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Image visibility improvement is a standout amongst the most vital assignments in digital image processing. It is a standout amongst the most mind-boggling and imperative undertakings in advanced image processing. Image visibility improvement procedures are utilized in enhancing the visual nature of images. Medicinal imaging is present as of late utilized in the greater part of the applications like Radiography, MRI, Ultrasound Imaging, Tomography, Cardiograph, and Fundus Imagery, etc. Contrast and Image quality are the serious issues in medicinal imaging. The image enhancement makes the image unmistakable for human discernment or machine vision. The procedure of image visibility improvement doesn`t raise the inbuilt data substance of the information, yet can feature the highlights important to recognize the protests in a basic and productive way.

mammogram, medical image enhancement, image enhancement, X-ray, CT images.

[1] M. Tiwari, S.S. Lamba, B. Gupta, "A software-supported approach for improving visibility of backlight images", Advances in Computer Communication and Computational Sciences, Springer, pp. 299-308, 2019.
[2] S. Hosseinian, H. Arefi, "Assessment Of Restoration Methods Of X-Ray Images With Emphasis on Medical Photogrammetric Usage", The International Archives of the Photogrammetric, Remote Sensing and Spatial Information Sciences, 2016, XXIII ISPRS Congress, 12–19 July 2016.
[3] A.P. Reeves, Y. Xie, and S. Liu, "Large-scale image region documentation for fully automated image biomarker algorithm development and evaluation," Journal of Medical Imaging, 4(2): 024505, Jun. 2017.
[4] VOLCANO`09 available at http://www.via.cornell.edu/challenge/.
[5] LITFL(Radiology Image Databases) available at https://lifeinthefastlane.com/resources/image-database/
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[7] A Hadjipanteli et al, "The effect of system geometry and dose on the threhold detectable calcification diameter in 2D-mammography and digital breast tomosynthesis". Phys Med Biol 62, pp. 858-877, 2017.
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[9] M. Tiwari, B. Gupta, "Brightness preserving contrast enhancement of medical images using adaptive gamma correction and homomorphic filtering", Electrical, Electronics and Computer Science (SCEECS), 2016 IEEE Students` Conference on, pp. 1-4, 2016.
[10] T.K. Agarwal, M. Tiwari, S.S. Lamba, "Modified histogram based contrast enhancement using homomorphic filtering for medical images", Advance Computing Conference (IACC), IEEE International, pp. 964-968, 2014.
[11] B. Gupta, M. Tiwari, "A tool supported approach for brightness preserving contrast enhancement and mass segmentation of mammogram images using histogram modified grey relational analysis", Multidimensional Systems and Signal Processing, vol. 28 (4), pp. 1549-1567, 2017.
[12] M. Sundaram, K. Ramar, N. Arumugam and G. Prabin, "Histogram based contrast enhancement for mammogram images." International Conference on Signal Processing, Communication, Computing and Networking Technologies, pp. 842-846, 2011.
[13] M. Sundaram, K. Ramar, N. Arumugam and G. Prabin, "Histogram modified local contrast enhancement for mammogram images." Applied Soft Computing, pp. 5809-5816, 2011.
[14] S.K. Badugu, R.K. Kontham, V.K. Vakulabharanam, B. Prajna, "Calculation of Texture Features for Polluted Leaves", Isroset-Journal (IJSRCSE) Vol.6 , Issue.1 , pp.11-21, Feb-2018.
[15] P.M. Ingale, "The importance of Digital Image Processing and its applications", (IJSRCSE) Vol.06 , Special Issue.01, pp.31-32, Jan-2018.
[16] S. Mohan and M. Ravishankar, "Optimized histogram based contrast limited enhancement for mammogram images." Short Paper, ACEEE International Journal on Information Technology, vol. 3(1), 2014.
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[18] J.J. Staal, M.D. Abramoff, M. Niemeijer, M.A. Viergever, B. van Ginneken, "Ridge based vessel segmentation in color images of the retina", IEEE Transactions on Medical Imaging, 2004, vol. 23, pp. 501-509.
[19] A. Hoover, V. Kouznetsova and M. Goldbaum, "Locating Blood Vessels in Retinal Images by Piece-wise Threhsold Probing of a Matched Filter Response", IEEE Transactions on Medical Imaging , vol. 19 no. 3, pp. 203-210, March 2000.
[20] Decencière et al., "Feedback on a publicly distributed database: the Messidor database." Image Analysis & Stereology, v. 33, n. 3, p. 231-234, aug. 2014. ISSN 1854-5165. available at: http://www.ias-iss.org/ojs/IAS/article/view/1155, http://dx.doi.org/10.5566/ias.1155.
[21] T. Kauppi et al., "DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy Algorithms".
[22] T. Kauppi et al., "DIARETDB1 diabetic retinopathy database and evaluation protocol, In Proc of the 11th Conf. on Medical Image Understanding and Analysis (Aberystwyth, Wales, 2007).
[23] M. Zhou, K. Jin, S. Wang, J. Ye and D. Qian, "Color retinal image enhancement based on luminosity and contrast adjustment," in IEEE Transactions on Biomedical Engineering, vol. 65, no. 3, pp. 521-527, March 2018.
[24] B. Gupta, M. Tiwari, "Color retinal image enhancement using luminosity and quantile based contrast enhancement", Multidimensional Systems and Signal Processing, pp. 1-9, 2019.

