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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Tsarapatsani, Konstantina-Helen Sakellarios, Antonis Tsakanikas, Vasilis D. Rudolf, Henrik Trampisch, Hans J. Pezoulas, Vasileios C. Matsopoulos, George K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR 45110 Greece Biomedical Research Institute Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350383386

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis's results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided. © 2023 IEEE.

关键词： Forecasting

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PhishGuard: A Convolutional Neural Network-Based Model for Detecting Phishing URLs with Explainability Analysis

PhishGuard: A Convolutional Neural Network-Based Model for D...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Md Robiul Islam Md Mahamodul Islam Mst. Suraiya Afrin Anika Antara Nujhat Tabassum Al Amin Dept. of Computer Science and Engineering Uttara University Dhaka Bangladesh Dept. of Informatics University of Oslo Oslo Norway Dept. of Building Engineering and Construction Management Khulna University of Engineering and Technology (KUET) Khulna Bangladesh Dept. of Electrical and Electronics Engineering BRAC University Dhaka Bangladesh Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science American International University Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Abstract-Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black-box intelligent models’ decision to detect Suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a $1 D$ Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85 %. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL.

关键词： Uniform resource locators Deep learning Analytical models Accuracy Phishing Organizations Feature extraction

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SMART TRANSPORT FOR SMART CITIES: A FUTURISTIC SCENARIO AND A REALISTIC PROJECT 7

SMART TRANSPORT FOR SMART CITIES: A FUTURISTIC SCENARIO AND ...

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7th International Conference on Smart Data and Smart Cities, SDSC 2022

作者： Pantazis, D.N. Argyrakis, P. Moussas, V. Geroulis, G. Daverona, A.C. Stergiou, C. Voyiatzis, I. Aposporis, P. SOCRATES LAB Dept. of Surveying and Geoinformatics Engineering University of West Attica Egaleo Athens Greece Dept. of Mechanical Engineering University of West Attica Aigaleo Athens Greece Dept. of Civil Engineering University of West Attica Aigaleo Athens Greece Dept. of Informatics and Computer Engineering University of West Attica Aigaleo Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece /NTUA Greece

Smart transport for smart cities: our article presents an innovative and original idea in the field of urban air transport with a new type of manned drone. This scenario is analysed starting from some initial futuristic ideas up to the reality of the construction of such a vehicle. After a brief presentation of a list of manned aerial vehicles (manned drones) that reflects the efforts that have been realized until now, a number of parameters and specifications related to such aerial urban transport, are also pointed out. In addition, various emerging issues comprising legislation matters, infrastructure for landing and take-off in cities, navigation methods and other possible impacts on the urban transport, have also been presented shortly. © Author(s) 2022.

关键词： Drones

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Carotid plaque stroke risk assessment using multiscale AM-FM analysis based on DoG filterbanks

Carotid plaque stroke risk assessment using multiscale AM-FM...

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International Conference on Digital Signal Processing (DSP)

作者： Kyriacos P. Constantinou Ioannis P. Constantinou Marios S. Pattichis Efthyvoulos Kyriacou Constantinos S. Pattichis Dept. of Computer Science University of Cyprus Nicosia Cyprus Istognosis Ltd Nicosia Cyprus Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA Dept. of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

The objective of this work was the investigation of multiscale Amplitude Modulation - Frequency Modulation (AM-FM) analysis based on Difference of Gaussians (DoG) filterbanks representations in order to predict the risk of stroke by analysing carotid plaques ultrasound images of individuals with asymptomatic carotid stenosis. We computed the instantaneous amplitude, instantaneous phase and the magnitude of instantaneous frequency to extract histogram features on each plaque region. The Support Vectors Machine classifier was implemented to classify asymptomatic versus symptomatic plaques. A dataset of 100 carotid plaque images (50 asymptomatic and 50 symptomatic) were tested, and showed that the AM-FM features based on DoG filterbanks and simple histograms performed better than the traditional AM-FM features. Best results were obtained when an eight scale filterbank with a combination of scales was used reaching the accuracy of 75%.

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Congestion Probabilities in a Mobile Hotspot Supporting Quasi-Random Traffic

Congestion Probabilities in a Mobile Hotspot Supporting Quas...

