i-manager Publications

i-manager's Journal on Computer Science (JCOM)

Current Issue Vol. 12 Issue 3

Blockchain Scalability Analysis and Improvement of Bitcoin Network through Enhanced Transaction Adjournment Techniques
Data Lake System for Essay-Based Questions: A Scenario for the Computer Science Curriculum
Creating Secure Passwords through Personalized User Inputs
Optimizing B-Cell Epitope Prediction: A Novel Approach using Support Vector Machine Enhanced with Genetic Algorithm
Gesture Language Translator using Morse Code

Most Cited

Volume 7 Issue 2 June - August 2019

Article

Significance of R in Research

G. Sateesh* , B. Padmaja, M. V. Bhuvaneswari*

*-*** Department of Computer Science Engineering, Lendi College of Engineering, Andhra Pradesh, India.

Satheesh, G., Padmaja, B., Bhuvaneswari, M. V.(2019). Significance of R in Research, i-manager's Journal on Computer Science, 7(2), 1-7. https://doi.org/10.26634/jcom.7.2.15639

Abstract

R is a great degree adaptable factual programming language and condition that is open source and unreservedly accessible for all standard working frameworks. The aim of this paper is to bring significance of R to the allied fields of Data science and development of R in different innovations like Data Analysis, Image Processing, Big Data Analytics and Machine Learning everything under data science advancements. R studio contributes numerous packages, which are valuable in their respective environment and projects effortlessly. Its short syntax structure to quicken tasks on the data, loading and storing information for both nearby and over web, an extensive rundown of long list of tools for data analysis pulls in clients to work with R. R demonstrates that imperative strategies not accessible somewhere else can be actualized in R effectively.

References

[1]. Babu, K. N. (2016). A novel approach on image processing using R Studio. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), 5(11), 399-402.

[2]. Babu, T. G., & Babu, G. A. (2016). A survey on data science technologies & big data analytics. International Journal of Advanced Research in Computer Science and Software Engineering, 6(2), 322-327.

[3]. Beaujean, A. A. (2013). Factor analysis using R. Practical Assessment, Research & Evaluation, 18(4), 1-11.

[4]. Dancik, G. (2018). Automated grading of templatebased R programming assignments using swirl-tbp. Journal of Computing Sciences in Colleges, 33(6), 179- 180.

[5]. Henao, J. D. V., & Bedoya, J. W. B. (2012). Examples in the classroom: Pattern classification using the R language. Dyna, 79(173), 81-88.

[6]. Huang, R., Xu, W., Liverani, S., Hiltbrand, D., & Stapleton, A. E. (2018, April). A case study of r performance analysis and optimization. In Proceedings of the Practice and Experience on Advanced Research Computing (p. 33). ACM.

[7]. Jatain, A., & Ranja, A. (2017). A review study on big data anaylsis using R Studio. International Journal of Computer Science and Mobile Computing, 6(6), 8-13.

[8]. Kelley, K., Lai, K., & Wu, P. J. (2008). 34 using R for Data Analysis -a Best Practice for Research (pp. 535-572). Retrieved from http://citeseerx.ist.psu.edu/viewdoc/ summary?doi=10.1.1.163.6787

[9]. Kolaczyk, E. D., & Csárdi, G. (2014). Statistical Analysis of Network Data with R (Vol. 65). New York: Springer.

[10]. Matloff, N. (2011). The Art of R programming: A Tour of Statistical Software Design. No Starch Press.

[11]. Pandey, R. (2015, December). Elective Recommendation Support through K-Means Clustering using R-Tool. In 2015 International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 851-856). IEEE.

[12]. Ramalakshmi, E., & Kompala, N. (2017, February). Multi-threading image processing in single-core and multi-core CPU using R language. In 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1-5). IEEE.

[13]. Shinde, P. P., Oza, K. S., & Kamat, R. K. (2017, February). Big data predictive analysis: Using R analytical tool. In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (pp. 839- 842). IEEE.

[14]. Siddiqui, T., Alkadri, M., & Khan, N. A. (2017). Review of programming languages and tools for Big Data Analytics. International Journal of Advanced Research in Computer Science, 8(5), 1112-1118.

[15]. Xu, W., Huang, R., Zhang, H., El-Khamra, Y., & Walling, D. (2016). Empowering R with high performance computing resources for big data analytics. In Conquering Big Data with High Performance Computing (pp. 191-217). Springer, Cham.

Full Article (HTML)

Pdf

Research Paper

Multi-defense Framework for Mitigating Man in the Cloud Attack (MitC)

Prabakeran Saravanan* , K. Swarnapriya , Remina Agnes Priscilla *

*-***Department of Computer Science & Engineering, K. C. G College of Technology, Chennai, Tamilnadu, India.