K. Gangrade, "A Review of Various Digital Image Preprocessing Methods for Medical Image Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.513-516, 2019.

Talent resource management is one of the complex tasks for human resource professionals to assign the right person for the right place at the right time in the organization. The sustainability of suitable employee in an organization is very crucial these days. In this competitive age, employees are switching the organization on some gain but the organization suffers a lot. This paper is mainly concerned with the application of the knowledge discovery technique in human resource management, particularly in talent resource management to conquer the employee attrition and predicting the possible attrition in future.

Soft Computing, Fuzzy logic, Decision Tree, Talent Management, KDD, Knowledge Discovery

[1] A. S. Chang, & Leu, S.S., "Data mining model for identifying project profitablility variables," International Journal of Project Management, vol. 24, pp. 199-206, 2006.
[2] A TP Track Research Report "Talent Management: A State of the Art," Tower Perrin HR Services 2005.
[3] E. Frank, Hall, M., et al., "Data mining in bioinformatics using Weka," Bioinformatics Application Note, vol. 20, pp. 2479-2481, 2004.
[4] H. Jantan, A. R. Hamdan, Z. A. Othman, and M. Puteh, "Applying Data Mining Classification Techniques for Employee`s Performance Prediction," in Knowledge Management 5th International Conference (KMICe2010), Kuala Terengganu, Terengganu Malaysia, 2010, pp. 645-652.
[5] I. Bose, & Mahapatra, R.K., "Business data mining - a machine learning perspective " Information & Management, vol. 39, pp. 211-225, 2001.
[6] Jantan, H., Hamdan, A. R., & Othman, Z. A. (2010). Human talent prediction in HRM using C4. 5 classification algorithm, International Journal on Computer Science and Engineering, 2(08-2010), pp 2526-2534
[7] Jantan H., Hamdan A.R., Othman Z.A. (2009) Classification Techniques for Talent Forecasting in Human Resource Management. In: Huang R., Yang Q., Pei J., Gama J., Meng X., Li X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science, vol 5678. Springer, Berlin, Heidelberg
[8] J. Hamidah, H. Abdul Razak, and A. O. Zulaiha, "Classification for Talent Management using Decision Tree Induction Techniques," in 2nd Data Mining and Optimization Seminar (DMO’09), Bangi, Selangor, 2009, pp. 15-20.
[9] Kurgan, L.A., Musilek, P. (2006). A survey of knowledge discovery and Data Mining Models, The Knowledge Engineering Review, 21(1), pp 1 – 24
[10] Phyu, T.N., (2009). Survey of classification techniques in data mining, Proceedings of the International Multi Conference Of Engineers And Computer Scientists, IMECS 2009, Vol 1
[11] S. H. Liao, Chen, Y.N., & Tseng, Y.Y., "Mining demand chain knowledge of life insurance market for new product development," Expert Systems with Applications, vol. 36, pp. 9422-9437, 2009.
[12] V. Cho, & Ngai, E.W.T., "Data mining for selection of insurance sales agents," vol. 20, pp. 123-132, 2003

K. P. Tripathi, Ashutosh Gaur, "Knowledge Discovery Techniques in Human Talent Management: “A Knowledge Discovery Databases Approach to Conquer Employee Attrition Problem Using Data Mining Techniques”," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.517-522, 2019.