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IEEE Microwave Theory and Techniques in Wireless Communications (MTTW)

作者： Marinos Vlasakis Ioannis Moscholios Panagiotis Sarigiannidis Michael Logothetis Dept. Informatics & Telecommunications University of Peloponnese Tripolis Greece Dept. Electrical & Computer Engineering University of W. Macedonia Kozani Greece Dept. Electrical & Computer Engineering University of Patras Patras Greece

In this paper, we consider a vehicle that has an access point of fixed capacity. The vehicle accommodates a finite number of users who generate calls (quasi-random process). Each call requires a single bandwidth unit to be serviced for an exponentially distributed service time. The applied call admission policy depends on which phase the vehicle is. Two phases are taken into account: a moving and a stop one. During the moving phase, only new calls are serviced under the complete sharing policy. During the stop phase, both new and handover calls are being serviced, while the latter are being prioritized utilizing a probabilistic bandwidth reservation policy. The analytical determination of the various performance metrics of the system presented in this paper is accurate and is based on three-dimensional Markov chains.

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Multicluster Kernel Intuitionistic Fuzzy C-Means and State Transition Algorithm : Framework for Low Light Segmentation Imaging of Tuberculosis Bacilli Base of Semisupervised Approach

Multicluster Kernel Intuitionistic Fuzzy C-Means and State T...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Sari Ayu Wulandari I Ketut Eddy Purnama Eko Mulyanto Yuniarno Mauridhi Hery Purnomo Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Biomedical Engineering Faculty of Electrical Engineering Universitas Dian Nuswantoro Semarang Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

Annotated images are required as ground truth for deep learning. Bacterial segmentation is requires a lot of time and effort when done manually. The autosegmentation task gets more challenging since bacterial images contain low light properties, which have an important effect on auto annotating tasks. In order to solve this issue, we present a system that includes a fuzzy-based clustering method that enhances bacterial object segmentation performance by utilizing the multicluster idea. The State Transform Algorithm (STA) is used to obtain starting centroids in order to increase stability, because the Kernel of Intuitionistic Fuzzy C- Means (KIFCM) is sensitive to starting centroids and hence sensitive to being stuck in local optima. The accuracy of KIFCM-STA with bicluster is poor in low-light images. To boost performance, the multicluster technique (MKIFCM-STA) is presented as a continuation hybrid of KIFCM-STA. This framework allows for the ideal amount of clusters (Silhouette) and cluster ranking to provide clusters containing bacterial objects (Topsis). In order to compare our method against four prior approaches (IFCM, KFCM, KIFCM, and KIFCM-STA), we compare its qualitative aspects (visualization of images) and quantitative aspects (average IOU, Dice, HD, ASD, and Accuracy). In low light image clustering tasks, our model significantly improves and achieves great results in terms of accuracy, with a score of 89.438%. This accomplishment highlights how crucial it is that our framework tackles the problem of low-light image clustering in images of bacteria, eventually improving the image auto-annotation procedure.

关键词： Deep learning Image segmentation Visualization Microorganisms Accuracy Clustering methods Clustering algorithms

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DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance 11

DCGAN-based Medical Image Augmentation to Improve ELM Classi...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Rando Setiawan, Noor Akhmad Permanasari, Adhistya Erna Rulaningtyas, Riries Suksmono, Andriyan Bayu Sitanggang, Imas Sukaesih Universitas Gadjah Mada Dept. of Electrical and Information Engineering Yogyakarta Indonesia Biomedical Engineering Program Dept. of Physics Universitas Airlangga Surabaya Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Ipb University Department of Computer Science Bogor Indonesia

ISBN: (纸本)9781665460309

Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a pathologist to diagnose. Hence, a rapid development of medical computerization for early detection to get the results quickly is needed. This paper proposes synthetic data augmentation by using Deep Convolutional Generative Adversarial Network (DCGAN) to increase number of pap smear samples in dataset. Gray Level Co-occurrence Matrix (GLCM) is employed to extract features from dataset. Classification of 3 classes which are Adenocarcinoma, High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC) is conducted using Extreme Learning Machine (ELM). The result shows that the addition of synthetic data improves the performance of ELM with the accuracy of 90%. This accuracy is better than the accuracy of ELM using only the original dataset which is 85%. © 2022 IEEE.

