scholarly journals Image Classification With Convolutional Neural Networks In MapReduce

2021 ◽  
Author(s):  
Min Chen
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Large Scale ◽  
Data Science ◽  
Fault Tolerant ◽  
Computational Cost ◽  
Data Intensive ◽  
Computationally Intensive

Abstract Deep learning (DL) techniques, more specifically Convolutional Neural Networks (CNNs), have become increasingly popular in advancing the field of data science and have had great successes in a wide array of applications including computer vision, speech, natural language processing and etc. However, the training process of CNNs is computationally intensive and high computational cost, especially when the dataset is huge. To overcome these obstacles, this paper takes advantage of distributed frameworks and cloud computing to develop a parallel CNN algorithm. MapReduce is a scalable and fault-tolerant data processing tool that was developed to provide significant improvements in large-scale data-intensive applications in clusters. A MapReduce-based CNN (MCNN) is developed in this work to tackle the task of image classification. In addition, the proposed MCNN adopted the idea of adding dropout layers in the networks to tackle the overfitting problem. Close examination of the implementation of MCNN as well as how the proposed algorithm accelerates learning are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvements in speedup, scaleup and sizeup compared to the standard algorithms.

scholarly journals Image Classification: A Survey

2020 ◽  
Vol 1 (2) ◽  
pp. 1-9
Author(s):  
Ankita Singh ◽  
◽  
Pawan Singh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Learning Ability ◽  
Final Decision

The Classification of images is a paramount topic in artificial vision systems which have drawn a notable amount of interest over the past years. This field aims to classify an image, which is an input, based on its visual content. Currently, most people relied on hand-crafted features to describe an image in a particular way. Then, using classifiers that are learnable, such as random forest, and decision tree was applied to the extract features to come to a final decision. The problem arises when large numbers of photos are concerned. It becomes a too difficult problem to find features from them. This is one of the reasons that the deep neural network model has been introduced. Owing to the existence of Deep learning, it can become feasible to represent the hierarchical nature of features using a various number of layers and corresponding weight with them. The existing image classification methods have been gradually applied in real-world problems, but then there are various problems in its application processes, such as unsatisfactory effect and extremely low classification accuracy or then and weak adaptive ability. Models using deep learning concepts have robust learning ability, which combines the feature extraction and the process of classification into a whole which then completes an image classification task, which can improve the image classification accuracy effectively. Convolutional Neural Networks are a powerful deep neural network technique. These networks preserve the spatial structure of a problem and were built for object recognition tasks such as classifying an image into respective classes. Neural networks are much known because people are getting a state-of-the-art outcome on complex computer vision and natural language processing tasks. Convolutional neural networks have been extensively used.

scholarly journals Designing convolutional neural networks with constrained evolutionary piecemeal training

2021 ◽  
Author(s):  
Dolly Sapra ◽  
Andy D. Pimentel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Network Architecture ◽  
High Performance ◽  
Search Algorithm ◽  
Search Space ◽  
Pareto Optimal Set

AbstractThe automated architecture search methodology for neural networks is known as Neural Architecture Search (NAS). In recent times, Convolutional Neural Networks (CNNs) designed through NAS methodologies have achieved very high performance in several fields, for instance image classification and natural language processing. Our work is in the same domain of NAS, where we traverse the search space of neural network architectures with the help of an evolutionary algorithm which has been augmented with a novel approach of piecemeal-training. In contrast to the previously published NAS techniques, wherein the training with given data is considered an isolated task to estimate the performance of neural networks, our work demonstrates that a neural network architecture and the related weights can be jointly learned by combining concepts of the traditional training process and evolutionary architecture search in a single algorithm. The consolidation has been realised by breaking down the conventional training technique into smaller slices and collating them together with an integrated evolutionary architecture search algorithm. The constraints on architecture search space are placed by limiting its various parameters within a specified range of values, consequently regulating the neural network’s size and memory requirements. We validate this concept on two vastly different datasets, namely, the CIFAR-10 dataset in the domain of image classification, and PAMAP2 dataset in the Human Activity Recognition (HAR) domain. Starting from randomly initialized and untrained CNNs, the algorithm discovers models with competent architectures, which after complete training, reach an accuracy of of 92.5% for CIFAR-10 and 94.36% PAMAP2. We further extend the algorithm to include an additional conflicting search objective: the number of parameters of the neural network. Our multi-objective algorithm produces a Pareto optimal set of neural networks, by optimizing the search for both the accuracy and the parameter count, thus emphasizing the versatility of our approach.

scholarly journals Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2414
Author(s):  
Ebtesam Almazrouei ◽  
Gabriele Gianini ◽  
Nawaf Almoosa ◽  
Ernesto Damiani
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Interference Management ◽  
Computational Cost ◽  
Spectrum Monitoring ◽  
Time Frequency ◽  
Radio Access Technology ◽  
Wide Range ◽  
Channel Conditions

This paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a crucial enabler for emerging wireless access environments that supports sharing of (and dynamic access to) spectral resources between multiple RATs and user classes. Emitter classification enables monitoring the varying patterns of spectral occupancy across RATs, which is instrumental in optimizing spectral utilization and interference management and supporting efficient enforcement of access regulations. Existing emitter classification approaches successfully leverage convolutional neural networks (CNNs) to recognize RAT visual features in spectrograms and other time-frequency representations; however, the corresponding classification accuracy degrades severely under harsh propagation conditions, and the computational cost of CNNs may limit their adoption in resource-constrained network edge scenarios. In this work, we propose a novel emitter classification solution consisting of a Denoising Autoencoder (DAE), which feeds a CNN classifier with lower dimensionality, denoised representations of channel-corrupted spectrograms. We demonstrate—using a standard-compliant simulation of various RATs including LTE and four latest Wi-Fi standards—that in harsh channel conditions including non-line-of-sight, large scale fading, and mobility-induced Doppler shifts, our proposed solution outperforms a wide range of standalone CNNs and other machine learning models while requiring significantly less computational resources. The maximum achieved accuracy of the emitter classifier is 100%, and the average accuracy is 91% across all the propagation conditions.

