Archive

Abstract:

The purpose of the study is to analyze the psychometric properties of a survey questionnaire, Malaysian Teachers’ Innovative Behavior Instrument (MTIB) using Rasch Measurement Model aided by Winstep software Version 3.73. The questionnaire was administered on 109 school teachers from Melaka. The data were analyses to examine the items functional accordingly from the aspect of items fit in measuring constructs, items polarity, unidimensionality, local independence and the reliability and separation of item and respondent. The Rasch analysis showed satisfying psychometric properties of MTIB after removal of some misfitting items. Fit statistic evaluation discovered that a sum of 10 items were out of range and leaving only 20 items remaining that are appropriate to measure the four constructs of the innovative behavior in the MTIB. Further analysis with the remaining 20 items revealed that each PTMEA Corr is in positive values and met the assumptions of unidimensionality and local independence. Reliability and separation index were also within acceptable range. As for future research, it is recommended that different studies should be organized by using a various sample to generate much better, detailed and comprehensive information which can be represented more extensively.

Keywords:

Innovative Behavior,Psychometric,Rash Model,Teacher,Instrument,

Refference:

I. Ariffin, T. F. T., Bush, T., & Nordin, H. “Framing the roles and responsibilities of excellent teachers: Evidence from Malaysia”. Teaching and Teacher Education, Volume: 73, pp. 14–23, 2018

II. Balsamo, M., Giampaglia, G., & Saggino, A. “Building a new Rasch-based self-report inventory of depression”.Neuropsychitric Disease and Treatment, Volume: 10, pp. 153–165. 2014

III. Bond, T. G., & Fox, C. M. Applying the rasch model: Fundamental measurement in the human sciences (3rd ed.). New York: Routledge. 2015

IV. Brem, A., Maier, M., & Wimschneider, C. “Competitive advantage through innovation: the case of Nespresso” European Journal of Innovation Management, Volume: 19, Issue: 1, pp. 133–148. 2016

V. De Jong, J., & Den Hartog, D. “Innovative Work Behavior: Measurement and Validation”. EIM Business and Policy Research, Volume: 8, Issue: 1,pp. 1–27. 2008

VI. Fisher, W. P. “Rating scale instrument quality criteria”. Rasch Measurement Transactions, Volume: 21, Issue: 1,pp. 1095. 2007

VII. George, L., & Sabapathy, T. “Work motivation of teachers: Relationship with organizational commitment”. Canadian Social Science, Volume: 7, Issue: 1,pp. 90–99. 2011

VIII. Hadi, F. H., Mohd, F., Ismail, N., & Nair, P. K. “Importance of Commitment in Encouraging Employees’ Innovative Behavior Introduction”. Asia-Pacific Journal of Business Administration, Volume: 8, Issue: 1,pp. 1–25. 2016

IX. Hattie, J. Visible learning: A synthesis of over 800 meta‐analyses relating to achievement. London: Routledge. 2009

X. Hon, A. H. Y., & Lui, S. S. “Employee creativity and innovation in organizations”. International Journal of Contemporary Hospitality Management, Volume: 28, Issue: 5,pp. 862–885. 2016

XI. Lee, W.-G., Jeon, Y.-H., Kim, J.-W., & Jung, C.-Y. “Effects of job security and psychological ownership on turnover intention and innovative behavior of manufacturing employees”. Journal of the Korea Safety Management and Science, Volume: 16, Issue: 1, pp. 53–68. 2014

XII. Linacre, J M. “A user’s guide to WINSTEPS: Rasch Model Computer Programs”. Chicago: Mesa-Press. 2016

XIII. Linacre, John M. “What do infit and outfit, mean-square and standardized mean? ” Rasch Measurement Transactions, Volume: 16, Issue: 2,pp. 878. 2002

XIV. Messmann, G., & Mulder, R. H. “Development of a measurement instrument for innovative work behaviour as a dynamic and context-bound construct”. Human Resource Development International, Volume: 15, Issue: 2, pp.43–59. 2012

XV. Naqshbandi, M. M. “Managerial ties and open innovation: examining the role of absorptive capacity”. Management Decision, Volume: 54, Issue: 9, pp.2256–2276. 2016

