Evaluation of Deep Learning models on UV ink : a Fake Money detection scheme with RPN

Abstract As soon as coins or money was invented, there were people trying to make counterfeits. Counterfeit money is fake money that is produced without the permission of the state or government, usually to imitate the currency and deceive the intended recipient. In Bangladesh, this is a significant problem and the problem is becoming more and more phenomenon as the days are passing by. Today’s modern bank notes have several security features that makes easier to identify fake notes. One of the security features is the use of UV ink. Bank notes deliberately put random flecks of color scattered all over the surface of the money - which acts as a extra layer of protection against counterfeiters. We propose an automatic authentication model for identifying counterfeit money based on these random flecks of color which is visible under UV light. To obtain a benchmark result, existing object detection pre-trained models were used, followed by MobileNet, Inception, ResNet50, ResNet101, and Inception-ResNet architectures. After that, using the Region Proposal Network (RPN) method with Convolutional Neural Network (CNN) based classification the optimal model was proposed. The proposed model had a 96.3 percent accuracy. It is critical to reduce the circulation of counterfeit money in a country’s economy to stop inflation. This study will aid in the detection of counterfeit money and, hopefully, reduce its spread.

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An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL

Electronics ◽

10.3390/electronics9091514 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1514

Author(s):

Ali Aljofey ◽

Qingshan Jiang ◽

Qiang Qu ◽

Mingqing Huang ◽

Jean-Pierre Niyigena

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Third Party ◽

Learning Models ◽

Phone Calls ◽

Learning Techniques ◽

Proposed Model ◽

Phishing Detection

Phishing is the easiest way to use cybercrime with the aim of enticing people to give accurate information such as account IDs, bank details, and passwords. This type of cyberattack is usually triggered by emails, instant messages, or phone calls. The existing anti-phishing techniques are mainly based on source code features, which require to scrape the content of web pages, and on third-party services which retard the classification process of phishing URLs. Although the machine learning techniques have lately been used to detect phishing, they require essential manual feature engineering and are not an expert at detecting emerging phishing offenses. Due to the recent rapid development of deep learning techniques, many deep learning-based methods have also been introduced to enhance the classification performance. In this paper, a fast deep learning-based solution model, which uses character-level convolutional neural network (CNN) for phishing detection based on the URL of the website, is proposed. The proposed model does not require the retrieval of target website content or the use of any third-party services. It captures information and sequential patterns of URL strings without requiring a prior knowledge about phishing, and then uses the sequential pattern features for fast classification of the actual URL. For evaluations, comparisons are provided between different traditional machine learning models and deep learning models using various feature sets such as hand-crafted, character embedding, character level TF-IDF, and character level count vectors features. According to the experiments, the proposed model achieved an accuracy of 95.02% on our dataset and an accuracy of 98.58%, 95.46%, and 95.22% on benchmark datasets which outperform the existing phishing URL models.

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A Convolutional Neural Network based System to Detect Plant Disease

Webology ◽

10.14704/web/v18si04/web18175 ◽

2021 ◽

Vol 18 (Special Issue 04) ◽

pp. 944-962

Author(s):

K. Niha ◽

Dr.S. Amutha ◽

Dr. Aisha Banu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Neural Network Model ◽

Plant Disease ◽

Performance Metrics ◽

Plant Diseases ◽

Detection Accuracy ◽

Learning Models ◽

Proposed Model

Disease in plants are a great challenge in the advancement of agriculture which affects farmers yield and the plants. In this modern research deep learning models got a spot light by increasing plant detection accuracy and classification. The proposed CNN (Convolutional Neural Network) model detect seven plant diseases out of healthy leaf, where the dataset considered in this work contain 8685 leaf images from Plant Village Dataset. The proposed modals performance are evaluated with respect to the performance metrics (F1 score, Precision and Recall) and are compared with SVM and ANN. Where the proposed CNN model outperforms the rest with the accuracy of 96.2% and the F1 score greater than 95%. The feasibility of the proposed model in plant detection and classification may provide a solution to the problem faced by farmers.

