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Innovation is the key factor for an organization to be successful. Innovation is result of ‘out of the box thinking’ in an organization and is essential in this competitive world for its survival and to be successful. Innovation is not linear but multidimensional and complex. It is very important to identify the variables which are significant for innovation. In this paper both tangible and intangible variables which may have significant influence on innovation are identified and listed. An empirical study is carried out to find out the kind of influence each of the various identified variables have on innovation. Since no proper tool is available there is a need also to have a tool which is helpful in measuring the impact of various identified variables which may have significant influence on innovation. An instrument developed to measure the significance of each of the variables on innovation is presented in this paper. Cluster sampling technique is used for the empirical study and the respondents are professionals from the IT sector.

Innovation, Information Technology, Tangible Variables, Intangible Variables,Innovation Measurement Instrument, Innovation Mechanism

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M. Vishwanath Pai, Sureshramana Mayya, H.G. Joshi, "Variables Responsible for Innovation in Information Technology Sector: An Empirical Study," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.457-461, 2018.

Searching for medical information on the Web is popular and important. However, medical search has its own unique requirements that are poorly handled by existing medical Web search engines (WSE). The first online medical Web search engine that extensively uses medical knowledge and questionnaire to facilitate ordinary internet users to search for medical information. All existing medical WSEs assume that searchers can form appropriate queries by themselves. However, most Internet users do not have much medical knowledge. Frequently, a medical information searcher has only a vague idea about the problem that he is facing and does not know the proper way to clearly describe his situation in sufficient detail. As a result, appropriate guidance is highly necessary during the medical search process. This can be illustrated by an analogy to the medical diagnosis process. In this paper we mainly focused on how health care data is analysed by a web user and how he is retrieving the information from the Data Processing Information Systems.

Data Processing, Information Systems, Medical search Process, WSE

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[4] John Wiley & Sons, American Medical Association Family Medical Guide, 4th ed., 2004.
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[8] R.A.Baez a-Yates and B.A.Ribeiro-Neto.Modern InformationRetrieval,ACM Press/Addison-Wesley, 1999
[9] R.D. Collins, Algorithmic Diagnosis of Symptoms and Signs: Cost-Effective Approach Lippincott Williams & Wilkins, 2002.
[10] S.E. Robertson, S. Walker, and M. Hancock-Beaulieu, “Okapi at TREC-7: automatic ad hoc Filtering, VLC and interactive,” in Proc.TREC’98, 1998, pp. 199-210.
[11] A survey on Internet of things for health care and medication management. IJSRCSE, Volume 2, Issue 1, January – February 2017, Pages 1 – 5.

Srikanth Bethu, B Sankara Babu, R. Aruna Flarence, "Data Processing in Information Systems: Health Care Data Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.462-470, 2018.

With the speedy growth of web base environment and extensive requirement of databases in many important fields, the databases owners are need to be care full about how to protect and verify originality of the database. Digital watermarking is an effective solution to protecting the copyright of databases from illegal copying by using the inherent properties of relational databases. In this paper, we propose a relational database watermarking scheme that partition the whole database in groups by using the tuple hash value of the databases. After the group formation we incorporate the use of hash byte function over the group tuples along with owner id to form a group string. Similarly in detection process, a string will be formed to verify the suspicious watermarked database. The proposed scheme does not require any additional storage to store the verification information in the original database so the approach is robust against the any distortion in original database. Cast comparison experiments are conducted over watermark embedding and detection algorithms with the group size variation and the results are analyzed to show the running cost of the proposed scheme.

ownership protection; group wise partition; tuple hash value; hashbyte; robustness