Convolutional neural networks as aid in core lithofacies classification

2019 ◽  
Vol 7 (3) ◽  
pp. SF27-SF40 ◽  
Author(s):  
Rafael Pires de Lima ◽  
Fnu Suriamin ◽  
Kurt J. Marfurt ◽  
Matthew J. Pranter
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Science ◽  
Science Studies ◽  
Deep Convolutional Neural Networks ◽  
Wide Range ◽  
Description Task ◽  
Self Driving Cars

Artificial intelligence methods have a very wide range of applications. From speech recognition to self-driving cars, the development of modern deep-learning architectures is helping researchers to achieve new levels of accuracy in different fields. Although deep convolutional neural networks (CNNs) (a kind of deep-learning technique) have reached or surpassed human-level performance in image recognition tasks, little has been done to transport this new image classification technology to geoscientific problems. We have developed what we believe to be the first use of CNNs to identify lithofacies in cores. We use highly accurate models (trained with millions of images) and transfer learning to classify images of cored carbonate rocks. We found that different modern CNN architectures can achieve high levels of lithologic image classification accuracy (approximately 90%) and can aid in the core description task. This core image classification technique has the potential to greatly standardize and accelerate the description process. We also provide the community with a new set of labeled data that can be used for further geologic/data science studies.

scholarly journals An Explainable Convolutional Neural Networks for Automatic Segmentation of the Left Ventricle in Cardiac MRI

2021 ◽  
Author(s):  
Jun Liu ◽  
Feng Deng ◽  
Geng Yuan ◽  
Xue Lin ◽  
Houbing Song ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Left Ventricle ◽  
Convolutional Neural Networks ◽  
Cardiac Mri ◽  
Large Scale ◽  
Data Science ◽  
Training System ◽  
Data Sets

Recently, the study on model interpretability has become a hot topic in deep learning research area. Especially in the field of medical imaging, the requirements for safety are extremely high; Moreover, it is very important for the model to be able to explain. However, the existing solutions for left ventricular segmentation by convolutional neural networks are black boxes; explainable CNNs remains a challenge; explainable deep learning models has always been a task often overlooked in the entire data science lifecycle by data scientists or deep learning engineers. Because of very limited medical imaging data, most solutions currently use transfer learning methods to transfer the model which used on large-scale benchmark data sets (such as ImageNet) to fine tune medical imaging models. Consequently, a large amount of useless parameters are generated, resulting in further barrier for the model to provide a convincing explanation. This paper presents a novel method to automatically segment the Left Ventricle in Cardiac MRI by explainable convolutional neural networks with optimized size and parameters by our enhanced Deep Learning GPU Training System. It is very suitable for deployment on mobile devices. We simplify deep learning tasks on DIGITS systems, monitoring performance, and displaying the heat map of each layer of the network with advanced visualizations in real time. Our experiment results demonstrated that the proposed method is feasible and efficient.

scholarly journals Clinical Text De-identification and Other Large Scale Processing Tasks in Resource Constrained Environments

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Richard Jackson ◽  
Richard Dobson ◽  
Robert Stewart
Keyword(s):  
Data Processing ◽  
Language Processing ◽  
Large Scale ◽  
Data Science ◽  
Fault Tolerant ◽  
Data Repositories ◽  
Clinical Text ◽  
Governance Model ◽  
Constrained Environments ◽  
Large Scale Data Processing

ABSTRACT ObjectivesClinical text de-identification is a common requirement of the ‘enclave’ governance model of ethical EHR research. However, there is often little consideration of the engineering task that is required to scale these approaches across the hundreds of millions of clinical documents containing personal identifiers that are resident in the data repositories of a typical NHS Trust. Similarly, natural language processing is an increasingly important field of clinical data science, yet it requires fault tolerant approaches to data processing. This work concerns the development of “turbo-laser” - a distributed document processing architecture based upon the popular ‘battle hardened’ Spring Batch framework - an industry standard for large scale processing tasks. ApproachUsing Spring Batch, we developed a highly scalable unstructured data processing framework, using the concept of remote partitioning. Remote partitioning allows us to offload processing tasks to any and all computers in a network. With this approach, it is possible to harness the entire compute available of an organisation, whether it be an office of 15 desktop PCs that go unused overnight, or a compute cluster of a thousand processors. This method is especially valuable in the NHS, where the provision of sufficient compute to make large scale analytics possible are often hindered by the lack of available hardware, or difficulties in navigating technical governance policies ill equipped for the demands of modern data science. ResultsTurbo-laser was developed in consideration of the processing challenges common in the NHS. Currently, four types of ‘job’ are available - De-identification, using the Cognition algorithm, generic GATE output, text extraction from binary files such as MS Office, PDF and scanned documents, and a document re-compiler to deal with EHR legacy issues. Examples of turbo-laser usage include processing 9 million binary documents on modest hardware, within 48 hours. ConclusionTurbo-laser is an enterprise grade processing tool, in keeping with the software engineering pattern of ‘batch processing’ that has been at the forefront of the informatics movement. An open source project, it is hoped that others may contribute and extend its principles, lowering the barrier of large scale data processing throughout the NHS.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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