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A Model of Convolutional Neural Network Combined with External Knowledge to Measure the Question Similarity for Community Question Answering Systems

International Journal of Machine Learning and Computing ◽

10.18178/ijmlc.2021.11.3.1035 ◽

2021 ◽

Vol 11 (3) ◽

pp. 194-201

Author(s):

Van-Tu Nguyen ◽

◽

Anh-Cuong Le ◽

Ha-Nam Nguyen

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Question Answering ◽

Learning Models ◽

Learning Methods ◽

Data Set ◽

External Knowledge ◽

Community Question Answering ◽

Proposed Model

Automatically determining similar questions and ranking the obtained questions according to their similarities to each input question is a very important task to any community Question Answering system (cQA). Various methods have applied for this task including conventional machine learning methods with feature extraction and some recent studies using deep learning methods. This paper addresses the problem of how to combine advantages of different methods into one unified model. Moreover, deep learning models are usually only effective for large data, while training data sets in cQA problems are often small, so the idea of integrating external knowledge into deep learning models for this cQA problem becomes more important. To this objective, we propose a neural network-based model which combines a Convolutional Neural Network (CNN) with features from other methods so that the deep learning model is enhanced with addtional knowledge sources. In our proposed model, the CNN component will learn the representation of two given questions, then combined additional features through a Multilayer Perceptron (MLP) to measure similarity between the two questions. We tested our proposed model on the SemEval 2016 task-3 data set and obtain better results in comparison with previous studies on the same task.

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Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Sensors ◽

10.3390/s19010210 ◽

2019 ◽

Vol 19 (1) ◽

pp. 210 ◽

Cited By ~ 32

Author(s):

Zied Tayeb ◽

Juri Fedjaev ◽

Nejla Ghaboosi ◽

Christoph Richter ◽

Lukas Everding ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Motor Imagery ◽

Classification Performance ◽

Feature Engineering ◽

Learning Models ◽

Eeg Signals ◽

Learning Methods

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

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Efficient Facial Emotion Recognition Model using Deep Convolutional Neural Network and Modified Joint Trilateral Filter

10.21203/rs.3.rs-866042/v1 ◽

2021 ◽

Author(s):

Naveen Kumari ◽

Rekha Bhatia

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Emotion Recognition ◽

Deep Convolutional Neural Network ◽

Facial Emotion Recognition ◽

Facial Emotion ◽

Learning Models ◽

Recognition Model ◽

Trilateral Filter

Abstract Facial emotion recognition extracts the human emotions from the images and videos. As such, it requires an algorithm to understand and model the relationships between faces and facial expressions, and to recognize human emotions. Recently, deep learning models are extensively utilized enhance the facial emotion recognition rate. However, the deep learning models suffer from the overfitting issue. Moreover, deep learning models perform poorly for images which have poor visibility and noise. Therefore, in this paper, a novel deep learning based facial emotion recognition tool is proposed. Initially, a joint trilateral filter is applied to the obtained dataset to remove the noise. Thereafter, contrast-limited adaptive histogram equalization (CLAHE) is applied to the filtered images to improve the visibility of images. Finally, a deep convolutional neural network is trained. Nadam optimizer is also utilized to optimize the cost function of deep convolutional neural networks. Experiments are achieved by using the benchmark dataset and competitive human emotion recognition models. Comparative analysis demonstrates that the proposed facial emotion recognition model performs considerably better compared to the competitive models.

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Recognition of Urdu Handwritten Characters Using Convolutional Neural Network

Applied Sciences ◽

10.3390/app9132758 ◽

2019 ◽

Vol 9 (13) ◽

pp. 2758 ◽

Cited By ~ 7

Author(s):

Mujtaba Husnain ◽

Malik Muhammad Saad Missen ◽

Shahzad Mumtaz ◽

Muhammad Zeeshan Jhanidr ◽

Mickaël Coustaty ◽

...

