Robust Attention Deraining Network for Synchronous Rain Streaks and Raindrops Removal

<div>Synchronous Rain streaks and Raindrops Removal (SR3) is a very hard and challenging task, since rain streaks and raindrops are two wildly divergent real-scenario phenomena with different optical properties and mathematical distributions. As such, most of existing deep learning-based Singe Image Deraining (SID) methods only focus on one of them or the other. To solve this issue, we propose a new, robust and hybrid SID model, termed Robust Attention Deraining Network (RadNet) with strong robustenss and generalztion ability. The robustness of RadNet has two implications :(1) it can restore different degenerations, including raindrops, rain streaks, or both; (2) it can adapt to different data strategies, including single-type, superimposed-type and blended-type. Specifically, we first design a lightweight robust attention module (RAM) with a universal attention mechanism for coarse rain removal, and then present a new deep refining module (DRM) with multi-scales blocks for precise rain removal. The whole process is unified in a network to ensure sufficient robustness and strong generalization ability. We measure the performance of several SID methods on the SR3 task under a variety of data strategies, and extensive experiments demonstrate that our RadNet can outperform other state-of-the-art SID methods.</div>

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Data science in economics: comprehensive review of advanced machine learning and deep learning methods

10.21203/rs.3.rs-91905/v1 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Prediction Accuracy ◽

Data Science ◽

State Of The Art ◽

Hybrid Models ◽

The Other ◽

Learning Models ◽

Comprehensive Review

Abstract This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

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Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

10.20944/preprints202010.0263.v1 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Prediction Accuracy ◽

Data Science ◽

State Of The Art ◽

Hybrid Models ◽

The Other ◽

Learning Models ◽

Comprehensive Review

This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

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Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest

Applied Sciences ◽

10.3390/app10186425 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6425

Author(s):

Natascha Claudia D’Amico ◽

Rosa Sicilia ◽

Ermanno Cordelli ◽

Lorenzo Tronchin ◽

Carlo Greco ◽

...

Keyword(s):

Lung Cancer ◽

Overall Survival ◽

Deep Learning ◽

Small Cell Lung Cancer ◽

State Of The Art ◽

Local Binary Patterns ◽

Research Field ◽

Small Cell ◽

The Other ◽

Small Cell Lung

Lung cancer accounts for the largest amount of deaths worldwide with respect to the other oncological pathologies. To guarantee the most effective cure to patients for such aggressive tumours, radiomics is increasing as a novel and promising research field that aims at extracting knowledge from data in terms of quantitative measures that are computed from diagnostic images, with prognostic and predictive ends. This knowledge could be used to optimize current treatments and to maximize their efficacy. To this end, we hereby study the use of such quantitative biomarkers computed from CT images of patients affected by Non-Small Cell Lung Cancer to predict Overall Survival. The main contributions of this work are two: first, we consider different volumes of interest for the same patient to find out whether the volume surrounding the visible lesions can provide useful information; second, we introduce 3D Local Binary Patterns, which are texture measures scarcely explored in radiomics. As further validation, we show that the proposed signature outperforms not only the features automatically computed by a deep learning-based approach, but also another signature at the state-of-the-art using other handcrafted features.

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Bitstream-Based Neural Network for Scalable, Efficient, and Accurate Deep Learning Hardware

Frontiers in Neuroscience ◽

10.3389/fnins.2020.543472 ◽

2020 ◽

Vol 14 ◽

Author(s):

