Volume-07 , Special Issue-01 , Jan - 2019 Go Back

Computers have become an indispensible part of our life. Advancements in the field of technology have led to smaller and more compact computers, while their processing power has increased. However, with parts getting smaller and more compact, quantum physics are making things a bit problematic. Transistors act like switches, either allowing electrons to pass through or blocking their passage. Transistors are already almost as small as atoms. Electrons will eventually be able to transfer themselves to the other side of the blocked passage via a process called quantum tunneling, making transistors useless. Classical computers and the way it works stop making sense in the quantum realm. A possible solution to this problem could be quantum computers, which takes advantage of quantum principles to work. In this paper, an overview of quantum computers, their working principles and the basics of the math behind such computers have been discussed. The future scope of such a computer, problems associated with implementing it as well as the problems to be dealt with in case a scalable quantum computer is built is also provided.

Quantum Computer, Qubit, Quantum Gates, Decoherence, CNOT, Hadamard Gate, Pauli Gates, Phase Gates, SWAP Gate, Entanglement, Quantum Algorithm, Quantum Fourier Transform

[1] What is quantum computing?- IBM Q
[2] Quantum Computers Explained- Limits of Human Technology- Kurzgesagt- In a Nutshell
[3] A Beginner’s Guide to Quantum Computing- IBM Research
[4] Quantum Computing for Computer Scientists- Microsoft Research
[5] Building the Bits and Qubits- Frame of Essence
[6] Quantum Logic Gate- Wikipedia
[7] Demystifying Quantum Gates- One Qubit At A Time- Towards Data Science
[8] How Does a Quantum Computer Work?- Veritasium
[9] Siddhartha Kasivajhula- Quantum Computing- A Survey
[10] Michael A. Nielsen & Isaac L. Chuang-Quantum Computation and Quantum Information
[11] Quantum Supremacy- Wikipedia
[12] Microsoft- Quantum Gates and Circuits: The Crash Course

Megha Roy, Soumen Santra, Sarasij Majumdar, "Quantum Computers", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.57-62, 2019.

In the present era of Internet, all sorts of digital media (text, audio, video and multimedia) is freely and easily available to everyone over Internet. Anyone can easily access those digital media without taking any sorts of permissions from the owner of the digital media, distribute the digital media freely to others and claim the ownership of the digital media and sometimes make unnecessary changes to the digital media itself. So, the need of the time is to protect the digital media from unauthorized access, restrict free distribution of digital media to others and also to establish the ownership of the digital media in an easy, consistent and efficient manner. In this paper, several approaches using digital watermarking method are proposed, which embeds ownership information and copy protection mechanism within the digital media, to provide a powerful mechanism for protecting digital media in efficient and convenient manner.

Digital Media, Digital Watermarking, Security, Privacy, Ownership, Authentication, Protection

[1] Dhaked D., Yadav S., Mathuria M., Agrawal S., “User Identification over Digital Social Network using Fingerprint Authentication”, Emerging Trends in Expert Applications and Security, pp 11-22, Advances in Intelligent Systems and Computing, Vol 841, Springer, Singapore (978-981-13-2284-6).
[2] F. Lynda-de-jason, Nitya M., “A Protocol for preventing insider attacks in untrusted infrastructure-as-a-service clouds”, International Journal for Research in Science Engineering and Technology (2394 739x), pp 1-6.
[3] J. C. Ingemar, L. M. Matthew, A. B. Jeffrey, J. Fridrich, T. Kalker, “Digital Watermarking and Steganography (2nd edition)”, Morgan Kaufmann Publishers, USA, 2008.
[4] Rathi V.O., Patil P.S., “Invertible message Hiding using Histogram Modification by LabView: A New Approach”, International Journal of Applied Research, Vol 7, Issue 1, Jan 2017 (2249 555X).
[5] Indoviya A., Sharma O.P., “A chronological Review in Digital Watermarking”, International Journal of Applied Engineering Research (0973 4562), Vol 13, No. 21 (2018), pp 15383-15385.
[6] B. Schneier, “Applied Cryptography”, John Wiley & Sons, New York, USA, 2nd ed., 1996.
[7] Lin X., “Image Forgery Detection”, Introductory Computer Forensics, Springer, Cham (978-3-030-00580-1), pp 507-555.
[8] J. Seitz, “Digital Watermarking for Digital Media”, Information Science Publication, Hershey, 2005.
[9] Mondal S.N., “Control of Corruption in India: A Socio-Legal Challenge”, International Journal of Advance Study and Research Work (2581 5997), Vol 1, Issue 5, August 2018.

