Volume-5 , Issue-9 , Sep - 2017 Go Back

The proposed approach represents novel and simple model approach based on a mixture of techniques and algorithms in a shared pool based on Viola–Jones object detection framework algorithm combined with geometric and symmetric information of the face parts from the image in a smart algorithm. The study is a continued part of previous work, the proposed model is modestly applied with hundreds of face images taken under different lighting conditions, a number of general assumptions used in this research field are identified. The proposed algorithm goes beyond the limits of all existing technologies as it obtains the unique functional features by enabling the proposed model to work with different skin color tone, applying it to low-quality images, detecting faces with eye glasses, determining the position of facial parts (e.g. eye pupils, nose, lips, etc.) and detect several faces on one image is typically designed to deal with single images.

Face Detection, Face Recognition, Viola-Jones Algorithm, Object Detection

[1] A.Maghraby M.Abdalla O.Enany, “Hybrid Face Detection System using Combination of Viola - Jones Method and Skin Detection”, International Journal of Computer Applications (0975 – 8887) Volume 71– No.6, May 2013
[2] P. Viola and M. J. Jones, “Robust real-time face detection”, International Journal of Computer Vision, 57 (2004), pp. 137–154.
[3] L. Sirovich and M. Meytlis. “Symmetry, probability, and recognition inface space”, PNAS - Proceedings of the National Academy of Sciences,106(17):6895–6899, April 2009.
[4] Fugat Ashlesha G., Gaikwad Shital S., Gangurde Jyoti P. and Sawant Aishwarya S., "A Survey on Secured Online Voting System Using Face Recognition", International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.214-216, 2015.
[5] Prabhjot Singh and Anjana Sharma, "Face Recognition Using Principal Component Analysis in MATLAB", International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.1, pp.1-5, 2015.
[6] Phillip I.W.,Dr. John F. “Facial Feature Detection Using Haar Classifiers”,Jcsc 21, (2006)
[7] Zhang, J., Yan, Y., and Lades, M., “Face Recognition: Eigenfaces, Elas-tic Matching, and Neural Nets”, Proc. IEEE, vol.85, no.9, pp.1423-1435, 1999
[8] Z.H. Zhou and X. Geng, “Projection functions for eye detection”, Pattern Recognition 37, no 5, pp. 1049-1056, 2004
[9] Daugman J (2006) "Probing the uniqueness and randomness of IrisCodes: Results from 200 billion iris pair comparisons." Proceedings of the IEEE, 94(11),
[10] Albiol,A., Albiol,A., Oliver,J., Mossi,J.M.(2012), “Who is who at different cameras: people re- identification using depth cameras”, Computer Vision, IET. Vol 6(5), 378-387.
[11] M. Hassaballah,KenjiMurakami, and Shun Ido, “Eye and Nose Fields Detection From Gray Scale Facial Images”, MVA2011 IAPR Conference on Machine Vision Applications, June 13-15, 2011, Nara, JAPAN
[12] S.Gurumurthy,B.K.Tripathy, “Design and Implementation of Face Recognition System in Matlab Using the Features of Lips”, I.J. Intelligent Systems and Applications,2012,8,30-36.
[13] A.Vora, A.Raj, K.Manikantan, and S.Ramachandran, “Enhanced Face Recognition using 8-Connectivity-of-Skin-Region and Standard- Deviation-based-Pose-Detection as preprocessing techniques”, International Conference on Medical Imaging, m-Health and Emerging Communication Systems,pp.364-369, 2014.
[14] B. Dahal, AbeerAlsadoon, “P.W.C. PrasadandAmrElchouemi, Incorporating Skin Color for Improved Face Detection and Tracking System”, 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp.-173 – 176, 2016
[15] M. DwisnantoPutro, TeguhBharataAdji, Bondhan Winduratna, “Adult Image Classifiers Based On Face Detection Using Viola-Jones Method”, 2015 1st International Conference on Wireless and Telematics (ICWT), pp. 1-6
[16] Lin, S.-H., Kung, S.Y., and Lin, L.-J., “Face Recognition/ Detection by Probabilistic Decision-Based Neural Network”, IEEE Trans. Neu-ral Networks, vol. 8, No. 1, pp.114-132, Jan. 1997
[17] Chellappa, R., Wilson, C.L., and Sirohey, S., “Human and Machinr Recognition of Faces: A Survey”, Proc. IEEE, vol.83, pp.705-741, May 1995.
[18] Donghe Yang, Jinsong Xia, “Face Tracking Based on CamshiftAlgorithm and Motion Prediction”, International Workshop onIntelligent Systems and Applications, 2009. ISA 2009.
[19] Corporation, “Open Source Computer Vision Library Reference Manual”, 123456-001, 2001.
[20] G. R. Bradski. “Computer vision face tracking for use in a perceptual user interface”, Intel Technology Journal, 2nd Quarter, 1998.
[21] Vikramsingh R. Parihar & Nileshsingh V. Thakur, “Graph Theory Based Approach For Image Segmentation Using Wavelet Transform”, International Journal of Image Processing (IJIP), Volume (8) : Issue (5) : 2014
[22] Vikramsingh R. Parihar, Roshani S. Nage, Atul S. Dahane, “Image Analysis and Image Mining Techniques: A Review”, Journal of Image Processing and Artificial Intelligence, MAT Journals Volume 3 Issue 2, 2017

Vikramsingh R. Parihar, Anagha P. Dhote, "A Novel Approach to Real Time Face Detection and Recognition," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.62-67, 2017.

