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Abstract:

This paper introduced four notions of Fuzzy pairwise regular (in short FP-R) bitopological spaces and established some relation among them. Also, prove that all of these definitions satisfy the “good extension” property. Further, prove that all of these notions are hereditary. Finally, observe that all concepts are preserved under one-one, onto, and continuous mapping.

Keywords:

Fuzzy bitopological space,Regular space,FP-Continuous,FP – Open,FP – Close Map,

Refference:

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III. Amin M.R., Hossain M.S. and Miah S.S., Fuzzy Pairwise Regular Bitopological Spaces in Quasi-coincidence Sense; J. Bangladesh Academy of Science. 44(2) (2021), 139-143.
IV. Banerjee G. K., On pairwise almost strongly -continuous mapping; Bull. Cal. Math. Soc. 79(1987)314-320.
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VIII. Garcia J.G., Romaguera S. and Sanehis M., An identification theorem for the completion of the Hausdorff fuzzy metric; Fuzzy sets and systems. 227(2013) 96-106.
IX. Islam M.S., Islam R. and Islam M.S. “On a study on Intuitionistic fuzzy r-normal Spaces” Notes on Intuitionistic Fuzzy Sets27(3)(2021),69-82
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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.

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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
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Media, Inc., 2022.
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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).
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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,

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