S. Sarbavidya, "Protection of Digital Media using Digital Watermarking", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.63-66, 2019.

With the advancement of technology and the widespread use of smart devices, the world has witnessed that the networking and/or the connectivity horizon has broadened to an exalted level. One of the prominent researches being undertaken in this digital era is the development of Text-to-Speech (TTS) engines; which is capable enough of offering more interactivity with the prevalent smart devices. There are various TTS engines available in the market currently, but these engines lack the capability of showing the effects of human voice e.g., they fail to provide credible indications of the sentiment, mood or emotional state of mind of the speaker etc. Further speaking, presently there is no comprehensible or consummate TTS engine that could replicate human behaviour and/or mannerisms with utmost precision and accuracy. This paper proposes a novel Text-to-Speech engine named ‘Twee’ whose pronunciation works in sync with real world human intelligence. The proposed system is an application of the interdisciplinary field of research whereby domains such as Natural Language Processing, Artificial Intelligence and Digital Signal Processing are amalgamated to perform sentiment analysis on text through the processing of phonemes. This system works well both in mono channel mode and in stereo mode and is capable of generating varied effects on a voice depending on the type of communication.

Artificial Intelligence, Natural Language Processing, Digital Signal Processing, Phoneme, Emotion

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[2] W.Y. Wang, K. Georgila, “Automatic Detection of Unnatural Word-Level Segments in Unit-Selection Speech Synthesis”, In the Proceedings of the 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, Waikoloa, HI, USA, pp.289-294, 2011.
[3] R.E. Remez, P.E. Rubin, D.B. Pisoni, T.D. Carrell, “Speech Perception without Traditional Speech Cues”, Science, New Series, Vol.212, Issue.4497, pp. 947-950, 1981. doi:10.1126/science.7233191
[4] J. Zhang, “Language Generation and Speech Synthesis in Dialogues for Language Learning”, Massachusetts Institute of Technology, pp.1-68, 2004.
[5] S. Lemmetty, “Review of Speech Synthesis Technology”, Helsinki Universty of Technology, pp.1-113, 1999.
[6] I.G. Mattingly,"Speech synthesis for phonetic and phonological models", Current Trends in Linguistics. Mouton, The Hague, Vol. 12, pp.2451–2487, 1974.
[7] FFmpeg Git, "FFmpeg 4.0 "Wu"", last accessed 2018-07-18.
[8] Takanishi Lab Webpage, "Anthropomorphic Talking Robot Waseda Talker Series", Retrieved from http://www.takanishi.mech.waseda.ac.jp/top/research/voice/index.htm, last accessed 2018-10-10.
[9] Deepmind Webpage, "WaveNet: A Generative Model for Raw Audio”, Retrieved from https://deepmind.com/blog/wavenet-generative-model-raw-audio/, last accessed 2018-09-08.

D. Das, H. Hassan, S. Gupta, "Twee: A Novel Text-To-Speech Engine", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.67-70, 2019.

In digital universe emerging trends of new technologies in different sectors are dramatically generating large amount of varieties [e.g.-structured and unstructured] of data every day. The low cost of memory is the reason to populate massive amount of data sets and it is too much heterogeneous to get a particular information is very troublesome. Day to day gathering of typical data makes a large complex datasets is called Big Data, which is difficult to capture and manage by conventional data processing system. The large complex datasets are stored among thousands of servers and Big Data technologies easily acquire and process those datasets. These technologies used in all sectors with big challenge to get better decision, quality assurance, risk management, etc. Adoption of big data technologies is opportunities to get the hidden pattern by acquired and analyzed a large scale, flexible, fault tolerance, and real time data which are resided in distributed files.

