scholarly journals Deep Learning Machine using Hierarchical Cluster Features

2019 ◽  
Vol 29 (3) ◽  
pp. 82
Author(s):  
Sara Salman ◽  
Jamila H. Soud
Keyword(s):  
Deep Learning ◽  
Computational Models ◽  
Hierarchical Cluster ◽  
Data Representation ◽  
Cluster Method ◽  
Classification Problems ◽  
Levels Of Abstraction ◽  
Image Translation ◽  
Multiple Levels ◽  
Learning Machine

Deep learning of multi-layer computational models allowed processing to recognize data representation at multiple levels of abstraction. These techniques have greatly improved the latest ear recognition technology. PNN is a type of radiative basis for classification problems and is based on the Bayes decision-making base, which reduces the expected error of classification. In this paper, strong features of images are used to give a good result, therefore, SIFT method using these features after adding improvements and developments. This method was one of the powerful algorithms in matching that needed to find energy pixels. This method gives stronger feature on features and gives a large number of a strong pixel, which is considered a center and neglected the remainder of it in our work. Each pixel of which is constant for image translation, scaling, rotation, and embedded lighting changes in lighting or 3D projection. Therefore, the interpretation is developed by using a hierarchical cluster method; to assign a set of properties (find the approximation between pixels) were classified into one.

scholarly journals An Overview Studying of Deep Learning

2020 ◽  
pp. 394-398
Author(s):  
Mya Sandar Kyin ◽  
Zaw Lin Oo ◽  
Khin Mar Cho
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Computational Models ◽  
Unstructured Data ◽  
Levels Of Abstraction ◽  
Large Numbers ◽  
Multiple Levels ◽  
Multiple Processing

Deep learning is a subfield of machine learning however both drop under the broad category of artificial intelligence. Deep learning is what powers the most human-like artificial intelligence that consents computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. Deep learning is making major advances in solving problems hence categorized in wider section of artificial intelligence. The main advantage of Deep Learning is to create an artificial neural network that can learn and make intelligent decisions on its own and to process large numbers of features makes deep learning very powerful when dealing with unstructured data.

scholarly journals The Understanding of Deep Learning: A Comprehensive Review

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ranjan Kumar Mishra ◽  
G. Y. Sandesh Reddy ◽  
Himanshu Pathak
Keyword(s):  
Deep Learning ◽  
Deep Neural Network ◽  
Computational Models ◽  
Review Paper ◽  
State Of The Art ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Levels Of Abstraction ◽  
Comprehensive Review ◽  
Computer Based

Deep learning is a computer-based modeling approach, which is made up of many processing layers that are used to understand the representation of data with several levels of abstraction. This review paper presents the state of the art in deep learning to highlight the major challenges and contributions in computer vision. This work mainly gives an overview of the current understanding of deep learning and their approaches in solving traditional artificial intelligence problems. These computational models enhanced its application in object detection, visual object recognition, speech recognition, face recognition, vision for driverless cars, virtual assistants, and many other fields such as genomics and drug discovery. Finally, this paper also showcases the current developments and challenges in training deep neural network.

scholarly journals Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

2020 ◽  
Vol 10 (2) ◽  
pp. 118 ◽  
Author(s):  
Muhammad Waqas Nadeem ◽  
Mohammed A. Al Ghamdi ◽  
Muzammil Hussain ◽  
Muhammad Adnan Khan ◽  
Khalid Masood Khan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Medical Image ◽  
Computational Models ◽  
Medical Image Analysis ◽  
Critical Discussion ◽  
Levels Of Abstraction ◽  
Discussion Section ◽  
Wide Range ◽  
Classification Prediction

Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.

scholarly journals Deep learning (pogłębianie procesu uczenia się) z perspektywy analizy potrzeb studentów języka angielskiego jako obcego

2019 ◽  
pp. 301-320
Author(s):  
Katarzyna Papaja ◽  
Artur Świątek ◽  
Kamil Mielnik
Keyword(s):  
Deep Learning ◽  
Foreign Language ◽  
Language Learning ◽  
Educational Process ◽  
Levels Of Abstraction ◽  
Proper Understanding ◽  
Fields Of Study ◽  
Potential Benefits ◽  
Multiple Levels ◽  
Definition Of

