A Deep Learning Approach for Malware and Software Piracy Threat Detection

Internet of Things (IoT) -based systems need to be up to date on cybersecurity threats. The security of IoT networks is challenged by software piracy and malware attacks, and much important information can be stolen and used for cybercrimes. This paper attempts to improve IoT cybersecurity by proposing a combined model based on deep learning to detect malware and software piracy across the IoT network. The malware’s model is based on Deep Convolutional Neural Networks (DCNNs). Apart from this, TensorFlow Deep Neural Networks (TFDNNs) are introduced to detect software piracy threats according to source code plagiarism. The investigation is conducted on the Google Code Jam (GCJ) dataset. The conducted experiments prove that the classification performance achieves high accuracy of about 98%.

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Multimodal Multi-tasking for Skin Lesion Classification Using Deep Neural Networks

10.1007/978-3-030-90439-5_3 ◽

2021 ◽

pp. 27-38

Author(s):

Rafaela Carvalho ◽

João Pedrosa ◽

Tudor Nedelcu

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Deep Learning ◽

Skin Lesion ◽

Deep Neural Networks ◽

Classification Performance ◽

Additional Information ◽

Deep Learning Model ◽

Lesion Classification

AbstractSkin cancer is one of the most common types of cancer and, with its increasing incidence, accurate early diagnosis is crucial to improve prognosis of patients. In the process of visual inspection, dermatologists follow specific dermoscopic algorithms and identify important features to provide a diagnosis. This process can be automated as such characteristics can be extracted by computer vision techniques. Although deep neural networks can extract useful features from digital images for skin lesion classification, performance can be improved by providing additional information. The extracted pseudo-features can be used as input (multimodal) or output (multi-tasking) to train a robust deep learning model. This work investigates the multimodal and multi-tasking techniques for more efficient training, given the single optimization of several related tasks in the latter, and generation of better diagnosis predictions. Additionally, the role of lesion segmentation is also studied. Results show that multi-tasking improves learning of beneficial features which lead to better predictions, and pseudo-features inspired by the ABCD rule provide readily available helpful information about the skin lesion.

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Assessment of bilateral knee pain from MR imaging using deep neural networks

10.1101/463497 ◽

2018 ◽

Author(s):

Gary H. Chang ◽

David T. Felson ◽

Shangran Qiu ◽

Terence D. Capellini ◽

Vijaya B. Kolachalama

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Neural Networks ◽

Deep Learning ◽

Knee Pain ◽

Deep Neural Networks ◽

Learning Approach ◽

Mr Images ◽

Bilateral Knee ◽

Machine Learning Approach

ABSTRACTBackground and objectiveIt remains difficult to characterize pain in knee joints with osteoarthritis solely by radiographic findings. We sought to understand how advanced machine learning methods such as deep neural networks can be used to analyze raw MRI scans and predict bilateral knee pain, independent of other risk factors.MethodsWe developed a deep learning framework to associate information from MRI slices taken from the left and right knees of subjects from the Osteoarthritis Initiative with bilateral knee pain. Model training was performed by first extracting features from two-dimensional (2D) sagittal intermediate-weighted turbo spin echo slices. The extracted features from all the 2D slices were subsequently combined to directly associate using a fused deep neural network with the output of interest as a binary classification problem.ResultsThe deep learning model resulted in predicting bilateral knee pain on test data with 70.1% mean accuracy, 51.3% mean sensitivity, and 81.6% mean specificity. Systematic analysis of the predictions on the test data revealed that the model performance was consistent across subjects of different Kellgren-Lawrence grades.ConclusionThe study demonstrates a proof of principle that a machine learning approach can be applied to associate MR images with bilateral knee pain.SIGNIFICANCE AND INNOVATIONKnee pain is typically considered as an early indicator of osteoarthritis (OA) risk. Emerging evidence suggests that MRI changes are linked to pre-clinical OA, thus underscoring the need for building image-based models to predict knee pain. We leveraged a state-of-the-art machine learning approach to associate raw MR images with bilateral knee pain, independent of other risk factors.

