Archive

Journal > Journal > Page 111

ECG HEARTBEAT CLASSIFICATION USING WAVELET PACKET ENTROPY AND RANDOM FOREST

Authors:

Seba Maity, Soumyadeep Jana

DOI NO:

https://doi.org/10.26782/jmcms.2023.08.00002

August 30, 2023

Abstract:

ECG or electrocardiogram is an electrical signal which is generated by our heart. It is the cardiac electrical activity that provides important information about heart conditions [2]. ECG is very popular to identify heart illnesses like arrhythmia, chest pain, heart abnormalities, measuring heart rate, etc. In the past, till now ECG is the primary technique to detect heart illness in medical. ECG is a non-invasive technique. A survey World Health Organization says that heart diseases are the main reason for most deaths worldwide. In most cardiovascular diseases, arrhythmia is the most common. For this ECG is very much famous in medical studies. The study of an individual ECG beat can provide meaningfully correlated clinical information for the automatic ECG recognition of an ECG signal but it is difficult to investigate more ECG signals of different patients because of their different physical conditions. So here the main problem to investigating an ECG signal is that it can be different in every person. Suppose two different types of diseases have the same type of properties in an ECG signal. Even sometimes different patients have the same type of ECG pattern graph. These are the main difficulties in diagnosing an ECG signal. Many methods of feature extraction and classification have been proposed but some of the techniques  remain to be improved. In this paper first of all we make our database with the help of the MIT-BIH database. After preprocessing and segmentation we decompose the signal by wavelet packet decomposition. Then calculate the entropy from the decomposed coefficients and extract the features.

Keywords:

ECG Beat classification,RF-based classifier,wavelet packet entropy,feature extraction,MIT-BIH,

Refference:

I. Allam, J. P., Samantray, S., & Ari, S. (2020). SpEC: A system for patient specific ECG beat classification using deep residual network. Biocybernetics and Biomedical Engineering, 40(4), 1446-1457.
II. Das, M. K., & Ari, S. (2014). ECG beats classification using mixture of features. International scholarly research notices, 2014.
III. Lin, C. H. (2008). Frequency-domain features for ECG beat discrimination using grey relational analysis- based classifier. Computers & Mathematics with Applications, 55(4), 680-690.
IV. Luz, E. J. D. S., Schwartz, W. R., Cámara-Chávez, G., & Menotti, D. (2016). ECG-based heartbeat classification for arrhythmia detection: A survey. Computer methods and programs in biomedicine, 127, 144-164.
V. Li, T., & Zhou, M. (2016). ECG classification using wavelet packet entropy and random forests. Entropy, 18(8), 285.
VI. Qin, Q., Li, J., Zhang, L., Yue, Y., & Liu, C. (2017). Combining low-dimensional wavelet features and support vector machine for arrhythmia beat classification. Scientific reports, 7(1), 1-12.
VII. Seba Maity. : IMAGE WATERMARKING ON DEGRADED COMPRESSED SENSING MEASUREMENTS. J. Mech. Cont. & Math. Sci., Vol.-18, No.-04, April (2023) pp 10-22
VIII. Thomas, M., Das, M. K., & Ari, S. (2015). Automatic ECG arrhythmia classification using dual tree complex wavelet based features. AEU-International Journal of Electronics and Communications, 69(4), 715-721.

View Download

Journal Vol – 18 No -8, August 2023

YOLO (YOU ONLY LOOK ONCE) ALGORITHM-BASED AUTOMATIC WASTE CLASSIFICATION SYSTEM

Authors:

Seba Maity, Tania Chakraborty, Ratnesh Pandey, Hritam Sarkar

DOI NO:

https://doi.org/10.26782/jmcms.2023.08.00003

August 30, 2023

Abstract:

Our paper presents the design and implementation of an automated waste management system that utilizes the You Only Look Once (YOLO) algorithm and computer vision techniques for efficient waste sorting. The escalating global concern regarding waste management necessitates the development of automated systems to address the challenges associated with waste sorting. By leveraging YOLO's object detection capabilities and the power of computer vision, our system accurately identifies and classifies various types of waste in real time. The YOLO algorithm's efficiency and speed enable the swift processing of waste items, facilitating efficient sorting into predefined placements. This automated system not only improves accuracy but also reduces health risks for workers and minimizes environmental harm. Complemented by public awareness campaigns promoting proper waste separation and recycling practices, our research contributes to advancing waste management technologies and fostering sustainable practices for a healthier environment.