Creating school time table lies under the category of NP hard problems. If we try to create a time table using exhaustive approach i.e applying all possible combinations, then it can take a huge amount of time to solve the problem. So Genetic Algorithm is a good way to solve such problems. Instead of using traditional techniques of Crossover and Mutation in genetic algorithm we have applied a better technique of crossover and mutation which reduces the search space by deducting invalid time tables from the search space. We have also done some experiments which show the variation of number of generations to solve the problem with mutation rate.

NP hard problem,exhaustive approach

[1] Man, K. F., Tang, K. S., &Kwong, S. (1996). Genetic algorithms: concepts and applications [in engineering design]. IEEE Transactions on Industrial Electronics, 43(5), 519–534.doi:10.1109/41.538609
[2] E. Canth-Paz, “A summary of research on parallel genetic algorithms,” Illinois Genetic Algorithms Lab., Univ. Illinois at Urbana-Champaign, IlliGAL Rep. 95007, July 1995.
[3] Manoj Garg and Dinesh Kumar, "Simple GA & Hybrid GA for Basis Path Testing under BDFF", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.6, pp.28-35, 2016
[4] S. Dubey, R. Jhaggar, R. Verma, D. Gaur, "Encryption and Decryption of Data by Genetic Algorithm", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.47-52, 2017
[5] https://en.wikipedia.org/wiki/Genetic_algorithm

Mohd. Irfan, "A Technique of Crossover and Mutation to solve School Time Table Problem using Genetic Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.523-525, 2019.

These days with intense use of mobile applications and cloud computing, mobile cloud computing has been acquainted. The cloud computing deals with high latency, limited flexibility, platform dependency. The mobile edge computing(MEC) combines cloud computing and wireless communication to overpower the obstacles of delay, scalability and mobility. MEC helps to bring the various resources and cloud computing services nearer and accessible anywhere by the user with the use of mobile edge clouds(MEC). In this paper we provide an extensive survey of mobile edge computing research, while highlighting the specific concerns in mobile cloud computing. We present a taxonomy based on the key issues in this area, and discuss the different approaches taken to tackle these issues.

