Archive

Abstract:

The leading infectious disease-related cause of mortality for people is tuberculosis (TB). India is one of the countries with the highest rates of TB worldwide, making it a serious public health problem. People with active lung TB can spread the illness by spitting, coughing, or sneezing. In healthcare, the application of machine learning (ML) that helps in diagnosis is on the rise. In this study, we suggest a stacked ensemble model that combines three base ML classifier models to predict treatment-unfavorable in Pulmonary TB (PTB) patients. Cases with unfavorable treatment are considered as the event of interest. Retrospectively, secondary data of 1236 PTB patients treated in randomized controlled clinical research were obtained and split into training and testing data in a 70:30 ratio. Several ML models had different levels of effectiveness in predicting treatment-unfavorable outcomes in PTB patients. The Support Vector Machines model struggled with sensitivity (0.246) but had high specificity (0.981). Likewise, the Logistic Regression model showed poor sensitivity (0.339) but strong specificity (0.959). The Decision Tree model, on the other hand, did well, with high sensitivity (0.755) and specificity (0.956). With the best accuracy (0.929), sensitivity (0.774), specificity (0.956), and F1-score (0.759), the stacked Ensemble Random Forest model performed better than the others. This illustrates the prospective of ensemble learning in the healthcare industry, where it is essential to identify negative effects early and accurately. To improve prediction accuracy and generalizability, future research should verify these results and explore other clinical characteristics.

Keywords:

Clinical Trial,Cross-Validation,Ensemble,Machine Learning,Pulmonary Tuberculosis,

Refference:

I. Abu Al-Haija, Qasem, Moez Krichen, and Wejdan Abu Elhaija. “Machine-learning-based darknet traffic detection system for IoT applications.” Electronics 11.4 (2022): 556. 10.3390/electronics11040556
II. Amini, Payam, et al. “Prevalence and determinants of preterm birth in Tehran, Iran: a comparison between logistic regression and decision tree methods.” Osong public health and research perspectives 8.3 (2017): 195. 10.24171/j.phrp.2017.8.3.06
III. Ayodele, Taiwo Oladipupo. “Types of machine learning algorithms.” New advances in machine learning 3.19-48 (2010): 5-1. https://web.archive.org/web/20160417233342id_/http:/cdn.intechweb.org:80/pdfs/10694.pdf
IV. Bakyarani ES, Srimathi H, Bagavandas M. A survey of machine learning algorithms in health care. Int JSci Technol Res. 2019 Nov; 8(11):223. https://api.semanticscholar.org/CorpusID:212513380
V. Bora, R.M.; Chaudhari, S.N.; Mene, S.P. A Review of Ensemble Based Classification and Clustering in Machine Learning. Int. J. New Innov. Eng. Technol. 2019, 12, 2319–6319. https://www.ijniet.org/wp-content/uploads/2020/01/10.pdf
VI. CDC. Tuberculosis: Causes and How It Spreads. Tuberculosis (TB). https://www.cdc.gov/tb/causes/index.html
VII. Cruz, Joseph A., and David S. Wishart. “Applications of machine learning in cancer prediction and prognosis.” Cancer informatics 2 (2006): 117693510600200030. 10.1177/117693510600200030
VIII. Doupe, Patrick, James Faghmous, and Sanjay Basu. “Machine learning for health services researchers.” Value in Health 22.7 (2019): 808-815. 10.1016/j.jval.2019.02.012
IX. Dye, Christopher. “Global epidemiology of tuberculosis.” The Lancet 367.9514 (2006): 938-940. 10.1016/S0140-6736(06)68384-0
X. Ekins, Sean, et al. “Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I.” Tuberculosis 103 (2017): 52-60. 10.1016/j.tube.2017.01.005
XI. Fawagreh, Khaled, Mohamed Medhat Gaber, and Eyad Elyan. “Random forests: from early developments to recent advancements.” Systems Science & Control Engineering: An Open Access Journal 2.1 (2014): 602-609. 10.1080/21642583.2014.956265
XII. Ganaie, Mudasir A., et al. “Ensemble deep learning: A review.” Engineering Applications of Artificial Intelligence 115 (2022): 105151. 10.1016/j.engappai.2022.105151
XIII. Garcıa-Gila, Diego, et al. “Smart Data based Ensemble for Imbalanced Big Data Classification.” arXiv preprint arXiv:2001.05759 (2020). 10.48550/arXiv.2001.05759
XIV. Global Pandemic. TB Alliance. https://www.tballiance.org/why-new-tb-drugs-global-pandemic/
XV. Hasan, Md Kamrul, et al. “Diabetes prediction using ensembling of different machine learning classifiers.” IEEE Access 8 (2020): 76516-76531. 10.1109/ACCESS.2020.2989857
XVI. Jaeger, Stefan, et al. “Automatic tuberculosis screening using chest radiographs.” IEEE transactions on medical imaging 33.2 (2013): 233-245. 10.1109/TMI.2013.2284099
XVII. Jiang, Tammy, Jaimie L. Gradus, and Anthony J. Rosellini. “Supervised machine learning: a brief primer.” Behavior therapy 51.5 (2020): 675-687. 10.1016/j.beth.2020.05.002
XVIII. Kearns, Michael J., and Umesh Vazirani. An introduction to computational learning theory. MIT press, 1994. 10.7551/mitpress/3897.001.0001
XIX. Kourou, Konstantina, et al. “Machine learning applications in cancer prognosis and prediction.” Computational and structural biotechnology journal 13 (2015): 8-17. 10.1016/j.csbj.2014.11.005
XX. Lin, Weiwei, et al. “An ensemble random forest algorithm for insurance big data analysis.” Ieee access 5 (2017): 16568-16575. 10.1109/ACCESS.2017.2738069
XXI. Logistic Regression: Overview and Applications. Keylabs: latest news and updates. https://keylabs.ai/blog/logistic-regression-overview-and-applications/
XXII. Mahesh, T. R., et al. “The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification.” Healthcare Analytics 4 (2023): 100247. 10.1016/j.health.2023.100247
XXIII. Mian Qaisar, Saeed, et al. “Machine learning with adaptive rate processing for power quality disturbances identification.” SN Computer Science 3.1 (2022): 14. 10.1007/s42979-021-00904-1
XXIV. Mihoub, Alaeddine, et al. “Predicting covid-19 spread level using socio-economic indicators and machine learning techniques.” 2020 first international conference of smart systems and emerging technologies (SMARTTECH). IEEE, 2020. 10.1109/SMART-TECH49988.2020.00041
XXV. Mitchell, Tom M., and Tom M. Mitchell. Machine learning. Vol. 1. No. 9. New York: McGraw-hill, 1997. https://www.cs.cmu.edu/~tom/files/MachineLearningTomMitchell.pdf
XXVI. Mitchell, Tom M., Jaime G. Carbonell, and Ryszard S. Michalski, eds. Machine learning: a guide to current research. Vol. 12. Springer Science & Business Media, 1986. https://link.springer.com/book/10.1007/978-1-4613-2279-5
XXVII. Nasteski, Vladimir. “An overview of the supervised machine learning methods.” Horizons. b 4.51-62 (2017): 56. 10.20544/HORIZONS.B.04.1.17.P05
XXVIII. Nitze, I., U. Schulthess, and H. Asche. “Comparison of machine learning algorithms random forest, artificial neural network and support vector machine to maximum likelihood for supervised crop type classification.” Proceedings of the 4th GEOBIA, Rio de Janeiro, Brazil 79 (2012): 3540. http://mtc-m16c.sid.inpe.br/col/sid.inpe.br/mtc-m18/2012/05.15.13.21/doc/015.pdf
XXIX. Nusinovici, Simon, et al. “Logistic regression was as good as machine learning for predicting major chronic diseases.” Journal of clinical epidemiology 122 (2020): 56-69. 10.1016/j.jclinepi.2020.03.002
XXX. Qaisar, Saeed Mian, et al. “Multirate processing with selective subbands and machine learning for efficient arrhythmia classification.” Sensors 21.4 (2021): 1511. 10.3390/s21041511
XXXI. Rhys, Hefin. Machine Learning with R, the tidyverse, and mlr. Simon and Schuster, 2020. https://www.manning.com/books/machine-learning-with-r-the-tidyverse-and-mlr
XXXII. Shouman, Mai, Tim Turner, and Rob Stocker. “Using Decision Tree for Diagnosing Heart Disease Patients.” AusDM 11 (2011): 23-30. https://crpit.scem.westernsydney.edu.au/confpapers/CRPITV121Shouman.pdf
XXXIII. Shrivastav, Lokesh Kumar, and Ravinder Kumar.”An ensemble of random forest gradient boosting machine and deep learning methods for stock price prediction.” Journal of Information Technology Research (JITR) 15.1 (2022): 1-19. 10.4018/JITR.2022010102
XXXIV. Srinivasan, Sriram, et al. “Deep convolutional neural network based image spam classification.” 2020 6th conference on data science and machine learning applications (CDMA). IEEE, 2020. 10.1109/CDMA47397.2020.00025
XXXV. Steyerberg, Ewout W., et al. “Internal validation of predictive models: efficiency of some procedures for logistic regression analysis.” Journal of clinical epidemiology 54.8 (2001): 774-781. 10.1016/S0895-4356(01)00341-9
XXXVI. Tiwari, Akshita, and Srabanti Maji. “Machine learning techniques for tuberculosis prediction.” International Conference on Advances in Engineering Science Management & Technology (ICAESMT)-2019, Uttaranchal University, Dehradun, India. 2019. 10.2139/ssrn.3404486
XXXVII. Tuberculosis (TB). https://www.who.int/news-room/fact-sheets/detail/tuberculosis
XXXVIII. Tuberculosis Research Centre (Indian Council of Medical Research), Chennai, India. “Split‐drug regimens for the treatment of patients with sputum smear‐positive pulmonary tuberculosis–a unique approach.” Tropical Medicine & International Health 9.5 (2004): 551-558. 10.1111/j.1365-3156.2004.01229.x
XXXIX. Veropoulos, Konstantinos, Colin Campbell, and Nello Cristianini. “Controlling the sensitivity of support vector machines.” Proceedings of the international joint conference on AI. Vol. 55. 1999. https://seis.bristol.ac.uk/~enicgc/pubs/1999/ijcai_ss.pdf
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XLI. Wilson, Robert A., and Frank C. Keil, eds. The MIT Encyclopedia of the cognitive sciences (MITECS). MIT press, 2001. 10.7551/mitpress/4660.001.0001
XLII. Yang, Kaixiang, et al. “Hybrid classifier ensemble for imbalanced data.” IEEE transactions on neural networks and learning systems 31.4 (2019): 1387-1400. 10.1109/TNNLS.2019.2920246
XLIII. Zhang, Yuzhen, Jingjing Liu, and Wenjuan Shen. “A review of ensemble learning algorithms used in remote sensing applications.” Applied Sciences 12.17 (2022): 8654. 10.3390/app12178654

