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6th International Conference on Advances in visual informatics, IVIC 2019

作者： Ng, Kok Weng Ang, Mei Choo Cher, Dong Theng Ahmad, Siti Azfanizam Abdul Wahab, Amelia Natasya Department of Mechanical Materials and Manufacturing Faculty of Science and Engineering University of Nottingham Malaysia Semenyih Malaysia Institute of Visual Informatics Universiti Kebangsaan Malaysia Bangi Malaysia Industrial Centre of Innovation in Smart Manufacturing SIRIM Berhad Kuala Lumpur Malaysia Department of Mechanical and Manufacturing Engineering Faculty of Engineering Universiti Putra Malaysia Serdang Malaysia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia

ISBN: (纸本)9783030340315

Among TRIZ (Theory of Inventive Problem Solving) methods that are applied to help solve design problems, ARIZ (Algorithm for Inventive Problem Solving) is considered to be the most powerful and is able to solve problems with "minimal changes to the system". However, the potential solutions derived from ARIZ are solution concepts that are general and are dependent on the engineers to further define these solution concepts into specific solutions. Shape grammars have been used to describe the shape of specific solution concepts or products with the intention to allow engineers to explore and generate a variation of specific solutions. This research work explores the application of a novel framework that combines ARIZ with shape grammars to facilitate and help engineers to translate the general solution concepts into a visualisation of specific solution concepts. © Springer Nature Switzerland AG 2019.

关键词： Engineers

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Inconsistencies among spectral robustness metrics 1

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14th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, Qshine 2018

作者： Wang, Xiangrong Feng, Ling Kooij, Robert E. Marzo, Jose L. Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Computing Science Department Institute of High Performance Computing A*STAR Singapore Singapore iTrust Centre for Research in Cyber Security Singapore University of Technology and Design Singapore Singapore Institute of Informatics and Applications University of Girona Girona Spain

ISBN: (数字)9783030144135

ISBN: (纸本)9783030144128

Network robustness plays a critical role in the proper functioning of modern society. It is common practice to use spectral metrics, to quantify the robustness of networks. In this paper we compare eight different spectral metrics that quantify network robustness. Four of the metrics are derived from the adjacency matrix, the others follow from the Laplacian spectrum. We found that the metrics can give inconsistent indications, when comparing the robustness of different synthetic networks. Then, we calculate and compare the spectral metrics for a number of real-world networks, where inconsistencies still occur, but to a lesser extent. Finally, we indicate how the concept of the R∗-value, a weighted sum of robustness metrics, can be used to resolve the found inconsistencies. © ICST Institute for Computer Sciences, Social informatics and Telecommunications engineering 2019.

关键词： Circuit theory

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Relativistic variational-quantum-eigensolver calculations of molecular electric dipole moments on quantum hardware

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Physical Review A 2025年第2期111卷 022817-022817页

作者： Palak Chawla Shweta K. R. Swain Tushti Patel Renu Bala Disha Shetty Kenji Sugisaki Sudhindu Bikash Mandal Jordi Riu Centre for Quantum Engineering Research and Education TCG Crest Kolkata 700091 India Department of Physics Indian Institute of Technology Delhi New Delhi 110016 India Present address: Department of Physics and Materials Science University of Luxembourg L-1511 Luxembourg Luxembourg. Department of Physics IIT Tirupati Chindepalle Andhra Pradesh 517619 India Present address: Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Grudziadzka 5 87-100 Toruń Poland. Graduate School of Science and Technology Keio University 7-1 Shinkawasaki Saiwai-ku Kawasaki Kanagawa 212-0032 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa 223-8522 Japan Keio University Sustainable Quantum Artificial Intelligence Center (KSQAIC) Keio University 2-15-45 Mita Minato-ku Tokyo Japan Qilimanjaro Quantum Tech Carrer de Veneçuela 74 Sant Martí 08019 Barcelona Spain Universitat Politècnica de Catalunya Carrer de Jordi Girona 3 08034 Barcelona Spain

The quantum–classical hybrid variational quantum eigensolver (VQE) algorithm is among the most actively studied topics in atomic and molecular calculations on quantum computers, yet few studies address properties other than energies or account for relativistic effects. This work presents high-precision 18-qubit relativistic VQE simulations for calculating the permanent electric dipole moments (PDMs) of alkaline-earth metal monohydride molecules (BeH to RaH) on traditional computers, and 6- and 12-qubit PDM computations for SrH on IonQ quantum devices. To achieve high precision on current noisy intermediate-scale era quantum hardware, we apply various resource-reduction methods, including reinforcement learning-based and causal flow preserving ZX–calculus routines, along with error mitigation and postselection techniques. Our approach reduces the two-qubit gate count in our 12-qubit circuit by 99.71%, with only a 2.35% trade-off in precision for PDM when evaluated classically within a suitably chosen active space. On the current generation IonQ Forte-I hardware, the error in PDM is −1.17% relative to classical calculations and only 1.21% compared with the unoptimized circuit.