Hyeonuk Sim ◽

Jongeun Lee

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recognition Accuracy ◽

Fault Tolerant ◽

State Of The Art ◽

The Other ◽

Stochastic Computing ◽

Hardware Implementations ◽

Neuromorphic Hardware ◽

Very High

While convolutional neural networks (CNNs) continue to renew state-of-the-art performance across many fields of machine learning, their hardware implementations tend to be very costly and inflexible. Neuromorphic hardware, on the other hand, targets higher efficiency but their inference accuracy lags far behind that of CNNs. To bridge the gap between deep learning and neuromorphic computing, we present bitstream-based neural network, which is both efficient and accurate as well as being flexible in terms of arithmetic precision and hardware size. Our bitstream-based neural network (called SC-CNN) is built on top of CNN but inspired by stochastic computing (SC), which uses bitstreams to represent numbers. Being based on CNN, our SC-CNN can be trained with backpropagation, ensuring very high inference accuracy. At the same time our SC-CNN is deterministic, hence repeatable, and is highly accurate and scalable even to large networks. Our experimental results demonstrate that our SC-CNN is highly accurate up to ImageNet-targeting CNNs, and improves efficiency over conventional digital designs ranging through 50–100% in operations-per-area depending on the CNN and the application scenario, while losing <1% in recognition accuracy. In addition, our SC-CNN implementations can be much more fault-tolerant than conventional digital implementations.

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A Method for Optimal Detection of Lung Cancer Based on Deep Learning Optimized by Marine Predators Algorithm

Computational Intelligence and Neuroscience ◽

10.1155/2021/3694723 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Xinrong Lu ◽

Y. A. Nanehkaran ◽

Maryam Karimi Fard

Keyword(s):

Neural Network ◽

Lung Cancer ◽

Deep Learning ◽

State Of The Art ◽

The Body ◽

The Other ◽

Optimal Detection ◽

Optimal Arrangement ◽

Marine Predators ◽

Deep Networks

Lung cancer is the uncontrolled growth of cells in the lung that are made up of two spongy organs located in the chest. These cells may penetrate outside the lungs in a process called metastasis and spread to tissues and organs in the body. In this paper, using image processing, deep learning, and metaheuristic, an optimal methodology is proposed for early detection of this cancer. Here, we design a new convolutional neural network for this purpose. Marine predators algorithm is also used for optimal arrangement and better network accuracy. The method finally applied to RIDER dataset, and the results are compared with some pretrained deep networks, including CNN ResNet-18, GoogLeNet, AlexNet, and VGG-19. Final results showed higher results of the proposed method toward the compared techniques. The results showed that the proposed MPA-based method with 93.4% accuracy, 98.4% sensitivity, and 97.1% specificity provides the highest efficiency with the least error (1.6) toward the other state of the art methods.

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A Deep Learning Based Approach for Localization and Recognition of Pakistani Vehicle License Plates

Sensors ◽

10.3390/s21227696 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7696

Author(s):

Umair Yousaf ◽

Ahmad Khan ◽

Hazrat Ali ◽

Fiaz Gul Khan ◽

Zia ur Rehman ◽

...

Keyword(s):

Deep Learning ◽

State Of The Art ◽

The Other ◽

License Plate ◽

Standard Size ◽

Current State ◽

Bounding Box ◽

Proposed Model ◽

Plate Area

License plate localization is the process of finding the license plate area and drawing a bounding box around it, while recognition is the process of identifying the text within the bounding box. The current state-of-the-art license plate localization and recognition approaches require license plates of standard size, style, fonts, and colors. Unfortunately, in Pakistan, license plates are non-standard and vary in terms of the characteristics mentioned above. This paper presents a deep-learning-based approach to localize and recognize Pakistani license plates with non-uniform and non-standardized sizes, fonts, and styles. We developed a new Pakistani license plate dataset (PLPD) to train and evaluate the proposed model. We conducted extensive experiments to compare the accuracy of the proposed approach with existing techniques. The results show that the proposed method outperformed the other methods to localize and recognize non-standard license plates.

