Journal Vol – 15 No -6, June 2020

Abstract:

Handover indicates transferring an ongoing telephone call or even information sessions from one cell to another. Handovers required due to the action of the mobile individual from one place to another place. Handovers are actually made use of to avoid an ongoing contact us to be actually separated. If we do not make use of handovers then whenever a user leaves the location of a certain tissue at that point its own ongoing call is instantly detached. The process of handovers needs a variety of guidelines e.g. what is actually the handover program we are making use of, how many stations are free of charge etc. In the handover procedure our service provider should additionally maintain the QoS approximately the specification. Vertical handover might be actually referred to a procedure of moving phone call attached to a network/data session from one network attached in a tissue to the core system of another.

Keywords:

Vertical handover,handoff,wireless,networks,

Refference:

I A. Hasswa, N. Nasser, H. Hassanein, Universal ethical handoff choice capacity for numerous wireless networks, 2005, Int. Conf. Wirel. Pick. Commun. Networks. (2005) 239– 243.
II A. Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131.doi: 10.1109/I-SMAC.2018.8653669
III Bura Vijay Kumar, YerrollaChanti, NagenderYamsani, SrinivasAluvala, BandiBhaskar, Design a Cost Optimum for 5g Mobile Cellular Network Footing on NFV and SDN, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-2S3, July 2019.
IV D. Todinca, C. Cernazanu-Glavan, Unfamiliar system variety formula based upon complex thinking, in: SACI 2013 – 8th IEEE Int. Symp. Appl. Comput. Intell. Informatics, Proc, 2013, pp. 467– 472.
V D. Deepika, a Krishna Kumar, MonelliAyyavaraiah, ShobanBabuSriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
VI J. Hou, D.C. O’Brien, Vertical handover decision-making formula making use of blurry reasoning for the packed radio-and-ow physical body, IEEE Trans. Wirel. Commun. 5 (2006) 176– 185.

VII Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
VIII Kiran Kumar S V N Madupu, “Data Mining Model for Visualization as a Process of Knowledge Discovery”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, ISSN: 2278 – 8875, Vol. 1, Issue 4, October 2012.
IX Kiran Kumar S V N Madupu, “Advanced Database Systems and Technology Progress of Data Mining”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319 – 8753, Vol. 2, Issue 3, March 2013
X KomuravellySudheer Kumar, J. Bhavana, “A Study on Data Mining towards Cloud Computing”, Indian Journal of Public Health Research & Development, Vol.9, No. 11,November 2018.
XI P. Pramod Kumar, S. Naresh Kumar, V. Thirupathi, Ch. Sandeep, “QOS AND SECURITY PROBLEMS IN 4G NETWORKS AND QOS MECHANISMS OFFERED BY 4G”, International Journal of Advanced Science and Technology, Vol. 28, No. 20, (2019), pp. 600-606
XII P. Pramod Kumar, K Sagar, “FLEXIBLE VERTICAL HANDOVER DECISION ALGORITHM FOR HETEROGENOUS WIRELESS NETWORKS IN 4G”, JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, Vol.-14, No.-6, November – December (2019) pp 54-66
XIII P Pramod Kumar and K Sagar 2019, “A Relative Survey on Handover Techniques in Mobility Management”, IOP Conf. Ser.: Mater. Sci. Eng. 594 012027
XIV P. Pramod Kumar, K. Sagar, “Vertical Handover Decision Algorithm Based On Several Specifications in Heterogeneous Wireless Networks”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), Volume-8 Issue-9, July 2019
XV Pramod Kumar P,Thirupathi V, Monica D, “Enhancements in Mobility Management for Future Wireless Networks”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 2, February 2013
XVI PushpaMannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XVII PushpaMannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XVIII PushpaMannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XIX Soumya, Pramod Kumar Poladi, VahiniSiruvoru. A Witness Oriented Secure Location Provenance Modelling for Location Proofs, International Journal TEST Engineering and Management, Volume 82, Jan-Feb 2020, Page Nos: 2793-2797, ISSN: 0193-4120.
XX SrinivasAluvala, K. Raja Sekhar, DeepikaVodnala, A novel technique for node authentication in mobile ad hoc networks, Perspectives in Science, Volume 8, 2016, Pages 680-682
XXI SiripuriKiran, ShobanBabuSriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XXII X. Yan, N. Peanut, Y.A. S¸ ekerciolu, A taking a trip stretch revelation set up technique to lower needless handovers arising from tissue systems to wlans, IEEE Commun. Lett. 12 (2008) 14– 16.

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Abstract:

The interdisciplinary field of Data Mining (DM) develops from the assemblage of statistics as well as machine learning (artificial intelligence). It provides a technology that helps to assess and also recognize the relevant information contained in a database, as well as it has been used in a large number of areas or requests. Exclusively, the idea DM originates from the correlation between the seek beneficial info in data banks as well as exploration useful minerals in a hill.

Keywords:

Data Mining,KDD process,security,

Refference:

I Bhavana Jamalpur, Komuravelly Sudheer Kumar, “Implementation of Bovw Model Towards Obtaining Discriminative Features of the Images”, International Journal of Advanced Science and Technology, Vol. 28, No. 17, (2019).
II Bhavana Jamalpur,“Analysis of Noise Reduction of Large Data sets Using Mathematical Tools in Data Mining”, International Journal of Pure and Applied Mathematics, Volume 120 No. 6 2018, 7061-7070 ISSN: 1314-3395
III D. Deepika, a Krishna Kumar, Monelli Ayyavaraiah, Shoban Babu Sriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
IV D. Ramesh, Sallauddin Md, Syed Nawaz Pasha “Enhancements of Artificial Intelligence and Machine Learning “ , International Journal of Advanced Science and Technology, vol 28,No.17(2019),pp-16-23
V Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi : https://doi.org/10.32628/CSEIT206271
VI Kiran Kumar S V N Madupu, “Tool to Integrate Optimized Hardware and Extensive Software into Its Database to Endure Big Data Challenges”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 5, pp. 272-279, September-October 2019. Available at doi : https://doi.org/10.32628/CSEIT206275

VII Kiran Kumar S V N Madupu, “Functionalities, Applications, Issues and Types of Data Mining System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 8, August 2017
VIII KomuravellySudheer Kumar, J. Bhavana, “A Study on Data Mining towards Cloud Computing”, Indian Journal of Public Health Research & Development, Vol.9, No. 11,November 2018.
IX Mohammed Ali Shaik, “A Survey on Text Classification methods through Machine Learning Methods”, International Journal of Control and Automation, Vol. 12, No.6, (2019), pp. 390 – 396.
X Mohammed Ali Shaik, “Time Series Forecasting using Vector quantization”, International Journal of Advanced Science and Technology, Vol. 29, No. 4, (2020), pp. 169-175.
XI Mohammed Ali Shaik, P.Praveen, Dr.R.VijayaPrakash, “Novel Classification Scheme for Multi Agents”, Asian Journal of Computer Science and Technology, ISSN: 2249-0701 Vol.8 No.S3, 2019, pp. 54-58.
XII Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131.doi: 10.1109/I-SMAC.2018.8653669
XIII P. Pramod Kumar, S. Naresh Kumar, V. Thirupathi, Ch. Sandeep, “QOS AND SECURITY PROBLEMS IN 4G NETWORKS AND QOS MECHANISMS OFFERED BY 4G”, International Journal of Advanced Science and Technology, Vol. 28, No. 20, (2019), pp. 600-606
XIV Praveen P., Rama B. (2018) A Novel Approach to Improve the Performance of Divisive Clustering- BST. In: Satapathy S., Bhateja V., Raju K., Janakiramaiah B. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542. Springer, Singapore
XV PushpaMannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XVI PushpaMannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013

XVII PushpaMannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XVIII S. Naresh Kumar, P. Pramod Kumar, C. H. Sandeep, and S. Shwetha. “Opportunities for applying deep learning networks to tumour classification.” Indian Journal of Public Health Research & Development, no. 11 (2018): 742-747.
XIX Sandeep, C. H., S. Naresh Kumar, and P. Pramod Kumar. “Security challenges and issues of the IoT system.” Indian Journal of Public Health Research & Development, no. 11 (2018): 748-753.
XX SiripuriKiran, ShobanBabuSriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XXI Thirupathi, Ch. Sandeep, S. Naresh Kumar, P. Pramod Kumar ,A COMPREHENSIVE REVIEW ON SDN ARCHITECTURE, APPLICATIONS AND MAJOR BENIFITS OF SDN, International Journal of Advanced Science and Technology, Volume 28, Issue 20, December 2019, Page Nos: 607-614, ISSN: 2005-4238.
XXII ThotapallyMounika Reddy, BhavanaJamalpur, “Dynamic and Secure Ranked Keyword Search over Encrypted Cloud Data”,International Journal of Research,Volume 05 Issue 07,March 2018.
XXIII Y.Chanti, J. Bhavana, “Fast Nearest Neighbor Search Partial Query With Keyword”,International Journal For Technological Research In Engineering ,Volume 3, Issue 4, December-2015, ISSN (Online): 2347 – 4718.