Digital watermarking is the technique with the use of that owner’s copyright information can be embedded into the original media either it is in the form of an image, audio or video. It is an essential to maintain the ownership for digital media in sharing of the information. For this audio watermarking is one of the most likely medium to maintain the robustness as well as imperceptibility against the piracy, malicious attacks, and various transformation operations. Though there are some challenges to achieve this results, in this paper, our proposed audio watermarking technique is used to improve the robustness, imperceptibility and accuracy of the information with security. For robustness, in our proposed work we are using synchronized secret key concept and two important transformation methods used are DWT (Discreet Wavelet Transformation) up to 2-level in which the modifications are done in low frequency sub-band and SVD (Singular value Decomposition), so that original audio file does not have any impact of watermark bits to get the better performance. Also, there are more efficient results are presented as per robustness value calculated in this paper with compare to previous work done.

Audio watermarking,DWT,SVD,watermark embedding and extraction, robustness, imperceptibilty, SNR, IDWT, D-matrix Formation.

[1] H. J. Kim, Y. H. Choi, J. W. Seok, and J. W. Hong, Audio watermarking techniques, ser. Innovative Intelligence. World Scientific Publishing Co., 2004, vol. 7 (Intelligent Watermarking Techniques), ch. 8, pp. 185–218.
[2] N.F. Johnson, S. Jajodia, and Z. Duric, Information hiding: Steganography and watermarking attacks and countermeasures, Kluwer academic Publishers, 2000.
[3] M. Navneet Kumar, “Watermarking Using Decimal Sequences,” M.S. thesis, Louisiana State University, Baton Rouge, LA, USA, 2004.
[4] Arnold, M., Schmucker, M. and Wolthusen, S.D. (2002) Techniques and Applications of Digital Watermarking and Content Protection. Artech House, Norwood.
[5] Yamamoto, K. and Iwakiri, M. (2010) Real-Time Audio Watermarking Based on Characteristics of PCM in Digital Instrument. Journal of Information Hiding and Multimedia Signal Processing, 1, 59-71.
[6] Natgunanathan, I., Xiang, Y., Rong, Y., Zhou, W. and Guo, S. (2012) Robust Patchwork-Based Embedding and Decoding Scheme for Digital Audio Watermarking. IEEE Transactions on Audio, Speech, and Language Processing, 20, 2232-2239.
[7] Noriega, R.M., Nakano, M., Kurkoski, B. and Yamaguchi, K. (2011) High Payload Audio Watermarking: Toward Channel Characterization of MP3 Compression. Journal of Information Hiding and Multimedia Signal Processing, 2, 91-107.
[8] Khushbu and Deepinder Kaur, "Enhancement in Watermarking Approach Using DCT-DWT-SVD Techniques by Applying Kalman Filter", International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.50-53, 2016.
[9] Shahriar, M.R., Cho, S. and Chong, U.P. (2012) Time-Domain Audio Watermarking using Multiple Marking Spaces. Proceedings of the International Conference on Informatics, Electronics and Vision (ICIEV12), Dhaka, 974-979.
[10] Shobha Elizabeth Rajan, Sreedevi P, "Enhancing Visual Cryptography Using Digital Watermarking", International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.152-156, 2015.
[11] Shore, S., Ismail, M., Zainal, N. and Shokri, A. (2013) Error Probability in Spread Spectrum (SS) Audio Watermarking. Proceedings of 2013 IEEE International Conference on Space Science and Communication (IconSpace), Melaka, 1-3 July 2013, 169-173.
[12] Pahlavani, F. and Pourmohammad, A. (2013) A Block Set Interpolation Technique Based Additive-White-Noise Robust Audio Watermarking Method. Proceedings of 2013 10th International ISC Conference on Information Security and Cryptology (ISCISC), Yazd, 29-30 August 2013, 1-5.
[13] Ai, H., Liu, Q. and Jiang, X. (2013) Synchronization Audio Watermarking Algorithm Based on DCT and DWT. Proceedings of IEEE Conference Anthology, Shanghai, 1-8 January 2013, 1-4.
[14] Can, Y.S. and Alagoz, F. (2013) Robust Frequency Hopping and Direct Sequence Spread Spectrum Audio Watermarking Technique on Wavelet Domain. Proceedings of 2013 International Conference on Electronics, Computer and Computation (ICECCO), Ankara, 7-9 November 2013, 382-385.
[15] Dhar, P.K. and Shimamura, T. (2013) Entropy-Based Audio Watermarking Using Singular Value Decomposition and Log-Polar Transformation. Proceedings of 2013 IEEE 56th International Midwest Symposium on Circuits and Systems (MWSCAS), Columbus, 4-7 August 2013, 1224-1227.
[16] Shahriar, M.R., Cho, S. and Chong, U. (2012) Time-Domain Audio Watermarking Using Multiple Marking Spaces. Proceedings of 2012 International Conference on Informatics, Electronics & Vision (ICIEV), Dhaka, 18-19 May 2012, 974-979.
[17] Kanchan Thakur, "Hybrid DWT, FFT and SVD based Watermarking Technique for Different wavelet Transforms", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.7-12, 2017.
[18] M. Jamal, S. Mudassar, F. S. Mehmood, M. R. Rehman, "Improved Consistency of Digital Image Watermarking Using RDWT and SVD", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.104-106, 2017.
[19] Cox, I.J., Kilian, J., Leighton, T. and Shamoon, T. (1997) Secure Spread Spectrum Watermarking for Multimedia. IEEE Transactions on Image Processing, 6, 1673-1687
[20] Uludag, U. and Arslan, L. (2001) Audio Watermarking Using DC-Level Shifting. Project Report, Bogazici University, Istanbul.
[21] Swanson, M.D., Zhu, B., Tewfik, A.H. and Boney, L. (1998) Robust Audio Watermarking Using Perceptual Masking. Signal Processing, 66, 337-355.
[22] Sehirli, M., Gurgen, F. and Ikizoglu, S. (2005) Performance Evaluation of Digital Audio Watermarking Techniques Designed in Time, Frequency and Cepstrum Domains. Proceedings of International Conference on Advances in Information Systems (ADVIS’04), Izmir, 20-22 October 2004, 430-440.
[23] Al-Yaman, M.S., Al-Taee, M.A. and Alshammas, H.A. (2012) Audio-Watermarking Based Ownership Verification System Using Enhanced DWT-SVD Technique. Proceedings of the 9th International Multi-Conference on Systems, Signals and Devices (SSD’12), Chemnitz, 20-23 March 2012, 1-5.
[24] Bender, W., Gruhl, D., Morimoto, N. and Lu, A. (1996) Techniques for Data Hiding. IBM Systems Journal, 35, 313- 336.
[25] Al-Haj, A. and Mohammad, A. (2010) Digital Audio Watermarking Based on the Discrete Wavelets Transform and Singular Value Decomposition. European Journal of Scientific Research, 39, 6-21.
[26] Al-Haj, A., Mohammad, A. and Bata, L. (2011) DWT-Based Audio Watermarking. The International Arab Journal of Information Technology, 8, 326-333.
[27] Al-Yaman, M.S., Al-Taee, M.A., Shahrour, A.T. and Al-Husseini, I.A. (2011) Biometric Based Audio Ownership Verification Using Discrete Wavelet Transform and SVD Techniques. Proceedings of the 8th International Multi- Conference on Systems, Signals and Devices (SSD’11), Sousse, 22-25 March 2011, 1-5.
[28] Khalid A. Darabkh, Imperceptible and Robust DWT-SVD-Based Digital Audio Watermarking Algorithm, Journal of Software Engineering and Applications, 2014, 7, 859-871.
[29] A. B. Watson, “Image Compression Using the Discrete Cosine Transform,” Mathematical Journal, vol. 4(1), pp. 81-88, 1994.
[30] S. Katzenbeisser, and F.A.P. Petitcolas, Information hiding techniques for steganography and digital watermarking, Artech House Publishers, 2000.
[31] Al-Yaman, M.S., Al-Taee, M.A. and Alshammas, H.A. (2012) Audio-Watermarking Based Ownership Verification System Using Enhanced DWT-SVD Technique. Proceedings of the 9th International Multi-Conference on Systems, Signals and Devices (SSD’12), Chemnitz, 20-23 March 2012, 1-5.

