scholarly journals Self-Adaptive Approximate Mobile Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2958
Author(s):  
Timotej Knez ◽  
Octavian Machidon ◽  
Veljko Pejović
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real World ◽  
Edge Computing ◽  
Major Drawback ◽  
High Resource ◽  
Average Accuracy ◽  
Resource Requirements ◽  
And Performance ◽  
Network Compression

Edge intelligence is currently facing several important challenges hindering its performance, with the major drawback being meeting the high resource requirements of deep learning by the resource-constrained edge computing devices. The most recent adaptive neural network compression techniques demonstrated, in theory, the potential to facilitate the flexible deployment of deep learning models in real-world applications. However, their actual suitability and performance in ubiquitous or edge computing applications has not, to this date, been evaluated. In this context, our work aims to bridge the gap between the theoretical resource savings promised by such approaches and the requirements of a real-world mobile application by introducing algorithms that dynamically guide the compression rate of a neural network according to the continuously changing context in which the mobile computation is taking place. Through an in-depth trace-based investigation, we confirm the feasibility of our adaptation algorithms in offering a scalable trade-off between the inference accuracy and resource usage. We then implement our approach on real-world edge devices and, through a human activity recognition application, confirm that it offers efficient neural network compression adaptation in highly dynamic environments. The results of our experiment with 21 participants show that, compared to using static network compression, our approach uses 2.18× less energy with only a 1.5% drop in the average accuracy of the classification.

scholarly journals Real-Time Automated Classification of Sky Conditions Using Deep Learning and Edge Computing

2021 ◽  
Vol 13 (19) ◽  
pp. 3859
Author(s):  
Joby M. Prince Czarnecki ◽  
Sathishkumar Samiappan ◽  
Meilun Zhou ◽  
Cary Daniel McCraine ◽  
Louis L. Wasson
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Precision Agriculture ◽  
Edge Computing ◽  
Training Data ◽  
Learning Approaches ◽  
Sky Conditions

The radiometric quality of remotely sensed imagery is crucial for precision agriculture applications because estimations of plant health rely on the underlying quality. Sky conditions, and specifically shadowing from clouds, are critical determinants in the quality of images that can be obtained from low-altitude sensing platforms. In this work, we first compare common deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. We then develop an artificial-intelligence-based edge computing system to fully automate the classification process. Training data consisting of 100 oblique angle images of the sky were provided to a convolutional neural network and two deep residual neural networks (ResNet18 and ResNet34) to facilitate learning two classes, namely (1) good image quality expected, and (2) degraded image quality expected. The expectation of quality stemmed from the sky condition (i.e., density, coverage, and thickness of clouds) present at the time of the image capture. These networks were tested using a set of 13,000 images. Our results demonstrated that ResNet18 and ResNet34 classifiers produced better classification accuracy when compared to a convolutional neural network classifier. The best overall accuracy was obtained by ResNet34, which was 92% accurate, with a Kappa statistic of 0.77. These results demonstrate a low-cost solution to quality control for future autonomous farming systems that will operate without human intervention and supervision.

CloudA: A Ground-Based Cloud Classification Method with a Convolutional Neural Network

2020 ◽  
Vol 37 (9) ◽  
pp. 1661-1668
Author(s):  
Min Wang ◽  
Shudao Zhou ◽  
Zhong Yang ◽  
Zhanhua Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Learning Ability ◽  
Visualization Method ◽  
Classification Methods ◽  
Recognition Method ◽  
Cloud Classification ◽  
Average Accuracy

AbstractConventional classification methods are based on artificial experience to extract features, and each link is independent, which is a kind of “shallow learning.” As a result, the scope of the cloud category applied by this method is limited. In this paper, we propose a new convolutional neural network (CNN) with deep learning ability, called CloudA, for the ground-based cloud image recognition method. We use the Singapore Whole-Sky Imaging Categories (SWIMCAT) sample library and total-sky sample library to train and test CloudA. In particular, we visualize the cloud features captured by CloudA using the TensorBoard visualization method, and these features can help us to understand the process of ground-based cloud classification. We compare this method with other commonly used methods to explore the feasibility of using CloudA to classify ground-based cloud images, and the evaluation of a large number of experiments show that the average accuracy of this method is nearly 98.63% for ground-based cloud classification.