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Abstract:

Abstract There are limited studies that address the quality of measurement items in motor observation questionnaire for teachers (MOQ-T)even though their use has gained attention locally. One aspect of the item quality that can be reviewed is content validity through expert consensus. Therefore, this study aims to examine the content validity of MOQ-T instrument items using an expert panel. A total of 15 experts in the areas of measurement and evaluation, occupational therapy, motor development and special education were selected for this study through purposive sampling. A total of 18 MOQ-T items were analysed using Content Validity Ratio (CVR) analysis and the items were reviewed through email correspondence and face to face during meeting sessions with experts. The findings showed that all items are significant as they exceed the critical CVR value of 0.49. However, one new item was added as one of the items was broken down to two sentences in response to expert suggestions to avoid items that are 'double barrelled' where conjunctions like 'and’ are used to describe two different issues for one intended response. Subsequently, new items were derived to measure the skills needed. This study contributed to new MOQ-T with 19 items that can be used by teachers to study special needs students in Malaysia. For further research, it is proposed that new psychometric measurement theories, such as the Rasch measurement model can be added to improve the reliability of motor measurement items for teachers including MOQ-T. In addition, this study created an opportunity to review the localised version of MOQ-T that can be used for the initial screening of developmental coordination disorder (DCD) problems in the context of special needs students in Malaysia.

Keywords:

content validity,expert panel,content validity ratio, Motor observation questionnaire for teachers (MOQ-T),Developmental coordination disorder (DCD),

Refference:

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II. Ahari, M.N., Azad, A., Alizadeh-Zarei, M., Ebadi, A., Parand, A. &Mohammadi, P. “Development and Validity of the School Interim Competency of Performance Skill Battery Scale (SICPSBS)”. International Journal of Paediatrics-Mashhad 6(11): 8451–8473, 2018.

III. Ahmad, N.A., Drus, S.M., Kasim, H. & Othman, M.M. “Assessing Content Validity of Enterprise Architecture Adoption Questionnaire (EAAQ) Among Content Experts”. Proceedings of 2019 IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE) 160–165, 2019.

IV. Ali, N., Tretiakov, A. &Whiddett, D. “A Content Validity Study for a Knowledge Management System Success Model in Healthcare”. Journal of Information Technology Theory and Application (JITTA) 15(2): 3, 2014.

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VIII. Asunta, P., Viholainen, H., Ahonen, T. &Rintala, P. “Reliability and validity of theFinnish version of Motor Observation Questionnaire for Teachers (MOQ-T-FI)”:Abstracts of 11th International Conference on Developmental Coordination Disorder (DCD11) . Journal of Comorbidity 5(2): 32, 2015.
IX. Asunta, P., Viholainen, H,.Ahonen, T.,Cantell, M., Westerholm, J., Schoemaker, MM.,Rintala, P. 2017. “Reliability and validity of the Finnish version of the motor observation questionnaire for teachers”. Hum Movement Sci. 53:63–71, 2017.

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LXVI. Schoemaker, M.M., Flapper, B., Verheij, N.P., Wilson, B.N., Reinders-Messelink, H.A. & de Kloet, A. “Evaluation of the developmental coordination disorder questionnaire as a screening instrument”. Developmental Medicine and Child Neurology 48(8): 668–673, 2006.

LXVII. Schoemaker, M.M., Flapper, B.C.T., Reinders-Messelink, H.A. &Kloet, A. de. “Validity of the motor observation questionnaire for teachers as a screening instrument for children at risk for developmental coordination disorder”. Human Movement Science 27(2): 190–199, 2008.
LXVIII. Schoemaker MM, Niemeijer AS, Flapper BC, Smits-Engelsman BC. “Validity and reliability of the movement assessment battery for children-2 checklist for children with and without motor impairments”. Dev Med Child Neurol. 54(4):368–75, 2012.

LXIX. Sekaran, U. &Bougie, R. “Research Methods for Business A Skill-Building Approach”. Illinois: Wiley, 2016.

LXX. Shalbafan, M., Najarzadegan, M., Soraya, S., Rashedi, V., Najafian, R., Ahmadkhaniha, H., Seddigh, R., Hassanzadeh, M., Saeedi, V. &Kamalzadeh, L. “Validity and Reliability of the Persian Version of Internet Sex Screening Test in Iranian Medical Students”. Sexual Addiction & Compulsivity 26 2019 3-4: 1–10, 2019.

LXXI. Shorten, A. &Moorley, C. “Selecting the sample”. Evidence-Based Nursing 17(2): 32–33, 2014.

LXXII. Shrotryia, V.K. &Dhanda, U. “Content Validity of Assessment Instrument for Employee Engagement”. SAGE Open January-March 2019: 1-7, 2019.

LXXIII. Sourani, A. &Sohail, M. “The Delphi Method: Review and Use in Construction Management Research”. International Journal of Construction Education and Research 11(1): 54–76, 2015.

LXXIV. Stevenson, W.A. “Examining School Readiness”. PhD Dissertation: University of Kentucky, 2019.

LXXV. Swami, V. & Barron, D. “Translation and validation of body image instruments: Challenges, good practice guidelines, and reporting recommendations for test adaptation”. Body Image 1–17. https://doi.org/10.1016/j.bodyim.2018.08.014, 2018.

LXXVI. Swan, M.A. & Hobbs, B.B. “Querying Rural Content Experts Using an Online Questionnaire”. Online Journal of Rural Nursing and Health Care 18(2): 189–203, 2018.

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Abstract:

This paper gives a complete assessment of the various strategies used to decorate the skills of Low Voltage Ride Through (LVRT) of Double Fed Induction Generators (DFIG) primarily based wind turbine systems (WT). As the world is using about 20% to 25% of renewable energy from wind using DFIG primarily based WT machine is at once connected to the grid without the digital interface of power, as a result the terminal voltage or reactive electricity output can’t manage. Therefore, unique LVRT approaches based at the implementing additional active interface technologies had been proposed within this paper. Many techniques are developed nowadays to overcome the issue of this low voltage due to faults. This paper tries to define such active methods to short the gap by way of presenting a complete analysis of these LVRT strategies for DFIG based WECS in terms of overall adaptive performance, complexity of controllers, and cost effectiveness. Here characteristic of this paper is to highlight the methods for increasing the ability of LVRT relying on the configuration of the relationship into 3 major areas according to its grid integrations. In this paper hybrid (series-shunt) connections of FACT devices are used in WECS to study its effectiveness and benefits. The mathematical models of the whole system are simulated through MATLAB simulink and results are discussed.  

