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AI-driven Risk Assessment and Decision-making in Healthcare Applications Using Machine Learning: A Case Study on Customer Twitter Feeds 2

AI-driven Risk Assessment and Decision-making in Healthcare ...

引用

2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Darwiesh, Abdelaziz Elhoseny, Mohamed El-Baz, A.H. Atassi, Reem Mathematics Dep. Faculty of Science Damietta University New Damietta Egypt Information Systems Dep. Faculty of Computing and Info University of Sharjah Sharjah United Arab Emirates Computer Science Dep. Faculty of Computers and Artificial Intelligence Damietta University New Damietta Egypt Higher Colleges of Technology Dubai United Arab Emirates

ISBN: (纸本)9798331504106

This study presents an AI-driven strategy for enhancing decision-making in healthcare applications by assessing and managing potential risks through the analysis of customer interactions on social media platforms, specifically Twitter. We propose the use of machine learning models to outperform traditional lexical approaches, addressing the complexities inherent in risk detection. Natural language processing methods are employed to efficiently process and analyze textual data, enabling healthcare institutions to make informed decisions about risk management. By leveraging customer feedback from the X platform, our approach aids in the development of effective healthcare programs by identifying and classifying risks. We create three specialized datasets for risk analysis, risk assessment, and hazard classification to optimize the training and dep.oyment phases. In our case study, which focuses on tweets related to healthcare for US consumers, we find that over 50% of the sample identifies potential risks, with nearly 80% indicating medium levels of risk. Commonly identified risks include financial, operational, and strategic threats. The proposed methodology demonstrates an average accuracy of 82.66% in risk detection, identification, and assessment, confirming its effectiveness in supporting AI-driven decision-making in healthcare application development. ©2024 IEEE.

关键词： Tweets

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Biological age estimation from ECG using Artificial Intelligence approaches 2

Biological age estimation from ECG using Artificial Intellig...

引用

2nd Jordanian International Biomedical Engineering Conference, JIBEC 2024

作者： Sleiman, Jana Daher, Mohamad Khalil, Mohamad Diab, Ahmad Kassem, Abdallah Lebanese University Computer and Communications Engineering Tripoli Lebanon Faculty of Science Faculty of Applied Sciences & Technology Dep. of Computer Science Dep. of Computer Science & Information Technology Tripoli Lebanon Lebanese University Electrical Engineering Tripoli Lebanon Lebanese International University Biomedical Engineering Beirut Lebanon Notre Dame University Faculty Of Engineering ECCE Dept. Zouk Mosbeh Lebanon

ISBN: (纸本)9798331539542

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age. © 2024 IEEE.

关键词： Convolutional neural networks

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MPI Impact in C+L+S Multiband Transmission Reach

MPI Impact in C+L+S Multiband Transmission Reach

引用

2024 Photonic Networks and Devices, NETWORKS 2024

作者： Cancela, Luís G. Pires, João O. Instituto de Telecomunicações Dep. Information Science and Technology Iscte Instituto Universitário de Lisboa Portugal Instituto de Telecomunicações Dep. Electrical and Computer Engineering Instituto Superior Técnico Portugal

Multipath interference (MPI) impact is assessed in a C+L+S multiband transmission scenario. For a typical -34 dB/span MPI, transmission reach in the L-band suffers a 20% reach penalty considering the QPSK modulation f... 详细信息

关键词： C++ (programming language)

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ERROR SENSITIVITY MODULATION BASED EXPERIENCE REPLAY: MITIGATING ABRUPT REPRESENTATION DRIFT IN CONTINUAL LEARNING 11

ERROR SENSITIVITY MODULATION BASED EXPERIENCE REPLAY: MITIGA...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Sarfraz, Fahad Arani, Elahe Zonooz, Bahram Advanced Research Lab NavInfo Europe Netherlands Dep. of Mathematics and Computer Science Eindhoven University of Technology Netherlands

Humans excel at lifelong learning, as the brain has evolved to be robust to distribution shifts and noise in our ever-changing environment. Deep neural networks (DNNs), however, exhibit catastrophic forgetting and the learned representations drift drastically as they encounter a new task. This alludes to a different error-based learning mechanism in the brain. Unlike DNNs, where learning scales linearly with the magnitude of the error, the sensitivity to errors in the brain decreases as a function of their magnitude. To this end, we propose ESMER which employs a principled mechanism to modulate error sensitivity in a dual-memory rehearsal-based system. Concretely, it maintains a memory of past errors and uses it to modify the learning dynamics so that the model learns more from small consistent errors compared to large sudden errors. We also propose Error-Sensitive Reservoir Sampling to maintain episodic memory, which leverages the error history to pre-select low-loss samples as candidates for the buffer, which are better suited for retaining information. Empirical results show that ESMER effectively reduces forgetting and abrupt drift in representations at the task boundary by gradually adapting to the new task while consolidating knowledge. Remarkably, it also enables the model to learn under high levels of label noise, which is ubiquitous in real-world data streams. Code: https://***/NeurAI-Lab/ESMER. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Errors

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Colex2Lang: Language Embeddings from Semantic Typology 24