Although the term Deep Learning does not seem to be a new term in language learning, it attracted relatively little attention until just a few years ago. Different fields of study show that Deep Learning leverages a sophisticated process to learn multiple levels of abstraction from the data; however, in languages, the term has been widely accepted as the key concept in the transformation and personalisation of the learning process. In this paper, we take the definition of Deep Learning, and we corroborate the theories by use of the study which aims to assess the needs of students in the context of language exercises, resources as well as tools and modern technological solutions. A proper understanding of Deep Learning is necessary to examine the potential benefits for students and the broadly defined society. Therefore, the essence of the research is to obtain the answers to what is important in the education of modern foreign languages and also what the teacher’s role is. A quantitative study was conducted on 441 students of English Philology. The results of the needs analysis of foreign language students allow for a greater understanding of their expectations towards themselves and their teachers; additionally, to answer the question about what kind of education recipients they are and whether they are active participants in the whole educational process.

scholarly journals Extended Hierarchical Extreme Learning Machine with Multilayer Perceptron

2017 ◽  
Vol 10 (2) ◽  
pp. 196-204
Author(s):  
Khanittha Phumrattanaprapin ◽  
Punyaphol Horata
Keyword(s):  
Deep Learning ◽  
Extreme Learning Machine ◽  
Multilayer Perceptron ◽  
High Performance ◽  
Single Layer ◽  
Classification Performance ◽  
Classification Problems ◽  
Hierarchical Learning ◽  
Deep Architecture ◽  
Learning Machine

The Deep Learning approach provides a high performance of classification, especially when invoking image classification problems. However, a shortcoming of the traditional Deep Learning method is the large time scale of training. The hierarchical extreme learning machine (H-ELM) framework was based on the hierarchical learning architecture of multilayer perceptron to address the problem. H-ELM is composed of two parts; the first entails unsupervised multilayer encoding, and the second is the supervised feature classification. H-ELM can give a higher accuracy rate than the traditional ELM. However, there still remains room to enhance its classification performance. This paper therefore proposes a new method termed the extending hierarchical extreme learning machine (EH-ELM), which extends the number of layers in the supervised portion of the H-ELM from a single layer to multiple layers. To evaluate the performance of the EH-ELM, the various classification datasets were studied and compared with the H-ELM and the multilayer ELM, as well as various state-of-the-art such deep architecture methods. The experimental results show that the EH-ELM improved the accuracy rates over most other methods.

Learning at Multiple Levels of Abstraction: The Case of Verb Argument Constructions

2007 ◽  
Author(s):  
Amy Perfors ◽  
Charles Kemp ◽  
Elizabeth Wonnacott ◽  
Joshua B. Tenenbaum
Keyword(s):  
Levels Of Abstraction ◽  
Multiple Levels

scholarly journals Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1579
Author(s):  
Dongqi Wang ◽  
Qinghua Meng ◽  
Dongming Chen ◽  
Hupo Zhang ◽  
Lisheng Xu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Channel Model ◽  
Expert Knowledge ◽  
Automatic Detection ◽  
Data Representation ◽  
Learning Technology ◽  
Arrhythmia Detection ◽  
Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

scholarly journals Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset

2021 ◽  
Vol 11 (9) ◽  
pp. 3863
Author(s):  
Ali Emre Öztürk ◽  
Ergun Erçelebi
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Corner Detection ◽  
Batch Size ◽  
Test Accuracy ◽  
Classification Problems ◽  
Auc Value ◽  
Classification Test

A large amount of training image data is required for solving image classification problems using deep learning (DL) networks. In this study, we aimed to train DL networks with synthetic images generated by using a game engine and determine the effects of the networks on performance when solving real-image classification problems. The study presents the results of using corner detection and nearest three-point selection (CDNTS) layers to classify bird and rotary-wing unmanned aerial vehicle (RW-UAV) images, provides a comprehensive comparison of two different experimental setups, and emphasizes the significant improvements in the performance in deep learning-based networks due to the inclusion of a CDNTS layer. Experiment 1 corresponds to training the commonly used deep learning-based networks with synthetic data and an image classification test on real data. Experiment 2 corresponds to training the CDNTS layer and commonly used deep learning-based networks with synthetic data and an image classification test on real data. In experiment 1, the best area under the curve (AUC) value for the image classification test accuracy was measured as 72%. In experiment 2, using the CDNTS layer, the AUC value for the image classification test accuracy was measured as 88.9%. A total of 432 different combinations of trainings were investigated in the experimental setups. The experiments were trained with various DL networks using four different optimizers by considering all combinations of batch size, learning rate, and dropout hyperparameters. The test accuracy AUC values for networks in experiment 1 ranged from 55% to 74%, whereas the test accuracy AUC values in experiment 2 networks with a CDNTS layer ranged from 76% to 89.9%. It was observed that the CDNTS layer has considerable effects on the image classification accuracy performance of deep learning-based networks. AUC, F-score, and test accuracy measures were used to validate the success of the networks.

Sign in / Sign up

Export Citation Format

Share Document