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Deep Learning Approach for Enhanced Cyber Threat Indicators in Twitter Stream 1

7th International Symposium on Security in Computing and Communications, SSCC 2019

作者： Simran, K. Balakrishna, Prathiksha Vinayakumar, R. Soman, K.P. Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Graduate School Computer Science Department Texas State University San Marcos United States Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Centre CincinnatiOH United States

ISBN: (数字)9789811548253

ISBN: (纸本)9789811548246

In recent days, the amount of Cyber Security text data shared via social media resources mainly Twitter has increased. An accurate analysis of this data can help to develop cyber threat situational awareness framework for a cyber threat. This work proposes a deep learning based approach for tweet data analysis. To convert the tweets into numerical representations, various text representations are employed. These features are feed into deep learning architecture for optimal feature extraction as well as classification. Various hyperparameter tuning approaches are used for identifying optimal text representation method as well as optimal network parameters and network structures for deep learning models. For comparative analysis, the classical text representation method with classical machine learning algorithm is employed. From the detailed analysis of experiments, we found that the deep learning architecture with advanced text representation methods performed better than the classical text representation and classical machine learning algorithms. The primary reason for this is that the advanced text representation methods have the capability to learn sequential properties which exist among the textual data and deep learning architectures learns the optimal features along with decreasing the feature size. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Deep learning

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Smart Digital Marketing of Financial Services to Millennial Generation using emerging technological tools and buyer persona 6

Smart Digital Marketing of Financial Services to Millennial ...

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6th HCT Information Technology Trends, ITT 2019

作者： Akre, Vishwesh Rajan, Amala Ahamed, Jinesh Al Amri, Aisha Al Daisi, Shamma Higher Colleges of Technology Dubai Women's Computer Information Science Division Dubai United Arab Emirates Higher Colleges of Technology Dubai Women's College Networking Computer and Information Science Division Dubai United Arab Emirates Applications Development Majors Higher Colleges of Technology Dubai Women's College CIS Division Dubai United Arab Emirates

ISBN: (纸本)9781728150611

Digital Marketing is term used to describe marketing products or services using digital technologies. It is mainly based on the internet, and it can refer to advertising through digital channels such as search engines, websites, social media, email, and mobile apps. Millennial generation consists of individuals born between 1982 and 2002, they are the demographic social influencers. Financial Institutions such as banks are searching for new ways to market products and services to attract millennials. It is important for banks to use digital marketing for millennial generations since they are avid fans of the visual platform. Buyer Persona is a semi-fictional representation to represent what prospective customers are thinking and doing, as it gives real data about existing customers. In order to focus on each category of customers, its necessarily to create buyer persona. It is one of the unique methods banks could use for marketing. This research has examined digital marketing tools and consumer behaviour of millennial generation and have proposed the techniques of creating specific buyer persona through a framework. This framework could be used by banks in order to create specific marketing strategies for marketing financial services to individuals of the millennial generations. © 2019 IEEE.

关键词： Internet of things

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Deep learning approach for enhanced cyber threat indicators in Twitter stream

arXiv

引用

arXiv 2020年

作者： Simran, K. Balakrishna, Prathiksha Vinayakumar, R. Soman, K.P. Center for Computational Engineering and Networking Amrita School Of Engineering Amrita vishwa vidyapeetham Coimbatore India Graduate School Computer Science Department Texas State University United States Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Centre CincinnatiOH United States

关键词： Social networking (online)

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Knee bone segmentation on three-dimensional MRI 18

Knee bone segmentation on three-dimensional MRI

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18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

作者： Almajalid, Rania Shan, Juan Zhang, Maolin Stonis, Garrett Zhang, Ming Department of Computer Science Seidenberg School of Csis Pace University New York CityNY United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Hopewell Valley Central High School PenningtonNJ United States Department of Computer Science and Networking Wentworth Institute of Technology BostonMA United States Division of Rheumatology Tufts Medical Center BostonMA United States

