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The mobility management of the mobile node are depending on the movement of mobile node, cell channel, handover and frequency reuse. When many MN are connected in a cell or sub cell, according to the TBMM cell are divided in two time phrase those are active and idle phrase. In check manager method the time are dynamic. It is depended on the number of mobile node at that time. When minimum MN are connected in the cell, a new MN are want to established the new connection or make the handover, the cell are check the free channel availability. If the channel is available in main cell then the connection are made through the main cell or generated a request signal to the corresponding sub cell to allocate the channel for new connection or make the handover successfully. The mathematical analysis and simulation result are shown that the check manager are better than the TBMM and standard mobility management methods.

Check Manager, Time base mobility management, Handover, Channel Allocation, Frequency Reuse

[1] Dr. Mahdi H. A. Ahmad, “Minimizing Handoff Ping-Pong Effect in 802.11 Data Networks Using Differential RSS and Extrapolation”, Journal of Science & Technology, vol. (14), No.(1), 2009.
[2] David Lopez-P ´ erez, Alvaro Valcarce, ´ Akos Lad ´ anyi, Guillaume de la Roche, and Jie Zhang ´ Centre for Wireless Network Design (CWiND), University of Bedfordshire, D109 Park Square, Luton LU1 3JU, UK, “Intracell Handover for Interference and Handover Mitigation in OFDMA Two-Tier Macrocell-Femtocell Networks”, Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2010, Article ID 142629, 15 pages
[3] David L´opez-P´erez, ˙Ismail G¨uvenc¸, Guillaume de la Roche, Marios Kountouris, Tony Q.S. Quek, Jie Zhang, “Enhanced Inter-Cell Interference Coordination Challenges in Heterogeneous Networks.”, Published in: IEEE Wireless Communications ( Volume: 18 , Issue: 3 , June 2011 )
[4] Yi-Bing Lin, Dept. of Comput. Sci. & Inf. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan Ai-Chun Pang, “Comparing soft and hard handoffs”, IEEE Transactions on Vehicular Technology ( Volume: 49 , Issue: 3 , May 2000 )
[5] M. Chopra, Cellular Infrastructure Group, Motorola, Fort Worth, TX, USA, K. Rohani, Cellular Infrastructure Group, Motorola, Fort Worth, TX, USA, J.D. Reed, Cellular Infrastructure Group, Motorola, Fort Worth, TX, USA, “Analysis of CDMA range extension due to soft handoff”, IEEE Xplore: 06 August 2002, Published in: 1995 IEEE 45th Vehicular Technology Conference. Countdown to the Wireless Twenty-First Century.
[6] Yu-Wen Chang, Inst. for Syst. Res., Maryland Univ., College Park, MD, USA, E. Geraniotis Inst. for Syst. Res., Maryland Univ., College Park, MD, USA, “Accurate computations of cell coverage areas for CDMA hard and soft handoffs”, IEEE Xplore: 06 August 2002, Published in: Proceedings of Vehicular Technology Conference – VTC.
[7] J. T. Malinen and C. Williams, “Micromobility taxonomy,” Internet Draft, IETF, Nov. 2001
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[9] A. T. Campbell, J Gomez, S. Kim, Z. Turanyi, C.-Y. Wan and A. Valko, “Comparison of IP micro-mobility protocols”, IEEE Wireless Commun. Mag., vol. 9, no. 1, Feb. 2002.
[10] E. Cayirei and I.F. Akyildiz, “User mobility pattern scheme for location update and paging in wireless systems,” IEEE Trans. Mobile Computing, vol. 1, no. 3, pp. 236 – 247, 2002
[11] D. Sarddar, “A time base mobility management method for Leo satellite network.” International Journal of computer application, vol. 42, no. 2, pp. 33 – 40, March 2012
[12] Jiang Xie, Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA & R. Akyildiz, Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA, Article: An optimal location management scheme for minimizing signaling cost in Mobile IP. 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333), DOI: 10.1109/ICC.2002.997445, 07 August 2002
[13] Xiaowei Zhang, Javier Gomez Castellanos, Andrew T. Campbell. Article: P-MIP: Paging Extensions for Mobile IP, Mobile Networks and Applications, April 2002, Volume 7, Issue 2, pp 127–141
[14] Debobrata Sarddar, Soumya Das, Dipsikha Ganguli, Sougata Chakraborty, Kunal Hui, Kalyan Kumar Das and Mrinal Kanti Naskar. Article: A New Method for First and Low Cost Handover in Leo Satellites. International Journal of Computer Application 37(7): 39 – 45, January, 2012. Published by Foundation of Computer Science, New York, USA.

