scholarly journals A Deep Learning Approach for Generating Pleasant Music

2021 ◽  
pp. 641-649
Author(s):  
Dax Jain ◽  
Diya Mistry ◽  
Dr. Nidhi Arora
Keyword(s):  
Deep Learning ◽  
Music Composition ◽  
Deep Neural Networks ◽  
Learning Approach ◽  
Musical Style ◽  
Simple Method ◽  
Creative Music ◽  
Musical Chords ◽  
Learning Architectures ◽  
Polyphonic Music

Advancement in deep neural networks have made it possible to compose music that mimics music composition by humans. The capacity of deep learning architectures in learning musical style from arbitrary musical corpora have been explored in this paper. The paper proposes a method for generated from the estimated distribution. Musical chords have been extracted for various instruments to train a sequential model to generate the polyphonic music on some selected instruments. We demonstrate a simple method comprising a sequential LSTM models to generate polyphonic music. The results of the model evaluation show that generated music is pleasant to hear and is similar to music played by humans. This has great application in entertainment industry which enables music composers to generate variety of creative music.

Automated pneumonia detection on chest X-ray images: A deep learning approach with different optimizers and transfer learning architectures

Measurement ◽  
2021 ◽  
pp. 109953
Author(s):  
Adhiyaman Manickam ◽  
Jianmin Jiang ◽  
Yu Zhou ◽  
Abhinav Sagar ◽  
Rajkumar Soundrapandiyan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Learning Approach ◽  
X Ray ◽  
Chest X Ray ◽  
Learning Architectures

scholarly journals A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits

2021 ◽  
Vol 12 ◽  
Author(s):  
Osval A. Montesinos-López ◽  
Abelardo Montesinos-López ◽  
Brandon A. Mosqueda-González ◽  
Alison R. Bentley ◽  
Morten Lillemo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Plant Breeding ◽  
Deep Neural Networks ◽  
Reference Population ◽  
Calibration Method ◽  
Data Sets ◽  
Simple Method ◽  
Continuous Response ◽  
Predicting Performance

Genomic selection (GS) has the potential to revolutionize predictive plant breeding. A reference population is phenotyped and genotyped to train a statistical model that is used to perform genome-enabled predictions of new individuals that were only genotyped. In this vein, deep neural networks, are a type of machine learning model and have been widely adopted for use in GS studies, as they are not parametric methods, making them more adept at capturing nonlinear patterns. However, the training process for deep neural networks is very challenging due to the numerous hyper-parameters that need to be tuned, especially when imperfect tuning can result in biased predictions. In this paper we propose a simple method for calibrating (adjusting) the prediction of continuous response variables resulting from deep learning applications. We evaluated the proposed deep learning calibration method (DL_M2) using four crop breeding data sets and its performance was compared with the standard deep learning method (DL_M1), as well as the standard genomic Best Linear Unbiased Predictor (GBLUP). While the GBLUP was the most accurate model overall, the proposed deep learning calibration method (DL_M2) helped increase the genome-enabled prediction performance in all data sets when compared with the traditional DL method (DL_M1). Taken together, we provide evidence for extending the use of the proposed calibration method to evaluate its potential and consistency for predicting performance in the context of GS applied to plant breeding.

Exemplar models are useful and deep neural networks overcome their limitations: A commentary on Ambridge (2020)

2020 ◽  
Vol 40 (5-6) ◽  
pp. 612-615
Author(s):  
James L. McClelland
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Language Processing ◽  
Deep Neural Networks ◽  
Machine Intelligence ◽  
Human Language ◽  
Language Usage ◽  
Primitive Elements ◽  
Exemplar Models ◽  
Learning Architectures

Humans are sensitive to the properties of individual items, and exemplar models are useful for capturing this sensitivity. I am a proponent of an extension of exemplar-based architectures that I briefly describe. However, exemplar models are very shallow architectures in which it is necessary to stipulate a set of primitive elements that make up each example, and such architectures have not been as successful as deep neural networks in capturing language usage and meaning. More work is needed bringing contemporary deep learning architectures used in machine intelligence to the effort to understand human language processing.

scholarly journals Assessment of bilateral knee pain from MR imaging using deep neural networks

2018 ◽  
Author(s):  
Gary H. Chang ◽  
David T. Felson ◽  
Shangran Qiu ◽  
Terence D. Capellini ◽  
Vijaya B. Kolachalama
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Neural Networks ◽  
Deep Learning ◽  
Knee Pain ◽  
Deep Neural Networks ◽  
Learning Approach ◽  
Mr Images ◽  
Bilateral Knee ◽  
Machine Learning Approach

