Archive

OBSERVED ISSUES IN CLOUD-BASED WEB COMMERCE ADOPTION FOR THE FINANCIAL TRANSACTIONS IN HYDERABAD

Authors:

Srinivasa Rao Gundu,Panem Charan Arur,Thimmapuram Anuradha,

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00001

Abstract:

In the present day scenario, maximum financial transactions are being carried out with the help of Cloud-Based Web Trade (CBWT). These Cloud Oriented Web-Based Financial Transactions provide numerous advantages to the end-users. The Commodities are available at a much cheaper rate and numerous choices are left over to the customers and they are also reducing the shopping time. Particularly the time like Pandemic Situation would provide a better way to purchase multiple goods at their fingertips. There are many numbers of reasons are leftover behind the success and the downfall of such Cloud Oriented Web-Based Financial Transactions. Some of these include financial conditions, technical feasibility, and geographical location, etc. However, nowadays there it is facing many Ethical, Service-oriented, and financial challenges in this area. There is needed to make a SWOT Analysis since it is going to be the major financial gateway for numerous people.

Keywords:

Cloud-Based Web Trade (CBWT),SWOT Analysis,Online Banking,Hacking,Security,Business,

Refference:

I. A. A. Abdulrazaq, M. B. Abdulrazaq, I. J. Umoh and E. A. Adedokun, “Fraud Detection in Credit Card and Application of VAT Clustering Algorithm: A Review,” 2019 2nd International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf), Zaria,Nigeria, 2019, pp. 1-7, doi: 10.1109/NigeriaComputConf45974.2019.8949660.
II. A. Mitrokotsa, N. Komninos and C. Douligeris, “Intrusion Detection with Neural Networks and Watermarking Techniques for MANET,” IEEE International Conference on Pervasive Services, Istanbul, 2007, pp. 118- 127.
III. Anitha Vemulapalli, Nandula Anuradha, Geeta Mahadeo Ambildhuke. ‘RISK ASSESSMENT FOR BIG DATA IN CLOUD COMPUTING ENVIRONMENT FROM THE PERSPECTIVE OF SECURITY, PRIVACY AND TRUST’. J. Mech. Cont. & Math. Sci., Vol.-15, No.-8, August (2020) pp 391-402. DOI : 10.26782/jmcms.2020.08.00036
IV. B. Li and Y. Wang, “RZKPB: A Privacy-Preserving Block chain-Based Fair Transaction Method for Sharing Economy,” 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), New York, NY, 2018, pp. 1164-1169, doi:10.1109/TrustCom/BigDataSE.2018.00161.
V. B. Galloway and G. P. Hancke, “Introduction to industrial control networks,” IEEE Communications Surveys Tutorials, vol. 15, no. 2, pp. 860–880, Second 2013.
VI. C. Chang, J. Yang and K. Chang, “An Efficient and Flexible Mobile Payment Protocol,” 2012 Sixth International Conference on Genetic and Evolutionary Computing, Kitakushu, 2012, pp. 63-66, doi: 10.1109/ICGEC.2012.43.

VII. C. Piao, Y. Zuo and C. Zhang, “Research on Hybrid-Cloud-Based User Privacy Protection of O2O Platform,” 2016 IEEE 13th International Conference on e-Business Engineering (ICEBE), Macau, 2016, pp. 214- 219, doi: 10.1109/ICEBE.2016.044.
VIII. G. A. Lopes Ferreira et al., “Internet of Things and the Credit Card Market: How Companies Can Deal with the Exponential Increase of Transactions with Connected Devices and Can Also be Efficient to Prevent Frauds,” 2015 12th International Conference on Information Technology – New Generations, Las Vegas, NV, 2015, pp. 107-111, doi: 10.1109/ITNG.2015.23.
IX. G. Drakopoulos, E. Kafeza and H. Al Katheeri, “Proof Systems In Blockchains: A Survey,” 2019 4th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Piraeus, Greece, 2019, pp. 1-6.
X. H. D. Thai, C. Lee, D. Niyato and P. Wang, “A Survey of mobile cloud computing: Architecture, applications, and approaches,” Wireless Communications and Mobile Computing, vol. 13, no. 18, 2013, pp. 1587– 1611.
XI. Hu Dongxing,et al. Information Network Security Situation Aware Technology Based on Artificial Intelligence [J]. Journal of Information Communication, 2012 (6):80-81.
XII. H. Elayan, O. Amin, R. M. Shubair, and M.-S. Alouini, “Terahertz communication: The opportunities of wireless technology beyond 5G,” in Proc. 2018 International Conference on Advanced Communication Technologies and Networking, Apr. 2018, pp. 1–5.
XIII. J. Ni, X. Lin, and X. S. Shen, “Efficient and secure service-oriented authentication supporting network slicing for 5G-enabled IoT,” IEEE JSAC, vol. 36, no. 3, Mar. 2018.
XIV. J. Zhu, “Capacity-power consumption and energy- efficiency evaluation of green wireless networks,” China Communications, vol. 9,no. 2, 2012, pp. 13–21.
XV. K. Kotobi and S. G. Bilen, “Secure blockchains for dynamic spectrum access: A decentralized database in moving cognitive radio networks enhances security and user access,” IEEE Veh. Technol. Mag., vol. 13, no. 1, pp. 32–39, Mar. 2018. doi:10.1109/MVT.2017.2740458.
XVI. M. H. Bhuyan, D. K. Bhattacharyya and J. K. Kalita, “Network Anomaly Detection: Methods, Systems and Tools,” in IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 303-336, First Quarter 2014.
XVII. M. T. Hossan, M. Z. Chowdhury, M. Shahjalal, and Y. M. Jang, “Human bond communication with head-mounted displays: scope,challenges, solutions, and applications,” IEEE Communications Magazine, vol. 57, no. 2, pp. 26-32, February 2019.
XVIII. R. Henry, A. Herzberg, and A. Kate, “Blockchain access privacy: challenges and directions,” IEEE Security & Privacy, vol. 16, no. 4, pp. 38-45, Jul./Aug. 2018.
XIX. R. Li, “Towards a New Internet for the Year 2030 and Beyond,” in Third Annual ITU IMT-2020/5G Workshop and Demo Day, 2018.
XX. R. Tripathi, S. Vignesh, V. Tamarapalli and D. Medhi, “Cost Efficient Design of Fault Tolerant Geo-Distributed Data Centers,” inIEEE Transactions on Network and Service Management, vol. 14, no. 2, pp. 289-301, June 2017, doi: 10.1109/TNSM.2017.2691007.
XXI. S. Ali, N. Rajatheva, and W. Saad, “Fast uplink grant for machine type communications: Challenges and opportunities,” IEEE Comm. Mag., vol. 57, no. 3, Mar. 2019.
XXII. T. Aste, P. Tasca, and T. Di Matteo, “Blockchain technologies: the foreseeable impact on society and industry,” Computer, vol. 50, no. 9, pp. 18-28, 2017.
XXIII. Tallapally Sampath Kumar, B. Manjula. : ‘EFFICIENT MULTI-LEVEL ENCRYPTION PROCEDURE FOR CLOUD SECURITY’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-6, June (2020) pp 402-410. DOI : 10.26782/jmcms.2020.06.00031.