Mobile Cloud Computing, Mobile Edge Clouds, Mobility, Internet of Things

[1] Lu, Gang, and Wen Hua Zeng. "Cloud computing survey." In Applied Mechanics and Materials, vol. 530, pp. 650-661. Trans Tech Publications, 2014.
[2] Zhang, Q., Cheng, L. &Boutaba, R. J Internet ServAppl (2010) 1: 7. https://doi.org/10.1007/s13174-010-0007-6
[3] H. T. Dinh, C. Lee, D. Niyato, and P. Wang, “A survey of mobilecloud computing: architecture, applications, and approaches,” WirelessCommunications and Mobile Computing, vol. 13, no. 18, pp. 1587-1611,2013.
[4] Bonomi, Flavio, Rodolfo Milito, Jiang Zhu, and SateeshAddepalli. "Fog computing and its role in the internet of things." In Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pp. 13-16. ACM, 2012.
[5] Lei, Lei, ZhangduiZhong, Kan Zheng, Jiadi Chen, and HanlinMeng. "Challenges on wireless heterogeneous networks for mobile cloud computing." IEEE Wireless Communications 20, no. 3 (2013): 34-44.
[6] Hayes, Brian. "Cloud computing." Communications of the ACM 51, no. 7 (2008): 9-11.
[7] Kumar, Karthik, and Yung-Hsiang Lu. "Cloud computing for mobile users: Can offloading computation save energy?." Computer 43, no. 4 (2010): 51-56.
[8] White Paper. Mobile Cloud Computing SolutionBrief. AEPONA, 2010.
[9] http://www.etsi.org/technologies-clusters/technologies/multi-access-edge-computing
[10] T. H. Luan, L. Gao, Z. Li, Y. Xiang, and L. Sun, “Fog computing: Focusing on mobile users at the edge,” CoRR, abs/1502.01815, 2015.
[11] Li, I. Santos, F.C. Delicato, P.F. Pires, L. Pirmez, W. Wei, H. Song, A. Zomaya, S. Khan, System modelling and performance evaluation of a three-tier cloud of things, Future Gener. Comput. Syst. 70 (2016) 104–125.
[12] I. Farris, L. Militano, M. Nitti, L. Atzori, A. Iera, Mifaas: a mobile-iot-federationas-a-service model for dynamic cooperation of iot cloud providers, Future Gener. Comput. Syst. 70 (2016) 126–137.
[13] Wang, Chenmeng, Chengchao Liang, F. Richard Yu, Qianbin Chen, and Lun Tang. "Computation offloading and resource allocation in wireless cellular networks with mobile edge computing." IEEE Transactions on Wireless Communications16, no. 8 (2017): 4924-4938.
[14] Sun, Xiang, and Nirwan Ansari. "EdgeIoT: Mobile edge computing for the Internet of Things." IEEE Communications Magazine 54, no. 12 (2016): 22-29.
[15] Corcoran, Peter, and Soumya Kanti Datta. "Mobile-edge computing and the internet of things for consumers: Extending cloud computing and services to the edge of the network." IEEE Consumer Electronics Magazine 5, no. 4 (2016): 73-74.
[16] D. Satria, D. Park, and M. Jo, “Recovery for overloaded mobile edge computing,” Future Generation Computer Systems, 2016.
[17] M. Smara, M. Aliouat, A.-S. K. Pathan, and Z. Aliouat, “Acceptance test for fault detection in component-based cloud computing and systems,” Future Generation Computer Systems, 2016.
[18] W. Tarneberg, A. Mehta, E. Wadbro, J. Tordsson, J. Eker, M. Kihl, and E. Elmroth, “Dynamic application placement in the mobile cloud network,” Future Generation Computer Systems, 2016.
[19] R.N.S. Widodo, H. Lim, M. Atiquzzaman, Sdm: Smart deduplication for mobile cloud storage, Future Gener. Comput. Syst. 70 (2016) 64–73.