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Abstract:

This paper presents the finite element approach to solving the Poisson equation. Using Dirichlet boundary conditions in a two-dimensional polygonal region, the polygon to be discretized is made up of twelve-noded quadrilateral structured meshes. To arrive at a numerical solution, the smaller components must first be solved, and the partial answers must then be combined to provide a solution for the complete mesh. The problem finds applications in various physical domains, such as fluid dynamics, heat conduction, electrostatics, and gravitational potential. However, due to the intricate nature of the domains, which include reentrant corners, fractures, and discontinuities in the solution along the borders, it can be challenging to find exact solutions to these problems. As a result, we propose using the MAPLE-18 program to provide numerical results that corroborate our theoretical conclusions and to suggest a twelve-noded quadrilateral mesh approach that facilitates the solution of the problem, the performance of the Galerkin weighted finite element technique on the generic polygonal domain is demonstrated numerically by use of twelve noded quadrilateral mesh.

Keywords:

FEM,Shape function,Twelve Noded Quadrilateral Mesh,Polygonal Domain,Poisson Equation,

Refference:

I. Bonito, A., Girault, V., and E. Suli, “Finite element approximation of a strain-limiting elastic model,” IMA Journal of Numerical Analysis, Vol. 40, pp. 29–86, 2018. 10.1093/imanum/dry065

II. Bulicek, M., Málek, J.,Rajagopal, K.R., and E. Suli. “On elastic solids with limiting small strain: modeling and analysis”, EMS Surveys in Mathematical Sciences, Vol. 1, pp. 283- 332, 2014. 10.4171%2Femss%2F7

III. Bulícek, M., Málek, J., and Suli, E. “Analysis and approximation of a strain-limiting nonlinear elastic model”, Journal of Mathematics and Mechanics of Solids, Vol. 20, pp. 92–118, 2015.
10.1177/1081286514543601

IV. Cameron Talischi., Glaucio, H.P., Anderson Pererira., and Ivan F. M. M. “PolyMesher: a general-purpose mesh generator for polygonal elements written in Matlab”, Journal of Structural Multidisciplinary Optimization, Vol. 45, pp. 309-328, 2012. 10.1007/s00158-011-0706-z