scholarly journals An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

2021 ◽  
Vol 11 (21) ◽  
pp. 10301
Author(s):  
Muhammad Shoaib Farooq ◽  
Attique Ur Rehman ◽  
Muhammad Idrees ◽  
Muhammad Ahsan Raza ◽  
Jehad Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real World ◽  
Early Stage ◽  
Binary Classification ◽  
X Rays ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

scholarly journals Implementation of Deep Learning for Slump Optimization Based on Concrete Quality Using Convolutional Neural Network in PT. SELO PROGO SAKTI

2020 ◽  
Vol 5 (2) ◽  
pp. 83-88
Author(s):  
Hedi Pandowo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Experimental Design ◽  
Convolutional Neural Network ◽  
Processing Unit ◽  
Training Process ◽  
Average Accuracy ◽  
Graphical Processing ◽  
Intelligence Science

Deep Learning is part of the scientific field of Machine Learning and Machine Learning is part of Artificial Intelligence science. Deep Learning has extraordinary capabilities by using a hardware Graphical Processing Unit (GPU) so that the artificial requirement network can run faster than using a Personal Computer Unit (CPU). Especially in terms of object classification in images using existing methods in the Convolutional Neural Network (CNN). The method used in this research is Preprocessing and Processing of Input Data, Training Process in which CNN is trained to obtain high accuracy from the classification carried out and the Testing Process which is a classification process using weights and bias from the results of the training process. This type of research is a pre experimental design (pre experimental design). The results of the object image classification test with different levels of confusion in the Concrete database with the Mix Design K-125, K-150, K-250 and K-300 produce an average accuracy value. This is also relevant to measuring the failure rate of concrete or slump

Classification of EgyptSat-1 Images Using Deep Learning Methods

2020 ◽  
Vol 10 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Hatem Keshk ◽  
Xu-Cheng Yin
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Performance ◽  
Satellite Images ◽  
Satellite Image ◽  
Aerial Photographs ◽  
Average Accuracy

Background: Deep Learning (DL) neural network methods have become a hotspot subject of research in the remote sensing field. Classification of aerial satellite images depends on spectral content, which is a challenging topic in remote sensing. Objective: With the aim to accomplish a high performance and accuracy of Egyptsat-1 satellite image classification, the use of the Convolutional Neural Network (CNN) is raised in this paper because CNN is considered a leading deep learning method. CNN is developed to classify aerial photographs into land cover classes such as urban, vegetation, desert, water bodies, soil, roads, etc. In our work, a comparison between MAXIMUM Likelihood (ML) which represents the traditional supervised classification methods and CNN method is conducted. Conclusion: This research finds that CNN outperforms ML by 9%. The convolutional neural network has better classification result, which reached 92.25% as its average accuracy. Also, the experiments showed that the convolutional neural network is the most satisfactory and effective classification method applied to classify Egyptsat-1 satellite images.

scholarly journals An Input-aware Factorization Machine for Sparse Prediction

2019 ◽  
Author(s):  
Yantao Yu ◽  
Zhen Wang ◽  
Bo Yuan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Overall Performance ◽  
Factorization Machine ◽  
The Impact ◽  
Novel Model ◽  
Individual Input ◽  
Better Than

Factorization machines (FMs) are a class of general predictors working effectively with sparse data, which represents features using factorized parameters and weights. However, the accuracy of FMs can be adversely affected by the fixed representation trained for each feature, as the same feature is usually not equally predictive and useful in different instances. In fact, the inaccurate representation of features may even introduce noise and degrade the overall performance. In this work, we improve FMs by explicitly considering the impact of individual input upon the representation of features. We propose a novel model named \textit{Input-aware Factorization Machine} (IFM), which learns a unique input-aware factor for the same feature in different instances via a neural network. Comprehensive experiments on three real-world recommendation datasets are used to demonstrate the effectiveness and mechanism of IFM. Empirical results indicate that IFM is significantly better than the standard FM model and consistently outperforms four state-of-the-art deep learning based methods.

scholarly journals Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period

2020 ◽  
Vol 11 (2) ◽  
pp. 36-50
Author(s):  
Aljaž Ferencek ◽  
Davorin Kofjač ◽  
Andrej Škraba ◽  
Blaž Sašek ◽  
Mirjana Kljajić Borštnar
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Predictive Models ◽  
Discrete Event ◽  
Quality Data ◽  
Call Rate ◽  
Learning Models ◽  
Data Set ◽  
Warranty Period ◽  
And Performance