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Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks

Applied Sciences ◽

10.3390/app10072488 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2488 ◽

Cited By ~ 5

Author(s):

Muhammad Naseer Bajwa ◽

Kaoru Muta ◽

Muhammad Imran Malik ◽

Shoaib Ahmed Siddiqui ◽

Stephan Alexander Braun ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Skin Diseases ◽

State Of The Art ◽

Disease Diagnosis ◽

Classification Performance ◽

Computer Aided Diagnosis ◽

Computer Aided ◽

Aided Diagnosis

Propensity of skin diseases to manifest in a variety of forms, lack and maldistribution of qualified dermatologists, and exigency of timely and accurate diagnosis call for automated Computer-Aided Diagnosis (CAD). This study aims at extending previous works on CAD for dermatology by exploring the potential of Deep Learning to classify hundreds of skin diseases, improving classification performance, and utilizing disease taxonomy. We trained state-of-the-art Deep Neural Networks on two of the largest publicly available skin image datasets, namely DermNet and ISIC Archive, and also leveraged disease taxonomy, where available, to improve classification performance of these models. On DermNet we establish new state-of-the-art with 80% accuracy and 98% Area Under the Curve (AUC) for classification of 23 diseases. We also set precedence for classifying all 622 unique sub-classes in this dataset and achieved 67% accuracy and 98% AUC. On ISIC Archive we classified all 7 diseases with 93% average accuracy and 99% AUC. This study shows that Deep Learning has great potential to classify a vast array of skin diseases with near-human accuracy and far better reproducibility. It can have a promising role in practical real-time skin disease diagnosis by assisting physicians in large-scale screening using clinical or dermoscopic images.

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Combining content and social features in a deep learning approach to Vietnamese email prioritization

REV Journal on Electronics and Communications ◽

10.21553/rev-jec.271 ◽

2021 ◽

Author(s):

Ha Thanh Nguyen ◽

Quan Dinh Dang ◽

Anh Quang Tran

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

New Method ◽

Learning Approach ◽

Field Of Study ◽

Learning Techniques ◽

Email Overload ◽

Social Features

The email overload problem has been discussed in numerous email-related studies. One of the possible solutions to this problem is email prioritization, which is the act of automatically predicting the importance levels of received emails and sorting the user’s inbox accordingly. Several learning-based methods have been proposed to address the email prioritization problem using content features as well as social features. Although these methods have laid the foundation works in this field of study, the reported performance is far from being practical. Recent works on deep neural networks have achieved good results in various tasks. In this paper, the authors propose a novel email prioritization model which incorporates several deep learning techniques and uses a combination of both content features and social features from email data. This method targets Vietnamese emails and is tested against a self-built Vietnamese email corpus. Conducted experiments explored the effects of different model configurations and compared the effectiveness of the new method to that of a previous work.

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Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1719367115 ◽

2018 ◽

Vol 115 (25) ◽

pp. E5716-E5725 ◽

Cited By ~ 184

Author(s):

Mohammad Sadegh Norouzzadeh ◽

Anh Nguyen ◽

Margaret Kosmala ◽

Alexandra Swanson ◽

Meredith S. Palmer ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Data Extraction ◽

High Volume ◽

Camera Trap ◽

Camera Traps ◽

Deep Convolutional Neural Networks ◽

Manual Task ◽

In The Wild

Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would improve our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into “big data” sciences. Motion-sensor “camera traps” enable collecting wildlife pictures inexpensively, unobtrusively, and frequently. However, extracting information from these pictures remains an expensive, time-consuming, manual task. We demonstrate that such information can be automatically extracted by deep learning, a cutting-edge type of artificial intelligence. We train deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2 million-image Snapshot Serengeti dataset. Our deep neural networks automatically identify animals with >93.8% accuracy, and we expect that number to improve rapidly in years to come. More importantly, if our system classifies only images it is confident about, our system can automate animal identification for 99.3% of the data while still performing at the same 96.6% accuracy as that of crowdsourced teams of human volunteers, saving >8.4 y (i.e., >17,000 h at 40 h/wk) of human labeling effort on this 3.2 million-image dataset. Those efficiency gains highlight the importance of using deep neural networks to automate data extraction from camera-trap images, reducing a roadblock for this widely used technology. Our results suggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild.

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A Deep Learning Approach to Detect Pornography Videos in Educational Repositories

10.5753/cbie.sbie.2020.1253 ◽

2020 ◽

Author(s):

Pedro V. A. de Freitas ◽

Antonio J. G. Busson ◽

Álan L. V. Guedes ◽

Sérgio Colcher

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Detection System ◽

Automated Detection ◽

Learning Approach ◽

Multimodal Approach ◽

Deep Convolutional Neural Networks ◽

High Level

A large number of videos are uploaded on educational platforms every minute. Those platforms are responsible for any sensitive media uploaded by their users. An automated detection system to identify pornographic content could assist human workers by pre-selecting suspicious videos. In this paper, we propose a multimodal approach to adult content detection. We use two Deep Convolutional Neural Networks to extract high-level features from both image and audio sources of a video. Then, we concatenate those features and evaluate the performance of classifiers on a set of mixed educational and pornographic videos. We achieve an F1-score of 95.67% on the educational and adult videos set and an F1-score of 94% on our test subset for the pornographic class.