[1] Saraju P. Mohanty , “Digital Watermarking : A Tutorial Review” Indian Institute of Science, Bangalore, 1999
[2] R. Agrawal, J. Kiernan. “Watermarking Relational Databases”, In: Proceeding of the 28th VLDB Conference. Hong Kong, 2002: 155-166.
[3] G.H. Gamal, M.Z. Rashad and M.A. Mohamed “A Simple Watermark Technique for Relational Databa” Mansoura Journal for Computer Science and Information Systems Vol. 4, No.4, Jan2008.
[4] Raju Halder, Shantanu Pal, Agostino Cortesi “Watermarking Techniques for Relational Databases: Survey, Classification and Comparison” Journal of Universal Computer Science, Vol. 16, no.21 2010, pp.3165-3190
[5] Min, Li, Wenyue, Zhao, “An Asymmetric Watermarking Scheme for Relational Database”, Communication Software and Networks (ICCSN), IEEE 3rd International Conference. 2011,pp.180-184
[6] Udai Pratap Rao a, Dhiren R. Patel a, Punitkumar M. Vikani, “Relational Database Watermarking for Ownership Protection 2nd International Conference on Communication, Computing & Security [ICCCS-2012] Science Direct pp.988-995.
[7] B. Wu, et aI., "Design and implementation of spatial data
watermarking service system", Geo-spatial Information Science, vol.13, no. I, pp. 40-48, 2010.
[8] Anuj Kumar Dwivedi ,Dr. B. K. Sharma ,Dr. A. K. Vyas “Relational databases watermarking technique based on embedded proportion” International Education & Research Journal [IERJ] E-ISSN No : 2454-9916 | Volume : 3 | Issue : 6 | June 2017 pp 34-36

Anuj Kumar Dwivedi, B.K. Sharma, A.K. Vyas, "Relational Databases Watermarking Technique Based on Specific String Verification," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.471-475, 2018.

Taxonomy learning is an important task for developing successful applications as well as knowledge obtaining, sharing and classification. The manual construction of the domain taxonomies is a time-consuming task. To reduce the time and human effort will build a new taxonomy learning approach named as TaxoFinder. TaxoFinder takes three steps to automatically build the taxonomy. First, it identifies the concepts from a domain corpus. Second, it builds CGraphs where a node represents each of such concepts and an edge represents an association between nodes. Each edge has a weight indicating the associative strength between two nodes. Lastly TaxoFinder derives the taxonomy from the graph using analytic graph algorithm. The main aim of TaxoFinder is to develop the taxonomy in such a way that it covers the overall maximum associative strengths among the concepts in the graph to build the taxonomy. In this evaluation, compare TaxoFinder with existing subsumption method and show that TaxoFinder is an effective approach and give a better result than subsumption method.

Taxonomy learning, ontology learning, TaxoFinder, concept taxonomy, concept graphs, similarity, associative strength.

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[3] [3] E.-A. Dietz, D. Vandic, and F. Frasincar, “TaxoLearn: A semantic approach to domain taxonomy learning,” in Proc. IEEE/WIC/ACM Int. Joint Conf. Web Intell. Intell. Agent Technol., 2012, pp. 58–65.
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D.R. Kamble, K.S. Kadam, "Implementation of taxonomy classification using Graph-based Approach," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.476-479, 2018.

In this paper a modified load flow algorithm is applied in unbalanced RDS to determine the exact power losses, voltage at each node and enhance the efficiency of unbalanced load flow solutions. In this paper, an attempt has been made to minimize real power loss with existing unbalanced radial distribution systems. The proposed method has been tested on several radial distribution systems and results found are noteworthy. The results are compared with existing methods for 25-IEEE node URDS system and a noticeable change in the power loss and node voltages were observed.

Modified unbalance load flow analysis (MULFA), radial distribution system, Real power loss (RPL)

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[3] Puthireddy Umapathi Reddy1 , Sirigiri Sivanagaraju2 , Prabandhamkam Sangameswararaju3, “power flow analysis of three phase unbalanced radial distribution system,” International Journal of Advances in Engineering & Technology, Vol. 3, Issue 1, pp. 514-524, March 2012.
[4] V.V.S.N.Murtya , Ashwani Kumarb, “Capacitor Allocation in Unbalanced Distribution System under Unbalances and Loading Conditions,” 4th International Conference on Advances in Energy Research 2013, ICAER 2013, Energy Procedia 54 ( 2014 ) 47 – 74.
[5] Mostafa Sedighizadeh *, Reza Bakhtiary,” Optimal multi-objective reconfiguration and capacitor placement of distribution systems with the Hybrid Big Bang–Big Crunch algorithm in the fuzzy framework,” Ain Shams Engineering Journal (2016) 7, 113–129
[6] Ujjwal Ghatak and V. Mukherjee “ A fast and efficient load flo technique for unbbabalnced distribution system’, electrical power and energy system 84 (2017) 99-110.
[7] S.Frank, J. Sexauer, and S. Mohagheghi, “Temperature Dependent Power Flow,” IEEE Trans. Power Syst., vol. 28, pp. 4007-4018, Oct. 2013
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Surender Singh, V.R Singh, R. Ranjan, "Modified Load Flow Analysis in Unbalanced Radial Distribution System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.480-483, 2018.

