Archive

Abstract:

Internet of Things (IoT) is an emerging trend in the field of technology, which has derived a lot of attention in the recent years. The ability of this technology for reducing the burden and strain on the education or academic system makes it possible for deriving a potential and raising the standards of academics. This study proposes a standard model for the educational system with the help of IoT. This paper gives an IoT based modal for the student engagement till the industry institute linkage plan. It gives a design in which the monitoring of RFID based data can be done and results could be discovered using the IoT techniques for the further selection criteria of industries. The results for any student shall be updated and made available based on the student data and business intelligence can be applied to the university system for giving the industry for best students. The study tries to relate various components which are later for the model generation, including the strength, weaknesses, opportunities and threats for a wearable IoT university system. A lot of challenges are based by the field of academics and University’s as far as security and privacy is concerned. Future direction in the research can be derived from the existing proposed model in the study.

Keywords:

IoT,e-learning,computational learning,System Adaption,Security,privacy,challenges,smart devices,sensors-based devices,

Refference:

I. Ansari, A.N., et al. Automation of attendance system using RFID, biometrics, GSM Modem with. Net framework. in Multimedia Technology (ICMT), 2011 International Conference on. 2011. IEEE.
II. Baradwaj, B.K. and S. Pal, Mining educational data to analyze students’ performance. arXiv preprint arXiv:1201.3417, 2012.
III. Bevitt, D., C. Baldwin, and J. Calvert, Intervening early: Attendance and performance monitoring as a trigger for first year support in the biosciences. Bioscience Education, 2010. 15(1): p. 1-14.
IV. Bsoul, Q., & Salim, Z. 2016. Effect Verb Extraction on Crime Traditional Cluster, world applied science journal.
V. Cambria, E., & White, b. 2014. Jumping NLP Curves: A Review of Natural Language Processing Research. IEEE Computational Intelligence Magazine, 9(1): 48-57.
VI. Chawathe, S.S., et al. Managing RFID data. in Proceedings of the Thirtieth international conference on Very large data bases-Volume 30. 2004. VLDB Endowment.
VII. Cleveland, B.W., Engaging spaces: Innovative learning environments, pedagogies and student engagement in the middle years of school. 2011: University of Melbourne, Faculty of Architecture, Building and Planning.
VIII. Darcy, P., B. Stantic, and A. Sattar. Applying a neural network to recover missed RFID readings. in Proceedings of the Thirty-Third Australasian Conferenc on Computer Science-Volume 102. 2010. Australian Computer Society, Inc.
IX. Darcy, P., S. Tucker, and B. Stantic, Integrating RFID technology with intelligent classifiers for meaningful prediction knowledge. Advances in Internet of Things, 2013. 3(2): p. 27-33.
X. Ding, X. & Tang, Y. 2013. Improved Mutual Information Method For Text Feature Selection. The 8th International Conference on Computer Science & Education. IEEE, pp: 163-166.
XI. Doyle, L., et al., An evaluation of an attendance monitoring system for undergraduate nursing students. Nurse education in practice, 2008. 8(2): p. 129-139.
XII. Dyer, M. 1995. Connectionist natural language processing: a status report. in Computational Architectures Integrating Neural and Symbolic Processes, Sun and L. Bookman, Eds. Dordrecht. The Netherlands: Kluwer Academic, 292(1):389–429.
XIII. Ferreira, D.D.J.S.S.F.B.V., Knowledge and technology transfer between university — Industry — Society: A new crowdsourcing framework for Internet of Things, in Microwaves, Antennas, Communications and Electronic Systems (COMCAS), 2017 IEEE International Conference, IEEE, Editor. 2017, IEEE Explore: Tel-Aviv, Israel.
XIV. Fodeh, S., Punch, W. & Tan, P. 2011. On ontology-driven document clustering using core semantic features. On ontology-driven document clustering using core semantic features, Journal of KnowlInfSyst, Springer-Verlag London. 28(2): 395-421.
XV. Gershenfeld, N., R. Krikorian, and D. Cohen, The internet of things. Scientific American, 2004. 291(4): p. 76-81.