Debabrata Sarddar, Pinaki Das, Rajat Pandit, "Cost Reduction for Mobile communication using Check Manager Method," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.495-501, 2019.

Designing a CAPTCHA requires choosing a challenge which is difficult for bots and easy for human being. Cognitive CAPTCHA provide much needed human advantage as compared to bots. Various available cognitive CAPTCHAs attempt to explore the potential AI hard problems in number of possible ways. In this paper we provide a framework to design cognitive CAPTCHAs. It will serve as a guideline to design the cognitive challenge in an efficient way. This will give baseline features to be considered while designing any cognitive CAPTCHA.

Cognitive CAPTCHA, HCI, Web security, Accessibility, Human Interactive Proof (HIP), Bots

[1] S. K. Saha, A. K. Nag, D. Dasgupta, “Human-Cognition-Based CAPTCHAs”, IT Professional, Vol. 17, No. 5, pp. 42-48, 2015.
[2] M. M. Tanvee, M. T. Nayeem, M. M. Rafee, “Move & Select: 2-Layer CAPTCHA Based on Cognitive Psychology for Securing Web Services”, International Journal of Video & Image Processing and Network Security, IJVIPNS/IJENS, Vol. 11, Issue. 5, pp. 9-17, 2011.
[3] M. J. M. Chowdhury, N. R. Chakraborty, “CAPTCHA Based on Human Cognitive Factor”, International Journal of Advanced Computer Science and Applications, Vol. 4, No. 11, pp. 144-149, 2013.
[4] A. Rusu, R. Docimo, “Leveraging Cognitive Factors in Securing WWW with CAPTCHA”, In the Proceedings of USENIX Conference on Web Applications, 2010.
[5] V. Dhaka, G. Gandhi, “Developing a CAPTCHA Utilizing Cognitive Ability of Human through PHP”, International Journal of Advanced Networking Applications, Special Issue, pp. 50–54, 2015.
[6] T. Yamamoto, T. Suzuki, M. Nishigaki, “A Proposal of Four-panel cartoon CAPTCHA”, In the Proceedings of IEEE International Conference on Advanced Information Networking and Applications 2011, pp. 159–166, 2011.
[7] D. G. S. Edgar, G. C. Salvador, M. F. R. Edgardo, “Designing New CAPTCHA Models Based on the Cognitive Abilities of Artificial Agents”, Research in Computing Science, Vol. 138, pp. 127-136, 2017.
[8] M. Mohamed, S. Gao, N. Saxena, C. Zhang, “Dynamic Cognitive Game CAPTCHA Usability and Detection of Streaming-Based Farming”, In Workshop on Usable Security (USEC), co-located with NDSS, 2014.
[9] M. R. Ogiela, N. Krzyworzeka, L. Ogiela, “Application of knowledge‐based cognitive CAPTCHA in Cloud of Things security”, Concurrency and Computation: Practice and Experience, Vol. 30, No. 21, pp. 1-11, 2018.
[10] N. Divyashree, “Secured Conversion and Generation of Cognitive Catch Implementing Honeypot Technique”, IOSR Journal of Computer Engineering (IOSR-JCE), Vol. 20, No. 3, pp. 24-26, May-June 2018.
[11] M. R Ogiela, L. Ogiela, “Authentication Protocols Using Multi-level Cognitive CAPTCHA, Advances in Internet, Data and Web Technologies”, EIDWT 2019, Lecture Notes on Data Engineering and Communications Technologies, Vol. 29, pp. 114–119, Springer, Cham, February 2019.
[12] A. Bhalerao, L. Rade, “A Basic Survey of CAPTCHA :Application and Challenges”, International Journal of Scientific Research in Computer Science and Engineering, Vol.06, No. 01, pp.1-5, 2018.
[13] P. Devi, "Attacks on Cloud Data: A Big Security Issue", International Journal of Scientific Research in Network Security and Communication, Vol.6, No.2, pp.15-18, 2018.
[14] K. A. Kluever, R. Zanibbi, “Balancing Usability and Security in a Video CAPTCHA”, In 5th Symposium on Usable Privacy and Security, SOUPS, ACM, p.p. 1–11, 2009.