View Download

COMPARATIVE ANALYSIS OF PREDICTION TECHNIQUES ON THE BASIS OF TELECOM CUSTOMER CHURN

Authors:

Yasser Khan,Zeeshan Rasheed,Naeem Ahmed,Minhaj Ullah,Malik Taimur Ali,Farrukh Hassan,Sheeraz Ahmed,

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00002

Abstract:

Telecommunication customer churn is considered as major cause for dropped revenue and customer baseline of voice, multimedia and broadband service provider. There is strong need on focusing to understand the contributory factors of churn. Now considering factors from data sets obtained from Pakistan major telecom operators are applied for modeling. On the basis of results obtained from the optimal techniques, comparative technical evaluation is carried out. This research study is comprised mainly of proposition of conceptual frame work for telecom customer churn that lead to creation of predictive model. This is trained tested and evaluated on given data set taken from Pakistan Telecom industry that has provided accurate & reliable outcomes. Out of four prevailing statistical and machine learning algorithm, artificial neural network is declared the most reliable model, followed by decision tree. The logistic regression is placed at last position by considering the performance metrics like accuracy, recall, precision and ROC curve.  The results from research has revealed main parameters found responsible for customer churn were data rate, call failure rate, mean time to repair and monthly billing amount. On the basis of these parameter artificial neural network has achieved 79% more efficiency as compare to low performing statistical techniques.

Keywords:

Artificial Neural Network,Prediction,Churn management,Telecom Churn,

Refference:

I. Asmaa Jamal Awad, Ahmed Abdulrasool Ahmed, Osamah Abdallatif. : ‘ESTIMATION TYPES OF FAILURE FOR THERMO-ELECTRIC UNIT BY USING ARTIFICIAL NEURAL NETWORK (ANN)’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-7, July (2020) pp 47-69. DOI : 10.26782/jmcms.2020.07.00005
II. Al-Weshah, Ghazi A., Excimirey Al-Manasrah, and Manar Al-Qatawneh. “Customer relationship management systems and organizational performance: Quantitative evidence from the Jordanian telecommunication industry.” Journal of Marketing Communications 25, no. 8 (2019): 799-819.
III. Bhattacharyya, Jishnu, and Manoj Kumar Dash. “Investigation of customer churn insights and intelligence from social media: a netnographic research.” Online Information Review (2020).
IV. DARMA, Jufri, Azhar SUSANTO, Sri MULYANI, and Jadi SUPRIJADI. “The Role of Top Management Support in the Quality of Financial Accounting Information Systems.” Journal of Applied Economic Sciences 13, no. 4 (2018).
IV. Dridi, Amna, Mohamed Medhat Gaber, R. Muhammad Atif Azad, and Jagdev Bhogal. “Scholarly data mining: A systematic review of its applications.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (2020)

