Electrocardiogram Classification Based on Deep Convolutional Neural Networks: A Review

Due to many new medical uses, the value of ECG classification is very demanding. There are some Machine Learning (ML) algorithms currently available that can be used for ECG data processing and classification. The key limitations of these ML studies, however, are the use of heuristic hand-crafted or engineered characteristics of shallow learning architectures. The difficulty lies in the probability of not having the most suitable functionality that will provide this ECG problem with good classification accuracy. One choice suggested is to use deep learning algorithms in which the first layer of CNN acts as a feature. This paper summarizes some of the key approaches of ECG classification in machine learning, assessing them in terms of the characteristics they use, the precision of classification important physiological keys ECG biomarkers derived from machine learning techniques, and statistical modeling and supported simulation.

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Anomaly Detection in Drone-Captured Images Using Machine Learning Techniques and Deep Learning Architectures

10.1007/978-981-16-4625-6_78 ◽

2021 ◽

pp. 783-791

Author(s):

Kartik Joshi ◽

G. Vidya ◽

Soumya Shaw ◽

Abitha K. Thyagarajan ◽

Akhil Pathak ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Anomaly Detection ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Learning Architectures

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Bike Sharing Prediction using Deep Neural Networks

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.1.3.30 ◽

2017 ◽

Vol 1 (3) ◽

pp. 83 ◽

Cited By ~ 4

Author(s):

Chandrasegar Thirumalai ◽

Ravisankar Koppuravuri

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Neural Nets ◽

Machine Learning Techniques ◽

Business Decisions ◽

Learning Techniques ◽

Bike Sharing ◽

The Way

In this paper, we will use deep neural networks for predicting the bike sharing usage based on previous years usage data. We will use because deep neural nets for getting higher accuracy. Deep neural nets are quite different from other machine learning techniques; here we can add many numbers of hidden layers to improve the accuracy of our prediction and the model can be trained in the way we want such that we can achieve the results we want. Nowadays many AI experts will say that deep learning is the best AI technique available now and we can achieve some unbelievable results using this technique. Now we will use that technique to predict bike sharing usage of a rental company to make sure they can take good business decisions based on previous years data.

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DETECTION OF WHALES USING DEEP LEARNING METHODS AND NEURAL NETWORKS

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2017.v10s1.20767 ◽

2017 ◽

Vol 10 (13) ◽

pp. 489 ◽

Cited By ~ 1

Author(s):

Saheb Ghosh ◽

Sathis Kumar B ◽

Kathir Deivanai

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Data Model ◽

Natural Habitat ◽

Machine Learning Techniques ◽

Learning Methods ◽

Learning Techniques ◽

Learning Technique ◽

Extraction Model

Deep learning methods are a great machine learning technique which is mostly used in artificial neural networks for pattern recognition. This project is to identify the Whales from under water Bioacoustics network using an efficient algorithm and data model, so that location of the whales can be send to the Ships travelling in the same region in order to avoid collision with the whale or disturbing their natural habitat as much as possible. This paper shows application of unsupervised machine learning techniques with help of deep belief network and manual feature extraction model for better results.

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A Comparison of Machine Learning and Deep Learning Techniques for Activity Recognition using Mobile Devices

Sensors ◽

10.3390/s19030521 ◽

2019 ◽

Vol 19 (3) ◽

pp. 521 ◽

Cited By ~ 6

Author(s):

Alejandro Baldominos ◽

Alejandro Cervantes ◽

Yago Saez ◽

Pedro Isasi

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Activity Recognition ◽

Leisure Activities ◽

Machine Learning Techniques ◽

Learning Networks ◽

Extremely Randomized Trees ◽

Learning Techniques ◽

Improved Accuracy ◽

Learning Architectures

We have compared the performance of different machine learning techniques for human activity recognition. Experiments were made using a benchmark dataset where each subject wore a device in the pocket and another on the wrist. The dataset comprises thirteen activities, including physical activities, common postures, working activities and leisure activities. We apply a methodology known as the activity recognition chain, a sequence of steps involving preprocessing, segmentation, feature extraction and classification for traditional machine learning methods; we also tested convolutional deep learning networks that operate on raw data instead of using computed features. Results show that combination of two sensors does not necessarily result in an improved accuracy. We have determined that best results are obtained by the extremely randomized trees approach, operating on precomputed features and on data obtained from the wrist sensor. Deep learning architectures did not produce competitive results with the tested architecture.

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Winsorization for Robust Bayesian Neural Networks

Entropy ◽

10.3390/e23111546 ◽

2021 ◽

Vol 23 (11) ◽

pp. 1546

Author(s):

Somya Sharma ◽

Snigdhansu Chatterjee

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Black Box ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Versatile Technique ◽

Aberrant Observations ◽

Probabilistic Machine Learning ◽

Different Levels

With the advent of big data and the popularity of black-box deep learning methods, it is imperative to address the robustness of neural networks to noise and outliers. We propose the use of Winsorization to recover model performances when the data may have outliers and other aberrant observations. We provide a comparative analysis of several probabilistic artificial intelligence and machine learning techniques for supervised learning case studies. Broadly, Winsorization is a versatile technique for accounting for outliers in data. However, different probabilistic machine learning techniques have different levels of efficiency when used on outlier-prone data, with or without Winsorization. We notice that Gaussian processes are extremely vulnerable to outliers, while deep learning techniques in general are more robust.

