38 Result(s)
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Chapter and Conference Paper
Kiwifruit Counting Using Kiwidetector and Kiwitracker
Efficient fruit detection and counting are crucial to improve fruit industrial efficiency and assist the farmers to develop reasonable harvesting strategies in advance while significantly reducing human labors...
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Chapter and Conference Paper
Enhancing Privacy Protection in Intelligent Surveillance: Video Blockchain Solutions
Blockchain has emerged as a contemporary innovation that ensures secure operations in distributed networks, including decentralized applications, finance, logistics, and cross-border organizational control. In...
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Chapter
Transfer Learning and Ensemble Learning
In this chapter, we start from transfer learning and introduce the relationship between learners. We use ensemble learning to combine them together and hope to get a strong learner from a weak learner by chang...
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Chapter
Deep Learning Platforms
There are a plethora of deep learning platforms available at present. The famous one is MATLAB deep learning toolbox developed by MathWorks which simplifies deep learning computations and reduces the workload ...
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Chapter
Generative Adversarial Networks and Siamese Nets
In this chapter, we will emphasize computational iterations in GANs (i.e., generative adversarial networks) [46] and Siamese nets [3, 6, 15] . In deep learning, these models are named as contrastive networks [3]...
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Chapter
Manifold Learning and Graph Neural Network
In this chapter, we will introduce manifold learning and graph neural networks. We hope to introduce graphical probability models as the starting point of basestone. We need to introduce our readers why we sho...
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Chapter
Introduction
This chapter covers the fundamentals of deep learning, therefore, we present relevant knowledge in chronological order so as to fully introduce the history of deep learning development; meanwhile, we review ho...
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Chapter
Convolutional Neural Networks and Recurrent Neural Networks
In this chapter, we will introduce the typical deep neural networks from the viewpoint of Convolutional Neural Network (CNN or ConvNet) family, especially , Single Shot MultiBox Detector (SSD) , and You Only...
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Chapter
Reinforcement Learning
In this chapter, we introduce fundamental concepts of reinforcement learning [21] such as , , deep Q- , and double Q- . We detail why reinforcement is thought as a method of deep learning.
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Chapter
CapsNet and Manifold Learning
is one of the relatively new methods in deep learning, which has taken topological of a scene into consideration. The output will be a vector to reflect this relationship. Meanwhile, manifold , which is em...
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Chapter
Transfer Learning and Ensemble Learning
In this chapter, we start from transfer learning and introduce the relationship between different learners; we use ensemble learning to combine them together and hope to get a strong learner from a weak learne...
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Chapter and Conference Paper
A New Noise Generating Method Based on Gaussian Sampling for Privacy Preservation
Centralised machine learning brings in side effect pertaining to privacy preservation, most of machine learning methods prone to using the frameworks without privacy protection, as current methods for privacy ...
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Chapter and Conference Paper
Tree Leaves Detection Based on Deep Learning
In this paper, digital images related to five kinds of leaves which are available at New Zealand are collected as our dataset, two deep learning models, namely, Faster R-CNN and YOLOv5, representing two-stage ...
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Chapter and Conference Paper
Apple Ripeness Identification Using Deep Learning
Deep learning models assist us in fruit classification, which allow us to use digital images from cameras to classify a fruit and find its class of ripeness automatically. Apple ripeness classification is a pr...
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Chapter and Conference Paper
Traffic Sign Recognition Using Guided Image Filtering
In challenging lighting conditions, such as haze, rain, and weak lighting condition, the accuracy of traffic sign recognition is not very high due to missed detection or incorrect positioning. In this paper, w...
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Chapter and Conference Paper
Sign Language Recognition from Digital Videos Using Deep Learning Methods
In this paper, we investigate the state-of-the-art deep learning methods for sign language recognition. In order to achieve this goal, Capsule Network (CapsNet) is proposed in this paper, which shows positive ...
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Chapter
Reinforcement Learning
In this chapter, we will introduce the fundamental concepts of reinforcement learning such as , , deep Q- , etc. We will introduce why reinforcement is thought as a method of deep learning. Then, mathemati...
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Chapter
Boltzmann Machines
In this chapter, we will introduce , restricted Boltzmann , and deep Boltzmann . We will generalize our deep neural networks from networks to general graphs, we will use probabilistic graphical to model t...
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Chapter and Conference Paper
Traffic-Sign Recognition Using Deep Learning
Traffic-sign recognition (TSR) has been an essential part of driver-assistance systems, which is able to assist drivers in avoiding a vast number of potential hazards and improve the experience of driving. How...
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Chapter
Deep Learning Platforms
There are many deep learning platforms available such as , , MXNet, , and Theano. Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, which originally was developed a...
