Book ChapterDOI
Recent Trends in Deep Learning with Applications
K. Balaji,K. Lavanya +1 more
- pp 201-222
TLDR
The main purpose of using deep learning algorithms are such as faster processing, low-cost hardware, and modern growths in machine learning techniques.Abstract:
Deep learning methods play a vital role in Internet of things analytics. One of the main subgroups of machine learning algorithm is Deep Learning. Raw data is collected from devices. Collecting data from all situations and doing pre-processing is complex. Monitoring data through sensors continuously is also complex and expensive. Deep learning algorithms will solve these types of issues. A deep learning method signifies at various levels of representation from lower level features to very higher level features of data. The higher level features provide more abstract thoughts of information than the lower level which contains raw data. It is a developing methodology and has been commonly applied in art, image caption, machine translation, natural language processing, object detection, robotics, and visual tracking. The main purpose of using deep learning algorithms are such as faster processing, low-cost hardware, and modern growths in machine learning techniques. This review paper gives an understanding of deep learning methods and their recent advances in Internet of things.read more
Citations
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Journal ArticleDOI
Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS.
Saeed H. Alsamhi,Saeed H. Alsamhi,Faris A. Almalki,Hatem Al-Dois,Soufiene Ben Othman,Soufiene Ben Othman,Jahan Hassan,Ammar Hawbani,Radyah Sahal,Brian Lee,Hager Saleh +10 more
TL;DR: In this paper, a survey on the use of ML for enhancing IoT applications is presented, and an in-depth overview of the various IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare.
Book ChapterDOI
Medical Image Analysis With Deep Neural Networks
K. Balaji,K. Lavanya +1 more
TL;DR: The essentials of deep learning methods with convolutional neural networks are presented and their achievements in medical image analysis, such as in deep feature representation, detection, segmentation, classification, and prediction are analyzed.
Posted Content
Human or Machine: Automating Human Likeliness Evaluation of NLG Texts
Erion Çano,Ondrej Bojar +1 more
TL;DR: An attempt to automate the human likeliness evaluation of the output text samples coming from natural language generation methods used to solve several tasks by using a discrimination procedure based on large pretrained language models and their probability distributions.
Classification of Lung Nodule Using Hybridized Deep Feature Technique
TL;DR: A hybridized approach has been followed to classify lung nodule as benign or malignant to help in early detection of lung cancer and help in the life expectancy of lungcancer patients thereby reducing the mortality rate by this deadly disease scourging the world.
References
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Book ChapterDOI
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.
Proceedings ArticleDOI
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
TL;DR: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories.
Proceedings ArticleDOI
Extracting and composing robust features with denoising autoencoders
TL;DR: This work introduces and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising 1 criterion
TL;DR: This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations.
Journal Article
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
TL;DR: Denoising autoencoders as mentioned in this paper are trained locally to denoise corrupted versions of their inputs, which is a straightforward variation on the stacking of ordinary autoencoder.