Big data, Hadoop, HPCC, Storm, Blockchain, healthcare, Smart Power Grid, Smart Traffic

[1]7 big data techniques that create business value [www.firemex.com/thedealroom/7-big-data-techniques-that-create-business-value/&grqid=YtOUQFYe&hl=en-IN]
[2] Apache Hadoop[ http://en.wikipedia.org/wiki/Apache-Hadoop]
[3]The Research on Risk Assesment of New product sample Development stage in manufacturing Enterprise [http://link.spinger.com]
[4] The Deciding Factor: Big Data & Decision Making
[5]Innovation and Insights in Big Data. http://bigdata.teradata.com/US/Success-Stories/Innovation-and-Insights/&grqid=dvBseRck& hl=en-IN
[6] Tasleema Nizam and Syed Imtiyaz Hassan. Big Data: A survey paper on Big Data Innovation and its technology
[7]HPCC[www.wikipedia.org/wiki/HPCC&grqid=Odl-mqLJ&hl=en-IN]
[8] A.M Middleton. ECL/HPCC : A united Approach to Big Data. Hand book of Data intensive computing. Springer 2011.
[9]HPCC System : Introduction to HPCC(High Performance Computing Cluster)
[10]Muhammad Hussian Iquabal, Tariq Rahim Soomro. Big Data Analysis: Apache Storm Perspective.
[11]Wullianallur Raghupathi and Viju Raghupathi. An Overview of health analytics
[12]IHTT: Transforming Healthcare through Big Data Strategies for leveraging Big Data in the healthcare industry.
[13]Wullianallur Raghupathi and Viju Raghupathi. Big Data analytics in healthcare: Promise and potential
[14] Peter Groves, Basel Kayyali, David David and Steve Van Kuiken. The Big Data revolution in healthcare
[15]Exploring next Generation Financial Services: The Big Data Revolution
[16] Top 10 Big Data Trends in 2017 for Financial Services.
https://mapr.com/bblog/top-10-Big-data-trends-2017-financial-services/
[17] Lansiti,Marco, Lakhani, Karim R. The truth about Blockchain. Harvard Business Review
[18] GridShaym R, Bharathi Ganash HB, Sachin Kumar, Pabaharan poornachandran, soman KP. Apache Spark a Big Data Analytics platform for Smart
[19] Swartz R. A, Laynch J.P, Zerbst S, Sweetman B, Rolfes R. Structural monitoring of wind turbines using sensor network smart structure and system
[20] Gang Zeng. Application of Big Data in intelligent Traffic System
[21]Smart Traffic Management with Big Data. https://inform.tmforum.org/features-and-analysis/
[22]Chen Haitao. Improving traffic management with Big Data Analytics
[24]Inside BIGDATA guide to scientific Research. http://www.insidebigdata.com/white-paper/
[25] Taylor & Francis. Big Data for scientific research and discovery

Ananda Khamaru, "Big Data Analytics: An Innovative approach to adopt in emerging Sectors", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.71-77, 2019.

The basic purpose of Distance Education (DE) is to provide quality education regardless of time and space for those who do not continue his/her education in traditional mode due to several reasons. In the digital era, DE uses Digital Rights Management (DRM) to promote quality teaching for all. DRM manages and protects any digital creation, in the education domain, it is used to protect and manage Intellectual Property Rights (IPR). There are many distance education system running around the globe and spreading the essence of quality teaching. In our survey we try to focus on DE, DRM, needs of DRM in DE and discuss some pioneer DE systems that use DRM worldwide and also propose a model DRMSDE for Distance Education system using DRM.

Distance Education (DE), Digital Rights Management (DRM), Intellectual Property Rights (IPR)

