Book ChapterDOI
Recent Trends in Deep Learning with Applications
K. Balaji,K. Lavanya +1 more
- pp 201-222
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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
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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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Proceedings Article
Efficient Learning of Sparse Representations with an Energy-Based Model
TL;DR: A novel unsupervised method for learning sparse, overcomplete features using a linear encoder, and a linear decoder preceded by a sparsifying non-linearity that turns a code vector into a quasi-binary sparse code vector.
Proceedings Article
Nonlinear Learning using Local Coordinate Coding
Kai Yu,Tong Zhang,Yihong Gong +2 more
TL;DR: It is shown that a high dimensional nonlinear function can be approximated by a global linear function with respect to this coding scheme, and the approximation quality is ensured by the locality of such coding.
Journal ArticleDOI
A tutorial survey of architectures, algorithms, and applications for deep learning
TL;DR: This tutorial survey is to introduce the emerging area of deep learning or hierarchical learning to the APSIPA community and provides a taxonomy-oriented survey on the existing deep architectures and algorithms in the literature, and categorize them into three classes: generative, discriminative, and hybrid.
Book ChapterDOI
Image classification using super-vector coding of local image descriptors
TL;DR: In this article, the authors proposed a new framework for image classification using local visual descriptors, which performs a nonlinear feature transformation on descriptors and aggregates the results together to form image-level representations, and finally applies a classification model.
Posted Content
Visualizing and Understanding Convolutional Neural Networks
Matthew D. Zeiler,Rob Fergus +1 more
TL;DR: In this paper, a novel visualization technique was introduced to give insight into the function of intermediate feature layers and the operation of the classifier, which enabled the authors to find model architectures that outperformed Krizhevsky et al. on ImageNet classification benchmark.