scholarly journals Emotional quantification of soundscapes by learning between samples

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.

scholarly journals p1-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning

2019 ◽  
Vol 2019 (3) ◽  
pp. 191-209 ◽  
Author(s):  
Se Eun Oh ◽  
Saikrishna Sunkam ◽  
Nicholas Hopper
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Feature Engineering ◽  
Engineering Process ◽  
Learning Techniques ◽  
Wide Range

Abstract Recent advances in Deep Neural Network (DNN) architectures have received a great deal of attention due to their ability to outperform state-of-the-art machine learning techniques across a wide range of application, as well as automating the feature engineering process. In this paper, we broadly study the applicability of deep learning to website fingerprinting. First, we show that unsupervised DNNs can generate lowdimensional informative features that improve the performance of state-of-the-art website fingerprinting attacks. Second, when used as classifiers, we show that they can exceed performance of existing attacks across a range of application scenarios, including fingerprinting Tor website traces, fingerprinting search engine queries over Tor, defeating fingerprinting defenses, and fingerprinting TLS-encrypted websites. Finally, we investigate which site-level features of a website influence its fingerprintability by DNNs.

scholarly journals Bike Sharing Prediction using Deep Neural Networks

2017 ◽  
Vol 1 (3) ◽  
pp. 83 ◽  
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.

scholarly journals DETECTION OF WHALES USING DEEP LEARNING METHODS AND NEURAL NETWORKS

2017 ◽  
Vol 10 (13) ◽  
pp. 489 ◽  
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.

scholarly journals Electrocardiogram Classification Based on Deep Convolutional Neural Networks: A Review

2021 ◽  
pp. 43-53
Author(s):  
admin admin ◽  
◽  
◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Machine Learning Techniques ◽  
Deep Convolutional Neural Networks ◽  
Learning Techniques ◽  
Good Classification ◽  
Learning Architectures ◽  
Ecg Data

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.

scholarly journals Application of Machine Learning in Rhinology: A State of the Art Review

2020 ◽  
Vol 63 (8) ◽  
pp. 341-349
Author(s):  
Myeong Sang Yu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Medical Records ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Machine Learning Technique ◽  
Learning Techniques ◽  
Learning Technique ◽  
Machine Learning Applications

The revolutionary development of artificial intelligence (AI) such as machine learning and deep learning have been one of the most important technology in many parts of industry, and also enhance huge changes in health care. The big data obtained from electrical medical records and digitalized images accelerated the application of AI technologies in medical fields. Machine learning techniques can deal with the complexity of big data which is difficult to apply traditional statistics. Recently, the deep learning techniques including convolutional neural network have been considered as a promising machine learning technique in medical imaging applications. In the era of precision medicine, otolaryngologists need to understand the potentialities, pitfalls and limitations of AI technology, and try to find opportunities to collaborate with data scientists. This article briefly introduce the basic concepts of machine learning and its techniques, and reviewed the current works on machine learning applications in the field of otolaryngology and rhinology.

scholarly journals Winsorization for Robust Bayesian Neural Networks

Entropy ◽  
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.

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

Philosophies ◽  
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.

scholarly journals Identifying Ear Abnormality from 2D Photographs Using Convolutional Neural Networks

2019 ◽  
Vol 9 (1) ◽  
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.

scholarly journals Deep Learning in Risk Assessment

2019 ◽  
Author(s):  
Janayna Moura ◽  
Lucas Bissaro ◽  
Fernanda Santos ◽  
Murillo Carneiro
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
State Of The Art ◽  
Risk Groups ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Credit Evaluation ◽  
Learning Techniques ◽  
Evaluation Problem ◽  
Consistent Performance

Credit evaluation models have been largely studied in the accounting and finance literature. With the support of such models, usually developed as part of a data mining process, it is possible to classify the credit applicants more accurately into ``good'' or ``bad'' risk groups. Despite many machine learning techniques have been extensively evaluated to this problem, deep learning models have been barely explored yet, although they have provided state-of-the-art results for a myriad of applications. In this paper, we propose deep learning models for the credit evaluation problem. To be specific, we investigate the abilities of deep neural networks (DNN) and convolutional neural networks (CNN) for such a problem and systematically compare their classification accuracy against five commonly adopted techniques on three real-world credit evaluation datasets. The results show that random forest, which is a state-of-the-art technique for such a problem, presented the most consistent performance, although CNN demonstrated a high potential to outperform it in bigger datasets.

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