Keywords:

Waste management automated system,YOLO algorithm,Computer vision,Image processing, keras,Tensorflow,Dataset,Arduino UNO,Servo motor,

Refference:

I. Abdul Vahab, Maruti S Naik, Prasanna G Raikar an Prasad S R4,
“Applications of Object Detection System”, International Research Journal of Engineering and Technology (IRJET)
II. Akar, Mehmet, and Ismail Temiz. “Motion controller design for the speed
control of dc servo motor.” International Journal of Applied Mathematics and Informatics 1.4 (2007): 131-137.
III. Aacha Gautam, Anjana Kumari, Pankaj Singh: “The Concept of Object
Recognition”, International Journal of Advanced Research in Computer
Scienceand Software Engineering, Volume 5, Issue 3, March 2015
IV. Banzi, Massimo, and Michael Shiloh. Getting started with Arduino. Maker
Media, Inc., 2022.
V. D. Hoornweg and P. Bhada-Tata, “A Global Review of Solid Waste Management,” (2012) 1-116.
VI. Geethapriya S, N. Duraimurugan, S.P. Chokkalingam, “Real-Time Object Detection with Yolo”, International Journal of Engineering and Advanced Technology (IJEAT)
VII. Hammad Naeem, Jawad Ahmad and Muhammad Tayyab, “Real-Time Object Detection and Tracking”, IEEE
VIII. https://img.livestrong.com/630x/photos.demandstudios.com/getty/article/ 228/54/178229012.jpg

IX. https://imageds.wisegeek.com/black-webcam.jpg
X. Real-Time Object Detection”, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779- 788
XI. Meera M K, & Shajee Mohan B S. 2016, “Object recognition in images”, International Conference on Information 60 Science (ICIS).
XII. Niehs.nih.gov, “Cancer and the Environment,” 46, 2018. [Online].
XIII. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, J. Santamaría, Mohammed A. Fadhel, Muthana Al-Amidie & Laith Farhan Journal of Big Data volume 8, Article number: 53 (2021)
XIV. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, J. Santamaría, Mohammed A. Fadhel, Muthana Al-Amidie & Laith Farhan Journal of Big Data volume 8, Article number: 53 (2021)
XV. V. Gajjar, A. Gurnani and Y. Khandhediya, “Human Detection and Tracking for Video Surveillance: A Cognitive Science Approach,” in 2017 IEEE International Conference on Computer Vision Workshops, 2017
XVI. You Only Look Once: Unified, Real-Time Object Detection. Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi,

View Download

Journal Vol – 18 No -8, August 2023

IDENTIFYING FRAUD IN ONLINE TRANSACTIONS

Authors:

Sneha Sen, Megha Adhikari, Dilip Kumar Gayen

DOI NO:

https://doi.org/10.26782/jmcms.2023.09.00001

September 17, 2023

Abstract:

 Fraudulent credit card transactions must be when customers are charged for items that they did not purchase. Such problems can be tackled with Data Science and its importance, along with Machine Learning, cannot be overstated. This project intends to illustrate the modelling of a data set using machine learning with  Identifying Fraud in Online Transactions. The Identifying Fraud in Online Transactions problem includes modelling past credit card transactions with the data of the ones that turned out to be a fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 99.99% of the fraudulent transactions while minimizing the incorrect fraud classifications. Identifying Fraud in Online Transactions is a typical sample of classification. In this process, we have focused on analyzing and pre-processing  data sets by using a Random Forest Algorithm.