V. Gordini, Niccolò, and Valerio Veglio. “Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry.” Industrial Marketing Management 62 (2017): 100-107.
VI. Idris, Adnan, Aksam Iftikhar, and Zia ur Rehman. “Intelligent churn prediction for telecom using GP-AdaBoost learning and PSO under sampling.” Cluster Computing 22, no. 3 (2019): 7241-7255.
VII. Jere, Mlenga G., and Alick Mukupa. “Customer satisfaction and loyalty drivers in the Zambian mobile telecommunications industry.” Journal of Business and Retail Management Research 13, no. 2 (2018).
VIII. Khdour, Naser, and Atef Al-Raoush. “The impact of organizational storytelling on organizational performance within Jordanian telecommunication sector.” Journal of Workplace Learning (2020).
IX. Lee, Hyunsong, Hyunhong Choi, and Yoonmo Koo. “Lowering customer’s switching cost using B2B services for telecommunication companies.” Telematics and Informatics 35, no. 7 (2018): 2054-2066.
X. Mashchak, Nataliia, and Oksana Dovhun. “Modern Marketing and Logistics Approaches in the Implementation of E-Commerce.” In Integration of Information Flow for Greening Supply Chain Management, pp. 375-391. Springer, Cham, 2020.
XI. Muthu, BalaAnand, C. B. Sivaparthipan, Gunasekaran Manogaran, Revathi Sundarasekar, Seifedine Kadry, A. Shanthini, and Antony Dasel. “IOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector.” Peer-to-peer networking and applications 13, no. 6 (2020): 2123-2134.
XII. Özata, Hatice Işık, Önder Demir, and Buket Doğan. “Analysis of Patents in Cyber Security with Text Mining.” International Journal of Computer Theory and Engineering 13, no. 1 (2021).
XIII. Pant, Laxmi Prasad, and Helen Hambly Odame. “Broadband for a sustainable digital future of rural communities: A reflexive interactive assessment.” Journal of Rural Studies 54 (2017): 435-450.
XIV. Rachid, Ait Daoud, Amine Abdellah, Bouikhalene Belaid, and Lbibb Rachid. “Clustering Prediction Techniques in Defining and Predicting Customers Defection: The Case of E-Commerce Context.” International Journal of Electrical and Computer Engineering 8, no. 4 (2018): 2367.
XV. Stiassny, Alfred, Agnes Somosi, and Krisztina Kolos. “Enhancing customer retention in case of service elimination? An empirical investigation in telecommunications.” (2019).
XVI. Taniguchi, Tadanari. “Self-organizing map analysis and classification of consumption trends of foreigners visiting Japan using a questionnaire survey.” Journal of Global Tourism Research 3, no. 2 (2018).
XVII. Thuethongchai, Nopsaran, Tatri Taiphapoon, Achara Chandrachai, and Sipat Triukose. “Adopt big-data analytics to explore and exploit the new value for service innovation.” Social Sciences 9, no. 3 (2020): 29.
XVIII. Van den Poel, Dirk, and Bart Lariviere. “Customer attrition analysis for financial services using proportional hazard models.” European journal of operational research 157, no. 1 (2004): 196-217.
XIX Wang, Li, Chaochao Chen, Jun Zhou, and Xiaolong Li. “Time-sensitive Customer Churn Prediction based on PU Learning.” arXiv preprint arXiv:1802.09788 (2018).
XX. Yasser Khan, Shahryar Shafiq, Sheeraz Ahmed, Nadeem Safwan, Mehr-e-Munir, Alamgir Khan. : ‘Factors affecting Service Quality, Customer Satisfaction and Customer Churn in Pakistan Telecommunication Services Market’. J. Mech. Cont.& Math. Sci., Vol.-14, No.-4, July-August (2019) pp 576-594. DOI : 10.26782/jmcms.2019.08.00048

View Download

IMPROVEMENT IN SIGNAL QUALITY THROUGH MEDIAN BASE FILTERING

Authors:

Junaid Masood,Sheeraz Ahmed,Asim Ali,Ubaid Ullah,Said-ul-Abrar,Muhammad Tayyab,Samhita Priyadarsini Gundala,

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00003

Abstract:

Speech signal segmental framing and the scaling factor is basis for the speech recognition process as first step. The next followed step is existing noise reduction in the recognized speech signal for quality improvement. In this work, the noise reduction is done using newly proposed adaptive median based filtering. Comparison of the observations based on adaptive median filtering with Minimum Mean-Square Error Short-time Spectral Amplitude (MMSE-STSA) and Minimum Mean-Square Error (MMSE) based noise reduction reveal a list of worthy to mention relevant observations. The drawn conclusion also accumulates possible contributions by the proposed adaptive median based filtering technique. Lastly is mentioning of Signal-to-noise ratio (SNR) as the primary metric for observations collection for the newly proposed adaptive median based filtering technique analysis.

Keywords:

Filters,Speech Signal,Signal to Noise Ratio,Mean Square Error,Scaling Factor,

Refference:

I. Brown, A., S. Garg, and J. Montgomery, Automatic and Efficient Denoising of Bioacoustics Recordings Using MMSE STSA. IEEE Access, 2018. 6: p. 5010-5022.

II. Di Liberto, G.M., et al., Atypical cortical entrainment to speech in the right hemisphere underpins phonemic deficits in dyslexia. NeuroImage, 2018.

III. Djaziri-Larbi, S., et al., Watermark-Driven Acoustic Echo Cancellation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018. 26(2): p. 367-378.

IV. Fu, J., L. Zhang, and Z. Ye, Supervised monaural speech enhancement using two-level complementary joint sparse representations. Applied Acoustics, 2018. 132: p. 1-7.

V. Heitkaemper, Jens, Joerg Schmalenstroeer, Joerg Ullmann, Valentin Ion, and Reinhold Haeb-Umbach. “A Database for Research on Detection and Enhancement of Speech Transmitted over HF links.” arXiv preprint arXiv:2106.02472 (2021).

VI. Heese, F., et al. Selflearning codebook speech enhancement. in Speech Communication; 11. ITG Symposium; Proceedings of. 2014. VDE.

VII. Kandagatla, R.K. and P. Subbaiah, Speech enhancement using MMSE estimation of amplitude and complex speech spectral coefficients under phase-uncertainty. Speech Communication, 2018. 96: p. 10-27.

VIII. Khaldi, K., A.-O. Boudraa, and A. Komaty, Speech enhancement using empirical mode decomposition and the Teager–Kaiser energy operator. The Journal of the Acoustical Society of America, 2014. 135(1): p. 451-459.

IX. Khaldi, K., et al., Speech enhancement via EMD. EURASIP Journal on Advances in Signal Processing, 2008. 2008(1): p. 873204.

