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

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

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

Decoding the Obfuscated: Advanced NER Techniques on Commercial Sex Advertisement Data

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Decoding the Obfuscated: Advanced NER Techniques on Commerci...

International Conference on Computational science and Computational Intelligence (CSCI)

作者： Alejandro Rodriguez Perez Pablo Rivas Javier Turek Korn Sooksatra Ernesto Quevedo Gisela Bichler Tomas Cerny Laurie Giddens Stacie Petter Dept. of Computer Science School of Eng. & Computer Science Baylor University Human-Robot Collaboration Lab Intelligent Systems Research Intel Labs School of Criminology & Criminal Justice California State University San Bernardino Dept. of Systems and Industrial Eng. College of Engineering The University of Arizona Information Tech. & Decisions Sci. Dept. G. Brint Ryan College of Business University of North Texas School of Business Wake Forest University

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

Detecting potential human trafficking activity within online Commercial Sex Advertisements (CSAs) presents unique challenges for Named Entity Recognition (NER) due to the complex and often obfuscated textual content. This paper thoroughly evaluates state-of-the-art language models and tokenization techniques, focusing on their efficacy in the NER task within the context of CSAs. Our results indicate that the Longformer model, equipped with byte-level BPE tokenization, outperforms other models regarding precision, recall, and the $F$ 1 score. The study also uncovers specific areas for improvement, offering avenues for future research. Our findings have significant implications for the automated analysis and monitoring of CSAs for suspected human trafficking activity, which contributes to developing more robust and transparent online ecosystems.

关键词： Adaptation models Biological system modeling Focusing Named entity recognition Tokenization Data models Web sites

A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data

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A Trustable LSTM-Autoencoder Network for Cyberbullying Detec...

IEEE International Conference on Big Data

作者： Mst Shapna Akter Hossain Shahriar Alfredo Cuzzocrea Fan Wu Juanjose Rodriguez-Cardenas Department of Intelligent Systems and Robotics University of West Florida USA Center of Cybersecurity University of West Florida USA iDEA Lab University of Calabria Rende Italy Dept. of Computer Science Tuskegee University Tuskegee USA Dept. of Information Technology Kennesaw State University Georgia USA

Social media cyberbullying has a detrimental effect on human life. As online social networking grows daily, the amount of hate speech also increases. Such terrible content can cause depression and actions related to suicide. This paper proposes a trustable LSTM-Autoencoder Network for cyberbullying detection on social media using synthetic data. We have demonstrated a cutting-edge method to address data availability difficulties by producing machine-translated data. However, several languages such as Hindi and Bangla still lack adequate investigations due to a lack of datasets. We carried out experimental identification of aggressive comments on Hindi, Bangla, and English datasets using the proposed model and traditional models, including Long Short-Term Memory (LSTM), Bidirectional Long ShortTerm Memory (BiLSTM), LSTM-Autoencoder, Word2vec, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer 2 (GPT-2) models. We employed evaluation metrics such as f1-score, accuracy, precision, and recall to assess the models’ performance. Our proposed model outperformed all the models on all datasets, achieving the highest accuracy of 95%. Our model achieves state-of-the-art results among all the previous works on the dataset we used in this paper.

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Effects of Different Optimization Formulations in Evolutionary Reinforcement Learning on Diverse Behavior Generation

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arXiv 2021年

作者： Villin, Victor Masuyama, Naoki Nojima, Yusuke Dept. of Computer Science and Intelligent Systems Osaka Prefecture University Sakai Osaka Japan

Generating various strategies for a given task is challenging. However, it has already proven to bring many assets to the main learning process, such as improved behavior exploration. With the growth in the interest of heterogeneity in solution in evolutionary computation and reinforcement learning, many promising approaches have emerged. To better understand how one guides multiple policies toward distinct strategies and benefit from diversity, we need to analyze further the influence of the reward signal modulation and other evolutionary mechanisms on the obtained behaviors. To that effect, this paper considers an existing evolutionary reinforcement learning framework which exploits multi-objective optimization as a way to obtain policies that succeed at behavior-related tasks as well as completing the main goal. Experiments on the Atari games stress that optimization formulations which do not consider objectives equally fail at generating diversity and even output agents that are worse at solving the problem at hand, regardless of the obtained behaviors. © 2021, CC BY.

关键词： Multiobjective optimization

Fast Measurement of Impedance Calculation for Electrochemical Impedance Spectroscopy

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Fast Measurement of Impedance Calculation for Electrochemica...