Keywords:

LVRT, DFIG,WT,FACT,WECS,

Refference:

I. Abdulhamed Hwas, Reza Katebi, Wind Turbine Control Using PI Pitch Angle Controller, IFAC Proceedings Volumes, Volume 45, Issue 3, 2012, Pages 241-246, ISSN 1474-6670, ISBN 9783902823182, https://doi.org/10.3182/20120328-3-IT-3014.00041.
II. B. P. Ganthia, V. Agarwal, K. Rout and M. K. Pardhe, “Optimal control study in DFIG based wind energy conversion system using PI & GA,” International Conference on Power and Embedded Drive Control (ICPEDC), Chennai, 2017, pp. 343-347.
III. Bekhada, Hamane Doumbia, Mamadou, BOUHAMIDA, Mohamed Draou, Azeddine CHAOUI, Hichamn Benghanem, Mustapha, “Comparative Study of PI, RST, Sliding Mode and Fuzzy Supervisory Controllers for DFIG based Wind Energy Conversion System”, International Journal of Renewable Energy Research (IJRER), Volume – 5, 2015/12/26, Page 1174 – 1185.
IV. Djeriri, Youcef & Meroufel, Abdelkader & Massoum, Ahmed & Boudjema, Zinelaabidine. (2014). A comparative study between field oriented control strategy and direct power control strategy for DFIG. Journal of Electrical Engineering. 14. 169-178.
V. B. P. Ganthia, S. Mohanty, P. K. Rana and P. K. Sahu, “Compensation of voltage sag using DVR with PI controller,” 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, 2016, pp. 2138-2142, doi: 10.1109/ICEEOT.2016.7755068.
VI. B.P. Ganthia, P.K. Rana, T. Patra, R. Pradhan and R. Sahu, “Design and Analysis of Gravitational Search Algorithm Based TCSC Controller in Power System”, Materials Today: Proceedings, vol. 5, no. 1, pp. 841-847, 2018.
VII. Moghadasi,aA.,aSarwat,aA.,a&aGuerrero,aJ.aM.a(2016).aAaComprehensiveaReviewaofaLow-Voltage-Ride-ThroughaMethodsaforaFixed SpeedaWindaPoweraGenerators.aRenewablea&aSustainableaEnergyaReviews,a55,a823–839.ahttps://doi.org/10.1016/j.rser.2015.11.020.
VIII. Ganthia, B.P., Rout, K.: Deregulated power system based study of agc using pid and fuzzy logic controller. Int. J. Adv. Res. 4(06) (2016)
IX. Siraj, Kiran, HarisSiraj, and MashoodNasir. “Modeling and control of a doubly fed induction generator for grid integrated wind turbine.” Power Electronics and Motion Control Conference and Exposition (PEMC), 2014 16th International. IEEE, 2014.
X. S. M. Muyeen, Md. Hasan Ali, R. Takahashi, T. Murata, J. Tamura, Y. Tomaki, A. Sakahara and E. Sasano, “Comparative Study on Transient Stability Analysis of Wind Turbine Generator System Using Different Drive Train Models”, IET Renewable Power Generation, Vol. 1, No, 2, pp. 131-141, June 2007.
XI. Zhang, B.; Hu, W.; Hou, P.; Tan, J.; Soltani, M.; Chen, Z. “Review of Reactive Power Dispatch Strategies for Loss Minimization in a DFIG-based Wind Farm” Energies 2017, 10, 856.

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Abstract:

The DC-DC converter steps up or steps down depending upon the application requirement. In soft robots, they are used to amplify the signal from milli-volts to several kilovolts. This allows soft robots to obtain diverse features. The current work describes the details of DC-DC converter based on the Cockcroft Walton Multiplier circuit which is developed to control the output voltage required to electrically potentially induce the actuation of dielectric elastomer films used in the bio-mimetic eye. Soft robots require careful manipulation of voltage and current signals. The input to converter is 12V Alternating Voltage whereas the output is 3.7kV. Dielectric elastomer films require voltages in several kilovolts for actuation. This converter is suitable for soft robot applications because of being low cost, lightweight and portability. In the study, a multiplier circuit is constructed based on the Cockcroft Walton generator.

Keywords:

Electro-active (EA),dielectric elastomer (DE),Human Machine Interference (HMI),Switched Mode Power Supply (SMPS),Cockcroft Walton Multiplier (CWM),

Refference:

I. Andrea Mari˜no-L´opez, Ana Sousa-Castillo, Enrique Carb´o-Argibay, Francisco Otero-Espinar, Ramon A Alvarez-Puebla, MoisesP´erezLorenzo, and Miguel A Correa-Duarte. Laser-protective soft contact lenses: Keeping an eye on the eye through plasmonics. Applied Materials Today, 15:1–5, 2019.
II. Chandra Shekhar and Shirshu Varma. An optimized 2.4 ghzrf energy harvester for energizing low-power wireless sensor platforms. Journal of Circuits, Systems and Computers, 28(06):1950104, 2019.
III. Chitra Sharma, AK Jhala, and Manish Prajapati. Selection of passive component for Cockcroft walton voltage multiplier: A low cost technique. International Research Journal of Engineering and Technology, 3(2):667–671, 2016.
IV. CK Dwivedi and MB Daigvane. Multi-purpose low cost dc high voltage generator (60kv output), using cockcroft-walton voltage multiplier circuit. In 2010 3rd International Conference on Emerging Trends in Engineering and Technology, pages 241–246. IEEE, 2010.
V. David F Spencer, Rahmat Aryaeinejad, and Edward L Reber. Using the cockroft-walton voltage multiplier with small photomultipliers. IEEE Transactions on nuclear Science, 49(3):1152–1155, 2002.
VI. David Frazer Spencer, Rahmat Aryaeinejad, and EL Reber. Using the cockroft-walton voltage multiplier design in handheld devices. In 2001 IEEE Nuclear Science Symposium Conference Record (Cat. No. 01CH37310), volume 2, pages 746–749. IEEE, 2001.
VII. DeveshMalviya, AK Bhardwaj, Mangesh Borage, and Sunil Tiwari. Simulation studies on current fed cockroft-waltonmultiplier. In Proceedings of the seventh DAE-BRNS Indian particle accelerator conference: book of abstracts, 2015.
VIII. Dmitri Vinnikov, Andrii Chub, Oleksandr Korkh, Elizaveta Liivik, FredeBlaabjerg, and Samir Kouro. Mppt performance enhancement of low cost pv micro converters. Solar Energy, 187:156–166, 2019.
IX. Edgar Everhart and Paul Lorrain. The cockcroft-walton voltage multiplying circuit. Review of Scientific Instruments, 24(3):221–226, 1953.
X. Ehsan Hajiesmaili and David R Clarke. Reconfigurable shape-morphing dielectric elastomers using spatially varying electric fields. Nature communications, 10(1):183, 2019.
XI. Hui Zhang, Min Dai, and Zhisheng Zhang. The analysis of transparent dielectric elastomer actuators for lens. Optik, 178:841–845, 2019.

XII. Jianing Wang, Sjoerd WH de Haan, JA Ferreira, and Peter Luerkens. Complete model of parasitic capacitances in a cascade voltage multiplier in the high voltage generator. In 2013 IEEE ECCE Asia Downunder, pages 18–24. IEEE, 2013
XIII. Jinrong Li, Yang Wang, Liwu Liu, Sheng Xu, Yanju Liu, JinsongLeng, and Shengqiang Cai. A biomimetic soft lens controlled by electrooculographic signal. Advanced Functional Materials, page 1903762, 2019.
XIV. Lukas M¨uller and Jonathan W Kimball. High gain dc–dc converter based on the cockcroft–walton multiplier. IEEE Transactions on Power Electronics, 31(9):6405–6415, 2015.
XV. Manxin Chen, Changqing Yin, Poh Chiang Loh, and Adrian Ioinovici. Improved large dc gain converters with low voltage stress on switches based on coupled-inductor and voltage multiplier for renewable energy applications. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2019.
XVI. NA Azmi, RC Ismail, SS Jamuar, SAZ Murad, MNM Isa, WY Lim, and MA Zulkifeli. Design of dc high voltage and low current power supply using cockroft-walton (cw) voltage multiplier. In 2016 3rd International Conference on Electronic Design (ICED), pages 13–17. IEEE, 2016.
XVII. Nor AAzmi, Sohiful AZ Murad, Azizi Harun, and Rizalafande C Ismail. 5v to 6kv dc-dc converter using switching regulator with Cockcroft Walton voltage multiplier for high voltage power supply module. Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), 12(2):162–171, 2019.
XVIII. Qiao Chen, Bin Zi, Zhi Sun, Yuan Li, and QingsongXu. Design and development of a new cable-driven parallel robot for waist rehabilitation. IEEE/ASME Transactions on Mechatronics, 2019.
XIX. RosaliaMoreddu, Daniele Vigolo, and Ali K Yetisen. Contact lens technology: From fundamentals to applications. Advanced healthcare materials, page 1900368, 2019.
XX. Sohiful Anuar Zainol Murad, Nor Afiqah Azmi, Azizi Harun, and Tun Zainal Azni Zulkifli. A novel 1.6 kv high voltage low current stepup dc-dc converter with cockcroft-walton voltage multiplier for power supply modules. Jurnal Teknologi, 81(5), 2019.
XXI. Stephen J Vincent and Daddi Fadel. Optical considerations for scleral contact lenses: A review. Contact Lens and Anterior Eye, 2019.
XXII. Trace Langdon. Very low power cockcroft-walton voltage multiplier for rf energy harvesting applications. 2019.
XXIII. Yu Qiu, Elric Zhang, Roshan Plamthottam, and Qibing Pei. Dielectric elastomer artificial muscle: Materials innovations and device explorations. Accounts of chemical research, 52(2):316–325, 2019.

