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Multivariate multi-horizon time-series forecasting for real-time patient monitoring based on cascaded fine tuning of attention-based models

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Computers in Biology and Medicine 2025年 194卷 110406页

作者： Saleh, Hager El-Sappagh, Shaker McCann, Michael Alsamhi, Saeed Hamood Breslin, John G. Insight Research Ireland Centre for Data Analytics School of Engineering University of Galway University Road GalwayH91 TK33 Ireland Atlantic Technological University Letterkenny Donegal Ireland Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha13518 Egypt Department of Computing Atlantic Technological University Letterkenny Donegal Ireland Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Department of Electronics Engineering IBB University Ibb Ibb Yemen

The real-time forecasting of critical physiological indicators in intensive care units (ICUs) is essential for early intervention and clinical decision support. This study introduces a novel framework, StreamHealth Multi-Horizon AI, which has been designed to perform multivariate, multi-horizon time-series forecasting for vital signs, specifically for a person's blood oxygen saturation level (SpO2) and respiratory rate (RR). The framework leverages advanced attention-based models, with a particular emphasis on the Temporal Fusion Transformer (TFT) and Temporal Convolutional Network (TCN), and we benchmark its performance against classical deep learning architectures, including LSTM, GRU, Bi-LSTM, Bi-GRU, CNN, and Sequence-to-Sequence (Seq2Seq) models with and without attention mechanisms. Both univariate and multivariate forecasting tasks are explored across multiple prediction horizons (i.e., 7, 15 and 25 min), using physiological time-series data from the MIMIC-III database. The proposed system incorporates a cascaded fine-tuning strategy, wherein the TFT model is sequentially fine-tuned on individual patients’ data, significantly enhancing the model's generalizability to unseen patient profiles. Empirical results demonstrate that the TFT model consistently outperforms baseline models across all forecasting settings, achieving lower RMSE and MAE values, and exhibiting superior capacity for capturing long-sequence dependencies and temporal feature dynamics. To validate its applicability in real-time clinical environments, the framework integrates a simulated streaming infrastructure using Apache Kafka and Apache Flink, enabling continuous data ingestion, forecasting, and visualization of vital signs. This end-to-end deployment underscores the system's potential for ICU monitoring, allowing clinicians to anticipate patient deterioration proactively. In summary, we introduce a comprehensive framework that uniquely integrates TFT with cascaded fine-tuning for multivar

关键词： Electrotherapeutics

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Implementation of Artificial Intelligence In engineering to Achieve Sustainable Development Goals (SDGs)

Implementation of Artificial Intelligence In Engineering to ...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Muhammad D. Zakaria Azwa Abdul Aziz Azrul Amri Jamal Wan Azani Mustafa Laith H. Alzubaidi A.H. A. Hussein Faculty of Informatics & Computing Universiti Sultan Zainal Abidin (UniSZA) Tembila Campus Terengganu Malaysia Data Science & Analytics (DASA) Special Interest Group (SIG) Universiti Sultan Zainal Abidin (UniSZA) Gong Badak Terengganu Malaysia Centre of Excellence Advanced Computing (CoE AdvComp) Universiti Malaysia Perlis Arau Perlis Malaysia College of Pharmacy Ahl Al Bayt University Karbala Iraq Faculty of Engineering The Islamic University Najaf Iraq

Artificial Intelligence (AI) is implicit to revolutionize the engineering industry by automating tasks, minimizing costs, improving quality, and creating new opportunities for innovation. The article explores the implementation of AI in engineering as a means to achieve the Sustainable Development Goals (SDGs) set forth by the United Nations. AI technologies hold significant potential in optimizing resource utilization, enabling data-driven decision-making, and fostering innovation in various engineering domains. This study provides an overview of AI applications highlighting their alignment with specific SDGs. It examines the advantages, challenges, and ethical considerations connected to AI integration in engineering practices. Moreover, the article provides recommendations for policymakers, researchers, and practitioners to ensure responsible and inclusive AI development. By harnessing the transformative power of AI in engineering, we can increase the process towards SDGs, creating a more sustainable and prosperous future for all.

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PPG Segmentation Using Deep Learning

PPG Segmentation Using Deep Learning

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Mohammad Tabbakha Hassan Al Masri Abdullatif Hammad Mohammed Alsulatie Hiam Alquran Wan Azani Mustafa Muntather Almusawi Abbas Hameed Abdul Hussein Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid Jordan King Hussein Medical Center Royal Jordanian Medical Service The Institute of Biomedical Technology Amman Jordan Faculty of Electrical Engineering & Technology Campus Pauh Putra Advanced Computing (AdvComp) Centre of Excellence Universiti Malaysia Perlis Arau Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraqs

As an indicator regarding cardiovascular health, vascular dynamics, and more, Photoplethysmography (PPG) consists of a baseline with two pulsatile peaks that reflect the blood flow variations. In the absence of critical studies and a precise understanding of PPG signal this study proposes a deep learning approach to predict and define each phase of PPG waveform utilizing Gated recurrent unit (GRU) due to its optimal performance in the short-term dependencies in sequences, the model is composed mainly of GRU layer with 20 hidden units applying tanh and sigmoid activation function, followed by dropout layer and a fully connected layer, afterward a softmax layer was added, we conducted our study on a dataset combined of an accessible Bed-Based Ballistocardiography Dataset (BBB), after that, the data was manually labeled. Following up, hyperparameters such as learning rate, batch size, and other parameters were adjusted after numerous trials, which resulted in test accuracy, precision, and recall of 0.875, 0.892, and 0.854, respectively in the used dataset. These promising results might be the first step of more future investigations concerning PPG.

