Archive

Abstract:

Localization algorithm plays the major rule for different applications such as tracking, positioning, and monitoring. The general framework presented by localization approaches may not work well in practical environments, due to many reasons related with dealing with 2 Dimensional space only or having high computational costs. As a result, Hybrid Localization Algorithm (HLA) was proposed and presented in this paper based on the use of both Received Signal Strength (RSS) and Angle-of-Arrival (AoA). The algorithm has been tested in a 3 Dimensional indoor scenario, with considering the effects of different building materials. Obtained result indicate an effectiveness in localizing the received points by using 2 transmitters for more accuracy in positioning coordination with average ranging error of less than 0.23m for both Line of Sight (LoS) and Non Line of Sight (NLoS) cases.

Keywords:

RSS,Localization algorithm, indoor,,hybrid,

Refference:

I. C. Feng, et al. “Received-signal-strength-based indoor positioning using compressive sensing.” IEEE Transactions on mobile computing, Vol. 11, no.12, pp: 1983-1993, 2011.‏

II. C. Wong, R. Klukas, and G. M. Geoffrey “Using WLAN infrastructure for angle-of-arrival indoor user location.” 2008 IEEE 68th Vehicular Technology Conference. IEEE, pp: 1-5, 2018. ‏

III. D. Dardari, P. Closas, & P. M. Djurić, “Indoor tracking: Theory, methods, and technologies”. IEEE Transactions on Vehicular Technology, Vol.64, no.4, pp: 1263-1278, 2015.

IV. F. Zafari, G. Athanasios, and K. L. Kin. “A survey of indoor localization systems and technologies.” IEEE Communications Surveys & Tutorials, Vol. 21, no.3, pp: 2568-2599, 2019‏.

V. G. Wang, H. Chen, Y. Li, & M. Jin. “On received-signal-strength based localization with unknown transmit power and path loss exponent”. IEEE Wireless Communications Letters, Vol.1, no.5, pp: 536-539, 2012.

VI. H. Nurminen, M. Dashti, & R. Piché, “A survey on wireless transmitter localization using signal strength measurements”, Wireless Communications and Mobile Computing, 2017. ‏

VII. I. Guvenc, & C. C Chong, “A survey on TOA based wireless localization and NLOS mitigation techniques”, IEEE Communications Surveys & Tutorials, Vol.11, no.3, pp: 107-124, 2009.

VIII. I. Guvenc, and C. Chia-Chin “A survey on TOA based wireless localization and NLOS mitigation techniques.” IEEE Communications Surveys & Tutorials, Vol.11, no.3 pp: 107-124, 2009.‏

IX. International Telecommunication Union, “Effects of building materials and structures on radio wave propagation above about 100 MHz”, Recommendation ITU-R P.2040-1, pp. 22–23, July 2015.

X. J. H. Huh, and Seo. K. “An indoor location-based control system using bluetooth beacons for IoT systems.” Sensors, Vol. 17, no.12, pp: 2917, 2017.‏

XI. J.Yim, G. Subramaniam, and H. K. Byeong. “Location-based mobile marketing innovations.”, Mobile Information Systems, 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. AL-Hakeem, I. M. Burhan, M. M. Abdulwahid, “Hybrid Localization Algorithm for Accurate Indoor Estimation Based IoT Services”, IJAST, vol. 29, no. 05, pp. 9921 – 9929, 2020.

XIV. M. M. Abdulwahid, et al. “Investigation and optimization method for wireless AP deployment based indoor network.” MS&E, Vol.745, no.1, pp: 012031, 2020.‏

XV. M. M. Abdulwahid, O. A. S. Al-Ani, M. F. Mosleh and R. A. Abd-Alhmeed..”Investigation of millimeter-wave indoor propagation at different frequencies”. In 2019 4th Scientific International Conference Najaf (SICN),pp. 25-30, 2019.