XVI. Halibas, A.S., I.G. Pillai, and A.C. Matthew. Utilization of RFID analytics in assessing student engagement. in Applications of Information Technology in Developing Renewable Energy Processes & Systems (IT-DREPS), 2017 2nd International Conference on the. 2017. IEEE.
XVII. Hanna, M., Data mining in the e-learning domain. Campus-wide information systems, 2004. 21(1): p. 29-34.
XVIII. Hotho, A., Staab, S., &Stumme, G. 2003. WordNet improves text document clustering. In Proc. of the SIGIR 2003 Semantic Web Workshop, pp: 541-544.
XIX. Hughes, G. and C. Dobbins, The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs). Research and Practice in Technology Enhanced Learning, 2015. 10(1): p. 10.
XX. Jeffery, S.R., M. Garofalakis, and M.J. Franklin. Adaptive cleaning for RFID data streams. in Proceedings of the 32nd international conference on Very large data bases. 2006. VLDB Endowment.
XXI. Jindal, N. and B. Liu. Mining comparative sentences and relations. in AAAI. 2006.
XXII. Jing, B.-Z., et al. RFID access authorization by face recognition. in Machine Learning and Cybernetics, 2009 International Conference on. 2009. IEEE.
XXIII. Jones, K., J. Thomson, and K. Arnold, Questions of data ownership on campus. 2014.
XXIV. Kassim, M., et al. Web-based student attendance system using RFID technology. in Control and System Graduate Research Colloquium (ICSGRC), 2012 IEEE. 2012. IEEE.
XXV. Kummer, O., Savoy, J., & Argand, E. 2012. Feature selection in sentiment analysis.
XXVI. Lewis, D. 1997. Reuters-21578 text categorization test collection. AT&T Labs Research.Matthew, A.S.H.I.G.P.A.C., Utilization of RFID analytics in assessing student engagement, in Applications of Information Technology in Developing Renewable Energy Processes & Systems (IT-DREPS), 2017 2nd International Conference, IEEE, Editor. 2017, IEEE: Amman, Jordan.
XXVII. Li, D.-Y., et al. Design of Internet of Things System for Library Materials Management using UHF RFID. in RFID Technology and Applications (RFID-TA), 2016 IEEE International Conference on. 2016. IEEE.
XXVIII. Lim, T., S. Sim, and M. Mansor. RFID based attendance system. in Industrial Electronics & Applications, 2009. ISIEA 2009. IEEE Symposium on. 2009. IEEE.
XXIX. Liu, Xin&Beyrend-Dur, Delphine&Dur, Gael & Ban, Syuhei. (2014).
XXX. Mihăescu, C., et al. Learning analytics solution for building personalized quiz sessions. in Carpathian Control Conference (ICCC), 2017 18th International. 2017. IEEE.
XXXI. Porter, F. 1997. An algorithm for suffix stripping in K. Sparck Jones, P. Willett (1st Eds) Readings in Information Retrieval, Morgan Kaufmann Multimedia Information and Systems Series, pp: 313–316.
XXXII. Rogati, Monica & Yang, Yiming. 2002. High-performing feature selection for text classification. 659. 10.1145/584902.584911.
XXXIII. Romero, C. and S. Ventura, Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2013. 3(1): p. 12-27.
XXXIV. Romero, C., et al., Web usage mining for predicting final marks of students that use Moodle courses. Computer Applications in Engineering Education, 2013. 21(1): p. 135-146.
XXXV. Srinidhi, M. and R. Roy. A web enabled secured system for attendance monitoring and real time location tracking using Biometric and Radio Frequency Identification (RFID) technology. in Computer Communication and Informatics (ICCCI), 2015 International Conference on. 2015. IEEE.
XXXVI. Teague, D.M. and M.W. Corney, Is anybody there? Bootstrapping attendance with engagement. 2011.
XXXVII. Welbourne, E., et al. Challenges for pervasive RFID-based infrastructures. in Pervasive Computing and Communications Workshops, 2007. PerCom Workshops’ 07. Fifth Annual IEEE International Conference on. 2007. IEEE.
XXXVIII. Wu, D.-L., et al. Access control by RFID and face recognition based on neural network. in Machine Learning and Cybernetics (ICMLC), 2010 International Conference on. 2010. IEEE.
XXXIX. Yao, H., Liu, C., Zhang, P., & Wang, L. 2017. A feature selection method based on synonym merging in text classification system. Journal on Wireless Communications and Networking. Springer. pp: 1-8.
XL. Zhou, Q., et al. Design and Implementation of Learning Analytics System for Teachers and Learners Based on the Specified LMS. in Educational Innovation through Technology (EITT), 2014 International Conference of. 2014. IEEE