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Abstract:

In the current article, we would give some results related to concave function and introduce the definition of concaviable function and new notion of concaviable functions and obtain the new results in which involved concaviable function and we would obtain new major ization type results for weighted concaviable function. This article also recaptures the similar results for concave function as well as for convex function.

Keywords:

Concave Function,Convex Function,Concavifiable Function,Majorization,

Refference:

Adil Khan, Majorization theorems for convexifiable functions, Math. Commun.,18 (2013), 61–65.

Asif R. Khan and FarazMehmood, Some Remarks on Functions with Non-decreasing Increments, Journal of Mathematical Analysis, 11 (1) (2020), 1–16.

Asif R. Khan, FarazMehmood, Faisal Nawaz and AamnaNazir, Some Remarks on Results Related to ∇−Convex Function, Submitted.

Asif R. Khan, FarazMehmood and M. AzeemUllahSiddique, Some Results Related to Convexifiable Functions, to appear.

EhtishamKarim, Asif R. Khan and SyedaSadia Zia, On Majorization Type Results, Commun. Optim.Theory, 2015, 2015:5, 1–17.

FarazMehmood, On Function with Nondecreasing Increments, (Unpublished doctoral dissertation), Department of Mathematics, University of Karachi, Karachi, Pakistan, 2019.

G. H. Hardy, J. E. Littlewood, G. Po ́lya, Inequalities, 2nd Ed. Cambbridge University Press, England, (1952).

I. C. P. Niculescu and L. E. Persson, Convex functions and their applications,A contemporary approach, Springer, New York, (2006).

J. E. Pec ̌aric ́, F. Proschan and Y. L. Tong, Convex functions, partial orderings and statistical applications, Academic Press, New York, 187(1992).

J. Karamata, Sur une ine ́galite ́ realitive aux fonctions convexes, Publ. Math. Univ. Belgrade, 1 (1932), 145–148.

Jr. W. A. Thompson and Darrel W. Parke, Some Properties of Generalized Concave Functions, Operations Research 21 1 (Jan. – Feb., 1973), 305–313.

L. Maligranda, J. E. Pecaric and L. E. Persson, Weighted favard and berwald inequalities, J. Math. Anal. Appl, (1995), pp.248–262.

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Martin J. Osborne, Mathematical methods for economic theory, version 2019-09-05, site built on the core of the OJS system.

Muhammad Adnan, A. R. Khan and FarazMehmood, Positivity of sums and integrals for higher order ∇−convex and completely monotonic functions, arXiv:1710.07182v1, [math.CA], 13 Oct 2017.

S. Zlobec, Characterization of convexiable function, Optimization 55(2006), 251–261.
Simon, Convex and concave function, Chapter 21, p. 505–522.

Z. Kadelburg, D. Dukic ́, M. Lukic ́, I. Matic ́, Inequalities of Karamata, Schur and Muirhead,and some application, The Teaching of Mathematics VIII (2005), 31–45.

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Abstract:

Aimed at the problems of Intensity, Contour and orientation information, a Human Action Recognition (HAR) method based on Fused Feature Vector (FFV) is proposed in this paper. The FFV is constructed based on three different features such as Intensity features, Gradient features, and Orientation features. These three set of features are obtained through three different feature extraction methods based on Gaussian Filter, Gradient Filter and Gabor filter. Further to ensure optimal discriminant subspace, Kernel Discriminant Analysis is employed as a dimensionality reduction technique. Given the FFV of each action image, Support Vector Machine (SVM) is employed for classification. The proposed recognition model is evaluated systematically on the three public datasets such as KTH dataset, Weizmann dataset and the challenging UCF YouTube action dataset. Experimental results prove that our method outperforms the conventional approaches in terms of recognition accuracy.

Keywords:

Human action recognition,Gaussian,Gradient,Gabor,Kernel ,Discriminant Analysis,Support vector Machine,Recognition Accuracy,

Refference:

I. A. Bobick and J. Davis, “The recognition of human movement using temporal templates,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 3, pp. 257–267, 2001.

II. A. Klaser and M. Marszalek, “A Spatio-temporal descriptor based on 3dgradients,” in Proc. Eur. Conf. Comput. Vision, 2008.

III. B. Mandal, How-Lung Eng, “Regularized Discriminant Analysis for Holistic Human Activity Recognition”, IEEE intelligent systems, 2012

IV. Bo Lin and Bin Fang, “A new spatial-temporal histograms of gradients descriptor and HOD-VLAD encoding for human action recognition”, International Journal of Wavelets, Multi-resolution and Information Processing, Vol. 17, No. 02, 2019.

V. B. Sch¨olkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT press, 2002.

VI. Chakraborty, B.; Holte, M.B.; Moeslund, T.B.; Gonzalez, J. Selective Spatio-temporal interest points. Comput. Vis. Image Underst. 2012, 116, 396–4100

VII. C Schuldt, I. Laptev, and B. Caputo, “Recognizing human actions: a local SVM approach,” in Proc. Int. Conf. Pattern Recognit., vol. 3, 2004, pp. 32–36.

VIII. C.Yang, M. Schmalz, W. Hu, and G. Ritter, “Center-surround filters for the detection of small targets in cluttered multispectral imagery: Background and filter design,” in Proc. SPIE, 1995, vol. 2496, pp. 637–648

IX. D.K. Vishwakarma and C. Dhiman, “A unified model for human activity recognition using spatial distribution of gradient and difference of Gaussian kernel”, Vis Comput. 35, 1595-1613, 2019.

X. D.K. Vishwakarma, PrachiRawat, and RajivKapoor, “Human Activity Recognition Using Gabor Wavelet Transform and Ridgelet Transform”, Procedia Computer Science,Volume 57, 2015, Pages 630-636.

XI. D. Song and D. Tao, “Biologically inspired feature manifold for scene classification,” IEEE Trans. Image Process., vol. 19, no. 1, pp. 174–184, Jan. 2010.

XII. Duta. I. C, Uijlings, J. R, Ionescu B, et al. Efficient Human Action recognition using Histograms of motion gradients and VLAD with descriptor shape information. Multimed Tools Appl. 76, 22445-22475, 2017.

XIII. D. Weinland and E. Boyer, “Action recognition using exemplar-based embedding,” in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2008, pp. 1–7.

XIV. G.Cheng, Y. Wan, A. N. Saudagar, K. Namuduri, and B. P. Buckles, “Advances in human action recognition: A survey,” New J. Phys., vol. 17, no. 8, pp. 1_30, 2015.

XV. G.Willems, T. Tuytelaars, and L. Van Gool, “An efficient dense and scale-invariant Spatio-temporal interest point detector,” in Proc. Eur. Conf. Comput. Vision, 2008, pp. 650–663.

XVI. I Laptev and T. Lindeberg, “Space-time interest points,” in Proc. IEEE Int. Conf. Comput. Vision, 2003, pp. 432–439.

XVII. J. Arunnehru1 and M. KalaiselviGeetha, “Motion Intensity Code for Action Recognition in Video Using PCA and SVM”, In: prasath R., Kathirvalavakumar T. (eds) Mining Intelligence and knowledge Exploration Lecture notes in computer science, Vol.8284, Spriger, Cham.

XVIII. Jin Wang et al. “Human action recognition based on Pyramid Histogram of Oriented Gradients”, IEEE International Conference on Systems, Man, and Cybernetics, AK, USA, 2011.

XIX. J. Liu, J. Luo and M. Shah, Recognizing realistic actions from videos “in the wild”, CVPR 2009, Miami, FL.

XX. Kishore K. Reddy, NareshCuntoor, AmithaPerera, Anthony Hoogs, “Human Action Recognition in Large-Scale Datasets Using Histogram of Spatiotemporal Gradients”, IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, Bejing China, 2012.

XXI. K. Ruben Raju, Yogesh Kumar Sharma, BirruDevender, “Composite Feature Vector Assisted Human Action Recognition through Supervised Learning”, International Journal of Recent Technology and Engineering (IJRTE), Volume-8 Issue-6, March 2020.

