at 10 years http://timesofindia.indiatimes.com/city/coimbatore/Survey-70-of-boys-began-watching-porn-at-10-years/articleshow/48209402.cms, download on May 2017
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Amresh Vijay Nikam, "Locating and Detecting Nipple for Pornographic Image Identification," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1343-1347, 2018.

Big Data is a term which now a days everyone is aware of. This has changed view of every normal human or technocrat towards world. Technocrats sees this as a world full of challenges yet more of opportunities to explore this world more vividly. The voluminous data evolving these days with exponential rate is one of the biggest concern of technological world, yet bigger challenge is to explore and extract useful information out of this world and exploit is to the fullest to get maximum benefit. This paper ca be taken as reference paper or a tutorial for the one who want to gain in depth basic knowledge of big data and pursue research further.

Big Data, Varacity, Variety, Value, Volume, Map Reduce, Hadoop

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Kamal Kumar Ranga, C.K. Nagpal, "Big Data: A New Way To Look At World.," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1348-1352, 2018.

In Elastic Optical Networks (EONs), overcoming spectrum contiguity and continuity constraints is a challenging task while allocating spectrum slots (SS) to an incoming traffic demand. The frequent setup and release of SS over spectrum paths (SP) lead to unused isolated non- contiguous SSs. These isolated SS becomes unusable for future connections and causes significant fragmentation of spectral resources and degrades the network performance. This paper presents a spectrum assignment (SA) strategy that allocates SS based upon the relative difference between the required SS width and available SS width. The performance of proposed SA technique is evaluated in terms of Network Blocking Probability (NBP) by carrying out simulations under variable load conditions. The comparative analysis shows that the proposed strategy reduces spectrum fragmentation effectively as compared to existing SA strategies.

EONs, Routing and Spectrum Assignment (RSA), SS, NBP, Orthogonal Frequency Division Multiplexing (OFDM), SP, National Science Foundation Network Topology (NSFNET)

[1] Deepak Sharma, Suresh Kumar, “An Overview of Elastic Optical Networks and its Enabling Technologies”. International Journal of Engineering and Technology (IJET) Vol. 9 No 3 Jun-Jul 2017, pp (1643-1649) ISSN 0975-4024 DOI: 10.21817/ijet/2017/v9i3/170903022.
[2] Deepak Sharma, Suresh Kumar, “Design and Evaluation of OFDM Based Optical Communication Network” Journal of Engineering and Applied Sciences Vol. 12 S. Issue 2 ,(2017)pp:6227-6233 DOI:10.3923/jeasci.2017.6227.6233.
[3] Shakya, Sunny, "Management of Spectral Resources in Elastic Optical Networks." Dissertation, Georgia State University, 2015.
[4] Deepak Sharma, Suresh Kumar, “Evaluation of Network Blocking Probability and Network Utilization Efficiency on Proposed Elastic Optical Network using RSA Algorithms.” Journal of Optical Communications, 0(0),(aop) (2018). doi:10.1515/joc-2017-0204
[5] B. C. Chatterjee, S. Ba and E. Oki, "Fragmentation Problems and Management Approaches in Elastic Optical Networks: A Survey," in IEEE Communications Surveys & Tutorials, vol. 20 pp. 183-210, 2018. doi: 10.1109/COM2017.276102
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D. Sharma, S. Kumar, "Network Blocking Probability Based Evaluation of Spectrum Fragmentation in Elastic Optical Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1353-1362, 2018.

Text mining has become a basic methodology for computational applications in the field of medical Reports. Text mining can be applied in the medical field for diagnosis of organs like Lung tumor, Head and neck, diabetes and other related diseases. Lung tumor is the most common disease, with more than one million cases being reported worldwide each year. The most effective way to reduce lung tumor deaths is by early diagnosis. This study aims to determine the lung tumor TNM Staging diagnosis. This research data uses National Cancer Institute (NCI) from UCI machine learning. Historical Medical Text Reports constitute a rich and varied source of information, which is today readily accessible, due to large-scale digitization efforts. But in spite of such large scale digitization efforts, stage data in Tumor (cancer) registries is often incomplete, inaccurate, or simply not collected. This paper describes a classification that automatically extracts Tumor staging information from the medical reports that identify malignant cases that are well suited for TNM staging using one class. This is because the decision is unaffected by the outliers and the form of the data fits more precisely. The system uses text classification techniques to extract elements of the stage listed in Tumor staging guidelines. When processing new reports, it classifies relevant sentences to help reach the staging decision and consequently, assigns the most likely stage. This Staging decision is appropriate for medical Text mining.

Text data mining, Tumor staging, Decision tree and PSO

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P. Jyotsna, P. Govindarajulu P, "Stage Prediction of Lung Tumor Identification: A Decision Tree Model for Particle Swarm Optimization Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1363-1372, 2018.