关键词： Qubits

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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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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

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Nucleus segmentation in pap smear images using image processing techniques

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AIP Conference Proceedings 2024年第1期2934卷

作者： Mohammad Alslatie Hiam Alquran Wan Azani Mustafa Mohammed Mansour Naser Yasmin Mohd Yacob The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman 11855 Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University 21163 Irbid Jordan Department of Biomedical Engineering Jordan University of Science and Technology 22110 Irbid Jordan Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Campus Pauh Putra 02600 Arau Perlis Malaysia Advanced Computing (AdvComp) Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Campus Pauh Putra 02600 Arau Perlis Malaysia Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Campus Pauh Putra 02600 Arau Perlis Malaysia

Cervical cancer is caused by the growth of abnormal cells in the cervix’s lining. Human papillomavirus (HPV), a sexually transmitted infection, plays a role in most cervical cancers. Therefore, cervical cancer can be avoided by having regular screenings and being vaccinated against HPV infection. The term pap-smear refers to human cell samples stained using the Papanicolaou method. The Papanicolaou method is used to detect precancerous cell changes before they become invasive cancer. The smear cell image is composed of a nucleus and cytoplasm. Cancer prevalence is determined by the shape and structure of the nucleus. Therefore, the segmentation of the nucleus is an essential step in detecting cancer. However, overlapping, poor contrast, uneven staining, and other factors make cervical nucleus segmentation difficult. This paper proposes a new segmentation method for the cervical nucleus using digital image processing. Our proposed method used a median filter to remove noise and a non-linear contrast stretching to enhance the Pap smear images. Then, we used Bradley thresholding for the segmented cervical nucleus. The main impact of this paper will assist doctors in diagnosing cervical cancer based on Pap smear images and increase the accuracy percentages compared to the conventional method.

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MRI detects blood-brain barrier alterations in a rat model of Alzheimer's disease and lung infection

Npj imaging

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Npj imaging 2025年第1期3卷 8页

作者： Yolanda Ohene William J Morrey Elizabeth Powell Katherine F Smethers Nadim Luka Kieron South Michael Berks Catherine B Lawrence Geoff J M Parker Laura M Parkes Hervé Boutin Ben R Dickie Division of Psychology Communication and Human Neuroscience School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK. Geoffrey Jefferson Brain Research Centre Manchester Academic Health Science Centre University of Manchester Manchester UK. Division of Neuroscience School of Biological Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK. Department of Medical Physics and Biomedical Engineering and Department of Neuroinflammation Centre for Medical Image Computing UCL London UK. Division of Informatics Imaging and Data Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK. Bioxydyn Limited Manchester UK. Imaging Brain & Neuropsychiatry iBraiN Université de Tours INSERM Tours France.

Pneumonia is a common infection in people suffering with Alzheimer's disease, leading to delirium, critical illness or severe neurological decline, which may be due to an amplified response of the blood-brain barrier (BBB) to peripheral insult. We assess the response of the BBB to repeated lung infection in rat model of Alzheimer's disease (TgF344-AD), at 13- and 18-months old, using dynamic contrast-enhanced (DCE) MRI and filter exchange imaging. Higher BBB water exchange rate is initially detected in infected TgF344-AD rats. BBB water exchange rates correlated with hippocampus aquaporin-4 water channel expression in infected animals. We detected no differences in BBB permeability to gadolinium contrast agent measured by DCE-MRI, confirmed by staining for tight junction proteins, occludin and claudin-5. These findings provide insight into the mechanisms of how peripheral inflammation impacts the BBB.