[20] A.S. Gomes, B. Sousa, D. Palma, V. Fonseca, Z. Zhao, E. Monteiro, T. Braun, P. Simoes, L. Cordeiro, Edge caching with mobility prediction in virtualized lte mobile networks, Future Gener. Comput. Syst. 70 (2016) 148–162.
[21] M. Sookhak, H. Talebian, E. Ahmed, A. Gani, and M. K. Khan, “A review on remote data auditing in single cloud server: Taxonomy and open issues,” Journal of Network and Computer Applications, vol. 43, pp. 121–141, 2014.
[22] W. Zhang, Y. Wen, K. Guan, D. Kilper, H. Luo, and D. O. Wu, “Energyoptimal mobile cloud computing under stochastic wireless channel,” IEEE Trans. Wireless Commun., vol. 12, no. 9, pp. 4569–4581, Sep. 2013.
[23] C. You, K. Huang, and H. Chae, “Energy efficient mobile cloud computing powered by wireless energy transfer,” IEEE J. Sel. Areas Commun., vol. 34, no. 5, pp. 1757–1771, May 2016.
[24] K. Kumar and Y. H. Lu, “Cloud computing for mobile users: Can offloading computation save energy?” Comput., vol. 43, no. 4, pp. 51– 56, Apr. 2010.
[25] Lu, Gang, and Wen Hua Zeng. "Cloud computing survey." In Applied Mechanics and Materials, vol. 530, pp. 650-661. Trans Tech Publications, 2014.
[26] Zhang, Q., Cheng, L. &Boutaba, R. J Internet ServAppl (2010) 1: 7. https://doi.org/10.1007/s13174-010-0007-6
[27] H. T. Dinh, C. Lee, D. Niyato, and P. Wang, “A survey of mobilecloud computing: architecture, applications, and approaches,” WirelessCommunications and Mobile Computing, vol. 13, no. 18, pp. 1587-1611,2013.
[28] Bonomi, Flavio, Rodolfo Milito, Jiang Zhu, and SateeshAddepalli. "Fog computing and its role in the internet of things." In Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pp. 13-16. ACM, 2012.
[29] Lei, Lei, ZhangduiZhong, Kan Zheng, Jiadi Chen, and HanlinMeng. "Challenges on wireless heterogeneous networks for mobile cloud computing." IEEE Wireless Communications 20, no. 3 (2013): 34-44.
[30] Hayes, Brian. "Cloud computing." Communications of the ACM 51, no. 7 (2008): 9-11.
[31] Kumar, Karthik, and Yung-Hsiang Lu. "Cloud computing for mobile users: Can offloading computation save energy?." Computer 43, no. 4 (2010): 51-56.
[32] White Paper. Mobile Cloud Computing SolutionBrief. AEPONA, 2010.
[33] http://www.etsi.org/technologies-clusters/technologies/multi-access-edge-computing
[34] T. H. Luan, L. Gao, Z. Li, Y. Xiang, and L. Sun, “Fog computing: Focusing on mobile users at the edge,” CoRR, abs/1502.01815, 2015.
[35] Li, I. Santos, F.C. Delicato, P.F. Pires, L. Pirmez, W. Wei, H. Song, A. Zomaya, S. Khan, System modelling and performance evaluation of a three-tier cloud of things, Future Gener. Comput. Syst. 70 (2016) 104–125.
[36] J. Liu, E. Ahmed, M. Shiraz, A. Gani, R. Buyya, and A. Qureshi, “Application partitioning algorithms in mobile cloud computing: Taxonomy, review and future directions,” Journal of Network and Computer Applications, vol. 48,pp. 99–117, 2015.
[37] ETSI White Paper No.11 - Mobile Edge Computing: A key technology towards 5G
[38] E. Ahmed, A. Gani, M. Sookhak, S. H. Ab Hamid, and F. Xia, “Application optimization in mobile cloud computing: Motivation, taxonomies, and open challenges,” Journal of Network and Computer Applications, vol. 52, pp.52–68, 2015.
[39] Ashutosh Gupta, Praveen Dhyani, O.P. Rishi, Vishwambhar Pathak, "A Novel Service Broker Policy for e-Governance using Federation of cloud", International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.515-521, 2018.

