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JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES > Journal > Page 72

ILICH METHOD OF DETERMINATION OF ACTIVATION ENERGY OF A DTA PEAK

Authors:

Sudipta Ghosh, Soumya Das, Sukriti Ghosh, Supriya Barman, P. S. Majumdar

DOI NO:

https://doi.org/10.26782/jmcms.2022.10.00002

October 29, 2022

Abstract:

Ilich method has been used to evaluate the activation energy of Differential Thermal Analysis (DTA) peaks. The method uses 10-20% of the initial rise portion of the peak. The suitability of the method has been adjudged by applying it both to the synthetic and experimental DTA peaks. It is found that the method can be used irrespective of the values of kinetic parameters of the peaks

Keywords:

Ilich method,Differential Thermal Analysis,kinetic parameters,

Refference:

I. B. M. Ilich, Sov.Phy-Sol.St. 21 1880 (1979).

II. CC Huang and T.S. Wu, Thermochim Acta 204 239(1992).

III. D.C.Sanyal and K Das, “A text book of Numerical Analysis” (U.N.Dhar, Kolkata), 2012.

IV. L.K. Singh and S. Mitra , J Chem. Soc. Dalton Trans, 21 2089, (1987).

V. M. Abramowitz and I.A. Stegun (Eds), “Hand Book of Mathematical Functions” (Dover, New York) Ch 5 (1965).

VI. M. Karmakar, Sk Azharuddin, S.Barman, PS Mazumdar and S D Singh. Material Science Research. 6.189 (2009).

VII. R.Chen and Y.Krish, “Analysis Of Thermally Stimulated Processes” (Oxford, Pergamon) (1981).

VIII. R.K. Gatria and H. N. K. Sarma, “Deconvoluation Methods in Thermally Stimulated processes” (Eureka Publishers, New Delhi), (1998).
IX. Sk Azaharuddin, B.Ghosh, A.Sarkar, S. Bhattacharya and P.S.Majumder J Mech Cont & Math Sci. Vol- 14, 121 (2019).
IX. S.K. Azaharuddin, B.Ghosh, S.Ghosh and P.S.Majumder, (J. Mech Cont & Math Sci.) 13, 29 (2018).

X. S.K. Azaharuddin, S.D.Singh and P.S.Majumdar J. Mech Cont & Math Sci. 12, 10 (2018).

XI. T.T. Yang and M. Steinberg. Anal chem 49, 998(1977).

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Journal Vol – 17 No -10, October 2022

CHLOROPHYLL a LEVEL IN THE COASTAL WATER OF DIGHA COAST: A SITUATION ANALYSIS

Authors:

Nabonita Pal, Sangita Agarwal, Mourani Sinha, Sufia Zaman, Abhijit Mitra

DOI NO:

https://doi.org/10.26782/jmcms.2022.10.00003

October 29, 2022

Abstract:

The time series analysis of chlorophyll a was carried out for more than 3 decades (1984-2018) from the coastal water of Digha and the data bank were subject to Nonlinear Autoregressive Neural Network Model to evaluate the status of the coastal water in 2050. The concentration of chlorophyll a ranged between 1.05 mgm-3 (in 2009) to 5.16 mgm-3 (in 1984) during the span of 35 years (real-time data). Chlorophyll a has a great role to drive the marine and estuarine food chain as it acts as the engine to transfer the energy derived from the Sun through different tires of the food chain. The decreasing trend of chlorophyll a with time is a warning signal for the fishery products from the region as the phytoplankton containing chlorophyll a serve as the major food of the fishes.

Keywords:

Chlorophyll a,Nonlinear Autoregressive ,Neural Network Model,Digha coast,,food chain,phytoplankton.,

Refference:

I. Jeffrey, S. W., Humphrey, G. F., “New spectrophotometric equations for determining chlorophyll a, b, c1 and c2 in higher plants, algae and natural phytoplankton”, Biochemie und Physiologie der Pflanzen, Vol. 167, pp: 191-19, 1975.
II. Mitra, A., “Ecosystem services of mangroves: An overview”, published by Springer. ISBN 978-81-322- 2106-7, DOI: 10.1007/978-3-030-20595-9_1, 2020.
III. Mitra, A., “Sensitivity of Mangrove ecosystem to changing Climate”, published by Springer. DOI: 10.1007/978-; 81-322-1509-7. Pp: 323, 2013.
IV. Mitra, A., Zaman, S., “Basics of Marine and Estuarine Ecology”, published by Springer. ISBN 978-81- 322-2705-2, 2016.
V. Mitra, A., Zaman, S., “Blue carbon reservoir of the blue planet”, published by Springer. ISBN 978-81-322-2106-7 (Springer DOI 10.1007/978- 81-322-2107-4), 2015.
VI. Mitra, A., Zaman, S., “Estuarine acidification”, published by Springer, ISBN 978-3-030-84792-0, 2021.
VII. https://earthobservatory.nasa.gov/global-maps

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Journal Vol – 17 No -10, October 2022

AN OPENING OF A NEW HORIZON IN THE THEORY OF QUADRATIC EQUATION : PURE AND PSEUDO QUADRATIC EQUATION – A NEW CONCEPT

Authors:

Prabir Chandra Bhattacharyya

DOI NO:

https://doi.org/10.26782/jmcms.2022.11.00001

November 28, 2022

Abstract:

In this paper, the author has opened a new horizon in the theory of quadratic equations. The author proved that the value of x which satisfies the quadratic equation cannot be the only criteria to designate as the root or roots of an equation. The author has developed a new mathematical concept of the dimension of a number. By introducing the concept of the dimension of number the author structured the general form of a quadratic equation into two forms: 1) Pure quadratic equation and 2) Pseudo quadratic equation. First of all the author defined the pure and pseudo quadratic equations. In the case of pure quadratic equation ax^2+bx+c=0 , the root of the equation will be a two-dimensional number having one root only while in the case of pseudo quadratic equation ax^2+bx+c=0, the root of the equation will be a one-dimensional number having two roots only. The author proved that all pseudo quadratic equation is factorizable but all factorizable quadratic equation is not a pseudo quadratic equation. The author begs to differ from the conventional theorem: "A quadratic equation has two and only two roots." By introducing the concept that any quadratic surd is a two-dimensional number, the author developed a new theorem: “In a quadratic equation with rational coefficients, irrational roots cannot occur in conjugate pairs” and proved it. Any form of quadratic equation ax^2+bx+c=0, can be solved by the application of the ‘Theory of Dynamics of Numbers’ even if the discriminant b^2-4ac<0 in real number only without introducing the concept of an imaginary number. Therefore, the question of imaginary roots does not arise in the method of solution of any quadratic equation

Keywords:

Dimension of Numbers,Dynamics of Numbers,,Quadratic Equation,Rectangular Bhattacharra’s Coordinates,significance of roots of a Quadratic Equation,

Refference:

I. Bhattacharyya, Prabir Chandra. : “AN INTRODUCTION TO THEORY OF DYNAMICS OF NUMBERS: A NEW CONCEPT”. J. Mech. Cont. & Math. Sci., Vol.-17, No.-1, January (2022). pp 37-53

II. Bhattacharyya, Prabir Chandra. : “A NOVEL CONCEPT IN THEORY OF QUADRATIC EQUATION”. J. Mech. Cont. & Math. Sci., Vol.-17, No.-3, March (2022) pp 41-63

III. Bhattacharyya, Prabir Chandra. : “AN INTRODUCTION TO RECTANGULAR BHATTACHARYYA’S CO-ORDINATES: A NEW CONCEPT”. J. Mech. Cont. & Math. Sci., Vol.-16, No.-11, November (2021). pp 76-86.