Krunalkumar N. Patel, Dipti B Shah, "Robust Audio Watermarking using Improved DWT-SVD approach," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.68-73, 2017.

Phishing is a form of internet fraud in which an attacker, also known as a phisher, attempts to fraudulently retrieve legitimate users` confidential or sensitive credentials by imitating electronic communications from a trustworthy or from the public organization in an automated fashion. There is an need of identify the phishing websites in this emerging digital era. Based on the URL and content based features of websites like length of URL, domain’s age, WHOIS properties, etc, we can draw an algorithm to identify the phishing websites. Furthermore, our approach checks the legitimacy of a webpage using hyperlink features. Hyperlinks are extracted from the source code of the given website and apply that into the proposed algorithm to detect phishing site. Our experiment shows that our proposed algorithm is very effective to detect the phishing websites and it have 89.16% True Positive Rate while greater than 82% of accuracy.

Phishing URL, Phishing URL/Hyperlink

[1] S. Abu-Nimeh and S. Nair, “Bypassing security toolbars and phishing filters via dns poisoning” In IEEE GLOBECOM: Global Telecommunications Conference, 2008.
[2] S. Sheng, M. Holbrook, P. Kumaraguru, L. F. Cranor, and J. Downs, “Who falls for phish? : a demographic analysis of phishing susceptibility and effectiveness of interventions” in Proceedings of the 28th international conference on Human factors in computing systems, ser. CHI ’10. New York, NY, USA: ACM, 2010, pp. 373–382.
[3]B. Krebs, “HBGary Federal hacked by Anonymous” http://krebsonsecurity.com/2011/02/hbgary-federal-hacked-by-anonymous/, 2011, accessed December 2011.
[4] V Karamchand Gandhi, “An Overview Study on Cyber crimes in Internet” Journal of Information Engineering and Applications from IISTE, Volume 2, Number 1, pages 1-5, February 2012. ISSN 2224-5782 (Paper) ISSN 2225-0506 (Online).
[5] Google safe browsing API Available at: https://developers.google.com/safe-browsing/. Accessed 30 Nov 2015.
[6] W Liu, X Deng, G Huang, AY Fu, “An antiphishing strategy based on visual similarity assessment”, IEEE Internet Comput. 10(2), 58–65 (2006)
[7] VP Reddy, V Radha, M Jindal, “Client side protection from phishing attack”, International Journal of advanced Engineering Science Technology 3(1), 039–045 (2011)
[8] A. Z. Broder, “Identifying and filtering near-duplicate documents”, In COM ’00: Proceedings of the 11th Annual Symposium on Combinatorial Pattern Matching, pages 1–10, London, UK, 2000. Springer-Verlag.

V. Karamchand Gandhi, M. Suriakala, "RSIPS: A Robust System to Identify Phishing Websites using Unique Addressing features of Web," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.74-78, 2017.

Agriculture is very helpful to meet the basic needs of human and their civilization by providing food, clothing, shelters, medicine and recreation. Hence, agriculture is the most important enterprise in the world. Agriculture provides a free fare and fresh environment, abundant food for driving out famine; favours friendship by eliminating fights. Red gram production is major plant or seed in world. Average world production of red gram is at 3.0 million tons in the last six years (2000-05). The area under cultivation is stagnant at 4.5 million hectares in the same period. Red gram is drought resistant and can be grown in areas with less than 650 mm annual rainfall. Production of red gram is estimated at 46,000 km² in all over world. Around 82% of this is grown in India. Hence it is necessary to develop android application for red gram crop. It is helpful for farmer to have all information related to red gram crop. This app will contain crop information and disease related to crop.