Keywords:

Frauds Classification,Online Transactions,credit card transactions,

Refference:

I. A. A. Taha and S. J. Malebary, : “An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine”, IEEE Access, 8, 25579–25587, 2020.
II. D. Varmedja, M. Karanovic, S. Sladojevic, M. Arsenovic and A. Anderla, : “Credit Card Fraud Detection – Machine Learning methods”, 18th International Symposium INFOTEH-JAHORINA (INFOTEH), East Sarajevo, Bosnia and Herzegovina, 1–5, 2019.
III. J. I-Z. Chen, K.-L. Lai, : “Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert”, Journal of Artificial Intelligence and Capsule Networks, 03(02), 101–112, 2021.
IV. Mehria Nawaz, Twinkle Agarwal, Dilip Kumar Gayen. : : ‘ONLINE SKILL TEST PLATFORM’. J. Mech. Cont. & Math. Sci., Vol.-17, No.-11, November (2022) pp 46-53. 10.26782/jmcms.2022.11.00003
V. R. B. Sulaiman, V. Schetinin, P. Sant, : “Review of Machine Learning Approach on Credit Card Fraud Detection”, Human-Centric Intelligent Systems. volume 2, 55–68, 2022.
VI. Q. Han, C. Gui, J. Xu, G. Lacidogna, : “A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm”, Construction and Building Materials. 226, 734-742, 2019.
VII. Y. Chen, W. Zheng, W. Li, Y. Huang, : “Large group activity security risk assessment and risk early warning based on random forest algorithm”. Pattern Recognition Letters, 144, 1–5, 2021.

View Download

Journal Vol – 18 No -9, September 2023

OSCILLATION OF HYDROLOGICAL PARAMETERS IN SHRIMP PONDS WITHIN MANGROVE-DOMINATED INDIAN SUNDARBANS

Authors:

Suvadeep Samanta, Prosenjit Pramanick, Sufia Zaman, Abhijit Mitra

DOI NO:

https://doi.org/10.26782/jmcms.2023.09.00002

September 17, 2023

Abstract:

The survival rate of Penaeus monodon was monitored for a decade (2010-2019) in two shrimp culture ponds at Chemaguri located at Sagar Island in the Indian Sundarbans delta complex. The two ponds exhibited significant variations in terms of the survival rate of the cultured species, which is attributed to variations in nitrate, phosphate, and dissolved oxygen. The root cause of such difference is related to variation in stocking density of the cultured species (10 PL20/m2 in pond 1 and 25 PL20/m2 in pond 2) which resulted in the generation of nutrients (except silicate) and alteration of Dissolved Oxygen (DO). Optimization of stocking density and introduction of a biotreatment pond may restore and ecologically balance the situation in the shrimp culture sector of the Sundarban region.

Keywords:

Penaeus monodon,Indian Sundarbans,survival rate,dissolved oxygen,dissolved nutrients,shrimp culture,

Refference:

I. Anh, P.T., Kroeze, C., Bush, S.R., Mol, A.P.J., : “Water pollution by intensive brackish shrimp farming in south-east Vietnam: Causes and options for control”. Agricultural Water Management, Vol. 97, No. 6, 872-882, 2010.
II. https://www.doctorshrimp.com/topics_uri/india-is-black-really-going-to-be-back/
III. https://www.indiacensus.net/states/west-bengal

IV. Mitra, A., : “Impact of COVID-19 Lockdown on Environmental Health : Exploring the Situation of the Lower Gangetic Delta”. Springer eBook. ISBN 978-3-031-27242-4, XIII, pp.310, 2023. 10.1007/978-3-031-27242-4

V. Mitra, A., : “Mangrove Forests in India: Exploring Ecosystem Services”. Springer, e-Book ISBN 978-3-030-20595-9 XV, pp. 361, 2020. 10.1007/978-3-030-20595-9

VI. Mitra, A., : “Sensitivity of mangrove ecosystem to changing climate”. Springer, New Delhi, Heidelberg, New York, Dordrecht, London. ISBN-10: 8132215087; ISBN-13. 2013. 97810.1007/978-81-322-1509-7

VII. Mitra, A., : “Status of coastal pollution in West Bengal with special reference to heavy metals”. Journal of Indian Ocean Studies, Vol. 5, No. 2, 135-138, 1998.