IV. Boyer, C. B. & Merzbach, U. C. (2011). A history of mathematics. New York: John Wiley & Sons.

V. Cajori, F., (1919). A History of Mathematics 2nd ed., New York: The Macmillan Company.

VI. Dutta, B.B. ( 1929). The Bhakshali Mathematics, Calcutta, West Bengal: Bulletin of the Calcutta Mathematical Society.

VII. Datta, B. B., & Singh, A. N. (1938). History of Hindu Mathematics, A source book. Mumbai, Maharashtra: Asia Publishing House.

VIII. Gandz, S. (1937). The origin and development of the quadratic equations in Babylonian, Greek, and Early Arabic algebra. History of Science Society, 3, 405-557.

IX. Gandz, S. (1940). Studies in Babylonian mathematics III: Isoperimetric problems and the origin of the quadratic equations. Isis, 3(1), 103-115.

X. Hardy G. H. and Wright E. M. “An Introduction to the Theory of Numbers”. Sixth Edition. P. 52.

XI. Katz, V. J. (1997), Algebra and its teaching: An historical survey. Journal of Mathematical Behavior, 16(l), 25-36.

XII. Katz, V., J. (1998). A history of mathematics (2nd edition). Harlow, England: Addison Wesley Longman Inc.

XIII. Katz Victor, (2007). The Mathematics of Egypt, Mesopotamia, China, India and Islam: A source book 1st ed., New Jersey, USA: Princeton University Press.

XIV. Kennedy, P. A., Warshauer, M. L. & Curtin, E. (1991). Factoring by grouping: Making the connection. Mathematics and Computer Education, 25(2), 118-123.

XV. Ling, W. & Needham, J., (1955). Horner’s method in Chinese Mathematics: Its root in the root extraction procedures of the Han Dynasty, England: T’oung Pao.
XVI. Nataraj, M. S., & Thomas, M. O. J. (2006). Expansion of binomials and factorisation of quadratic expressions: Exploring a vedic method. Australian Senior Mathematics Journal, 20(2), 8-17.

XVII. Rosen, Frederic (Ed. and Trans). (1831). The algebra of Mohumed Ben Muss. London: Oriental Translation Fund; reprinted Hildesheim: Olms, 1986, and Fuat Sezgin, Ed., Islamic Mathematics and Astronomy, Vol. 1. Frankfurt am Main: Institute for the History of Arabic-Islamic Science 1997.

XVIII. Smith, D. (1951). History of mathematics, Vol. 1. New York: Dover. Smith, D. (1953). History of mathematics, Vol. 2. New York: Dover. Stols, H. G. (2004).

XIX. Smith, D. (1953). History of mathematics, Vol. 2. New York: Dover.

XX. Thapar, R., (2000). Cultural pasts: Essays in early Indian History, New Delhi: Oxford University Press.
XXI. Yong, L. L. (1970). The geometrical basis of the ancient Chinese square-root method. The History of Science Society, 61(1), 92-102.

XXII. http://en. wikipedia.org/wiki/Shridhara

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Journal Vol – 17 No -11, November 2022

BREAST CANCER HISTOLOGICAL IMAGES CLASSIFICATION AND PERFORMANCE EVALUATION OF DIFFERENT CLASSIFIERS

Authors:

Md. Rakibul Islam, Shariful Islam, Md. Shahadot Hosen (Rony) , Md. Nur Alam

DOI NO:

https://doi.org/10.26782/jmcms.2022.11.00002

November 28, 2022

Abstract:

                  Breast cancer is a serious trouble and one of the greatest causes of death for women throughout the world. Computer-aided diagnosis (CAD) techniques can help the doctor make more credible decisions. We have determined the possibility of knowledge transfer from natural to histopathological [IX][XII] images by employing a pre-trained network ResNet-50.This pre-trained network has been utilized as a feature generator and extracted features are used to train support vector machine (SVM), random forest, decision tree, and K nearest neighbor(KNN) classifiers[X]. We altered the softmax layer to support the vector machine classifier, random forest classifier, decision tree classifier, and k-nearest neighbor classifier, to evaluate the classifier performance of each algorithm. These approaches are applied for breast cancer classification and evaluate the performance and behavior of different classifiers on a publicly available dataset named Bttheeak-HIS dataset. In order to increase the efficiency of the ResNet[III] model, we preprocessed the data before feeding it to the network. Here we have applied to sharpen filter and data augmentation techniques, which are very popular and effective image pre-processing techniques used in deep models.

Keywords:

Machine learning,Support Vector Machine (SVM),K-Nearest Neighbor (KNN),RESNET (Residual Network) model,Random Forest.[VII],

Refference:

I. Altman, N. S. (1992). “An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression” (Pdf). The American Statistician.
II. Araújo, Teresa, Guilherme Aresta, Eduardo Castro, José Rouco, Paulo Aguiar, Catarina Eloy, António Polónia, and Aurélio Campilho. “Classification of breast cancer histology images using Convolutional Neural Networks.” PloS one 12, no. 6 (2017): e0177544.
III. “Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception,” Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 1069612 (13 April 2018);
IV. Balestriero, R. Neural Decision Trees. Arxiv E-Prints, 2017.
V. Bayramoglu, Neslihan, Juho Kannala, and Janne Heikkilä. “Deep learning for magnification independent breast cancer histopathology image classification.” In Pattern Recognition (ICPR), 2016 23rd International Conference on, pp. 2440- 2445. IEEE, 2016.
VI. B. E. Bejnordi, G. Zuidhof, M. Balkenhol et al., “Contextaware stacked convolutional neural networks for classifcation of breast carcinomas in whole-slide histopathology images,” Journal of Medical Imaging, vol. 4, no. 04, p. 1, 2017.
VII. Diaz-Uriarte R, Alvarez De Andres S: Gene Selection And Classification Of Microarray Data Using Random Forest. Bmc Bioinformatics 2006, 7:3.

VIII. George, Yasmeen Mourice, Hala Helmy Zayed, Mohamed Ismail Roushdy, and Bassant Mohamed Elbagoury. “Remote computer-aided breast cancer detection and diagnosis system based on cytological images.” IEEE Systems Journal 8, no. 3 (2014): 949-964.

IX. Gupta, Vibha, and Arnav Bhavsar. “Breast Cancer Histopathological Image Classification: Is Magnification Important?.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 17- 24. 2017.
X. Hall P, Park Bu, Samworth Rj (2008). “Choice Of Neighbor Order In Nearest-Neighbor Classification”. Annals Of Statistics. 36 (5): 2135–2152.
XI. Hammer B, Gersmann K: A Note On The Universal Approximation Capability Of Support Vector Machines. Neural Processing Letters 2003, 17:43-53.
XII. Han, Zhongyi, Benzheng Wei, Yuanjie Zheng, Yilong Yin, Kejian Li, and Shuo Li. “Breast cancer multi-classification from histopathological images with structured deep learning model.” Scientific reports 7, no. 1 (2017): 4172
XIII. Kahya, Mohammed Abdulrazaq, Waleed Al-Hayani, and Zakariya Yahya Algamal. “Classification of breast cancer histopathology images based on adaptive sparse support vector machine.” Journal of Applied Mathematics and Bioinformatics 7.1 (2017): 49
XIV. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun,”Deep Residual Learning for Image Recognition,” arXiv:1512.03385 [cs.CV], 10 Dec 2015;
XV. Kowal, Marek, Paweł Filipczuk, Andrzej Obuchowicz, Józef Korbicz, and Roman Monczak. “Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images.” computers in biology and medicine 43, no. 10 (2013): 1563-1572.
XVI. Mehdi Habibzadeh, Mahboobeh Jannesari, Zahra Rezaei, Hossein Baharvand, Mehdi Totonchi,
XVII. Pan, S.J. And Yang, Q., 2010. A Survey On Transfer Learning. Ieee Transactions On Knowledge And Data Engineering, 22(10), Pp.1345–1359.
XVIII. Prinzie, A., Van Den Poel, D. (2008). “Random Forests For Multiclass Classification: Random Multinomial Logit”. Expert Systems With Applications
XIX. Qicheng Lao, Thomas Fevens,”Cell Phenotype Classification using Deep Residual Network and its Variants,” International Journal of Pattern Recognition and Artificial Intelligence © World Scientific Publishing Company, 01/17/19;
XX. Rawat, W. And Wang, Z., 2017. Deep Convolutional Neural Networks For Image Classification: A Comprehensive Review. Neural Computation, 29(9), Pp.2352–2449.