Agriculture, red gram, android application, crop information

[1]. Sanjay Chaudhary, Minal Bise, Asim Banerje, Aakas Goyal, Chetan Moradiya, “Agro Advisory System for Cotton” Agrinet Workshop, 6-10 Jan.2015.IEEE, PISSN: 2155-2487, EISSN: 2155-2509
[2]. S. Chaudhary and M. Bhise, “Restful Services for Agricultural Recommendation System,” in Proceedings of NSDI-2013 , llTB, Mumbai, pp. 46-52, November 29-30,2013).
[3]. P. Krishna Reddy, G. V. Ramaraju, and G. S. Reddy. ``eSagu™: a data warehouse enabled personalized agricultural advisory system,” in SIGMOD`07 Proceedings of the 2007ACM SIGMOD international conference on Management of data, pp.910-914, June 2007.
[4]. G. Narayanan, P. Periasamy, “Web Resources Development Methdology Based on Web Composition Using Ontology for User`s Optimal Goal”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.78-86, 2016.
[5]. [5] “Kissan Kerela: An Integrated multi-modal agricultural information system for kerela,” llITM-K, Thiruvanathapuram,http://www.kissankerala.net.June. 2014.
[6]. Ramamritham Krithi, Anil Bahuman, Ruchi Kumar, Aditya Chand, Subhasri Duttagupta, G.V. Raja Kumar, and Chaitra Rao. “aAQUA – A Multilingual, Multimedia Forum for the community,” in IEEE International Conference on Multimedia and Expo, vol. 3, 2004.
[7]. Arun Pande, Bhushan G. Jagyasi, Sanjay Kimbahune, Pankaj Doke,Ajay Mittal, Dineshkumar Singh, and Ramesh Jain. “Mobile Phone based Agro-Advisory System for Agricultural Challenges in Rural India,” in IEEE Conference on Technology for Humanitarian Challenges, Aug 2009.
[8]. Daksh Agrawal, Hirali Sanghani, Sonali Jadhav and Supriya Shinde, “Ontology based Domain Specific Web Search Engine”, International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.12-15, 2015.
[9]. “RDF: Resource Description Framework,” http://www.w3.org/RDF/, June, 2014.
[10]. “SPARQL: SPARQL Protocol and Query Language for RDF,”http://www. w3 .0rgITRlrdf-sparq I-query/, June, 2014.
[11]. “Apache Jena: A free and open source Java framework for building Semantic Web and Linked Data applications,” http://jena.apache.org, June, 2014.
[12]. “CartoDB: A cloud based solution for mapping services,” http://developers.cartodb.com, June, 2014.
[13]. G.K. Koutu, P.P Shastry, D.K. Mishra, and K.c. Mandloi, “Handbook of COTTON,” Studium Press Pvt. Ltd, India,2014.
[14]. M. Sabesh , “ClCR: Approved Package of Practices for cotton,” http://www.cicr.org.in/pop/gj.pdf, June, 2014.
[15]. “Agropedia: A free and open source Java framework for building Semantic Web and Linked Data applications,” http://agropedia.iitk.ac.in/, June, 2014.
[16]. “Climate:CropInformation,”http://www.ikisan.com/crop%20specific/eng/Iinks/apcoconutCIimate%20And%20Soils.shtml, June 2014.

S.Y. Arjunagi, N.B.Patil, "Red Gram Agro Advisory System," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.79-83, 2017.

Clause boundary identification for compound sentences in Punjabi language is one of the basic necessity for processing of compound sentences. For grammar checking of compound sentences, it is necessary to identify the structure of various independent clauses present in compound sentence. Once the sentence is identified as compound sentence, the next step is to identify its pattern. After identification of patterns, various clauses present in the sentence are extracted as it is the basic step for performing grammar checking. In this paper, author has explored a technique to identify the clause boundaries present in compound sentence. This study will be helpful in identifying and separating the compound sentences from Punjabi language corpus. Also this study will be helpful in developing other Natural Language Processing (NLP) applications like simplification compound sentence in simple sentences, Improving Machine translation system and grammar checking of compound sentences.

NLP, Compound Sentences, Independent clause, grammar checking

[1]. Sobha, L. D., & Lakshmi, S. Malayalam. 2013. Clause Boundary Identifier: Annotation and Evaluation. WSSANLP-2013, p. 83.
[2]. Kaur, N., Garg, K., Sharma, Sanjeev. Kumar. 2013. Identification and Separation of Complex Sentences from Punjabi Language. International Journal of Computer Applications, 69(13), pp. 21-24.
[3]. Sharma, Sanjeev Kumar ‘Assigning the Correct Word Class to Punjabi Unknown Words using CRF’ International Journal of Computer Applications (0975 – 8887) Volume 142 – No.2, May 2016
[4]. Brill, E. 1992. A simple rule-based part of speech tagger. In Proceedings of the workshop on Speech and Natural Language. Association for Computational Linguistics. pp. 112-116
[5]. Brill, E. 1993. Automatic grammar induction and parsing free text: A transformation-based approach. In Proceedings of the workshop on Human Language Technology. Association for Computational Linguistics. pp. 237242
[6]. Kasbon, R., Amran, N., Mazlan, E., & Mahamad, S. 2011. Malay language sentence checker. World Appl. Sci. J. (Special Issue on Computer Applications and Knowledge Management), 12, pp. 19-25.
[7]. Kubon V., & Platek, M. 1994. A grammar based approach to a grammar checking of free word order languages. In Proceedings of the 15th conference on Computational linguistics-Volume 2. Association for Computational Linguistics. pp. 906-910
[8]. Leffa, V. J. 1998. Clause processing in complex sentences. In Proceedings of the First International Conference on Language Resources and Evaluation Vol. 1, pp. 937-943.
[9]. Narula, R., & Sharma, S. K. 2014. Identification and Separation of Simple, Compound and Complex Sentences in Punjabi Language. International Journal of Computer Applications & Information Technology. Vol. 6, Issue II Aug- September 2014.
[10]. Orasan, C. 2000. A hybrid method for clause splitting in unrestricted English texts. Proceedings of ACIDCA` 2000
[11]. Parveen, D., Sanyal, R., & Ansari, A. 2011. Clause Boundary Identification using Classifier and Clause Markers in Urdu Language. Polibits Research Journal on Computer Science, 43, pp. 61-65.