[1] M. Simonson, S. Smaldino, S. Zuvacek, “Teaching and Learning at a Distance: Foundations of Distance Edcation”, Information Age Publishing Inc. , Charlotte , North Carolina, pp. 1-31.
[2] S. Wang, Q. Liu, “ERDRM: A Digital Rights Management System Model for Educational Resources”, 7th International Conference on Advances in Web Based Learning ICWL-2008 , Jinhua, China, pp 69-78, 2008.
[3] T. K. Shih , C.T. Chang, T. Arndt, J. C. Hung, “A Survey of Distance Education Challenges and Technologies”, International Journal of Distance Education Technologies,Vol-1, Issue-1, pp 1-20,2003.
[4] S. Downes, M. Mourad , H. Piccariello , R. Robson , “Digital Rights Management in E-Learning Problem Statement and Terms of Reference”, Proceedings of E-Learn 2003--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education , Phoenix, Arizona, USA ,pp. 696-699,2003.
[5] A.K. Singh , “DRMSDE: A Digital Right Managment Syatem For Distance Education”, International Journal of Advanced Research in Computer Science, Vol-8, Issue-7, pp. 652-654, 2017.
[6] P . K. Verma , “Why We Need Digital Rights Management”, International journal of Engineering Technology , Management and Applied Sciences , Vol-5, Issue-2 , pp.177-182, 2017
[7] S. Sarkar ,” History and Evaluation of Copyright in India”, Indian journal of research , Vol-5, Issue-11, pp.274-275, 2016
[8] N. Mclean , R. Iannella , “Digital Rights Management (DRM) in Education – The Need for Standardisation”, A Briefing Paper for IMS , Australian IMS Centre ,February 2002.
[9] G. Collier , R Robson ,”Digital Rights Management for Research and Education”, Article , December 2,2004.
[10] K. Coyle , ” The Technology of Rights: Digital Rights Management “, Based on a talk originally given at the Library of Congress, November 19, 2003.
[11] F. V. Assche and D. Massart, "Federation and brokerage of Learning Objects and their metadata". In Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT`04), Joensuu, Finland, pp.316-320. IEEE 2004.
[12] R. McGreal, T. Anderson, G. Babin, S. Downes, N. Friesen, K. Harrigan, M. Hatala, D. MacLeod, M. Mattson, , G. Paquette, G. Richards, , T. Roberts, & S. Schafer, “EduSource: Canada`s learning object repository network”, International Journal of Instructional Technology and Distance Learning, March, Vol-1,Issue-3,2004.
[13] J. Dalziel, “Reflections on the COLIS (Collaborative Online Learning and Information Systems) Demonstrator Project and the "Learning Object Lifecycle"”, Winds of Change in the Sea of Learning: Proceedings of the 19th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education, Auckland, New Zealand, pp. 159-166, 2002.
[14] S. Mukhopadhyay , A.K.Singh and A. M. Midda , “A Prototype Design for Digital Right management of Digital Library in E-Education : A UML Based Approach”,8th International Caliber , Goa India , pp. 164-173, 2011.
[15] A.K. Singh , S Mukhopadhya, “E-Learning in Distance Education using UML”, International Journal of Information and Computing System, Vol-2, Issue-2, pp 30-35 , 2009.
[16] A.K. Singh , S. Mukhopadhyaya , “An Agent Based Prototype of Digital Rights Management for Distance Education”, International Journal of Advance Research and Innovative Ideas in Education, Vol-3 , Issue-4 ,2017.
[17] D. Yu , An Application of Digital Rights Management in Network Learning System, Applied Mechanics and Materials Vol.409-410, pp. 1644-1648,2013
[18] S. Mukhopadhyaya , S. Dan , A. K. Singh , “Object Oriented Design of E-Learning System for Distance Education “ , International Journal of Innovative Technology & Creative Engineering vol 1, Issue 1, pp 27-30, 2011.
[19] J. Simon. Federated schools architecture for national collaborative environments. Sun MicroSystems White Paper. Also available as https://www.sun. com/products-n-solutions/edu/whitepapers/pdf/ federated_schools.pdf, June 2003.
[20] S. Mandala, A. H. Abdullah, A. S. Ismail ,” A survey of e-learning security”, International Conference on ICT for Smart Society, Jakarta, Indonesia,pp 1-6 , 2013.
[21] Z. Zhang , Q. Pei , J. Ma , and L. Yang, “Security and Trust in Digital Rights Management: A Survey”, International Journal of Network Security, Vol-9, Issue-3, pp.247–263, 2009.
[22] W. Ku , C. H. Chi, “Survey on the technological aspects of Digital Rights Management”, International Conference on Information Security ISC 2004: Information Security, Palo Alto , CA, USA, pp 391-403, 2004
[23] N. S. Harinarayana, C. S. Somu, M. V. Sunil, “Digital Rights Management in Digital Libraries: An Introduction to Technology, Effects and the Available Open Source Tools “,7 th International CALIBER-2009, Pondicherry University,Puducherry, India, pp 455-462, 2009.

Ajit Kumar Singh, Sunil Karforma, Sripati Mukhopadhyay, "A Survey on Digital Rights Management in Distance Education", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.78-81, 2019.