Colex2Lang: Language Embeddings from Semantic Typology

引用

24th Nordic Conference on Computational Linguistics, NoDaLiDa 2023

作者： Chen, Yiyi Biswas, Russa Bjerva, Johannes Dep. of Computer Science Aalborg University Copenhagen Denmark FIZ Karlsruhe AIFB Karlsruhe Inst. of Technology Karlsruhe Germany

ISBN: (纸本)9789916219997

In semantic typology, colexification refers to words with multiple meanings, either related (polysemy) or unrelated (homophony). Studies of cross-linguistic colexification have yielded insights into, e.g., psychology, historical linguistics and cognitive science (Xu et al., 2020;Brochhagen and Boleda, 2022;Schapper and Koptjevskaja-Tamm, 2022;Karjus et al., 2021;François, 2022). While NLP research up until now has mainly focused on integrating syntactic typology (Naseem et al., 2012;Täckström et al., 2013;Zhang and Barzilay, 2015;Daiber et al., 2016;de Lhoneux et al., 2018;Ponti et al., 2019;Chaudhary et al., 2019;Üstün et al., 2020;Oncevay et al., 2020;Yu et al., 2021;Ansell et al., 2021;Zhao et al., 2021;Oncevay et al., 2022), we here investigate the potential of incorporating semantic typology, of which colexification is an example. We propose a framework for constructing a large-scale synset graph and learning language representations with node embedding algorithms. We demonstrate that cross-lingual colexification patterns provide a distinct signal for modelling language similarity and predicting typological features. Our representations achieve a 9.97% performance gain in predicting lexico-semantic typological features and expectantly contain a weaker syntactic signal. This study is the first attempt to learn language representations and model language similarities using semantic typology at a large scale, setting a new direction for multilingual NLP, especially for low-resource languages. © 2023 Association for Computational Linguistics.

关键词： Computational linguistics

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Deep Learning Technique for Real-Time Forest Fire Detection from Video Streams 11

Deep Learning Technique for Real-Time Forest Fire Detection ...

引用

11th International Conference on Intelligent Systems and Embedded Design, ISED 2023

作者： Gupta, Nidhi Chakravarty, Sujata Dash, Satyabrata Centurion University of Technology & Management Dept. of Computer Science & Engineering Odisha India Gandhi Institute of Technology & Management Gitam Deemed to Be University Dep. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350318159

Forest fires are currently the most common issue that we are dealing with. The resources that have been lost are irreplaceable. Forest fires are uncontrollable fires that occur in nature as a result of human involvement or any other natural event. Forest fires can be so large that putting them out takes a long time. Contrasting with previous years, there has been a striking surge in the frequency of forest fires. Changes in climatic conditions are the key factor affecting the scope of forest fires. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. The incorporation of a Video Fire Detection model significantly enhances the efficiency of fire detection systems, particularly in large spaces. Establishing a video fire detection system involves focusing on spatial, spectral, and temporal indicators, as well as flame picture segmentation, recognition, tracking, and prediction. Analytical techniques, specifically utilizing ResNet50 and VGG16 models, can be applied to identify pool fire images through these segmentation processes, contributing to more robust fire detection capabilities. © 2023 IEEE.

关键词： Fires

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Performance Analysis of Intelligent Reflecting Surfaces for 5G/6G-Enabled Future Smart Industries with a Focus on Millimeter-Wave Band Communications 59

Performance Analysis of Intelligent Reflecting Surfaces for ...

引用

2024 Annual IEEE International Conference on Communications Workshops, ICC Workshops 2024

作者： Khan, Muhammad Farhan Raza, Ali Iqbal, Adeel Rashid, Adnan Jamshed, Muhammad Ali Pesch, Dirk School of Computer Science and Information Technology University College Cork Cork Ireland Comsats University Islamabad Dep. of Electrical and Computer Engineering Pakistan Yeungnam University Dep. of Info. & Com. Engineering Gyeongsan-si38541 Korea Republic of Politecnico di Bari Dep. of Electrical & Information Engineering Bari Italy James Watt School of Engineering University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9798350304053

Industry 4.0's emergence necessitates reliable communication systems. Millimeter-wave (mmWave) communication is a promising solution, but challenges like limited coverage and atmospheric absorption persist. The Intelligent Reflecting Surfaces (IRS) offers transformative potential by dynamically manipulating wave propagation to improve millimeter-wave (mmWave) communication. This research investigates the impact of the distance of Industrial User Equipment (iUE) from the IRS and Base Station (BS), and the height of the IRS from the ground, along with the impact of sum throughput by increasing the number of IRS elements. The IRS-assisted system exhibits enhanced performance within the mmWave frequency band compared to the sub-6 GHz band. The observed achievable rate gains, exceeding 200% on average, underscore the significant performance enhancement enabled by IRS dep.oyments. Moreover, increasing the number of IRS elements significantly enhances the overall throughput. The decrease in required transmit power with an increasing number of Intelligent Reflecting Surfaces IRS elements can be attributed to the enhanced ability of IRS to efficiently reflect and manipulate electromagnetic waves. As more IRS elements are dep.oyed, they collectively contribute to optimizing signal propagation, allowing for better control and redirection of the transmitted signals. This phenomenon enables the system to achieve the desired communication objectives with less overall transmit power. Additionally, the attainable capacity of the examined IRS-assisted wireless communication system exhibits growth alongside an increase in the elevation of the IRS installation. Results showcase the potential of IRSs to enhance coverage and communication reliability, providing valuable insights for designing efficient wireless systems in smart factories and Industry 4.0 applications. © 2024 IEEE.