XVI. O. A Shareef, M. M. Abdulwahid, M. F. Mosleh, & R. A. Abd-Alhameed. “The optimum location for access point deployment based on RSS for indoor communication”, International Conference on Modelling and Simulation (UKsim2019), Vol.20, p 2.1, 2019.
XVII. N. B. Mohamadwasel, & Bayat, O. Improve DC Motor System using Fuzzy Logic Control by Particle Swarm Optimization in Use Scale Factors, 2019.

XVIII. REMCOM Inc, “The Wireless InSite user’s manual.” version 2.6.3, romcom inc., 315 s. allen st., suite 416 state college, pa16801,2009.

XIX. R. Peng, and L. S. Mihail. “Angle of arrival localization for wireless sensor networks.” 2006 3rd annual IEEE communications society on sensor and ad hoc communications and networks. Vol. 1, pp. 374-382, 2006.‏

XX. S. Aditya, F. M. Andreas, and M. B. Hatim. “A survey on the impact of multipath on wideband time-of-arrival based localization.” Proceedings of the IEEE, Vol. 106, no.7 pp: 1183-1203, 2018.‏

XXI. S. Sen, R. C. Romit, and N. Srihari. “SpinLoc: Spin once to know your location.” Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications, pp: 1-6, 2012.‏

XXII. V. Carrizales, Y. Samantha, N. Marco Aurelio, and R. L. Javier. “A platform for e-health control and location services for wandering patients.” Mobile Information Systems, 2018.‏

XXIII. W. Dai, S. Yuan, and Z. W. Moe. “Distributed power allocation for cooperative wireless network localization.” IEEE Journal on Selected Areas in Communications Vol.33, no.1, pp: 28-40, 2014.

XXIV. Y. R. Mohammed, N. Basil, O. Bayat, and A. Hamid, “A New Novel Optimization Techniques Implemented on the AVR Control System using MATLAB-SIMULINK A New Novel Optimization Techniques Implemented on the AVR Control System using MATLAB-SIMULINK,” no. May, 2020.

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Abstract:

The area of fractal modeling is a present-day applicative growth. Fractals contain unlimited amount of information in contradiction to conventional geometric shapes. A well-established method of creating fractals is by means of Iterated Function Systems, with extra - ordinary work done on 2D IFS, where the rendering of the same acquired in an easy and effective manner. Though the 3D IFS transpires/takes shape as a natural world derived add-on, more research has to be carried on it in real-world fractal science and engineering. Here 3D IFS is used to get enchanting fractals by applying algorithms.  The methods used here have a wide- spread use in fractal science very, an example being, recursive fractals elucidated through algebraic transformations. Also presented is a suitable algorithm for processing of arrays. Finally, the outputs obtained are passed through shading and exposure to get a viewing picture. The processes used above result in producing modified versions of objects in a variety of shape andtexture.

Keywords:

Fractals,IFS,Self-similarity,Time Complexity,Time Image,

Refference:

I D.M. Monro and F. Budbridge, “Rendering Algorithms for deterministic fractals,” IEEE Computer Graphics and its Applications, Pages 32-41, 1995.
II G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April1955.
III S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350.
IV J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
V M. Young, the Technical Writer’s Handbook.Mill Valley, CA: University Science, 1989.
VI M. Young, the Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989.
VII Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987

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Abstract:

In this paper, we accomplished the concept of convolution of Laguerre transform for the study of continuous Laguerre wavelet transform and discuss some of its basic properties. Finally our main goal is to find out the Plancherel and inversion formula for the Continuous Laguerre WaveletTransform.

Keywords:

Laguerre transforms,Laguerre convolution,Wavelet transform,2010 Mathematics Subject Classification,42C40,65R10,44A35,

Refference:

I A. Erdèlyi (ed.), Tables of Integral Transforms, Vol. II, Mc Graw-Hill Book Co., New York, 1954.

II C.K. Chui, An introduction to Wavelets, Academic Press, NewYork, 1992.

III Market, Mean Cesaro summability of Laguerre expansions and norm estimates with sift parameter, Anal. Math. 8 (1982), pp. 19–37.