Saravanan, P., Swarnapriya, K., Priscilla, R. A.(2019). Multi-defense Framework for Mitigating Man in the Cloud Attack (MitC), i-manager's Journal on Computer Science, 7(2), 8-18. https://doi.org/10.26634/jcom.7.2.15674

Abstract

Cloud computing is the technology of forming a network of remote servers hosted on the Internet to manage process and store data. This technology has its own drawback from the security point of view. This research work aims to address the most recent attack called the man in the cloud attack and the possible solution to overcome it. The attack is tried to be defended at multilevel, so that we can protect our system to the at most level. The first level is to notify the user by detecting the phishing sites, through which the malware is sent into the user's system. At the second level, the user's token id is encrypted, so that the switching of credentials can be avoided.

References

[1]. Chennam, K. K., Muddana, L., & Aluvalu, R. K. (2017, May). Performance analysis of various encryption algorithms for usage in multistage encryption for securing data in cloud. In 2017 2^nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2030-2033). IEEE.

[2]. Dataaspirant. (2017). The Random Forest Algorithm works in machine learning. Retrieved from http://dataaspirant.com/2017/05/22/random-forestalgorithm- machine-learing/

[3]. Github. (n.d). frankosterfeld/qtkeychain: Platform-independent QtAPI for storing passwords securely. Retrieved from https://github.com/frankoster feld/ qtkeychain

[4]. Guleria, S., & Vatta, D. S. (2013). To enhance multimedia security in cloud computing environment using crossbreed algorithm. International Journal of Application or Innovation in Engineering and Management (IJAIEM), 2(6), 562-568.

[5]. Imperva, (n.d). Man in the Cloud (MITC) Attack. Retrieved from https://www.imperva.com/docs/HII_Man_ In_The_Cloud_Attacks.pdf

[6]. Islam, M., & Chowdhury, N. K. (2016). Phishing websites detection using machine learning based classification techniques. In 1^stInternational Conference on Advanced Information and Communication Technology.

[7]. James, J., Sandhya, L., & Thomas, C. (2013, December). Detection of phishing URLs using machine learning techniques. In 2013 International Conference on Control Communication and Computing (ICCC) (pp. 304-309). IEEE.

[8]. Kao, C. H., Dai, J. H., Ko, R., Kuang, Y. T., Lai, C. P., & Mao, C. H. (2016, December). MITC Viz: Visual Analytics for Man-in-the-Cloud Threats Awareness. In 2016 International Computer Symposium (ICS) (pp. 306-311). IEEE.

[9]. Khan, N., & Al-Yasiri, A. (2016). Identifying cloud security threats to strengthen cloud computing adoption framework. Procedia Computer Science, 94, 485-490.

[10]. Liang, X., Shetty, S., Zhang, L., Kamhoua, C., & Kwiat, K. (2017, June). Man In The Cloud (MITC) defender: Sgxbased user credential protection for synchronization applications in cloud computing platform. In 2017 IEEE 10^th International Conference on Cloud Computing (CLOUD) (pp. 302-309). IEEE.

[11]. OneConnect. (2017). Prevention of Man in the Cloud (MITC) Attack. Retrieved from https://www.one connectinc.com/how-to-prevent-man-in-the-cloudattacks/

[12]. OwnCloud (2019). The last cloud collaboration platform you'll ever need. Retrieved from https://own cloud.org/

[13]. Ramachandran, M. (2016). Software security requirements management as an emerging cloud computing service. International Journal of Information Management, 36(4), 580-590.

[14]. Saravanan, P., Sethukarasi, T., & Indumathi, V. (2018a). An efficient software defined network based cooperative scheme for mitigation of Distributed Denial of Service (DDoS) Attacks. Journal of Computational and Theoretical Nanoscience, 15(6-7), 2221-2226.

[15]. Saravanan, P., Sethukarasi, T., & Indumathi, V. (2018b). authentic novel trust propagation model with deceptive recommendation penalty scheme for distributed Denial of Service attacks. Journal of Computational and Theoretical Nanoscience, 15(6-7), 2383-2389.

[16]. Saravanan, P., Sethukarasi, T., & Indumathi, V. (2018c). Two level trust propagation model with global weighted index for detecting and mitigating denial of service. TAGA Journal of Graphic Technology, 14, 2491- 2504.

[17]. Saravanan, P., & Sethukarasi, T. (2019). Optimal hop selection based novel trust based DDoS attack removal framework for reliable and secured transmission of data in VANETs. Wireless Personal Communications, 1-29.