XXII. K. Soomro and A. R. Zamir, “Action recognition in realistic sports videos,” in Advances in Computer Vision and Pattern Recognition, vol. 71. Cham, Switzerland: Springer, 2014, pp. 181_208.

XXIII. M. B. Abidine, B. Fergani, “Evaluating a new classification method using PCA to human activity recognition”, ICCMA 2013: IEEEInternational Conference on Computer Medical Applications, At Sousse, Tunisia.

XXIV. M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri, “Actions as space-time shapes,” in Proc. IEEE Int. Conf. Comput. Vision, vol. 2, 2005, pp. 1395–1402.

XXV. M. Bregonzio, S. Gong, and T. Xiang, Recognizing action as clouds of space-time interest points, in CVPR, 2009.

XXVI. Md. Zia Uddin, J.J. Lee, and T.-S. Kim, “Shape-Based Human Activity Recognition Using Independent Component Analysis and Hidden Markov Model”, In: Nguyen N. T., Borzemski L., Grzech A., Ali M. (eds) New frontiers in applied artificial intelligence. IEA/AIE 2008. Lecture notes I computer science, vol. 5027, Springer, Berlin, Heidelberg.

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XXVIII. Michael W. Davidson, Mortimer Abramowitz, “Molecular Expressions Microscopy Primer: Digital Image Processing – Difference of Gaussians Edge Enhancement Algorithm”, Olympus America Inc., and Florida State University.

XXIX. Ning Ii, De Xu, “2D Log-Gabor wavelet based action recognition”, IEICE Trans.Inf& Sys. Vol.E92-D, No.11, 2009.

XXX. P A. Dhulekar, S. T. Gandhe, “Action recognition based on Histogram of Oriented gradients and Spatio-temporal interest points, IJET, 7(4), 2018, 2153-2160.

XXXI. ParulArora, SmritiSrivastava, KunalArora, ShreyaBareja, “Improved Gait Recognition using Gradient Histogram Gaussian Image”, Procedia Computer Science 58 ( 2015 ) 408 – 413.

XXXII. P. Doll´ar, V. Rabaud, G. Cottrell, and S. Belongie, “Behavior recognition via sparse spatio-temporal features,” in Proc. Joint IEEE Int. Workshop Visual Surveillance Perform. Eval. Tracking Surveillance, 2005, pp. 65–72.

XXXIII. R. Gonzalez and R. Woods, Digital image processing. Pearson/Prentice Hall, 2008.

XXXIV. R. Poppe, “A survey on vision-based human action recognition”, Image and Vision Computing 28 (2010) 976–990

XXXV. S Kanagamalliga, and S. Vasuki “Contour-based object tracking in video scenes through optical flow and Gabor features”, Optik, Volume 157, March 2018, Pages 787-797.

XXXVI. S. Maheswari and P. ArockiaJansi Rani, “RVM-based human action classification through Gabor and Haar feature extraction”, Int. J. Computational Vision and Robotics, Vol. 6, Nos. 1/2, 2016.

XXXVII. Su, Y., Li, Y. & Liu, A., “open-view human action recognition based on Linear Discriminant Analysis”, Multimedia tools Appl, 78, 767-782, 2019

XXXVIII. T. B. Moeslund, A. Hilton, and V. Kr¨uger, “A survey of advances in vision-based human motion capture and analysis,” Computer Vision and Image Understanding, vol. 104, no.2-3, pp. 90–126, 2006.

XXXIX. T. Ko, “A survey on behavior analysis in video surveillance for homeland security applications,” in Proc. 37th IEEE Appl. Imagery Pattern Recog. Workshop, Washington, DC, 2008, pp. 1–8.

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XLII. V. Thanikachalam and K.K. Thyagarajan, “Human Action Recognition using Accumulated motion and gradient of motion from video”, ICCCNT 2012.

XLIII. Y. Ke, R. Sukthankar, and M. Hebert, “Event detection in crowded videos,” in Proc. IEEE Int. Conf. Comput. Vision, 2007, pp. 1–8.

XLIV. Yuan Shen, Zhenjiang Miao, “Oriented Gradients for Human Action Recognition”, ICIMCS’10, December 30–31, 2010, Harbin, China.

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Abstract:

The objective of this paper is to monitor and control the environmental conditions for proper food grain repository. We have implemented a monitoring and controlling system that monitors and controls the weather parameters like Temperature, Humidity, Gas and Light intensity. The users can control and monitor the above said parameters of the repository using IOT. These sensor values are sent to the cloud. When these values get exceeded by the threshold values then the user can take an action against the conditions. By using of Thingspeak to retrieve the cloud sensor data is monitored and controlled.

Keywords:

ESP32,Temperature and Humidity Sensor (DHT11),Gas Sensor,Buzzer,Light Dependent Resistor (LDR),Thing Speak,

Refference:

I. Deepak, N., Rajendra Prasad, C., & Sanjay Kumar, S. (2018). “Patient health monitoring using IOT”, International Journal of Innovative Technology and Exploring Engineering, 8(2), 454–457. https://doi.org/10.4018/978-1-5225-8021-8.ch002.
II. Kannamma, M. &Baskar, Chanthini & Manivannan, D.. (2013). Controlling and monitoring process in industrial automation using Zigbee, pp:806-810. 10.1109/ICACCI.2013.6637279.
III. Mukesh. K and Ch. Rajendra Prasad, “Web Based Monitoring System for Nuclear Power Plant” International Journal of research and Applications July –September 2015 Transactions 2(7): 346-350(ISSN: 2394-4544), Volume 2 Issue 7. DOI: 10.17812/IJRA.2.7(59)2015.
IV. Prasad, C. R., & Bojja, P. (2020), “The energy-aware hybrid routing protocol in WBBSNs for IoT framework”, International Journal of Advanced Science and Technology, Volume-29, Issue-4, pp:1020–1028.
V. Pravalika, V., & Rajendra Prasad, C. (2019), “Internet of things based home monitoring and device control using Esp32”. International Journal of Recent Technology and Engineering, 8 (1 Special Issue 4), 58–62.
VI. Priyanka D., et.al, “Smart Food Quality Testing and Ordering System Using at Mega328 in Restaurants”, International Journal of Scientific Research and Engineering Development,Volume-3, Issues-1, Jan- Feb, 2020, pp: 645-650.
VII. Ramchandar Rao, P., Srinivas, S., & Ramesh, E. (2019). A report on designing of wireless sensor networks for IoT applications”, International Journal of Engineering and Advanced Technology, Volume-8, Issue-3, 2005-09, https://doi.org/10.35940/ ijeat.F1236.0986S319.
VIII. Sanjay Kumar, S., Ramchandar Rao, P., &Rajendra Prasad, C. (2019), “Internet of things based pollution tracking and alerting system”, International Journal of Innovative Technology and Exploring Engineering, volume-8, issue-8, pp: 2242–2245.
IX. Shreyas S K, Shridhar Katgar, Manjunath Ramaji, Yallaling Goudar, Ramya Srikanteswara, “Efficient Food Storage Using Sensors, Android and IoT”, International Journal Of Advanced Research in Basic Engineering Sciences and Technology (IJARBEST), Volume-3,Special Issue-23, April 2017, pp.8-12.
X. Suryawanshi VS &Kumbhar MS, “Real Time Monitoring & Controlling System for Food Grain Storage”, International Journal of Innovative Research in Science, Engineering and Technology, Volume-3, 2014, pp:734-738.
XI. TSGC, Tri-States Grain Conditioning, Inc., “Grain Temperature Monitoring Systems” www.tsgcinc.com
XII. Verdouw, Cor & Wolfert, Sjaak & Beulens, Adrie & Rialland, Agathe. (2015). Virtualization of food supply chains with the internet of things. Journal of Food Engineering. 176. 10.1016/j.jfoodeng.2015.11.009.
XIII. Vinayaka H, Roopa J “Intelligent System for Monitoring and Controlling Grain Condition Based on ARM 7 Processor”, India International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS),Volume-5, Issue-7, July 2016, pp:6-10, ISSN 2278-2540

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Abstract:

Recommender systems are implemented as information filtering agents. In most of the conventional recommender systems, the data about domain is available in limited volumes and suggestions are made to users based on their profile information. This lead to two major problems, insufficient representation of domain knowledge, called 'data sparsity' and lack of user-item interaction, called cold start. These two issues can be addressed with ontology based recommender systems, as they cam map domain information with user preferences without losing the semantic richness of the content. This work uses knowledge based method in knowledge aware recommendations to recommend most relevant research papers in digital literature collections. It uses simple methods to construct ontology knowledge graph and uses it for training incremental k-means clustering model. Finally, learning to rank, Adarank algorithm is used to list the top most recommendations for the given user query. The experiments were conducted based on real world unstructured datasets, and results have shown that the proposed model performs well over some of the state-of-the-art baselines.