S. K. Sharma, "Marking Clause Boundary in Compound Sentences of Punjabi Language," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.84-88, 2017.

In the present paper, the notion of δ-open sets, g-closed sets, δg-closed sets; and the relation between them has been studied. It is also noted that the collection of δ-open sets form the topology. A new concept of δ*-continuity has been established which is a generalization of the classical form of continuity. By introducing the idea of δ-fine ope n set and δg-fine open set, δ-fine continuity and δg-fine continuity have been defined. In support of these new concepts, several illustrative examples have been given.

δ-open sets, g-closed sets, δg-closed sets

[1] N. Levine, “Generalized closed sets in topology”, Rend Circ Math Palermo, Vol. 19, Issue 2, pp. 89 – 96, 1970.
[2] N. Bourbaki, “General topology”, Park I. Reading MA: Addition Wesley; 1966.
[3] M. Genster, I. L. Reilly, “ Locally closed sets and LC-continuous function”, Int. J. Math. Sci. Vol. 12, pp. 417- 424, 1989.
[4] J. Dontchev, M. Genster, “On δ-generalized closed sets and T3/4-spaces”. Mem. Fac. Sci. Kochi Univ. Ser. Math. Vol. 17, pp.15 -31, 1996.
[5] A. A. Nasef, “ On b-locally closed sets and related topics”, Chaos, Solitons and Fractals, Vol.12, pp. 1910 – 1915, 2001.
[6] J. K. Park, J. H. Park, B.Y. Lee, “On locally δ-generalized closed sets and LδGC-continuous functions”, Chaos, Solitons and Fractals, Vol. 19, pp. 995 -1002, 2004.
[7] E. Ekici, “On locally closedness and continuity”, Chaos, Solitons and Fractals, Vol. 36, pp. 1244 -1255, 2008.
[8] Y. Beceren, T. Noiri, “Some functions defined by α-open and pre-open sets”, Chaos Solitons and fractals, Vol. 37, pp. 1097 -1103, 2008.
[9] N. Levine, “Semi-open sets and semi-continuity in topological spaces”, Am. Math. Monthly, Vol. 70, pp. 36-41, 1963.
[10] M. E. Abd El-Monsef, S. N. El-Deeb, R. A.Mahmoud, “β-open sets and β-continuous mapping”, Bull. Fac. Sci. Assiut. Univ. A, Vol. 12, pp.77 – 90, 1983.
[11] S. Z. Bai, Y. P. Zuo, “On g-α-irresolute functions”, Acta. Math. Hungar., Vol. 130, Issue 4, pp. 382-389, 2011.
[12] P. L. Powar, K. Rajak, “Fine irresolute mapping”, Journal of Advanced Studies in Topology, Vol. 3, Issue 4, pp. 125-139, 2012.
[13] J. R. Munkers, Topology, Second Edition, Pearson Education Asia.
[14] P. L. Powar, P. Dubey, “A Concise form of continuity in fine topological space”, Advances in computational sciences and technology (ACST), Vol. 10, Issue 6, pp. 1785-1805, 2017.

P.L. Powar, Pratibha Dubey, "Decomposition of δ-Continuity and δ*-Continuity," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.89-96, 2017.

Computer easily recognize Semantic Web Services (OWL-S) instead of web services like WSDL. we are going to convert web services to semantic web services so discovering and selecting the services get easier. Ontology repository and standardization engine are basic steps for this conversion. The proposed system presents a distributed Web service discovery architecture This architecture is based on the concept of distributed shared space and intelligent search among a subset of spaces. It allows the publishing of Web service descriptions as well as to submit requests to discover the Web service of user’s interests.

WSDL, OWL, Mapping, OSSE

[1]. T. A. Farrag , A. I. Saleh , “Toward SWSs discovery : Mapping from WSDL to OWL-S Based on Ontology Search and Standardization engine”, IEEE Transaction on Knowledge & Data Engg, vol. 25 , No. 5 , May 13.
[2]. D. Martin , M. Burstein, D. Mcdermott, S. Mcilraith, M. Paolucci , K. Sycara , D.L. Mcguinness, E. Sirin, and N. Srinivasan , “ Bringing Semantics to Web Services with OWL-S ” World Wide Web, vol. 10, no. 3, pp. 243-277, Sept. 2007.
[3]. S. Vinotha, J. Vikneshwaran, “Relevency Based ContentSearch in Semantic Web “, International Journal of Emerging Trends and Technology in Computer Science(IJETTCS),Vol. 3, Issue 2 , March – April 2014.
[4]. S. Pradeepha, B.Lakshmipathi, “ Augmenting the SWS Discovery by Categorization of Web Service" , International Journal of Advanced Research in Computer Science and Tech , Vol . 2 Issue Special1 Jan-March 2014.
[5]. J. Praba. & M. A. Hema , " Semantic Web Service To Support Modeling In Mapping From Web Service Description Language", International Journal of Com_Puter Science and Engg,Vol. 3, Issue 3, May 2014.
[6]. M . Burstein , C . Bussler , M. Zaremba , T. Finin , M.N. Huhns , M. Paolucci , A.P. Sheth , and S. illiams , “ A Semantic Web Services Architecture ” , IEEE Internet Computing, vol. 3, no. 5, pp. 72-81, Sept./Oct. 2005.
[7]. T.A. Farrag “ A Cluster-Based Semantic Web Services Discovery and Classification ” , Proc . ACME Second Int’l Conf . Advanced Computer Theory and Eng., pp 1825-1834, 2003.
[8]. “Web Services Description Language (WSDL)”, W3C Note, 2001.
[9]. “Web Ontology Language for Service OWL-S” , W3C Member Submission, 2004.
[10]. B .Di Martino , “Semantic Web Services Discovery Based on Structural Ontology match”, Int’l J. Web &Grid Services , vol. 5 , no. 1, pp. 46-65, 2003.
[11]. M. Paolucci, N. Srinivasan, K. Sycara, and T. ishimura,“ Towards a Semantic Choreography of Web Services: From WSDL to DAML-S”, Proc. First Int’l Conf. Web Services (ICWS ’03), pp 22-26, June 2003.