V. Chuanming Ju., Jianming Zhang., Rongxiong Xiao., and Baotao Chi. “Automatic surface mesh generation by a binary-tree method”, Journal of Engineering Analysis with Boundary Elements, Vol. 152, pp. 473-495, 2023. 10.1016/j.enganabound.2023.04.023

VI. Haris Suhendar., Muhammad Ridho Pratama., and Michael Setyanto Silambi. “Mesh-Free Solution of 2D Poisson Equation with High Frequency Charge Patterns Using Data- Free Physics Informed Neural Network”, Journal of Physics: Conference Series, Vol. 2866, pp. 1-6, 2024. 10.1088/1742-6596/2866/1/012053
VII. Ling, W., Liu, B., and Guo, Q. “Convergence Analysis of a Symmetrical and Positivity Preserving Finite Difference Scheme for 1D Poisson–Nernst–Planck System”, Journal of Symmetry, Vol. 14, pp. 1-11, 2022.
10.3390/sym14081589

VIII. Manikkan, S., and Srinivasan, B. “Transfer physics informed neural network: a new framework for distributed physics informed neural networks via parameter sharing”, Journal of Engineering Computers, Vol. 39, pp. 2961–2988, 2023. 10.1007/s00366-022-01703-9

IX. Muller, J.D., Roe, P.L., and Deconinck, H. “A frontal approach for node generation in Delaunay triangulations”, International Journal for Numerical Methods in Fluids, Vol. 3, pp. 241-255, 1993.
10.1002/fld.1650170305

X. Nicolas Kowalski, Franck Ledoux and Pascal Frey. “Automatic domain partitioning for quadrilateral meshing with line constraints”, Journal of Engineering with computers, Vol. 31, pp. 405-421, 2015.
10.1007/s00366-014-0387-5

XI. Noack, R.W., and Anderson, D.A “Solution-Adaptive Grid Generation using Parabolic Partial Differential Equations”, AIAA Journal., Vol. 28, pp. 1016-1023, 2012. 10.2514/3.25159

XII. Rebay, S. “Efficient Unstructured Mesh Generation by Means of Delaunay Triangulation and Bowyer-Watson Algorithm”, Journal of Computational Physics, Vol. 1, pp. 125-138, 1993.
10.1006/jcph.1993.1097

XIII. Reddy, J.N. “Finite Element Method”, Third Edition, Tata Mc Graw-Hill, 2005. https://www.accessengineeringlibrary.com/content/book/9780072466850
XIV. Shivaram, K.T., and Jyothi, H.R. “Finite element approach for numerical integration over family of eight node linear quadrilateral element for solving Laplace equation”, Materials Today Proceedings, Vol. 46, pp. 4336-4340, 2021. 10.1016/j.matpr.2021.03.437

XV. Shivaram, K.T., Harshitha, L.M.P., Chethana, H. P., and K. Nidhi. “Finite element mesh generation technique for numerical computation of cutoff wave numbers in rectangular and L-shaped wave-guide”, 3rd International Conference on Inventive Computation Technologies (ICICT), IEEE Xplore, 2018.
https://ieeexplore.ieee.org/document/9034432
XVI. Shivaram, K.T. “Generalised gaussian quadrature rules over an arbitrary tetrahedron in euclidean three-dimensional space”, International Journal of Applied Engineering Research, Vol. 8, pp. 1533-1538, 2013. https://www.ripublication.com/ijaerv6/ijaerv8n13_03.pdf
XVII. Tao Shan., Wei Tang., Xunwang Dang., and Maokun Li. “Study on a Fast Solver for Poisson’s Equation Based On Deep Learning Technique”, IEEE Transactions on Antennas and Propagation, pp. 6725 – 6733, 2020. 10.1109/TAP.2020.2985172

XVIII. Thompson, D.S., and Soni, B.K. “Semi structured Grid Generation in Three Dimensions using a Parabolic Marching Scheme”, AIAA Journal, Vol. 40, pp.1-3, 2000. 10.2514/2.1659

XIX. Wei Tang., Tao Shan., Xunwang Dang., Maokun, Li., Fan Yang., Shenheng Xu, and Ji Wu. “Study on a Poisson’s Equation Solver Based On Deep Learning Technique”, in IEEE Electrical Design of Advanced Packaging and Systems Symposium(EDAPS), pp. 1-3, 2017.
10.48550/arXiv.1712.05559

XX. Yoon, H.C., Vasudeva, K.K., and Mallikarjunaiah, S.M. “Finite element model for a Coupled thermo-mechanical system in nonlinear strain-limiting thermos elastic body”, Journal of Communications in Nonlinear Science and Numerical Simulation, Vol. 108, pp. 1-4, 2022.
10.1016/j.cnsns.2022.106262

XXI. Yogitha, A.M., Shivaram, K.T., Rajesh, S.M., and Mahesh Kumar, N. “Numerical computation of cut off wave number in polygonal wave guide by eight node finite element mesh generation approach”, Journal of the Nigerian Society of Physical Sciences, Vol. 6, pp. 1-6, 2024.
10.46481/jnsps.2024.2149

XXII. Zhu, J.Z., Zienkiewicz, O.C., Hinton E., and Wu, J. “A new approach to the development of automatic quadrilateral mesh generation”, International Journal for Numerical Methods in Engineering, Vol. 32, pp. 849-866, 1991. 10.1002/nme.1620320411

XXIII. Zienkiewicz, O.C., Taylor, R.L., and Zhu,J.Z. “Finite Element Method”, its basis and Fundamentals, 6th Edition, Elsevier, April, 2005. https://shop.elsevier.com/books/the-finite-element-method-its-basis-and-fundamentals/zienkiewicz/978-0-08-047277-5

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Abstract:

This study examines the flow of a hybrid nanofluid from Cu-Al2O3/ water through an exponentially stretched porous surface under the influence of an inclined magnetic field, chemical reaction, and heat source. The reduced ordinary differential equations derived from the governing equations of continuity, momentum, energy, and concentration are solved using the Keller Box Technique, and results are presented through graphs. The effects of magnetic parameter, radiation parameter, heat source, and porosity parameter on velocity, temperature, and concentration profiles are studied.