International SoC Design Conference, ISOCC

作者： En-Chi Yang Suz-Ting Wang Kusn-Lin Liu Wen-Ho Juang Ming-Hwa Sheu How-Chiun Wu Shin-Chi Lai Dept. Electronics Engineering / Dept. Doctor’s Program of Smart Industry Technology Research and Design National Yunlin University of Science and Technology / National Formosa University Douliu Taiwan / Huwei Taiwan Dept. Doctor’s Program of Smart Industry Technology Research and Design National Formosa University Huwei Taiwan Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Natural Biotechnology Nanhua University Chiayi Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

This paper presents a design for a fast measurement electrochemical impedance spectroscopy (EIS) system, which differs from the traditional single-frequency point calculation approach. The proposed system is capable of simultaneously measuring 23 frequency points within the frequency range of 1Hz-10KHz. Additionally, it has an impedance measurement range of 100$\omega$ - 1M$\omega$. The experimental results demonstrate that the average MRE and PAE are both below 0.4% and 1°, with conditions of R=9940 $\omega$ and C=1.02uF.

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Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detection

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arXiv 2024年

作者： Rahman, Md Abdur Shahriar, Hossain Wu, Fan Cuzzocrea, Alfredo Dept. of Intelligent Systems and Robotics University of West Florida PensacolaFL United States Center for Cybersecurity University of West Florida PensacolaFL United States Department of Computer Science Tuskegee University TuskegeeAL United States iDEA Lab University of Calabria Rende Italy

Large language models (LLMs) are renowned for their exceptional capabilities, and applying to a wide range of applications. However, this widespread use brings significant vulnerabilities. Also, it is well observed that there are huge gap which lies in the need for effective detection and mitigation strategies against malicious prompt injection attacks in large language models, as current approaches may not adequately address the complexity and evolving nature of these vulnerabilities in real-world applications. Therefore, this work focuses the impact of malicious prompt injection attacks which is one of most dangerous vulnerability on real LLMs applications. It examines to apply various BERT (Bidirectional Encoder Representations from Transformers) like multilingual BERT, DistilBert for classifying malicious prompts from legitimate prompts. Also, we observed how tokenizing the prompt texts and generating embeddings using multilingual BERT contributes to improve the performance of various machine learning methods: Gaussian Naive Bayes, Random Forest, Support Vector Machine, and Logistic Regression. The performance of each model is rigorously analyzed with various parameters to improve the binary classification to discover malicious prompts. Multilingual BERT approach to embed the prompts significantly improved and outperformed the existing works and achieves an outstanding accuracy of 96.55% by Logistic regression. Additionally, we investigated the incorrect predictions of the model to gain insights into its limitations. The findings can guide researchers in tuning various BERT for finding the most suitable model for diverse LLMs vulnerabilities. © 2024, CC BY.

关键词： Embeddings

Provable concept learning for interpretable predictions using variational autoencoders

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arXiv 2022年

作者： Taeb, Armeen Ruggeri, Nicolò Schnuck, Carina Yang, Fanny Seminar for Statistics ETH Zürich Switzerland Max-Planck-Institute for Intelligent Systems Tübingen Germany Computer Science Dept. ETH Zürich Switzerland Mathematics Dept. ETH Zürich Switzerland

In safety-critical applications, practitioners are reluctant to trust neural networks when no interpretable explanations are available. Many attempts to provide such explanations revolve around pixel-based attributions or use previously known concepts. In this paper we aim to provide explanations by provably identifying high-level, previously unknown ground-truth concepts. To this end, we propose a probabilistic modeling framework to derive (C)oncept (L)earning and (P)rediction (CLAP) - a VAE-based classifier that uses visually interpretable concepts as predictors for a simple classifier. Assuming a generative model for the ground-truth concepts, we prove that CLAP is able to identify them while attaining optimal classification accuracy. Our experiments on synthetic datasets verify that CLAP identifies distinct ground-truth concepts on synthetic datasets and yields promising results on the medical Chest X-Ray dataset. © 2022, CC BY.

关键词： Safety engineering

Extreme Age of Information for Wireless-Powered Communication systems

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arXiv 2022年

作者： Miridakis, Nikolaos I. Shi, Zheng Tsiftsis, Theodoros A. Yang, Guanghua The School of Intelligent Systems Science & Engineering Jinan University Zhuhai Campus Zhuhai519070 China Dept. of Informatics and Computer Engineering University of West Attica Aegaleo 12243 Greece

The extreme or maximum age of information (AoI) is analytically studied for wireless communication systems. In particular, a wireless powered single-antenna source node and a receiver (connected to the power grid) equipped with multiple antennas are considered when operated under independent Rayleigh-faded channels. Via the extreme value theory and its corresponding statistical features, we demonstrate that the extreme AoI converges to the Gumbel distribution whereas its corresponding parameters are obtained in straightforward closed-form expressions. Capitalizing on this result, the risk of the extreme AoI realization is analytically evaluated according to some relevant performance metrics, while some useful engineering insights are manifested. Copyright © 2022, The Authors. All rights reserved.

关键词： Antennas

Heart Failure: Machine Learning Prediction Within a 5-Year Framework

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Heart Failure: Machine Learning Prediction Within a 5-Year F...

IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

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

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction.

关键词： Accuracy Sensitivity Explainable AI Biological system modeling Peptides Receivers Predictive models Indexes Cardiovascular diseases Random forests