Keywords:

Ontology,NLP,Recommender System,Knowledge Graph,Incremental Learning,Hybrid model,Semantic data model,

Refference:

I Agarwal, N., Haque, E., Liu, H., & Parsons, L. (2005, October). Research paper recommender systems: A subspace clustering approach. In International Conference on Web-Age Information Management. Springer, Berlin, Heidelberg. (pp. 475-491)
II Beel, J., Gipp, B., Langer, S., &Breitinger, C. (2016). paper recommender systems: a literature survey. International Journal on Digital Libraries, 17(4), 305-338.
III BessSchrader(2020). What’s the Difference Between an Ontology and a Knowledge Graph? Global Knowledge & Information Management Services. https://enterprise-knowledge.com/cms/assets/uploads/2020/01/Ontologies-vs.-Knowledge-Graphs.pdf.

IV Cao, Y., Wang, X., He, X., Hu, Z., & Chua, T. S. (2019, May). Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In The world wide web conference. (pp. 151-161).
V Cheyer, A. (2018). U.S. Patent No. 10,002,189. Washington, DC: U.S. Patent and Trademark Office.
VI Garanina, N., Sidorova, E., Kononenko, I., &Gorlatch, S. (2017). Using multiple semantic measures for coreference resolution in ontology population. International Journal of Computing, 16(3), 166-176.

VII Ge, J., Chen, Z., Peng, J., & Li, T. (2012, August). An ontology-based method for personalized recommendation. In 2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing (pp. 522-526). IEEE.
VIII George, G., &Lal, A. M. (2019). Review of ontology-based recommender systems in e-learning. Computers & Education, 142, 103642.
IX GiorgosPapachristoudis.(2019). Popular evaluation metrics in recommender systems explained. https:// medium.com/qloo/popular-evaluation-metrics-in-recommender-systems-explained-324ff2fb427d.
X Grossmann, R. (2019). The existence of the world: an introduction to ontology. Routledge. 06-Mar-Philosophy – 146 pages.
XI Iannacone, M., Bohn, S., Nakamura, G., Gerth, J., Huffer, K., Bridges, R., …&Goodall, J. (2015, April). Developing an ontology for cyber security knowledge graphs. In Proceedings of the 10th Annual Cyber and Information Security Research Conference (pp. 1-4).
XII Isinkaye, F. O., Folajimi, Y. O., &Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261-273.
XIII James Mishra. (2017).Mean Reciprocal Rank (MRR). https://machinelearning.wtf/terms/mean-reciprocal-rank-mrr/.
XIV Joachims, T., Granka, L., Pan, B., Hembrooke, H., & Gay, G. (2017, August). Accurately interpreting clickthrough data as implicit feedback. In ACM SIGIR Forum (Vol. 51, No. 1, pp. 4-11). New York, NY, USA: Acm.
XV Kawamura, T., Sekine, M., & Matsumura, K. (2017). Detecting Hypernym/Hyponym in Science and Technology Thesaurus Using Entropy-Based Clustering of Word Vectors. International Journal of Semantic Computing, 11(04), 433-449.
XVI Konys, A. (2018). Knowledge systematization for ontology learning methods. Procedia computer science, 126, 2194-2207.
XVII Li, G., & Chen, Q. (2016). Exploiting explicit and implicit feedback for personalized ranking. Mathematical Problems in Engineering.

XVIII Li, H. (2011). A short introduction to learning to rank. IEICE TRANSACTIONS on Information and Systems, 94(10), 1854-1862.
XIX Lv, F., Jin, T., Yu, C., Sun, F., Lin, Q., Yang, K., & Ng, W. (2019, November). SDM: Sequential deep matching model for online large-scale recommender system. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2635-2643).
XX Munir, K., &Anjum, M. S. (2018). The use of ontologies for effective knowledge modelling and information retrieval. Applied Computing and Informatics, 14(2), 116-126.
XXI Neethukrishnan, K. V., &Swaraj, K. P. (2017, February). Ontology based research paper recommendation using personal ontology similarity method. In 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1-4). IEEE.
XXII Obeid, C., Lahoud, I., El Khoury, H., &Champin, P. A. (2018, April). Ontology-based recommender system in higher education. In Companion Proceedings of the The Web Conference 2018 (pp. 1031-1034).
XXIII Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert systems with applications, 39(11), 10059-10072.
XXIV Parlak, B., &Uysal, A. K. (2019). On classification of abstracts obtained from medical journals. Journal of Information Science, 0165551519860982
XXV Pedregosa et al., (2011).Scikit-learn: Machine Learning in Python, JMLR 12, pp. 2825-2830.

XXVI Prokofyev, R., Tonon, A., Luggen, M., Vouilloz, L., Difallah, D. E., &Cudré-Mauroux, P. (2015, October). SANAPHOR: Ontology-based coreference resolution. In International Semantic Web Conference (pp. 458-473). Springer, Cham.
XXVII Raj.(2019). Creating Smart — Knowledge Base Systems (KBS) using advanced NLP library.Towards Data Science.May 4. https://towardsdatascience.com/creating-smart-knowledge-base-systems-kbs-using-advanced-nlp-library-b5c21dfafcd1.

XXVIII Sheridan, P., Onsjö, M., Becerra, C., Jimenez, S., &Dueñas, G. (2019). An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise. Future Internet, 11(9), 182.
XXIX Sure, Y., Tempich, C., &Vrandecic, D. (2006). Ontology engineering methodologies. Semantic Web Technologies: Trends and Research in Ontology‐based Systems, 171-190.
XXX Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., &Guo, M. (2019). Exploring high-order user preference on the knowledge graph for recommender systems. ACM Transactions on Information Systems (TOIS), 37(3), 1-26.
XXXI Weng, S. S., & Chang, H. L. (2008). Using ontology network analysis for research document recommendation. Expert Systems with Applications, 34(3), 1857-1869.
XXXII Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent trends in deep learning based natural language processing. ieee Computational intelligence magazine, 13(3), 55-75.
XXXIII Zhang, F., Yuan, N. J., Lian, D., Xie, X., & Ma, W. Y. (2016, August). Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 353-362).
XXXIV Thangaraj .M &ArunaSaraswathy.P. (2019, October). Ontology Based Recommender System using Fuzzy Clustering Technique. International journal of Engineering and Advanced Technology (IJEAT). 9(1). 6412-6418.

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Abstract:

The rapid development in the technology of information and the necessity of transferring the information lead to the importance of the valuable and sensitive information protection is the major demand of users.‏ Current research papers presenting a method for protection of a secret gray scale image and it is composed of four phases. First phase calculates the hash value using the SHA-256 type of hash function to make sure that there is no manipulating, altering or changing on the content of the secret image. The second phase is the encryption process for the secret image using the AES encryption algorithm. Third phase applied Shamir secret sharing scheme by splitting the encryption key of the encryption algorithm used in the previous phase into a number of shares. The final phase is for embedding secret image into an appropriate cover image using Discrete Wavelet Haar (DWH), the cover image is divided into four or more parts according to the iteration numbers that chooses manually. The Least Significant Bit (LSB) technique used for hiding the secret image in a cover image. The results obtained from the proposed method approved that the secret image completely restored without any change, moreover the correlation coefficient between the secret and the retrieved image is high. After the process of reconstruction of the stego image by the proposed method, the test results of quality of image were good with MSE 1.63 and PSNR 46.008 in Lena image.