Komal Kharbanda, Kiranbir Kaur, "Mobile Edge Computing: Review and Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.525-532, 2019.

Aspect oriented programming is a young concept in Computer science. It is succeeding from research projects towards commercial applications. Most of the current AOP tools suitable for commercial projects are proposed for Java platform only, which bounds their applicability. AspectJ is the leading tool for Java technology, the only way to implement a new programming paradigm such as Aspect Oriented Programming is either to extend the Java language or to develop Java API to support it. For .NET, the situation is different — it is a multilanguage programming environment. Today, Aspect Oriented Programming is supported in most languages and platforms. For Microsoft .NET, PostSharp is the most advanced and mature framework, and has been used commercially for several years. There are various known Aspect Oriented Programming tools for Microsoft.NET also. This paper present the analysis and overview of the all various popular AOP tools for .Net framework in detail.

Aspect Oriented Programming, .Net, AOP tools, CLR, MSIL

[1] Jatin Arora, Jagandeep Singh Sidhu and Pavneet Kaur, "Applying Dependency Injection Through AOP Programming to Analyze the Performance of OS", International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.45-50, 2015.
[2] Geeta Bagade, Shashank Joshi, "Analysis of Aspect Oriented Systems: Refactorings using AspectJ", International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.76-80, 2016.
[3] Safonov, V. O. (Vladimir Olegovich) Using aspect-oriented programming for trustworthy software development /Vladimir O. Safonov. p. cm. ISBN 978-0-470-13817-5QA76. 64. S253 2008.
[4] Safonov V. Aspect.NET: a new approach to aspect - oriented programming, .NET Developer’s Journal 2003 ;( 4): 36 – 40.
[5] LOOM.NET Web pages. Available at http://www.rapier - loom.net/.
[6] Mono. Available at http://www.mono - project.com.
[7] AspectDNG Web pages. Available at http://sourceforge.net/projects/aspectdng/.
[8] Aspect# Web pages. Available at http://www.castleproject.org/aspectsharp/.
[9] PostSharp Web pages. Available at http://www.postsharp.org/.
[10] DotSpect Web pages. Available at http://dotspect.tigris.org/.
[11] Encase Web pages. Available at http://theagiledeveloper.com/articles/Encase.aspx.
[12] Compose* Web pages. Available at http://composestar.sourceforge.net/.
[13] Weave.NET. Available at http://www.dsg.cs.tcd.ie/dynamic/?category_id= - 26.
[14] Wicca and Phx.Morph Web site. Available at http://www.cs.columbia. edu/eaddy/wicca.
[15] Microsoft Phoenix. Available at http://research.microsoft.com/phoenix.
[16] Microsoft Managed Debugger (mdbg) Web pages. Available at http://msdn. microsoft.com/msdntv/episode.aspx?xml=episodes/en/20060302clrjs/manifest.xml.
[17] Seasar.Net Web pages available at http://s2container.net.seasar.org/en/index.html
[18] Spring.Net Framework for Aop Available at http://www.springframework.net/doc-latest/reference/html/aop.html.
[19] Angela Hantelmann, Cui Zhang: “Adding Aspect-Oriented Programming Features to C#.NET by using Multidimensional Separation of Concerns (MDSOC) Approach”, in Journal of Object Technology, vol. 5 no. 4 Mai-June 06, pp. 59-83.
[20] CrosscutterN Available at https://www.codeproject.com/Tips/CrossCutterN-A-Light-Weight-AOP-Tool-for-NET

P.R. Sarode, R.N. Jugele, "Aspect Oriented Programming Tools for .Net Framework," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.533-538, 2019.

A Smart Mirror is the most recent in innovative home stylistic theme and in the market. In view of the client studies and model execution, we present the improvement of an enhancing machine that fuses intelligent administrations of data, offered through a UI on the surface of a mirror. Our work depends on the possibility that we as a whole take a gander at the mirror when we go out, so for what reason wouldn`t the mirror end up smart. It is a custom mirror establishment that is both a mirror and a display. It takes information from the web including time, climate, stock reports, quotes and more to stay up with the latest. The smart mirrors, which continue to work today and will take place in future technology, provide users with both mirror and computer-assisted information services to the user. These systems can be connected to the Web, and the information in certain locations on the mirror may transmit data. In the smart mirror system performed in the working scope, using the Raspberry Pi 3 micro-controller card, the weather, time and location information taken from the Web services, the activity calendar information of the user, the phone rings information and the camera image is included. Some enhancements can be controlled via the microphone in the smart mirror, with the help of the voice commands.

Smart Mirror, Raspberry Pi, Node JS

[1] "Voice controlled automation system," in Multitopic Conference, 2008. INMIC 2008. IEEE International , vol., no., pp.508-512, 23-24 Dec. 2008 doi: 10.1109/ INMIC 2008.4777791
[2] Preeti Pannu Vaibhav Khanna, Yash Vardhan, Dhruva Nair, “Design and Development of a Smart Mirror Using Raspberry PI”, IJEEDC, Volume-5, Issue 1, January 2017
[3] D.K. Mittal, R. Rastogi, A Comparative Study and New Model for Smart Mirror, International Journal of Scientific Research in Research Paper. Computer Science and Engineering Vol.5, Issue.6, pp.58-61, December (2017)
[4] Jadhav, Gaurav, Kunal Jadhav, and Kavita Nadlamani. "Environment Monitoring System Using Raspberry-Pi". International Research Journal of Engineering and Technology (IRJET) Volume: 03.Issue: 04 (2016)
[5] Piyush Maheshwari, Maninder Jeet Kaur, Sarthak Anand, “Smart Mirror: A Reflective Interface to Maximize Productivity”, International Journal of Computer Applications (0975 – 8887), Year: May-2017.
[6] Biljana Cvetkoska, Ninoslav Marina, Dijana Capeska Bogatinoska, Zhanko Mitreski, "Smart mirror E-health assistant — Posture analyze algorithm proposed model for upright posture", Smart Technologies IEEE EUROCON 2017 -17th International Conference on, pp. 507-512, 2017.
[7] Jonathan Rodriguez, Zhenyu Zhou, “;An IoT-Based E-Health Monitoring System Using Rapsberry Pi” GLOBECOM 2017 - 2017 IEEE Global Communications Conference, pp. 1-6, 2017.
[8] John See and Sze-Wei Lee, “An Integrated Vision-based Architecture for Home Security System,”IEEE Transactions on Consumer Electronics, Vol. 53, pp: 489-498, No. 2, May 2007M. Young,
[9] Derreck y Otros GOLD, "SmartReflect: A Modular Smart Mirror Application Platform", 2016 IEEE 7th Annual Information Technology Electronics and Mobile Communication Conference (IEMCON), 2016.
[10] Jun-Ren Ding, Chien-Lin Huang, Jin-Kun Lin, Jar-Ferr Yang and Chung-Hsien Wu, “Magic Mirror”,Ninth IEEE International Symposium on Multimedia 2007.

