Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices

Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

التفاصيل البيبلوغرافية
المؤلفون الرئيسيون:	Ghosh, Kawshik Kumar, Islam, MD.Fahim-Ul, Efaz, Abrar Ahsan, Ratul, Md. Wahid Sadiq, Shatil, Md Zaid Hassan Khan
مؤلفون آخرون:	Chakrabarty, Amitabha
التنسيق:	أطروحة
اللغة:	English
منشور في:	Brac University 2023
الموضوعات:	Deep learning Edge devices Mastitis Livestock Machine learning Cognitive learning theory
الوصول للمادة أونلاين:	http://hdl.handle.net/10361/18377

مواد مشابهة

Application of machine learning techniques on the context of livestock analysis
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منشور في: (2018)

Blockchain-based edge computing for medical data storage & processing using federated learning
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منشور في: (2021)

Use of machine learning and IoT for monitoring and tracking of livestock
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منشور في: (2023)

LSTM based content prediction for edge caching using federated learning approach
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منشور في: (2021)

Exploring machine learning techniques for symptom-based detection of livestock diseases
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منشور في: (2024)

Deep learning based predictive analytics for decentralized content caching in hierarchical edge networks
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منشور في: (2022)

Federated ensemble-learning for transport mode detection in vehicular edge network
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Data security model using deep learning and edge computing for Internet of Things (IoT) in smart city
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Heart disease prediction using machine learning algorithms
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Comprehensive fingerprint recognition utilizing one shot learning with Siamese Network
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منشور في: (2023)

Damaged road detection using Image Processing and Deep Learning
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Implementing machine learning techniques to forecast floods In Bangladesh based on historical data
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منشور في: (2024)

Analysis of the impact of online education using EEG signals and machine learning algorithms
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Document template identification and data extraction using machine learning and deep learning approach
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