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Detection of Cotton Plant Diseases Using Deep Transfer Learning

Journal of Mobile Multimedia ◽

10.13052/jmm1550-4646.1828 ◽

2021 ◽

Author(s):

Vani Rajasekar ◽

K Venu ◽

Soumya Ranjan Jena ◽

R. Janani Varthini ◽

S. Ishwarya

Keyword(s):

Neural Networks ◽

Image Processing ◽

Deep Learning ◽

State Of The Art ◽

Plant Diseases ◽

The Other ◽

Convolution Kernel ◽

Learning Method ◽

Specific Task ◽

Classification Problems

Agriculture is a vital part of every country’s economy, and India is regarded an agro-based nation. One of the main purposes of agriculture is to yield healthy crops without any disease. Cotton is a significant crop in India in relation to income. India is the world’s largest producer of cotton. Cotton crops are affected when leaves fall off early or become afflicted with diseases. Farmers and planting experts, on the other hand, have faced numerous concerns and ongoing agricultural obstacles for millennia, including much cotton disease. Because severe cotton disease can result in no grain harvest, a rapid, efficient, less expensive and reliable approach for detecting cotton illnesses is widely wanted in the agricultural information area. Deep learning method is used to solve the issue because it will perform exceptionally well in image processing and classification problems. The network was built using a combination of the benefits of both the ResNet pre-trained on ImageNet and the Xception component, and this technique outperforms other state-of-the-art techniques. Every convolution layer with in dense block is tiny, so each convolution kernel is still in charge of learning the tiniest details. The deep convolution neural networks for the detection of plant leaf diseases contemplate utilising a pre-trained model acquired from usual enormous datasets, and then applying it to a specific task educated with their own data. The experimental results show that for ResNet-50, a training accuracy of 0.95 and validation accuracy of 0.98 is obtained whereas training loss of 0.33 and validation loss of 0.5.

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Fake Banknote Recognition Using Deep Learning

Applied Sciences ◽

10.3390/app11031281 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1281

Author(s):

César G. Pachón ◽

Dora M. Ballesteros ◽

Diego Renza

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

State Of The Art ◽

Design Strategy ◽

The Other ◽

Massive Data ◽

Design Strategies ◽

Art Works ◽

Classifier Performance ◽

Banknote Recognition

Recently, some state-of-the-art works have used deep learning-based architectures, specifically convolutional neural networks (CNNs), for banknote recognition and counterfeit detection with promising results. However, it is not clear which design strategy is more appropriate (custom or by transfer learning) in terms of classifier performance and inference times for massive data applications. This paper presents a comparison of the two design strategies in various types of architecture. For the transfer learning (TL) strategy, the most appropriate freezing points in CNN architectures (sequential, residual and Inception) are identified. In addition, a custom model based on an AlexNet-type sequential CNN is proposed. Both the TL and the custom models were trained and compared using a Colombian banknote dataset. According to the results, ResNet18 achieved the best accuracy, with 100%. On the other hand, the network with the shortest inference times was the proposed custom network, since its performance is up to 6.48-times faster in CPU and 16.29-times faster in GPU than the inference time with the models by transfer learning.

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DEFSI: Deep Learning Based Epidemic Forecasting with Synthetic Information

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33019607 ◽

2019 ◽

Vol 33 ◽

pp. 9607-9612 ◽

Cited By ~ 8

Author(s):

Lijing Wang ◽

Jiangzhuo Chen ◽

Madhav Marathe

Keyword(s):

Deep Learning ◽

High Resolution ◽

State Of The Art ◽

Synthetic Data ◽

State Level ◽

The Other ◽

County Level ◽

Short Term ◽

Neural Network Structure ◽

Epidemic Forecasting

Influenza-like illness (ILI) is among the most common diseases worldwide. Producing timely, well-informed, and reliable forecasts for ILI is crucial for preparedness and optimal interventions. In this work, we focus on short-term but highresolution forecasting and propose DEFSI (Deep Learning Based Epidemic Forecasting with Synthetic Information), an epidemic forecasting framework that integrates the strengths of artificial neural networks and causal methods. In DEFSI, we build a two-branch neural network structure to take both within-season observations and between-season observations as features. The model is trained on geographically highresolution synthetic data. It enables detailed forecasting when high-resolution surveillance data is not available. Furthermore, the model is provided with better generalizability and physical consistency. Our method achieves comparable/better performance than state-of-the-art methods for short-term ILI forecasting at the state level. For high-resolution forecasting at the county level, DEFSI significantly outperforms the other methods.

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