X. Kuortti, J., J. Malinen, and A. Ojalammi, Post-processing speech recordings during MRI. Biomedical Signal Processing and Control, 2018.

XI. Masood, J., Shahzad, M., Khan, Z.A., Akre, V., Rajan, A., Ahmed, S. and Masood, F., 2020, November. Effective Classification Algorithms and Feature Selection for Bio-Medical Data using IoT. In 2020 Seventh International Conference on Information Technology Trends (ITT) (pp. 42-47). IEEE.

XII. Michelsanti, Daniel, Zheng-Hua Tan, Shi-Xiong Zhang, Yong Xu, Meng Yu, Dong Yu, and Jesper Jensen. “An overview of deep-learning-based audio-visual speech enhancement and separation.” IEEE/ACM Transactions on Audio, Speech, and Language Processing (2021).

XIII. Nabi, W., et al., A dual-channel noise reduction algorithm based on the coherence function and the bionic wavelet. Applied Acoustics, 2018.

XIV. Rao, C.V.R., M.R. Murthy, and K.S. Rao. Speech enhancement using perceptual Wiener filter combined with unvoiced speech—A new scheme. in Recent Advances in Intelligent Computational Systems (RAICS), 2011 IEEE. 2011. IEEE.

XV. Tabassum Feroz, Uzma Nawaz. : ‘SUPPRESSION OF WHITE NOISE FROM THE MIXTURE OF SPEECH AND IMAGE FOR QUALITY ENHANCEMENT’. J. Mech. Cont. & Math. Sci., Vol.-16, No.-7, July (2021) pp 67-78. DOI : 10.26782/jmcms.2021.07.00006

XVI. Wang, X., et al., A comparison of recent waveform generation and acoustic modeling methods for neural-network-based speech synthesis. preprint arXiv:1804.02549, 2018.

XVII. Wiem, B., P. Mowlaee, and B. Aicha, Unsupervised single channel speech separation based on optimized subspace separation. Speech Communication, 2018. 96: p. 93-101.

XVIII. Yang, Fan, Ziteng Wang, Junfeng Li, Risheng Xia, and Yonghong Yan. “Improving generative adversarial networks for speech enhancement through regularization of latent representations.” Speech Communication 118 (2020): 1-9.

XIX. Yilmaz, O. and S. Rickard, Blind separation of speech mixtures via time-frequency masking. IEEE Transactions on signal processing, 2004. 52(7): p. 1830-1847.

View Download

A NEW EVOLUTIONARY METHOD TO PARAMETERS AND ORDERS IDENTIFICATION AND SYNCHRONIZATION OF CHAOTIC FRACTIONAL-ORDER SYSTEMS

Authors:

Ali Soleimanizadeh,Mohammad Ali Nekui,

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00004

Abstract:

System identification is an important task in the control theory. Classical control theory is usually known for integer-order processes. Nowadays real processes are fractional order usually. According to a large number of fractional-order systems, identification of these systems is so important. This paper aims to evaluate an improved Biogeography-based Optimization (BBO) approach to estimate the parameters and orders of fractional-order systems. After that, a method based on this algorithm has been introduced to synchronization of chaotic systems. Results show that the proposed scheme has high accuracy.

Keywords:

Fractional-order system,System identification,Biogeography-based Optimization,

Refference:

I. Al_ A, Modares H. System identification and control using adaptive particle swarm optimization. Applied Mathematical Modelling. 2011 Mar 1;35(3):1210-21.
II. Antonik P, Gulina M, Pauwels J, Massar S. Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronization and cryptography. Physical Review E. 2018 Jul 24;98(1):012215.
III. Behinfaraz R., Badamchizadeh M. New approach to synchronization of two different fractional-order chaotic systems, International Symposium on Artificial Intelligence and Signal Processing (AISP), 2015, 149-153.
IV. Behinfaraz, Reza, and Mohammad Ali Badamchizadeh. Synchronization of different fractional-ordered chaotic systems using optimized active control. Modeling, Simulation, and Applied Optimization (ICM- SAO), 2015 6th International Conference on. IEEE, 2015.
V. Bouzeriba A. Fuzzy Adaptive Controller for Synchronization of Uncertain Fractional-Order Chaotic Systems. In Advanced Synchronization Control and Bifurcation of Chaotic Fractional-Order Systems 2018 (pp. 190-217). IGI Global.
VI. D.Simon, “Biogeography-based optimization” ,IEEE Trans. on Evo. Com. vol.12,pp.702-713, 2008.
VII. Doye IN, Salama KN, Laleg-Kirati TM. Robust fractional-order proportional-integral observer for synchronization of chaotic fractional-order systems. IEEE/CAA Journal of Automatica Sinica. 2019 Jan;6(1):268-77.
VIII. H. Ma M. Fei, Z. Ding, J. Jin, “Biogeography-based optimization ensemble of migration models for global numerical optimization”, Proc. IEEE Congress on Evolutionary Computation, June 2012.
IX. H. Ma, D. Simon, “Blended Biogeography-based optimization for constrained optimization”, Evolutionary Comp., Vol. 24, pp. 517-525,2011.
X. Hartley TT, Lorenzo CF. Fractional-order system identification based on continuous order-distributions. Signal processing. 2003 Nov 1;83(11):2287-300.
XI. I. Podlubny, “The Laplace Transform Method for Linear Differential Equations of the Fractional Order”, UEF-02-94, The Academy of Sciences Institute of Experimental Physics, Kosice, Slovak Republic, 1994.
XII. Johnson T, Husbands P. System identification using genetic algorithms. In International Conference on Parallel Problem Solving from Nature 1990 Oct 1 (pp. 85-89). Springer, Berlin, Heidelberg.
XIII. Kazemi A, Behinfaraz R, Ghiasi AR. Accurate model reduction of large scale systems using adaptive multi-objective particle swarm optimization algorithm. 2017 International Conference on In Mechanical, System and Control Engineering (ICMSC), 2017 May 19 (pp. 372-376).
XIV. Kilbas AA, Srivastava HM, Trujillo JJ. Theory and applications of fractional differential equations . Elsevier Science Limited; 2006.
XV. L . Dorcak , “Numerical Methods for Simulation the Fractional-Order Control Systems”, UEF SAV, The Academy of Sciences Institute of Experimental Physics, Kosice, Slovak Republic, 1994.
XVI. Li C, Peng G.”Chaos in Chens system with a fractional order” Chaos Solitons Fract 2004; 22:44350
XVII. Ouannas A, Grassi G, Azar AT. Fractional-Order Control Scheme for QS Chaos Synchronization. InInternational Conference on Advanced Machine Learning Technologies and Applications 2019 Mar 28 (pp. 434-441). Springer, Cham.
XVIII. Ouannas A, Odibat Z. Reduced-Increased Synchronization Between Fractional Chaotic Systems with Different Dimensions and Orders. Available at SSRN 3274053. 2018 Jun 20.
XIX. P. J. Torvik, R.L. Bagley, “On the appearance of the fractional derivative in the behaviour of real mate- rials”, Transactions of the ASME, vol. 51, June 1984, pp. 294-298.
XX. Pham VT, Ouannas A, Volos C, Kapitaniak T. A simple fractional-order chaotic system without equi- librium and its synchronization. AEU-International Journal of Electronics and Communications. 2018 Mar 1;86:69-76.
XXI. Pillai N, Schwartz SL, Ho T, Dokoumetzidis A, Bies R, Freedman I. Estimating parameters of nonlinear dynamic systems in pharmacology using chaos synchronization and grid search. Journal of Pharmacoki – netics and Pharmacodynamics. 2019:1-8.
XXII. Reza Behinfaraz , Mohammadali Badamchizadeh, Amir Rikhtegar Ghiasi, An adaptive method to parameter identification and synchronization of fractional-order chaotic systems with parameter uncertainty, Applied Mathematical Modelling, Volume 40, Issues 78, April 2016, Pages 4468-4479.
XXIII. Sanaullah Mastoi, Wan Ainun Mior othman, Umair Ali, Umair Ahmed Rajput, Ghulam Fizza. : ‘NUMERICAL SOLUTION OF THE PARTIAL DIFFERENTIAL EQUATION USING RANDOMLY GENERATED FINITE GRIDS AND TWO-DIMENSIONAL FRACTIONAL-ORDER LEGENDRE FUNCTION’. J. Mech. Cont. & Math. Sci., Vol.-16, No.-6, June (2021) pp 39-51. DOI : 10.26782/jmcms.2021.06.00004.
XXIV. Singh S, Azar AT, Vaidyanathan S, Ouannas A, Bhat MA. Multi switching Synchronization of Com- mensurate Fractional Order Hyperchaotic Systems Via Active Control. In Mathematical Techniques of Fractional Order Systems 2018 (pp. 319-345).
XXV. Sontakke Bhausaheb, Rajashri Pandit : ‘NUMERICAL SOLUTION OF TIME FRACTIONAL TIME REGULARIZED LONG WAVE EQUATION BY ADOMINAN DECOMPOSITION METHOD AND APPLICATIONS’. J. Mech. Cont. & Math. Sci., Vol.-16, No.-2, February (2021) pp 48-60. DOI : 10.26782/jmcms.2021.02.00005.
XXVI. Vaseghi B, Pourmina MA, Mobayen S. Finite-time chaos synchronization and its application in wireless sensor networks. Transactions of the Institute of Measurement and Control. 2018 Sep;40(13):3788-99.

View Download

A NEW CONSTRUCTION OF OS OF SUBALGEBRAS AND INVARIANT SOLUTION OF THE BLACK-SCHOLES EQUATION

Authors:

Zahid Hussain,Sadaqat Hussain,Suhail Abbas,Shams-ur-Rehman,Shahid Hussain,

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00005

Abstract:

In this manuscript, the Lie group technique is applied to construct a new OS and invariant solutions of a one-dimensional LA, which describes the symmetries properties of a nonlinear Black-Scholes model. The structure of LA depends on one parameter. We have shown a novel way to construct the so-called OS of subalgebras of the Black-Scholes equation by utilizing the given symmetries. We transform the symmetries of the Black-Scholes equation into a simple ordinary differential equation called the Lie equation, which provides us a way through which to construct a new optimal scheme of subalgebras of the Black-Scholes through applying the concept of LE. The OS which consists of minimal representatives is utilized to develop the invariant solution for the Black-Scholes equation. The fundamental use of the Lie group analysis to the differential equation is the categorization of group invariant solutions of differential equations via OS. Finally, we have utilized the OS to construct the invariant solution of the Black-Scholes equation.