IV E. Gorlich and C. Market, A convolution structure for Laguerre series, Indag. Math. 44 (1982), pp. 61–171.

V F.M. Cholewinski and D.T. Haimo, The dual Poisson–Laguerre transform, Trans. Amer. Math. Soc. 144 (1969), pp. 271–300.

VI G. Kaiser, A Friendly Guide to Wavelets, Birkhauser Verlag, Boston, 1994.

VII R.S. Pathak and M.M. Dixit, Continuous and discrete Bessel wavelet transform, J. Comput. Appl. Math. 160 (2003), pp. 241–250.

VIII S.K. Upadhyay, A. Tripathi, Continuous Watson Wavelet Transform, Integral Transforms and Special Functions, 23:9, 639-647.

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Abstract:

In the paper, identification and classification of high impedance faults (HIF) are analyzed with the Extended Kalman filter and Deep Belief Neural Network (DBN). Here, the proposed method is utilized for classifying the HIF in power system. To extract the features of the signals, EKF is introduced and the DBN is used for classify the signals. Initially, the distribution system, the No Fault (NF) signals are analyzed. After that, in the distribution system linear load and non-linear loads are applied to the system. In this proposed method, radial distribution system and meshed distribution systems are analyzed under the HIF conditions. Here, harmonic coefficients of 3rd, 5th, 7th, 9th and 13th are analyzed with the help of proposed method. The feature signals of current and voltage under the harmonic components are taken as the input of DBN. The feature signals are classified with the help of DBN classifier. The proposed method is implemented in MATLAB/Simulink working platform and the detection performance evaluated. The evaluated results are compared with Artificial Neural Network (ANN) and Neuro Fuzzy Controller (NFC) methods. In addition, the proposed method is tested with the statistical measures like, Accuracy, Sensitivity, and Specificity etc

Keywords:

DBN,EKF,linear load,non-linear load,ANN,NFS,harmonic coefficients,HIF,

Refference:

I Bokka Krishna Chaitanya, Anamika Yadav and Mohammad Pazoki, “An Intelligent Detection of High-Impedance Faults for Distribution Lines Integrated with Distributed Generators”, IEEE Systems Journal, Vol. 14, No. 1, pp. 870 – 879, March 2020

II Chengye Lu, Sheng Wu, Chunxiao Jiang and Jinfen, “Weak Harmonic Signal Detection Methodin Chaotic Interference based on Extended Kalman Filter”, Digital Communications and Networks, Vol.5, No.1, pp.51-55, February 2019

III Érica Mangueira Lima, Núbia Silva Dantas Brito and Benemar Alencar de Souza, “High impedance fault detection based on Stockwell transform and third harmonic current phase angle”, Electric Power Systems Research, Vol.175, pp.1-14, October 2019,

IV Junbo Zhao, Marcos Netto and Lamine Mili, “A Robust Iterated Extended Kalman Filter for Power System Dynamic State Estimation”, IEEE Transactions on Power Systems, Vol. 32, No. 4, pp. 3205 – 3216, July 2017

V J.U.N. Nunes, A.S. Bretas, N.G. Bretas, A.R. Herrera-Orozco and L.U. Iurinice, “Distribution systems high impedance fault location: A spectral domain model considering parametric error processing”, Elsevier, International Journal of Electrical Power & Energy Systems, Vol. 109, pp. 227-241, July 2019

VI Kumari Sarwagya, Sourav De and Paresh Kumar Nayak, “High-impedance fault detection in electrical power distribution systems using moving sum approach”, IET Science, Measurement & Technology, Vol. 12, No. 1, pp. 1-8, 2018

VII Meera R.Karamta and J.G.Jamnani, “Implementation of Extended Kalman Filter Based Dynamic State Estimation on SMIB System Incorporating UPFC Dynamics”, Energy Procedia, Vol.100, pp. 315-324, November 2016

VIII MuhammadSarwar, FaisalMehmood, Muhammad Abid, Abdul QayyumKhan, Sufi TabassumGul and Adil SarwarKhan, “High impedance fault detection and isolation in power distribution networks using support vector machines”, Journal of King Saud University – Engineering Sciences, July 2019