Fingerprints are commonly used biometric trait used for identification. Latent prints are the fingerprint impressions which are inadvertently left by a person on different surfaces that come in contact with the finger at the crime scene. These latent fingerprints are used as evidence in the forensics to identify the suspect. Sometimes one fingerprint gets overlapped on another fingerprint, due to which it becomes difficult to extract features from the fingerprint and identify the suspect. Till now, overlapped and non-overlapped regions were segmented manually, which need extra human effort and consumes a lot of time. So, separating overlapped fingerprint automatically is necessary to identify the correct person. In this paper, machine learning algorithm is used to segment the overlapped fingerprint regions automatically by extracting features using Random Decision Forest (RDF) classifier and then separating the two fingerprints. Also, a database of overlapped latent fingerprints is collected using the touch less based sensor called Reflected Ultra Violet Imaging System (RUVIS). This device is used to search, view, detect and capture the latent fingerprints on non-porous surfaces. The performance of the proposed approach is evaluated on the developed database by computing False Rejection Rate (FRR).

Biometrics, latent fingerprints, Overlapped fingerprints, Random Decision Forest, Reflected Ultra Violet Imaging System

[1] F. Chen, J. Feng, A. K. Jain, J. Zhou, and J. Zhang, “Separating overlapped fingerprints,” IEEE Trans. Inf. Foren. Secur., Vol 76, Issue 10, pp.346–359, 2011.
[2] Y. Shi, J. Feng, J. Zhou, “Separating overlapped fingerprints using constrained relaxation labeling”, In: Proceedings of the 2011 international joint conference on biometrics, 2011.
[3] J. Feng, Y. Shi, J. Zhou, “Robust and efficient algorithms for separating latent overlapped fingerprints”, IEEE Trans Inf Forensics Secur Vol. 7, Issue 5, pp.1498–1510, 2012.
[4] Q. Zhao, A. Jain, “Model based separation of overlapping latent fingerprints”, IEEE Trans Inf Forensics Secur Vol.7, Issue 3, pp.904–918, 2012.
[5] N. Zhang, Y. Zang, X. Yang, X. Jia, J. Tian, “Adaptive orientation model fitting for latent overlapped fingerprints separation”, IEEE Trans Inf Forensics Secur, Vol 9, Issue 10, pp.1547–1556, 2014.
[6] S.Jeyanthi, N.U. Maheswari and R. Venkatesh. "Neural network based automatic fingerprint recognition system for overlapped latent images." Journal of Intelligent & Fuzzy Systems, Vol 28, Issue 6, pp.2889-2899, 2015.
[7] S. Jeyanthi, N.U. Maheswari and R. Venkatesh. "An Efficient Automatic Overlapped Fingerprint Identification and Recognition Using ANFIS Classifier", International Journal of Fuzzy Systems, Vol 18, Issue 3, pp.478-491, 2015.
[8] B. Stojanović, A. Nešković, O. Marques, “A novel neural network based approach to latent overlapped fingerprints separation”, Multimedia Tools and Applications, Vol 76, Issue 10, pp.12775–12799, 2017.
[9] B. Stojanović, O. Marques, A. Nešković, S. Puzović, "Fingerprint ROI segmentation based on deep learning", Telecommunications Forum (TELFOR), IEEE, Volume 76, Issue 10, pp.1-4, 2016.
[10] A. Sankaran, A. Jain, T. Vashisth, M. Vatsa, R. Singh, "Adaptive latent fingerprint segmentation using feature selection and random decision forest classification", Information Fusion, Elsevier, Volume 34, Issue 10, pp.1-15, 2017.
[11] K. Tejas, C. Swathi, D.A. Kumar and R. Muthu, "Automated region masking of latent overlapped fingerprints", In Power and Advanced Computing Technologies (i-PACT), IEEE, Innovations in pp.1-6, 2017.
[12] S.U. Maheswari, and E. Chandra. "An Enhanced Active contour based Segmentation for Fingerprint Extraction." International Journal on Computer Science and Engineering, Vol 4, Issue 9, pp.1633, 2012.
[13] K. Qian, M. Schott and J. Dittmann, “Separation of contactless captured high-resolution overlapped latent fingerprints: parameter optimisation and evaluation”, In Biometrics and Forensics (IWBF), International Workshop, IEEE, pp.1-4, 2013.
[14] K. Qian, M. Schott, W. Zheng and J. Dittmann, “Context-based approach of separating contactless captured high-resolution overlapped latent fingerprints”, IET biometrics, Vol. 3, Issue 2, pp,101-112, 2014.
[15] S. Jeyanthi, N.U. Maheswari and R. Venkatesh, “Separation and recognition of overlapped latent images”, In Computing, Communications and Networking Technologies (ICCCNT), IEEE, Fourth International Conference on pp.1-6, 2013.