Keywords:

Chemical reaction,Heat source,Hybrid nanofluid,Porosity,Stretching sheet,

Refference:

I. Asifa Tassaddiq., Sadam Khan., Muhammad Bilal, Taza Gul., Safyan Mukhtar., Zahir Shah and Ebenezer Bonyah: ‘Heat and mass transfer together with hybrid nanofluid flow over a rotating disk’. AIP Advances: Vol. 10(5), 2020, Article ID 055317. 10.1063/5.0010181
II. Boyd.J., Buick.J.M., Green S.: ‘Analysis of the Casson and Carreau-Yasuda non- Newtonian blood models in steady and oscillatory flow using the lattice Boltzmann method’. Physics of Fluids: Vol. 19, 2007, 10.1063/1.2772250
III. Ch. Achi Reddy., B. Shankar.: ‘Magneto Hydrodynamics Stagnation PointFlow of a Nano Fluid over an Exponentially Stretching Sheet with an Effect of Chemical Reaction, Heat Source and Suction/Injunction’. World Journal of Mechanics: Vol.5, pp.211-221, 2015. 10.4236/wjm.2015.511020
IV. C.Y. Wang: (1989) . ‘Free Convection on a Vertical Stretching Surface’, J. Appl. Math. Mech: Vol. 69 (11), pp.418–420, 1989,
V. Elbashbeshy. E.M.A.: ‘Heat transfer over an exponentially stretching continuous surface with suction’. Arc. of Mech: Vol. 53(6), pp. 643- 65, 2001.
VI. G. Mahanta., S. Shaw.: ‘3D Casson fluid flow past a porous linearly stretching sheet with convective boundary condition’. Alexandria Engineering Journal: 10.1016/j.aej.2015.04.014
VII. I Waini., A Ishak and I Pop.: ‘Hybrid nanofluid flow and heat transfer past a permeable stretching/shrinking surface with a convective boundary condition’. Journal of Physics: Conference Series: 2019. 10.1088/1742-6596/1366/1/012022.
VIII. M.A. Samad., M., Mohebujjaman.: ‘MHD Heat and Mass Transfer Free Convection Flow along a Vertical Stretching Sheet in Presence of Magnetic Field with Heat Generation’. Research Journal of Applied Sciences, Engineering and Technology: Vol. 1(3), pp: 98-106, 2009.
IX. M. Santhi., K. V. SuryanarayanaRao., P. Sudarsana Reddy and P. Sreedevi. ‘Heat and mass transfer characteristics of radiative hybrid nanofluid flow over a stretching sheet with chemical reaction’. Heat Transfer: pp.1–21, 2020, 10.1002/htj.22012
X. M. Shanmugapriya., R. Sundareswaram and P. Senthil Kumar. ‘Heat and Mass Transfer Enhancement of MHD Hybrid Nanofluid Flow in the Presence of Activation Energy’, Hindawi, International Journal of Chemical Engineering:2021, 10.1155/2021/9473226
XI. Maria Immaculate Joyce., JaganKandasamy and Sivasankaran Sivanandam.‘Entropy Generation of Cu-Al2O3/Water Flow with Convective Boundary Conditions through a Porous Stretching Sheet with Slip Effect, Joule Heating and Chemical Reaction’. Mathematical and Computational Applications: 2023, 10.3390/mca28010018
XII. Mubashar Arshad., Fahad M. Alharbi., Ali Hassan., Qusain haider., Abdullah Alhushaybari., Sayed M.Eldin., Zubair Ahmed., Laila AAl.Essa and Ahmed M. Galal. ‘Effect of inclined magnetic field on radiative heat and mass transfer in chemically reactive hybrid nanofluid flow due to dual stretching’. Scientific Reports: 2023, 10.1038/s41598-023-34871-9
XIII. N S Yousef., A M Megahed and E Fares. ‘Influence of chemical reaction and variable mass diffusivity on non Newtonian fluid flow due to a rough stretching sheet with magnetic field and Cattaneo-Christov fluxes’, Indian Journal of Phyicss: Vol. 97(8), pp.2475–2483, 2023, 10.1007/s12648-023-02609-y
XIV. Naveed Ahmed., Fitnat Saba., Umar Khan., Syed Tauseef Mohyud –Din, El-Sayed M-Sherif and Ilyas Khan. ‘Nonlinear Thermal Radiation and Chemical Reaction Effects on a (Cu−CuO)/NaAlg Hybrid Nanofluid Flow Past a Stretching Curved Surface’. Processes: Vol. 7, 962, pp.1-24, 2019. 10.3390/pr7120962
XV. Paluru Sreedevi and Patakota Sudarsana Reddy. ‘Heat and mass transfer analysis of MWCNT-kerosene nanofluid flow over a wedge with thermal radiation’. Heat Transfer: Vol. 50(1), pp. 10–33, 2021, 10.1002/htj.21892
XVI. Peri K Kameswarn., S Shaw and Sibanda, P.: ‘Dual solution Casson fluid flow over a stretching or shrinking sheet’. Sadhana: Vol.39, pp.1573-1583, 2014.
XVII. Preeti Jain. ‘Exact Solutions for Chemically Reactive Species in Casson Fluid Past an Exponentially Accelerated Surface with Newtonian Heating in the presence of Thermal radiation’. International Journal of Engineering Research and General Science: Vol. 3(6), pp.521-540, 2015.
XVIII. R. N. Barik., G. C. Dash and P. K. Rath.: ‘Heat and mass transfer on MHD flow through a porous medium over a stretching surface with heat source’. Mathematical Theory and Modeling: Vol.2(7),2012.
XIX. Suriya Uma Devi., S.P. Anjali Devi.: ‘Heat transfer enhancement of Cu -Al2O3/water hybrid nanofluid flow over a stretching sheet’. Journal of Nigerian Mathematical Society: Vol. 36 (2), pp. 419–433, 2017.
XX. Sohail Ahmad., Kashif Ali., Muhammad Rizwan and Muhammad Ashraf.: ‘Heat and mass transfer attributes of copper–aluminum oxide hybrid nanoparticles flow through a porous medium’. Case Studies in Thermal Engineering: Vol. 25, 2021, 10.1016/j.csite.2021.100932.
XXI. Sujit Kumar Khan.: ‘Boundary layer visco-elastic fluid flow over an exponentially stretching sheet’. International Journal of Applied Mechanics and Engineering: Vol. 11(2), pp.321-335.
XXII. Ubaidullah Yashkun., KahairyZaimi., Noor Ashikin Abu Bakar . MHD ‘Hybrid Nanofluid Flow over a permeable stretching/shrinking sheet with thermal radiation effect’, International Journal of Numerical Methods for Heat and Fluid Flow: 10.1108/HFF-02-2020-0083
XXIII. W.A. Khan., I. Pop.: ‘Boundary-layer flow of a nanofluid past a stretching sheet’. International Journal of Heat Mass Transfer: Vol. 53 (11), pp.2477–2483, 2010. 10.1016/j.ijheatmasstransfer.2010.01.032

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Abstract:

We study the NP-hard problem of determining the secure metric dimension of graphs. A resolving set uniquely identifies each vertex by its distance vector to the set; the smallest is the metric basis, and its size is the metric dimension. A set is secure if each outside vertex can replace an inside one while preserving resolvability. Computing this parameter is NP-complete and has applications in routing, image processing, and network verification. This paper determines the secure metric dimension for alternate snake graphs, including k-polygonal, double, and triple alternate triangular snakes

Keywords:

Metric Basis,Metric Dimension,Alternate Snake Graph,

Refference:

I. A. Ahmad, A. N. Koam, M. Azeem, I. Masmali, R. Alharbi. : ‘Edge based metric dimension of various coffee compounds’. PLOS ONE. Vol. 19, No. 4, e0294932, 2024. 10.1371/journal.pone.0294932
II. A. H. Karbasi, R. E. Atani. : ‘Application of dominating sets in wireless sensor networks’. International Journal of Security and Its Applications. Vol. 7, No. 4, pp. 185–202, 2013.
III. B. Mohamed. : ‘A comprehensive survey on the metric dimension problem of graphs and its types’. International Journal of Theoretical and Applied Mechanics. Vol. 9, No. 1, pp. 1–5, 2023. 10.11648/j.ijtam.20230901.11
IV. B. Mohamed, L. Mohaisen, M. Amin. : ‘Binary Archimedes optimization algorithm for computing dominant metric dimension problem’. Intelligent Automation & Soft Computing. Vol. 38, No. 1, pp. 19–34, 2023. 10.32604/iasc.2023.031947
V. B. Mohamed, M. Amin. : ‘Domination number and secure resolving sets in cyclic networks’. Applied and Computational Mathematics. Vol. 12, No. 2, pp. 42–45, 2023. 10.11648/j.acm.20231202.12
VI. B. Mohamed, M. Amin. : ‘Hybridizing slime mould algorithm with simulated annealing for solving metric dimension problem’. Machine Learning Research. Vol. 8, No. 1, pp. 9–16, 2023. 10.11648/j.mlr.20230801.12
VII. C. Zhang, G. Haidar, M. U. I. Khan, F. Yousafzai, K. Hila, A. U. I. Khan. : ‘Constant time calculation of the metric dimension of the join of path graphs’. Symmetry. Vol. 15, No. 3, Article ID 708, 2023. 10.3390/sym15030708
VIII. E. A. Alhenawi, R. A. Khurma, R. Damaševicius, A. G. Hussien. : ‘Solving Traveling Salesman Problem using parallel river formation dynamics optimization algorithm on multi-core architecture using Apache Spark’. International Journal of Computational Intelligence Systems. Vol. 17, No. 1, Article 4, 2024. 10.1007/s44196-023-00385-5
IX. E. Banaian, A. Sen. : ‘A generalization of Markov numbers’. The Ramanujan Journal. Vol. 63, No. 4, pp. 1021–1055, 2024. 10.1007/s11139-023-00801-6
X. E. Hitzer, C. Lavor, D. Hildenbrand. : ‘Current survey of Clifford geometric algebra applications’. Mathematical Methods in the Applied Sciences. Vol. 47, No. 3, pp. 1331–1361, 2024.
XI. F. Harary, R. A. Melter. : ‘On the metric dimension of a graph’. Ars Combinatoria. Vol. 2, pp. 191–195, 1976.
XII. G. Chartrand, L. Eroh, M. A. Johnson, O. R. Oellermann. : ‘Resolvability in graphs and the metric dimension of a graph’. Discrete Applied Mathematics. Vol. 105, Issues 1–3, pp. 99–113, 2000. 10.1016/S0166-218X(00)00198-0
XIII. H. Al-Zoubi, H. Alzaareer, A. Zraiqat, T. Hamadneh, W. Al-Mashaleh. : ‘On ruled surfaces of coordinate finite type’. WSEAS Transactions on Mathematics. Vol. 21, pp. 765–769, 2022. 10.37394/23206.2022.21.87
XIV. H. Subramanian, S. Arasappan. : ‘Secure resolving sets in a graph’. Symmetry. Vol. 10, No. 10, Article 439, 2018. 10.3390/sym10100439
XV. I. M. Batiha, B. Mohamed, I. H. Jebril. : ‘Secure metric dimension of new classes of graphs’. Mathematical Models in Engineering. Vol. 10, No. 3, pp. 1–7, 2024. 10.21595/mme.2024.24168
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Abstract:

The paper specializes in developing a real-time power management device (EMS) to ensure resilient power distribution for medical institution operations by integrating renewable energy sources, vehicle batteries, and stationary energy storage systems. The EMS is designed to seamless transition between regular grid operation and outages with a specific consciousness on prioritizing strength supply to crucial infrastructure at some stage in grid downtimes by way of switching to island operation by utilizing strength saved in automobile batteries and sustainable sources like solar panels and wind turbines (Quiet Revolution QR5 – less noise level, compact and more efficient for sensitive environment). The gadget ensures uninterrupted energy for critical systems which includes the Intensive Care Unit and emergency gadget whilst deprioritizing non-vital loads to optimize aid allocation. Advanced manage algorithms dynamically manage power flows ensuring effective operation even in the face of renewable power variability. With real-time tracking and smart load management, this gadget enhances the resilience and sustainability of strength networks, imparting a reliable and eco-conscious solution for crucial medical institution infrastructure for the duration of outages.

Keywords:

EMS,Energy flexibility,Island operation,Power optimization,Real-time power management,Sustainable energy,Vehicle batteries,

Refference:

I. Baran, Mesut E., and Felix F. Wu. “Network reconfiguration in distribution systems for loss reduction and load balancing.” IEEE Transactions on Power delivery 4.2 (1989): 1401-1407. 10.1109/61.25627
II. Bracco, Stefano, et al. “On the integration of solar PV and storage batteries within a microgrid.” 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe). IEEE, 2019.
III. Fotopoulou, Maria, Dimitrios Rakopoulos, and Stefanos Petridis. “Decision Support System for Emergencies in Microgrids.” Sensors 22.23 (2022): 9457. 10.3390/s22239457
IV. Fotopoulou, Maria, et al. “Assessment of smart grid operation under emergency situations.” Energy 287 (2024): 129661. 10.1016/j.energy.2023.129661
V. Joshi, Aditya, et al. “Survey on AI and machine learning techniques for microgrid energy management systems.” IEEE/CAA Journal of Automatica Sinica 10.7 (2023): 1513-1529. 10.1109/JAS.2023.123657
VI. Kumar, Raushan, N. P. Patidar, and Satyam Patel. “Designing of Microgrid With Different Renewable Energy Sources.” 2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES). IEEE, 2022. 10.1109/PEDES56012.2022.10080577
VII. Lujano-Rojas, Juan M., et al. “Optimum load management strategy for wind/diesel/battery hybrid power systems.” Renewable Energy 44 (2012): 288-295. 10.1016/j.renene.2012.01.097
VIII. Noghreian, Elizabeth, and Hamid Reza Koofigar. “Power control of hybrid energy systems with renewable sources (wind-photovoltaic) using switched systems strategy.” Sustainable Energy, Grids and Networks 21 (2020): 100280. 10.1016/j.segan.2019.100280
IX. Pandya, Margi, Ankur Singh Rana, and Aneesa Farhan. “Energy Management in DC Microgrid Using Machine Learning.” 2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON). IEEE, 2023.
X. Pavić, Ivan, Hrvoje Pandžić, and Tomislav Capuder. “Electric vehicle aggregator as an automatic reserves provider under uncertain balancing energy procurement.” IEEE transactions on power systems 38.1 (2022): 396-410. 10.48550/arXiv.2012.11158
XI. Quijano, Darwin A., et al. “Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators.” IET Generation, Transmission & Distribution 15.2 (2021): 359-370. 10.1049/gtd2.12026
XII. Ramachandran, M., et al. “Microgrid energy optimization and realization by means of plug-in electric vehicles in both v2g-g2v environment.” 2023 8th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2023. 10.1109/ICCES57224.2023.10192878
XIII. Rani, S. Leela, and V. Vijaya Rama Raju. “V2G and G2V technology in micro-grid using bidirectional charger: A review.” 2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T). IEEE, 2022. 10.1109/ICPC2T53885.2022.9777085
XIV. Shakeel, Femina Mohammed, and Om P. Malik. “Vehicle-to-grid technology in a micro-grid using DC fast charging architecture.” 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). IEEE, 2019. 10.1109/CCECE.2019.8861592
XV. Shipman, Rob, et al. “We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network.” Energy 221 (2021): 119813. 10.1016/j.energy.2021.119813
XVI. Wang, Yue, Hao Liang, and Venkata Dinavahi. “Decentralized stochastic programming for optimal vehicle‐to‐grid operation in smart grid with renewable generation.” IET Renewable Power Generation 15.4 (2021): 746-757. 10.1049/rpg2.12064
XVII. Witharama, W. M. N., et al. “Optimal scheduling of a solar-powered microgrid using ML-based solar and load forecasting.” 2023 IEEE World AI IoT Congress (AIIoT). IEEE, 2023. 10.1109/AIIoT58121.2023.10174588

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Abstract:

A system with a single functional unit and one or two cold standby units is studied by developing two Models. In one of the two models, there is a provision for one cold standby unit, whereas in the other, the provision is for two cold standby units. The operative unit, whenever it fails, may either fail due to a major fault or a minor fault, which is revealed by carrying out an inspection. When the operative unit fails, the cold standby becomes operational with an activation time between the operative unit failing and the standby operating state. Reliability and profit analysis have been carried out for both two models. A comparative study has also been made between the two models to decide whether one or two cold standby units should be used for such a system, as far as the profitability aspect is concerned. The models have been examined using the regenerating point method.

Keywords:

Cold Standby unit(s),Comparative Analysis,Major/Minor failure,Operative unit,Regenerative Point Technique,

Refference:

I. A. Kumar, S. Baweja: ‘Cost-benefit analysis of a cold standby system with preventive maintenance subject to the arrival time of the server’. Int J Agriculture Stat Sci, Vol 11, No. 2, (2015), pp 375–380. https://connectjournals.com/file_full_text/2414202H_375-380.pdf
II. A. Manocha, and G. Taneja: ‘Stochastic analysis of a two-unit cold standby system with arbitrary distribution for life, repair, and waiting times’. International Journal of Performability Engineering, Vol11,No.3,(2015),pp293-299. 10.23940/ijpe.15.3.p293.mag
III. A. Roy, and N. Gupta: ‘A study on the utilization of a cold standby component to enhance the mean residual life function of a coherent system’. Communications in Statistics-Theory and Methods, Vol 53,No.19,(2024),pp6977-6996. 10.1080/03610926.2023.2255323
IV. K. Murari, and V. Goyal: ‘Reliability of a system with two types of repair facilities’. Microelectronic Reliability, Vol 23, (1983), pp 1015–1025. 10.1016/0026-2714(85)90400-7
V. L.R. Goel, A. Kumar, and A.K. Rastogi: ‘Stochastic behavior of man-machine systems operating under different weather conditions’. Microelectronic Reliability, Vol 25, (1985), pp 87–91. 10.1016/0026-2714(85)90447-0
VI. M.A.W. Mahmoud, and M.E. Mosherf: ‘On a two-unit cold standby system considering hardware, human error failures, and preventive maintenance’. Mathematical and Computer Modeling, Vol 5, No. (5-6),(2010),pp736-745. 10.1016/j.mcm.2009.10.019
VII. M.S. EL-Sherbeny, M.A.W. Mahmoud, & Z.M. Hussien: ‘Reliability analysis of a two-unit cold standby system with arbitrary distributions and change in units’. Life Cycle Reliable Safe Eng, Vol 9, (2020), pp 261–272. 10.1007/s41872-020-00127-y
VIII. L. Munda, G. Taneja, K. Sachdeva: ‘Reliability and economic analysis of a system comprising three units, i.e., operative, hot standby, and warm standby’. Journal of Mechanics of Continua and Mathematical Sciences Vol 20, No.-1, January (2025), pp 17 – 33. 10.26782/jmcms.2025.01.00002
IX. P. Kumar, & A. Sirohi: ‘Profit analysis of a two-unit cold standby system with the delayed repair of the partially failed unit and better utilization of units’. International Journal of Computer Applications, Vol 117, No. 1, (2015), pp 41-46. 10.5120/20522-2633
X. P.K. Tyagi, and K. Agarwal: ‘Cost-benefit analysis of a two-unit cold standby system with correlated failures and repairs and inspection time’. International Journal of Engineering, Management & Technology (IJEMT), Volume 1, No 5, (2022), pp 9-16. 10.1108/13552519710161544
XI. Parveen, D. Singh, A.K. Taneja: ‘Redundancy Optimization of N+1-Unit Cold Standby System Working with a Single Operative Unit with Activation Time’. International Journal of Engineering Trends and Technology, Vol 71, No. 8, August (2023), pp 458-466. 10.14445/22315381/IJETT-V71I8P239
XII. Parveen, D. Singh, A.K. Taneja: ‘Redundancy Optimization for a System Comprising One Operative Unit and N Warm Standby Units with Switching Time’. International Journal of Agricultural & Statistical Sciences, Vol 19, (2023), pp 1339-1350. 10.59467/IJASS.2023.19.1339
XIII. Parveen, D. Singh, A.K. Taneja: ‘Redundancy Optimization for a System Comprising One Operative Unit and N Hot Standby Units’.Reliability: Theory & Applications, Vol 18, No. 4, (2023), pp 547-562. https://www.gnedenko.net/Journal/2023/042023/RTA_4_2023-46.pdf
XIV. R. Singh and R. K. Bhardwaj: ‘Steady-state performance of a cold standby system with conditional server replacement’. Journal of Statistics Applications & Probability, Vol 3, (2021), pp 759-766. 10.18576/jsap/100314
XV. S. Batra and G. Taneja: ‘Reliability and Optimum Analysis for Number of Standby Units in a System Working with One Operative Unit’. International Journal of Applied Engineering Research, Vol 13, No.5, (2018),pp2791-2797. https://www.ripublication.com/ijaer18/ijaerv13n5_93.pdf
XVI. S. Batra, and G. Taneja: ‘Optimization of the Number of Hot Standby Units through Reliability Models for a System Operative with One Unit’. International Journal of Agricultural and Statistical Sciences, Vol 14, No. 1, (2018), pp 365-370. https://connectjournals.com/file_html_pdf/2838401H_365-370a.pdf
XVII. S. Batra and G. Taneja: A ‘Reliability Model for the Optimum Number of Standby Units in a System Working with Two Operative Units’. Ciencia e Tecnica Vitivinicola, Vol 33, No. 8, (2018), pp 20-49. https://www.academia.edu/download/79254481/ijaerv13n5_93.pdf
XVIII. S.K. Singh, and A.K. Mishra: ‘Profit evaluation of a two-unit cold standby redundant system with two operating systems’. Microelectronic Reliability, Vol 34, No. 4, (1994), pp 747–750. 10.1016/0026-2714(94)90040-X
XIX. S.K. Singh, B. Srinivasu: ‘Stochastic analysis of a two-unit cold standby system with preparation time for repair’. Microelectronics Reliability, Volume 27, No. 1, (1987), pp 55-60. 10.1016/0026-2714(87)90620-2
XX. V. Singh, P. Poonia: ‘Probabilistic assessment of two-unit parallel system with correlated lifetime under inspection using regenerative point technique’. Int J Reliabil Risk Safety Theory Appl, Vol 2, No. 1, (2019) pp 5–14. 10.30699/IJRRS.2.1.2