[18]. Seahorse. (2005). A gnome encryption interface. Retrieved from https://wiki.gnome.org/Apps/Seahorse

[19]. Shahzad, F. (2014). State-of-the-art survey on cloud computing security challenges, approaches and solutions. Procedia Computer Science, 37, 357-362.

[20]. Singh, A. P., & Pasupuleti, S. K. (2016). Optimized public auditing and data dynamics for data storage security in cloud computing. Procedia Computer Science, 93, 751-759.

[21]. Suguna, M., Anusia, R., Shalinie, S. M., & Deepti, S. (2017, March). Secure identity management in mobile cloud computing. In 2017 International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2) (pp. 42-45). IEEE.

[22]. UCI. (2007). Machine Learning Repository. Retrieved from http://archive.ics.uci.edu/ml/index.php

[23]. Wang, Q., Wang, C., Ren, K., Lou, W., & Li, J. (2011). Enabling public auditability and data dynamics for storage security in cloud computing. IEEE Transactions on Parallel and Distributed Systems, 22(5), 847-859.

[24]. Zimba, A., Hongsong, C., & Zhaoshun, W. (2016, October). Attack tree analysis of Man in the Cloud attacks on client device synchronization in cloud computing. In 2016 2^ndIEEE International Conference on Computer and Communications (ICCC) (pp. 2702-2706). IEEE.

Full Article (HTML)

Pdf

Research Paper

Security Improvement for Realistic Data Using International Data Encryption Cryptographic Algorithm

Anuraj Malav*

Department of R & D, University of Petroleum and Energy Studies (UPES), Dehradun, India.

Malav, A.(2019). Security Improvement for Realistic Data Using International Data Encryption Cryptographic Algorithm, i-manager's Journal on Computer Science, 7(2), 19-25. https://doi.org/10.26634/jcom.7.2.15465

Abstract

Now-a-days as the trend is to go online in every field like E-commerce, Banking, security is one of the main concerns. Some of this information additionally incorporates delicate data such as personal information or top secret government documents. So it is very important to protect personal data and information. Only DES or IDEA may be attacked by different sorts of cryptanalysis utilizing parallel procedure. In this paper, the author implements a symmetric key algorithm combining with two symmetric key algorithms, i.e. IDEA and 3DES with some modifications in order to improve the security and make it more complex for the attackers to break this algorithm. Using the combinations of these keys, we get 2^ (56+128) =2^184 that is 3.32 times stronger than conventional DES and 1.44 times stronger than conventional IDEA algorithm. The recommendation of S-IDEA algorithm is that it likewise be actualized in equipment utilizing VLSI innovation.

References

[1]. Bhalla, J. S., & Nagrath, P. (2013). Nested digital image watermarking technique using blowfish encryption algorithm. International Journal of Scientific and Research Publications, 3(4), 1-6.

[2]. Biham, E., & Shamir, A., (1992). Differential Cryptanalysis of the Full 16-Round DES. In Annual International Cryptology Conference (pp. 487-496). (Vol. 740). Springer, Berlin, Heidelberg.

[3]. Chen, J., Xue, D., & Lai, X. (2008). An analysis of International Data Encryption Algorithm (IDEA) security against differential cryptanalysis. Wuhan University Journal of Natural Sciences, 13(6), 697-701.

[4]. Cheung, O. Y., Tsoi, K. H., Leong, P. H. W., & Leong, M. P. (2001, May). Tradeoffs in parallel and serial implementations of the International Data Encryption Algorithm (IDEA). In International Workshop on Cryptographic Hardware and Embedded Systems (pp. 333-347). Springer, Berlin, Heidelberg.

[5]. Clavier, C., Gierlichs, B., & Verbauwhede, I. (2008, April). Fault analysis study of IDEA. In Cryptographers' Track at the RSA Conference (pp. 274-287). Springer, Berlin, Heidelberg.

[6]. Curiger, A., Bonnenberg, H., Zimmermann, R., Felber, N., Kaeslin, H., & Fichtner, W. (1993, May). VINCI: VLSI implementation of the new secret-key block cipher IDEA. In Proceedings of IEEE Custom Integrated Circuits Conference-CICC'93, IEEE. 9-12.

[7]. Demirci, H., Selçuk, A. A., & Türe, E. (2003, August). A new meet-in-the-middle attack on the IDEA block cipher. In International Workshop on Selected Areas in Cryptography (pp. 117-129). Springer, Berlin, Heidelberg.

[8]. Forouzan, B. A. (2008). Network Security. McGraw Hill (international edition).

[9]. Hellman, M. E. (1979). IDES will be totally insecure within ten years. IEEE Spectrum, 16(7), 32-40.