Keywords:

Black-Scholes Equation,Generators,LE,OS,Invariant solution,

Refference:

I. A.K. Yadav,. And, A.T Ali,. “An OS and Invariant solution of Dark Energy Models in cylindrically symmetric space-time”, The European Physical Journal plus, Eur. Phys. J. Plus, pp. 129-179 (2014).
II. A.P. Coggeshall and Meyer-ter-Vehn, “Group Invariant solutions and OSs for multidimensional hydrodynamics”, J. Math. Phys 33,( 1994).
III. A.P. hupakin. “OS of sub algebras of one solvable algebra L7”, Lie Groups and Their Applications 1 (1994).
IV. C.A. Pooe. Mahomed F.M. and Wafo Soh C. Invariant Solutions of the Black- Scholes Equation, Math. & Compt. Apps 8, p p. 63- 70 (2003).
V. F. Black. and M. Scholes, “The pricing of options and corporate liabilities”, J. Pol. Econ. 81, pp. 637 – 659 (1973).
VI. G. Baumann, “Symmetry Analysis of Differential Equations with Mathematica. Telos, Springer Verlag”, New York, (2000).
VII. G.W. Bluman,.and S.C. Anco,. “Symmetry and Integration Methods for Differential Equations”, Springer-Verlag, New York (2002).
VIII. J. Stampfli, and Victor, “The Mathematics of finance, in: Brooks/ Cole series in Advance Mathematics, Brooks/ cole, Pacific Grove”, CA, Modeling and Hedging, (2001).
IX. L. A. Bordag. “Option-valuation in illiquid markets: invariant solutions to a nonlinear model in mathematical Control Theory and Finance”, eds.A. Sarychev, M. Guerra and M. R. Grossinho, 81, pp.72-94, “Springer”,2008.
X. L.V Ovsyannikov,. “Group analysis of differential equations”, Nuaka, Moscow, 1978. English transl., ed W.F. Ames, Academic Press, New York (1982).
XI. L.V. Ovsyannikov ,“Group properties of differential equations”, Siberian Branch, USSR Academy of Sciences, Novosibirsk (1962).
XII. Mohammad Asif Arefin, Biswajit Gain, Rezaul Karim, Saddam Hossain. : ‘A COMPARATIVE EXPLORATION ON DIFFERENT NUMERICAL METHODS FOR SOLVING ORDINARY DIFFERENTIAL EQUATIONS’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-12, December (2020) pp 1-11. DOI : 10.26782/jmcms.2020.12.00001.
XIII. N. H. Ibragimov. “CRC Handbook of Lie group analysis of differential equations. Volume1,2,3 CRC Press”, Boca Raton, Ann Arbor, London, Tokyo, (1994,1995,1996).
XIV. N.H. bragimov. “Exercises for courses based on Lie group analysis”, ALGA Publication, Karlskrona (2008).

XV. N.H. Ibragimov, “Analytical Methods in Mathematical Modelling”, ALGA Publication Karlskrona (2008).
XVI. N.H. Ibragimov, “Elementary Lie group analysis and ordinary differential equations”, John Wiley and Sons, Chichester (1999).
XVII. N.H. Ibragimov, “Transformation Groups Applied to Mathematical Physics CRC Press”, Boca Raton, F.L, (1985).
XVIII. N.H. bragimov, “A practical course in differential equations and mathematical modelling”, Third Edition, ALGA Publication, Karlskrona (2006)
XIX. Ovsyannikov,. OS of subalgebras, Lie Groups and Their Applications 1 (1994).
XX. P. J. Olver,. “Applications of Lie Groups to Differential Equations”, GTM 107, Second edn., Springer-Verlag (1993).
XXI. P. Wilmot, S. Howison, and Dewynne, “The Mathematics of financial Derivatives”, Cambridge University Press, Cambridge, (1997).
XXII. R. K, Gazizov. and , N.H Ibragimov1, “Lie symmetry analysis of differential equations in finance”, Nonlinear Dynamics, 17, pp. 387 – 407(1998).
XXIII. Sivaram. B. : ‘COMPARATIVE STUDY OF SOLUTION METHODS OF NON-HOMOGENEOUS LINEAR ORDINARY DIFFERENTIAL EQUATIONS WITH CONSTANT COEFFICIENTS’. J. Mech. Cont.& Math. Sci., Vol.-16, No.-1, January (2021) pp 1-18. DOI : 10.26782/jmcms.2021.01.00001.

View Download

REAL-TIME MONITORING SYSTEM OF POWER TRANSFORMER USING IoT AND GSM

Authors:

Jehan Parvez,Salman Khan,Imran Khan,

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00006

Abstract:

The power transformer is the most important and expensive element in the power system. It is used to change the voltage levels at different stages in a power system. The foremost responsibility of the utility grid is to ensure smooth and reliable availability of power through the transformer. But there are different abnormal conditions that can occur in the transformer such as overheating, overexcitation, abnormal frequency, overload, abnormal voltage, open circuit, and breaker failure. These abnormal conditions reduce the life, efficiency, and performance of the transformer, as a result, the overall reliability of the power system gets decreased. Moreover, in case of any failure of the power transformer, the consumers will suffer a severe power outage and consequently, a massive economic loss will occur. During abnormal conditions, the health of a transformer is deteriorating, and it is very important, that the operator should act quickly and accurately in terms of any abnormality occurred. For this purpose, need a proper health monitoring system that should properly monitor the health of the transformer and take proper action to prevent it from greater damages. The proposed system is user-friendly, flexible, reliable, and presenting more functionalities with almost 10 times lower cost than the existing system. This research work has developed a low-cost GSM and internet of things (IoT) based indigenous prototype for transformer monitoring that will be able to early inform the relevant staff through SMS and web data for the different abnormal conditions.