XVI. Ngann, S. W. “Hubungan Antara Pembelajaran Berorganisasi Dengan Tingkah Laku Kerja Inovatif Dalam Kalangan Guru Sekolah Rendah Bai’ah”. Universiti Pendidikan Sultan Idris. 2016

XVII. Noorsafiza, M. S. “Pembelajaran di Organisasi dan Persekitaran Kerja terhadap pembentukan tingkah laku kerja inovatif. ” Universiti Kebangsaan Malaysia. 2016

XVIII. Nur Atiqah, A. Modal Psikologi Positif & Nilai Kerja sebagai Peramal kepada Tingkah Laku Inovatif. Universiti Kebangsaan Malaysia. 2014

XIX. Serdyukov, P. “Innovation in education: what works, what doesn’t, and what to do about it? ” Journal of Research in Innovative Teaching & Learning, Volume: 10, Issue: 1, pp. 4–33. 2017. https://doi.org/10.1108/jrit-10-2016-0007

XX. West, M. A., & Farr, J. L. “Innovation at work: Psychological perspectives” Social Behaviour, Volume: 4, Issue: 1, 15–30. 1989.

XXI. Wu, M., & Adams, R. Applying the Rasch model to psycho-social measurement: A practical approach. Melbourne: Educational Measurement Solutions. 2007.

XXII. Zainal, M. A., & Matore, M. E.E. M. “Tingkah Laku Inovatif Sebagai Pemangkin Dalam Meneroka Idea Kamikaze Guru Pada Masa Depan”. Prosiding Seminar Kebangsaan Pendidikan Negara (SKEPEN) Ke-6. 2019, pp. 2337–2350. 2019

XXIII. Zhu, C. “Organisational culture and technology-enhanced innovation in higher education”. Technology, Pedagogy and Education, Volume: 24, Issue: 1, pp.65–79. 2015

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Abstract:

One of the major social networking services provided by the social network these days is image recommendation. As day to day trend is increasing, knowing the user preferences and recommending the images have become urgent need in social network. Earlier recommendation models or frameworks were done by considering upload history of the user and interests. Most of the previous models were not considering other factors like reaction to the image, admiration to the image, sharing, reporting the image and so on. This paper, proposes a new socio recommend framework by considering the above factors using aspect importance attention (AIAM) model which improve the recommendation of the images, which keeps users engaged with social networking app.

Keywords:

Attention aspect,Hit ratio,Cumulative Gain,

Refference:

I. Anagnostopoulos, R. Kumar, and M. Mahdian. Influence and correlation in social networks. In KDD, pages 7–15. ACM, 2008.

II. F. Gelli, T. Uricchio, X. He, A. Del Bimbo, and T.-S. Chua. Beyond the product: Discovering image posts for brands in social media.In MM. ACM, 2018.

III. G. Adomavicius and A. Tuzhilin. Toward the next generationof recommender systems: A survey of the state-of-the-art andpossible extensions. TKDE, 17(6):734–749, 2005.

IV. H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King. Recommender systems with social regularization. In WSDM, pages 287–296. ACM, 2011.

V. J. Chen, H. Zhang, X. He, L. Nie, W. Liu, and T.-S. Chua. Attentive collaborative filtering: Multimedia recommendation with itemand component-level attention. In SIGIR, pages 335–344. ACM, 2017.

VI. J. Tang, X. Shu, G.-J. Qi, Z. Li, M. Wang, S. Yan, and R. Jain. Triclustered tensor completion for social-aware image tag refinement. PAMI, 39(8):1662–1674, 2017.

VII. L. Wu, L. Chen, R. Hong, Y. Fu, X. Xie and M. Wang, “A Hierarchical Attention Model for Social Contextual Image Recommendation,” in IEEE Transactions on Knowledge and Data Engineering,doi: 10.1109/TKDE.2019.2913394.

VIII. L. Wu, P. Sun, R. Hong, Y. Ge and M. Wang, “Collaborative Neural Social Recommendation,” in IEEE Transactions on Systems, Man, and Cybernetics: Systems,doi: 10.1109/TSMC.2018.2872842.

IX. M. Jiang, P. Cui, F. Wang, W. Zhu, and S. Yang. Scalable recommendation with social contextual information. TKDE, 26(11):2789– 2802, 2014.

X. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. InUAI, pages 452–461. AUAI Press, 2009.