XXI. Rokach, Lior; Maimon, O. (2008). Data Mining With Decision Trees: Theory And Applications. World Scientific Pub Co Inc.
XXII. Shalev-Shwartz, Shai; Ben-David, Shai (2014). “18. Decision Trees”. Understanding Machine Learning. Cambridge University Press.
XXIII. Scornet, Erwan (2015). “Random Forests And Kernel Methods”.

XXIV. Simonyan, K. And Zisserman, A., 2014. Very Deep Convolutional Networks For Large-Scale Image Recognition. Arxiv Preprint Arxiv:1409.1556.
XXV. Spanhol, Fabio A., Luiz S. Oliveira, Caroline Petitjean, and Laurent Heutte. “A dataset for breast cancer histopathological image classification.” IEEE Transactions on Biomedical Engineering 63, no. 7 (2016): 1455-1462.
XXVI. Spanhol, Fabio Alexandre, Luiz S. Oliveira, Caroline Petitjean, and Laurent Heutte. “Breast cancer histopathological image classification using convolutional neural networks.” In Neural Networks (IJCNN), 2016 International Joint Conference on, pp. 2560-2567. IEEE, 2016.
XXVII. Spanhol, Fabio A., Luiz S. Oliveira, Paulo R. Cavalin, Caroline Petitjean, and Laurent Heutte. “Deep features for breast cancer histopathological image classification.” In Systems, Man, and Cybernetics (SMC), 2017 IEEE International Conference on, pp. 1868-1873. IEEE, 2017.
XXVIII. Tang, Y., 2013. Deep Learning Using Linear Support Vector Machines. Arxiv Preprint Arxiv: 1306.0239.
XXIX. T. Araujo, G. Aresta, E. Castro et al., “Classifcation of breast cancer histology images using convolutional neural networks,” PLoS ONE, vol. 12, no. 6, Article ID e0177544, 2017.
XXX. Veta, Mitko, Josien PW Pluim, Paul J. Van Diest, and Max A. Viergever. “Breast cancer histopathology image analysis: A review.” IEEE Transactions on Biomedical Engineering 61, no. 5 (2014): 1400-1411
XXXI. Yosinski, J., Clune, J., Bengio, Y. And Lipson, H., 2014. How Transferable Are Features In Deep Neural Networks?. In Advances In Neural Information Processing Systems (Pp. 3320–3328).
XXXII. Zeiler, M.D. And Fergus, R., 2014, September. Visualizing And Understanding Convolutional Networks. In European Conference On Computer Vision (Pp. 818–833). Springer, Cham.
XXXIII. Zhang, Yungang, Bailing Zhang, Frans Coenen, and Wenjin Lu. “Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles.” Machine vision and applications 24, no. 7 (2013): 1405-1420.

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Journal Vol – 17 No -11, November 2022

ONLINE SKILL TEST PLATFORM

Authors:

Mehria Nawaz, Twinkle Agarwal, Dilip Kumar Gayen

DOI NO:

https://doi.org/10.26782/jmcms.2022.11.00003

November 28, 2022

Abstract:

Information communication and technology are the most important skills for 21st-century learning and help promote other skills, including life and career skills and learning and innovation skills. This kind of learning allows the learner to connect as a learning network without barriers or borders. The growth of online education has taken our education system to another level. Now anyone can learn from anywhere, anytime as per convenience. In different platforms, questions link are shared with a submission time. Although, learners are taking up unfair means to clear the test provided online in which students usually search up the topic, use different means to get the answers, and get good marks. Hence, teachers cannot get an idea of who is good in the class and who needs extra attention. So, our idea is to make such a platform where the teacher will be taking the test just like our offline classes. In this platform, the teacher will be discussing every question after the students submit the answer in a time duration which will also be proctored and at the same time, the teacher will get the top performer and their submission time. This way we can assure minimal malpractice and identify the students who need more explanation for the questions. This will clear their doubts and the teacher understands the actual performance ratio.

Keywords:

Skill test platform,MongoDB,MERN Stack, MongoDB,student's performance,

Refference:

I. C. Ying-ying, “The Design and Implementation of Online Examination System with Characteristics of Cloud Service”, Beijing University of Posts and Telecommunications. 2013.
II. H-R. Ouyang, H-F. Wei, H-X. Li, A-Q. Pan, Y. Huang, “Checking Causal Consistency of MongoDB”, Journal of Computer Science and Technology. Vol. 37, pp: 128–146, 2022.
III. M. Radoev, “A Comparison between Characteristics of NoSQL Databases and Traditional Databases,” Comput. Sci. Inf. Technol. vol. 5, no. 5, pp: 149–153, 2017.
IV. M.Yagci, M. Unal, “Designing and implementing an adaptive online examination system”, Procedia – Social and Behavioral Sciences. Vol. 116, pp: 3079-3083, 2014.
V. W. Schultz, T. Avitabile, A. Cabral, “Tunable consistency in MongoDB.”, Proc. VLDB Endow. Vol. 12, No. 12, pp 2071-2081, 2019.
VI. Z. Yong-Sheng, F. Xiu-Mei and B. Ai-Qin, “The Research and Design of Online Examination System,” 2015 7th International Conference on Information Technology in Medicine and Education (ITME). pp.: 687-691, 2015.

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Journal Vol – 17 No -11, November 2022

GENERAL ANALYTICAL SOLUTION OF AN ELASTIC BEAM UNDER VARYING LOADS WITH VALIDATION

Authors:

Hafeezullah Channa, Muhammad Mujtaba Shaikh, Kamran Malik

DOI NO:

https://doi.org/10.26782/jmcms.2022.11.00004

November 28, 2022

Abstract:

In this paper, we take into account the system of differential equations with boundary conditions of a fixed elastic beam model (EBM). Instead of finding a solution of EBM for a particularly specified load, which is the usual practice, we derive the general analytical solution of the model using techniques of integrations. The proposed general analytical solutions are not load-specific but can be used for any load without having to integrate successively again and again. We have considered load in a general polynomial form and obtained a general analytical solution for the deflection and slope parameters of EBM. Direct solutions have been determined under two types of loads: uniformly distributed load and linearly varying load. The formulation derived has been validated on the known cases of uniformly distributed load as appears frequently in the literature.

Keywords:

Elastic beam,General analytical solution,Deflection,Slope,

Refference:

I. Babak Mansoori, Ashkan Torabi, Arash Totonch (2020). ‘Numerical investigation of the reinforced concrete beams using cfrp rebar, steel sheets and gfrp’. J. Mech. Cont.& math. Sci., vol.-15, no.-3, pp 195-204.
II. Cheng XL, Han W, Huang HC (1997). Finite element methods for Timoshenko beam, circular arch and Reissner-Mindlin plate problems. J. Comput. Appl. Math.,79(2): 215- 234.
III. Li L (1990). ‘Discretization of the Timoshenko beam problem by the p and h/p versions of the finite element method’. Numer. Math., 57(1): 413-420.
IV. Malik, Kamran, Shaikh, Abdul Wasim and Shaikh, Muhammad Mujtaba. (2021). “An efficient finite difference scheme for the numerical solution of Timoshenko beam model. Journal of mechanics of continua and mathematical sciences”, 16 (5): 76-88..
V. Malik, Kamran, Shaikh, Muhammad Mujtaba and Shaikh, Abdul Wasim. (2021).“On exact analytical solutions of the Timoshenko beam model under uniform and variable loads. Journal of mechanics of continua and mathematical sciences”, 16 (5): 66-75.
VI. Timoshenko SP (1921). On the correction for shear of the differential equation for transverse Vibrations of prismatic bars, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 41(245): 744-746.
VII. Timoshenko, S. (1953). History of strength of materials. New York: McGraw-Hill Charles V. Jones, “The Unified Theory of Electrical Machines”, London, 1967.