ABSTRACTBackground and objectiveIt remains difficult to characterize pain in knee joints with osteoarthritis solely by radiographic findings. We sought to understand how advanced machine learning methods such as deep neural networks can be used to analyze raw MRI scans and predict bilateral knee pain, independent of other risk factors.MethodsWe developed a deep learning framework to associate information from MRI slices taken from the left and right knees of subjects from the Osteoarthritis Initiative with bilateral knee pain. Model training was performed by first extracting features from two-dimensional (2D) sagittal intermediate-weighted turbo spin echo slices. The extracted features from all the 2D slices were subsequently combined to directly associate using a fused deep neural network with the output of interest as a binary classification problem.ResultsThe deep learning model resulted in predicting bilateral knee pain on test data with 70.1% mean accuracy, 51.3% mean sensitivity, and 81.6% mean specificity. Systematic analysis of the predictions on the test data revealed that the model performance was consistent across subjects of different Kellgren-Lawrence grades.ConclusionThe study demonstrates a proof of principle that a machine learning approach can be applied to associate MR images with bilateral knee pain.SIGNIFICANCE AND INNOVATIONKnee pain is typically considered as an early indicator of osteoarthritis (OA) risk. Emerging evidence suggests that MRI changes are linked to pre-clinical OA, thus underscoring the need for building image-based models to predict knee pain. We leveraged a state-of-the-art machine learning approach to associate raw MR images with bilateral knee pain, independent of other risk factors.

scholarly journals Exploring Deep Physiological Models for Nociceptive Pain Recognition

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4503 ◽  
Author(s):  
Patrick Thiam ◽  
Peter Bellmann ◽  
Hans A. Kestler ◽  
Friedhelm Schwenker
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Expert Knowledge ◽  
Electrodermal Activity ◽  
Nociceptive Pain ◽  
Feature Engineering ◽  
Inference Model ◽  
Inference Models ◽  
Learning Architectures

Standard feature engineering involves manually designing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features for the subsequent optimisation of an inference model. Several studies have shown that this whole manual process can be efficiently replaced by deep learning approaches which are characterised by the integration of feature engineering, feature selection and inference model optimisation into a single learning process. In the following work, deep learning architectures are designed for the assessment of measurable physiological channels in order to perform an accurate classification of different levels of artificially induced nociceptive pain. In contrast to previous works, which rely on carefully designed sets of hand-crafted features, the current work aims at building competitive pain intensity inference models through autonomous feature learning, based on deep neural networks. The assessment of the designed deep learning architectures is based on the BioVid Heat Pain Database (Part A) and experimental validation demonstrates that the proposed uni-modal architecture for the electrodermal activity (EDA) and the deep fusion approaches significantly outperform previous methods reported in the literature, with respective average performances of 84.57 % and 84.40 % for the binary classification experiment consisting of the discrimination between the baseline and the pain tolerance level ( T 0 vs. T 4 ) in a Leave-One-Subject-Out (LOSO) cross-validation evaluation setting. Moreover, the experimental results clearly show the relevance of the proposed approaches, which also offer more flexibility in the case of transfer learning due to the modular nature of deep neural networks.

scholarly journals Combining content and social features in a deep learning approach to Vietnamese email prioritization

2021 ◽  
Author(s):  
Ha Thanh Nguyen ◽  
Quan Dinh Dang ◽  
Anh Quang Tran
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
New Method ◽  
Learning Approach ◽  
Field Of Study ◽  
Learning Techniques ◽  
Email Overload ◽  
Social Features

The email overload problem has been discussed in numerous email-related studies. One of the possible solutions to this problem is email prioritization, which is the act of automatically predicting the importance levels of received emails and sorting the user’s inbox accordingly. Several learning-based methods have been proposed to address the email prioritization problem using content features as well as social features. Although these methods have laid the foundation works in this field of study, the reported performance is far from being practical. Recent works on deep neural networks have achieved good results in various tasks. In this paper, the authors propose a novel email prioritization model which incorporates several deep learning techniques and uses a combination of both content features and social features from email data. This method targets Vietnamese emails and is tested against a self-built Vietnamese email corpus. Conducted experiments explored the effects of different model configurations and compared the effectiveness of the new method to that of a previous work.

scholarly journals Using deep neural networks to detect complex spikes of cerebellar Purkinje cells

2020 ◽  
Vol 123 (6) ◽  
pp. 2217-2234
Author(s):  
Akshay Markanday ◽  
Joachim Bellet ◽  
Marie E. Bellet ◽  
Junya Inoue ◽  
Ziad M. Hafed ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Purkinje Cell ◽  
Purkinje Cells ◽  
Deep Neural Networks ◽  
Learning Approach ◽  
Sorting Algorithms ◽  
Accuracy Level ◽  
Cerebellar Purkinje Cells ◽  
Complex Spikes