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Abstract:

Software Reliability Growth Models (SRGMs) are essential for assessing software dependability. The reliability evaluation process involves two key steps: model building and variable estimation, with this study focusing on the latter. Traditional methods like Least Squares Estimation (LSE) and Maximum Likelihood Estimation (MLE) were widely used for parameter estimation. However, these methods have limitations, increasing interest in metaheuristic optimization techniques. Metaheuristics overcome traditional drawbacks by employing strategies such as search field exploration and neighbourhood exclusion. This study evaluates four metaheuristic methods for SRGM variable estimation: Gravitational Search Algorithm (GSA), Sine-Cosine Algorithm (SCA), Grey-Wolf Optimizer (GWO), and Regenerative Genetic Algorithm (RGA). These methods were tested on three real loss datasets generated by four well-known SRGMs. The estimated variables using metaheuristic approaches closely align with those derived from LSE, demonstrating their accuracy. Results showed that RGA and GWO outperformed other techniques, offering superior parameter estimation capabilities. Additionally, RGA and GWO showed better integration and R2 dispersion values, making them more effective for practical failure data analysis. This research highlights the potential of RGA and GWO as reliable tools for SRGM parameter estimation, indicating their suitability for handling complex optimization challenges in software reliability studies.

Keywords:

Software reliability growth models,gravitational search algorithm,least squares estimation,maximum likelihood estimation,regenerative genetic algorithm,

Refference:

I. Aggarwal, K. K., et al. “Software Maintenance Effort Prediction Using Genetic Algorithm.” ACM SIGSOFT Software Engineering Notes, vol. 30, no. 2, 2005, pp. 1–7.
II. Aljahdali, H. M., and A. F. Sheta. “Software Reliability Prediction Using Multi-Gene Symbolic Regression Genetic Programming.” 2011 International Conference on Innovations in Information Technology, IEEE, 2011, pp. 104–109.

III. Arora, A., and G. Sikka. “Software Reliability Prediction Using Fuzzy Logic: Modeling and Performance Analysis.” International Journal of Computer Applications, vol. 75, no. 6, 2013, pp. 27–31.

IV. Arunachalam, V. “Software Reliability Models: Assumptions, Limitations and Applicability.” Indian Institute of Management Bangalore Research Paper, no. 207, 2002.

V. Capretz, L. F. “Implications of Software Testing Strategies in Software Reliability Engineering.” International Journal of Computer Applications in Technology, vol. 21, no. 1–2, 2004, pp. 40–48.

VI. Gokhale, S. S. “Architecture-Based Software Reliability Analysis: Overview and Limitations.” IEEE Transactions on Dependable and Secure Computing, vol. 4, no. 1, 2007, pp. 32–40.

VII. Gokhale, S. S., et al. “Important Milestones in Software Reliability Modeling.” Software Engineering Conference Proceedings, 1998, pp. 225–236.
VIII. Gokhale, S. S. “Software Reliability: Models and Applications.” IEEE Software, vol. 22, no. 3, 2005, pp. 75–77.

IX. Goel, A. L. “Software Reliability Models: Assumptions, Limitations, and Applicability.” IEEE Transactions on Software Engineering, vol. SE-11, no. 12, 1985, pp. 1411–1423.

X. Kapur, P. K., et al. Contributions to Hardware and Software Reliability. World Scientific, 1999.

XI. Kapur, P. K., et al. Software Reliability Assessment with OR Applications. Springer, 2011. DOI: 978-0-85729-204-9_1.

XII. Kapur, P. K., and S. M. Younes. “An NHPP Based Software Reliability Growth Model for Open Source Software Using Testing Effort and Change-Point.” Proceedings of the World Congress on Engineering, vol. 1, 2009.

XIII. Kapur, P. K., P. Goyal, and S. M. Younes. “A Unified Approach for Developing Software Reliability Growth Models in the Presence of Imperfect Debugging and Error Generation.” International Journal of Systems Assurance Engineering and Management, vol. 1, 2010, pp. 35–48.