R. Jamgekar, N. Sawant, P. Bansode, "Review On Toward SWSs Discovery: Mapping from WSDL to OWL-S Based on Ontology Search and Standardization Engine," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.97-101, 2017.

The work proposed here addresses the task of organizing the geo-referenced media on Flickr to generate Visual and Thematic Summarization of specified geo-location. The major challenge addressed in this work is: how to use the unstructured and unrestricted community contributed media and annotations to generate knowledge? The social tags associated with social images suffer from various problems such as, “free nature of tags”, “tag relevance”, and “cold start”. To deal with these problems, we consider ternary interrelations and multiple intra-relations among user, image and tag and model the relations using HOSVD, a Tensor Reduction Technique. As a result, context information for geo-location images is generated using user’s potential annotations. Content and context information for geo-location images and probabilistic generative model is utilized for location visualization. The novel visualization scheme proposed here summarizes the geo-location with a rich display landscape and provides location description using location representative tags. Experiments are performed on geo-tagged Flickr images for various geo-locations. The experimental results have validated the proposed method.

Tag Refinement, Tensor Factorization, Geo-Referenced Photos, Summarization, Clustering, Image Search, Collection Visualization

[1] Dimitrios Rafailidis, Petros Daras, (2013), “The TFC Model: Tensor Factorization and Tag Clustering for Item Recommendation in Social Tagging Systems”, IEEE Transactions On Systems, Man, And Cybernetics: Systems, Vol. 43, No. 3, May 2013
[2] Jaffe A, Naaman M, Tassa T, Davis M, (2006), “Generating Summaries And Visualization For large Collections Of Geo-Referenced Photographs”. In: Proceedings of the 8th ACM international workshop on multimedia information retrieval. ACM, New York, pp 89–98.
[3] Jitao Sang, Changsheng Xu, Dongyuan Lu,(2012), “User-Aware Image Tag Refinement via Ternary Semantic Analysis”, IEEE Transactions On Multimedia, Vol. 14, No. 3, June 2012.
[4] Kennedy L, Naaman M (2008) “Generating Diverse and Representative Image Search Results for Landmarks”. In: Proceeding of the 17th international conference on World Wide Web. ACM, New York, pp 297–306.
[5] Yan-Tao Zheng • Zheng-Jun Zha • Tat-Seng Chua (2011), “Research and applications on georeferenced multimedia: a survey," Multimed Tools Appl 51:77–98 DOI 10.1007/s11042-010-0630-z.
[6] Panagiotis Symeonidis, Alexandros Nanopoulos, and Yannis Manolopoulos, (2010), “A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis”, IEEE Tra nsactions On Knowledge And Data Engineering, Vol. 22, No. X, 2010 .
[7] Qiang Hao, Rui Cai, Xin-Jing Wang, Jiang-Ming Yang, Yanwei Pang, and Lei Zhang, 2009, “Generating Location Overviews with Images and Tags by Mining User-Generated Travelogues”, MM’09, October 19–24, 2009, Beijing, China.
[8] Quan Fang, Jitao Sang, Changsheng Xu, and Ke Lu, 2013, “Paint the City Colorfully: Location Visualization from Multiple Themes”, MMM 2013, Part I, LNCS 7732, pp. 92–105, 2013.
[9] Stevan Rudinac, Alan Hanjalic, Martha Larson, (2013) “Generating Visual Summaries of Geographic Areas Using Community-Contributed Images”, IEEE Transactions On Multimedia, Vol. 15, No. 4, June 2013.
[10] T. G. Kolda and B. W. Bader, (2009), “Tensor decompositions and applications,” SIAM Rev., vol. 51, no. 3, pp. 455–500, 2009.
[11] Thomas Hofmann, (1999), “Probabilistic Latent Semantic Indexing”. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’99, pages 50–57, New York, NY, USA, 1999. ACM.
[12] S.A. Takale, P.J. Kulkarni, “Extract Knowledge About Geo-Location Using Context and Content Information of Geo-Tagged Social Media”, LNCS Proceedings, Part I, of the 16th International Conference on Web Information Systems Engineering - WISE 2015 - Volume 9418 Pages 601-615.

S.A. Takale, P.J. Kulkarni, "How Flickr Helps to Know the Place: Visual and Textual Summarization of Geo-location," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.102-107, 2017.

Cloud computing is an emerging technology based on the network that provides access to the data from server. It Cloud computing is an emerging technology based on the network that provides access to the data from server. It illustrates an extremely scalable computing asset offered as an external service Paid by Internet method. In cloud computing, mainly we have focused on virtualization, energy efficiency and security. In this paper, we conducted a survey of cloud computing which is a framework that uses different services, IaaS, PaaS, SaaS and HaaS. A comparison for the same is also explained. The concept of virtualization is also discussed following Bare metal hypervisor, Hosted hypervisor and VMM (Virtual machine migration). Different security threats have been mentioned being considered for cloud services. For the calculation of energy consumption, cloud computing uses energy efficiency concept. Work done by various authors in cloud computing has been discussed with the research gap as well.