VIII. Mitra, A., Banerjee, K., Chakraborty, R., Banerjee, A., Mehta, N., Berg, H., : “Study on the water quality of the shrimp culture ponds in Indian Sundarbans”. Indian Science Cruiser, Vol. 20, No. 1, 34-43, 2006.

IX. Mitra, A., Bhattacharyya, D.P., : “Environmental issues of shrimp farming in mangrove ecosystem”. Journal of Indian Ocean Studies, Vol. 11, No. 1, 120-129, 2003.

X. Mitra, A., Zaman, S., : “Estuarine Acidification: Exploring the Situation of Mangrove Dominated Indian Sundarban Estuaries”. Springer eBook. ISBN 978-3-030-84792-0, XII, pp.402, 2021. 10.1007/978-3-030-84792-0

XI. Mitra, A., Zaman, S., Pramanick, P., : “Blue Economy in Indian Sundarbans: ExploringLivelihood Opportunities”. Springer . ISBN 978-3-031-07908-5 (e-Book), XIV, pp. 403, 2022. 10.1007/978-3-031-07908-5

XII. Mitra, A., Zaman, S.,Pramanick, P., : “Climate Resilient Innovative Livelihoods in Indian Sundarban Delta: Scopes and Challenges”. Springer, 2023 (In press).

XIII. M. N. Sarker, Shampa Mitra, Prosenjit Pramanick, Sufia Zaman, Abhijit Mitra, : “MANGROVE ASSOCIATE BASED SHRIMP FEED: AN INNOVATION IN THE AQUACULTURE SECTOR”. J. Mech. Cont. & Math. Sci., Vol.-16, No.-11, November (2021) pp 43-61. 10.26782/jmcms.2021.11.00005

XIV. Strickland, J.D.H., Parsons, T. R., : “A practical handbook of seawater analysis. 2nd (Ed.)”. Journal of the Fisheries Research Board of Canada, 167, 1-310, 1972.

XV. Winkler, L.W., : “Die Bestimmung des in Wasser gelöstenSauerstoffen, Berichte der Deutschen Chemischen Gesellschaft”. 21, 2843-2855, 1998.

XVI. Yang, W., Zheng, C., Zheng, Z., Wei, Y., Lu, K., Zhu, J. : “Nutrient enrichment during shrimp cultivation alters bacterioplankton assemblies and destroys community stability”. Ecotoxicology and Environmental Safety, Vol. 30, No. 156, 366-374, 2018. 10.1016/j.ecoenv.2018.03.043.

View Download

Journal Vol – 18 No -9, September 2023

A NEW CONCEPT TO PROVE, √(- 1)= -1 IN BOTH GEOMETRIC AND ALGEBRAIC METHODS WITHOUT USING THE CONCEPT OF IMAGINARY NUMBERS

Authors:

Prabir Chandra Bhattacharyya

DOI NO:

https://doi.org/10.26782/jmcms.2023.09.00003

September 17, 2023

Abstract:

A particular branch of mathematics is coordinate geometry where geometry is studied with the help of algebra. According to the new concept of three types of Rectangular Bhattacharyya’s coordinate systems, plane coordinate geometry consists of four axes. In type – I, Rectangular Bhattacharyya’s coordinate system, the four axes are all neutral straight lines having no direction; in the type – II coordinate system the four axes are all count up straight lines and, in the type – III coordinate system all the axes are countdown straight lines. The author has considered all four axes to be positive in type II and type III coordinate systems. Ultimately, the author has established relations among the three types of coordinate systems and used the extended form of Pythagoras Theorem to prove √(- 1)= -1. In this paper, algebra is studied with the help of geometry. The equation, x2 + 1 = 0, means x2 = – 1 and therefore, the value of √(- 1)= -1, has been proved by the author with the help of geometry by using the new concept of the three types of coordinate systems without using the concept of the imaginary axis. Also, the author has given an alternative method of proof of √(- 1)= -1 algebraically by using the concept of the theory of dynamics numbers. The square root of any negative number can be determined in a similar way. This is the basic significance of that paper. This significance can be widely used in Mathematics, Science, and Technology and also, in Artificial Intelligence (AI), and Crypto-system