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Journal Vol – 17 No -11, November 2022

FEASIBILITY OF ADOPTION AND OVERVIEW OF ONLINE LEARNING IN INSTITUTES OF PAKISTAN AFTER COVID-19: AN INSTRUCTORS AND LEARNERS PERSPECTIVE

Authors:

Fariha Shaikh, Sania Bhatti, Shafqat Shahzoor Chandio, Muhammad Mujtaba Shaikh

DOI NO:

https://doi.org/10.26782/jmcms.2022.12.00001

December 27, 2022

Abstract:

The educational process grows intellectual and critical thinking which helps a person to make correct or optimal decisions by using logic, calculations, and experiments. This factor helps a person to use available resources in an optimal way to maximize the outcome. Unfortunately, along with all other areas, the educational process was seized initially during COVID-19 as well. To continue the education process in lockdowns, academia has shifted from traditional learning (TL) towards the online learning (OL) process. Instructors and learners of different academies belong to different fields and backgrounds. Thus, it is not easy to smoothly adopt OL for all of them. Therefore, this study is aimed to conduct a survey to check the feasibility of the adoption of OL for both types of audiences i.e. instructors and learners. The purpose is to compare the thoughts of both audiences and find the difference between them by using different descriptive and inferential statistical techniques and to have a brief overview of OL and TL in the academies of Pakistan. This study will help academies to understand the flaws, gaps, and limitations of OL from instructors' and learners' perspectives as the gaps can be filled by improving existing approaches to make the OL system smoothly adoptable by everyone in Pakistan in the future.

Keywords:

Online Learning,Traditional Learning,COVID-19,Instructor,Learners,

Refference:

I. Adane, Fentahun, Yoseph Merkeb Alamneh, and Melaku Desta. “Computer vision syndrome and predictors among computer users in Ethiopia: a systematic review and metaanalysis.” Tropical Medicine and Health 50.1 (2022): 1-12
II. Al-adwan A. &Smedly J. Implementing E-learning in the Jordanian Higher Education System: Factors Affecting Impact. International Journal of Education and Development using Information and communication technology (IJEDICT). 2012; 8(1): 121 – 135.
III. Alias NA &ZainuddinAM. Innovation for Better Teaching and Learning: Adopting the Learning Management System.Malaysian Online Journal of Instructional Technology. 2005; 2(2): 27 – 40.
IV. Berstorff PC & Lowes SL. Student Perceptions and Opinions towards E-learning in the college environment. Academy of Educational Leadership Journal. 2007; 11(2): 13 – 30.
V. Bisht, Raj Kishor, Sanjay Jasola, and Ila Pant Bisht. “Acceptability and challenges of online higher education in the era of COVID-19: a study of students’ perspective.” Asian Education and Development Studies (2020).
VI. Blehm, Clayton, et al. “Computer vision syndrome: a review.” Survey of ophthalmology 50.3 (2005): 253-262. 15
VII. Chandra, Yamini. “Online education during COVID-19: perception of academic stress and emotional intelligence coping strategies among college students.” Asian education and development studies (2020).
VIII. Das, Pamela, and Richard Horton. “Rethinking our approach to physical activity.” Lancet (London, England) 380.9838 (2012): 189-190.
IX. Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of educational technology systems, 49(1), 5-22.
X. Elfaki, Nahid Khalil, Itedal Abdulraheem, and Rashida Abdulrahim. “Impact of e-learning vs traditional learning on student’s performance and attitude.” International Journal of Medical Research & Health Sciences 8.10 (2019): 76-82.
XI. Fariha Shaikh, Shafiq-ur-Rehman, et al. (2022) “Effects of Online Educational System on Personal Health of Students and Teachers in COVID-19 Crises.” [in press].
XII. Hannay, Maureen, and Tracy Newvine. “Perceptions of distance learning: A comparison of online and traditional learning.” Journal of online learning and teaching 2.1 (2006): 1-11.Hscodhod
XIII. Holley D. Which Room is the Virtual Seminar in Please? Educational and Training. 2002; 44(3): 112 – 121.
XIV. Kumar, Naveen, and Nageshwar Sharma. “To determine the prevalence of computer vision syndrome among computer users: a descriptive study.” European Journal of Molecular & Clinical Medicine (EJMCM) 7.10 (2020): 2020.
XV. Lee, I-Min, et al. “Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy.” The lancet 380.9838 (2012): 219-229.
XVI. Valentina A. & Nelly A. The role of E-learning, the advantages and disadvantages of its adoption in higher education.International Journal of Education and Research. 2014; 2(12): 397 -410.
XVII. Zarei-Zavaraki E &Rezael I. The impact of Using Electronic Portfolio on Attitude, Motivation and Educational Progress of Students’ Khaje Nasir Toosi University.Educational Measurement Periodical. 2011; 2(5): 67 – 96.

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Journal Vol – 17 No -12, December 2022

GRAVITY SCORE: NEW METRIC TO MEASURE PLAGIARISM IN TEXT DOCUMENTS USING THE CONCEPT OF GRAVITATIONAL FORCE

Authors:

Srijit Panja

DOI NO:

https://doi.org/10.26782/jmcms.2022.12.00002

December 27, 2022

Abstract:

Present-day computational capabilities allow digital assets like images, videos, text, and audio have features comparable to those in real-world entities. Location is one such aspect. Similar to real-world bodies being represented by vectors on cartesian coordinates, digital media entities (like text, as discussed in this paper) when encoded, each component of the encoding representing a feature, conceptually should have a vector representation in each such encoding. The concept is put to practice by text encodings (embedding) techniques like Bag-of-words, TF-IDF, Word2Vec, Glove, and Transformer models like BERT, AlBERT etc which create vectors out of the text. This paper aims to use a combination of features in text analogous to mass and distance and propose a new plagiarism index cloning the formula of gravitational force. Parameters like the length of documents/number of words, semantics, frequency of each word, etc, one or many of which are often missed out in prevalent algorithms of text similarity calculations, are important for detecting and measuring plagiarism. The paper aims to consider all such possible parameters in the formulation of a new plagiarism metric to be coined as Gravity Score.

Keywords:

Natural Language Processing,Text Embedding,Text token,Gravitation,

Refference:

I. Abdi, H., and L. J. Williams. 2010. “Principal component analysis.” Wiley interdisciplinary reviews: computational statistics 2 (4): 433–459.
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VII. Edelbaum, T. N. 1962. “Theory of maxima and minima.” In Mathematics in Science and Engineering, 5:1–32. Elsevier.
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XIII. Mikolov, T., K. Chen, G. Corrado, and J. Dean. 2013. “Efficient estimation of word representations in vector space.” arXiv preprint arXiv:1301.3781.

XIV. Morita, K., E.-S. Atlam, M. Fuketra, K. Tsuda, M. Oono, and J.-i. Aoe. 2004. “Word classification and hierarchy using co-occurrence word information.” Information processing & management 40 (6): 957–972.
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XXII. Zhang, Y., R. Jin, and Z.-H. Zhou. 2010. “Understanding bag-of-words model: a statistical framework.” Interna- tional journal of machine learning and cybernetics 1 (1): 43–52.