Keywords:

Transformer,Health,Monitoring,GSM,IoT,

Refference:

I. A. Küchler, High Voltage Engineering: Fundamentals-Technology-Applications. Springer, 2017.
II. A. M. Elmashtoly and C.-K. Chang, “Prognostics Health Management System for Power Transformer with IEC61850 and Internet of Things,” Journal of Electrical Engineering & Technology, vol. 15, no. 2, pp. 673-683, 2020.
III. G. Arun, R. Arunkumar, K. K. Kumar, P. Muthupattan, and G. Kannayeram, “GSM BASED SINGLE PHASE DISTRIBUTION TRANSFORMER MONITORING AND CONTROL,” Journal of Critical Reviews, vol. 7, no. 12, pp. 637-640, 2020.
IV. I. Aniebiet and I. S. Fidelis, “Design and Implementation of Gsm Enabled Remote Sensor for Monitoring Power Transformer Operation,” American Journal of Electrical and Computer Engineering, vol. 4, no. 2, pp. 62-71, 2020.
V. J. Jiang, R. Chen, M. Chen, W. Wang, and C. Zhang, “Dynamic fault prediction of power transformers based on hidden Markov model of dissolved gases analysis,” IEEE Transactions on Power Delivery, vol. 34, no. 4, pp. 1393-1400, 2019.
VI. M. Ghiasi, N. Ghadimi, and E. Ahmadinia, “An analytical methodology for reliability assessment and failure analysis in distributed power system,” SN Applied Sciences, vol. 1, no. 1, pp. 1-9, 2019.
VII. M. Subba Rao, SakilaGopal Reddy, K. Sai Janardhan, Sangu Harish Reddy. : ‘DESIGN OF SINGLE LINE TO THREE LINE POWER CONVERTER’. J. Mech. Cont. & Math. Sci., Vol.-15, No.-8, August (2020) pp 822-835. DOI : 10.26782/jmcms.2020.08.00067.
VIII. Maheswari Muthusamy, A.K. Parvathy. : ‘ARTIFICIAL INTELLIGENCE TECHNIQUES-BASED LOW VOLTAGE RIDE THROUGH ENHANCEMENT OF DOUBLY FED INDUCTION WIND GENERATOR’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-3, March (2020) pp 125-139. DOI : 10.26782/jmcms.2020.03.00010
IX. P. Mercy, N. U. Maheswari, S. D. Devi, and V. Dhamodharan, “Wireless protection and monitoring of power transformer using PIC,” IJCSMC, vol. 4, no. 3, pp. 0634-640, 2015.
X. R. V. Jadhav, S. S. Lokhande, and V. N. Gohokar, “Monitoring of transformer parameters using Internet of Things in Smart Grid,” in 2016 International Conference on Computing Communication Control and automation (ICCUBEA), 2016: IEEE, pp. 1-4.
XI. Y. Sun et al., “A temperature-based fault pre-warning method for the dry-type transformer in the offshore oil platform,” International Journal of Electrical Power & Energy Systems, vol. 123, p. 106218, 2020.

View Download

GASOLINE CONSUMPTION PREDICTION VIA DATA MINING TECHNIQUE

Authors:

Soma Gholamveisy,

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00007

Abstract:

Due to the increasing dependence of human life on energy, it plays a crucial role in the functioning of the various economic sectors of the countries, potentially and actually. Fuel products, especially gasoline, given their importance in the transportation sector, play major roles in the economic growth and development of countries. Hence, the authorities in each country have to control the fuel supply and demand parameters accurately with a more accurate prediction of fuel consumption and proper planning in the direction of consumption. The purpose of this study is to find appropriate methods and approaches for forecasting gasoline consumption in Tehran using data mining methods. For this purpose, daily consumption data of gasoline stations were collected in 5 different regions of Tehran during the period of 2008-2013. Then, these numbers were predicted on a daily, weekly, monthly, and seasonal basis for analyzing the consumption at different time intervals. The standardization method was also used to match the scales. After data pre-processing, gasoline consumption was predicted using the multi-layer perceptron (MLP) neural network method. The gasoline consumption forecast was evaluated based on the mean squared error (MSE), mean, and mean absolute error (MAE) criteria. The results indicate that the artificial neural network (ANN) can accurately predict gasoline consumption in five different regions of Tehran.

Keywords:

data mining,gasoline consumption,ANN-MLP,prediction,

Refference:

I. Elnaz Siami-Irdemoosaa Saeid R.Dindarloo, 2015 “Prediction of fuel consumption of mining dump trucks: A neural networks approach” Applied Energy.Volume 151, 1 August 2015, Pages 77-84.

II. Fatemeh Rahimi-Ajdadi Yousef Abbaspour-Gilandeh, 2011. Artificial Neural Network and stepwise multiple range regression methods for prediction of tractor fuel consumption, Measurement,Volume 44, Issue 10, December 2011, Pages 2104-2111.
III. G. E. Nasr E.A. Badr C.Joun, Backpropagation neural networks for modeling gasoline consumption, Energy Conversion and Management.Volume 44, Issue 6, April 2003, Pages 893-905.
IV. Karisa M. Pierce Janiece L. Hope Kevin J. Johnson Bob W.Wright Robert E.Synovec 2005” Classification of gasoline data obtained by gas chromatography using a piecewise alignment algorithm combined with feature selection and principal component analysis”, Journal of Chromatography A Volume 1096, Issues 1–2, 25 November Pages 101-110.
V. Mohanad Aldhaidhawi, Muneer Naji, Abdel Nasser Ahmed. : ‘EFFECT OF IGNITION TIMINGS ON THE SI ENGINE PERFORMANCE AND EMISSIONS FUELED WITH GASOLINE, ETHANOL AND LPG’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-6, June (2020) pp 390-401. DOI : 10.26782/jmcms.2020.06.0003.
VI. Necla Kara .Togun Sedat Baysec, 2010” Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks” Applied Energy,Volume 87, Issue 1, January 2010, Pages 349-355.