S.S. Kulkarni, H.S. Fadewar, "A Framework for Cognitive CAPTCHA Designing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.502-505, 2019.

Biometric techniques are utilized to provide authentication to prevent unauthorized access and it handle physical or behavioral that identifies the identity of a person. In our proposed paper the security mechanism named PAD is proposed that provides more accuracy than the existing system. In this technique first of all sensor capture the biometric images and then it is utilized to identify one or more characteristics of a person like face, fingerprint and other feature. Results section shows that it gives better accuracy and recognition rate utilizes various combination of biometric technique.

Biometric, PAD security mechanism, Security, Sensors

[1] R. W. Anwar, M. Bakhtiari, A. Zainal, A. H. Abdullah, K. N. Qureshi, F. Computing, and J. Bahru, “Security Issues and Attacks in Wireless Sensor Network,” World Appl. Sci. J., vol. 30, no. 10, pp. 1224–1227, 2018.
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[11] L. R. Haddada, B. Dorizzi, and N. Essoukri Ben Amara, “Watermarking signal fusion in multimodal biometrics,” Int. Image Process. Appl. Syst. Conf. IPAS 2014, 2014.
[12] N. Dey, “Motion Detection and Tracking in Video Processing Applications ( Preface ),” IEEE Access, no. September, 2016.
[13] R. Snehkunj, A. N. Jani, and N. N. Jani, “Brain MRI / CT Images Feature Extraction to Enhance Abnormalities Quantification,” Springer Int. Publ., vol. 11, no. January, pp. 1–10, 2018.
[14] L. Best-Rowden, H. Han, C. Otto, B. F. Klare, and A. K. Jain, “Unconstrained face recognition: Identifying a person of interest from a media collection,” IEEE Trans. Inf. Forensics Secur., vol. 9, no. 12, pp. 2144–2157, 2014.
[15] P. Naraei, V. Street, V. Street, and V. Street, “Application of Multilayer Perceptron Neural Networks and Support Vector Machines in Classification of Healthcare Data,” IEEE Access, no. December, pp. 848–852, 2016.
[16] A. V. Flevaris, A. Martínez, and S. A. Hillyard, “Attending to global versus local stimulus features modulates neural processing of low versus high spatial frequencies: An analysis with event-related brain potentials,” Front. Psychol., vol. 5, no. APR, pp. 1–11, 2014.
[17] S. Africa, “From Local to Global Processing: The Development of Illusory Contour Perception,” vol. 4, no. 11, pp. 38–55, 2017.

Surinder Pal Kaur, Anil Kumar, "Enhanced PAD security Mechanism Using Fingerprint and Face Scanning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.506-509, 2019.

This research is an attempt to substitute an agent in place of a customer care executive in a helpdesk kind of scenario by building an emotional cognitive conversational agent. Some of the cognition that may be required to achieve human like dialog system are goal, desire, belief, intention, decision making, emotions, compassion, emotional pragmatics etc. The functionality of this Cognitive Architectures is Decision making, Prediction, Problem solving, Reasoning and Belief, action, communication between agents, Learning and reflection. Conversational agent or Chatbot is become to rule our day to day lives. Alexa, Siri, Watson, etc have become the buzz words of today. This research paper is an attempt to implement the architecture for Conversational agents based on human cognition using Python and Machine Learning.

Artificial Intelligence, Cognitive science, Chatbot, Machine Learning, Python, Conversational Agents

[1] Pat Langley, John E. Laird, Seth Rogers, Cognitive architectures: Research issues and challenges, Cognitive Syst ems Research, ISSN 1389-0417, Volume 10, Issue 2, Pages 141-160, 2009
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[3] Gnanaguru Gnaneswari, Venkatamuni, Vijayakumar Maragal , Building a Conversational Agent based on the principles of Cognitive Pragmatics using Cognitive Architecture, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 6 Issue 02, February 2017.
[4] AbuShawar, Bayan & Atwell, Eric., ALICE chatbot: Trials and outputs, Computación y Sistemas, Vol. 19, No. 4, 2015, pp. 625–63, 2005
[5] Arend Hintze, Understanding the four types of AI, from reactive robots to self-aware beings, Michigan State University, 2016.
[6] P Singh, EMONE: An Architecture for Reflective Commonsense Thinking, Dissertation of Doctor of Philosophy in Computer Science and Engineering. Cambridge, MA: Massachusetts Institute of Technology, 2005.
[7] Weitzenfeld, A., Arbib, M., Alexander, A.: NSL—Neural Simulation Language: A System for Brain Modeling, MIT Press, Cambridge, MA,2002.
[8] N, Davis D., Computational Architectures for Intelligence and Motivation. International Symposium on Intelligent Control, Vancouver, Canada :17th IEEE, 2002.
[9] A.K.Gupta, S.Gupta, "Neural Network through Face Recognition", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.38-40, 2018