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Using deep neural networks to detect complex spikes of cerebellar Purkinje cells

Journal of Neurophysiology ◽

10.1152/jn.00754.2019 ◽

2020 ◽

Vol 123 (6) ◽

pp. 2217-2234

Author(s):

Akshay Markanday ◽

Joachim Bellet ◽

Marie E. Bellet ◽

Junya Inoue ◽

Ziad M. Hafed ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Purkinje Cell ◽

Purkinje Cells ◽

Deep Neural Networks ◽

Learning Approach ◽

Sorting Algorithms ◽

Accuracy Level ◽

Cerebellar Purkinje Cells ◽

Complex Spikes

Purkinje cell “complex spikes,” fired at perplexingly low rates, play a crucial role in cerebellum-based motor learning. Careful interpretations of these spikes require manually detecting them, since conventional online or offline spike sorting algorithms are optimized for classifying much simpler waveform morphologies. We present a novel deep learning approach for identifying complex spikes, which also measures additional relevant neurophysiological features, with an accuracy level matching that of human experts yet with very little time expenditure.

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Deep learning in Arabic sentiment analysis: An overview

Journal of Information Science ◽

10.1177/0165551519865488 ◽

2019 ◽

pp. 016555151986548

Author(s):

Amal Alharbi ◽

Mounira Taileb ◽

Manal Kalkatawi

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Sentiment Analysis ◽

Deep Neural Networks ◽

The Internet ◽

Learning Approach ◽

Comprehensive Overview ◽

Exponential Increase ◽

Arabic Sentiment Analysis

Sentiment analysis became a very motivating area in both academic and industrial fields due to the exponential increase of the online published reviews and recommendations. To solve the problem of analysing and classifying those reviews and recommendations, several techniques have been proposed. Lately, deep neural networks showed promising outcomes in sentiment analysis. The growing number of Arab users on the Internet along with the increasing amount of published Arabic reviews and comments encouraged researchers to apply deep learning to analyse them. This article is a comprehensive overview of research works that utilised the deep learning approach for Arabic sentiment analysis.

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Deep distributed convolutional neural networks: Universality

Analysis and Applications ◽

10.1142/s0219530518500124 ◽

2018 ◽

Vol 16 (06) ◽

pp. 895-919 ◽

Cited By ~ 19

Author(s):

Ding-Xuan Zhou

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Mathematical Foundation ◽

Deep Convolutional Neural Networks ◽

The Family ◽

Fully Connected

Deep learning based on structured deep neural networks has provided powerful applications in various fields. The structures imposed on the deep neural networks are crucial, which makes deep learning essentially different from classical schemes based on fully connected neural networks. One of the commonly used deep neural network structures is generated by convolutions. The produced deep learning algorithms form the family of deep convolutional neural networks. Despite of their power in some practical domains, little is known about the mathematical foundation of deep convolutional neural networks such as universality of approximation. In this paper, we propose a family of new structured deep neural networks: deep distributed convolutional neural networks. We show that these deep neural networks have the same order of computational complexity as the deep convolutional neural networks, and we prove their universality of approximation. Some ideas of our analysis are from ridge approximation, wavelets, and learning theory.

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A pilot study of a deep learning approach to detect marginal bone loss around implants

BMC Oral Health ◽

10.1186/s12903-021-02035-8 ◽

2022 ◽

Vol 22 (1) ◽

Author(s):

Min Liu ◽

Shimin Wang ◽

Hu Chen ◽

Yunsong Liu

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Neural Networks ◽

Deep Learning ◽

Bone Loss ◽

Classification Performance ◽

Learning Approach ◽

Marginal Bone Loss ◽

Radiographic Images ◽

Sensitivity Specificity

Abstract Background Recently, there has been considerable innovation in artificial intelligence (AI) for healthcare. Convolutional neural networks (CNNs) show excellent object detection and classification performance. This study assessed the accuracy of an artificial intelligence (AI) application for the detection of marginal bone loss on periapical radiographs. Methods A Faster region-based convolutional neural network (R-CNN) was trained. Overall, 1670 periapical radiographic images were divided into training (n = 1370), validation (n = 150), and test (n = 150) datasets. The system was evaluated in terms of sensitivity, specificity, the mistake diagnostic rate, the omission diagnostic rate, and the positive predictive value. Kappa (κ) statistics were compared between the system and dental clinicians. Results Evaluation metrics of AI system is equal to resident dentist. The agreement between the AI system and expert is moderate to substantial (κ = 0.547 and 0.568 for bone loss sites and bone loss implants, respectively) for detecting marginal bone loss around dental implants. Conclusions This AI system based on Faster R-CNN analysis of periapical radiographs is a highly promising auxiliary diagnostic tool for peri-implant bone loss detection.

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