VII. Pierhuigi Barbieri. (2001) Robust cluster analysis for detecting physico-chemical typologies of freshwater from wells of the plain of friuli. Analytica Chimica Acta,, pp.161-170.
VIII. Răzvan Andonie. (2010) “Extreme Data Mining: Inference from Small Datasets”; International Journal of Computers Communications & Control, 5: 280-291.
IX. Reza Babazadeh ,2017”A Hybrid ARIMA-ANN approach for optimum estimation and forecasting of gasoline consumption”, RAIRO-Oper. Res.Volume 51, Number 3, July-September 2017.

View Download

EFFECTS OF TRAFFIC LOAD, TEMPERATURE AND MATERIAL PROPERTIES ON RUTTING IN FLEXIBLE PAVEMENTS

Authors:

Muhammad Asim,Haseeb Ullah,Haider Khan,Muhammad Yahya,

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00008

Abstract:

Rutting (permanent deformation) is one of the most common and serious kinds of damage to flexible pavement, particularly in countries with high summer temperatures. Rutting also occurs when there is a lot of traffic and the use of poor materials. Pavement engineering is greatly influenced by the use of materials such as asphalt and cement in modern times. To study the effect of load, high temperature, and materials properties on rutting damage of flexible pavement this paper is the best approach to all these concerned issues related to rutting. Abaqus ver.6.12.1 has been used to simulate flexible pavement under different loading and thermal conditions. Three models have been developed in this paper, the first model simulated against traffic loading only, the second model shows combined traffic and thermal loading while the third model related with the change of materials property in terms of Young’s modulus (E).

Keywords:

Flexible Pavements,FEM,Rutting,Traffic loads,Temperature,

Refference:

I. Abd-Ali, M. S. (2013). A Finite Element Model for Rutting Prediction of Flexible Pavement Considering Temperature Effect. Engineering and Technology Journal, 31(21 Part (A) Engineering).
II. Abd-Ali, M. S. (2013). A Finite Element Model for Rutting Prediction of Flexible Pavement Considering Temperature Effect. Engineering and Technology Journal, 31(21 Part (A) Engineering).
III. Abed, A. H., & Al-Azzawi, A. A. (2012). Evaluation of rutting depth in flexible pavements by using finite element analysis and local empirical model. American Journal of Engineering and Applied Sciences, 5(2), 163-169.
IV. Abu Al-Rub, R. K., Darabi, M. K., Huang, C. W., Masad, E. A., & Little, D. N. (2012). Comparing finite element and constitutive modelling techniques for predicting rutting of asphalt pavements. International Journal of Pavement Engineering, 13(4), 322-338.
V. Ahmed, Z., Jabbar, A., Ghassan, E. G., & Masood, A. K. K. SURFACE DEFORMATION OF FLEXIBLE PAVEMENT WITH DIFFERENT BASE LAYER USING FINITE ELEMENT ANALYSIS.
VI. Ali, B., Sadek, M., & Shahrour, I. (2008). Elasto-Viscoplastic Finite Element Analysis of the Long-Term Behavior of Flexible Pavements: Application to Rutting. Road materials and pavement design, 9(3), 463-479.
VII. Al-Khateeb, L. A., Saoud, A., & Al-Msouti, M. F. (2011). Rutting prediction of flexible pavements using finite element modeling. Jordan Journal of Civil Engineering, 5(2), 173- 190.
VIII. Alkaissi, Z. A. (2020). Effect of high temperature and traffic loading on rutting performance of flexible pavement. Journal of King Saud University-Engineering Sciences, 32(1), 1-4.
IX. Alkaissi, Z. A., & Al-Badran, Y. M. (2018). FINITE ELEMENT MODELING OF RUTTING FOR FLEXIBLE PAVEMENT. Journal of Engineering and Sustainable Development, 22(3), 1-13.
X. Hossain, M. I., Mehta, R., Shaik, N. A., Islam, M. R., & Tarefder, R. A. (2016). Rutting Potential of an Asphalt Pavement Exposed to High Temperatures. In International Conference on Transportation and Development 2016 (pp. 1194-1205).
XI. Hulsey, J. L., Ahmed, M. J., & Connor, B. (2008). SOLVING PLASTIC DEFORMATION PROBLEMS FOR ANCHORAGE FLEXIBLE PAVEMENTS.
XII. Khodary, f., & mashaan, n. Behaviour of different pavement types under traffic loads using finite element modelling.
XIII. Leonardi, G. I. O. V. A. N. N. I. (2014). Finite element analysis of airfield flexible pavement. Archives of Civil Engineering, 60(3).
XIV. Nazarian, S., & Boddapati, K. M. (1995). Pavement-falling weight deflectometer interaction using dynamic finite-element analysis. Transportation Research Record, 1482, 33.
XV. Siang, A. J. L. M., Wijeyesekera, D. C., Mei, L. S., & Zainorabidin, A. (2013). Innovative laboratory assessment of the resilient behaviour of materials (rigid, elastic and particulates). Procedia Engineering, 53, 156-166.
XVI. Ullah Irfan, Dr. Rawid Khan, Manzoor Elahi, Ajab Khurshid. : ‘CHARACTERIZATION OF THE NONLINEAR BEHAVIOR OF FLEXIBLE ROAD PAVEMENTS’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-12, December (2020) pp 111-126. DOI :0.26782/jmcms.2020.12.00010

View Download