MODELING AND PERFORMANCE EVALUATION OF PACKET SCHEDULING IN UPLINK 3GPP LTE SYSTEMS

Authors:

Samia Dardouri, Rund Fareed Mahafdah, Omaia Mohammad Al Omari, Ridha bouallegue

DOI NO:

https://doi.org/10.26782/jmcms.2020.03.00023

Abstract:

The radios must be distributed in the best way possible to provide higher quality of service (QoS) to users. A main component of Long-Term Evolution (LTE) processing is the packet scheduler, which includes all time and frequency support in active flows. We evaluate in this article three different scheduling algorithms in the uplink transmission path for the mixed forms of traffic flows for the Single Carrier Frequency Division Multiple Access (SC-FDMA). We apply metrics which allow fast evaluation of performance measures such as throughput, Packet Loss Ratio (PLR), Fairness Index (FI) and Spectral Eﬃciency (SE) by using the LTE-Sim open source simulator. The main contribution of this paper is to determine the appropriate uplink scheduling algorithm for VOIP and video traﬃcs in 3GPP LTE

Keywords:

SC-FDMA,QoS,LTE,Scheduling algorithms,Resource allocation,Uplink direction,throughput,fairness,Packet loss ratio,Spectral Eﬃciency,

Refference:

I. 3GPP TS 36.213 V10.1.0, Technical Speciﬁcation Group Radio Access Network (E-UTRA); Physical layer procedures, (2011-04).
II. 3GPP TS 36.213: Evolved Universal Terrestrial Radio Access (EUTRA); Physical layer procedures. Version 8.8.0 Release 8, 2009.
III. Abrignani, M., Giupponi, L., Lodi, A. et al. Scheduling M2M traffic over LTE uplink of a dense small cell network. J Wireless Com Network 2018, 193 (2018)..
IV. B. Nsiri, M. Nasreddine, M. Ammar,W. Hakimi, M. Soﬁen, Modeling and Performance Evaluation of Scheduling Algorithms For Downlink LTE cellular Network ICWMC 2014 : The Tenth International Conference on Wireless and Mobile Communications.
V. B. P. S. Sahoo, Deepak Puthal, Satyabrata Swain and Sambit Mishra, A Comparative Analysis of Packet SchedulingSchemes for Multimedia Services in LTE Networks in 2015 International Conference on Computational Intelligence Networks (CINE 2015).
VI. G. Piro, L. Alfredo Grieco, G. Boggia, F. Capozzi, Simulating LTE Cellular Systems: an Open Source Framework, Octobre 2010.
VII. H. Jang and Y. Lee, QoS-Constrained Resource Allocation Scheduling for LTE Network in International Symposium on Wireless and Pervasive Computing (ISWPC), 2013.
VIII. H. Safa , K. Tohme , Low Complexity Scheduling Algorithms for the LTE Uplink in the ISCC 2009 proceedings.
IX. http://trace.eas.asu.edu/, H.264/AVC and SVC video trace library.
X. J. Lim, H.G. Myung, and D.J. Goodman, Single Carrier FDMA for Uplink Wireless Transmission, in IEEE Vehicular Technology Magazine, Volume 1, Issue 3, pp 3039, 2007. 4. 3GPP, Tech. Specif. Group Radio Access Network – Physical Channel and Modulation (Release 8), 3GPP TS 36.211.
XI. K. Elgazzar, M. Salah, M. Abd-Elhamid Taha, H. Hassanein, Comparing Uplink Schedulers for LTE in IWCMC ’10 Proceedings of the 6th International Wireless Communications and Mobile Computing Conference 2010.
XII. Long Term Evolution (LTE): A Technical Overview. Motorola. Retrieved July 3, 2010.
XIII. M. Iturralde, S. Martin and T. Ali Yahiya, Resource Allocation by Pondering Parameters for Uplink System in IEEE 38th Conference on LTE Networks in Local Computer Networks (LCN), 2013.
XIV. M. Salah , Najah Abu Ali , Abd-ElhamidTaha , HosamHassanein, Evaluating Uplink Schedulers in LTE in Mixed Traﬃc Environments in the IEEE ICC 2011 proceedings.
XV. Mamman M, Hanapi ZM, Abdullah A, Muhammed A (2019) Quality of Service Class Identifier (QCI) radio resource allocation algorithm for LTE downlink. PLoS ONE 14(1): e0210310. https://doi.org/10.1371/journal.pone.0210310
XVI. Mandeep Singh, HarpreetKaur, 0, Performance Enhancement of Heterogeneous LTE Networks Using EXP/PF Packet Scheduling Algorithm, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 07, Issue 08 (August – 2018),
XVII. Patra, A. , Pauli, V. , Yu Lang, Packet Scheduling for Real-Time Communication over LTE Systems in Wireless Days (WD)proceedings, 2013.
XVIII. R. Jain et al. A quantitative measure of fairness and discrimination for resource allocation in shared computer systems. Dig. Eq. Corp., Lit, MA, DEC Rep-DECTR-301 , Sep. 1984.
XIX. S. Hussain, Dynamic Radio Resource Management in 3GPP LTE, Blekinge Institute of Technology, January, 2009.
XX. S. Kwon, Neung-Hyung Lee, Uplink QoS Scheduling for LTE System, in the Vehicular Technology Conference (VTC Spring), 2011 proceedings.
XXI. S. Mohamed, Comparative Performance Study of LTE Uplink Schedulers, Thesis (Master, Electrical Computer Engineering) Queen’s University, 2011.
XXII. S. Nawaz Khan Marwat, T. Weerawardane, Yasir Zaki1 , Carmelita Goerg1 , and Andreas Timm-Giel2, Performance Evaluation of Bandwidth and QoS Aware LTE Uplink Scheduler, in Wired/Wireless Internet Communication Lecture Notes in Computer Science Volume 7277, 2012, pp 298-306.
XXIII. Yıldız, Önem & Sokullu, Radosveta. (2019). Mobility and traffic-aware resource scheduling for downlink transmissions inLTE-A systems. TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES. 27. 2021-2035. 10.3906/elk-1808-56.

FEATURE EXTRACTION FOR MOBILE HANDSET IN COHERENCY WITH PRICING FACTORS

Authors:

Anurag Tiwari, Vivek Kumar Singh, Praveen Kumar Shukla, Manuj Darbari

DOI NO:

https://doi.org/10.26782/jmcms.2020.03.00024

Abstract:

This paper presents a showcase of analysis of Mobile price with respect to the features it is able to analyse for the buyer. The paper gives machine learning approach in identification of the right price and its subsequent features detail. ANN with Back propagation algorithm has been chosen by developing a customized mobile selection algorithm using Kaggle database for modelling and Analysis. Various cost factors are adjusted in relation with the features to be incorporated in the Handset. The adjustment of input variables is done by the help of the machine learning technique giving the exact relationship in three main factors Requirement of the customer based on their segmentation, Price and Features.

Keywords:

Mobile Selection Criteria,MachineLearning,ANN,DSS,

Refference:

I. A Chaudhary, S. Kolhe and Rajkamal, “Performance Evaluation of feature selection methods for Mobile devices”, ISSN: 2248-9622, Vol. 3, Issue 6, NovDec 2013, pp.587-594.

II. A Lapedes and R. Farber, “How Neural Networks Works”, in Neural Information Processing Systems(D.Z. Anderson,ed.),(Denver), American Institute of Physics,NewYork,pp. 442-456,1988.

III. Bourassa, S.C., Cantoni, E. and Hoesli, M. 2007. “Spatial dependence, housing submarkets, and house price prediction”, The Journal of Real Estate Finance and Economics, 35(2), p.143-160.

IV. GONGGI, S., 2011. New model for residual value prediction of used cars based on BP neural network and non-linear curve fit. In: Proceedings of the 3 rd IEEE International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

V. H. Liu and R. Setiono, “A probabilistic approach to feature selection – A filter solution,” the 13th International Conference on Machine Learning, pp. 319-327, 1996.

VI. H.White, “Learning in Artificial Neural Networks :A Statistical Perspective ”,Neural Computation,1(4),pp.425-464,1989.

VII. Kanwal Noor and Sadaqat Jan, “Vehicle Price Prediction System using Machine Learning Techniques” , International Journal of Computer Applications (0975 – 8887) Volume 167 – No.9, June 2017.

VIII. Khaidem, Luckyson&Saha, Snehanshu&Basak, Suryoday&Kar, Saibal&Dey, Sudeepa. (2016). Predicting the direction of stock market prices using random forest.