[10]. Nakahara, J., Preneel, B., & Vandewalle, J. (2003, October). A note on weak keys of PES, IDEA, and some extended variants. In International Conference on Information Security (pp. 267-279). Springer, Berlin, Heidelberg.

[11]. Nakahara, J., Preneel, B., & Vandewalle, J. (2004, July). The Biryukov-Demirci attack on reduced-round versions of IDEA and MESH ciphers. In Australasian Conference on Information Security and Privacy (pp. 98- 109). Springer, Berlin, Heidelberg.

[12]. NBS. (1977). Data Encryption Standard. Washington DC: FIPS.

[13]. NIST. (2012). 800-67 Revision 1, Recommendation for the Triple Data Encryption Algorithm (TDEA) Block Cipher. National Institute of Standards and Technology.

[14]. Yang, W. Z., Li, T., & Hao, L. (2004). Expanding key size of IDEA. Computer Engineering and Design, 25(11), 1903- 1904.

[15]. Yide, M. (2005). Some improvements on international data encryption algorithm in developing system. Computer Engineering and Application, 41(7), pp. 114-115.

Full Article (HTML)

Pdf

Research Paper

Exploration of Heed Clustering Algorithm for Performance Improvement in Heterogenous WSNs

Nikita Gandotra*

Department of Computer Science & IT, University of Jammu, Jammu and Kashmir, India.

Gandotra, N.(2019). Exploration of Heed Clustering Algorithm for Performance Improvement in Heterogenous WSNs, i-manager's Journal on Computer Science, 7(2), 26-35. https://doi.org/10.26634/jcom.7.2.16142

Abstract

Wireless Sensor Networks are extensively used for monitoring in difficult terrains since these could be easily deployed due to their small size and their ability to work on their own without additional equipment as they communicate in adhoc manner. Each node consists of an individual power source in the form of a battery and remains active in the network till its energy is exhausted. To extend the network lifetime and optimising the use of restricted power supply, clustering algorithms are widely used to group neighbouring nodes and work in small clusters imitating the behaviour of the actual network. Hybrid Energy Efficient Distributed Clustering (HEED) algorithm was proposed to address the limited power supply and the network lifetime in WSNs. HEED selects cluster heads periodically according to their residual energy and node degree. This paper suggests a few improvements in the original HEED algorithm and a new model has been proposed based on these improvements. The algorithms are analysed with both homogeneous and heterogeneous node batteries and it was found that the proposed model improves the average energy of each node and extends the network lifetime.

References

[1]. Aierken, N., Gagliardi, R., Mostarda, L., & Ullah, Z. (2015 March). RUHEED- Rotated Unequal Clustering Algorithm for Wireless Sensor Networks. 2015 IEEE 29^th International Conference on Advanced Information Networking and Applications Workshops (WAINA) (pp. 170-174). doi:10.1109/WAINA.2015.86

[2]. Al-Baz, A., & El-Sayed, A. (2018). A new algorithm for cluster head selection in LEACH protocol for Wireless Sensor Networks. International Journal of Communication System. 31(1), e3407. https://doi.org/10.1002/dac. 3407

[3]. Amis, A. D., Prakash, R., Vuong, T. H., & Huynh, D. T. (2000). Max-min d-cluster formation in wireless ad hoc networks. In Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No. 00CH37064) (Vol. 1, pp. 32-41). IEEE.

[4]. Baker, D., & Ephremides, A. (1981). The architectural organization of a mobile radio network via a distributed algorithm. IEEE Transactions on Communications, 29(11), 1694-1701.

[5]. Chatterjee, M., Das, S., & Turgut, D. (2002). WCA: A weighted clustering algorithm for mobile ad hoc networks. Clustering Computing, 5(2), 193-204.

[6]. Culler-Mayeno, E. (2006). A Technical Report: Wireless Sensor Networks and how they work. IEEE Communications Magazine, 40(8), 1-9.

[7]. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2002). An application specific protocol architecture for wireless micro sensor networks. IEEE Transactions on Wireless Communications, 1(4), 660-670.

[8]. Jadidoleslamy, H. (2013, February). An introduction to various basic concepts of clustering techniques on Wireless Sensor Networks. International Journal of Mobile Network Communications & Telematics (IJMNCT), 3(1), 1- 17. doi:10.5121/ijmnct.2013.3101

[9]. Kour, H., & Sharma, A. K. (July 2010). Hybrid Energy Efficient Distributed Protocol for Heterogeneous Wireless Sensor Network. International Journal of Computer Applications, 4(6), 1-5.