The knowledge about missile we want to make is represented in ontology. In philosophy, an ontology is “a particular theory about the nature of being or the kinds of existents”. It also covers the basic question on how to record the real world. Tom R Gruber discuss a more technical view of ontology, the most prominent definition being “An ontology is an explicit specification of a conceptualization”. This definition is often extended by three additional conditions: “Ontology is an explicit, formal specification of a shared conceptualization of a domain of interest”. The reference to a domain of interest indicates that for domain ontology one is not interested in modeling the whole world, but rather in modeling just the parts which are relevant to the task at hand. Ontology of missile can be used for proper classification of articles and it helps for information extraction and answering to a search query. Ontology knowledge base for missile is generated using OWL ontology language and it helps to extract information and answering to user queries. This ontology is capable of answering any missile related queries of user interest and it knowledgebase can be enriched by populating new information related to missile. This ontology can help to prioritise multi sensor tracking systems for better tracking coverage and accuracy.

Ontology, Missile, Class, Property

[1] Pike, W. and Gahegan, M. (2007). Beyond ontologies: Toward situated representationsof scientific knowledge, International Journal of Human-Computer Studies.
[2] Grigoris Antoniou, Frank van Harmelen Web Ontology Language: OWL
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[4] M. Dean, G. Schreiber (eds), F. van Harmelen, J. Hendler, I. Horrocks, D.McGuinness,P. Patel-Schneider, L. Stein, OWL Web Ontology Language Referencehttp://www.w3.org/TR/2003/WD-owl-ref-20030331/
[5] M. Smith, C.Welty, D. McGuinness, OWL Web Ontology Language Guidehttp://www.w3.org/TR/2003/WD-owl-guide-20030331/
[6] Natalya F. Noy, Deborah L. McGuinness. Ontology Development 101: A Guide toCreating Your First Ontology
[7] TomR. Gruber. Towards Principles for the Design of Ontologies Used for KnowledgeSharing. In N. Guarino and R. Poli, editors, Formal Ontology in Conceptual Analysisand Knowledge Representation, Deventer, TheNetherlands, 1993. Kluwer AcademicPublishers.
[8] Merriam-Webster. Dictionary.
[9] Wikipedia

S. Kaity, P. K. Das Gupta, B. Jana, "A Method of Building Ontology Based Information knowledgebase", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.82-87, 2019.

Optics has been recognizing as a good probable signal for realization of logic circuits, digital optical systems for optical communication and computation using optical switching devices for the utilization of super fast speed. Some optical logic gates can operate on the basis of frequency conversion process of some nonlinear materials. Semiconductor optical amplifier is recognized as a very promising and reliable optical device for implementing various all optical logical operations. Recently, more and more advanced technology should be needed to design compact and integrated version of devices. Here the authors have proposed an alternative approach to realize frequency encoded all optical integrated AND - OR logic gate with control input by which a single circuit operates on different logics using reflected semiconductor optical amplifier and add/drop multiplexer type optical switches. Again, this scheme of all optical frequency encoded integrated logic gate using optical switches with controlling input is successfully verified by proper simulation technique.

Semiconductor Optical Amplifier, Reflected Semiconductor Optical Amplifier, Add/Drop Multiplexer, Pump Beam, Probe Beam