Keywords:

Cartesian Coordinate System,Dynamics of Numbers,Extended form of Pythagoras Theorem,Imaginary Number,Quadratic Equation,Three Types of Rectangular Bhattacharyya’s coordinate systems,

Refference:

I. Ahmad Raza, : “Research Work of Mathematics in Algebra. New Inventory Work in Quadratic Equation (P.E. Degree 2), Cubic Equation (P.E. Degree 3) & Reducible Equation General Term”. European Journal of Mathematics and Statistics. pp. 310-315. Vol 4, Issue 1, January 2023. 10.24018/ejmath.2023.4.1.159
II. Ashwannie Harripersaud, : “The Quadratic Equation Concept”. American Journal of Mathematics and Statistics. 2021; 11(3): 67-71. 10.5923/j.ajms.20211103.03
III. B. B. Dutta, (1929). : “The Bhakshali Mathematics”. Calcutta, West Bengal: Bulletin of the Calcutta Mathematical Society.
IV. B. B. Datta, & A. N. Singh, (1938). : “History of Hindu Mathematics, A source book”. Mumbai, Maharashtra: Asia Publishing House.
V. 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.
VI. H. Lee Price, Frank R. Bernhart, : “Pythagorean Triples and a New Pythagorean Theorem”. arXiv:math/0701554 [math.HO]. 10.48550/arXiv.math/0701554
VII. Katz, V., J. (1998). : “A history of mathematics (2nd edition)”. pp. 226-227. Harlow, England: Addison Wesley Longman Inc.
VIII. L. Nurul , H., D., (2017). : “Five New Ways to Prove a Pythagorean Theorem”. International Journal of Advanced Engineering Research and Science. Volume 4, issue 7, pp.132-137. 10.22161/ijaers.4.7.21
IX. M. Janani et al, : “Multivariate Crypto System Based on a Quadratic Equation to Eliminate in Outliers using Homomorphic Encryption Scheme”. Homomorphic Encryption for Financial Cryptography. pp 277–302. 01 August 2023. Springer.
X. M. Sandoval-Hernandez et al, : “The Quadratic Equation and its Numerical Roots”. International Journal of Engineering Research & Technology (IJERT). Vol. 10 Issue 06, June-2021 pp. 301-305. 10.17577/IJERTV10IS060100
XI. Prabir Chandra Bhattacharyya, : “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. 10.26782/jmcms.2021.11.00008
XII. Prabir Chandra Bhattacharyya, : “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. 10.26782/jmcms.2022.01.00003
XIII. Prabir Chandra Bhattacharyya, : ‘A NOVEL CONCEPT IN THEORY OF QUADRATIC EQUATION’. J. Mech. Cont. & Math. Sci., Vol.-17, No.-3, March (2022) pp 41-63. 10.26782/jmcms.2022.03.00006
XIV. Prabir Chandra Bhattacharyya. : “A NOVEL METHOD TO FIND THE EQUATION OF CIRCLES”. J. Mech. Cont. & Math. Sci., Vol.-17, No.-6, June (2022). pp 31-56. 10.26782/jmcms.2022.06.00004
XV. Prabir Chandra Bhattacharyya, : “AN OPENING OF A NEW HORIZON IN THE THEORY OF QUADRATIC EQUATION: PURE AND PSEUDO QUADRATIC EQUATION – A NEW CONCEPT”. J. Mech. Cont. & Math. Sci., Vol.-17, No.-11, November (2022). pp 1-25. 10.26782/jmcms.2022.11.00001
XVI. Prabir Chandra Bhattacharyya, : “A NOVEL CONCEPT FOR FINDING THE FUNDAMENTAL RELATIONS BETWEEN STREAM FUNCTION AND VELOCITY POTENTIAL IN REAL NUMBERS IN TWO-DIMENSIONAL FLUID MOTIONS”. J. Mech. Cont. & Math. Sci., Vol.-18, No.-02, February (2023) pp 1-19. 10.26782/jmcms.2023.02.00001
XVII. Prabir Chandra Bhattacharyya, : “A NEW CONCEPT OF THE EXTENDED FORM OF PYTHAGORAS THEOREM”. J. Mech. Cont. & Math. Sci., Vol.-18, No.-04, April (2023) pp 46-56. 10.26782/jmcms.2023.04.00004
XVIII. Salman Mahmud, : “14 New Methods to Prove the Pythagorean Theorem by using Similar Triangles”. International Journal of Scientific and Innovative Mathematical Research (IJSIMR). vol. 8, no. 2, pp. 22-28, 2020. 10.20431/2347-3142.0802003
XIX. S. Mahmud, (2019). : “Calculating the area of the Trapezium by Using the Length of the Non Parallel Sides: A New Formula for Calculating the area of Trapezium”. International Journal of Scientific and Innovative Mathematical Research. volume 7, issue 4, pp. 25-27. 10.20431/2347- 3142.0704004
XX. Smith, D. (1953). : “History of mathematics”, Vol. 2. Pp. 293. New York: Dover
XXI. Smith, D. (1953). : “History of mathematics”, Vol. 2. pp. 443. New York: Dover.
XXII. T. A. Sarasvati Amma, : “Geometry in Ancient and Medieval India”. Pp. – 17. Motilal Banarasidass Publishers Pvt. Ltd. Delhi.
XXIII. T. A. Sarasvati Amma, : “Geometry in Ancient and Medieval India”. pp. – 219. Motilal Banarasidass Publishers Pvt. Ltd. Delhi.
XXIV. Thapar, R., (2000). : “Cultural pasts: Essays in early Indian History”, New Delhi: Oxford University Press.