AbstractBackground: This paper addresses the problem of products’ terminal call rate (TCR) prediction during the warranty period. TCR refers to the information on the amount of funds to be reserved for product repairs during the warranty period. So far, various methods have been used to address this problem, from discrete event simulation and time series, to machine learning predictive models.Objectives: In this paper, we address the above named problem by applying deep learning models to predict terminal call rate.Methods/Approach: We have developed a series of deep learning models on a data set obtained from a manufacturer of home appliances, and we have analysed their quality and performance.Results: Results showed that a deep neural network with 6 layers and a convolutional neural network gave the best results.Conclusions: This paper suggests that deep learning is an approach worth exploring further, however, with the disadvantage being that it requires large volumes of quality data.

scholarly journals Cloud-based architecture for face identification with deep learning using convolutional neural network

2021 ◽  
Vol 23 (2) ◽  
pp. 811
Author(s):  
Aditya Herlambang ◽  
Putu Wira Buana ◽  
I Nyoman Piarsa
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Face Identification ◽  
K Nearest Neighbor ◽  
Non Invasive ◽  
The Face ◽  
And Performance ◽  
University Of Manchester

The use of a face as a biometric to identify a person in order to keep the system safe from an unauthorized person has advantages over other biometric characteristics. The face as a biometric has more structure and a wider area than other biometrics, while can be retrieved in a non-invasive manner. We proposed a cloud-based architecture for face identification with deep learning using convolutional neural network. Face identification in this study used a cloud-based engine with four stages, namely face detection with histogram of oriented gradients (HOG), image enhancement, feature extraction using convolutional neural network, and classification using k-nearest neighbor (KNN), SVM, as well as random forest algorithm. This study conducted a classification experiment with cloud-based architecture using three different datasets, namely Faces94, Faces96 and University of Manchester Institute of Science and Technology (UMIST) face dataset. The results from this study are with the proposed cloud-based architecture, the best accuracy is obtained by KNN algorithm with an accuracy of 99% on Faces94 dataset, 99% accuracy on Faces96 dataset, 97% on UMIST face dataset, and performance of the three algorithms decreased in UMIST face dataset with facial variations from various angles from left to right profile.

A Novel Hybrid Deep Multi-Criteria Model for Recommender System

2021 ◽  
Author(s):  
Nour Salim Nassar
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Real World ◽  
Deep Neural Network ◽  
Recommendation Systems ◽  
The Other ◽  
Learning Techniques ◽  
Novel Model

Abstract Recommender systems are everywhere books, products, movies, and more. Traditional recommender systems typically use a single criterion in the recommendation, while studies have shown that multi-criteria recommending is more accurate. Novel deep learning techniques have produced remarkable achievements in many fields. The use of such techniques in recommendation systems has started to get attention recently, and several models of recommendation have been proposed based on deep learning. However, there is still no work for using deep learning in hybrid multi-criteria recommender systems. In this work, a model for a hybrid deep multi-criteria recommender system was presented. The model mainly includes two major parts: In the first one, the model obtains the user ID, item ID, and the item metadata to be used as input to a deep neural network in order to predict the criteria ratings. In the second part, the obtained ratings act as an input to another deep neural network, where the overall rating is predicted. Our experiments were conducted on a real-world dataset. They demonstrated the superiority of the proposed novel model over the other models in all measures used to evaluate performance. This indicates the successful use of hybrid deep multi-criteria in the recommendation systems.

Automated pill quality inspection using deep learning

2021 ◽  
pp. 2140050
Author(s):  
Thi Thoa Mac ◽  
Nguyen Thanh Hung
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
Internal Crack ◽  
Quality Inspection ◽  
Statistical Representation ◽  
Financial Costs ◽  
Average Accuracy ◽  
Processing Techniques

The pill manufacturing process accrues substantial financial costs due to quality. Pill quality inspection is laborious, time-consuming and subjective, resulting in poor statistical representation and inconsistent results. In this study, we developed an approach that integrates deep learning algorithms and computer-vision-based processing with an optimization algorithm to fully automate the image analysis of internal crack/contamination detection. This approach exploits the features learned by convolutional neural network using various sub-processing techniques and Adam optimization. It achieves robust quantification of internal pill defects with an average accuracy of 95%.

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