[1] Vinita, “A Comprehensive Review of Recent Advancement in Optical Communication Networks”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.617-626, 2018.
[2] B. Ghosh, et al. “A Novel Approach to Realize of All Optical Frequency Encoded Dibit Based XOR and XNOR Logic Gates Using Optical Switches with Simulated Verification”, Optics and Spectroscopy, Vol.124, Issue.3, pp.337-342, 2018.
[3] C. Taraphdar, T. Chattopadhyay, J.N. Roy, “Polarization encoded all-optical ternary Max gate”, In the Proceedings of the 4th International Conference on Computers and Devices for Communication, Kolkata, India, pp.1–4, 2009.
[4] I.S. Amiri, S.E. Alavi, M. Bahadoran, A. Afroozeh, H. Ahmad, “Nanometer bandwidth soliton generation and experimental transmission within nonlinear fiber optics using an add-drop filter system”, Journal of Computational and Theoretical Nanoscience, Vol.12, Issue.2, pp.221-225, 2015.
[5] S. Chen, Y. Shi, S. He, D. Dai, “Compact eight-channel thermally reconfigurable optical add/drop multiplexers on silicon”, IEEE Photonics Technology Letters, Vol.28, Issue.17, pp.1874-1877, 2016.
[6] J. Wang, L.R. Chen, “Low crosstalk Bragg grating/Mach-Zehnder interferometer optical add-drop multiplexer in silicon photonics”, Optics express, Vol.23, Issue.20, pp.26450-26459, 2015.
[7] R. Mansoor, S. Koziel, H. Sasse, A. Duffy, “Crosstalk suppression bandwidth optimisation of a vertically coupled ring resonator add/drop filter”, IET Optoelectronics, Vol.9, Issue.2, pp.30-36, 2015.
[8] S. Saha, S. Biswas, S. Mukhopadhyay, “Optical scheme of conversion of a positionally encoded decimal digit to frequency encoded Boolean form using Mach–Zehnder interferometer-based semiconductor optical amplifier”, IET Optoelectronics, Vol.11, Issue.5, pp.201-207, 2017.
[9] S. Singh, S. Kaur, R. Kaur, R.S. Kaler, “Photonic processing of all-optical Johnson counter using semiconductor optical amplifiers”, IET Optoelectronics, Vol.11, Issue.1, pp.8-14, 2016.
[10] D.K. Gayen, T. Chattopadhyay, K.E. Zoiros, “All-optical D flip-flop using single quantum-dot semiconductor optical amplifier assisted Mach–Zehnder interferometer”, Journal of Computational Electronics, Vol.14, Issue.1, pp.129-138, 2015.
[11] Q. Cheng, A. Wonfor, J.L. Wei, R.V. Penty, I.H. White, “Demonstration of the feasibility of large-port-count optical switching using a hybrid Mach–Zehnder interferometer–semiconductor optical amplifier switch module in a recirculating loop”, Optics letters, Vol.39, Issue.18, pp.5244-5247, 2014.

B. Ghosh, N. Halder, D. Roy, S. Hazra, S. Mukherjee, PP. Sarkar, "An alternative approach to realize all optical frequency encoded integrated AND-OR logic gate with control input using optical switches and its simulative verification", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.88-93, 2019.

This paper provides a brief overview of a paradigm that has been used to identify, classify and cluster the negations consist in the Tweets. Usually unambiguous short text messages, collected from the famous microblogging service Twitter, are called Tweets. It has a maximum character limit of 280 characters. People usually express their standpoints or perspectives about a situation or fact through Tweets. In this collected dataset of Tweets, some negations may be overlapped or/and misclassified. So, our objective is to improve the accuracy using fine classification and increase the sharpness by reducing the overlap or/and misclassification. Here, we have used two different techniques of Sentiment Analysis, such as Lexicon Based Approach and Supervised Learning Approach to train our model. This proposed system has also analyzed Tweets and Emoticons into three categories- Positive, Negative and Neutral. In this analysis, we have used a data set of 2000 Tweets and found 88.14 percentage of accuracy.

sentiment, negation, polarity, emoticons

[1] L. Jia, C. Yu, W. Meng, “The effect of negation on sentiment analysis and retrieval Effectiveness”, In the Proceeding of the 18th ACM conference (CIKM `09), Hong Kong, China, pp.1827-1830, 2009.
[2] A. Hogenboom, P. V. Iterson, B. Heerschop, F. Frasincar, U. Kaymak, “Determining negation scope and strength in sentiment analysis”, In the Proceedings of 2011 IEEE International Conference on Systems, Man, and Cybernetics, Alaska, USA, pp. 2589-2594, 2011.
[3] B. Pang, L. Lee, S. Vaithyanathan, “Thumbs Up?Sentiment Classification Using Machine Learning Techniques”, In the Proceedings of the 7th conference on Empirical methods in Natural Language Processing (EMNLP `02), Philadelphia, USA, pp.79-86, 2002.
[4] R. Amalia, M. A. Bijaksana, D. Darmantoro, “Negation handling in sentiment classification using rule-based adapted from Indonesian language syntactic for Indonesian text in Twitter”, Journal of Physics: Conference Series, Vol.971, Issue.1, 2018.
[5] F. Zwarts, “Negatief polaire uitdrukkingen I”, Journal of GLOT, Vol.4, pp.35-132, 2002.
[6] T. Givón, “English grammar: A function-based introduction”, John Benjamins Publishing Company, Amsterdam, Netherlands, pp.1-318, 1993.
[7] M. Wiegand, A. Balahur, B. Roth, D. Klakow and A. Montoyo, “A Survey on the Role of Negation in Sentiment Analysis”, In the Proceedings of the Workshop on Negation and Speculation in Natural Language Processing, Uppsala, Sweden, pp.60-68, 2010.
[8] O. Bojar, Franky, K. Veselovská, “Resources for Indonesian Sentiment Analysis”, Journal of The Prague Bulletin of Mathematical Linguistics, Vol.103, Issue.1, pp.21-41, 2015.
[9] S. R. Das, M. Y. Chen, “Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web”, Journal of Management Science, INFORMS, pp.1375-1388, Vol.53, No.9, 2007.
[10] H. Wang, A. J. Castanon, “Sentiment analysis via emoticons using twitter data”, In the Proceedings of 2015 IEEE International Conference on Big Data, Santa Clara, CA, USA, pp.2404-2408, 2015.
[11] M. Dadvar, C. Hauff, F. D. Jong, “Scope of Negation Detection in Sentiment Analysis”, In the Proceedings of the 11th Dutch-Belgian Information Retrieval Workshop (DIR 2011), Amsterdam, Netherlands, pp.16-20, 2011.
[12] U. Farooq, H. Mansoor, A. Nongaillard, Y. Ouzrout, M. A. Qadir, “Negation Handling in Sentiment Analysis at Sentence Level”, Journal of Computers, Vol.12, Issue.5, pp.470-478, 2017.