IX. Lei Dong, Carlo Ratti, Siqi Zheng,”Predicting neighborhoods’ socioeconomic attributes using restaurant data Proceedings of the National Academy of Sciences Jul 2019, 116 (31) 15447-15452;

X. Limsombunchai, V. 2004. “House Price Prediction: Hedonic Price Model vs. Artificial Neural Network”, New Zealand Agricultural and Resource Economics Society Conference, New Zealand, pp. 25-26. 2004

XI. M. Hall, “Feature Selection for Discrete and Numeric Class Machine Learning”, Department of Computer Science.

XII. M. Robnik and I. Kononenko, “Theoretical and Empirical Analysis of ReliefF and RReliefF”, Machine Learning Journal, 2003.

XIII. M.C. Mozer,”A Focused Back – propagation Algorithm for Temporal Pattern Recognition”,Complex Systems,3,pp.349-381,1989.

XIV. Mariana Listiani , 2009. “Support Vector Regression Analysis for Price Prediction in a Car Leasing Application”. Master Thesis. Hamburg University of Technology.

XV. Minitab Express Support. Interpret all statistics and graphs for Multiple Regression.[Online] Available

XVI. Mobile data and specifications online available from https://www.flipkart.com/ (Last Accessed on Friday, ‎ December ‎22, ‎2019, ‏ ‎ 3:14:34 PM)

XVII. NamhyoungK,Kyu J, Yong.K, A New Ensemble Model for Efficient Churn Prediction in Mobile Telecommunication, 2012 45th Hawaii international conf on system sciences.

XVIII. Nau, R. 2014. Notes on linear regression analysis, Lecture handouts, Duke University, Furqa School of Business, 26 nov 2014.

XIX. Nisha Thomas and Mercy.”Implementation of Back propagation Algorithm in Reconfigurable Hardware”.2011.

XX. R.Linsker, “From Basic Network Principles to Neural Architecture”, in Processings of the National Academy of Sciences,83,(USA),pp 7508-7512,8390-8394,8779-8783,1986.

XXI. S.E .Fahlman, “Fast Learning Variations on Backpropagation : An EmpricialStudy”,in Proc . 1998 connectionist Model Summer School (D.S. Touretzky, G.E. Hinton, and T.J. Sejnowski ,eds.), San Mateo ,CA:MorganKaufmann,pp. 38-51,1989.

XXII. S.Titri, H. Bourmeridja.”New Reuse Design Methodology for Artificial Neural Network Implementation”.1999.

XXIII. SameerchandPudaruth . “Predicting the Price of Used Cars using Machine Learning Techniques”, International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 7 (2014), pp. 753764

XXIV. Shonda Kuiper, “Introduction to Multiple Regression: How Much Is Your Car Worth? ” , Journal of Statistics Education • November 2008

XXV. Singh, Y., Bhatia, P. K., &Sangwan, O. 2007. “A review of studies on machine learning techniques”, International Journal of Computer Science and Security, 1(1), 70-84.

XXVI. SireeshaJasti ,TummalaSitaMahalakshmi”Multiple Linear Regression”,IJRTE,pp. 1919-1925,August 2019.

XXVII. Suebsing and N. Hiransakolwong, “Euclideanbased Feature Selection for Network Intrusion Detection”, International Conference on Machine Learning and Computing IPCST, 2011.

XXVIII. Sundsøy, Pål&Bjelland, Johannes &reme, bjørn-atle&jahani, eaman. (2016). Deep Learning Applied to Mobile Phone Data for Individual Income Classification. 10.2991/icaita-16.2016.24.

XXIX. TadayoshiHorita, Takuroa Murata and ItsuoTakanami.”A Multiple Weight and Neuron Fault Tolerant Digital Multilayer Neural Network”.2006.

XXX. Thu ZarPhyu, NyeinNyeinOo. Performance Comparison of Feature Selection Methods. MATEC Web of Conferences42, (2016).

XXXI. X.Yao ,”A New Evolutionary System for Evolving Artificial Neural Networks”,IEEETrans.Neural Networks,8,May 1997.

XXXII. Z. Karimi and M. Mansour and A. Harounabadi “Feature Ranking in Intrusion Detection Dataset using combination of filtering”, International Journal of Computer Applications,Vol.78,September 2013.

BLENDING MULTI-OBJECTIVE OPTIMIZATIONAND QUALITY FUNCTION DEPLOYMENT FOR DETERMINING COST AND QUALITY

Authors:

Anurag Tiwari, Vivek Kumar Singh, Praveen Kumar Shukla, Manuj Darbari

DOI NO:

https://doi.org/10.26782/jmcms.2020.03.00025

Abstract:

The Blending problem is one of the oldest and best known optimization problems. It is generally formulated as a linear program and has been applied in many fields. However, the mixing problem encountered in the industry requires much more than direct linear programming formulation. Indeed, the classic blending model would almost always be impossible due to the problem of blending in the industry. Indeed, it is often not possible to combine the characteristics of the mixtures as desired, which leads decision makers to seek solutions as close as possible to specific solutions. In this article, we develop and solve a versatile optimization model for the problem of blending, in which we minimize the total cost of the raw materials to be used, as well as violations of the desired characteristic scores of the final blends. We also present a parametric model which is used as a reference point to compare the multi-objective optimization model.

Keywords:

MOO,QFD,Mobile Handsets,

Refference:

I. D Yagyasen, M Darbari, PK Shukla, VK Singh (2013), “Diversity and convergence issues in evolutionary multiobjective optimization: application to agriculture science”, IERI Procedia.

II. D Yagyasen, M Darbari (2014),”Application of semantic web and petri calculus in changing business scenario”Modern Trends and Techniques in Computer Science.

III. R Asthana, NJ Ahuja, M Darbari (2011),”Model proving of urban traffic controls using neuro Petri nets and fuzzy logic”International Review on Computers and Software (IRECOS.