Purkinje cell “complex spikes,” fired at perplexingly low rates, play a crucial role in cerebellum-based motor learning. Careful interpretations of these spikes require manually detecting them, since conventional online or offline spike sorting algorithms are optimized for classifying much simpler waveform morphologies. We present a novel deep learning approach for identifying complex spikes, which also measures additional relevant neurophysiological features, with an accuracy level matching that of human experts yet with very little time expenditure.

scholarly journals A Deep Learning Approach for Malware and Software Piracy Threat Detection

2021 ◽  
Vol 11 (6) ◽  
pp. 7757-7762
Author(s):  
K. Aldriwish
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Software Piracy ◽  
Classification Performance ◽  
Learning Approach ◽  
Threat Detection ◽  
Deep Convolutional Neural Networks ◽  
Combined Model ◽  
Security Of Iot

Internet of Things (IoT) -based systems need to be up to date on cybersecurity threats. The security of IoT networks is challenged by software piracy and malware attacks, and much important information can be stolen and used for cybercrimes. This paper attempts to improve IoT cybersecurity by proposing a combined model based on deep learning to detect malware and software piracy across the IoT network. The malware’s model is based on Deep Convolutional Neural Networks (DCNNs). Apart from this, TensorFlow Deep Neural Networks (TFDNNs) are introduced to detect software piracy threats according to source code plagiarism. The investigation is conducted on the Google Code Jam (GCJ) dataset. The conducted experiments prove that the classification performance achieves high accuracy of about 98%.

scholarly journals Mobile-based Deep Learning Models for Banana Disease Detection

2020 ◽  
Vol 10 (3) ◽  
pp. 5674-5677 ◽  
Author(s):  
S. L. Sanga ◽  
D. Machuve ◽  
K. Jomanga
Keyword(s):  
Deep Learning ◽  
Food Security ◽  
Early Detection ◽  
Smallholder Farmers ◽  
Disease Detection ◽  
Learning Approach ◽  
Learning Models ◽  
Banana Production ◽  
Pests And Diseases ◽  
Learning Architectures

In Tanzania, smallholder farmers contribute significantly to banana production and Kagera, Mbeya, and Arusha are among the leading regions. However, pests and diseases are a threat to food security. Early detection of banana diseases is important to identify the diseases before too much damage is done on the plants. In this paper, a tool for early detection of banana diseases by using a deep learning approach is proposed. Five deep learning architectures, namely Vgg16, Resnet18, Resnet50, Resnet152 and InceptionV3 were used to develop models for banana disease detection, achieving all high accuracies, varying from 95.41% for InceptionV3 to 99.2% for Resnet152. InceptionV3 was selected for mobile deployment because it demands much less memory. The developed tool was capable of detecting diseases with a confidence of 99% of the captured leaves from the real environment. This tool will help smallholder farmers conduct early detection of banana diseases and improve their productivity.

scholarly journals VIDHOP, viral host prediction with deep learning

2020 ◽  
Author(s):  
Florian Mock ◽  
Adrian Viehweger ◽  
Emanuel Barth ◽  
Manja Marz
Keyword(s):  
Deep Learning ◽  
Viral Genome ◽  
Influenza A ◽  
Deep Neural Networks ◽  
Supplementary Information ◽  
Learning Approach ◽  
Virus Species ◽  
Genome Sequences ◽  
Rotavirus A ◽  
The Core

Abstract Motivation Zoonosis, the natural transmission of infections from animals to humans, is a far-reaching global problem. The recent outbreaks of Zikavirus, Ebolavirus and Coronavirus are examples of viral zoonosis, which occur more frequently due to globalization. In case of a virus outbreak, it is helpful to know which host organism was the original carrier of the virus to prevent further spreading of viral infection. Recent approaches aim to predict a viral host based on the viral genome, often in combination with the potential host genome and arbitrarily selected features. These methods are limited in the number of different hosts they can predict or the accuracy of the prediction. Results Here, we present a fast and accurate deep learning approach for viral host prediction, which is based on the viral genome sequence only. We tested our deep neural network (DNN) on three different virus species (influenza A virus, rabies lyssavirus and rotavirus A). We achieved for each virus species an AUC between 0.93 and 0.98, allowing highly accurate predictions while using only fractions (100–400 bp) of the viral genome sequences. We show that deep neural networks are suitable to predict the host of a virus, even with a limited amount of sequences and highly unbalanced available data. The trained DNNs are the core of our virus–host prediction tool VIrus Deep learning HOst Prediction (VIDHOP). VIDHOP also allows the user to train and use models for other viruses. Availability and implementation VIDHOP is freely available under https://github.com/flomock/vidhop. Supplementary information Supplementary data are available at Bioinformatics online.

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