[10]. Li, C., Ye, M., Chen, G., & Wu, J. (2005). An Energy- Efficient Unequal Clustering Mechanism for Wireless Sensor Networks. Proceedings of the 2^nd IEEE International Conference on Mobile Ad-hoc and Sensor Systems Conference (MASS) (pp. 596-604). Washington, DC.

[11]. Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power-Efficient GAthering in Sensor Information Systems. IEEE Aerospace Conference Proceeding (Vol. 3, pp. 1125-1130).

[12]. Loscri, V., Morabito, G., & Marano, S. (2005). A two-level hierarchy for low-energy adaptive clustering hierarchy. Proceedings of IEEE VTC Conference, 62(3), pp. 1809-1813.

[13]. Mamalis, B., Gavalas, D., Konstantopoulos, C., & Pantziou, G. (2009). Clustering in Wireless Sensor Networks. In Zhang /RFID and Sensor Networks (p. 323). Retrieved from http://www.syros.aegean.gr/users/dgavalas/en/ iframe_files/papers/2009/Clustering-chapter.pdf

[14]. Nack, F. (2010). An overview on Wireless Sensor Network. 1-8. Institute of Computer Science (ICS), University, Berlin. Retrieved from https://www.mi.fu-b erlin.de/inf/groups/ag-tech/teaching/2008- 09_WS/S_19565_Proseminar_Technische_Informatik/nac k09verview.pdf

[15]. Reddy, G. K., & Devi, L. N. (2018, February). Review on Clustering Protocols with Energy heterogeneity in Wireless Sensor Networks. In 2018 International Conference on Communication, Computing and Internet of Things (IC3IoT) (pp. 243-246). IEEE.

[16]. Selvi, G. V., & Manoharan, R. (2014). Balanced unequal clustering algorithm for wireless sensor networks. International Journal of Communication and Networking System, 3(2), 49-53. https://doi.org/10.20894/ IJCNES.103.003.002. 001.

[17]. Sharma, S., & Mittal, D. P. (2013, January). Wireless Sensor Networks: Architecture, Protocols. International Journal of Advanced Research in Computer Science and Software Engineering, 3(1). 303-308.

[18]. Usha, M., & Sankarram, N. (2014). A survey on energy efficient hierarchical (LEACH) clustering algorithms in Wireless Sensor Network. In International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), Proceedings of International Conference On Global Innovations In Computing Technology (ICGICT'14) (Vol. 2, No. 1, pp. 601-609).

[19]. Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for Ad Hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366-379.

Full Article (HTML)

Pdf

Research Paper

Language Translation: Enhancing Bi-Lingual Machine Translation Approach Using Python

Manish* , Mohammad Atique**

*-**Department of Computer Science & Engineering, SantGadge Baba Amravati University, Amravati, India.

Rana, M., Atique, M.(2019). Language Translation: Enhancing Bi-Lingual Machine Translation Approach Using Python, i-manager's Journal on Computer Science, 7(2), 36-42. https://doi.org/10.26634/jcom.7.2.15597

Abstract

This paper shows the improvement in the work carried in Machine Translation as compared to the other techniques used. The work is the enhancement of “Enhancing Bi-Lingual Machine Translation Approach”. It shows the development in a Language Translation using Python, which consist of predefined packages like TextBlob and Google-API. The paper talks about enhancing Bilingual Machine Translation. English language into Indian Languages like Hindi, Marathi, Gujarati, Sindhi, Punjabi, Bengali, Urdu, and Dravidian languages. The work shown in the paper is implemented using a speech_recognition module, where speech input is taken from user so as to translate into any Indian language. After comparison with various techniques and research, the paper shows efficient result up to 94% accuracy.

References

[1]. Aasha, V. C., & Ganesh, A. (2015, August). Machine translation from English to Malayalam using transfer approach. In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1565-1570). IEEE.

[2]. Abiola, O. B., Adetunmbi, A. O., & Oguntimilehin, A. (2015). A review of the various approaches for text to text machine translations. International Journal of Computer Applications, 120(18), 7-12.

[3]. Ahmed, S. Rahman, M. O. Pir, S. R. Mottalib, M. A., & Islam, M. S. (2003). A new approach towards the development of English to Bengali machine translation system. International Conference on Computer Information and Technology (ICCIT), 360-364.

[4]. Anju, E. S., & Kumar, K. V. M. (2014). Malayalam to English Machine Translation: An EBMT system. IOSR Journal of Engineering (IOSRJEN), 4(1), 18-23.

[5]. Ayala, B. R., & Chen, J. (2017, June). A machine learning approach to evaluating translation quality. In Proceedings of the 17^thACM/IEEE Joint Conference on Digital Libraries (pp. 281-282). IEEE Press.