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Deep Learning ◽

Convolutional Neural Network ◽

Pattern Matching ◽

Character Recognition ◽

Learning Models ◽

Series Of Experiments

In the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritten characters in an unconstrained environment. We also propose a novel dataset of Urdu handwritten characters since there is no publicly-available dataset of this kind. A series of experiments are performed on our proposed dataset. The accuracy achieved for character recognition is among the best while comparing with the ones reported in the literature for the same task.

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Sign Board Recognition Based on Convolutional Neural Network Using Yolo-3

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9214 ◽

2020 ◽

Vol 17 (8) ◽

pp. 3478-3483

Author(s):

V. Sravan Chowdary ◽

G. Penchala Sai Teja ◽

D. Mounesh ◽

G. Manideep ◽

C. T. Manimegalai

Keyword(s):

Neural Network ◽

Deep Learning ◽

Object Detection ◽

Convolutional Neural Network ◽

Road Accidents ◽

Learning Models ◽

The Neural Network

Road injuries are a big drawback in society for a few time currently. Ignoring sign boards while moving on roads has significantly become a major cause for road accidents. Thus we came up with an approach to face this issue by detecting the sign board and recognition of sign board. At this moment there are several deep learning models for object detection using totally different algorithms like RCNN, faster RCNN, SPP-net, etc. We prefer to use Yolo-3, which improves the speed and precision of object detection. This algorithm will increase the accuracy by utilizing residual units, skip connections and up-sampling. This algorithm uses a framework named Dark-net. This framework is intended specifically to create the neural network for training the Yolo algorithm. To thoroughly detect the sign board, we used this algorithm.

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

Applied Sciences ◽

10.3390/app112110301 ◽

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.

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Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network

Applied Sciences ◽

10.3390/app11157147 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7147

Author(s):

Jinmo Gu ◽

Jinhyuk Na ◽

Jeongeun Park ◽

Hayoung Kim

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

False Positive Rate ◽

Direct Marketing ◽

Network Models ◽

Learning Models ◽

Insurance Policy ◽

Neural Network Models ◽

Potential Customers

Outbound telemarketing is an efficient direct marketing method wherein telemarketers solicit potential customers by phone to purchase or subscribe to products or services. However, those who are not interested in the information or offers provided by outbound telemarketing generally experience such interactions negatively because they perceive telemarketing as spam. In this study, therefore, we investigate the use of deep learning models to predict the success of outbound telemarketing for insurance policy loans. We propose an explainable multiple-filter convolutional neural network model called XmCNN that can alleviate overfitting and extract various high-level features using hundreds of input variables. To enable the practical application of the proposed method, we also examine ensemble models to further improve its performance. We experimentally demonstrate that the proposed XmCNN significantly outperformed conventional deep neural network models and machine learning models. Furthermore, a deep learning ensemble model constructed using the XmCNN architecture achieved the lowest false positive rate (4.92%) and the highest F1-score (87.47%). We identified important variables influencing insurance policy loan prediction through the proposed model, suggesting that these factors should be considered in practice. The proposed method may increase the efficiency of outbound telemarketing and reduce the spam problems caused by calling non-potential customers.

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Application of deep learning models for aspect based sentiment analysis.

Bulletin of Bryansk state technical university ◽

10.12737/22917 ◽

2015 ◽

Vol 2015 (3) ◽

pp. 117-126

Author(s):

Дмитрий Будыльский ◽

Dmitriy Budylskiy ◽

Александр Подвесовский ◽

Aleksandr Podvesovskiy

Keyword(s):

Neural Network ◽

Deep Learning ◽

Natural Language Processing ◽

Convolutional Neural Network ◽

Language Processing ◽

Recurrent Neural Network ◽

Russian Text ◽

Learning Models ◽

Actual Problem ◽

Good Efficiency

This paper describes actual problem of sentiment based aspect analysis and four deep learning models: convolutional neural network, recurrent neural network, GRU and LSTM networks. We evaluated these models on Russian text dataset from SentiRuEval-2015. Results show good efficiency and high potential for further natural language processing applications.

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