Fake Banknote Recognition Using Deep Learning

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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Deep Learning for text in limted data settings

10.36227/techrxiv.12100692 ◽

2020 ◽

Author(s):

Pathikkumar Patel ◽

Bhargav Lad ◽

Jinan Fiaidhi

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Sentiment Analysis ◽

Transfer Learning ◽

Text Classification ◽

State Of The Art ◽

Time Series Forecasting ◽

Text Data ◽

Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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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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Deep Transfer Learning for Source Code Modeling

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194020500230 ◽

2020 ◽

Vol 30 (05) ◽

pp. 649-668

Author(s):

Yasir Hussain ◽

Zhiqiu Huang ◽

Yu Zhou ◽

Senzhang Wang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Transfer Learning ◽

Automated Final Lesion Segmentation in Posterior Circulation Acute Ischemic Stroke Using Deep Learning

Diagnostics ◽

10.3390/diagnostics11091621 ◽

2021 ◽

Vol 11 (9) ◽

pp. 1621

Author(s):

Riaan Zoetmulder ◽

Praneeta R. Konduri ◽

Iris V. Obdeijn ◽

Efstratios Gavves ◽

Ivana Išgum ◽

...

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Detection Rate ◽

Posterior Circulation ◽

Lesion Detection ◽

The Other ◽

Lesion Volume ◽

Learning Approaches ◽

Lesion Segmentation ◽

Anterior Circulation

Final lesion volume (FLV) is a surrogate outcome measure in anterior circulation stroke (ACS). In posterior circulation stroke (PCS), this relation is plausibly understudied due to a lack of methods that automatically quantify FLV. The applicability of deep learning approaches to PCS is limited due to its lower incidence compared to ACS. We evaluated strategies to develop a convolutional neural network (CNN) for PCS lesion segmentation by using image data from both ACS and PCS patients. We included follow-up non-contrast computed tomography scans of 1018 patients with ACS and 107 patients with PCS. To assess whether an ACS lesion segmentation generalizes to PCS, a CNN was trained on ACS data (ACS-CNN). Second, to evaluate the performance of only including PCS patients, a CNN was trained on PCS data. Third, to evaluate the performance when combining the datasets, a CNN was trained on both datasets. Finally, to evaluate the performance of transfer learning, the ACS-CNN was fine-tuned using PCS patients. The transfer learning strategy outperformed the other strategies in volume agreement with an intra-class correlation of 0.88 (95% CI: 0.83–0.92) vs. 0.55 to 0.83 and a lesion detection rate of 87% vs. 41–77 for the other strategies. Hence, transfer learning improved the FLV quantification and detection rate of PCS lesions compared to the other strategies.

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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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Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer Learning

10.1101/2020.04.11.20054643 ◽

2020 ◽

Cited By ~ 4

Author(s):

Arshia Rehman ◽

Saeeda Naz ◽

Ahmed Khan ◽

Ahmad Zaib ◽

Imran Razzak

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Bacterial Pneumonia ◽

State Of The Art ◽

High Accuracy ◽

X Rays ◽

Early Screening ◽

X Ray ◽

Learning Techniques ◽

The World

AbstractBackgroundCoronavirus disease (COVID-19) is an infectious disease caused by a new virus. Exponential growth is not only threatening lives, but also impacting businesses and disrupting travel around the world.AimThe aim of this work is to develop an efficient diagnosis of COVID-19 disease by differentiating it from viral pneumonia, bacterial pneumonia and healthy cases using deep learning techniques.MethodIn this work, we have used pre-trained knowledge to improve the diagnostic performance using transfer learning techniques and compared the performance different CNN architectures.ResultsEvaluation results using K-fold (10) showed that we have achieved state of the art performance with overall accuracy of 98.75% on the perspective of CT and X-ray cases as a whole.ConclusionQuantitative evaluation showed high accuracy for automatic diagnosis of COVID-19. Pre-trained deep learning models develop in this study could be used early screening of coronavirus, however it calls for extensive need to CT or X-rays dataset to develop a reliable application.

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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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