[6]. Brants, T., Popat, A. C., Xu, P., Och, F. J., & Dean, J. (2007, June). Large language models in machine translation. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (pp. 858-867).

[7]. Chen, J. (2016). Multilingual Access and Services for Digital Collections. ABC-CLIO.

[8]. Chen, K., Zhao, T., Yang, M., & Liu, L. (2017, February). Translation prediction with source dependency-based context representation. In Thirty-First AAAI Conference on Artificial Intelligence.

[9]. Chen, K., Zhao, T., Yang, M., Liu, L., Tamura, A., Wang, R., ... & Sumita, E. (2018). A neural approach to source dependence based context model for statistical machine translation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(2), 266-280.

[10]. Corston-Oliver, S., Gamon, M., & Brockett, C. (2001, July). A machine learning approach to the automatic evaluation of machine translation. In Proceedings of the 39^th Annual Meeting on Association for Computational Linguistics (pp. 148-155). Association for Computational Linguistics.

[11]. Dasgupta, S., Wasif, A., & Azam, S. (2004). An optimal way of machine translation from English to Bengali. In ICCIT.

[12]. Dhariya, O., Malviya, S., & Tiwary, U. S. (2017, January). A hybrid approach for Hindi-English machine translation. In 2017 International Conference on Information Networking (ICOIN) (pp. 389-394). IEEE.

[13]. Ethnologue. (2016). Most Widely Spoken Languages in the World. Retrieved from http//www.infoplease.com/ ipa/A0775272.html

[14]. Haroon, R. P., & Shaharban, T. A. (2016, March). Malayalam machine translation using hybrid approach. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (pp. 1013-1017). IEEE.

[15]. Heafield, K., Pouzyrevsky, I., Clark, J. H., & Koehn, P. (2013). Scalable modified Kneser-Ney language model estimation. In Proceedings of the 51^st Annual Meeting of the Association for Computational Linguistics (Vol. 2, pp. 690-696).

[16]. Hu, X. (2016, August). Statistical Machine Translation Approach based on Dependency-to-String Model. In 2016 International Conference on Smart Grid and Electrical Automation (ICSGEA) (pp. 241-244). IEEE.

[17]. Kulkarni, A. P. (2003). Design and Architecture of 'Anusaaraka'-An Approach to Machine Translation. Satyam Techical Review, 3, 57-64.

[18]. Muntarina, K., Moazzam, G. M., & Bhuiyan, A. A. (2013). Tense based english to Bangla translation using MT system. International Journal of Engineering Science Invention, 2(10), 30-38.

[19]. Nahar, S., Huda, M. N., Nur-E-Arefin, M., & Rahman, M. M. (2017, December). Evaluation of machine translation approaches to translate English to Bengali. In 2017 20^th International Conference of Computer and Information Technology (ICCIT) (pp. 1-5). IEEE.

[20]. Nair, L. R., Peter, D., & Ravindran, R. P. (2012). Design and development of a Malayalam to English translator-a transfer based approach. International Journal of Computational Linguistics (IJCL), 3(1), 1-11.

[21]. Owczarzak, K., van Genabith, J., & Way, A. (2007). Evaluating machine translation with LFG dependencies. Machine Translation, 21(2), 95-119.

[22]. Pal, S., Pakray, P., & Naskar, S. K. (2014). Automatic building and using parallel resources for SMT from rd comparable corpora. In Proceedings of the 3 Workshop on Hybrid Approaches to Machine Translation (HyTra) (pp. 48-57).

[23]. Pareek, P. K., Swathi, K., & Shetteppanavar, P. (2017, May). An efficient machine translation model for Dravidian language. In 2017 2^nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2101-2105). IEEE.

[24]. Quirk, C., & Menezes, A. (2006). Dependency treelet translation: the convergence of statistical and example- based machine-translation? Machine Translation, 20(1), 43-65.

[25]. Rahman, S. A., Mahmud, K. S., Roy, B., & Hasan, K. M. A. (2003). English to Bengali Translation using a new Natural Language Processing algorithm. In International Conference on Computer Information and Technology (ICCIT) (pp. 294-298).

[26]. Rana, M. (2015). A Review: Machine Translation Using Various Soft-Computing Tools. Multiconw-2015 Proceedings.

[27]. Rana, M., & Atique, M. (2015a). Example based machine translation using fuzzy logic from English to Hindi. In Proceedings on the International Conference on Artificial Intelligence (ICAI) (p. 354). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).

[28]. Rana, M., & Atique, M. (2015b). A Review: Machine Translation using Various Soft-Computing Tools: International Conference on Communication Computing & Virtualization (Vol. 2, pp. 813-816).