关键词： Diseases Imaging the immune system

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Air Pollution and Cardiorespiratory Hospitalization, Modeling and Analysis Using Artificial Intelligence Techniques

Research Square

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Research Square 2021年

作者： Usmani, Raja Sher Afgun Pillai, Thulasyammal Ramiah Hashem, Ibrahim Abaker Targio Marjani, Mohsen Shaharudin, Rafiza Binti Latif, Mohd Talib School of Computer Science and Engineering Taylor’s University Selangor Malaysia College of Computing and Informatics Department of Computer Science University of Sharjah Sharjah27272 United Arab Emirates Institute for Medical Research Environmental Health Research Centre Occupational Health Unit Jalan Pahang Kuala Lumpur50588 Malaysia Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia

Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the Artificial Intelligence (AI) techniques. We propose the Enhanced Long Short-Term Memory (ELSTM) model and provide a comparison with other AI techniques, i.e., Long Short-Term Memory (LSTM), Deep Learning (DL), and Vector Autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia. © 2021, CC BY.

关键词： Long short-term memory

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Design and Implementation of Hybrid Vehicle using Control of DC Electric Motor 54

Design and Implementation of Hybrid Vehicle using Control of...

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54th International Universities Power engineering Conference, UPEC 2019

作者： Hussain, Muhammad Majid Rashid Mustafa, Al Chaudary, Muhammad Akmal Razaq, Abdul University of South Wales Faculty of Computing Engineering and Science Cardiff United Kingdom University of South Wales Centre for Automotive Power Systems Engineering Cardiff United Kingdom Ajman University Department of Electrical and Computer Engineering Ajman United Arab Emirates Abertay University School of Design and Informatics Dundee United Kingdom

ISBN: (纸本)9781728133492

The electric motors and its control technology are key components of hybrid electric vehicles (HEVs). Control of the electric motor is a fundamental issue for traction application in electric vehicles and HEVs. This paper presents the design, development and implementation of a hybrid vehicle using both an electric motor and petrol engine to increase efficiency and decrease carbon footprint. Initially, a prototype of a HEV is designed and the performance values are calculated, before a control system is developed and implemented to control the DC motor speed using a microcontroller as the vehicle's electronic control unit along with simple proportional integral derivative (PID) control using speed as a feedback mechanism. The prototype made incorporated voltage, current, speed and torque sensors for feedback resulting in a closed loop control system which successfully matched the speed input of a user-controlled pedal sensor. A user interface was developed to show the driver of the vehicle key variables such as the revolutions per minute (RPM) of the motor, the speed of the vehicle along with the current being drawn, and the voltage applied to the motor with overall power. To output a variable voltage from the Arduino, a digital output was used with pulse width modulation (PWM) capabilities in order to provide a variable DC voltage to the speed controller. © 2019 IEEE.

关键词： User interfaces

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Contrast enhancement on pap smear cell images: A comparison

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AIP Conference Proceedings 2023年第1期2608卷

作者： Mohamed Saifuddin Shahul Hameed Wan Azani Mustafa Syed Zulkarnain Syed Idrus Mohd Aminudin Jamlos Hiam Alquran Faculty of Electrical Engineering Technology University of Malaysia Perlis UniCITI Alam Campus Sungai Chuchuh 02100 Padang Besar Perlis Malaysia Advanced Computing (AdvComp) Centre of Excellence (CoE) Universiti Malaysia Perlis 02600 Arau Perlis Malaysia Faculty of Applied and Human Sciences Universiti Malaysia Perlis 01000 Kangar Perlis Malaysia Faculty of Electronic Engineering Technology University of Malaysia Perlis UniCITI Alam Campus Sungai Chuchuh 02100 Padang Besar Perlis Malaysia Biomedical Systems and Medical Informatics Engineering Yarmouk University 21163 Irbid Jordan

Cervical cancer is a common disease that can be carried by women. It is the 3rd leading cause of female cancer in Malaysia and the 4th most common type of cancer for women globally. Referring to the HPV Information centre in 2018, it is estimated that 1682 women are diagnosed and 944 die from having cervical cells. In 2018, 569, 847 of the 18,078,957 cases were categorised as cervical cancer, which is 3.2%. With the advancement in science and technology, cervical cancer can be detected at an early stage by conducting a Pap smear test. This test will filter abnormal cervical cells and detect precancerous changes in cervical cells based on the colour and shape properties of their nuclei and cytoplasm. The problem is that performing the procedure manually can be time-consuming and cause inconsistencies and errors even further because the cervical cell itself does not show an obvious difference in texture and colour from normal cells. This paper will focus on colour contrast enhancement of the cervical cell using a few methods such as Contrast Stretching (CS), CLAHE, Histogram Equalization (HE), Image Adjustment, and Multi Scale Retinex (MSR). From this research, the cervical cell colour contrast can be enhanced to a better level and the detection of cervical cells can happen faster and more accurately. Hence, the errors in detecting can be reduced again so they can be treated soon.

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