IS-USG: INTELLIGENCE SYSTEM ULTRASONOGRAPHY Machine Learning Implementation for Recognition of Pregnant Sheep in Gumukmas Multifarm Jember
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In 2023, the government's program related to food independence emphasizes the importance of meat independence, including goat meat. CV. Gumukmas Multi Farm Jember faces several challenges in managing pregnant sheep, such as early detection of pregnancy, preventing abortion, and managing sheep health history. Therefore, the Intelligence System Ultrasonography (IS-USG) system was developed to detect sheep pregnancy more accurately and integrate medical data with the help of machine learning. This system allows automatic monitoring of pregnancy conditions and fetal health, and recommends interventions such as probiotic feeding. IS-USG is implemented using a web and mobile-based client-server architecture, with Laravel and MySQL backends, and RESTful API integration for real-time data synchronization. Testing was conducted to evaluate the effectiveness of the system, and initial results showed that this system can improve sheep reproductive management, accelerate pregnancy detection, and minimize the risk of birth failure.
Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
Sannella, M. J. 1994 Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398., University of Washington.
Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE.
Y.T. Yu, M.F. Lau, "A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions", Journal of Systems and Software, 2005, in press.
Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullende
Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.Bowman, M., Debray, S. K., and Peterson, L. L. 1993. Reasoning about naming systems.
[8] N. Muhammad Hussain, AU Rehman, MTB Othman, J. Zafar, H. Zafar, and H. Hamam, "Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data,"Sensors, vol. 22, no. 14, Art. no. 14, Jul. 2022, doi: 10.3390/s22145103.
H. Li et al., “Deep learning in ultrasound elastography imaging: A review,”Med. Phys., vol. 49, no. 9, pp. 5993–6018, 2022, doi: 10.1002/mp.15856.
Department of Computer Sciences, Ajayi Crowther University, Oyo, Nigeria. and JA Ayeni, “Convolutional Neural Network (CNN): The architecture and applications,” Appl. J. Phys. Sci., vol. 4, no. 4, pp. 42–50, Dec. 2022, doi: 10.31248/AJPS2022.085.
O. Dib and B. Rababah, “Decentralized Identity Systems: Architecture, Challenges, Solutions and Future Directions,” Ann. Emergency. Technol. Comput. AETiC, vol. 4, no. 5, Art. no. 5, 2020, doi: 10.33166/AETiC.2020.05.002.
FD Amrizal and F. Nugrahanti, "IMPLEMENTATION OF LARAVEL FRAMEWORK IN ANIMAL HEALTH CONSULTATION APPLICATION BASED ON WEBSITE," Pros. Nas. Technol. Inf. And Commun. SENATIC, vol. 3, no. 1, Art. no. 1, Sept. 2020.
A.-A. Tulbure, A.-A. Tulbure, and E.-H. Dulf, “A review on modern defect detection models using DCNNs – Deep convolutional neural networks,”J. Adv. Res., vol. 35, pp. 33–48, Jan. 2022, doi: 10.1016/j.jare.2021.03.015.
H. Gholamalinezhad and H. Khosravi, “Pooling Methods in Deep Neural Networks, a Review,” Sep. 16, 2020, arXiv: arXiv:2009.07485. doi: 10.48550/arXiv.2009.07485.
[15] B. Petrovska, E. Zdravevski, P. Lameski, R. Corizzo, I. Štajduhar, and J. Lerga, “Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification,” Sensors, vol. 20, no. 14, Art. no. 14, Jul. 2020, doi: 10.3390/s20143906.
SR Dubey, SK Singh, and BB Chaudhuri, “Activation functions in deep learning: A comprehensive survey and benchmark,”Neurocomputing, vol. 503, pp. 92–108, Sept. 2022,
doi: 10.1016/j.neucom.2022.06.111.
Siska Narulita, Ahmad Nugroho, and M. Zakki Abdillah, “Unified Modeling Language (UML) Diagram for Designing a Research and Community Service Management Information System (SIMLITABMAS),” Bridge J. Publ. Sist. Inf. And Telecommun., vol. 2, no. 3, pp. 244–256, Aug. 2024, doi: 10.62951/bridge.v2i3.174.
MZ Prasetyo, E. Susanto, and A. Wantoro, “THALASSEMIA PATIENT MEDICAL RECORD INFORMATION SYSTEM (CASE STUDY: POPTI BANDAR LAMPUNG Branch) | Prasetyo | Journal of Technology and Information Systems”, Accessed: Oct. 12, 2024. [Online]. Available: https://jim.teknokrat.ac.id/index.php/sisteminformasi/article/view/3140/931
X. Xiang, M. Wang, and W. Fan, “A Permissioned Blockchain- Based Identity Management and User Authentication Scheme for E-Health Systems,” IEEE Access, vol. 8, pp. 171771–171783, 2020, doi: 10.1109/ACCESS.2020.3022429.
A. Iftekhar, X. Cui, M. Hassan, and W. Afzal, "Application of Blockchain and Internet of Things to Ensure Tamper-Proof Data Availability for Food Safety," J. Food Qual., vol. 2020, no. 1, p. 5385207, 2020, doi: 10.1155/2020/5385207.
M. Zhang, H. Feng, H. Luo, Z. Li, and X. Zhang, “Comfort and health evaluation of live mutton sheep during the transportation based on wearable multi-sensor system,” Comput. Electron. Agric., vol. 176, p. 105632, Sept. 2020, doi: 10.1016/j.compag.2020.105632.
GE Dal, SO Enginler, K. Baykal, and A. Sabuncu, “Early pregnancy diagnosis by semiquantitative evaluation of luteal vascularity using power Doppler ultrasonography in sheep | Acta Veterinaria Brno”,
doi: 10.2754/avb201988010019.