Pest detection system using machine learning techniques

Pest detection system using machine learning techniques

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς:	Prithvi, Protyusha Barua, Zahin, Fabliha, Anny, Sanjida Sultana
Άλλοι συγγραφείς:	Chakrabarty, Amitabha
Μορφή:	Thesis
Γλώσσα:	English
Έκδοση:	Brac University 2022
Θέματα:	Deep learning Transfer learning Pest detection Data augmentation Loss function Hyperparameter tuning Support Vector Machine (SVM) Inceptionv3 Xception You Only Look Once version 5 (YOLOv5) Convolutional Neural Network (CNN) Machine learning Cognitive learning theory (Deep learning) Neural networks (Computer science)
Διαθέσιμο Online:	http://hdl.handle.net/10361/17127

Παρόμοια τεκμήρια

Classifying insect pests from image data using deep learning
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Recognising license plate from image data using deep learning
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Diabetic retinopathy detection and classification by using deep learning
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Deep learning-based real-time pothole detection for avoiding road accident
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An efficient deep learning approach to classify white blood cells
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An efficient deep learning approach to detect skin Cancer
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Corn leaf disease detection using deep convolution neural network
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Document template identification and data extraction using machine learning and deep learning approach
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Visual object classification from fMRI data
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An efficient ML approach to detect brain tumor using MRI images
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Demystifying second hand car market in Bangladesh using multimodal machine learning techniques
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Occluded object detection for autonomous vehicles employing YOLOv5, YOLOX and Faster R-CNN
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Finding location-specific aggregated air quality index with smartphone images using deep convolutional neural network
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Comparative analysis and implementation of credit risk prediction through distinct machine learning models
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A conventional & deep learning strategy for analyzing & detecting Bengali fake news in online medium
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Skin cancer detection and classification using multiple optimized deep convolutional neural network
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Density based traffic control system for a four way intersection
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Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification
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Automated detection of Malignant Lesions in the ovary using deep learning models and XAI
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Prostate cancer detection using deep learning neural network with transfer learning approach
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An efficient deep learning approach to detect skin cancer using image data
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Performance analysis of stacking neural network and machine learning model for detecting fraudulent transaction
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Implementing machine learning techniques to forecast floods In Bangladesh based on historical data
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ProteoKnight: phage virion protein classification with CNN and uncertainty quantification
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Application of CNN based architectures in detection of distracted drivers
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Leveraging sequential deep learning models for detecting multitude of human action categories
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Recall-Net: A CNN-based Model for Four-class Classification of Alzheimer’s Disease
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Intracranial hemorrhage detection using CNN-LSTM fusion model
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Visual object recognition using deep convolutional neural network
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Classification of peripheral blood cell images using deep learning
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Έκδοση: (2024)

Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network
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Έκδοση: (2020)

Fighting depression: psychological approaches among Bangladeshi university students
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A modern technique to detect potholes by Computer Vision and Deep Learning
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Detection of handwritten text using convolutional neural network
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Child Safe Browser Extension: A Browser Extension to Detect Adultery and Violent Content to Make Safer Web for Children
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Deep learning approaches for Bengali cyberbullying cetection on social media: a comparative study of BiLSTM, BiGRU and BERT models
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Cassava leaf disease classification using deep learning and convolutional neural network ensemble
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Detection and classification of Mango Leaf diseases utilizing convolutional neural network models
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Classification of retinal diseases from OCT images using deep learning models
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Classification of respiratory diseases and COVID-19 from respiratory and cough sound using deep learning techniques
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