Cloud computing, Virtualization, service model, Virtual machine migration, security threats and energy efficiency

[1]. Ostermann, S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T., & Epema, D. (2009, October). A performance analysis of EC2 cloud computing services for scientific computing. In International Conference on Cloud Computing (pp. 115-131). Springer, Berlin, Heidelberg..
[2]. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., ... & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
[3]. Qian, L., Luo, Z., Du, Y., & Guo, L. (2009). Cloud computing: An overview. Cloud computing, 626-631.
[4]. Subashini, S., & Kavitha, V. (2011). A survey on security issues in service delivery models of cloud computing. Journal of network and computer applications, 34(1), 1-11.
[5]. Alshammari, A., Alhaidari, S., Alharbi, A., & Zohdy, M. (2017, June). Security Threats and Challenges in Cloud Computing. In Cyber Security and Cloud Computing (CSCloud), 2017 IEEE 4th International Conference on (pp. 46-51). IEEE.
[6]. Wei, Y., & Blake, M. B. (2010). Service-oriented computing and cloud computing: Challenges and opportunities. IEEE Internet Computing, 14(6), 72-75.
[7]. Tsai, W. T., Sun, X., & Balasooriya, J. (2010, April). Service-oriented cloud computing architecture. In Information Technology: New Generations (ITNG), 2010 Seventh International Conference on (pp. 684-689). IEEE
[8]. Lombardi, F., & Di Pietro, R. (2011). Secure virtualization for cloud computing. Journal of Network and Computer Applications, 34(4), 1113-1122.
[9]. Jain, R., & Paul, S. (2013). Network virtualization and software defined networking for cloud computing: a survey. IEEE Communications Magazine, 51(11), 24-31.
[10]. Shaikh, F. B., & Haider, S. (2011, December). Security threats in cloud computing. In Internet technology and secured transactions (ICITST), 2011 international conference for (pp. 214-219). IEEE.
[11]. Kajal, N., & Ikram, N. (2015, May). Security threats in cloud computing. In Computing, Communication & Automation (ICCCA), 2015 International Conference on (pp. 691-694). IEEE.
[12]. Jain, R., & Paul, S. (2013). Network virtualization and software defined networking for cloud computing: a survey. IEEE Communications Magazine, 51(11), 24-31..
[13]. Shivlal Mewada, Arti Sharivastava, Pradeep Sharma, N Purohit and S.S. Gautam, "An Performance Analysis of Encryption Algorithm in Cloud Computing", International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.83-89, 2015.
[14]. Cao, Y., Song, F., Liu, Q., Huang, M., Wang, H., & You, I. (2017). A LDDoS-aware Energy-efficient Multipathing Scheme for Mobile Cloud Computing Systems. IEEE Access.
[15]. Razaque, A., Vennapusa, N. R., Soni, N., & Janapati, G. S. (2016, April). Task scheduling in cloud computing. In Long Island Systems, Applications and Technology Conference (LISAT), 2016 IEEE (pp. 1-5). IEEE.
[16]. Mathew, T., Sekaran, K. C., & Jose, J. (2014, September). Study and analysis of various task scheduling algorithms in the cloud computing environment. In Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on (pp. 658-664). IEEE.
[17]. Arabnejad, H., Barbosa, J. G., & Prodan, R. (2016). Low-time complexity budget–deadline constrained workflow scheduling on heterogeneous resources. Future Generation Computer Systems, 55, 29-40.
[18]. Yu, J., & Buyya, R. (2006, June). A budget constrained scheduling of workflow applications on utility grids using genetic algorithms. In Workflows in Support of Large-Scale Science, 2006. WORKS`06. Workshop on (pp. 1-10). IEEE.
[19]. Ting, T. O., Rao, M. V. C., & Loo, C. K. (2006). A novel approach for unit commitment problem via an effective hybrid particle swarm optimization. IEEE Transactions on Power Systems, 21(1), 411-418.
[20]. He, Q., & Wang, L. (2007). A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Applied mathematics and computation, 186(2), 1407-1422.
[21]. Meena, J., Kumar, M., & Vardhan, M. (2016). Cost Effective Genetic Algorithm for Workflow Scheduling in Cloud Under Deadline Constraint. IEEE Access, 4, 5065-5082.
[22]. Visheratin, A. A., Melnik, M., & Nasonov, D. (2016). Workflow scheduling algorithms for hard-deadline constrained cloud environments. Procedia Computer Science, 80, 2098-2106.
[23]. Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future generation computer systems, 28(5), 755-768.
[24]. Chen, W., Xie, G., Li, R., Bai, Y., Fan, C., & Li, K. (2017). Efficient task scheduling for budget constrained parallel applications on heterogeneous cloud computing systems. Future Generation Computer Systems, 74, 1-11.

Amanpreet Kaur, Sawtantar Singh Khurmi, "A Study Of Cloud Computing Based On Virtualization And Security Threats," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.108-112, 2017.

Nowadays, with rapid use of internet, a very large number of reviews are posted by visitors on different website related to the various movies that describe the polarity between movies. Customers share their feelings with others in the form of comments or reviews that describe their opinion as either in negative or in positive or in neutral. Such websites are essential to people for decision making. In this paper, the sentiment analysis is done in order to analyze the movie reviews, so we use the machine learning classifier Random Forest with Gini Index based Feature Selection and also compared it with another algorithm such as SVM. The results show that Gini Index method with Random Forest classifier has better performance in terms of Accuracy, Root Mean Square Error, Precision, Recall and F-Measure.