XI. S. Wang, Y. Wang, J. Tang, K. Shu, S. Ranganath, and H. Liu. What your images reveal: Exploiting visual contents for point-of-interest recommendation. In WWW, pages 391–400, 2017.

XII. T.-S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y. Zheng. Nuswide: a real-world web image database from national university of Singapore. In MM, page 48. ACM, 2009.

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Abstract:

This paper examines an (in) stability of an axially moving string system under the effect of external (viscous) damping. The string is taken to be fixed at both ends and general initial conditions are taken into consideration. The belt (string) speed is assumed to be non-constant harmonically varying about a relatively large means speed. The external damping is also considered to be small. Mathematically, the transverse vibrations of damped axially moving string system are modeled as second order linear homogeneous partial differential equation with variable coefficients. The approximate-analytic solution of the given initial-boundary value problem has been obtained by the application of two timescales perturbation method in conjunction of with Laplace transform method. It is found out that there are infinitely many values of resonant frequency parameter that gives rise to internal resonance in the system. However, in this study only non-resonant and the fundamental resonant cases has been studied. It turned out that the mode-response and the energy of system exhibits stability under certain values of damping parameter and mode-truncation for those parametric values is not problematic.

Keywords:

axially moving string,viscous damping,mode response,internal resonance,Laplace transform method,

Refference:

I. A. Maitlo, S. H. Sandilo, A. H. Sheikh, R. A. Malookani, and S. Qureshi, “On aspects of viscous damping for an axially transporting string”. Sci. int. (Lahore)., Vol. 28, No. 4, pp.3721–3727 (2016).

II. A. H. Nayfeh, “Introducation to Perturbation techniques”. John Wiley and Sons, Inc, 1981.

III. Darmawijoyo, W. T. Van Horssen and P. H. Clément, “On a Rayleigh wave equation with boundary damping”. Nonlinear Dyn., Vol. 33, pp. 399–429 (2003).

IV. Darmawijoyo and W. T. Van Horssen, “On boundary damping for a weakly nonlinear wave equation”. Nonlinear Dyn.,Vol.30, No. 2, pp.179–191(2002).

V. G. Suweken and W. T. Van Horssen, “On the transversal vibrations of a conveyor belt with a low and time-varying velocity. Part I: the string-like case”. J. Sound Vib.,Vol. 264, No. 1, pp.117–133 (2003).

VI. I. V. Andrianov and J. Awrejcewicz, “Dynamics of a string moving with time-varying speed”. J. Sound Vib.,Vol. 292, No. 3–5, pp.935–940 (2006).

VII. J. Kevorkian and J. D. Cole, “Multiple scale and singular perturbation methods”. Springer-Verlag, New Yark Inc.Vol. 114 (1996).

VIII. K. Marynowski and T. Kapitaniak, “Zener internal damping in modelling of axially moving viscoelastic beam with time-dependent tension”. Int. J. Non. Linear. Mech., Vol. 42, No. 1, pp. 118–131 (2007).

IX. L. Debnath and D. Bhatta, “Integral Transforms and their applications”. Chapman and Hall/CRC, second edit. (2007).

X. M. A. Zarubinskaya and W. T. van Horssen, “On aspects of boundary damping for a rectangular plate”. J. Sound Vib.,Vol. 292,No. 3–5, pp. 844–853 (2006).

XI. N. Jakšić and M. Boltežar, “Viscously damped transverse vibrations of an axially-moving string”. J. of Mech. Eng., Vol. 51, No. 9, pp. 560–569 (2005).

XII. N. V. Gaiko and W. T. Van Horssen, “On the transverse, low frequency vibrations of a traveling string with boundary damping”. J. Vib. Acoust. Trans. ASME.,Vol. 137, No. 4, pp. 10–12 (2015).

XIII. P. Zhang, J. H. Bao, and C. M. Zhu, “Dynamic analysis of hoisting viscous damping string with time-varying length”. J. Phys. Conf. Ser., Vol. 448, pp. 1-9 (2013).

XIV. Rajab A. Malookani, S. H. Sandilo, and A. H. Sheikh, “On (non) applicability of a mode-truncation of a damped traveling string”. Mehran Univ. research J. of Eng. & Tech., Vol. 38, No. 2, pp. 471–478 (2019).
XV. Rajab A. Malookani and W. T. Van Horssen, “On the asymptotic approximation of the solution of an equation for a non-constant axially moving string”. J. Sound Vib., Vol. 367, pp. 203–218 (2016).