[29]. Rana, M., & Atique, M. (2015c). Example based Machine Translation using Various Soft Computing Techniques Review. International Journal of Scientific and Engineering Research, 6(4), 1100-1107.

[30]. Rana, M., & Atique, M. (2016a). Enhancing Bi- Lingual Example based Machine Translation Approach. International Journal of Advanced Engineering Research and Science, 3(10), 8-12.

[31]. Rana, M., & Atique, M. (2016b). Use of Fuzzy tool for example based machine translation. Procedia Computer Science, 79, 199-206.

[32]. Remya, L. B. et al. (2012). Analysis of Interlingual Translation Issues for Building Parallel Corpora. National Seminar on Relevance of Malayalam.

[33]. Ren, F., & Shi, H. (2001). Parallel machine translation: Principles and practice. In Proceedings Seventh IEEE International Conference on Engineering of Complex Computer Systems (pp. 249-259). IEEE.

[34]. Shen, L., Xu, J., & Weischedel, R. (2008, June). A new string-to-dependency machine translation algorithm with a target dependency language model. In Proceedings of ACL-08: HLT (pp. 577-585).

[35]. Sindhu, D. V., & Sagar, B. M. (2016, December). Study on machine translation approaches for Indian languages and their challenges. In 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT) (pp. 262-267). IEEE.

[36]. Sinha, R. M. K., Sivaraman, K., Agrawal, A., Jain, R., Srivastava, R., & Jain, A. (1995, October). ANGLABHARTI: A multilingual machine aided translation project on translation from English to Indian languages. In 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21^stCentury (Vol. 2, pp. 1609-1614). IEEE.

[37]. Tiwary, U. S., & Siddiqui, T. (2008). Natural Language Processing and Information Retrieval. Oxford University Press, Inc.

Full Article (HTML)

Pdf

Research Paper

Indian Sign Language Recognition System using Openpose

Pooja Malhotra* , Chirag K. Maniar, Nikhil V. Sankpal*, Hardik R. Thakkar****

*-**** Department of Information Technology, K.J. Somaiya College of Engineering, Mumbai University, Mumbai, Maharashtra, India.

Malhotra, P., Maniar, C. K., Sankpal, N. V., Thakkar, H. R. (2019). Indian Sign Language Recognition System using Openpose, i-manager's Journal on Computer Science, 7(2), 43-50. https://doi.org/10.26634/jcom.7.2.15993

Abstract

Human beings communicate through language, be it verbal or be it a sign language that makes use of body motion. Hearing and Speech impaired people, having no way to communicate verbally, make use of Sign Language. They perform gestures using a sign language in order to convey their message and effectively communicate with each other. Since, not everyone knows about Indian Sign Language (ISL), it becomes difficult for normal people to fluently communicate with Hearing and Speech impaired community. This paper proposes ISL gesture recognition system in order to decrease this communication gap. The dataset consists of videos of ISL gestures, which are performed by different Subjects. The proposed system uses OpenPose library, which helps in creating the skeleton of human body and thus it provides keypoints of the whole human body frame by frame. The use of this library removes the dependency on lighting conditions and background. It helps in focusing on just the gesture movements. After extracting the keypoints, Long Short Term Memory (LSTM) is used for classification of gestures. LSTM model classifies which ISL gesture the particular video belongs to.

References

[1]. Caffe. (n.d). Deep learning framework by BAIR. Retrieved from https://caffe.berkeleyvision.org/

[2]. Cao, Z., Hidalgo, G., Simon, T., Wei, S. E., & Sheikh, Y. (2018). OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields. arXiv preprint arXiv:1812.08008.

[3]. Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7291- 7299).

[4]. Gupta, B., Shukla, P., & Mittal, A. (2016, January). K-nearest correlated neighbor classification for Indian Sign Language gesture recognition using feature fusion. In 2016 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-5). IEEE. doi: 10.1109/IC-CCI.2016.7479951

[5]. Huang, J., Zhou, W., Li, H., & Li, W. (2015, July). Sign language recognition using real-sense. In 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP) (pp. 166-170). IEEE. doi: 10.1109/ChinaSIP 2015.7230384

[6]. Kumar, S., & Kaurav, A. (2018, January). Hand gesture through geometric moments (HCI based). In 2018 2^nd International Conference on Inventive Systems and Control (ICISC) (pp. 561-565). IEEE. doi: 10.1109/ICISC.2018.8398862.

[7]. OpenPose (2019). Real-time multi-person keypoint detection library for body, face, hands, and foot estimation. Retrieved from https://github.com/CMUPerceptual- Computing-Lab/openpose

[8]. Simon, T., Joo, H., Matthews, I., & Sheikh, Y. (2017). Hand keypoint detection in single images using multiview bootstrapping. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1145- 1153).