IX Sinha, Pampa, and Manoj Kumar Maharana, “Artificial Intelligence in Classifying High Impedance Faults in Electrical Power Distribution System”, In proceedings of International Conference on Recent Trends in Computing, Communication and Networking Technologies (ICRTCCNT’19), Kings Engineering College, pp.1-5, 2019

X VicenteTorres-Garcia, DanielGuillen, JimenaOlveres, BorisEscalante-Ramirez and Juan R.Rodriguez-Rodriguez, “Modelling of high impedance faults in distribution systems and validation based on multiresolution techniques”, Computers & Electrical Engineering, Vol. 83, pp.1-15, May 2020

XI Yuming Hua, Junhai Guo and Hua Zhao, “Deep Belief Networks and deep learning”, In Proceedings of International Conference on Intelligent Computing and Internet of Things, pp.1-4, 2015

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Abstract:

Softwarecost and schedule estimation is usually based on the estimated size of the software. Advanced estimation techniques also make use of the diverse factors viz, nature of the project, staff skills available, time constraints, performance constraints, technology required and so on. Usually, estimation is based on an estimation model prepared with the help of experienced project managers. Estimation of software cost is predominantly a crucial activity as it incurs huge economic and strategic investment. However accurate estimation still remains a challenge as the algorithmic models used for Software Project planning and Estimation doesn’t address the true dynamic nature of Software Development. This paper presents an efficient approach using the contemporary Constructive Cost Model (COCOMO) augmented with the desirable feature of fuzzy logic to address the uncertainty and flexibility associated with the cost drivers (Effort Multiplier Factor). The approach has been validated and interpreted by project experts and shows convincing results as compared to simple algorithmic models. The proposed model cost is close to the actual cost to a tune of 98%.

Keywords:

COCOMO,fuzzy logic,software development,cost estimation,

Refference:

I Ali Bou Nassif, Mohammad Azzeh, Ali Idri and Alain Abran, Software Development Effort Estimation Using Regression Fuzzy Models, Computational Intelligence and Neuroscience, Hindawi publications, Feb 2019, Volume 2019 |Article ID 8367214 | 17 pages | https://doi.org/10.1155/2019/8367214

II Attarzadeh, I., Siew Hock Ow, “A novel soft computing model to increase the accuracy of software development cost estimation”, Published in 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010, Volume: 3, Pages: 603 – 607, DOI: 10.1109/ICCAE.2010.5451810

III Ashish Sharma Manu Vardhan, A Versatile Approach for the Estimation of Software Development Effort Based on SRS Document , International Journal of Software Engineering and Knowledge Engineering (IJSEKE), 2014pp 1-42

IV Attarzadeh, I., Siew Hock Ow, “Improving estimation accuracy of the COCOMO II using an adaptive fuzzy logic model”, IEEE International Conference on Fuzzy Systems (FUZZ), 2011, Pages: 2458 – 2464, DOI: 10.1109/FUZZY.2011.6007471

V Attarzadeh, I.; Siew Hock Ow, Proposing a New High Performance Model for Software Cost Estimation””, ICCEE ’09. Second International Conference on Computer and Electrical Engineering, 2009, Volume: 2 Pages: 112 – 116, DOI: 10.1109/ICCEE.2009.97

VI [BOE81] Boehm, B., Software Engineering Economics, Prentice-Hall, 1981.