XVI. Rajab A. Malookani, S. Dehraj, and S. H. Sandilo, “Asymptotic approximations of the solution for a traveling string under boundary damping”. J. of appl. and compt. Mech.,Vol. 5, No. 5, pp. 918–925 (2019).

XVII. R. A. Malookani and W. T. Van Horssen, “On resonances and the applicability of Galerkin’s truncation method for an axially moving string with time-varying velocity”. J. Sound Vib.,Vol.344, pp. 1–17 (2015).

XVIII. S. Krenk, “Vibrations of a taut cable with an external damper”. J. Appl. Mech., Vol. 67, No. 4, pp. 772–776 (2000).

XIX. S. H. Sandilo, “On boundary damping for an axially moving beam and on the variable length induced vibrations of an elevator cable”. ENOC (Conference paper) (2011).

XX. S. V. Ponomareva and W. T. Van Horssen, “On transversal vibrations of an axially moving string with a time-varying velocity”. Nonlinear Dyn.,Vol. 50, No. 1–2, pp. 315–323 (2007).

XXI. S. H. Sandilo and W. T. Van Horssen, “On boundary damping for an axially moving tensioned beam”. J. Vib. Acoust. Trans. ASME.,Vol.134, pp. 011005-1-011005-8 (2012).

XXII. S. H. Sandilo, Rajab A. Malookani, and A. H. Sheikh, “On vibrations of an axially moving beam under material damping”. IOSR J. Mech. Civ. Eng., Vol. 13, No. 05, pp. 56–61(2016).

XXIII. S. H. Sandilo, A. H. Sheikh, M. A. Soomro, and Rajab A. Malookani, “On oscillations of an axially translating tensioned string-like equation under internal damping”. Sci. int. (Lahore).,Vol. 28, No. 4, pp.3897–3901(2016).

XXIV. S. Dehraj, S.H. Sandilo, Rajab A. Malookani, “On applicabitlity of Galerkin’s truncation method for damped axailly moving string”. J. of Vibroeng., Vol. 22, No. 2, pp. 337-352 (2020).

XXV. T. Akkaya and W. T. van Horssen, “On constructing a Green’s function for a semi-infinite beam with boundary damping”.Meccanica., Vol. 52, No. 10, pp. 2251–2263 (2017).

XXVI. T. Akkaya and W. T. van Horssen, “On boundary damping to reduce the rain–wind oscillations of an inclined cable with small bending stiffness” Nonlinear Dyn.,Vol.95, No. 1, pp.783–808 (2019).

XXVII. V. S. Sorokin, “On the effects of damping on the dynamics of axially moving spatially periodic strings”. Wave motion, Vol. 85, pp. 165–175 (2019).

XXVIII. W. T. Van Horssen, “On the weakly damped vibrations of a string attached to a spring mass dashpot system”.Journal Vib. Control, Vol.9, No. 11, pp. 1231–1248 (2003).

XXIX. W. T. Van Horssen and S. V. Ponomareva, “On the construction of the solution of an equation describing an axially moving string”.J. Sound Vib., Vol. 287, No. 1–2, pp. 359–366 (2005).

XXX. Y. Li and Y. Tang, “Analytical analysis on nonlinear parametric vibration of an axially moving string with fractional viscoelastic damping”. Math. Probl. Eng., Vol. 2017,pp.1-9 (2017).

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Abstract:

Food Safety is largely important for any society because food is the major source of living. Also, there is an inherent lack of trust in the food sector. One simply cannot determine if the vegetables or meat that he/she is picking off the shelves of the supermarket aren’t genetically modified or not. Sure, one could manually trace back all records of the item in question and arrive at the decision, but that would take days upon days of unworthy effort. Thus, the consumption of such products is vulnerable to several diseases. Diseases transmitted through contaminated food are a persistent concern, not only for each one of us but also for governments. This article explains the influence of Blockchain in Food Safety. Implementing this technology in edible products makes food traceability possible, tracking products to their source for enhanced food authenticity and safety. Although the term Blockchain is widely known and Blockchain’s frameworks are finding applications in a variety of fields such as the Internet of Things, Artificial Intelligence, Banking, and healthcare; its framework can also be implemented to trace each processing stage of a food product. A ledger framework that consists of blocks where each block containing the information of each process state, will help the consumer to track the authenticity of the food product. If any product defects in any particular process, it can be easily identified by using this framework. This framework is implemented by a combination of RFID (Radio Frequency Identification) and Blockchain Technology.