ISBN: (纸本)9781728145495

Three-dimensional (3D) images are widely used in the medical field (e.g., CT, MRI). In osteoarthritis research, 3D magnetic resonance imaging (MRI) provides a noninvasive way to study soft-tissue structures including hyaline cartilage, meniscus, muscle, bone marrow lesion, etc. The measurement of those structures can be greatly improved by accurately locating the bone structure. U-net is a convolutional neural network developed for biological image segmentation using limited training data. The original U-net takes a single 2D image as input and generates a binary 2D image as output. In this paper, we modified the U-net model to identify the bone structure on 3D knee MRI, which is a sequence of multiple 2D slices. Instead of taking a single image as input, the modified U-net takes multiple adjacent slices as input. The output is still a single binary image which is the segmentation result of the center slice in the input sequence. By using 99 knee MRI cases, where each knee case includes 160 2D slices, the proposed model was trained, validated, and tested. The dice coefficient, similarity, and area error metrics rate were tallied to assess the performance and the quality of the testing sets. Without any post-processing of the images, the model achieved promising segmentation performance with the Dice coefficient (DICE) 97.22% on the testing dataset. To achieve the best performance, diverse models were trained using different strategies including different numbers of input channels and different input image sizes. The experiment results indicate that the incorporation of neighboring slices generated better segmentation performance than using the single slice. We also found that a larger image size (uncompressed) corresponds to better performance. In summary, our best segmentation performance was achieved using five adjacent neighbor slices (two left neighbors + two right neighbors + the center slice) with the original image size of 352 × 352 pixels. © 2019 IEEE.

关键词： Magnetic resonance imaging

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Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients

arXiv

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arXiv 2021年

作者： Khozeimeh, Fahime Sharifrazi, Danial Izadi, Navid Hoseini Joloudari, Javad Hassannataj Shoeibi, Afshin Alizadehsani, Roohallah Gorriz, Juan M. Hussain, Sadiq Sani, Zahra Alizadeh Moosaei, Hossein Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Deakin University Geelong Australia Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Department of Psychiatry University of Cambridge Cambridge United Kingdom Dibrugarh University Assam 786004 India Omid Hospital Iran University of Medical Sciences Tehran Iran Department of Mathematics Faculty of Science University of Bojnord Iran Institute for Physical Activity and Nutrition School of Exercise and Nutrition Sciences Deakin University GeelongVIC3220 Australia Cardiovascular Division The George Institute for Global Health Newtown Australia Sydney Medical School University of Sydney Camperdown Australia

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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Uncertainty-aware semi-supervised method using large unlabelled and limited labeled COVID-19 data

arXiv

引用

arXiv 2021年

作者： Alizadehsani, Roohallah Sharifrazi, Danial Izadi, Navid Hoseini Joloudari, Javad Hassannataj Shoeibi, Afshin Gorriz, Juan M. Hussain, Sadiq Arco, Juan E. Sani, Zahra Alizadeh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Acharya, U. Rajendra Deakin University Geelong Australia Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Dept. of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The new coronavirus has caused more than 1 million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. In this paper, relying on Generative Adversarial Networks (GAN), we propose a Semi-supervised Classification using Limited Labelled Data (SCLLD) for automated COVID-19 detection. Our motivation is to develop learning method which can cope with scenarios that preparing labelled data is time consuming or expensive. We further improved the detection accuracy of the proposed method by applying Sobel edge detection. The GAN discriminator output is a probability value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid hospital. Also, we validate our system using the public dataset. The proposed method is compared with other state of the art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a COVID-19 semi-supervised detection method is presented. Our method is capable of learning from a mixture of limited labelled and unlabelled data where supervised learners fail due to lack of sufficient amount of labelled data. Our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) in case labelled training data is scarce. Our method has achieved an accuracy of 99.60%, sensitivity of 99.39%, and specificity of 99.80% where CNN (trained supervised) has achieved an accuracy of 69.87%, sensitivity of 94%, and specificity of 46.40%. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Smart Digital Marketing of Financial Services to Millennial Generation using emerging technological tools and buyer persona

Smart Digital Marketing of Financial Services to Millennial ...

引用

HCT Information Technology Trends (ITT)

作者： Vishwesh Akre Amala Rajan Jinesh Ahamed Aisha Al Amri Shamma Al Daisi Computer & Information Science Division Higher Colleges of Technology Dubai Women's Dubai United Arab Emirates CIS Division Higher Colleges of Technology Dubai Women's College Dubai United Arab Emirates Networking Computer and Information Science Division Higher Colleges of Technology Dubai Women's College Dubai United Arab Emirates CIS Division Applications Development Majors Higher Colleges of Technology Dubai Women's College Dubai United Arab Emirates

ISBN: (数字)9781728150611

ISBN: (纸本)9781728150628

关键词： Social network services Banking Tools Millennials Internet of Things Search engines

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Knee Bone Segmentation on Three-Dimensional MRI

Knee Bone Segmentation on Three-Dimensional MRI

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International Conference on Machine Learning and Applications (ICMLA)

作者： Rania Almajalid Juan Shan Maolin Zhang Garrett Stonis Ming Zhang Department of Computer Science Pace University New York City NY USA College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Hopewell Valley Central High School NJ USA Department of Computer Science & Networking Wentworth Institute of Technology Boston MA USA Division of Rheumatology Tufts Medical Center Boston MA USA

关键词： Image segmentation Bones Magnetic resonance imaging Three-dimensional displays Two dimensional displays Testing Training