Keywords:

DWH,AES,LSB,MES,PSNR,

Refference:

I. Ashutosh Gupta, and Sheetal Kaushik. “A Review: RSA and AES Algorithm.” IITM Journal of Management and IT, Vol.8, Issue 1, pp: 82-85, 2017.‏
II. Dahat, V. Ankush, and V. ChavanPallavi. “Secret sharing based visual cryptography scheme using CMY color space.” Procedia Computer Science, Vol 78, Issue C, pp: 563-570, 2016.‏
III. Essam H. Houssein, Mona AS Ali, and Aboul Ella Hassanien. “An image steganography algorithm using haar discrete wavelet transform with advanced encryption system.” 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), pp:641-644, 2016.‏
IV. Je SenTeh, Kaijun Tan, and Moatsum Alawida. “A chaos-based keyed hash function based on fixed point representation.” Cluster Computing, Vol. 22, Issue 2, pp: 649-660, 2019.‏
V. Jr. Wenceslao, V. Felicisimo “Enhancing the Performance of the Advanced Encryption Standard (AES) Algorithm Using Multiple Substitution Boxes.” International Journal of Communication Networks and Information Security, Vol. 10, issue 3, p.496, 2018.
VI. Kaiser J. Giri, Mushtaq Ahmad Peer, and P. Nagabhushan. “A robust color image watermarking scheme using discrete wavelet transformation.” IJ Image, Graphics and Signal Processing, Vol. 1, pp: 47-52, 2015.‏
VII. K. Shankar, and P. Eswaran. “Sharing a secret image with encapsulated shares in visual cryptography.” Procedia Computer Science, Vol. 70, pp: 462-468, 2015‏
VIII. K. Shankar, and P. Eswaran. “A new k out of n secret image sharing scheme in visual cryptography.” 2016 10th International Conference on Intelligent Systems and Control (ISCO), pp:1-6, 2016.‏
IX. K. Shankar, M. Elhoseny, R. S. Kumar, S. K. Lakshmanaprabu and X. Yuan, “Secret image sharing scheme with encrypted shadow images using optimal homomorphic encryption technique.” Journal of Ambient Intelligence and Humanized Computing, pp: 1-13, 2018.‏
X. L. Liu, Y. Lu, X. Yan, and S. Wan, “A progressive threshold secret image sharing with meaningful shares for gray-scale image.” 2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), pp:380-385, 2016.‏
XI. M. Abdullah, “Advanced encryption standard (AES) algorithm to encrypt and decrypt data”. Cryptography and Network Security, Vol. 16, 2017.
XII. M. M. Abdulwahid, O. A. S. Al-Ani, M. F. Mosleh and R. A. Abd-Alhmeed. “Optimal access point location algorithm based real measurement for indoor communication”. In Proceedings of the International Conference on Information and Communication Technology, pp: 49-55, 2019.‏
XIII. M. S. Sudha, and T. C. Thanuja. “Randomly tampered image detection and self-recovery for a text document using Shamir secret sharing.” 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, pp: 688-691, 2016.‏
XIV. Po-Cheng Wu, and Liang-Gee Chen. “An efficient architecture for two-dimensional discrete wavelet transform.” IEEE Transactions on circuits and systems for video technology, Vol. 11, Issue 4, pp: 536-545, 2001.‏

XV. R. Rahim, N. Kurniasih, F. Handayanna, L. S. Dewi, E. G. Sihombing, E. Arisawati, and I. Sulistiyowati. “Enhanced pixel value differencing with cryptography algorithm”. In MATEC Web of Conferences, Vol. 197, p. 03011. EDP Sciences‏ 2018.
XVI. Sahar A. El_Rahman, “A comparative analysis of image steganography based on DCT algorithm and steganography tool to hide nuclear reactors confidential information.” Computers & Electrical Engineering, Vol. 70, pp: 380-399, 2018.‏
XVII. Thakral, Shaveta, and PratimaManhas. “Image Processing by Using Different Types of Discrete Wavelet Transform.” International Conference on Advanced Informatics for Computing Research. Springer, Singapore,pp: 499-507, 2018.‏
XVIII. V. Kalist, P. Ganesan, B. S. Sathish and J. M. M. Jenitha “Possiblistic-Fuzzy C-means clustering approach for the segmentation of satellite images in HSL color space”. Procedia Computer Science, Vol. 57, pp: 49-56, 2015.

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Abstract:

Let be an additive mapping of a 2-torsion free semiprime inverse semiring in to itself, satisfying holds for all , then is a centralizer.

Keywords:

Semiprime Semiring,Inverse Semiring,Commutator,Centralizer,Left (right) Centralizer,

Refference:

I. Bandlet and Petrich, Subdirect products of rings and distributive lattices, Proceedings of the Edinburgh Mathematical Society, 25, 155-171 (1982).
II. Golan, The theory of semirings with applications in mathematics and theoretical computer science, Longman Scientific & Technical; New York: Wiley, (1992).
III. Javed, Aslam and Hussain, On Condition (A2) of Bandlet and Petrich for inverse semirings, International Mathematical Forum, Vol.7, 2903−2914 (2012).
IV. Karvellas P.H., Inversivesemirings, J. Aust. Math. Soc. 18, 277 – 288 (1974).
V. Maryam K. Rasheed, Abdulrahman. H. Majeed, Some results of (α, β) derivations on prime semirings, Iraqi Journal of Science, Vol. 60, No.5, pp: 1154-116 (2019).
VI. Sara, Aslam and Javed, Oncentralizer of semiprime inverse semiring, Discuss. Math. Gen. Algebra and Applications, 36, 71 – 84 (2016).
VII. M. K. Sen and S. K. Maity, Regular additively inverse semirings, Acta Math. Univ. Comenianae, 1, 137-146 (2006).
VIII. Vukman, An identity related to centralizers in semiprime rings, Comment. Math. Univ. Carolin. 40, 3, 447–456 (1999).

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Abstract:

The concept of intuitionistic fuzzy d-filter of d-algebra introducing in this paper and also several properties are discussing, with studding some relations on this notation with the concept of intuitionistic fuzzy d-algebra.

Keywords:

d-algebra,filter,d-filter,intuitionistic fuzzy set,fuzzy set,

Refference:

I. A. K. Hassan, “fuzzy filter spectrum of d-algebra”, M.Sc. Thesis, Faculty of Education for Girls, University of Kufa. (2014)
II. D. Coker, “An introduction to intuitionistic fuzzy topological spaces”, Fuzzy Sets and Systems 88 (1997), 81–89.
III. J. Neggers, A. Dvurecenskij and H. S. Kim, “On d-fuzzy Function in d-algebras” foundations of physics, 30(2000), No. 10, 1807-1816.
IV. J. Neggers and H. S. Kim, “on d-algebra “, Math. Slovaca. 49(1999) No.1, 19-26.
V. K. Iseki, “An algebra Relation with Propositional Calculus” Proc. Japan Acad, 42 (1966) 26-29.
VI. K. T. Atanassov, “Intuitionistic fuzzy sets” , Fuzzy sets and Systems 35 (1986), 87–96.
VII. L. A. Zadeh, “Fuzzy set”,Inform. And Control. 8(1965), 338-353.
VIII. P. A. Ejegwa, S.O. Akowe, P.M. Otene, J.M. Ikyule,”An Overview On Intuitionistic Fuzzy Sets ” International Journal of scientific & technology research , 3(2014) , 3, 2277-8616
IX. P. J. Allen, H. S. Kim, and J. Neggers, “Companion d-algebra” , Math. Slovaca 57(2007), No. 2 , 93-106
X. Y. B. Jun, H. S. Kim and D.S. Yoo, “Intuitionistic fuzzy d-algebra”, Scientiae Mathematicae Japonicae Online, e-(2006), 1289–1297.
XI. Y. Iami and K. Iseki, “On Axiom System of Propositional Calculi XIV” Proc. Japan Acad, 42 (1966) 19-20.

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Abstract:

The aim of this project is to develop an android application software package “EASY CHITS” for small scale chit organizers who could not afford chit fund software. This is an end-to-end application  which covers almost all the activities involved in managing a chit, everything in this application is systematically organized and arranged for both the chit organizers and users, unlike other applications each and every activity is arranged in three modules namely total balance, chit details, history , which makes it simple to use and navigate through the entire application for chit organizers, In addition to that all the necessary information is included for users at the user end.Chit Funds are indigenous monetary establishments in India that consolidates credit and investment funds in a solitary plan. In a chit support plot, a gathering of people meet up for a foreordained timespan and add to a typical pool at customary interims. The quantity of chit plans enlisted has been diminishing throughout the years. The chit support individuals show that as much as 72 percent of the individuals take an interest in chit assets for sparing. Moreover, 96 percent of the current and non-current chit finance individuals feel that chit reserves are sheltered. Larger part of the current and non-current chit support individuals have a place with low-salary family units. Our discoveries point to the way that however chit reserves are a significant wellspring of money for independent companies and low-pay family units in India; there has been a general mass migration of low worth chit plans from the enrolled chit support showcase. This is for the most part in light of the fact that enlisted chit subsidizes think that its less worthwhile to serve the poor because of the expanded expense of working such plans forced by the controllers. We find that the chit finance industry tends to the reserve funds needs of individuals, is viewed as sheltered and furthermore offers credits at lower loan costs than moneylenders.               