Keywords:

Blockchain,Ledger,Framework,Traceability,Authenticity,Food Safety,RFI,

Refference:

I Amendment to the Criminal Law of the People ‘s Republic of China (8), Article 24, May 1, 2011

II Brito, Jerry & Castillo, Andrea (2013). “Bitcoin: A Primer for Policymakers” (PDF). Fairfax, VA: Mercatus Center, George Mason University. Retrieved 22 October 2013.

III DING Hua, 2004. ‘On the Application of Supply Chain Theory in Enterprises Distributing Farm Produce’, China Business and Market, p.17-21.

IV FENG Tian, 2016, An Agrifood Supply Chain Traceability System for China Based on RFID & Blockchain Technology

V Gerrit Willem Ziggers, Jacques Trienekens, 1999. Quality assurance in food and agribusiness supply chains: Developing successful partnerships

VI Michael Crosby, Nachiappan, Pradan Pattanayak, Sanjeev Verma, Vignesh Kalyanaraman, Blockchain Technology: Beyond Bitcoin, 2016

VII SU Pin, 2012, ‘‘Research on Quality Safety Traceability System of Agricultural Products Based on Multi-Agent’, Science & Technology Information, p.24.

VIII XU Demin, 2010. The Application of RFID Technology in Supply Chain Management’, The Light & Textile Industries of Fujian, p. 43-46

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Abstract:

The service sector in India is remarkably diverse.  It comprises a wide array of industries that promote to individual customers and organizational customers, as well as to government agencies and non-profit organizations.  The service sector in India is the most vibrating sector which is contributing above 54.0% of India’s GVA (Gross Value Added) in 2017-18 and employed more than 28.6 % of the India’s total population. India’s Net Services exports in 2017-18 grew to 14.98% year by year to US$ 77,562.89 million. Both domestic factors and global factors significantly affect the services sector. The facilities management market of India is expected to grow at 17% compound annual growth rate (CAGR) between 2015 and 2020 and surpass the US$19 billion mark supported by growing sectors like retail, ,tourism hospitality, healthcare and  real estate  sectors.

Keywords:

Service Quality,Customer satisfaction,SERVQUAL,

Refference:

I. Arasli H., Mehtap Smadi S., and Katircioglu S. T,(2005), “Customer Service Qual.ity in the Greek Cypriot Banking Industry”. Managing Service Quality. Vol. 15 No. 1. pp. 41-576.

II. Arasli H., Mehtap-Smadi S., and Katircioglu S. T, “Customer Service Qual.ity in the Greek Cypriot Banking Industry”. Managing Service Quality. Vol. 15 No. 1. Pp. 41-576,2005.

III. Brogowicz, A. A., Delene, L. M., & Lyth, D. M. (1990), A synthesized service quality model with managerial implications. International Journal of Service Industry Management, 1(1), 27-44. Http://dx.doi.org/10.1108/ 0956423901000164.

IV. D.Srinivas and N. Hanumantha Rao, Service Quality in Commercial Banks: A Study of Public Sector Banks in Warangal District. Journal of Management, 5(4), 2018, pp. 9–17.

V. D.Srinivas,”A Swot Analysis Based Business Process Management System”, ISSN: 2005-4297 IJCA, Vol.12, No.6, pp. 397 -404, 2019.

VI. D.Srinivas and N. Hanumantha Rao, “Service Quality and Customer Satisfaction of Select Public and Private Sectors Banks”, International Journal of Management, Technology and Engineering,SSN NO: 2249-7455,Volume IX, Issue IV, APRIL/2019, pp. 4436-4440.

VII. D.Srinivas, “A Comprehensive Study on Functions and Levels of Management”, International Journal of Advanced Science and Technology, ISSN-22076360, 20054238,Volume-28,issue-17,pp-24-30.

VIII. Khan, F., Tabassum, A. And Jahan, K., Assessment of Service Gap In Superstores of Bangladesh by using SERVQUAL Model, World Review of Business Research, Vol. 4, No. 1, pp.109 – 128, 2014.

