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Abstract:

This paper focuses on the study of the algebraic theoretic properties and relationships within each stage of the cement production processes viewed as sub-states of a designed finite automata scheme as an extension of the compact and detailed models. It was discovered that from the initial stage to the final stage of the cement production process, each stage can have a finite automata scheme and a transition table that gives rise to a symmetrical matrix representation with the upper diagonal having distinct transition entries while the lower diagonal entries are zero. The diagonal non-zero entries represent activity scores (penalties), which can be used to specify the entire movement from one state to another in the extended models.

Keywords:

Cement,Deterministic Finite automata,Compact model,Detailed model,Extended model,Transition table,

Refference:

I. Adelana S. M. A., Olasehinde P. I. and Vrbka P. (2003), : “Isotope and Geochemical Characterization of Surface and Subsurface Waters in the Semi-Arid Sokoto Basin, Nigeria.” African Journal of Science and Technology (AJST), Science and Engineering Series Vol. 4 No. 2 pp. 80-89.

II. Kim N., Shin D, Wysk R. A. and Rothrock L. (2010) : “Using Finite State Automata for Formal Modeling of affordances in Human-Machine Cooperative Manufacturing System.” International Journal of Production Research, vol. 48, No. 5 Pg. 1303 – 1320. 10.1080/00207540802582235

III. Lawson M. V. (2005) : Lecture Note, Department of Mathematics, School of Mathematic and Computer Science Hderiott Watt University.

IV. Lea F.M. (1970). The Chemistry of Cement and Concrete (3rd edition); Edward Arnold Publishers Ltd.

V. O’ Castillo O. L. and Tapia C. G. (2009). An Environmental and Production Policy Application of Multi-objective Mathematical Programming for Cement Manufacturing in the Philippines. www.math.upd.edu.ph.

VI. Smith P., Esta C., Jha S. and Kong S. (2008). Deflating the Big Bang: Fast and Scalable Deep Packet Inspection with Extended Finite Automata, Seattle, Washington, USA.

VII. Robert L. Constable, (1980) : “The Role of Finite Automata in the Development of Modern Computing Theory. Studies in Logic and the Foundations of Mathematics.” Elsevier, Volume 101, Pages 61-83, ISSN 0049-237X, ISBN 9780444853455. 10.1016/S0049-237X(08)71253-9

VIII. Yalcin A., Tai T. and Boucher T. O. (2004) : “Deadlock Avoidance in Automated Manufacturing Systems Using Finite Automata and State Space Search.” www.researchgate.net

IX. Zaid, I., Ibrahim, A. A., and Garba, I. A. (2014a). : “Modeling of Sokoto cement production process using a finite automata scheme: An analysis of the detailed model.” International Journal of Computational Engineering Research (IJCER), 4(5):2250 – 3005.

X. Zaid, I., Ibrahim, A. A., Garba, I. A., and Sahabi, D. M. (2014b). : “Modeling of Sokoto Cement Production Process Using Finite Automata: Compact Model.” International Journal of Scientific Research (IOSR)–Journal of Applied Physics, 6(3):21 – 22

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Abstract:

In the present article, we have developed a method of determining the activation energy of thermoluminescence (TL) peaks recorded under a hyperbolic heating scheme. Usual methods of determination of activation energy of TL peaks require prior knowledge of the order of kinetics. However, the determination of the order of kinetics is not straightforward. In view of this, we have proposed a method of determination of the activation energy of a TL peak recorded under a hyperbolic heating scheme. The method does not require prior knowledge of the order of kinetics. The suitability of the present method has been assessed by applying it both to numerically computed and experimental TL peaks.

Keywords:

Thermoluminescence,Hyperbolic heating scheme,Kinetics,Activation energy,

Refference:

I. C. Christdoulides, J. Phys, D: Appl. Phys, 18, 1501 (1985).
II. D.C. Sanyal and K. Das. : “A text book of Numerical Analysis” (U.N. Dhar, Kolkata, 2013).
III. Flemming R.J., Can J Phys, 46,1509 (1968).
IV. Kelly P. J. and Laubitz M.J. Can J Phys, 45,311 (1967).
V. M.R. Spigel and L. J. Stephens, : “Theory and problems of statistics”, Third edition (Tata McGrowhill publishing company, New Delhi, 2007).
VI. R. Chen and S. W. S. Mckeevar S. W. S. : “Theory of Thermoluminescence and related phenomena”, World Scientific, Singapore (1997).
VII. R. Chen and V. Pagonis. : “Thermally stimulated Luminescence, A simulation approach”. Wiley and Sons LTD, Chichester, U.K. (2011).
VIII. R. Chen and Y. Krish. : “Analysis of Thermally Stimulated Process”, Paragon, Oxford (1981).
IX R. Chen, J. Electrochem. Soc, 116, 1254 (1969).
X Stammer K., J Physics E, 12, 637 (1979).
XI. W. Arnold and H. Sherwood; J-PhysChem. 63, 2(1959).
XII. W. M. Ziniker, J.M. Rusin, T.G. Stoebe, J. Meterial Sc, 8,407 (1973).

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Abstract:

The human brain is the major controller of the humanoid system [1]. The abnormal growth and division of cells in the brain lead to a brain tumor, and the further growth of brain tumors leads to brain cancer. In the area of human health, Computer Vision plays a significant role, which reduces the human judgment that gives accurate results. CT scans, X-rays, and MRI scans are the common imaging methods among magnetic resonance imaging (MRI) that are the most reliable and secure. MRI detects every minute of objects. Our project aims to focus on the use of different techniques for the discovery of brain cancer using brain MRI. In this study, we performed pre-processing using the bilateral filter (BF) for the removal of the noises that are present in an MR image. This was followed by the binary thresholding and Convolution Neural Network (CNN) segmentation techniques for reliable detection of the tumor region [2]. Training, testing, and validation datasets are used. Based on our machine, we will predict whether the subject has a brain tumor or not. The resultant outcomes will be examined through various performance metrics that include accuracy, sensitivity, and specificity. It is desired that the proposed work would exhibit a more exceptional performance over its counterparts.

Keywords:

Brain tumor detection,CNN system,Tumor detection system,Image Segmentation,

Refference:

I. A. Sivaramakrishnan And Dr. M. Karnan. : “A Novel Based Approach For Extraction Of Brain Tumor In MRI Images Using Soft Computing Techniques.” International Journal Of Advanced Research In Computer And Communication Engineering, Vol. 2, Issue 4, April 2013.
II. Asra Aslam, Ekram Khan, M.M. Sufyan Beg, : “Improved Edge Detection Algorithm for Brain Tumor Segmentation.” Procedia Computer Science, Volume 58, 2015, Pp 430-437, ISSN 1877-0509. 10.1016/j.procs.2015.08.057
III. B. Sathya and R. Manavalan : “Image Segmentation by Clustering Methods: Performance Analysis.” International Journal of Computer Applications (0975 – 8887) Volume 29– No.11, September 2011. 10.5120/3688-5127
IV. Seba Maity. “IMAGE WATERMARKING ON DEGRADED COMPRESSED SENSING MEASUREMENTS”. J. Mech. Cont. & Math. Sci., Vol.-18, No.-04, April (2023) pp 10-22. 10.26782/jmcms.2023.04.00002

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