Keywords:

Classification,Cluster,Easy chits,Android,UPI,

Refference:

I. http://business.mapsofindia.com/investment-industry/chit-funds.html

II. https://faculty.iima.ac.in/~iffm/literacy/Chit-fund-field-survey-report.pdf

III. https://ijrar.com/upload_issue/ijrar_issue_1459.pdf

IV. https://journals.sagepub.com/doi/abs/10.1177/097492921100300305.

V. http://shreyaschits.com/faq_aboutchits.html

VI. Mohammed Ali Shaik, P. Praveen, Dr. R. Vijaya Prakash, “Novel Classification Scheme for Multi Agents”, Asian Journal of Computer Science and Technology, ISSN: 2249-0701 Vol.8 No.S3, 2019, pp. 54-58.

VII. P. Praveen, B. Rama and T. Sampath Kumar, “An efficient clustering algorithm of minimum Spanning Tree,” 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, 2017, pp. 131-135.doi: 10.1109/AEEICB.2017.7972398

VIII. P. Praveen, B. Rama, “An Efficient Smart Search Using R Tree on Spatial Data”,Journal of Advanced Research in Dynamical and Control Systems, Issue 4,ISSN:1943-023x.

IX. Praveen P., Rama B. (2018) A Novel Approach to Improve the Performance of Divisive Clustering-BST. In: Satapathy S., Bhateja V., Raju K., Janakiramaiah B. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542. Springer, Singapore.

X. Praveen P., Rama B(2020). “An Optimized Clustering Method To Create Clusters Efficiently” Journal Of Mechanics Of Continua And Mathematical Sciences , ISSN (Online) : 2454 -7190 Vol.-15, No.-1, January (2020) pp 339-348 ISSN (Print) 0973-8975,https://doi.org/10.26782/jmcms.2020.01.00027

XI. Sallauddin Mohmmad, Dr. M. Sheshikala, Shabana,” Software Defined Security (SDSec):Reliable centralized security system to decentralized applications in SDN and their challenges”, Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 10-Special Issue, 2018, pp. (147-152).

XII. M. Sheshikala, D. Rajeswara Rao and R. Vijaya Prakash, Computation Analysis for Finding Co– Location Patterns using Map–Reduce Framework, Indian Journal of Science and Technology, Vol 10(8), DOI: 10.17485/ijst/2017/v10i8/106709, February 2017.

XIII. https://www.drishtiias.com/to-the-points/paper3/chit-fund

XIV. https://www.dvara.com/wp-content/uploads/2011/03/REPORT-Chit-Funds-Innovative-Access-to-Finance.pdf

XV. http://www.mca.gov.in/Ministry/pdf/Chit_Fund_Companies_6nov2008.pdf

XVI. http://www.telegraphindia.com/1130428/jsp/7days/story_16836319.jsp#.U4ghv_mSwoo

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Abstract:

One of the significant areas of Indian Economy is Agriculture. Work to practically half of the nation’s workforce is given by Indian horticulture segment. As a part of Agriculture, Cotton plays a major role in economic resource of Telangana. Huge number of farmers grows cotton in their fields as the lands fit to that crop. Beside the advantage the major problem affecting the crop are the diseases that are unknown to the farmers at early stages and losing the entire crop when he gets aware on that.  As a solution, we can identify the disease in the early stage and rectify before it affects the entire crop. This can be done by looking into images collected from the crop and given it as a test sample to the convolution neural network, where we test the sample with the existing training data and identify the major areas that are affected with the disease.  As an improvement we can also identify the disease that is also affected and apply the required pesticides. As a result, 91% of the diseases were correctly identified.

Keywords:

Neural Networks,Layers,Filter,Pooling,Padding,softmax,

Refference:

I. Aakanksha Rastogi, Ritika Arora, Shanu Sharma, “Leaf Disease Detection and Grading using Computer Vision Technology &Fuzzy Logic,” presented at the 2nd International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, 2015, pp. 500–505.
II. A. Harshavardhan, S. Babu and T. Venugopal, “An Improved Brain Tumor Segmentation Method from MRI Brain Images,” 2017 2nd International Conference On Emerging Computation and Information Technologies (ICECIT), Tumakuru, 2017, pp. 1-7.
III. Barbedo, J.G., 2018. Factors influencing the use of deep learning for plant disease recognition. Biosystems engineering, 172, pp.84-91.
IV. Harshavardhan, A. Mohammad, M.D. S. Ramesh, D. RaviChythanya, K,” Design methods for detecting sensor node failure and node scheduling scheme for WSN”, International Journal of Engineering and Advanced Technology 9 (1) ,pp.5430, 2019
V. Khirade, Sachin D. and A. B. Patil. “Plant disease detection using image processing.” In 2015 International conference on computing communication control and automation, pp. 768-771. IEEE, 2015.
VI. Liu, Bin, Yun Zhang, DongJian He, and Yuxiang Li. “Identification of apple leaf diseases based on deep convolutional neural networks.” Symmetry 10, no. 1 (2018): 11
VII. Md. Nazrul Islam, M.A. Kashem, MahmudaAkter and Md. Jamilur Rahman, “An Approach to Evaluate Classifiers for Automatic Disease Detection and Classification of Plant Leaf,” presented at the International Conference on Electrical, Computer and Telecommunication Engineering, RUET, Rajshahi-6204, Bangladesh, 2012, pp. 626–629.
VIII. Prakash, RajanalaVijaya, and SrinathTaduri. “Safe Navigation for Elderly and Visually Impaired People Using Adhesive Tactile Walking Surface Indicators in Home Environment.” In Information and Communication Technology for Sustainable Development, pp. 771-778. Springer, Singapore, 2020.
IX. Roopa, Goje, and M. Sampath Reddy. “A study on pattern matching intrusion detection system for providing network security to improve the overall performance of security system.” Indian Journal of Public Health Research & Development 9, no. 11 (2018): 683-687.
X. Sallauddin, M. Ramesh, D. Harshavardhan, A. Pasha, S.N. Shabana, “A comprehensive study on traditional AI and ANN architecture”, International Journal of Advanced Science and Technology 28 (17) ,pp.479, 2019
XI. Shaik, Mohammed Ali, P. Praveen, and R. VijayaPrakash. “Novel Classification Scheme for Multi Agents.”, Asian Journal of Computer Science and Technology 8, no. S3 (2019): 54-58.
XII. Traore, B.B. Kamsu-Foguem, B. and Tangara, F., 2018. Deep convolution neural network for image recognition. Ecological informatics, 48, pp.257-268.
XIII. Praveen P., Rama B(2020). “An Optimized Clustering Method To Create Clusters Efficiently” Journal Of Mechanics Of Continua And Mathematical Sciences , ISSN (Online) : 2454 -7190 Vol.-15, No.-1, January (2020) pp 339-348 ISSN (Print) 0973-8975, https://doi.org/10.26782/jmcms.2020.01.00027

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Abstract:

The engine performance, combustion characteristics and exhaust gas emissions of a four-cylinder, four-stroke indirect injection spark ignition engine has been numerically investigated at constant engine speed and different ignition timings when using gasoline, ethanol and LPG fuels. For this purpose, a model has been suggested by using a two-zone burnt and unburnt gas for in-cylinder combustion. The experimental data related to the cylinder pressures have been carried out to validate the engine model. The optimal effective power and effective torque were shown at advanced crank angle degrees before the top dead center. It is observed that the brake specific fuel consumption decreases if the ignition timings increase. The ethanol fuel exhausted a minimum level of carbon monoxide, unburnt hydrocarbon and oxide nitrogen emissions when compared with the gasoline fuel at all operating conditions. LPG fuel produced promising good emission results than that obtains from gasoline fuel.

Keywords:

LPG and Ethanol fuels,SI engine,Engine performance,Emissions,

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