Gnaneswari Gnanaguru, "Programming a Chatbot in Python using Emotional Cognitive Conversational Agent Architecture (ECCAA)," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.510-516, 2019.

Software testing is process for improving the quality of software by removing all sorts of errors before deployment of software system. The quality of the testing also depends on the test data used for the testing. If the test data cover all the statements and branches of a source program, then it increases the chances of revealing most of the errors from the given program. Normally test data is selected by tester based on his past experience of similar projects. This is time consuming and person oriented approach. Automation of this process can make the testing efficient, cost-effective and reliable. So we present here the Effective Search Based Algorithm (ESBA) which automatically generates test data to reveal the errors at structural test. Here we used branch distance as the optimization function to generate the test data. We applied this method on three benchmark programs to generate the test data. The experimental results indicate that our method outperforms genetic algorithm, many objective sorting algorithm based upon following criteria: average statement coverage 0.91, average branch coverage 0.84 and the average number of evaluations 23824.

Automated Software Testing, Automated Test Data Generation, Structural Testing,, Search based Algorithm, Branch Coverage

[1] Shaukat Ali, Muhammad Zohaib Iqbal, Andrea Arcuri, and Lionel C. Briand, "Generating Test Data from OCL Constraints with Search Techniques", IEEE Transaction on Software Engineering, Vol. 39, NO. 10, October 2013.
[2] Mark Harman and Phil McMinn, "A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search", IEEE Transaction on Software Engineering, Vol. 36, NO. 2, March/April 2010.
[3] T. Mantere and J.T. Alander, “Evolutionary Software Engineering, a Review,” Applied Soft Computing, vol. 5, pp. 315-331, 2005.
[4] Roy P. Pargas, Mary Jean Harrold, Robert R. Peck, "Test Data Generation using Genetics Algorithms", Journal of Software Testing, Verification and Reliability, 1999.
[5] Gilles Bernot, Marie Claude Gaudel, Bruno Marre, "Software Testing based on Formal Specifications:a theory and a tool", Software Engineering Journal (SEJ), Vol.6, No-6, p.387-405, 1991.
[6] Sandra Rapps and Elaine J. Weyuker, "Selecting Software Test Data Using Data Flow Information", IEEE Transactions On Software Engineering, Vol. SE-1l, No. 4, April 1985.
[7] Phil McMinn, "Search-based Software Test Data Generation:A Survey", Software Testing, Verification and Reliability 14(2), pp. 105-156, June 2004.
[8] Mark Harman and Phil McMinn, "A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search", IEEE Transaction on Software Engineering, Vol. 36, NO. 2, March/April 2010.
[9] Hwa-You Hsu and Alessandro Orso, "MINTS: A General Framework and Tool for Supporting Test-suite Minimization", IEEE ICSE’09, May 16 - 24, 2009, Vancouver, Canada.
[10] Christoph C. Michael, Gary McGraw, and Michael A. Schatz, "Generating Software Test Data by Evolution", IEEE Transaction on Software Engineering, Vol. 27, No. 12, December 2001.
[11] Shahid Mahmood, “A Systematic Review of Automated Test Data Generation Techniques”, Mater Thesis, Software Engineering MSE-2007:26, October 2007.
[12] Saswat Anand, Edmund K. Burke, Tsong Yueh Chen, John Clark, Myra B. Cohen, Wolfgang Grieskamp, Mark Harman, Mary Jean Harrold, Phil McMinn, “An Orchestrated Survey Of Methodologies For Automated Software Test Case Generation”, Elsevier, April 2013.
[13] Corina S.Pasareanu and Willem Visser, “ A survey of new trends in symbolic execution for software testing and analysis”, Springer-Verlag- 2009.
[14] Lionel Briand, Yvan Labiche, "A UML-Based Approach to System Testing", Software Quality Engineering Laboratory, Systems and Computer Engineering Department, Carleton University, 2002.
[15] Shaukat Ali, Lionel C. Briand, Hadi Hemmati, Rajwinder K. Panesar-Walawege, "A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation", IEEE Transaction on Software Engineering, Vol. 36, NO. 6, November/December 2010.
[16] Annibale Panichella, Fitsum Meshesha, Paolo Tonella, "Automated Test Case Generation as a many-Objective Optimization Problem with Dynamic Selection of the Targets", IEEE 2018.
[17] Bogdan Korel, “Automated Software Test Data Generation”, IEEE Transactions on Software Engineering, August 1990.
[18] Simone Scalabrino, Giovanni Grano, Darrio Di Nucci, Rocco Oliveto, and Andrea De Lucia, “Search-based Testing of Procedural Programs: Iterative Single-Target Approach?”, Conference Paper October-2016.
[19] Zoreh Karimi Aghdam and Bahman Arasteh, “An Efficient Method to Genearate Test Data for Software Structural Testing Using Artificial Bee Colony Optimization Algorithm”, International Journal of Software Engineering and Knowledge Engineering Vol-27, No-6, 2017.
[20] Simone Scalabrino, Giovanni Grano, Darrio Di Nucci, Michele Guerra, Andria De Lucia, Harald C Gall and Rocco Oliveto, “OCELOT: A Search Based Test Data Generation Tool for C”, An International Conference on Automated Software Engineering ASE’18.