Ankita Sharma, Manvjeet Kaur, "Automatic segmentation for separation of overlapped latent fingerprints," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.484-490, 2018.

Image Interpolation is an important operation in many image processing software and applications. It is a process of enlarging or reducing the image size. To resize an image, every pixel in new image is calculated using the values of the pixels in old image. There are many algorithms available for determining new value of the pixel, most of which involve some form of interpolation among the nearest pixels in the old image. After interpolating new values for pixel, it is important to preserve the image quality. As a result of digital image operations, various methods suffer from different edge-related visual artifacts such as aliasing, edge blurring, and jaggies effect. For our study we have used Nearest-neighbor, Bilinear, Bicubic, Cubic B-spline, Catmull-Rom, Lanczos of order two and Lanczos of order three image interpolation algorithms. In this paper, an attempt is made to evaluate different image interpolation algorithms to compare time performance on Intel Core i3, i5 and i7 processors supported with different hardware configuration. The result shows that more time is required to compute the larger image. However, the time can be minimized using higher end hardware configuration.

Interpolation, Computational Complexity, adaptive, non-adaptive, image quality, resize, scaling

[1] P. Bhatt, S. Patel, A. Shah, S. Patel, “Image Enhancement Using Various Interpolation Methods” IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS), Vol. 2, No.4, pp.799-803, 2012.
[2] D. Doma, “Comparison of Different Image Interpolation Algorithms”, West Virginia University Libraries, 2008.
[3] J. Titus, S. Geroge, “A Comparison Study On Different Interpolation Methods Based On Satellite Images”, International Journal of Engineering Research & Technology, Vol. 2, Issue 6, pp. 82-85 2013.
[4] P. S. Parsania, P. V. Virparia, “Image Quality Comparison using PSNR and UIQI for Image Interpolation Algorithms”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, Issue 12, pp. 21679-21687, 2016.
[5] K. Shah, J. Pandya, S. Vahora, “A Survey On Super Resolution Image Reconstruction Techniques”, International Journal of Engineering Research & Technology, Vol. 2, No. 4, pp. 1897-1901, 2013.
[6] G. Kaur, J. Kaur, “A Comparative Study of Image Demosaicing”, International Journal of Computer Sciences and Engineering, Vol. 3, Issue. 7, pp. 98-102, 2015.
[7] S. Fadnavis, “Image Interpolation Techniques in Digital Image Processing: An Overview”, International Journal of Engineering Research and Applications, Vol,. 4, No. 10, pp.70-73, 2015.
[8] A. Sinha, M. kumar, A. Jaiswal, R. Saxena, “Performance Analysis of High Resolution Images Using Interpolation Techniques in Multimedia Communication System”, Signal & Image Processing: An International Journal, Vol. 5, No. 2, pp. 39-49, 2014.
[9] A. Prajapati, S. Naik, S. Mehta, “Evaluation of Different Image Interpolation Algorithms”, International Journal of Computer Applications, Vol. 58, Issue. 1, pp. 6-12, 2012.
[10] T. Wu, B. Bai, P. Wang, “Parallel Catmull-Rom Spline Interpolation Algorithm for Image Zooming Based on CUDA”, International Journal of Applied Mathematics & Information Science, Vol. 7, No. 2, pp. 533-537, 2013.
[11] W. Burger, M. J. Burge, “Digital image processing: an algorithmic introduction using Java”, 1st ed., Springer, India, pp. 400-401, 2009.
[12] D. Han, “Comparison of Commonly Used Image Interpolation Methods”, In the Proceedings of 2nd International Conference on Computer Science and Electronics Engineering, France, pp.1556-1559, 2013.
[13] S. Singh, T. Gulati, “Upscaling Capsule Endoscopic Low Resolution Images”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, Issue. 5, pp. 40-46, 2014.
[14] C. Suresh, S. Singh, R. Saini, A. K. Saini, “A Comparative Analysis of Image Scaling Algorithms”, International Journal of Image, Graphics and Signal Processing, Vol. 5, Issue. 4, pp - 55-62, 2013.
[15] P. S. Parsania, P. V. Virparia, “Performance Analysis of Image Scaling Algorithms”, International Journal on Recent and Innovation Trends in Computing and Communication, vol. 4, Issue 6, pp. 521-526, 2016.