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From Reflex to Reflection: Two Tricks AI Could Learn from Us

Philosophies ◽

10.3390/philosophies4020027 ◽

2019 ◽

Vol 4 (2) ◽

pp. 27

Author(s):

Jean-Louis Dessalles

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Cognitive Processes ◽

Real World ◽

Machine Learning Techniques ◽

Human Intervention ◽

Real World Data ◽

World Data ◽

Learning Techniques

Deep learning and other similar machine learning techniques have a huge advantage over other AI methods: they do function when applied to real-world data, ideally from scratch, without human intervention. However, they have several shortcomings that mere quantitative progress is unlikely to overcome. The paper analyses these shortcomings as resulting from the type of compression achieved by these techniques, which is limited to statistical compression. Two directions for qualitative improvement, inspired by comparison with cognitive processes, are proposed here, in the form of two mechanisms: complexity drop and contrast. These mechanisms are supposed to operate dynamically and not through pre-processing as in neural networks. Their introduction may bring the functioning of AI away from mere reflex and closer to reflection.

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Identifying Ear Abnormality from 2D Photographs Using Convolutional Neural Networks

Scientific Reports ◽

10.1038/s41598-019-54779-7 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Rami R. Hallac ◽

Jeon Lee ◽

Mark Pressler ◽

James R. Seaward ◽

Alex A. Kane

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Machine Learning Techniques ◽

Test Accuracy ◽

Linear Measurements ◽

Softmax Classifier ◽

Ear Deformity ◽

Learning Techniques

AbstractQuantifying ear deformity using linear measurements and mathematical modeling is difficult due to the ear’s complex shape. Machine learning techniques, such as convolutional neural networks (CNNs), are well-suited for this role. CNNs are deep learning methods capable of finding complex patterns from medical images, automatically building solution models capable of machine diagnosis. In this study, we applied CNN to automatically identify ear deformity from 2D photographs. Institutional review board (IRB) approval was obtained for this retrospective study to train and test the CNNs. Photographs of patients with and without ear deformity were obtained as standard of care in our photography studio. Profile photographs were obtained for one or both ears. A total of 671 profile pictures were used in this study including: 457 photographs of patients with ear deformity and 214 photographs of patients with normal ears. Photographs were cropped to the ear boundary and randomly divided into training (60%), validation (20%), and testing (20%) datasets. We modified the softmax classifier in the last layer in GoogLeNet, a deep CNN, to generate an ear deformity detection model in Matlab. All images were deemed of high quality and usable for training and testing. It took about 2 hours to train the system and the training accuracy reached almost 100%. The test accuracy was about 94.1%. We demonstrate that deep learning has a great potential in identifying ear deformity. These machine learning techniques hold the promise in being used in the future to evaluate treatment outcomes.

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Emotional quantification of soundscapes by learning between samples

Multimedia Tools and Applications ◽

10.1007/s11042-020-09430-3 ◽

2020 ◽

Vol 79 (41-42) ◽

pp. 30387-30395

Author(s):

Stavros Ntalampiras

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

State Of The Art ◽

Emotional Responses ◽

Machine Learning Techniques ◽

Specific Domain ◽

Learning Framework ◽

Learning Techniques ◽

Wide Range

Abstract Predicting the emotional responses of humans to soundscapes is a relatively recent field of research coming with a wide range of promising applications. This work presents the design of two convolutional neural networks, namely ArNet and ValNet, each one responsible for quantifying arousal and valence evoked by soundscapes. We build on the knowledge acquired from the application of traditional machine learning techniques on the specific domain, and design a suitable deep learning framework. Moreover, we propose the usage of artificially created mixed soundscapes, the distributions of which are located between the ones of the available samples, a process that increases the variance of the dataset leading to significantly better performance. The reported results outperform the state of the art on a soundscape dataset following Schafer’s standardized categorization considering both sound’s identity and the respective listening context.

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An Optimum Tea Fermentation Detection Model Based on Deep Convolutional Neural Networks

Data ◽

10.3390/data5020044 ◽

2020 ◽

Vol 5 (2) ◽

pp. 44

Author(s):

Gibson Kimutai ◽

Alexander Ngenzi ◽

Rutabayiro Ngoga Said ◽

Ambrose Kiprop ◽

Anna Förster

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Machine Learning Techniques ◽

Support Vector ◽

K Nearest Neighbor ◽

Deep Convolutional Neural Networks ◽

Linear Discriminant ◽

Learning Techniques ◽

Classification Tasks

Tea is one of the most popular beverages in the world, and its processing involves a number of steps which includes fermentation. Tea fermentation is the most important step in determining the quality of tea. Currently, optimum fermentation of tea is detected by tasters using any of the following methods: monitoring change in color of tea as fermentation progresses and tasting and smelling the tea as fermentation progresses. These manual methods are not accurate. Consequently, they lead to a compromise in the quality of tea. This study proposes a deep learning model dubbed TeaNet based on Convolution Neural Networks (CNN). The input data to TeaNet are images from the tea Fermentation and Labelme datasets. We compared the performance of TeaNet with other standard machine learning techniques: Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Naive Bayes (NB). TeaNet was more superior in the classification tasks compared to the other machine learning techniques. However, we will confirm the stability of TeaNet in the classification tasks in our future studies when we deploy it in a tea factory in Kenya. The research also released a tea fermentation dataset that is available for use by the community.

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