IV. S Bansal, M Darbari(2012),”Application of Multi Objective Optimization in Prioritizing and Machine Scheduling: a Mobile Scheduler Toolkit”International Journal of Applied Information Systems 3 (2), 24-28.

V. SS Ahmad, M Darbari, H Purohit (2015),”Handling web dynamics of internet marketing supply chain using evolutionary algorithms and semantic breakdown strategy”International Business Information Management ConferenceNetherlands.

VI. SS Ahmad, H Purohit, F Alshaikhly, M Darbari (2013),”Information granules for medical infonomics”International Journal of Information and Operations Management Education.

VII. SaviturPrakash and ManujDarbari, “‘Quality & Popularity’ Prediction Modeling of TV Programme through Fuzzy QFD Approach,” Journal of Advances in Information Technology, Vol. 3, No. 2, pp. 77-90, May, 2012.doi:10.4304/jait.3.2.77-90

VIII. Sofia Angeletou, Matthew Rowe, and HarithAlani: Modelling and Analysis of User Behaviour in Online Communities, 10th International Semantic Web Conference Bonn, Germany, October 23-27, 2011, Lecture Notes in Computer Science, Springer-Verlag Berlin Heidelberg.

IX. Yang. S.Y, Hyun Ko, Seung, Wok. H, Hee. Y. Y, (2007), “Priority-Based Message Scheduling for the Multi-agent System in Ubiquitous Environment”, IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology – Workshops, pp. 395-398.

X. Yujian. Fu, Kan. W, Junwei. Y (2006), “A Multi-Agent System for Manufacturing Material Resource Planning”, Sixth International Conference on Intelligent Systems Design and Applications (ISDA’06) Volume 2, pp. 1118-1123.

XI. Zhanjie. W, Yanbo. L (2006), “A Multi-Agent Agile Scheduling System for Job-Shop Problem”, Sixth International Conference on Intelligent Systems Design and Applications (ISDA’06) Volume 2, pp. 679-683.

TOOLS OF ICT FOR LEARNING AND TEACHING MATHEMATICS

Authors:

Madhu Aggarwal, Satinder Bal Gupta

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00001

Abstract:

The utilization of Information Communication Technology for learning and teaching is mandatory now a day’s for the overall development of the students as well the teachers. Research reveals that ICT is useful in developing higher order skills and increasing student’s interest in Mathematics. In this paper, the authors discussed some tools of ICT that are helpful in learning and teaching mathematics and making mathematics an interesting subject for the learners.

Keywords:

Mathematical Tools,ICT,Software,Websites,Mobile Apps,Teaching,

Refference:

References
I. Adrian Old know, Ron Taylor and Linda Tetlow, “Teaching mathematics using ICT”, Bloomsbury Publishing India private Limited, 2010.

II. Albano G., Desiderio M., “Improvements in teaching and learning using CAS”, Proceedings of the Vienna International Symposium on Integrating Technology into Mathematics Education, Viena, Austria, 2002.

III. Artigue, M., “Learning mathematics in a CAS environment”, Proceeding of CAME, http://Itsn.mathstore.ac.uk/came/events/freudenthal, 2001.

IV. Crompton, H., &Traxler, J., “Mobile learning and mathematics. Foundations, design and case studies”. Florence, KY: Routledge, 2015.

V. Harding, A., &Engelbrecht, J., “Personal learning network clusters: A comparison between mathematics and computer science students”. Journal of Educational Technology and Society, 18(3), pp. 173–184, 2015.

VI. Jenni Way and Toni Beardon, “ICT and Primary Mathematics”, Open University Press, Philadelphia, 2003.

VII. SatinderBal Gupta, Monika Gupta., “Technology and E-Learning in Higher Education”, International Journal of Advanced Science and Technology, Vol. 29, No.4, pp. 1320-1325, 2020.

VIII. SatinderBal Gupta, Raj Kumar Yadav, Shivani., “Study of Growing Popularity of Payment Apps in India”, Test Engineering & Management, Vol. 82, pp. 16110-16119, Jan-Feb, 2020.

IX. Sue Johnston Wilder and David Pimm, “Teaching Secondary Mathematics with ICT”, McGraw Hill Education, UK, 2004.

X. White, T., & Martin, L., “Mathematics and mobile learning”. Tech Trends, 58(1), pp. 64–70, 2014.

XI. Wijers, M., Jonker, V., &Drijvers, P., “Mobile Math: exploring mathematics outside the classroom”. ZDM–TheInternational Journal on Mathematics Education, 42(7), pp. 789–799, 2010.

GENERALIZATION OF SOME WEIGHTED C ̆EBYS ̆EV-TYPE INEQUALITIES

Authors:

Faraz Mehmood, Asif R. Khan, Maria Khan, Muhammad Awais Shaikh

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00002

Abstract:

In present paper, we give generalisation of inequalities of eby ev type involving weights for absolutely continuous functions whose derivatives belong to (Lebesgue space), where r ≥ 1. Our results recapture many established results of diﬀerent authors. Applications are also given in probability theory.

Keywords:

C ̆ebys ̆evInequality,Probability Density Function,Cumulative Density Function.,

Refference:

Asif R. Khan, J. E. Pec ̆aric ́, M. Praljak, Weighted Montgomery’s Identities for Higher Order Diﬀerentiable Functions of Two Variables, , Rev. Anal. Numer. Theor. Approx., 42 (1) (2013), 49-71

Asif R. Khan, J. E. Pec ̆aric ́, M. Praljak, Generalized Cebysev and Ky Fans Identities and Inequalities, J. Math. Inequal.,10 (1) (2016), 185-204.

Asif R. Khan, J. E. Pec ̆aric ́, M. Praljak and S. Varos ̆anec, Genral linear Inequalities and Positivity/Higher Order Convexity, Monographs in inequalities, 12, Element, Zagreb, 2017.