View Download

Journal Vol – 18 No -9, September 2023

CONSTRUCTION AND ANALYSIS OF EXTENDED MODEL USING DETERMINISTIC FINITE AUTOMATA: AN APPLICATION TO SOKOTO CEMENT PRODUCTION SYSTEM

Authors:

Zaid Ibrahim

DOI NO:

https://doi.org/10.26782/jmcms.2023.10.00001

October 25, 2023

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

View Download

Journal Vol – 18 No -10, October 2023

DETERMINATION OF ACTIVATION ENERGY FOR TL PEAKS RECORDED UNDER HYPERBOLIC HEATING SCHEME

Authors:

B. Romesh Sharma, S.D. Singh, Siddhartha Bhattacharjya, Indranil Bhattacharyya, Partha Sarathi Majumdar, S. K. Azharuddin

DOI NO:

https://doi.org/10.26782/jmcms.2023.10.00002

October 25, 2023

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).

View Download

Journal Vol – 18 No -10, October 2023

BRAIN TUMOR DETECTION SYSTEM USING CONVOLUTIONAL NEURAL NETWORK

Authors:

Seba Maity, Soumyadeep Jana, Sagnik Dar, Swastika Ghosh, Arijit Sai

DOI NO:

https://doi.org/10.26782/jmcms.2023.10.00003

October 25, 2023

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

View Download

Journal Vol – 18 No -10, October 2023

APPLICATION OF THE ALGORITHM OF PARAMETRIC AND NON-PARAMETRIC CONFIDENCE INTERVALS IN PRE-PROCESSING IMAGE DATA

Authors:

Mohammad Kaisb Layous Alhasnawi

DOI NO:

https://doi.org/10.26782/jmcms.2023.11.00001

November 26, 2023

Abstract:

Digital image processing and enhancement is one of the most important and frequently used issues in many fields of image processing. When handling images or sending them over a particular channel, they are subject to certain noise and require filtering methods. In this paper, the parametric confidence interval algorithm was compared to the nonparametric confidence interval algorithm for processing the noisy images. The results showed that a nonparametric confidence interval algorithm is better at defining the external parameters of an image in terms of noise elimination and enhancement landmarks.