IX. Najjar, L. And Ram, R.B. (2006), Service Quality: A Case Study of a Bank. The Quality Management Journal, 5(3), pp. 35-44.

X. Parasuraman, A., Berry, L. & Zeithaml, V. “A Conceptual Model of SQ And itsimplications for Future Research”, Journal of Marketing. 49. Pp. 41–50,1985.

XI. Rohini, R, “Service quality in Bangalore hospitals – An empirical study”. Journal of Services Research, 6(1), 2006.

XII Rust, R. T., & Olive, R. L. (1994), Service quality: New directions in theory and Practice. Sage Publications
.
XIII. Taylor, S.A., & Baker, T.L. (1994), An assessment of the relationship between service quality and customer satisfaction in the formation of consumer purchase intentions. Journal of Retailing, 70, summer, 163-78.

XIV. Zeithaml, V. A., Berry, L. L. & Parasuraman, A. (1998), Communication and control processes in the delivery of service quality. Journal of Marketing, 52, 35–48.

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Abstract:

Generally communication with people in our daily life is by speaking with voice but some communications can possible with body language,facial expressions and hand signs. Expect the voice also we can communicate with others. Apart from that hand gestures are playing very important role in communication. Here we developed a gesture identification system which interpretsthe American Sign Language .This system helps the people who are deficiency with deaf and dumb. This system lead them to understand communicate as like normal people.Lot of proposals is introduced on gestures specified with their languages like ASL, ISL, etc.Here we are introducing new static gestures using MATLAB on bases of existing systems. Our input captured from camera then system applies the preprocessing on captured image. The set of features are retrieved using PCA. Comparison of the features is done using Euclidean Distance with the help of training sets. Finally optimal gestures identify and produce the output inwards of text or voice.

Keywords:

Static gesture recognition,PCA,Euclidean Distance,MATLABsoftware,

Refference:

I. Anushree Pillai, Spandan Sinha, Piyanka Das,OinamRobitaChanu,”Contrivance OfRecognised Hand Gestures Into Voice And TextOutput,” Proceedings of 35th IRF International Conference, pp.41-45,2017.

II. C. Motoche, M.E. Benalcázar, “Real-time hand gesture recognition based on electromyographic signals and artificial neural networks,” International Conference on Artificial Neural Networks, pp. 352-361, 2018.

III. S. Saha, A. Konar, and J. Roy, “Single Person Hand Gesture Recognition Using Support Vector Machine,” Computational Advancement in Communication Circuits and Systems, Springer, pp. 161-167, 2015.

IV. 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

V. Pappula, Praveen, and Rama B. Ramesh Javvaji. “Experimental Survey on Data Mining Techniques for Association rule mining.” International Journal of Advanced Research in Computer Science and Software Engineering (2014).

VI. M Sheshikala, D Rajeswara Rao, R Vijaya Prakash, “A Map-Reduce Framework for Finding Clusters of Colocation Patterns-A Summary of Results”,Advance Computing Conference (IACC), 2017 IEEE 7th International, Pages 129-131.

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

VIII. D. Kothandaraman, M. Shesikala, K. SeenaNaik, Y. Chanti, B. Vijyakumar, “Design of an Optimized Multicast Routing Algorithm for Internet of Things”, International Journal of Recent Technology and Engineering (IJRTE), vol. 8, Issue 2,2019.

IX. S. Saha, A. Konar, and J. Roy, “Single Person Hand Gesture Recognition Using Support Vector Machine,” Computational Advancement in Communication Circuits and Systems, Springer, pp. 161-167, 2015.

X. Joshi, C. Monnier, M. Betke, and S. Sclaroff, “Comparing random forest approaches to segmenting and classifying gestures,” Image and Vision Computing, vol. 58, pp. 86-95, 2017.

XI. Zhang, Y.; Cao, C.; Cheng, J.; Lu, H. Egogesture: a new dataset and benchmark for egocentric hand gesturerecognition. IEEE Trans. Multimedia 2018, 20, 1038–1050.

XII. Coteallard, U.; Fall, C.L.; Drouin, A.; Campeaulecours, A.; Gosselin, C.; Glette, K.; Laviolette, F.; Gosselin, B.Deep learning for electromyographic hand gesture signal classification using transfer learning. IEEE Trans.Neural Syst. Rehabil. Eng. 2019, 27, 760–771.