Pratibha Jha, Prashant Jha, Mufeed Khan, Kajol Mittal, "Smart Mirror: A Journey to the new world," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.539-545, 2019.

This paper analyzes, compares, and contrasts the various methods in text detection and extraction. Existing techniques are categorized as either stepwise or integrated. Text detection and extraction can be categorized into sub-problems including text localization, verification, segmentation and recognition. It gives an elaborate view of the various methods applied for these sub problems. A number of benchmark datasets are discussed in details with their attributes.

Text detection, text localization, text recognition, text segmentation, survey

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[12] C. Yi Y. Tian "Text string detection from natural scenes by structure-based partition and grouping" IEEE Trans. Image Process. vol. 20 no. 9 pp. 2594-2605 Sep. 2011.
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Sheetal Garg, Akshatha P S., Kavyashree C., "An Extensive Survey on Text Detection and Recognition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.546-551, 2019.

In this survey we will converse the classification algorithms which are useful on credit card datasets used for finding the scam detection of credit cards. The two major mechanisms to avoid frauds along with losses due to fraudulent actions are fraud avoidance and fraud discovery system. Fraud prevention is the practical mechanism with the objective of disable the incidence of fraud. Fraud discovery systems come interested in play when the fraudsters exceed the fraud prevention scheme and begin a fraudulent transaction. No one can recognize whether a fraudulent transaction has accepted the prevention method. Consequently, the objective of the fraud detection method is to ensure all transaction for the prospect of being fraudulent despite of the prevention method, along with to recognize fraudulent ones as rapidly as probable after the fraudster has begin to commit a fraudulent transaction.

Credit card, classification, SVM, fraud, Naïve Bayes

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Ashish Kumar, Shivank Kumar Soni, Chetan Agrawal, "A Survey Paper On Credit Card Fraud Detection Using Different Classifiers," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.552-559, 2019.

A kernel J of a digraph D is an independent set of vertices of D such that for every vertex w∈V(D)J there exists an arc from w to a vertex in J. The Mycielskian μ(D) of a digraph D = (V,A) is the digraph with vertex set V ∪ V^`∪ {u}, where V` = {v`:v ∈ V}, and the arc set A ∪ {(x,y^` ):(x,y)∈ A}∪ {(x^`,y):(x,y)∈ A}∪ {(x`,u):x` ∈ V`} ∪ {(u,x`):x` ∈ V`}. In this paper, we have proved that, for any digraph D, the Mycielskian of D, μ(D), contains a kernel.

Kernel, Mycielskian of a digraph

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R. Lakshmi, S. Vidhyapriya, "Kernels in Mycielskian of a Digraph," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.560-562, 2019.

The field of image processing is addressed significantly by the role of CBIR. Peculiar query is the main feature on which the image retrieval of content based problems is dependent. Relevant information is required for the submission of sketches or drawing and similar type of features. Many algorithms are used for the extraction of features which are related to similar nature. The process can be optimized by the use of feedback from the retrieval step. Analysis of color and shape can be done by the visual contents of image. Here neural network, Relevance feedback techniques based on image retrieval are discussed.

CBIR, Hypothesis, Image Retrival, Image Processing, Extraction

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S. Selvaraj, K. Seetharaman, "Content Based Image Retrivel on Non-Parametric Statistical Tests of Hypothesis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.563-568, 2019.