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Journal Vol – 17 No -12, December 2022

LINEAR TREND LINE ANALYSIS BY THE METHOD OF LEAST SQUARE FOR FORECASTING RICE YIELD IN BANGLADESH

Authors:

Saddam Hossain, Suman Kar, Mohammad Asif Arefin, Md. Kawsar Ahmed Asif, Hossain Ahmed5

DOI NO:

https://doi.org/10.26782/jmcms.2022.12.00003

December 27, 2022

Abstract:

The method of curve fitting by the principle of the Least Square (L.S) method is a relevant and well-received method of trend analysis, especially to make a project for the future time. The Least Square (L.S) method helps to fit mathematical functions to a given data set. For this research, we accumulated data from the Yearbook of Agricultural Statistics of Bangladesh for the year 2007-08 to 2019-20 with the help of the Bangladesh Bureau of Statistics (BBS) website. We arranged the data according to the proposed method and graphically represented it. This research aimed to forecast the production of rice in Bangladesh with trend line analysis by the method of Least Square (L.S) for the years 2020-21 to 2024-25. As a result, we found an upward trend line for the production of rice in Bangladesh. Therefore the production will be maximum in the year 2024-25.

Keywords:

Least Square Method,Linear Trend Line,Forecasting,Time series,Bangladesh,

Refference:

I. Abdulkabir, M., Tunde, R. S., & Edem, U. A. (2015). Trend analysis on road traffic accident in Nigeria. Science Innovation, 3(5), 52-57.
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III. Alam, M. S., Kalpoma, K., Karim, M. S., Al Sefat, A., & Kudoh, J. I. (2019, July). Boro Rice Yield Estimation Model Using Modis Ndvi Data for Bangladesh. In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 7330-7333). IEEE.
IV. Ara, J., Md. Moheuddin, M., Hossain, S., & Abdus Sattar Titu, M. (2020). A mathematical study of break-even analysis based on dairy farms in Bangladesh. International Journal of Economic Behavior and Organization, 8(2), 38. https://doi.org/10.11648/j.ijebo.20200802.13
V. Athiyarath, S., Paul, M., & Krishnaswamy, S. (2020). A comparative study and analysis of time series forecasting techniques. SN Computer Science, 1(3). https://doi.org/10.1007/s42979-020-00180-5
VI. Awal, M. A., & Siddique, M. A. B. (2011). Rice production in Bangladesh employing by ARIMA model. Bangladesh Journal of Agricultural Research, 36(1), 51-62.
VII. Bangladesh Bureau of Statistics. Bangladesh Bureau of Statistics-Government of the People’s Republic of Bangladesh. (n.d.). Retrieved December 15, 2021, from http://bbs.gov.bd/site/page/3e838eb6-30a2-4709-be85-40484b0c16c6/-
VIII. Bangladesh Rice Research Institute. Bangladesh Rice Research Institute-Government of the People’s Republic of Bangladesh. (n.d.). Retrieved December 15, 2021, from http://www.brri.gov.bd/
IX. Bangladesh. Ricepedia. (n.d.). Retrieved December 15, 2021, from https://ricepedia.org/index.php/bangladesh
X. CAI, S., ZHANG, H., CHEN, H., & SHA, J. (2007). Research of piecewise cubic curve-fitting method based on least-square principle. Science technology and Engineering, 3.
XI. Chatfield, C. (2000). Time-series forecasting. CRC press.
XII. Chen, G., Ren, Z. L., & Sun, H. Z. (2005). Curve fitting in least-square method and its realization with Matlab. Ordnance industry automation, 3, 063.
XIII. Chen, L., & Zheng, Y. (2012). Study on curve fitting based on least square method. Journal of Wuxi Institute of Technology, 11(5), 52-55.
XIV. De Gooijer, J. G., & Hyndman, R. J. (2006). 25 years of time series forecasting. International journal of forecasting, 22(3), 443-473.
XV. Finance Division, Ministry of Finance. Finance Division, Ministry of Finance-Government of the People\’s Republic of Bangladesh. (n.d.). Retrieved December 15, 2021, from https://mof.gov.bd/site/page/44e399b3-d378-41aa-86ff-8c4277eb0990/BangladeshEconomicReview
XVI. Hossain, M. A., Uddin, M. N., Hossain, M. A., & Jang, Y. M. (2017, October). Predicting rice yield for Bangladesh by exploiting weather conditions. In 2017 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 589-594). IEEE.
XVII. Hossain, M. M., & Abdulla, F. (2015). Jute production in Bangladesh: A time series analysis. Journal of Mathematics and Statistics, 11(3), 93–98. https://doi.org/10.3844/jmssp.2015.93.98
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XXII. Mahmood, R., Meo, M., Legates, D. R., & Morrissey, M. L. (2003). The CERES-Rice model-based estimates of potential monsoon season rainfed rice productivity in Bangladesh. The Professional Geographer, 55(2), 259-273.
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Journal Vol – 17 No -12, December 2022

A STUDY INTO THE CUTTING-EDGE ADVANCEMENTS IN MATHEMATICS WITH REFERENCE TO COMPUTER SCIENCE

Authors:

Gundu Srinivasa Rao, Panem Charanarur

DOI NO:

https://doi.org/10.26782/jmcms.2022.12.00004

December 27, 2022

Abstract:

  Mathematical research in the ancient world was especially interesting when put in the context of philosophical ideas. No country has ever thrived without investing heavily in its children's education. It is crucial to achieving this requirement in order to be classified as a “Developed Nation” within a certain time limit. Allocating sufficient funds to Math and Computer Science programs at all educational levels is essential. In contrast to the study of mathematics for practical purposes, pure mathematics focuses only on the study of mathematical ideas themselves. Although the inspiration for these ideas sometimes comes from real-world problems, and the solutions often have practical applications, pure mathematicians are not typically driven by the potential utility of their work. Mathematics has been essential in the IT revolution. There are many examples of how computer science has contributed to modern life, including the information technology sector, the manufacturing sector, satellites, electronic banking and commerce, the communication revolution, the global positioning system (GPS), the geographic information system (GIS), remote sensing, and many more.

Keywords:

Mathematics,Education,Computer Science,Pure mathematics,Applied Mathematics,Real-world Applications,Practical Applications,Information Technology,Satellites,E-Banking,E-Commerce,Communication Technology,Remote Sensing,

Refference:

I. A. Ali, S. Talpur and S. Narejo, “Detecting Faulty Sensors by Analyzing the Uncertain Data Using Probabilistic Database,” 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2020, pp. 1-7, doi: 10.1109/iCoMET48670.2020.9074069.
II. B. Musil, S. Gartner, I. Pesek and M. Krašna, “ICT competences assessment through ICT escape room,” 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2019, pp. 622-626, doi: 10.23919/MIPRO.2019.8757043.
III. C. H. Hsu, C. E. Montenegro Marin, R. Gonzalez Crespo and H. F. Mohamed El-sayed, “Guest Editorial Introduction to the Special Section on Social Computing and Social Internet of Things,” in IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 947-949, 1 May-June 2022, doi: 10.1109/TNSE.2022.3167460.