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Automated Detection and Quantification of Sewage Pipe Cracks Using Deep Learning for Urban Water Environment Management

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SSRN 2024年

作者： Yang, Chenhao Zheng, Feifei Kapelan, Zoran Savic, Dragan Pan, Gang Feng, Yu Ma, Yiyi College of Civil Engineering and Architecture Zhejiang University China Department of Water Management Delft University of Technology Netherlands Centre for Water Systems University of Exeter North Park Road ExeterEX4 4QF United Kingdom KWR Water Research Institute Netherlands Faculty of Civil Engineering University of Belgrade Serbia College of Intelligence and Computing Tianjin University China Cartography and Visual Analytics Technical University of Munich Germany

Sewage pipe defects can significantly affect urban water environment, like leakage of pollutants through pipe cracks to groundwater. Currently, the detection of sewage pipe defects is mainly through closed-circuit television inspection, which is conducted manually and time-consuming. In this study, an integrated deep-learning-based algorithm is proposed to detect and quantify pipe cracks from images, namely the Crack Detection and Characterization (CDC) method. The method is based on models created in two steps (i) crack detection by semantic segmentation, and (ii) crack length quantification using an innovative algorithm. The CDC algorithm is verified by images both artificially created in the laboratory and from real filed inspection. For both laboratory and field cases, the CDC method performs well in crack identification and length quantification, and exhibits a higher accuracy than other existing models. The results also show the deblurring process can improve the accuracy greatly. This study can contribute to decision-making in sewage pipe maintenance and water environment management by providing an innovative way capable of more efficient and accurate pipe defect assessment compared with traditional labor-intensive work. © 2024, The Authors. All rights reserved.

关键词： Crack detection

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A Deep-Learning-Based Method for Automated Detection and Characterization of Cracks in Drainage Pipes

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SSRN 2024年

Currently, the interpretation of closed-circuit television images for structural defect analysis of drainage pipes is mostly conducted manually, which is labor-intensive. In this study, an integrated deep-learning-based algorithm is proposed, namely the Crack Detection and Characterization (CDC) method, which can detect and quantify (e.g., length) pipe cracks. The CDC algorithm is verified by images of pipe cracks both artificially created in the laboratory and from real filed inspection. From the results, the CDC algorithm with the DeepLab V3+ crack detection model performs best for both the laboratory and field cases. The results also show the significant effect of the deblurring process in improving the accuracy of the CDC algorithm. The process increases the mean intersection over union (mIoU) of the calculated results by 1.2%. This study offers an innovative way of detection and characterization of drainage pipe cracks, capable of more accurate pipe defect assessment compared with traditional work. © 2024, The Authors. All rights reserved.

关键词： Crack detection

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Evaluation of Machine Learning Techniques for Prediction of Breast Cancer

Evaluation of Machine Learning Techniques for Prediction of ...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Jared Tan Yi Hoong Samuel-Soma M. Ajibade Muhammed Basheer Jasser Anwar P.P. Abdul Majeed David Olayemi Alebiosu Bayan Issa Anthonia Oluwatosin Adediran Charis Shwu Chen Kwan Department of Data Science and Artificial Intelligence School of Engineering and Technology Sunway University No.5 Jalan Universiti Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Sunway University No.5 Jalan Universiti Selangor Darul Ehsan Malaysia Faculty of Informatics Engineering University of Aleppo Syria Department of Real Estate Faculty of Built Environment Universiti Malaya Kuala Lumpur Malaysia Department of Smart Computing and Cyber Resilience School of Engineering and Technology Sunway University No.5 Jalan Universiti Selangor Darul Ehsan Malaysia

ISBN: (数字)9798331522193

ISBN: (纸本)9798331522209

The prediction of breast cancer prognosis has been a significant challenge in medical research due to the complex nature of cancer progression and the variability in patient-specific factors. Machine learning techniques have emerged as powerful tools for enhancing prediction accuracy in cancer diagnosis and prognosis. This study evaluates the performance of three widely used machine learning classifiers—Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM)—in predicting breast cancer outcomes using the Wisconsin Breast Cancer Database. A robust framework was designed to preprocess the dataset, scale features, and assess the classifiers using key evaluation metrics, including accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). The results reveal that all classifiers demonstrated high predictive accuracy, with SVM achieving the highest accuracy of 97.08%, followed by RF and ANN with 96.67%. While RF recorded superior precision (95.40%), SVM outperformed others in recall (97.70%) and F1-score (96.05%), showcasing its ability to balance prediction precision and sensitivity. These findings underscore the potential of machine learning to augment traditional cancer prognosis methods, providing healthcare professionals with data-driven insights for personalized treatment strategies. This research highlights the critical role of machine learning in transforming cancer care and lays the groundwork for future studies focused on hybrid models and real-world implementation.