Asif. R. Khan and Faraz Mehmood, Double Weighted Integrals Identities of Montgomery for Diﬀerentiable Function of Higher Order, Journal of Mathematics and Statistics, 15 (1) (2019), 112-121.

Asif R. Khan and FarazMehmood, Generalized Identities and Inequalities of C ̆ebys ̆ev and Ky Fan for ∇-convex function, Submitted.

B. G. Pachpatte, On Trapezoid and Gru ̈ss like integral inequalities, Tamkang J. Math., 34(4) (2003) 365-369.

B. G. Pachpatte, On Ostrowski-Gru ̈ss-C ̆ebys ̆ev type inequalities for functions whose modulus of derivatives are convex, J. Inequal. Pure and Appl. Math., 6(4) (2005). Art. 128.

B. G. Pachpatte, On C ̆ebys ̆ev type inequalities involving functions whose derivatives belong to Lp spaces, J. Inequal. Pure Appl. Math., 7(2) (2006). Art. 58.

D. S. Mitrinovic ́, J. E. Pec ̆aric ́ and A. M. Fink, Classical and new inequalities in analysis, Kluwer Academic Publishers Group, Dordrecht, 1993.

FarazMehmood, On Functions WithNondecreasing Increments, (Unpublished doctoral dissertation), Department of Mathematics, University of Karachi, Karachi, Pakistan, 2019.

FizaZafar, Some generalizations of Ostrowski inequalities and their applications to numerical integration and special means, (Unpublished doctoral dissertation). BahauddinZakariya University, Multan, 2010.

H. P. Heining and L. Maligranda, C ̆ebys ̆ev inequality in function spaces, Real Analysis Exchange, 17 (1991-1992) 211-247.

J. Pec ̆aric ́, F. Proschan and Y. L. Tong, Convex functions, partial orderings, and statistical applications, Mathematics in science and engineering, vol. 187, Academic Press, 1992.

P. Cerone, OnC ̆ebys ̆ev Functional Bounds, Proceedings of the Conference on Differential and Difference Equations and Applications, Hindawi Publishing Corporation, 267-277.

P. L. C ̆ebys ̆ev, Sur les expressions approximatives des integrales par les auters prises entre les memes limites, Proc. Math. Soc. Charkov, 2 (1882) 93-98.

S. S. Dragomir, Th. M. Rassias (Editors). Ostrowski Type Inequalities and Applications in Numerical Integration. Kluwer Academic Publishers, Dordrecht/Boston/London 2002.

Zheng Liu, Generalizations of some new C ̆ebys ̆evtype inequalities, J. Inequal. Pure Appl. Math, 8(1) (2007). Art. 13.

NUMERICAL STUDY OF A THERMAL ENERGY STORAGE SYSTEM WITH DIFFERENT SHAPES INNER TUBES

Authors:

Ali N. Abdul Ghafoor, Munther Abdullah Mussa

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00003

Abstract:

A numerical study to investigate the behaviour and impact of different inner ‎tube geometric shapes on the thermal performance of the latent heat thermal energy storage (LHTES) unit have been done. Current work includes a horizontal concentric shell filling with paraffin wax as phase change material (PCM). The tested inner tube geometric shapes were circular tube, horizontal elliptical tube, and vertical elliptical tube. Finite-volume method with a single-domain enthalpy method have been used for the simulation. The results showed that the circular tube is the best due to keeping absorbing heat from PCM through HTF for a long time with 66.37% efficiency and 240.5 minutes.

Keywords:

Energy storage,solidification,Shell and tube,Natural convection,PCM,

Refference:

I. Agarwal, Ashish, and R. M. Sarviya. 2016. “An Experimental Investigation of Shell and Tube Latent Heat Storage for Solar Dryer Using Paraffin Wax as Heat Storage Material.” Engineering Science and Technology, an International Journal 19 (1): 619–31. https://doi.org/10.1016/j.jestch.2015.09.014.

II. Al-Abidi, Abduljalil A., Sohif Bin Mat, K. Sopian, M. Y. Sulaiman, and Abdulrahman Th Mohammed. 2013. “CFD Applications for Latent Heat Thermal Energy Storage: A Review.” Renewable and Sustainable Energy Reviews 20: 353–63. https://doi.org/10.1016/j.rser.2012.11.079.

III. Avci, Mete, and M. Yusuf Yazici. 2013. “Experimental Study of Thermal Energy Storage Characteristics of a Paraffin in a Horizontal Tube-in-Shell Storage Unit.” Energy Conversion and Management 73: 271–77. https://doi.org/10.1016/j.enconman.2013.04.030.

IV. Esapour, M., M. J. Hosseini, A. A. Ranjbar, Y. Pahamli, and R. Bahrampoury. 2016. “Phase Change in Multi-Tube Heat Exchangers.” Renewable Energy 85: 1017–25. https://doi.org/10.1016/j.renene.2015.07.063.

V. Hosseini, M. J., M. Rahimi, and R. Bahrampoury. 2014. “Experimental and Computational Evolution of a Shell and Tube Heat Exchanger as a PCM Thermal Storage System.” International Communications in Heat and Mass Transfer 50: 128–36. https://doi.org/10.1016/j.icheatmasstransfer.2013.11.008.

VI. Hosseini, M. J., A. A. Ranjbar, K. Sedighi, and M. Rahimi. 2012. “A Combined Experimental and Computational Study on the Melting Behavior of a Medium Temperature Phase Change Storage Material inside Shell and Tube Heat Exchanger.” International Communications in Heat and Mass Transfer 39 (9): 1416–24. https://doi.org/10.1016/j.icheatmasstransfer.2012.07.028.