IV. D. Połap, G. Srivastava, A. Jolfaei and R. M. Parizi, “Blockchain Technology and Neural Networks for the Internet of Medical Things,” IEEE INFOCOM 2020 – IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2020, pp. 508-513, doi: 10.1109/INFOCOMWKSHPS50562.2020.9162735.
V. H. Dehghani, “The effectiveness of a mobile application “Kalcal” on the learning of mathematics in students with dyscalculia,” 2019 International Serious Games Symposium (ISGS), 2019, pp. 1-6, doi: 10.1109/ISGS49501.2019.9047035.
VI. M. de Berg, H. L. Bodlaender, S. Kisfaludi-Bak and S. Kolay, “An ETH-Tight Exact Algorithm for Euclidean TSP,” 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS), 2018, pp. 450-461, doi: 10.1109/FOCS.2018.00050.
VII. M. T. Azhar, M. B. Khan and M. M. Zafar, “Architecture of an Enterprise Project Life Cycle using Hyperledger platform,” 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), 2019, pp. 1-5, doi: 10.1109/MACS48846.2019.9024764.
VIII. R. A. Canessane, R. Dhanalakshmi, B. Pavithra, B. Sasikanth and C. Sandeep, “HUSP Mining Techniques to Detect Most Weighted Disease and Most Affected Diseases for the Healthcare Industry,” 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), 2019, pp. 25-32, doi: 10.1109/ICONSTEM.2019.8918784.
IX. R. A. Canessane, R. Dhanalakshmi, B. Pavithra, B. Sasikanth and C. Sandeep, “HUSP Mining Techniques to Detect Most Weighted Disease and Most Affected Diseases for the Healthcare Industry,” 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), 2019, pp. 25-32, doi: 10.1109/ICONSTEM.2019.8918784.
X. S. Dragoumanos, L. Garcia, D. Schultz and L. Bowlby, “IEEE pre-university science, technology, engineering, and mathematics outreach: Share. Give back. Inspire,” in IEEE Potentials, vol. 41, no. 5, pp. 12-14, Sept.-Oct. 2022, doi: 10.1109/MPOT.2022.3181437.
XI. S. H. Said Abdelaziz Abdelrazek, H. B. Kutty Mammi and M. M. Din, “Privilege Escalation Focused Offensive Security Training Platform,” 2021 International Conference on Data Science and Its Applications (ICoDSA), 2021, pp. 169-174, doi: 10.1109/ICoDSA53588.2021.9617497.
XII. S. Hu, L. Shuai, Q. Yang and H. Chen, “Study on Wireless Signal Propagation in Residential Outdoor Activity Area Based on Deep Learning,” 2021 International Conference on Computer, Control and Robotics (ICCCR), 2021, pp. 225-230, doi: 10.1109/ICCCR49711.2021.9349418.
XIII. S. Mistry and L. Wang, “Efficient Prediction of Heart Disease Using Cross Machine Learning Techniques,” 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), 2022, pp. 1002-1006, doi: 10.1109/IPEC54454.2022.9777309.
XIV. S. Xiong, Q. Cao and W. Si, “Adaptive Path Tracing with Programmable Bloom Filters in Software-Defined Networks,” IEEE INFOCOM 2019 – IEEE Conference on Computer Communications, 2019, pp. 496-504, doi: 10.1109/INFOCOM.2019.8737387.
XV. T. Ostojic, “Applicability of knowledge: Motivational factor for beginning learners of programming,” 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2018, pp. 0491-0493, doi: 10.23919/MIPRO.2018.8400093.

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Journal Vol – 17 No -12, December 2022

ON MATHEMATICAL METHODS TO BALANCE EQUATIONS OF CHEMICAL REACTIONS – A COMPARISON AND WAY FORWARD

Authors:

Muhammad Mujtaba Shaikh, Mumtaz Yousaf

DOI NO:

https://doi.org/10.26782/jmcms.2023.01.00001

January 31, 2023

Abstract:

In this study, a comparative analysis is to be conducted between different mathematical techniques to find out the best one which can be more suitable from all perspectives to balance equations of chemical reactions and to provide case-to-case recommendations for the practitioners. The linear algebra approach, linear programming approach, and integer linear programming approach have been successfully utilized for chemical equation balancing.  Some chemical equations have been taken from the literature to see the performance of the above approaches. After highlighting the advantages and disadvantages of the existing approaches, some proposals for modification are presented. The proposed modifications have been worked out on all problems, and the integer solution is attained for all problems; even in cases where existing methods failed. The final recommendations on easier and better techniques have been provided. The two modified methods achieved top ratings among the existing and proposed methods.

Keywords:

Mathematical methods,Chemical equations,Linear Algebra,Linear Programming,Integer Linear Programming,FLOPs, Mathematical Chemistry,

Refference:

I. Abdelrahim M. Zabadi and RamizAssaf, (2017), “Balancing a Chemical Reaction Equation Using Algebra Approach”, International Journal of Advanced Biotechnology and Research(IJBR), vol 8, Issue 1, pp: 24-33.
II. Arcesio Garcia, (1987), “A new method to balance chemical equations”, Journal of chemical equation, vol 64, pp: 247-248.
III. Charnock, N. L, (2016), “Teaching Method for Balancing Chemical Equations: An Inspection versus an Algebraic Approach”. American Journal of Educational Research, vol 4, pp: 507-511
IV. Dr. Kakde Rameshkumar Vishwambharrao, Sant Gadage Maharaj Mahavidyalaya, Loha (Maharashtra), (September 2013), “Balancing Chemical Equations by Using Mathematical Model”, International Journal of Mathematical Research & Science (IJMRS), vol. 1, Issue 4, pp: 129-132.
V. E.V. Krishnamurthy, (1978), “Generalized matrix inverse approach to automatic balancing of chemical equation”, International Journal of Mathematical Education in Science and Technology, vol 9, no 3,
pp: 323–328.
VI. Ice B Risteski, (2012), “New very Hard Problems of Balancing Chemical Reactions”, Bulgarian Journal of Science Education, vol 21, No 4, pp: 574
VII. Ice.B Ristaki, (2014), “A new generalized algebra for the balancing of chemical reactions”, Materiali technologije/materials and technology, vol 48, Issue 2, pp: 215-219.
VIII. Lochte, H.L, (1997), “Pole reaction method (ion electron/half reaction)”, Journalof chemical education, vol 74, Issue 11, pp: 146-157.
IX. Mansoor Niaz and Anton E Lawson, (1985), “Balancing Chemical Equations: The Role of Developmental level and Mental Capacity”, Journal of Research in Science Teaching, vol 22, No 1, pp: 41-51.
X. Mumtaz Yousaf, Muhammad Mujtaba Shaikh, Abdul Wasim Shaikh (2020). Efficient Mathematical Programming Techninques For Balancing Equations of Complex Chemical Reactions. Journal of Maecahnics of Continua and Mathemtical Sciences. 15 (10): 53-66.
XI. Paul M. Treichel John C. Kotz, at https://www.britannica.com/science/chemical-reaction/Photolysis-reactions
XII. R. David Jones, A. Paul Schwab, (1989), “Balancer: A computer program for balancing chemical equations”, Journal of agronomic education, vol 18, Issue 1, pp: 29-32.
XIII. R.O Akinola, S.Y. Kutchin, I.A Nyam, (2016), “using row reduced echelon form in balancing chemical equations”, Advance in linear algebra and Matrix Theorey, vol 6, Issue 4, pp: 429-445.
XIV. Risteki I.B, (2016b), “A new generalzed matrix inverse method for balancing chemical equations and their stability”, Journal of Chinese chemical Soc, vol 56, Issue 4, pp: 65-79.
XV. S K Sen, Hans Agarwal, Sagar Sen, (Feburary 2006), “Chemical Equation Balancing: An integer programming appoach”, Mathematical and Computer Modelling, vol 44, pp: 678–691
XVI. William C. Herndon, (November 1997), “On Balancing Chemical Equations: Past and Present”, Journal of Chemical Education, vol 74, No 11, pp: 1359-1362.

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Journal Vol – 18 No -1, January 2023

UNET MOBILENETV2: MEDICAL IMAGE SEGMENTATION USING DEEP NEURAL NETWORK (DNN)

Authors:

Bikash Chandra Bag, Hirak Kumar Maity, Chaitali Koley

DOI NO:

https://doi.org/10.26782/jmcms.2023.01.00002

January 31, 2023

Abstract:

In this paper, the framework of polyp image segmentation is developed using a Deep neural network (DNN). Here Unet Mobile NetV2 is considered to evaluate the performance of the image from the CVC-612 dataset for the segmentation method. The proposed model outperformed earlier results. To compare our results two parameters, normally Dice co-efficient and Intersection over Union (IoU) are considered. The proposed model may be used for accurate computer-aided polyp detection and segmentation during colonoscopy examinations to find out abnormal tissue and thereby decrease the chances of polyps growing into cancer. MobileNetV2 significantly outperforms U-Net and MobileNetV2, two key state-of-the-art deep learning architectures, by achieving high evaluation scores with a dice coefficient of 89.71%, and an IoU of 81.64%.