Sachin D. Shelke, S.T. Patil, "An Effective Search Based Algorithm for Structural Test Data Generation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.517-522, 2019.

In a modern world, people use internet daily. They prefer to buy any types of products through online. There are many online websites and applications which provides service to the people of interacting with products. These services reduce people’s time in buying a product. They provide variety of options to the buyers. Researches have proved that the new way of advertising with the help of advanced technology has improved the business in all the fields. Advertisement promotes business and the way of people interacting with the products is one of the main keys of a business. Particularly musical instruments have less interaction with the buyers in online. Technologies like Artificial Intelligence and Machine learning is playing a major role in current business scenarios. Many revolutionary ideas are evolving which This article examines about the various missing factors of people interacting with products.

Musical instruments, Machine Learning, Product modelling, Business analysis

[1] A.Jenita jebamalar “Efficiency of Data Mining Algorithms Used In Agnostic Data Analytics Insight Tools ”, IJSRNSC, Vol.6 , Issue.6 , pp.14-18, Dec-2018.
[2]Gabriel Krummenacher, Chen Soon Ong, Stefan Koller, Seijin Kobayashi, Joachim M. Buhmann “Wheel Defect detection with Machine Learning”, 2018 IEEE Transactions on Intelligent Transportation Systems.
[3]Lisa Avila, Mike Bailey, “The Wearable Revolution”, 2015 IEEE Computer Graphics and Applications
[4]YU SANG, LAIXI SHI, AND YIMIN LIU “Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing”, 2018 International Conference on National Natural Science Foundation of China.
[5]Victor Zappi, Andrew Allen, Sidney Fels, “Extended Playing Techniquesonan Augmented Virtual Percussion Instrument”, 2017 Computer Music Journal.
[6]Zhenyuan Zhang, Zengshan Tian, Mu Zhou, “Lantern: Dynamic Continuous Hand Gesture Recognition Using FMCW Radar Sensor,
[7]Alberto de Santos Sierra et al.., “A Speech-driven Hand Gesture Generation Method and Evaluation in Android Robots”, IEEE.
[8]Zengshan Tian, Jiacheng Wang, Xiao long Yang, Mu Zhou “WiCatch: A Wi-Fi Based Hand Gesture Recognition System” IEEE.
[9]Carlos T. Ishi, Daichi Machiyashiki, Ryusuke Mikata, Hiroshi Ishiguro “A Speech-driven Hand Gesture Generation Method and Evaluation in Android Robots”, 2018 JST, ERATO, Grant Number JPMJER1401.
[10] H. Agarwal, A.Agarwal “Human Computer Interaction congregate with computer vision: A Review on Sixth Sense Technology ”, IJCSE, India, pp.887-891, 2018.