VII. Jesumathy, S. P., M. Udayakumar, S. Suresh, and S. Jegadheeswaran. 2014. “An Experimental Study on Heat Transfer Characteristics of Paraffin Wax in Horizontal Double Pipe Heat Latent Heat Storage Unit.” Journal of the Taiwan Institute of Chemical Engineers 45 (4): 1298–1306. https://doi.org/10.1016/j.jtice.2014.03.007.

VIII. Kibria, M. A., M. R. Anisur, M. H. Mahfuz, R. Saidur, and I. H.S.C. Metselaar. 2014. “Numerical and Experimental Investigation of Heat Transfer in a Shell and Tube Thermal Energy Storage System.” International Communications in Heat and Mass Transfer 53: 71–78. https://doi.org/10.1016/j.icheatmasstransfer.2014.02.023.

IX. Longeon, Martin, Adèle Soupart, Jean François Fourmigué, Arnaud Bruch, and Philippe Marty. 2013. “Experimental and Numerical Study of Annular PCM Storage in the Presence of Natural Convection.” Applied Energy 112: 175–84. https://doi.org/10.1016/j.apenergy.2013.06.007.

X. Mahdi, Mustafa S., Hameed B. Mahood, Ahmed F. Hasan, Anees A. Khadom, and Alasdair N. Campbell. 2019. “Numerical Study on the Effect of the Location of the Phase Change Material in a Concentric Double Pipe Latent Heat Thermal Energy Storage Unit.” Thermal Science and Engineering Progress 11: 40–49. https://doi.org/10.1016/j.tsep.2019.03.007.

XI. Rathod, M. K., and J. Banerjee. 2014. “Experimental Investigations on Latent Heat Storage Unit Using Paraffin Wax as Phase Change Material.” Experimental Heat Transfer 27 (1): 40–55. https://doi.org/10.1080/08916152.2012.719065.

XII. Seddegh, Saeid, Xiaolin Wang, and Alan D. Henderson. 2016. “A Comparative Study of Thermal Behaviour of a Horizontal and Vertical Shell-and-Tube Energy Storage Using Phase Change Materials.” Applied Thermal Engineering 93: 348–58. https://doi.org/10.1016/j.applthermaleng.2015.09.107.

XIII. Senthil, Ramalingam, and Marimuthu Cheralathan. 2016. “Melting and Solidification of Paraffin Wax in a Concentric Tube PCM Storage for Solar Thermal Collector.” International Journal of Chemical 14 (4): 2634–40. http://www.tsijournals.com/abstract/melting-and-solidification-of-paraffin-wax-in-a-concentric-tube-pcm-storage-for-solar-thermal-collector-12762.html.

XIV. Yazici, Mustafa Yusuf, Mete Avci, Orhan Aydin, and Mithat Akgun. 2014. “On the Effect of Eccentricity of a Horizontal Tube-in-Shell Storage Unit on Solidification of a PCM.” Applied Thermal Engineering 64 (1–2): 1–9. https://doi.org/10.1016/j.applthermaleng.2013.12.005.

EFFECT OF BINDER CONTENT ON SUPER PLASTICIZER DOSAGE FOR SELF-COMPACTING CONCRETE

Authors:

A. Nagaraju, S.Vijaya Bhaskar Reddy

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00004

Abstract:

The most incredible inventions in concrete is Self-Compacting Concrete, i.e. concrete will flow by its own weight. Self-compacting concrete can be achieved by high powder content and with combination of different mineral admixtures or secondary supplementary cementetious materials, high range water reducing super plasticizers and the performance of concrete will also enhanced. All above mentioned qualities make Self-compacting concrete as Special concrete. The more research works were going on to get the generalized mix code for SCC and effect of each ingredient of concrete also examine for SCC. In the present study, the effect super plasticize dosage and binder content on properties of Self-Compacting Concrete (SCC) had been studied. As a part of experimental study, the mix design was developed based on EFNARC guidelines. The SCC mixes were made with different proportion of binder content (450, 500, 550) at various dosages of super plasticize. Slump flow, V-funnel, l-box and J-ring tests were conducted for checking the properties of SCC. 3-days, 7-days, 28-days UPV test was conducted. The experimental results reveal that at higher binder content, at lower dosage, SCC was formed. The strength was achieved M45, M60 with 500 and 550 grades of cement respectively.

Keywords:

Self-compacting concrete,binder content,Super plasticizer dosage,mix design,

Refference:

I. B.Łaz´niewska-Piekarczyk, “The influence of chemical admixtures on cement hydration and mixture properties of very high performance self-compacting concrete”, Construction and Building Materials, Dec. 2013.

II. B. Beeralingegowda and V. D. Gundakalle, “The effect of addition ofLimestone powder on the Properties of self-compactingConcrete”, International Journal of Innovative Research in Science, Engineering and Technology, vol. 2, no. 9, Sep. 2013.

III. B. G. Patel, A. K. Desai and S. G. Shah, “Effect of Binder Volume on Fresh and Harden Properties of Self Compacting Concrete”, International Journal of Engineering Research & Technology, vol. 4, no. 09, Sept. 2015.
IV. H. Omkura and M.Ouchi, “Self-compacting concrete”, journal of Advanced Concrete Technology, vol. 1, no.1, pp. 5-15, Apr. 2003.
V. L.O Larsen and V.V. Naruts, “Self-compacting concrete with limestone powder for transport infrastructure”, Magazine of Civil Engineering, vol. 68, no. 8, pp. 76-85, 2016.
VI. M. A.Sikandar, Z.Baloch and Q. Jamal, “Effect of w/b ratio and binder content on the properties of self-compacting high-performance concrete (SCHPC)”, Journal of Ceramic Processing Research, vo. 6, no. 1, pp. 40-48, Jan. 2018.
VII. P.Aggarwal, R.Siddique, Y.Aggarwal and S. M. Gupta, “Self-Compacting Concrete – Procedure for Mix Design”, Leonardo Electronic Journal of Practices and Technologies, no. 12, Jan-Jun 2008, pp. 15-24.
VIII. P. D Viramgama, Prof. S. R. Vaniya and Prof. Dr. K.B.Parikh, “Effect of Ceramic Waste Powder in Self Compacting Concrete Properties: A Critical Review”, IOSR Journal of Mechanical and Civil Engineering, vol. 13, no. 1 Ver. V, Jan. – Feb. 2016.
IX. S.Grzeszczyk and P.Podkowa, “The Effect of Limestone Filler on the Properties of Self Compacting Concrete”, Annual Transactions of The Nordic Rheology Society, vol. 17, 2009.