Sentiment Analysis, Related Work, Feature Selection, Classification Algorithms, Evaluation Matrices

[1] V. Krishnaiah, Dr.G.Narsimha and Dr.N.Subhash Chandra, “Survey of Classification Techniques in Data Mining”, International Journal of Computer Sciences and Engineering Vol.2, Issue.9, pp 65-74, 2014.
[2] N. Nehra, “A Survey On Sentiment Analysis Of Movie Reviews”, International Journal Of Innovative Research In Technology (IJIRT), Vol.1, Issue.7, pp 36-40, 2014 .
[3] R. Feldman, “Techniques and Applications for Sentiment Analysis”,Communications of the ACM, Vol.56, Issue.4, pp 82-89, 2013.
[4] C. Catal, M. Nangir, “A Sentiment Classification Model Based On Multiple Classifiers”, Applied Soft Computing Elsevier, Vol.50, pp 135–141, 2017.
[5] R. Piryani, V. Gupta, V. K. Singh and U. Ghose, “A Linguistic Rule-Based Approach for Aspect-Level Sentiment Analysis of Movie Reviews”, Advances in Computer and Computational Sciences, Springer Nature Singapore Pte Ltd, Vol 1, pp 201-209, 2017.
[6] A.S. Manek, P.D. Shenoy,M.C. Mohan and Venugopal K R, “Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier”, World Wide Web Internet and Web Information Systems Springer, Volume 20, Issue 2, pp 135–154, 2016 .
[7] D. Anand, D. Naorem, “Semi-supervised Aspect Based Sentiment Analysis for Movies using Review Filtering”, 7th International conference on Intelligent Human Computer Interaction, IHCI, Science Direct Elsevier, Vol 84, pp 86-93, 2016.
[8] B. Batrinca, P. C. Treleaven, “Social media analytics: a survey of techniques, tools and platform”,AI & Society Springer, Vol.30, Issue.1, pp 89-116, 2015.
[9] Mrs. R.Nithya, Dr. D.Maheshwari, “Sentiment Analysis on Unstructured Review”, 14 Proceedings of the International Conference on Intelligent Computing Applications IEEE, , pp 367-371, 2014.
[10] I. Maks, P. Vossen , “A lexicon model for deep sentiment analysis and opinion mining applications”, Decision Support Systems Elsevier, Vol.53, Issue.4, pp 680-688, 2012.
[11] T. P. Sahu and S. Ahuja, “Sentiment Analysis of Movie Reviews: A study on Feature Selection & Classification Algorithms”, IEEE International Conference of Microelectronics, Computing and Communications (MicroCom) Durgapur, India, 2016.
[12] V. kumar, B. Vaghela and B. M. Jadav, “Analysis of Various Sentiment Classification Techniques”, International Journal of Computer Applications, Vol.140 , Issue.3, pp 22-27, 2016.
[13] F.H. Khan, U. Qamar, S. Bashir, “SentiMI: Introducing point-wise information with SentiWordNet to improve sentiment polarity detection”, Applied Soft Computing, Elsevier, Vol.39, pp 140-153, 2016.
[14] A. Tripathy, A. Agrawal, S.K. Rath, “Classification of sentiment reviews using n-gram machine learning approach”, Expert Systems with Applications, Elsevier, Vol.57, pp 117-126 , 2016.
[15] S.H. Bhojani and Dr. N. Bhatt, “Data Mining Techniques and Trends – A Review”, Global Journal For Research Analysis, Vol.5, Issue.5, pp 252-254 , 2016.
[16] Y.S. You, S. Lee and J. Kim, “Design and Development of Visualization Tool for Movie Review and Sentiment Analysis”, Sixth International Conference on Emerging Databases: Technologies, Applications, and Theory, Jeju, Repbulic of Korea, pp 117-123, 2016.
[17] P. Gupta, A. Sharma, J. Grover, “Rating based Mechanism to Contrast Abnormal Posts on Movies Reviews using MapReduce Paradigm”, 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO),IEEE, pp 262-266, 2016.
[18] M. Kannvdiya, K. Patidar and R.S. Kushwaha , “A Survey On: Different Techniques And Features Of Data Classification”, International Journal of Research In Computer Applications And Robotics, Vol.4, Issue.6, pp 1-6, 2016.
[19] S. Kaur and A.K. Grewal, “A Review Paper on Data Mining Classification Techniques for Detection of Lung Cancer”, International Research Journal of Engineering and Technology (IRJET), Vol.03, Issue11, pp 1334-1338, 2016.
[20] C.H. Chu, C.A. Wang, Y.C. Chang, Y.W. Wu, Y.L. Hsieh and W.L. Hsu, “Sentiment Analysis on Chinese Movie Review with Distributed Keyword Vector Representation ”, Technologies and Applications of Artificial Intelligence (TAAI), IEEE Conference, pp 84-89, 2016.
[21] Z. Teng, D.T. Vo and Y. Zhang, “Context-Sensitive Lexicon Features for Neural Sentiment Analysis”, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp 1629–1638, 2016.
[22] P. Chikersal, S. Poria, E. Cambria, “SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning”, Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval), pp 647–651, 2015.
[23] A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay and C. A. Coello, “A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I”, IEEE Transactions on Evolutionary Computation, Vol.18, Issue.1, pp 4-19, 2014.
[24] V.K. Singh, R.Piryani, A. Uddin, P.Waila, “Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification”, International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), IEEE, pp 712-717, 2013.
[25] J.S. Modha, G.S. Pandi, S.J. Modha, “Automatic Sentiment Analysis for Unstructured Data”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.3, Issue.12, pp 91-97, 2013 .

Rajwinder Kaur, Prince Verma, "Sentiment Analysis of Movie Reviews: A Study of Machine Learning Algorithms with Various Feature Selection Methods," International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.113-121, 2017.