关键词： 5G mobile communication systems

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Sorting Red and Green Chilies by Digital Image Processing 2

Sorting Red and Green Chilies by Digital Image Processing

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2nd International Conference on Cybernetics and Innovations, ICCI 2023

作者： Chumuang, Narumol Ketcham, Mahasak Ganokratanaa, Thittaporn Pramkeaw, Patiyuth Chongsri, Jiratha Yimyam, Worawut Dep. of Digital Media Technology Thailand King Mongkut's University of Technology North Bangkok Dep. of Information Technology Management Thailand King Mongkut's University of Technology Thonburi Applied Computer Science Program Bangkok Thailand King Mongkut's University of Technology Thonburi Media Technology Program Bangkok Thailand Phetchaburi Rajabhat University Dep. of Business Information Management Thailand

ISBN: (纸本)9798350336573

The purpose of this research is to help save electricity within organizations and workplaces that use computers. Due to the behavior of general users today, they do not give importance to saving electricity. This work has developed an automatic computer shutdown system. with image processing via a webcam by using image processing techniques (image processing) applied to a webcam by using a webcam to capture the user on the computer screen. Then the images are analyzed and examined with the principles of image processing with the face detection technique using the algorithm of Haar -like features to detect human faces and ROI. define a specific area of interest. The system will then check the amount of inactivity on the screen. or does not show the face image within 10 minutes, the system will automatically turn off the computer. with image processing. This work can make it easier to turn off computers when not in use. and helps to save electricity for the organization or agencies as well. © 2023 IEEE.

关键词： Face recognition

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Examining Spurious Information through Text Categorization Methods

Examining Spurious Information through Text Categorization M...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Warinthaksa, Watcharin Yimyam, Worawut Ketcham, Mahasak Ganokratanaa, Thittaporn Pinthong, Thidarat Rajamangala University Of Technology Rattanakosin Nakhonprathom Thailand Phetchaburi Rajabhat University Dep. Of Business Information Management Phetchaburi Thailand King Mongkut's University Of Technology North Bangkok Dep. Of Management Information System Bangkok Thailand King Mongkut's University Of Technology Thonburi Applied Computer Science Program Bangkok Thailand

ISBN: (纸本)9798350370096

In recent years, deceptive content such as fake news and fake reviews, also known as opinion spams, have increasingly become a dangerous prospect for online users. Fake reviews have affected consumers and stores alike. Furthermore, the problem of fake news has gained attention in 2016, especially in the aftermath of the last U.S. presidential elections. Fake reviews and fake news are a closely related phenomenon as both consist of writing and spreading false information or beliefs. The opinion spam problem was formulated for the first time a few years ago, but it has quickly become a growing research area due to the abundance of user-generated content. It is now easy for anyone to either write fake reviews or write fake news on the web. The biggest challenge is the lack of an efficient way to tell the difference between a real review and a fake one;even humans are often unable to tell the difference. In this paper, we introduce a new n-gram model to detect automatically fake contents with a particular focus on fake reviews and fake news. © 2024 IEEE.

关键词： Text processing

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Automatic computer Shutdown with Image Processing via Webcam to Save Energy 2

Automatic Computer Shutdown with Image Processing via Webcam...

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2nd International Conference on Cybernetics and Innovations, ICCI 2023

作者： Chumuang, Narumol Ketcham, Mahasak Ganokratanaa, Thittaporn Pramkeaw, Patiyuth Yimyam, Worawut Chuamoo Dep. of Digital Media Technology Thailand King Mongkut's University of Technology North Bangkok Dep. of Information Technology Management Thailand King Mongkut's University of Technology Thonburi Applied Computer Science Program Bangkok Thailand King Mongkut's University of Technology Thonburi Media Technology Program Bangkok Thailand Phetchaburi Rajabhat University Dep. of Business Information Management Thailand

ISBN: (纸本)9798350336573

The purpose of this research is to help save electricity within organizations and workplaces that use computers. Due to the behavior of general users today, they do not give importance to saving electricity. The researcher has developed an automatic computer shutdown system. with image processing via a webcam By using image processing techniques (Image Processing) applied to a webcam. By using a webcam to capture the user on the computer screen. Then the images are analyzed and examined with the principles of image processing with the Face Detection technique using the algorithm of Haar-like Features to detect human faces and ROI. Define a specific area of interest The system will then check the amount of inactivity on the screen. or does not show the face image within 10 minutes, the system will automatically turn off the computer. with image processing This research can make it easier to turn off computers when not in use. and helps to save electricity for the organization or agencies as well. © 2023 IEEE.

关键词： Face recognition

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