[9]. Singh, A. K., John, B. P., Subramanian, S. V., Kumar, A. S., & Nair, B. B. (2016, December). A low-cost wearable Indian sign language interpretation system. In 2016 International Conference on Robotics and Automation for Humanitarian Applications (RAHA) (pp. 1-6). IEEE. doi: 10.1109/RAHA.2016.7931873.

[10]. Vishwakarma, D. K., & Ansari, S. (2017, November). A framework for human-computer interaction using dynamic time warping and neural network. In 2017 International Conference on Inventive Computing and Informatics (ICICI) (pp. 242-246). IEEE. doi : 10.1109/ICICI.2017.8365346.

[11]. Wei, S. E., Ramakrishna, V., Kanade, T., & Sheikh, Y. (2016). Convolutional pose machines. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4724-4732).

i-manager's Journal on Computer Science (JCOM)

Current Issue Vol. 12 Issue 3

Most Read

Most Cited

Volume 7 Issue 2 June - August 2019

Significance of R in Research

G. Sateesh* , B. Padmaja**, M. V. Bhuvaneswari***

*-*** Department of Computer Science Engineering, Lendi College of Engineering, Andhra Pradesh, India.

Satheesh, G., Padmaja, B., Bhuvaneswari, M. V.(2019). Significance of R in Research, i-manager's Journal on Computer Science, 7(2), 1-7. https://doi.org/10.26634/jcom.7.2.15639

Abstract

References

Full Article (HTML)

Pdf

Multi-defense Framework for Mitigating Man in the Cloud Attack (MitC)

Prabakeran Saravanan* , K. Swarnapriya **, Remina Agnes Priscilla ***

*-***Department of Computer Science & Engineering, K. C. G College of Technology, Chennai, Tamilnadu, India.

Saravanan, P., Swarnapriya, K., Priscilla, R. A.(2019). Multi-defense Framework for Mitigating Man in the Cloud Attack (MitC), i-manager's Journal on Computer Science, 7(2), 8-18. https://doi.org/10.26634/jcom.7.2.15674

Abstract

References

Full Article (HTML)

Pdf

Security Improvement for Realistic Data Using International Data Encryption Cryptographic Algorithm

Anuraj Malav*

Department of R & D, University of Petroleum and Energy Studies (UPES), Dehradun, India.

Malav, A.(2019). Security Improvement for Realistic Data Using International Data Encryption Cryptographic Algorithm, i-manager's Journal on Computer Science, 7(2), 19-25. https://doi.org/10.26634/jcom.7.2.15465

Abstract

References

Full Article (HTML)

Pdf

Exploration of Heed Clustering Algorithm for Performance Improvement in Heterogenous WSNs

Nikita Gandotra*

Department of Computer Science & IT, University of Jammu, Jammu and Kashmir, India.

Gandotra, N.(2019). Exploration of Heed Clustering Algorithm for Performance Improvement in Heterogenous WSNs, i-manager's Journal on Computer Science, 7(2), 26-35. https://doi.org/10.26634/jcom.7.2.16142

Abstract

References

Full Article (HTML)

Pdf

Language Translation: Enhancing Bi-Lingual Machine Translation Approach Using Python

Manish* , Mohammad Atique**

*-**Department of Computer Science & Engineering, SantGadge Baba Amravati University, Amravati, India.

Rana, M., Atique, M.(2019). Language Translation: Enhancing Bi-Lingual Machine Translation Approach Using Python, i-manager's Journal on Computer Science, 7(2), 36-42. https://doi.org/10.26634/jcom.7.2.15597

Abstract

References

Full Article (HTML)

Pdf

Indian Sign Language Recognition System using Openpose

Pooja Malhotra* , Chirag K. Maniar**, Nikhil V. Sankpal***, Hardik R. Thakkar****

*-**** Department of Information Technology, K.J. Somaiya College of Engineering, Mumbai University, Mumbai, Maharashtra, India.

Malhotra, P., Maniar, C. K., Sankpal, N. V., Thakkar, H. R. (2019). Indian Sign Language Recognition System using Openpose, i-manager's Journal on Computer Science, 7(2), 43-50. https://doi.org/10.26634/jcom.7.2.15993

Abstract

References

Full Article (HTML)

Pdf

G. Sateesh* , B. Padmaja, M. V. Bhuvaneswari*

Prabakeran Saravanan* , K. Swarnapriya , Remina Agnes Priscilla *

Pooja Malhotra* , Chirag K. Maniar, Nikhil V. Sankpal*, Hardik R. Thakkar****