XIII. Rekha, J. Bhattacharya and S. Majumder, Shape, Texture and Local Movement Hand Gesture Features for Indian Sign Language Recognition , IEEE 2011.

XIV. Y. Xu, Y. Dai, “Review of hand gesture recognition study and application. Contemp, ” Eng. Sci.10, pp:375–384,2017

XV. H. Mizuno, N. Tsujiuchi, T. Koizumi, “Forearm motion discrimination technique using real-time EMG signals,” 2011 Annual International Conference of the IEEE, Engineering in Medicine and Biology Society, EMBC, pp. 4435–4438,2011.

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Abstract:

The present paper deals with the comparison of output performance of fuzzy Proportional plus Derivative controller with conventional controllers. Fuzzy PD controller having 2 fuzzy sets for everyi/p variable and 3 fuzzy sets for o/p variable in the universe of discourse. Mathematical input-output relationships of simple fuzzy Proportional plus Derivative controller is developed via arbitrary membership functions for fuzzification, Zadeh AND operation for the evaluation of antecedent part of the rules and centroid method for defuzzification. Computer simulations show the effectiveness of Fuzzy Proportional plus Derivative controllers over the conventional controllers for time delay and nonlinear systems by choosing Triangular and Trapezoidal membership functions as input and output fuzzy sets. As a case study P.M.D.C. Servo system with saturation nonlinearity is considered with load disturbance and without load disturbance. MATLAB environment developed results are added to show the importance of the fuzzy controllers for P.M.D.C. Servo system with saturation nonlinearity with and without load disturbance using Triangular and Trapezoidal membership functions as input and output fuzzy sets.   

Keywords:

Analytical structures,fuzzification,defuzzification,fuzzy Proportional plus Derivative controllers,membership functions,MATLAB/simulink,

Refference:

I. Chun Chien Lee “Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part I, Part II”, IEEE Trans. Syst., vol.20, no.2, pp. 404-435, March/April. 1990.
II. E. H. Mamdani “Advances in the linguistic synthesis of fuzzy controllers”, Intern. Jour. Man Machine Studies, pp.669-678, 1976.
III. E. H. Mamdani and Assilian. S. (1975), “An experiment in linguisti synthesis with a fuzzy system”, Int. Journal of Man Machine Studies, No.7, pp.1-13, 1975.
IV. E. H. Mamdani and Baaklini. N “Prescriptive method for driving control policy in fuzzy logic control”, in electronic letter, pp.625-626, 1975.
V. Fuzzy PID controller: Design, performance evaluation, and stability analysis. Information Scinces 123 (2000) 249-270.
VI. G. Chen and Ying. H “Stability analysis of nonlinear fuzzy PI controller systems “, Proc. 3rd Int. conf. on Fuzzy logic applications, pp. 128-133, 1993.
VII. G. Chen, H. Li and H. A. Malki “New design and stability analysis of fuzzy proportional-derivative control systems”, IEEE Trans. Fuzzy systems 2 (4), pp. 245-254, 1994.
VIII. I. Hashimoto and S. Yamamoto Present status and future needs: The view from Japanese Industry, Chemical process control, CPCIV Padre, TX (1991) 1-28.
IX. L. A Zadeh “Fuzzy sets”, Inf. Control, 8, pp-338-353, 1965.
X. P.Atherton, Ibrahim Kaya and Nusret Tan by A refinement procedure for PID controllers in Electrical Engineering (2006) 88: 215–221.
XI. Wei Li, Xiaoguang Chang “Application of hybrid fuzzy logic proportional plus conventional integral-derivative controller to combustion control of stoker-fired boilers”, Fuzzy sets and systems. vol.111, pp.267-284, 2000.

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Abstract:

Today, in the technologized generation the utilization of smart computing devices has been increasing with a rapid pace in every walk of life, such as the adoption of smart watches, fitness bands, diabetic actuators, automatic machines and digital medical equipment for personal and organizational activities.In all these areas from personal to organizational and medical to satellites, the networking of devices and data transfer plays a key role. The autonomous networked computing system, that connects the physical and software components together to access, analyze and process the data for computing, communicating through networking is known as Cyber Physical System (CPS). When these systems used in different areas, they access and process a voluminous data called big data. As the big data is increasing in a large volume day by day, it has become challenging to handle such gigantic data in Cyber Physical System. So, there evolved a need to develop different tools and techniques to handle the big data in various Cyber Physical Systems. Focus of this review is to present the various tools and techniques used to manage big data in heterogeneous Cyber Physical Systems,in addition to this, it also briefs the growth and applications of Cyber Physical System.