Keywords:

Image processing,image pre-processing,Image,Noise,parametric confidence interval,nonparametric confidence interval,

Refference:

I. Buenestado, Pablo Iand Acho, Leonardo,(2018). : “Image Segmentation Based on Statistical Confidence Intervals.” Journals Entropy. Vol. 20, Number 46, Issue 1.
II. Carlo V. Fiorio,(2004), : “Confidence intervals for kernel density estimation.” The Stata Journal. vol. 4, Number 2, pp. 168–179.
III. Hmood, Munaf Yousif ,(2011), : “Estimate The Nonparametric Regression Function Using Canonical kernel.” Journal of economics and administrative sciences, Vol. 17, Issue 61, pp.212-225.
IV. Jaber, Asmaa Ghalib, Eesa, Aseel Muslim & Bushra Saad Jasim, (2021), : “Image Segmentation by Using Thresholding Technique in Two Stages.” Periodicals of engineering and natural sciences. Issa 2303-4521, Volume 9, Number 4 pp531-541 .

V. J. Verne, Image Pre-Processing.

VI. Li, Thing,(2012), : “Contributions to Mean Shift filtering and segmentation : Application to MRI ischemic data.”

VII. Prabhishek Singh & Raj Shree,( 2016), : “Speckle Noise: Modelling and Implementation.” International Science Press. 9 (17), pp. 8717-8727.

VIII. RafaelC.Gonzalez.,Woods, R.E.,(2002), “Digital Image Processing.” 2nd edition , Publisher Prentic Hal, New jersey.

IX. S. Rajeshwari& T. Sree Sharmila,(2013), : “Efficient quality analysis of MRI image using preprocessing techniques.”

X. Umbaugh Cott E., (1998), : “Computer Vision and Image Processing Practical Approach using CVIP tools.” Practice Hall PTR.

XI. Wasserman, Larry, : “All of Nonparametric Statistics.”

XII. Yen-Chi Chen,(2017), : “Tutorial on Kernel Density Estimation and Recent Advances.” Biostatistics & Epidemiology. Volume 1, Issue 1, pp.161-187.

View Download

Journal Vol – 18 No -11, November 2023

A STUDY ON WOMEN ATTENDING GYNAECOLOGY OUTPATIENT DEPARTMENT UNDERGOING PAP SMEAR EXAMINATION AND FOLLOW-UP OF ABNORMAL PAP SMEAR USING COLPOSCOPY

Authors:

Bidisha Biswas Basu, Priyanka M Lal, Shreya Sengupta

DOI NO:

https://doi.org/10.26782/jmcms.2023.11.00002

November 26, 2023

Abstract:

Cervical cancer is the fourth most common cancer in women. In 2018, an estimated 570,000 women were diagnosed with cervical cancer worldwide and about 311,000 women died from the disease. It is the most common genital cancer among women in India. Pap smear is very useful in detecting abnormal cells and colposcopy locates the abnormal lesion when pap smear is abnormal. The objective of the study is to evaluate the women coming to the Gynaecology Outpatient Department undergoing pap smear and follow-up of abnormal smear using colposcopy. 103 women in the age group of 26-75(50.29+2.56) years who attended the gynecology outpatient department (GOPD)at a tertiary care teaching hospital in central Kerala with various clinical symptoms were screened by pap smear testing over two weeks. The smear was obtained using an Ayre spatula and it was spread over a glass slide which was placed in 95% ethyl alcohol in a coplin Jar. It was then sent to the department of pathology for cytopathological examination followed up using colposcopy and biopsy was done in the cases with abnormal colposcopy. Among these women,46.6 percentage have a normal pap smear 47.6% have an abnormal smear, and 5. 8 % have an atrophic smear. Among abnormal smear 4.8% are ASCUS, 1.9% LSIL, 0.97% SCC, 0.97% high-grade adenocarcinoma, and 37.86% inflammatory smears. As per International guidelines, women with abnormal pap smear tests should undergo colposcopy and those with abnormal colposcopy findings should undergo biopsy. Awareness about cancer should be increased and women should be motivated to undergo screening.