Keywords:

Deep Neural network,Semantic segmentation,UNet MobileNetV2,

Refference:

I. A. Arezzo, A. Koulaouzidis, A. Menciassi, D. Stoyanov, E. B. Mazomenos, F. Bianchi, G. Ciuti, P. Brandao, P. Dario, R. Caliò, Fully convolutional neural networks for polyp segmentation in colonoscopy. In: International Society for Optics and Photonics. Medical Imaging 2017: Computer-Aided Diagnosis, v. 10134, p. 101340F, 2017.
II. A. Howard, M. Zhu, A. Zhmoginov, L-C. Chen, M. Sandler, MobileNetV2: Inverted Residuals and Linear Bottlenecks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, p. 4510-4520, 2018.
III. A. Howard, M. Zhu, A. Zhmoginov, L-C. Chen, M. Sandler, MobileNetV2: Inverted Residuals and Linear Bottlenecks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, p. 4510-4520, 2018.
IV. A. V. Mamonov, I. N. Figueiredo, P. N. Figueiredo Y-H. R. Tsai, Automated polyp detection in colon capsule endoscopy. IEEE transactions on medical imaging, v. 33, n. 7, p. 1488–1502, 2014.
V. B. Paul, S. A. Fattah, T. Mahmud, Polypsegnet: A modified encoderdecoder architecture for automated polyp segmentation from colonoscopy images. Computers in Biology and Medicine, p. 104119, 2020.
VI. D-P. Fan, G. Chen, G-P. Ji, H. Fu, J. Shen, L. Pranet Shao, T. Zhou, : Parallel reverse attention network for polyp segmentation. In: SPRINGER.International Conference on Medical Image Computing and ComputerAssisted Intervention, p. 263–273, 2020.
VII. D. Jha, D. Johansen, H. D. CVC-612, Johansen, M. A. Riegler, P. Halvorsen, P. H. Smedsrud, T. Lange : A Segmented Polyp Dataset. In Proc. of International Conference on Multimedia Modeling (MMM), p. 451-462, 2019.
VIII. D. Jha, H. D. Johansen, M. A. Riegler, P. Halvorsen, P. H. Smedsrud, T. Lange, ResUNet++: An Advanced Architecture for Medical Image Segmentation. 2019 IEEE International Symposium on Multimedia (ISM),2019.
IX. E. Dekker, J. C. V. Rijn, J. B. Reitsma, J. Stoker, P. M. Bossuyt, S. J. V. Deventer, Polyp miss rate determined by tandem colonoscopy: a systematic review. The American journal of gastroenterology, v. 101, p. 343, 2006.
X. E. Nasr-Esfahani, K. Najarian, M. Akbari, M. Mohrekesh, N. Karimi, S. M. R. Soroushmehr, S. Samavi, Polyp segmentation in colonoscopy images using fully convolutional network. In: IEEE. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), p. 69–72, 2018.

XI. F. Chollet, Keras. https://github.com/fchollet/keras, 2015.
XII. J. Deng, K. Li, L. Fei-Fei, L-J. Li, R. Socher, W. Dong, Imagenet: A large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition, p. 248–255, 2009.
XIII. J. Liang, N. Tajbakhsh, S. R. Gurudu, Automated polyp detection in colonoscopy videos using shape and context information. IEEE transactions on medical imaging, v. 35, n. 2, p. 630–644, 2015.
XIV. O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks forbiomedical image segmentation. In: SPRINGER. International Conference on Medical image computing and computer-assisted intervention, p. 234–241, 2015.
XV. N. K. Tomar, Automatic Polyp Segmentation using Fully Convolutional Neural Network, 2021.

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Journal Vol – 18 No -1, January 2023

SIMULATION OF WAVE SOLUTIONS OF A MATHEMATICAL MODEL REPRESENTING ELECTRICAL ENGINEERING BY USING AN ANALYTICAL TECHNIQUE

Authors:

Md. Nur Alam

DOI NO:

https://doi.org/10.26782/jmcms.2023.01.00003

January 31, 2023

Abstract:

The existing article examines the mathematical model (MM) representing electrical engineering (EE). We implement the unified technique (UT) to discover new wave solutions (WS) and to erect numerous kinds of solitary wave phenomena (SWP) for the studied model (SM). The SM is one of the models that have vital applications in the area of EE. The taken features provide a firm mathematical framework and may be necessary to the WSs. As an outcome, we get new kinds of WSs from. With 3-d, density, contour, and 2-d for different values of time parameters, mathematical effects explicitly manifest the suggested algorithm's full reliability and large display. We implement a few figures in 3-d, density, contour, and 2-d for diverse values of time parameters to express that these answers have the properties of soliton waves.

Keywords:

The UT method,MM,the modified Zakharov-Kuznetsov equation,EE,WSs,

Refference:

I. Abdul Majeed, Muhammad Naveed Rafiq, Mohsin Kamran, Muhammad Abbas and Mustafa Inc, Analytical solutions of the fifth-order time fractional nonlinear evolution equations by the unified method, Modern Physics Letters BVol. 36, No. 02, 2150546 (2022)
II. A. Akgül, D. Baleanu, M. Inc, F. Tchier, On the solutions of electrohydrodynamic flow with fractional differential equations by reproducing kernel method, Open Physics, 14 (1) (2016), pp. 685-689.
III. A.B.T. Motcheyo, C. Tchawoua and J.D.T. Tchameu, Supra transmission induced by waves collisions in a discrete electrical lattice, Phys. Rev. E, 88 (4), 040901 (2013).
IV. A. Das, Explicit weierstrass traveling wave solutions and bifurcation analysis for dissipative Zakharov-Kuznetsov modified equal width equation, Comput. Appl. Math., 37(3), 3208-3225 (2018).
V. A. Korkmaz, O.E. Hepson, K. Hosseini, H. Rezazadeh, M. Eslami, Sine-Gordon expansion method for exact solutions to conformable time fractional equations in RLW-class, J. King Saud University-Science, 32 (1) (2020), pp. 567-574
VI. A.S. Fokas and I.M. Gelfand, A Unified Method for Solving Linear and Nonlinear Evolution Equations and an Application to Integrable Surfaces. In: Gelfand, I.M., Lepowsky, J., Smirnov, M.M. (eds) The Gelfand Mathematical Seminars, 1993–1995. (1996). Birkhäuser Boston. https://doi.org/10.1007/978-1-4612-4082-2_5
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VIII. B.S. Bardin and E.A. Chekina, On the constructive algorithm for stability analysis of an equilibrium point of aperiodic hamiltonian system with two degrees of freedom in the case of combinational resonance, Regul. Chaotic Dynam., 24 (2), 127-144 (2019).
IX. Deiakeh RA, Ali M, Alilquran M, Sulaiman TA, Momani S, Smadi MA. On finding closed-form solutions to some nonlinear fractional systems via the combination of multi-Laplace transform and the Adomian decomposition method. Romanian Reports in Physics. 2022; 74(2): 111.
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XVI. K.S. Nisar, O.A. Ilhan, S.T. Abdulazeez, J. Manafian, S.A. Mohammed, M.S. Osman, Novel multiple soliton solutions for some nonlinear PDEs via multiple Exp-function method Results Phys., 21 (2021), p. 103769
XVII. Ma WX, Soliton solutions by means of Hirota bilinear forms. Partial Differential Equations in Applied Mathematics. 2022; 5: 100220.
XVIII. M. Eslami and H. Rezazadeh, The first integral method for Wu–Zhang system with conformable time-fractional derivative, Calcolo, 53 (2016), pp. 475-485, (2016).
XIX. M. N. Alam, A. R. Seadawy and D. Baleanu, Closed-form solutions to the solitary wave equation in an unmagnatized dusty plasma, Alexandria Engineering Journal, 59(3): 1505-1514, (2020).
XX. M. N. Alam, A. R. Seadawy and D. Baleanu, Closed-form wave structures of the space-timefractional Hirota–Satsuma coupled KdVequation with nonlinear physical phenomena, Open Physics, 18(1): 555-565, (2020).