C. Punithadevi, V. Hariram, J. AravinthVarman, M. Sabaresan, "Technology for Music: A Study on Musical Instruments Online Shopping," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.523-527, 2019.

Nowadays many people want to connect with each other, for this purpose, they are using many applications like SMS, Whatsapp, Facebook, etc. Short Message Service (SMS) is the oldest application for exchanging messages between communicating parties used by mobile phones. Current scenarios of hacks and exploitation demand confidentiality but unfortunately, SMS transmission will be hacked easily. To avoid information loss, encryption is one of the techniques that is used in this project for designing a secure SMS android application. The application will perform cryptographic manipulation of data that can protect a message from unauthorized access and disclosure over networks by using RSA, AES, and IDEA algorithms. RSA is a calculation utilized by present day PCs to encode and unscramble messages. It is an asymmetric cryptographic algorithm. IDEA is a calculation was proposed as a substitution for the Information Encryption Standard. Advanced Encryption Standard (AES) algorithm can be one of the widely used symmetric cryptographic algorithms used worldwide. It will be difficult for hackers to get the real data when encrypted by the AES algorithm.

RSA, AES, IDEA, Encryption, Decryption

[1] D. Lisonek, M. Drahansky, “SMS encryption for mobile communication”, International Conference on Security Technology, Hainan Island, pp 198 – 201, 2008.
[2] J. J. Garza-Saldana, A. Diaz-Perez, “State of security for SMS on mobile devices”, In the Proceedings of the 2008 Electronics, Robotics and Automotive Mechanics Conference, pp. 110 – 115, 2008.
[3] R. Soram, “Mobile sms banking security using elliptic curve cryptosystem”, International Journal of Computer Science and Network Security, vol. 9, no. 6, pp. 30-38.
[4] P.S.Patil, “New encryption technique for secure SMS transmission”, An international journal of advanced computer technology, 3 (11), November-2014.
[5] P. H. Kuaté, J. L. Lo and J. Bishop, “Secure asynchronous communication for mobile devices”, Proceedings of the Warm Up Workshop for ACM/IEEE ICSE 2010,Cape Town, South Africa, pp. 5 – 8,2009.
[6] Ako Muhammad Abdullah, “Advanced Encryption Standard to encrypt and decrypt the data”,Research Gate,3, June-2007
[7] Kumar Shanu and Itiram R. Khan, “Secure message encryption using Nth prime”, International Journal of research in Computer Science, Volume 8, No-5, May-June 2017.
[8] NIST, “Fips197: Advanced Encryption Standard (AES)”, FIPS PUB 197 Federal Information Processing Standard Publication 197, Technical report, National Institute of Standards and Technology, 2001.
[9] M.P. Leong, O.Y.H. Cheung, K.H. Tsoi and P.H.W. Leong “A Bit-Serial Implementation of the International Data Encryption Algorithm IDEA “ @cse.cuhk.edu. hk Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin, N.T. Hong Kong. 2000.

Shaik Quadar Janbee, Reddem Mouneeswari, Viswanadhapalli Bhanuja, Atmakuri Prashant, "SMS Encryption on Android Application," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.528-533, 2019.

Polycystic Ovaries in females in today’s age is a matter of concern.It can hinder the fertile nature of female and cause many more issues.Polycystic Ovaries can be detected by ultrasound.It can create a lot of problems if not taken seriously.For leading a good life females should be aware of this disease also.In approximate 70 per cent of this kind of cases remain undiagnosed. In past studies feature extraction using Convolutional Neural Network has proposed manually,here we try to propose a methodology in which we will add segmentation prior CNN so as to delete or eliminate redundant data and to achieve better accuarcy .Segmentation allows to divide the data or images so as deeply extract the exact information what is needed.

