scholarly journals Structured (De)composable Representations Trained with Neural Networks

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 79
Author(s):  
Graham Spinks ◽  
Marie-Francine Moens
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Contextual Information ◽  
Learning To Learn ◽  
Class Label ◽  
Impact Performance ◽  
Novel Technique ◽  
Language Data ◽  
Concept Classes ◽  
Generic Representation

This paper proposes a novel technique for representing templates and instances of concept classes. A template representation refers to the generic representation that captures the characteristics of an entire class. The proposed technique uses end-to-end deep learning to learn structured and composable representations from input images and discrete labels. The obtained representations are based on distance estimates between the distributions given by the class label and those given by contextual information, which are modeled as environments. We prove that the representations have a clear structure allowing decomposing the representation into factors that represent classes and environments. We evaluate our novel technique on classification and retrieval tasks involving different modalities (visual and language data). In various experiments, we show how the representations can be compressed and how different hyperparameters impact performance.

Facemask Detection Based on Double Convolutional Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Guoqiang Chen ◽  
Bingxin Bai ◽  
Hongpeng Zhou ◽  
Mengchao Liu ◽  
Huailong Yi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Feature Fusion ◽  
Contextual Information ◽  
Detection Accuracy ◽  
Learning Networks ◽  
Position Information ◽  
Detection Model ◽  
Multi Stage

Background: The study on facemask detection is of great significance because facemask detection is difficult, and the workload is heavy in places with a large number of people during the COVID-19 outbreak. Objective: The study aims to explore new deep learning networks that can accurately detect facemasks and improve the network's ability to extract multi-level features and contextual information. In addition, the proposed network effectively avoids the interference of objects like masks. The new network could eventually detect masks wearers in the crowd. Method: A Multi-stage Feature Fusion Block (MFFB) and a Detector Cascade Block (DCB) are proposed and connected to the deep learning network for facemask detection. The network's ability to obtain information improves. The network proposed in the study is Double Convolutional Neural Networks (CNN) called DCNN, which can fuse mask features and face position information. During facemask detection, the network extracts the featural information of the object and then inputs it into the data fusion layer. Results: The experiment results show that the proposed network can detect masks and faces in a complex environment and dense crowd. The detection accuracy of the network improves effectively. At the same time, the real-time performance of the detection model is excellent. Conclusion: The two branch networks of the DCNN can effectively obtain the feature and position information of facemasks. The network overcomes the disadvantage that a single CNN is susceptible to the interference of the suspected mask objects. The verification shows that the MFFB and the DCB can improve the network's ability to obtain object information, and the proposed DCNN can achieve excellent detection performance.

scholarly journals Microscopy images segmentation algorithm based on shearlet neural network

2021 ◽  
Vol 10 (2) ◽  
pp. 724-731
Author(s):  
Nemir Ahmed Al-Azzawi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Contextual Information ◽  
Computational Method ◽  
Feed Forward Neural Network ◽  
Electron Microscopic ◽  
Electron Microscopic Image ◽  
Hidden Layer ◽  
Microscopy Images

Microscopic images are becoming important and need to be studied to know the details and how-to quantitatively evaluate decellularization. Most of the existing research focuses on deep learning-based techniques that lack simplification for decellularization. A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced. The proposal is to link the concept of shearlets transform and neural networks into a single unit. The method contains a feed-forward neural network and uses a single hidden layer. The activation functions are depending on the standard shearlet transform. The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data. The shearlet neural networks capture the features of full accuracy and contextual information, respectively. The expected value for specific inputs is estimated by learning the functional configuration of a network for the sequence of observed value. Experimental results on the segmentation of two-dimensional microscopy images are promising and confirm the benefits of the proposed approach. Lastly, we investigate on a challenging datasets ISBI 2012 that our method (SNN) achieves superior outcomes when compared to classical and deep learning-based methods.

scholarly journals SMART ELECTRICITY CONSERVATION SYSTEM USING EFFICIENTNET

2021 ◽  
Vol 9 (2) ◽  
pp. 978-983
Author(s):  
Ushasukhanya S, Et. al.
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Electric Power ◽  
Power Supply ◽  
Electric Power Supply ◽  
Novel Technique ◽  
Learning Technique ◽  
Energy Conserving ◽  
Arduino Microcontroller ◽  
Electricity Conservation

Conservation of electric resource has been one of the important challenges over the decades. Worldwide, many nations are struggling to conserve and to bridge the gap between the demand and production of the resource. Though many measures like several Government acts, replacing existing products with energy conserving products and many solar based systems are being invented and used in practise, the demand and the need to preserve the resource still persists. Hence, this paper focuses on a novel technique to conserve the electric resource using a deep learning technique. The system uses Convolutional Neural Networks to identify and localize humans in the CCTV footages using EfficientNet, a deep transfer learning model. The classifier processes and yields its output to an embedded Arduino microcontroller, after detecting the presence/absence of human. The microcontroller enables/disables the electric power supply in the area of human’s presence/absence, based on the classifier’s output respectively. The system achieves an accuracy percentage of 84.2% in detecting humans in the footages with the subsequent enabling/disabling of electric power resource to conserve electricity.

scholarly journals Recognition of Fine-Grained Walking Patterns Using a Smartwatch with Deep Attentive Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6393
Author(s):  
Hyejoo Kim ◽  
Hyeon-Joo Kim ◽  
Jinyoon Park ◽  
Jeh-Kwang Ryu ◽  
Seung-Chan Kim
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Contextual Information ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Wearable System ◽  
Fine Grained ◽  
Feature Based ◽  
Recent Attention

Generally, people do various things while walking. For example, people frequently walk while looking at their smartphones. Sometimes we walk differently than usual; for example, when walking on ice or snow, we tend to waddle. Understanding walking patterns could provide users with contextual information tailored to the current situation. To formulate this as a machine-learning problem, we defined 18 different everyday walking styles. Noting that walking strategies significantly affect the spatiotemporal features of hand motions, e.g., the speed and intensity of the swinging arm, we propose a smartwatch-based wearable system that can recognize these predefined walking styles. We developed a wearable system, suitable for use with a commercial smartwatch, that can capture hand motions in the form of multivariate timeseries (MTS) signals. Then, we employed a set of machine learning algorithms, including feature-based and recent deep learning algorithms, to learn the MTS data in a supervised fashion. Experimental results demonstrated that, with recent deep learning algorithms, the proposed approach successfully recognized a variety of walking patterns, using the smartwatch measurements. We analyzed the results with recent attention-based recurrent neural networks to understand the relative contributions of the MTS signals in the classification process.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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 Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

2021 ◽  
Vol 11 (5) ◽  
pp. 2284
Author(s):  
Asma Maqsood ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Marcin Grzegorzek
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Red Blood Cells ◽  
Convolutional Neural Networks ◽  
Blood Cells ◽  
Superior Performance ◽  
Learning Models ◽  
Infected Female ◽  
The World ◽  
Augmentation Techniques

Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.

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