Keywords:

Big data,Cyber Physical System,Data Analytics,

Refference:

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Abstract:

Cognitive Radio Networks (CRNs) are new type of communication networks which solves the problems of spectrum utilization and channel assignments in an important manner. Cognitive users are two types i.e Primary and Secondary users. Secondary users use the unused spectrum which is not used by the primary user i.e unlicensed users uses the licensed bandwidth with their permission. Hence, Trust and Reputation management of secondary users has gained more attention. Mainly Reputation management models are required for CRNs to clearly identify whether the Secondary user is Malicious or trusted. If the secondary user is malicious he will attack the network at different layers and degrades the performance. In this paper, a method called Multiple Nash Reputation (MNR) method is proposed to secure the CRN at two different layers namely physical and network. First, trust is separately calculated for each CR user at two different layers, physical layer and network layer using trust parameters. After that the classification of malicious and normal user is made by applying the Multiple Nash Game Theory model. The performance of MNR method is evaluated based on Energy consumption and detection accuracy.

Keywords:

Trust,Reputation,Cross Layer attack,Cognitive Radio Networks,Multiple Nash Equilibrium,

Refference:

I. Brochure, “Coexistence of wireless systems in automation technology,” in Proc. ZVEI – Central Association for Electrical and Electronic Industry, Germany, April 2018.(1)

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III. Ernesto Cadena Muñoz, Enrique Rodriguez-Colina, Luis Fernando Pedraza, Ingrid Patricia Paez, “Detection of dynamic location primary user emulation on mobile cognitive radio networks using USRP”, EURASIP Journal on Wireless Communications and Networking, Springer Open, Feb 2020

IV. GaneshDavanam,T.Pavan Kumar,M.Sunil Kumar,”Mean Bid Trust Cross Layer Trust Evaluation Model for Cognitive Radio Networks”,International Journal of Advanced Science and Technology, Vol. 29, No. 5, (2020), pp. 11450-11461

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VIII. Jithin Jagannath, Sean Furman, Tommaso Melodia, Andrew Drozd, “Design and Experimental Evaluation of a Cross-Layer Deadline-Based Joint Routing and Spectrum Allocation Algorithm”, IEEE Transactions on Mobile Computing, Vol. 18, No. 8, Aug 2019 (2)

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Abstract:

Artificial Intelligence (AI) is poised to disrupt our world. Along with smart equipment making it possible for high-level cognitive processes like thinking, regarding, knowing, problem-solving as well as decision making, paired along with breakthroughs in data collection as well as aggregation, analytics as well as computer system processing energy, Artificial Intelligence shows opportunities to enhance as well as individual supplement intellect as well as enrich the method folks stay and function. To market sustainability, wise creation calls for a global point of view of smart manufacturing function technology. In this regard, due to demanding study efforts in the field of artificial intelligence (AI), a variety of AI-based approaches, such as machine learning, have been set up in the industry to obtain lasting manufacturing. This paper provides efficiency of data technologies that are driving the current surge in artificial intelligence.

Keywords:

Artificial Intelligence,industrial AI,sustainability,

Refference:

I. Carvalho,T.P.;Soares,F.A.A.M.N.;Vita,R.;daFrancisco,P.R.;Basto,J.P.;Alcalá,S.G.S.Asystematicliterature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 2019, 1,1–12.
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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, “Opportunities and Challenges towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255
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
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X. Pushpa Mannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278
XI. Pushpa Mannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi : https://doi.org/10.32628/IJSRST207254
XII. Pushpa Mannava, “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
XIII. Pushpa Mannava, “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
XIV. Shoban Babu Sriramoju, Naveen Kumar Rangaraju, Dr .A. Govardhan, “An improvement to the Role of the Wireless Sensors in Internet of Things” in “International Journal of Pure and Applied Mathematics”, Volume 118,No. 24,2018, ISSN: 1314-3395 (on-line version), url: http://www.acadpubl.eu/hub/
XV. Siripuri Kiran, Shoban Babu Sriramoju, “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

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