Deep neural network,region growing,CNN

[1] Y. Deng, Y. Wang, and P. Chen, “Automated detection of Polycystic Ovary Syndrome from ultrasound images,” Conf. Proc. IEEE Eng. Med. Biol. Soc., vol. 2008, pp. 4772–4775, 2008.
[2] P. Mehrotra, B. Ghoshdastidar, and S. Ghoshdastidar, “Automated Screening of Polycystic Ovary Syndrome using Machine Learning Techniques Palak,” 2011 Annu. IEEE India Conf., 2011.
[3] R. Sitheswaran and S. Malarkhodi, “An effective automated system in follicle identification for Polycystic Ovary Syndrome using ultrasound images,” 2014 Int. Conf. Electron. Commun. Syst. ICECS 2014, 2014.
[4] C. Panchasara, “Application of Image Segmentation Techniques on Medical Reports,” vol. 6, no. 7, pp. 2931–2933, 2015.
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[6] P. S. Anushalin and S. I. J, “Ultrasound Image Analysis of Kidney Stone using Wavelet Transform,” vol. 1, no. August, pp. 39–49, 2014.
[7] K. Viswanath, “Design and analysis performance of Kidney Stone Detection from Ultrasound Image by Level Set Segmentation and ANN Classification,” pp. 407–414, 2014.
[8] B. Cahyono, Adiwijaya, M. S. Mubarok, and U. N. Wisesty, “An implementation of convolutional neural network on PCO classification based on ultrasound image,” 2017 5th Int. Conf. Inf. Commun. Technol. ICoIC7 2017, vol. 0, no. c, pp. 3–6, 2017.
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[10] C. Science and S. Hospital, “Exploring Female Infertility Using Predictive Analytic,” 2017.
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[12] B. Purnama, U. N. Wisesti, Adiwijaya, F. Nhita, A. Gayatri, and T. Mutiah, “A classification of polycystic Ovary Syndrome based on follicle detection of ultrasound images,” 2015 3rd Int. Conf. Inf. Commun. Technol. ICoICT 2015, pp. 396–401, 2015.
[13]https://towardsdatascience.com/applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2
[14]https://www.abc.net.au/news/2018-04-28/polycystic-ovary- syndrome-women-on-life-with-pcos/9607494
[15]https://www.researchgate.net/figure/Deep-CNN-architecture-to-classify-between-healthy-and-exudate-patches_fig1_318910427
[16]http://mathalytics.blogspot.com/2015/04/k-nearest-neighbor-algorithm-machine.html

Palvi Soni, Sheveta Vashisht, "Image Segmentation for detecting Polycystic Ovarian Disease using Deep Neural Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.534-537, 2019.

Graph labeling is the mapping of graph elements like vertices or edges or both to set of integers under some conditions. In a binary labeling to vertices, if we assign the edge label as the difference of the label of end vertices and if the difference of the number of vertices with label 0 and 1 and difference of edges with label 0 and 1 is at most 1, then the labeling is cordial labeling. If the mapping is a one to one mapping from the vertices to the labels from {1, 2, …, p}, where p is the cardinality of the vertex set, edge label as in the cordial labeling and if the difference of the number of edges labeled with 1 and not labeled with 1 is at most 1, then the labeling is difference cordial labeling. Neighborhood difference cordial labeling is a variation of difference cordial labeling, if the difference of the number of edges labeled with 1 and not labeled with 1 at each vertex is at most 1 then the labeling is neighbourhood difference cordial labeling In this paper we investigate the neighbourhood difference cordial labeling of honey comb network, butterfly network, benes network and grid network.

Honey comb network, butterfly network, benes network, grid and difference cordial label

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V.M.Chitra, D. Antony Xavior, "On Neighbourhood Difference Cordial Labeling of Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.538-543, 2019.

Web Log Mining, also known as Web Usage Mining (WUM) is the application of Data Mining techniques, which is applied on web log data to extract interesting patterns. An enormous increase in the use of web applications as medium of the organizations and institutions, the web page hits are consistently increasing. The web servers have the facility to save the web navigational sequence as web log file. The enormous amount of irrelevant information in the web log file demands proper preprocessing. This renders the file, with the intent of making it more appropriate for a variety of downstream purposes such as analytics. There are various traditional techniques involved in preprocessing. The implementation of preprocessing model presented in this paper over other traditional preprocessing methods is to employ an efficient Structured Query Language (SQL) based technique. The proposed SQL based preprocessing technique reduces process time drastically. The resulting structured log file is well suited for further pattern mining and analytics.

Preprocessing, Web Log Mining, Server log, User Identification, Session Identification

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K. Vadivazhagan, M. Karthikeyan, "Preprocessing Application based on Structured Query Language for Web Log Mining," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.544-549, 2019.