A. Dian, D. Saha, "Classification of Negation in Sentiment Analysis using Twitter Data", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.94-99, 2019.

The process of recognizing scanned documents or machine printed documents using automated tools are used in different real life domains. Designing a method with cent percent accuracy of character recognition is a challenging and unachievable task. Presence of noise, distinct styles of font under real time environment makes character recognition more difficult. In this paper we describe recognition of handwritten basic characters of OlChiki script, used by more than 10 million tribal people in India mostly from Assam, Bengal, Bihar, Odisha and Jharkhand. There are 30 basic characters and 10 numeral digits in OlChiki and we have used a dataset of 10000 handwritten isolated character samples written by 50 persons. Samples in this dataset are composed of one stroke. Curvelet and Geometry based feature extraction has been used for comparison of performance. Strokes are recognized dynamically by using KNN and SVM classifier together. We have received an encouraging recognition result of 87% accuracy.

OlChiki Script, Basic Character Recognition, Curvelet based Feature, Geometry based Feature, KNN Classifier, SVM Classifier

[1] Marine Carrin, “The impact of cultural diversity and globalization in developing a Santali peer culture in Middle India”,EMIGRA Working Papers, ISSN 2013-3804.
[2] R.C. Hansdah, N.C. Murmu, “Encoding of OlChiki in Universal Character Set” - ISO/IEC 10646.
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[4] V.N. Vapnik, “The Nature of Statistical Learning Theory”, 2nd ed., Springer, 2000.
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[6] Singh, Mittal, Ghosh, “An Evaluation of Different Feature Extractors and Classifiers for Offline Handwritten Devnagari Character Recognition” – Journal of Pattern Recognition Research 2 (2011), pp. 269-277.
[7] SumantaDaw, Abhoy Chand Mondal, “A Font Invariant OlChiki Basic Character Recognition using Digital Curvlet Transformation”-ICCS 2013.

SumantaDaw, Abhoy Chand Mondal, "A Hybrid Recognition System of Handwritten OlChiki Character and Digit", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.100-104, 2019.

Optical Kerr materials are strongly recommended for optical self-focusing and defocusing, optical switching activities and optical signal processing. The refractive index of Kerr medium depends on the intensity of applied light pulse passing through it. Kerr medium can be used in frequency conversion if signal having time dependent intensity pattern is passed through such a medium. Earlier this Kerr material was successfully used to convert the frequency of the applied light using a saw-tooth type of intensity varying signal. The shift of frequency can be enhanced by sending the output modulated wave from Kerr medium again to the input of the same medium. Greater amount of frequency shift can be achieved by increasing the number of passing of the light wave through the Kerr medium. Here in this paper the authors propose a method of using Kerr medium for change of light frequency with saw-tooth intensity pattern accommodating the multi-passing technique.

Optical Kerr materials, Optical switching, Frequency conversion, Multi-passing technique

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A. Chatterjee, S. Mukhopadhyay, "Kerr Medium for Change of Light Frequency With Saw-Tooth Intensity Pattern Accommodating Multi-Passing Technique", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.105-108, 2019.