P.S. Parsania, P. V. Virparia, "Computational Time Complexity of Image Interpolation Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.491-496, 2018.

An increased interest has been noticed in Named Entity Recognition, particularly in the field of biomedical domain as identifying and extracting biomedical entities such as genes, diseases, proteins and drugs are tedious and demanding due to its ambiguity in biomedical terms. Named entity Recognition task consists of two phases. The first phase recognizes and extracts the entities whereas the second phase classifies the extracted entities under its associated classes. This research work is focused on the second phase of NER that is classifying the extracted entities with its associated class. In order to classify the entities, Relevance Vector Machine is trained and tested on two different datasets. For comparison purpose, HMM and SVM methods have been applied on the same datasets. The evaluation results shows that the RVM classifier performs better than HMM and SVM with high accuracy and less period of execution time.

Named Entity Recognition, Biomedical domain , Biomedical entities, Entities Classification, RVM

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[12] Janet Piñero et al., ”DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants.” Nucl. Acids Res. (2016) doi:10.1093/nar/gkw943
[13] Janet Piñero et al. , “ DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes”, Database (2015) doi:10.1093/database/bav028
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S. Vijaya, R. Radha, "Classifying gene disease entities using Relevance Vector Machine Classifier," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.497-502, 2018.

cloud computing delivers a service over the network by the use of hardware as well as of software that is the internet. Cloud computing is technology that are rapidly increase in terms of both academia and industry. Cloud computing allows everyone to use software and computing services on-demand at anytime, anywhere and anyplace using the internet. With the help of the cloud computing, users can access the files as well as can use the applications from any other device which can access the internet device. In Scheduling, cloud computing infrastructures contain several challenging issues like time estimation and load balancing etc. But main challenge for cloud computing environment is load balancing. Basically load balancing distributes the load to get lesser makespan (MS) and higher resource utilization. Load balancing algorithms ensure that neither a Virtual Machine is overloaded nor it is under loaded. This paper presents comparison of the metaheuristic approach which is inspired by Ant Behaviors (AB) and Swarm Intelligence (SI): The Ant Colony Optimization (ACO) and The Genetic algorithm (GA).

Cloud Computing, Load Balancer, The Ant Colony Optimization (ACO), The Genetic Algorithm (GA), Make-span, Resource-utilizations

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Nishu Rana, Pardeep Kumar, "Comparative Analysis of Metaheuristic Techniques: Ant Colony Optimization (ACO) and Genetic Algorithm (GA)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.503-507, 2018.

With the advances in geo-positioning technologies and location-based services, it is nowadays quite common for road networks to have textual contents on the vertices. Previous work on identifying an optimal route that covers a sequence of query keywords has been studied in recent years. However, in many practical scenarios, an optimal route might not always be desirable. For example, a personalized route query is issued by providing some clues that describe the spatial context between Pose along the route, where the result can be far from the optimal one. Therefore, in this paper, we investigate the problem of clue-based route search (CRS), which allows a user to provide clues on keywords and spatial relationships. First, we propose a greedy algorithm and a dynamic programming algorithm as baselines. To improve efficiency, we develop a branch-and-bound algorithm that prunes unnecessary vertices in query processing. In order to quickly locate candidate, we propose an AB-tree that stores both the distance and keyword information in tree structure. To further reduce the index size, we construct a PB-tree by utilizing the virtue of 2-hop label index to pinpoint the candidate. Extensive experiments are conducted and verify the superiority of our algorithms and index structures.

Spatial keyword queries, clue, Point-of-Interest, travel route search, query processing

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Shaik Sharmila, U. Mohan Srinivas, "Effective Road Networks Using Clue Based Route Search," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.508-523, 2018.