Keywords:

Carcinoma cervix,Colposcopy biopsy,Cytopathological examination Evaluation,Pap Smear,

Refference:

I. Ashok Verma, Suresh Verma, Shivani Vashest, Sumit Altri, Amrita Singhal, : “A study on cervical cancer screening in symptomatic women using paps smear in a tertiary care hospital in rural area of Himachal Pradesh, India.” Middle East fertility Society Journal. (2017) 22, 39-42
II. Bijoy Sree Sen Gupta, Sisir K Chattopadhyay, Thankam P Varma. : “Gynaecology for post graduate and practioners.” Second edition. Chapter 47 p.no.634-35
III. Howkins and Bourne Shaw’s textbook of Gynaecology,17th edition.
IV. Integrating HPV testing in cervical screening programs, Pan American health organization.https://wnow.paho.org
V. Mayank Singh, Ravi Prakash Jha, Deepak Dhamnetiya, : “Secular trends in incidence and mortality of cervical cancer in India and its States,1990-2019: data from the global burden of disease 2019 study.” 7th Feb 2022 BMC cancer 22, article number: 149( 2022)
VI. Meenakshi Venkatesh, Usha Devi Gopalan, : “A comparative study of pap smear and colposcopy guided biopsy in the evaluation of unhealthy cervix.” International journal of reproduction, volume 9, No. 6(2020) June 2020.https://doi.org/10.18203/23201770.ijrcog20202301
VII. Nayar,: “The Bethesda System for reporting cervical cytology.” 3rd edition,2015
VIII. Paul Uchizi Kasika, Alfred Kajira et al, : “Histopathological profile of cervical biopsies in Northern Malawi: retrospective cross sectional study BMJ”, Google scholar vol12, issue 3
IX. Poil U R, Bidinger P D, : “Visual inspection with acetic acid (VIA)screening program: 7 years experience in early detection of cervical cancer and precancers in rural South India.” Indian J community medicine 2015; 40(3): 203
X. Priyanka Poonam. Role of pap smear in detection of pre cancerous lesions of cervix -a case study, IAIM, 2019; 6(2)44-47
XI Push Lata Sachan, Meenakshi Singh, Rekha Sachan, : “A study on screening using pap smear test and clinical correlation”. Asia Pacific Journal of Oncology Nursing.Vol.5, Issue 3, P337-41, July 2018. https://ncbi.nlm.nik.gov
XII. Reaching 2020 Cervical cancer elimination targets. World Health Organization. https://www.who.int >…> detail
XIII. Saslow P, Solomon P, Lawson H W, Killackey M, Kulasingam S L, Cain J, et al., : American Cancer society, American society for colposcopy and cervical pathology and American society for clinical pathology
XIV. Screening guidelines for the prevention and early detection of cervical cancer – CA cancer J cin 2012;62:147-72 (pubmed) (Google scholar)
XV. Singh P., Ilancheran A. : “The ‘pap’ or cervical smear and the role of colposcopy in screening for carcinoma of cervix.” : Singapore Med J. 1989 June; 30 (3) : 302-5
XVI. Syeda Rumman Aktar Siddiqui, M Ariful Islam, M Zillur Rahman, : “Diagnosis of cervical lesion by colposcopy, VIA, pap smear test and their correlation with histopathology in a tertiary level laboratory in chattogram”. Bangladesh international Journal of research in medicine Vol .11, No. 3( 2023 ) March 2023
XVII. Yasamin Hamza Sharif, : “Clinical correlation of cc screening using Pap Smear test.” Journal of population therapeutics and clinical pharmacology. vol 29, number 01 (2022) JPTCP

View Download

Journal Vol – 18 No -11, November 2023