VII D. Manikavelan, R. Ponnusamy, Software quality analysis based on cost and error using fuzzy combined COCOMO model, Journal of Ambient Intelligence and Humanized Computing (2020), Springerlink, March 2020

VIII Huang, X.; Ho, D.; Ren, J.; Capretz, L.F., “A neuro-fuzzy tool for software estimation”, 20th IEEE International Conference on Software Maintenance, 2004. Proceedings. Page: 520, DOI: 10.1109/ICSM.2004.1357862

IX Iman Attarzadeh ; Siew Hock Ow, Proposing a new software cost estimation model based on artificial neural networks, 2nd International Conference on Computer Engineering and Technology, IEEE April 2020, DOI: 10.1109/ICCET.2010.5485840

X Kushwaha, N., Suryakant, “Software cost estimation using the improved fuzzy logic framework”, Conference on IT in Business, Industry and Government (CSIBIG), 2014, Pages: 1 – 5, DOI: 10.1109/CSIBIG.2014.7056959

XI Mirseidova, S.; Atymtayeva, L., “Definition of software metrics for software project development by using fuzzy sets and logic “,13th International Symposium on Advanced Intelligent Systems (ISIS), Joint 6th International Conference on Soft Computing and Intelligent Systems (SCIS) and 2012, Pages: 272 – 276, DOI: 10.1109/SCIS-ISIS.2012.6505336

XII [PUT92] Putnam, L. and W. Myers, Measures for Excellence, Yourdon Press, 1992.

XIII Rama, S.P., “Analytical structure of a fuzzy logic controller for software development effort estimation”, International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), Year: 2015, Pages: 1 – 4, DOI: 10.1109/EESCO.2015.725

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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:

I A. A. Yavuz, “An efficient real-time broadcast authentication scheme for command and control messages,” IEEE Transactions on Information Forensics and Security, vol. 9, no. 10, pp. 1733–1742, Oct 2014.
II A. Pantelopoulos and N. G. Bourbakis, “A survey on wearable sensor based systems for health monitoring and prognosis,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, no. 1, pp. 1–12, Jan 2010.
III A. Ukil and R. Zivanovic, “Automated analysis of power systems disturbance records: Smart grid big data perspective,” in Proc. 2014 IEEE Innovative Smart Grid Technologies – Asia (ISGT ASIA), May 2014, pp. 126–131.
IV Ada Bagozia, Devis Bianchini*, a, Valeria De Antonellisa, Alessandro Marinia, Davide Ragazzia a Dept. of Information Engineering, University of Brescia, Italy, Big Data Conceptual Modelling in Cyber-Physical Systems International Journal of Conceptual Modeling, February 20VI. DOI:10.VI417/emisa.si.hcm.24
V Ashish Jadhao, Swapnaja Hiray, Big Data Analytics in Cyber Physical Systems, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 10, October – 2013 ISSN: 2278-0VI1
VI Available at https:// www.kennethresearch.com/ report-details/ global-cyber-physical-system-market/ 1005892139
VII Available at https://ieeexplore.ieee.org/document/8220372
VIII Available at: http://cps.kaist.ac.kr/.
IX Available at: http://cpschina.org/.
X Available at: http://investopedia.com/.
XI Available at: http://newsinfo. nd.ed u/news/17248-nsf-fund s-cyber-physical-systems-project/.
XII Available at: http://searchdatamanagement.techtarget.com/.
XIII Available at: http://www.cps-cn.org/.
XIV Available at: http://www.cpsweek.org/.
XV C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos, “Context aware computing for the internet of things: A survey,” IEEE Communications Surveys and Tutorials, vol. 16, no. 1, pp. 414–454, First 2014.
XVI C. W. Tsai, C. F. Lai, M. C. Chiang, and L. T. Yang, “Data mining for internet of things: A survey,” IEEE Communications Surveys Tutorials, vol. 16, no. 1, pp. 77–97, First 2014.
XVII Carina Andrade1[0000-0001-8783-9412] 1 ALGORITMI Research Centre, University of Minho, Guimarães, Portugal carina.andrade@dsi.uminho.pt, A Big Data Perspective on Cyber-Physical Systems for Industry 4.0: Modernizing and Scaling Complex Event Processing
XVIII E. Kartsakli, A. S. Lalos, A. Antonopoulos, S. Tennina, M. D. Renzo, L. Alonso, and C. Verikoukis, “A survey on M2M systems for mhealth: A wireless communications perspective,” Sensors, vol. 14, no. 10, pp. VI 009–VI 052, 2014.
XIX E.A. Lee and S.A. Seshia, Introduction to Embedded Systems: A Cyber-Physical Systems Approach, 2nd ed., lulu.com, 2015.
XX E.A. Lee, “Fundamental Limits of Cyber-Physical Systems Modeling,” ACM Trans. Cyber-Physical Systems, vol. 1, no. 1, 2017, article no. 3.
XXI E.A. Lee, “The Past, Present and Future of Cyber-Physical Systems: A Focus on Models,” Sensors, vol. 15, no. 3, 2015, pp. 4837–4869.
XXII Foundations for Innovation in Cyber-Physical Systems: Workshop Report, tech. report, NIST, 2013; www.nist.gov/sites/default/files/documents/el/CPS-WorkshopReport-1-30-13-Final.pdf.
XXIII G. M. Lehto, G. Edlund, T. Smigla, and F. Afinidad, “Protection evaluation framework for tactical satcom architectures,” in Proc. MILCOM 2013 – 2013 IEEE Military Communications Conference, Nov 2013, pp. 1008–1013.
XXIV Hausi A. Muller, The Rise of Intelligent Cyber-Physical Systems University of Victoria December 2017, pp. 7-9, vol. 50DOI Bookmark: 10.1109/MC.2017.4451221
XXV J. Baek, Q. H. Vu, J. K. Liu, X. Huang, and Y. Xiang, “A secure cloud computing based framework for big data information management of smart grid,” IEEE Transactions on Cloud Computing, vol. 3, no. 2, pp. 233–244, April 2015.
XXVI J. Wu, I. Bisio, C. Gniady, E. Hossain, M. Valla, and H. Li, “Context a ware networking and communications: Part 1 [guest editorial],” IEEE Communications Magazine, vol. 52, no. 6, pp. 14–15, June 2014.
XXVII J. Wu, S. Guo, H. Huang, W. Liu, and Y. Xiang, “Information and communications technologies for sustainable development goals: State of- the-art, needs and perspectives,” IEEE Communications Surveys & Tutorials, vol. 20, no. 3, pp. 2389–2406, 20VI.
XXVIII J. Wu, S. Guo, J. Li, and D. Zeng, “Big data meet green challenges: Greening big data,” IEEE Systems Journal, vol. 10, no. 3, pp. 873–887, Sept 2016.
XXIX J. Yang, J. Zhao, F. Wen, W. Kong, and Z. Dong, “Mining the big data of residential appliances in the smart grid environment,” in Proc. 2016 IEEE Power and Energy Society General Meeting (PESGM), July 2016, pp. 1–5.
XXX J.Z. Li, H. Gao, B. Yu, “Concepts, Features, Challenges, and Research Progresses of CPSs,” Development Report of China Computer Science in 2009, pp. 1-17, 2009.
XXXI Jiafu Wan1, 2, Hehua Yan2, Hui Suo2 and Fang Li1, Advances in Cyber-Physical Systems Research, KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 5, NO. 11, November 2011
XXXII L. Atzori, A. Iera, and G. Morabito, “The internet of things: A survey,” Comput. Netw., vol. 54, no. 15, pp. 2787–2805, Oct. 2010. [Online]. Available: http://dx.doi.org/10.1016/j.comnet.2010.05.010
XXXIII L. Liu and Z. Han, “Multi-block admm for big data optimization in smart grid,” in Proc. 2015 International Conference on Computing, Networking and Communications (ICNC), Feb 2015, pp. 556–561.
XXXIV L. Sanchez, M. Bauer, J. Lanza, R. Olsen, and M. Girod-Genet, “A generic context management framework for personal networking environments,” in Proc. the 2006 Third Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services. Los Alamitos, CA, USA: IEEE Computer Society, 2006, pp. 1–8.
XXXV M. A. Alsheikh, D. Niyato, S. Lin, H. p. Tan, and Z. Han, “Mobile big data analytics using deep learning and apache spark,” IEEE Network, vol. 30, no. 3, pp. 22–29, May 2016.
XXXVI M. M. Rathore, A. Ahmad, A. Paul, and G. Jeon, “Efficient graph oriented smart transportation using internet of things generated big data,” in Proc. 2015 11th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), Nov 2015, pp. 512–519.