关键词： Support vector machines Accuracy Machine learning Medical services Artificial neural networks Breast cancer Prognostics and health management Random forests Medical diagnostic imaging Classification tree analysis

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Preface: The 1^stInternational Conference on Control, Optimization and Mathematical engineering 2021 (ICOCOME 2021)

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AIP Conference Proceedings 2024年第1期2867卷

作者： Sukono Supian, Sudradjat Chaerani, Diah Sambas, Aceng Mamat, Mustafa Vaidyanathan, Sundarapandian Sulaiman, Ibrahim Mohammed Department of Mathematics Universitas Padjadjaran Indonesia Department of Mathematics Universitas Padjadjaran Bandung Indonesia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Malaysia Research and Development Centre Vel Tech University India Department of Mathematics and Statistics Universiti Utara Malaysia Kedah Malaysia

Preface: The 1st International Conference on Control, Optimization and Mathematical engineering 2021 (ICOCOME 2021), AIP Conference Proceedings, Volume 2867, Is

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Digital Innovations Development: Causal factors and relations - A Case Study of Montenegro

Digital Innovations Development: Causal factors and relation...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Ivana Ognjanović Luka Laković Ramo Šendelj Laszlo Bokor Peter A. Kara Christoph Reich Manav Madan Milovan Roganović Emmanouil Zouilas John Mantas Jevto Eraković Tanja Radusinović Nada Rakočević Faculty for Information Systems and Technologies University of Donja Gorica Podgorica Montenegro Humanistic Studies University of Donja Gorica Podgorica Montenegro Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Institute of Data Science Cloud Computing and IT Security Furtwangen University Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens Goudi Greece Chamber of Economy of Montenegro Podgorica Montenegro

In order for economies to expand and develop globally, innovation is essential. A popular statistic for evaluating innovation between countries is the Global Innovation Index (GII), which provides a thorough analysis of the state of innovation worldwide. In this context, this paper aims to contribute to the literature by examining the multi-factoral effects of the innovation ecosystem on innovation development (internal causal effects), and the impact of global trends and crises on the innovation ecosystem (external causal effects) in Montenegro. The resources provided in this paper offer valuable insights into the innovation landscape in Montenegro and the Western Balkans, as well as the impact of globalization, global trends, and crises on innovation in the region. The paper also discusses the function of innovation hubs such as the DigNest project, in promoting cooperation between academic institutions, government agencies and corporations, and supporting the exchange of knowledge and fresh concepts.

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Challenges in Eye-Gaze Interaction Systems: A Survey

Challenges in Eye-Gaze Interaction Systems: A Survey

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International Conference on Platform Technology and Service (PlatCon)

作者： Aslina Baharum Rozita Ismail Punitha Turumogan Ismassabah Ismail Noorsidi Aizuddin Mat Noor Farhana Diana Deris School of Engineering and Technology Sunway University Bandar Sunway Selangor Malaysia College of Computing and Informatics Universiti Tenaga Nasional Kajang Selangor Malaysia Department of Computing and Information Technology Tunku Abdul Rahman University of Management and Technology Penang Malaysia Centre of Foundation Studies Universiti Teknologi MARA Cawangan Selangor Dengkil Selangor Malaysia Real Estate Department Mass Appraisal Housing and Planning Research Group Faculty of Built Environment and Surveying Centre for Real Estate Studies Universiti Teknologi Malaysia Johor Bahru Johor Malaysia Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia Johor Bahru Johor Malaysia

ISBN: (数字)9798350367874

ISBN: (纸本)9798350367881

Eye-gaze, also referred to as eye tracking, enables users to control their computers or communication devices using their eyes as a mouse. Achieving accurate and precise eye-gaze positioning with low-cost devices is a crucial advancement in this field. This paper presents a survey of eye-gaze-based interaction systems, aiming to identify current techniques employed in this domain. Key issues related to eye-gaze-based interaction systems are discussed, along with the challenges they pose and potential future developments. Recent eye-gaze system review methods are also presented. Additionally, the paper provides a summary of research findings on eye-gaze methods, comparing the main phases of eye-gaze-based interaction. Over the past few years, researchers have proposed various methods and solutions to enhance the accuracy and performance of eye-gaze-based systems. These techniques find applications in diverse areas, such as virtual keyboard typing, immersive video technologies, virtual communication, assistive devices, screen control, effortless scrolling, and enriched reading experiences. Overall, eye-gaze-based interaction systems have the potential to enhance user-human experiences by identifying eye-gaze positions or points, thereby improving users' lifestyles and understanding of their machine

关键词： Surveys Performance evaluation Location awareness Accuracy Target tracking Reviews Keyboards Gaze tracking People with disabilities Pupils

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