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Hypertrophic cardiomyopathy diagnosis based on cardiovascular magnetic resonance using deep learning techniques

Research Square

引用

Research Square 2021年

作者： Sharifrazi, Danial Alizadehsani, Roohallah Izadi, Navid Hoseini Roshanzamir, Mohamad Shoeibi, Afshin Khozeimeh, Fahime Sani, Fariba Alizadeh Sani, Zahra Alizadeh Hussain, Sadiq Harlapur, Chandrashekhar Gorriz, Juan M. Khosravi, Abbas Nahavandi, Saeid Sarrafzadegan, Nizal Islam, Sheikh Mohammed Shariful Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Deakin University Geelong Australia Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Mashhad University of Medical science Mashhad Iran Department of Cardiac MRI Omid Hospital Tehran Iran Dibrugarh University Assam786004 India Emergency Department Epworth Hospital Geelong Australia Department of Signal Theory Networking and Communications Universidad de Granada Spain Department of Psychiatry University of Cambridge United Kingdom Faculty of Medicine SPPH University of British Columbia VancouverBC Canada Isfahan Cardiovascular Research Center Cardiovascular Research Institute Isfahan University of Medical Sciences Khorram Ave Isfahan Iran Institute for Physical Activity and Nutrition School of Exercise and Nutrition Sciences Deakin University GeelongVIC3220 Australia Cardiovascular Division The George Institute for Global Health Newtown Australia Sydney Medical School University of Sydney Camperdown Australia

Hypertrophic cardiomyopathy (HCM) can lead to serious cardiac problems. HCM is often diagnosed by an expert using cardiovascular magnetic resonance (CMR) images obtained from patients. In this research, we aimed to develop a deep learning technique to automate HCM diagnosis. CMR images of 37421 healthy and 21846 HCM patients were obtained during two years. Images obtained from female patients form 53% of the collected dataset. The mean and standard deviation of the dataset patients’ age are 48.2 and 19.5 years, respectively. Three experts inspected images and determined whether a case has HCM or not. New data augmentation was used to generate new images by employing color filtering on the existing ones. To classify the augmented images, we used a deep convolutional neural network (CNN). To the best of our knowledge, this is the first time CNN is used for HCM diagnosis. We designed our CNN from scratch to reach acceptable diagnosis accuracy. Comparing the designed algorithm output with the experts’ opinions, the method could achieve accuracy of 95.23%, recall of 97.90%, and specificity of 93.06% on the original dataset. The same performance metrics for the designed algorithm on the augmented dataset were 98.53%, 98.70%, and 95.21%, respectively. We have also experimented with different optimizers (e.g. Adadelta and Adagrad) and other data augmentation methods (e.g. height shift and rotation) to further evaluate the proposed method. Using our data augmentation method, accuracy of 98.53% were achieved which is higher than the best accuracy (95.83%) obtained by the other data augmentation methods which have been evaluated. The upper bound on difference between true error rate and empirical error rate of the proposed method has also been provided in order to present better performance analysis. The advantages of employing the proposed method are elimination of contrast agent and its complications, decreased CMR examination time, lower costs for patients and cardiac imagi

关键词： Diagnosis

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Exploring choice overload in related-article recommendations in digital libraries 5

Exploring choice overload in related-article recommendations...

引用

5th Workshop on Bibliometric-Enhanced Information Retrieval, BIR 2017

作者： Beierle, Felix Aizawa, Akiko Beel, Joeran Service-centric Networking Technische Universität Berlin Telekom Innovation Laboratories Berlin Germany Digital Content and Media Sciences Research Division Tokyo Japan Trinity College Dublin School of Computer Science and Statistics Intelligent Systems Discipline Knowledge and Data Engineering Group ADAPT Centre Dublin Ireland

We investigate the problem of choice overload - the difficulty of making a decision when faced with many options - when displaying related-article recommendations in digital libraries. So far, research regarding to how many items should be displayed has mostly been done in the fields of media recommendations and search engines. We analyze the number of recommendations in current digital libraries. When browsing fullscreen with a laptop or desktop PC, all display a fixed number of recommendations. 72% display three, four, or five recommendations, none display more than ten. We provide results from an empirical evaluation conducted with GESIS' digital library Sowiport, with recommendations delivered by recommendations-as-a-service provider Mr. DLib. We use click-through rate as a measure of recommendation effectiveness based on 3.4 million delivered recommendations. Our results show lower click-through rates for higher numbers of recommendations and twice as many clicked recommendations when displaying ten instead of one related-articles. Our results indicate that users might quickly feel overloaded by choice. © 2017, CEUR-WS. All rights reserved.

关键词： Digital libraries

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