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
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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
Learning Deep Energy Models
TL;DR: This work proposes deep energy models, which use deep feedforward neural networks to model the energy landscapes that define probabilistic models, and is able to efficiently train all layers of this model simultaneously, allowing the lower layers of the model to adapt to the training of the higher layers, and thereby producing better generative models.
Journal ArticleDOI
3D object retrieval with stacked local convolutional autoencoder
TL;DR: A novel 3D object retrieval method based on stacked local convolutional autoencoder (SLCAE) with greedy layerwise strategy is applied to train SLCAE, and gradient descent method is used for training each layer.
Proceedings Article
Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines
TL;DR: This work presents an enhanced gradient which is derived such that it is invariant to bit-flipping transformations and proposes a way to automatically adjust the learning rate by maximizing a local likelihood estimate.
Proceedings ArticleDOI
Spatio-Temporal Convolutional Sparse Auto-Encoder for Sequence Classification.
TL;DR: A novel learning-based approach for video sequence classification that automatically learns a sparse shift-invariant representation of the local 2D+t salient information, without any use of prior knowledge is presented.