XXI. M. N. Alam and C. Tunc, The new solitary wave structures for the (2 + 1)-dimensional time-fractional Schrodinger equation and the space-time nonlinear conformable fractional Bogoyavlenskii equations, Alexandria Engineering Journal, 59(4): 2221-2232, (2020).
XXII. M.N. Alam, O.A. Ilhan, J. Manafian, M.I. Asjad, H. Rezazadeh and H.M. Baskonus, New results of some conformable models arising in dynamical systems, Advances in Mathematical Physics, Volume 2022 (2022), Article ID 7753879 DOI: https://doi.org/10.1155/2022/7753879.
XXIII. M. N. Alam, S. Aktar and C. Tunc, New solitary wave structures to time fractional biological population model, Journal of Mathematical Analysis-JMA, 11(3): 59-70, (2020).
XXIV. M. N. Alam and X. Li, New soliton solutions to the nonlinear complex fractional Schrödinger equation and the conformable time-fractional Klein–Gordon equation with quadratic and cubic nonlinearity, Physica Scripta, 95: 045224, (2020).
XXV. M.N. Alam, O.A. İlhan, M.S. Uddin and M.A. Rahim, Regarding on the results for the fractional Clannish Random Walker’s Parabolic equation and the nonlinear fractional Cahn-Allen equation, Advances in Mathematical Physics, 2022, Article ID 5635514, (2022).
XXVI. N. Raza, M.H. Rafiq, M. Kaplan, S. Kumar and Y.M. Chu, The unified method for abundant soliton solutions of local time fractional nonlinear evolution equations, Results in Physics, 22, 103979, (2021).
XXVII. N. Raza and M.H. Rafiq, Abundant fractional solitons to the coupled nonlinear Schrödinger equations arising in shallow water waves, Internat J Modern Phys B, 34 (18) (2020), Article 2050162, (2020).
XXVIII. R. Saleh, S.M. Mabrouk and A.M. Wazwaz, Lie symmetry analysis of a stochastic gene evolution in double-chain deoxyribonucleic acid system. Waves in Random and Complex Media, https://doi.org/10.1080/17455030.2020.1871109 (2021).
XXIX. Shakeel M, Attaullah, Shah NA, Chung JD. Application of modified exp-function method for strain wave equation for finding analytical solutions. Ain Shams Engineering Journal. 2022; 2022: 101883. DOI: https://doi.org/10.1016/j.asej.2022.101883
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XXXIII. Shakeel M, Attaullah, El-Zahar ER, Shah NA, Chung JD. Generalized exp-function method to find closed form solutions of nonlinear dispersive modified Benjamin-Bona-Mahony equation defined by seismic sea waves. Mathematics. 2022; 10(7): article no. 1026. doi.org/10.3390/math 10071026.
XXXIV. Thottoli SR, Tamsir M, Dhiman N, Souadi G. Computational modeling of the Balitsky–Kovchegov equation and its numerical solution using hybrid B-spline collocation technique. Partial Differential Equations in Applied Mathematics. 2022; 5: 100348.
XXXV. Tripathy A, Sahoo S. The new optical behaviour of the LPD model with Kerr law and parabolic law of nonlinearity. Partial Differential Equations in Applied Mathematics.2022; 5: 100334.
XXXVI. T.T. Guy and J.R. Bogning, Construction of Breather soliton solutions of a modeled equation in a discrete nonlinear electrical line and the survey of modulationnal in stability, J. Phys. Commun., 2 (11), 115007 (2018).
XXXVII. Yiren Chen1 and Shaoyong Li, New Traveling Wave Solutions and Interesting Bifurcation Phenomena of Generalized KdV-mKdV-Like Equation, Advances in Mathematical PhysicsVolume 2021 |Article ID 4213939 | https://doi.org/10.1155/2021/4213939
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Journal Vol – 18 No -1, January 2023

A NOVEL CONCEPT FOR FINDING THE FUNDAMENTAL RELATIONS BETWEEN STREAM FUNCTION AND VELOCITY POTENTIAL IN REAL NUMBERS IN TWO-DIMENSIONAL FLUID MOTIONS

Authors:

Prabir Chandra Bhattacharyya

DOI NO:

https://doi.org/10.26782/jmcms.2023.02.00001

February 27, 2023

Abstract:

In this paper, the author has presented the fundamental relations between stream function or current function,  and velocity potential or velocity function, φ which are ∂φ/∂x= ∂/∂y and ∂φ/∂y= - ∂/∂x where x,y,φ(x_(, ) y),  (x_(, ) y) are all real in two-dimensional fluid motions using real variables only whereas these relations had been established by using complex variables by Cauchy – Riemann which are known as Cauchy – Riemann equations in fluid dynamics.

Keywords:

Riemann equations,Quadratic equations,Rectangular Bhattacharyya’s Coordinates,Stream function,Theory of Dynamics of Numbers,Velocity potential,

Refference:

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Journal Vol – 18 No -2, February 2023

SIMULATION OF WAVE SOLUTIONS OF A FRACTIONAL-ORDER BIOLOGICAL POPULATION MODEL

Authors:

Md. Sabur Uddin, Md. Nur Alam, Kanak Chandra Roy

DOI NO:

https://doi.org/10.26782/jmcms.2023.02.00002

February 27, 2023

Abstract:

In this analysis, we apply prominent mathematical systems like the modified (G'/G)-expansion method and the variation of (G'/G)-expansion method to the nonlinear fractional-order biological population model. We formulate twenty-three mathematical solutions, which are clarified hyperbolic, trigonometric, and rational. Using MATLAB software, we illustrate two-dimensional, three-dimensional, and contour shapes of our obtained solutions. These mathematical systems depict and display its considerate and understandable technique that generates a king type shape, singular king shapes, soliton solutions, singular lump and multiple lump shapes, periodic lump and rouge, the intersection of king and lump wave profile, and the intersection of lump and rogue wave profile. Measuring our return and that gained in the past released research shows the novelty of our analysis. These systems are also capable to represents various solutions for other fractional models in the field of applied mathematics, physics, and engineering.

Keywords:

Nonlinear fractional order biological model, the modified -expansion method,the variation of -expansion method, mathematical solutions,nonlinear partial differential equations, lump, and rogue wave,

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XXIII. M. N. Alam, A. R. Seadawy and D. Baleanu,‘ Colsed-form wave solutions to the solitary wave equation in an unmaganatized dusty plasma’, Alexandria Engineering Journal , vol. 59,no.3, pp, 1505–1514, 2020.
XXIV. M. N. Alam and C. Tunc,‘The new solitary wave structures for the (2+1)-dimensional time fractional Schrodinger equation and the space-time nonlinear conformal fractional Bogoyav-lenskii equations,’ Alexandria Engineering Journal , vol. 59,no.4, pp, 2221–2232, 2020.
XXV. M. N. Alam, S. Aktar and C. Tunc,‘New solitary wave structures to time fractional biological population model ,’ Journal of Mathematical Analysis-JMA, vol. 11,no.3,pp, 59–70, 2020.
XXVI. M. N. Alam and X. Li,‘ New soliton solutions to the nonlinear complex fractional Schrodinger equation and conformal time-fractional Klein-Gordon equation with quadratic and cubic nonlinearity,’ Physics Scripta , vol. 95, no.4, pp, 045224, 2020.
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Journal Vol – 18 No -2, February 2023