关键词： Generative adversarial networks

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Performance Comparison of 15-Level Multilevel Inverter Topologies 15

Performance Comparison of 15-Level Multilevel Inverter Topol...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Anusha, G. Arora, Krishan Sharma, Himanshu Thota, Surya Prakash Verma, Chaman Raboaca, Maria Simona Filote, Constantin Lovely Professional University Dept. of Eee Punjab India Gnits Dept. of Eee Hyderabad India Eötvös Loránd University Faculty of Informatics Dept. of Media and Educational Informatics Budapest Hungary National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania Stefan Cel Mare University of Suceava Faculty of Electrical Engineering and Computer Science Suceava Romania

ISBN: (纸本)9798350321388

Multilevel inverters (MLI) have gained a significant emphasis in the area of renewable energy applications in recent years. The popularity of MLIs is due to their ability to efficiently convert the sustainable sources of energy, such as wind and solar power, into usable electrical energy. One key advantage of using reduced multilevel inverter configurations is that it can help to reduce the system overall cost by decreasing number of components required. The reduction of components in the inverter circuit results in simplification of design and lower expenditure. This paper proposes a fifteen-level modified multilevel inverter with cascaded h-bridge with a minimized switch count. The simulation results of the proposed inverter demonstrate that it is able to effectively attenuate harmonics and with reduced number of components required while still providing good performance. The suggested inverter design is also evaluated on its harmonic reduction characteristics, providing a cleaner output waveform and less distortion. © 2023 IEEE.

关键词： Solar energy

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Bringing Educational Neuroscience to Distance Learning with Design Thinking: The Design and Development of a Hybrid E-learning Platform for Skillful Training 7

Bringing Educational Neuroscience to Distance Learning with ...

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7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Dimitropoulos, Konstantinos Mystakidis, Stylianos Fragkaki, Maria University of Patras Dept. of Computer Engineering and Informatics Patras Greece University of Patras School of Natural Sciences Patras Greece University of Patras Dept. of Educational Sciences and Early Childhood Education Patras Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

Continuing, life-long education for flexible upskilling and reskilling has been a systemic challenge, a ruthless race against time, accelerated technological change and unforeseen adversities. Especially in the post-pandemic fourth industrial revolution era, higher education learners need systems that can facilitate durable, reliable knowledge construction and retention. Many e-learning platforms and methods have emerged and tried to solve this problem, however not all with the desired success. This kind of deeper learning is a lot trickier to implement effectively;it requires multifaceted thoughtful instructional and interaction design to operate as it should and keep learners engaged. In this paper, the design and development of a dual, hybrid, neuroscience-informed online learning platform is presented based on the design thinking methodology. This platform was used to facilitate an innovative didactic course based on neuroscience to higher education faculty members. © 2022 IEEE.

关键词： E-learning

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Self-Reported Learning Strategies and Preferences in Health informatics

Self-Reported Learning Strategies and Preferences in Health ...

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34th Medical informatics Europe Conference, MIE 2024

作者： Rohani, Narjes Gallagher, Michael Gal, Kobi Banas, Kasia Manataki, Areti Usher institute University of Edinburgh United Kingdom Moray House School of Education and Sport University of Edinburgh United Kingdom School of Informatics University of Edinburgh United Kingdom Dept. of Software and Information Systems Engineering Ben-Gurion University Israel School of Computer Science University of St Andrews United Kingdom

ISBN: (纸本)9781643685335

Despite the proliferation of educational programmes in Health informatics (HI) worldwide, there is limited knowledge regarding students' preferences and learning strategies in HI courses. To address this gap, we conducted a study to gather and analyse data from three HI courses. Employing the Motivated Strategies for Learning Questionnaire (MSLQ) and theories of deep and surface learning, we designed a questionnaire to collect data. The analysis of students' responses indicates that machine learning emerges as one of the most interesting topics, while certain topics such as data wrangling of genomics data were more challenging for students. Students expressed a preference for sequential learning. They exhibited multimodal tendencies regarding the type of learning resources, with tendency to prefer learning resources that have more visual contents. In all three courses, learners reported using deep learning strategy rather than surface learning, yet they appear to struggle with employing organisation, elaboration, and peer learning tactics. This study provides valuable insights into HI education, offering recommendations for educators, learners, and researchers to enhance HI education. © 2024 The Authors.

关键词： Contrastive Learning

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