Prediction of stock market is acclaimed by many as one of the most challenging areas for machine learning. The existence of quant industry that makes use of artificial intelligence based computational methods to predict the market provide enough evidence contrary to Efficient Market Hypothesis and Random Walk theory. Recent research on the financial market has focused on machine learning based approaches where instead of specifying the rules, learning algorithms are employed to make use of existing data. Financial markets provide one of the most organized data sets where data from each tick is recorded. Both generative and discriminative class of machine learning techniques have been explored in search of improved accuracy. Even with the abundance of structured financial data, complex, chaotic and nonlinear nature of the market that can ride on public emotions keeps the scope for probabilistic generative methods. This paper discusses the usability of machine learning techniques from both the classes: generative and discriminative along with the characteristics of data that enables them.

Discriminative, Financial market, Generative, Machine Learning, Sentiment Analysis, Supervised Learning

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P. Misra, S. Chaurasia, "Financial Market Predictions: Generative Vs Discriminative Methods," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1373-1378, 2018.

In this paper, a grid-connected hybrid system consisting of photovoltaic (PV) array, wind turbine, and battery storage are considered and a control strategy for power flow management of the considered hybrid system with an efficient transformer-coupled bidirectional dc-dc converter is presented. This transformer is used to interface the non-conventional energy sources to the main dc bus of the system. The specified converter consists of a buck-boost converter fused with multi-input dc-dc converter and a full bridge dc–ac inverter. A boost half-bridge converter is used to tackle power from the wind generator and a bipolar buck-boost converter is used to tackle power from PV panel along with battery charging and discharging control. Compared with the traditional methods, the proposed method can coordinate all the sources in such a way that to utilize it in an efficient way using Maximum power point tracking (MPPT) control and State of charge (SOC) control for PV, Wind and battery respectively and a coordinated control strategy for MPPT and battery SOC control by reducing control complexity, also provide galvanic isolation using High frequency transformer thus significant savings in component count and also switching and conduction losses will be reduced. The presented system aims to meet the load demand, manage the power flow from considered sources, inject surplus power into the grid and charge the battery from the grid as and when required. Simulation results obtained using MATLAB/Simulink

Bidirectional buck-boost DC-DC converter, full-bridge boost DC-AC Inverter, hybrid system, maximum power point tracking, solar photovoltaic (PV), transformer coupled boost dual-half-bridge bidirectional DC-DC converter

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J. Mano Priya, T. Narasimha Prasad, "A Grid Connected Hybrid System with A Transformer coupled Bidirectional DC-DC Converter," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1379-1385, 2018.

Dynamic PoS is valuable cryptographic crude that empowers a user to check the trustworthiness of outsourced documents and to proficiently refresh the records in a cloud server. In spite of the fact that analysts have arranged a few dynamic PoS plots in single user situations, the issue in multi-user conditions has not been examined adequately. A sensible multi-user cloud stockpiling framework needs the safe client-side cross-user de-duplication strategy that allows a user to skirt the transferring technique and get the possession of the records now, once elective house proprietors of proportional documents have uploaded them to the cloud server. To the most straightforward of our information, none of the present dynamic PoS will bolster this framework. amid this paper, we tend to present the origination of de-duplicatable dynamic evidence of capacity related propose a development refers to as DeyPoS, to acknowledge dynamic PoS and secure cross-user duplication, in the meantime. Considering the difficulties of structure decent variety and individual tag age, we tend to abuse a one of a kind apparatus alluded to as Homomorphic Authenticated Tree (HAT).

De-duplication, Proof of ownership, Dynamic proof of storage, Cloud Computing

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K. N. Priyanka, K. Aruna Kumari, "HAT: An Efficient Deduplicatable Dynamic POS Scheme," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1386-1391, 2018.

OSN`s have turned into the real world of data and excitement for many clients because of the colossal increment of the availability alternatives. Portable web has altered the clients to get to long range interpersonal communication destinations effortlessly and furthermore permits to different social multimedia content whenever, anyplace and in the interest of any character. Thusly, the association behaviors amongst clients and MSNs are becoming more extensive and entangled. This makes the investigation of client cooperations and behaviors more muddled. This paper principally expanded and enhanced the situation examination system for the particular social area, named as SocialSitu, and further proposed a novel calculation for clients` intention serialization investigation in light of great Generalized Sequential Pattern (GSP). We utilized the enormous volume of client behaviors records to investigate the continuous arrangement mode that is important to foresee client intention. Our test chosen two general sorts of intentions: playing and sharing of multimedia, which are the most widely recognized in MSNs, in view of the intention serialization calculation under various minimum support limit (Min_Support). By utilizing the clients` tiny behaviors examination on intentions, we found that the ideal personal conduct standards of every client under the Min_Support, and a client`s standards of conduct are diverse because of his/her character varieties in a huge volume of sessions information.

Multimedia social networks, situation analytics, intention prediction, behavior pattern, big data

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N. Ramavathy, V.Priyadarshini, "User Behavior Patterns in Social Networks Using Generalized Sequence Pattern," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1392-1397, 2018.