XXXVII M. Parameswaran and A. B. Whinston, “Social computing: An overview,” Communications of the Association for Information Systems, vol. 19, 2007. [Online]. Available: http://aisel.aisnet.org/cais/vol19/iss1/ 37/
XXXVIII M. Szvetits and U. Zdun, “Systematic Literature Review of the Objectives, Techniques, Kinds, and Architectures of Models at Runtime,” Software &Systems Modeling, vol. 15, no. 1, 2016, pp. 31–69.
XXXIX M. Tang, H. Zhu, and X. Mao, “A lightweight social computing approach to emergency management policy selection,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 8, pp. 1075–1087, Aug 2016.
XL M. Zhang, K. Szwaykowska, W. Wolf, and V. Mooney, Task scheduling for control oriented requirements for Cyber-Physical Systems, in Proc. of 2008 Real-Time Systems Symposium, 2005, pp. 47-56.
XLI Matthew N. O. Sadiku , Adebowale E. Shadare and Sarhan M. Musa, Social Computing, International Journal of Innovative Science, Engineering & Technology, Vol. 4 Issue 1, January 2017 ISSN (Online) 2348 – 7968 | Impact Factor (2015) – 4.332
XLII N. Bencomo et al., eds., Models@run.time: Foundations, Applications, and Roadmaps, Springer, 2014.
XLIII O. Vermesan and P. Friess, eds., Internet of Things—Converging Technologies for Smart Environments and Integrated Ecosystems, River Publishers, 2013.
XLIV P. Bellavista, A. Corradi, M. Fanelli, and L. Foschini, “A survey of context data distribution for mobile ubiquitous systems,” ACM Computing Surveys, vol. 44, no. 4, pp. 24:1–24:45, Sep. 2012. [Online]. Available: http://doi.acm.org/10.1145/2333112.2333119
XLV R Rajkumar, I Lee, L Sha… -, Cyber-physical systems: the next computing revolution, Design Automation …, 2010 – ieeexplore.ieee.org
XLVI R. de Lemos et al., eds., Software Engineering for Self-Adaptive Systems II, LNCS 7475, Springer, 2013.
XLVII Rachad Atat, Lingjia Liu, Senior Member, IEEE, Jinsong Wu, Senior Member, IEEE, Guangyu Li, Chunxuan Ye, and Yang Yi, Senior Member, IEEE, Big Data Meet Cyber-Physical Systems: A Panoramic Survey
XLVIII Rachad Atat1, Lingjia Liu2, (Senior Member, IEEE), Jinsong Wu3, (Senior Member, IEEE), Guangyu Li4, Chunxuan Ye5, Yang Yi2, (Senior Member, and IEEE), Big Data Meet Cyber-Physical Systems: A Panoramic Survey Digital Object Identifier x/ACCESS.20VI.
XLIX Radu F. Babiceanua,*, Remzi Sekerb, Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook, journal homepage: www.elsevier.com/locate/compind
L V. Nguyen, M. Guirguis, and G. Atia, “A unifying approach for the identification of application-driven stealthy attacks on mobile cps,” in Proc. 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton), Sept 2014, pp. 1093–1101.
LI X. Zhang, Z. Yi, Z. Yan, G. Min, W. Wang, A. Elmokashfi, S. Maharjan, and Y. Zhang, “Social computing for mobile big data,” Computer, vol. 49, no. 9, pp. 86–90, Sept 2016.
LII Y. Simmhan, S. Aman, A. Kumbhare, R. Liu, S. Stevens, Q. Zhou, and V. Prasanna, “Cloud-based software platform for big data analytics in smart grids,” Computing in Science Engineering, vol. 15, no. 4, pp. 38– 47, July 2013.