ON CENTRALIZERS OF MA-SEMIRINGS

Authors:

Yaqoub Ahmed, M. Nadeem, M. Aslam

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00005

Abstract:

An additive mapping γ : S → S is α − centralizer, if γ(xy) = γ(x)α(y) where α is an endomorphism on S, holds for all x, y ∈ S. In this article, we discuss some functional identities on additive mapping γ : S → S on a semiring S, which makes it α-centralizer. Further, we investigate some conditions on α – centralizers which enforces commutativity in semirings.

Keywords:

Semirings,centralizers ,α– centralizer.,

Refference:

I. B. Zalar, On centralizers of semiprime rings, Comment. Math. Univ. Carol. 32 (1991), 609614.
II. H. J. Bandlet and M. Petrich, Subdirect products of rings and distrbutive lattices, Proc. Edin Math. Soc. 25 (1982), 135171.
III. I. N. Herstein, Jordan derivations of prime rings, Proc. Amer. Math. Soc. 8 (1957), 11041119.
IV. J. Vukman, Centralizer on semiprime rings, Comment. Math. Univ. Carolinae, Vol 42(2001), pp 237-245.
V. J. Vukman, An identity related to centralizer in semiprime rings. Comment. Math. Univ. Carolinae, Vol 40 (1999) pp 447-456
VI. K. Glazek, A Guide to Literature on Semirings and their Applications in Mathematics and Information Sciences with Complete Bibliography, Kluwer Acad. Publ., Dodrecht, 2002.
VII. M.A Javed, M. Aslam and M. Hussain, On condition (A2) of Bandlet and Petrich for inverse semirings, International Mathematical forum, vol 7(2012), no.59, 2903-2914.
VIII. M. Bresar, Zalar B., On the structure of jordan *-derivations, Colloquium Math. (1992), 163-171.
IX. M. Bresar and J. Vukman, Jordan derivations on prime rings, Bull. Austral. Math. Soc. 37 (1988), 321322.
X. M. Bresar, Jordan derivations on semiprime rings, Proc. Amer. Math. Soc. 104 (1988), 10031006.
XI. P.H. Karvellas, Inversive semirings, J. Austral. Math. Soc. 18 (1974), 277-288
XII. P. Kostolnyi, F. Mi sn, Alternating weighted automata over commutative semirings, Theoret. Comput. Sci. 740 (2018), 127.
XIII. S. Ali, N. A. Dar and J. Vukman, jordan left centralizers of prime and semiprime rngs with involution, Beitr. Algebra. Geom. 54 (2) (2013), 609-624.
XIV. S. Sara, M. Aslam, M.A Javed, On centralizer of Semiprime inverse semirings, Discussiones Mathematicae, General Algebra and Applica- tions 36 (2016) 71-84
XV. U. Hebisch, H. J.Weinert, Semirings: Algebraic Theory and Applica- tions in the Computer Science, World Scientific, 1998.
XVI. V.N. Kolokoltsov, V. Maslov, Idempotent Analysis and Applications, Kluwer, Dordrecht, 1997.
XVII. V. Maslov, S.N. Sambourskii, Idempotent Analysis, Advances Soviet Math. 13, Amer. Math. Soc., Providence, R.I., 199

DATA DRIVEN ANALYSIS ON SPREAD OF CORONAVIRUS IN INDIA – A TIME DEPENDENT NON-PARAMETRIC MATHEMATICAL APPROACH

Authors:

Geetha Narayanan Kannaiyan, Bridjesh Pappula

DOI NO:

https://doi.org/10.26782/jmcms.2020.04.00006

Abstract:

Statistical analysis is a qualitative research used to quantify data adapting a statistical tool. The present study is to device a time dependent non-parametric mathematical model to analyze the spread of COVID-19 in INDIA based on the statistics available. As the medicine to treat COVID-19 is not invented yet, the best possible way to break the chain of spreading virus is, “Personal Hygiene and Social Distancing”.

Keywords:

COVID-19,Statistical analysis,Non-parametric analysis,

Refference:

I. www.who.int/health-topics/coronavirus#tab=tab_1 (accessed on 26-03-2020)
II. www.cdc.gov/coronavirus/2019-ncov/about/symptoms.html (accessed on 26-03-2020)
III. Y. Liu, A. A. Gayle, A. Wilder-Smith, J. Rocklöv, “The reproductive number of COVID-19 is higher compared to SARS coronavirus”, J Travel Med, 27(2), 2020.
IV. www.who.int/docs/default-source/coronaviruse/situation-reports/20200121-sitrep-1-2019-ncov.pdf?sfvrsn=20a99c10_4 (accessed on 26-03-2020)
V. www.who.int/docs/default-source/wrindia/situation-report/india-situation-report-8bc9aca340f91408b9efbedb3917565fc.pdf?sfvrsn=5e0b8a43_2 (accessed on 26-03-2020)
VI. www.covid19india.org/ (accessed on 26-03-2020)
VII. Z. Ali, S. Balabhaskar, “Basic statistical tools in research and data analysis”, Indian J Anaesth, 60(9), 662–669, 2016
VIII. D.G. Altman, J. M. Bland, “Parametric v non-parametric methods for data analysis”,. BMJ 338, 3167-3173, 2009