XIV. Kapur, P. K., and R. B. Garg. “A Software Reliability Growth Model for an Error Removal Phenomenon.” Software Engineering Journal, vol. 7, no. 4, 1992, pp. 291–293. DOI: 10.1049/sej.1992.0030.

XV. Kumar, D., and M. Yadav. “Software Reliability Prediction Using Artificial Neural Networks and Fuzzy Logic.” Journal of Engineering Research and Applications, vol. 7, no. 2, 2017, pp. 50–55.

XVI. Lyu, M. R., editor. Handbook of Software Reliability Engineering. McGraw-Hill, 1996.

XVII. Malhotra, R., and A. Jain. “Software Reliability Prediction Using Neural Network Ensemble.” International Journal of Computer Applications, vol. 25, no. 6, 2011, pp. 8–13.

XVIII. Malhotra, R. Empirical Research in Software Engineering: Concepts, Analysis, and Applications. CRC Press, 2015.
XIX. Musa, J. D. Software Reliability Engineering: More Reliable Software, Faster Development, and Lower Cost. McGraw-Hill, 1987.

XX. Musa, J. D. “Software Reliability Engineering: More Reliable Software, Faster Development, and Cheaper Development.” IEEE Software, vol. 22, no. 3, 2005, pp. 56–63.

XXI. Pham, H. System Software Reliability. Springer Science & Business Media, 2006.

XXII. Pham, H. Software Reliability. Springer Science & Business Media, 2007.

XXIII. Shooman, M. L. Software Engineering: Design, Reliability, and Management. McGraw-Hill, 1983.

XXIV. Shooman, M. L. Reliability of Computer Systems and Networks: Fault Tolerance, Analysis, and Design. Wiley, 2003.

XXV. Sheta, A. F. “Reliability Prediction of Software Using Genetic Programming.” 2006 IEEE International Symposium on Industrial Electronics, vol. 4, 2006, pp. 3257–3261.

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Abstract:

Cholera remains a significant public health concern globally, offering an opportunity to construct robust transmission models that elucidate its dynamics and guide intervention strategies. In this study, we develop a refined cholera transmission model that accounts for time-dependent recovery rates and persistent environmental reservoirs, extending beyond the assumptions of traditional SIR-type frameworks. The model segments the population into compartments—susceptible, infected, and statistical storage of relevant variables—allowing for dynamic epidemic progression under specified parameter values. Historical data on cholera cases, fatalities, and case-fatality rates spanning multiple years underwent rigorous preprocessing, including linear interpolation, to ensure robustness. We employed a least-squares curve-fitting approach to estimate key parameters, which optimizes model accuracy and allows simulation of disease progression and intervention effectiveness over time. Results from our model yield critical insights into cholera transmission, including the roles of environmental bacterial reservoirs and drug treatments in moderating infection rates. These estimated parameters provide policymakers with actionable data for designing targeted interventions, enhancing public health responses, and mitigating cholera's impact on vulnerable populations. This work emphasizes the value of mathematical modeling as a tool for understanding infectious disease dynamics and developing strategies to reduce epidemic impacts.

Keywords:

Cholera,Curve Fitting,Numerical Simulation,Seasonal Outbreak,Transmission dynamics,

Refference:

I. Al-Tawfiq, J. A., Chopra, H., Dhama, K., Sah, R., Schlagenhauf, P., & Memish, Z. A. (2022). The Cholera Challenge: How Should the World Respond? New Microbes and New Infections, 51, 101077. 10.1016/j.nmni.2022.101077
II. Ayoade, A. A., Ibrahim MO, Peter OJ, and F. A. Oguntolu. “A mathematical model on cholera dynamics with prevention and control.” Covenant Journal of Physical and Life Sciences (2018). https://journals.covenantuniversity.edu.ng/index.php/cjpls/article/view/933
III. Brhane, Kewani Welay, et al. “Mathematical modelling of cholera dynamics with intrinsic growth considering constant interventions.” Scientific Reports 14.1 (2024): 4616. 10.1038/s41598-024-55240-0

IV. Cirri, Emilio, and Georg Pohnert. “Algae-bacteria interactions that balance the planktonic microbiome.” New Phytologist 223.1 (2019): 100-106. 10.1111/nph.15765
V. Emmanuel, Sabastine, et al. “Population Growth Forecasting Using the Verhulst Logistic Model and Numerical Techniques.” Intelligent Systems Modeling and Simulation III: Artificial Intelligent, Machine Learning, Intelligent Functions and Cyber Security. Cham: Springer Nature Switzerland, 2024. 191-202. https://link.springer.com/chapter/10.1007/978-3-031-67317-7_12.
VI. Ezeagu, Nneamaka Judith, Houénafa Alain Togbenon, and Edwin Moyo. “Modelling and analysis of cholera dynamics with vaccination.” American Journal of Applied Mathematics and Statistics 7.1 (2019): 1-8. 10.12691/ajams-7-1-1
VII. Ghosh, Ahona, Sandip Roy, Haraprasad Mondal, Suparna Biswas, and Rajesh Bose. “Mathematical modelling for decision making of lockdown during COVID-19.” Applied Intelligence 52.1 (2022): 699-715. 10.1007/s10489-021-02463-7
VIII. Hailemariam Hntsa, Kinfe, and Berhe Nerea Kahsay. “Analysis of cholera epidemic control using mathematical modelling.” International Journal of Mathematics and Mathematical Sciences 2020.1 (2020): 7369204. 10.1155/2020/7369204
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Abstract:

The prime reason for human sustainability is Agriculture. With the frequent advances in technology, researchers should not forget the root and focus on improving the agriculture sector as well. A foremost challenge in the industry of agriculture is the detection of diseases in plants and its diagnosis which has gained significant attention over the past few years. Plant diseases have significantly degraded the overall food production. This is adversely affecting both the quantity and quality of products of agricultural. In this paper, several deep learning (DL) models are proposed to recognize the multiple classes of diseases present in plants from the images of leaves taken under various resolutions and different environmental conditions. Employing a Deep Convolutional Neural Network (CNN) in multi-class classification for detecting plant diseases can be beneficial in the early identification of these diseases and also in dealing with the negative impact of these diseases on agriculture. In the proposed method, five deep CNN models such as Sequential, ResNet50, InceptionV3, VGG16, and VGG19 are used. Comparative analysis of the implemented models suggested that DL helps in extracting the significant features and biomarkers related to these diseases. Based on the testing results, the VGG16 model beats other architectures in terms of training accuracy of 97.73% with validation accuracy of 88.82%.

Keywords:

Convolutional Neural Networks,Deep Learning,Plant Disease,Machine Learning,Transfer Learning,

Refference:

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