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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)

II. Deanna Hlavacek, J. Morris Chang, “A layered approach to cognitive radio network security: A survey”, Computer Networks, Elsevier, Oct 2014

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

V. G. Staple and K. Werbach, “The end of spectrum scarcity [spectrum allocation and utilization],” IEEE Spectrum, vol. 41, no. 3, March 2010.(4)

VI. Jaydip Sen, “A Survey on Security and Privacy Protocols for Cognitive Wireless Sensor Networks”, Journal of Network and Information Security, Jun 2013

VII. Jihen Bennaceur Hanen Idoudi Leila Azouz Saidane, “Trust management in cognitive radio networks: A survey”, International Journal of Network Management, Wiley, Aug 2017(10)

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)

IX. Linyuan Zhang, Guoru Ding, Qihui Wu, Yulong Zou, Zhu Han, Jinlong Wang, “Byzantine Attack and Defense in Cognitive Radio Networks: A Survey”, Elsevier, Jun 2015

X. Mee Hong Ling, Kok-Lim Alvin Yau, Geong Sen Poh, “Trust and reputation management in cognitive radio networks: a survey”, Security and Communication Networks, Wiley, Nov 2013

XI. Mitola, “Cognitive Radio Architecture Evolution,” in Proc. of the IEEE, vol. 97, no. 4, April 2009.(5)

XII. MouniaBouabdellah, Naima Kaabouch, Faissal El Bouanani, Hussain Ben-Azza, “Network layer attacks and countermeasures in cognitive radio networks: A survey”, Journal of Information Security and Applications, Elsevier, Jul 2018

XIII. Nadine Abbas, Youssef Nasser, Karim El Ahmad, “Recent advances on artificial intelligence and learning techniques in cognitive radio networks”, EURASIP Journal onWireless Communications and Networking, Springer, Jul 2015

XIV. Quanyan Zhu, Stefan Rass, “Game Theory Meets Network Security”, ACM, Oct 2019

XV. Saim Bin Abdul Khaliq, Muhammad Faisal Amjad, Haider Abbas, Narmeen Shafqat, Hammad Afzal, “Defence against PUE attacks in ad hoc cognitive radio networks: a mean field game approach”, Telecommunication Systems, Springer, May 2018 (3)

XVI. Wang Zhendong, Wang Huiqiang, Zhu Qiang, “A Trust Game Model and Algorithm for Cooperative Spectrum Sensing in Cognitive Radio Networks”, International Journal of Future Generation Communication and Networking Vol. 8, No. 3 (2015).(7)

XVII. W.Saad, et al., “Coalitional game theory for communication networks,” in IEEE Signal Processing Magazine, vol. 26, no. 5, Sept 2017.(8)

XVIII. Y. Zhao, S. Mao, J. O. Neel, and J. H. Reed, “Performance evaluation of cognitive radios: metrics, utility functions, and methodology,” in Proc. Of the IEEE, vol. 97, no. 4, April 2009.(6)

XIX. Z. Han et al., Game Theory in Wireless and Communication Networks: Theory, Models, and Applications, Cambridge Press, Cambridge, 2012.(9)

XX. Z. Jin, S. Anand, K. P. Subbalakshmi, “Impact of Primary User Emulation Attacks on Dynamic Spectrum Access Networks”, IEEE Transactions on Communication, Vol. 60, Issue 9, Sep 2012

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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.
II. 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
III. Kiran Kumar S V N Madupu, “Tool to IntegrateOptimized 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
IV. Kotsiantis,S.B.;Zaharakis,I.;Pintelas, P. Supervisedmachinelearning:Areviewofclassificationtechniques.Emerg. Artif. Intell. Appl. Comput. Eng. 2007, 160, 3–24.
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
VIII. Markham,I.S.;Mathieu,R.G.;Wray,B.A.Kanbansettingthroughartificialintelligence:Acomparativestudy of artificial neural networks and decision trees. Integr. Manuf. Syst. 2000, 11, 239–246.
IX. 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
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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