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International Scientific-Professional Conference on Information Technology (IT)

作者： Aniko Simon Luka Laković Pavle Kovačević Peter A. Kara Ivana Ognjanović Ramo Šendelj Christoph Reich Milovan Roganović John Mantas Laszlo Bokor Sigma Technology 1093 Budapest Hungary Faculty for Information Systems and Technologies University of Donja Gorica 81000 Podgorica Montenegro Budapest University of Technology and Economics 1111 Budapest Hungary Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 1111 Budapest Hungary Department of Computer Science School of Computer Science and Mathematics Faculty of Science Engineering and Computing Wireless Multimedia and Networking Research Group Kingston University London KT1 2EE UK Humanistic Studies University of Donja Gorica 81000 Podgorica Montenegro Institute of Data Science Cloud Computing and IT Security Furtwangen University 78120 Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro 81000 Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens 11527 Goudi Greece

With the advancement of information technology in the recent decades, digital and online services have become essential components of everyday life in Europe. Montenegro is no different in this regard. However, as digital literacy in the country is still in a developing phase in comparison with most countries of the EU, a higher portion of its citizens may rely on assistive technical communication. In this paper, we provide a case study on the assistive technical communication of Montenegrin eServices. The work addresses the sectors of online banking, healthcare services, higher education, national administrative services, and utilities. Our findings highlight the diversity in terms of implemented assistive technical communication, and distinguish usage contexts on the level of adequacy. Yet as the functionalities of Montenegrin eServices are expected to gradually expand in the upcoming years, assistive technical communication must aim to handle the higher extents of service complexity.

关键词： Online banking Education Europe Medical services Complexity theory Information technology

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Evaluation of the Montenegrin Academic Digital Innovation Hub

Evaluation of the Montenegrin Academic Digital Innovation Hu...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Luka Laković Peter A. Kara Ivana Ognjanović Laszlo Bokor Ramo Šendelj Christoph Reich Milovan Roganović Emmanouil Zoulias Jevto Eraković Tanja Radusinović Nada Rakočević Aniko Simon Faculty for Information Systems and Technologies University of Donja Gorica 81000 Podgorica Montenegro Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 1111 Budapest Hungary Department of Computer Science School of Computer Science and Mathematics Wireless Multimedia and Networking Research Group Faculty of Science Engineering and Computing Kingston University London KT1 2EE UK Humanistic Studies University of Donja Gorica 81000 Podgorica Montenegro Institute of Data Science Cloud Computing and IT Security Furtwangen University 78120 Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro 81000 Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens 11527 Goudi Greece Chamber of Economy of Montenegro 81000 Podgorica Montenegro Sigma Technology 1093 Budapest Hungary

In the era of global digitalization, digital innovation hubs have become essential, as they function as transforming forces. In this paper, we provide an analysis of the Montenegrin Academic Digital Innovation Hub by applying multi-criteria qualitative evaluation over several criteria: process flow of fully digitized operations of the Hub (with aspects of the innovation life-cycle, ethics, and security), resource management, strategic goals, and feasibility. The paper also proposes a model of comprehensive resource management via the perspective of smart specialization priorities of Montenegro.

关键词： Technological innovation Ethics Resource management Security Information technology

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Classification of Lung Cancer by Using Machine Learning Algorithms

Classification of Lung Cancer by Using Machine Learning Algo...

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International Conference on engineering Technology and their Applications (IICETA)

作者： Joman Al-Tawalbeh Baraa Alshargawi Hiam Alquran Waleed Al-Azzawi Wan Azani Mustafa Ahmed Alkhayyat Department of Biomedical Engineering Jordan University of Science and Technology Irbid Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Medical technical college Al-Farahidi University Baghdad Iraq Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Pauh Putra Campus Arau Perlis Malaysia Pauh Putra Campus Advanced Computing Centre of Excellence Universiti Malaysia Perlis (UniMAP) Pauh Putra Campus Arau Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

Due to the structure of cancer cells, where the majority of the cells are overlapped with each other, early diagnosis of lung cancer is a difficult challenge, so the cause of lung cancer remains unknown and prevention is difficult, early discovery of lung cancer is the only approach to cure it. The classification of lung cancer is a crucial process, based on signs that appear on patients; cancers could easily be predicted and treated. This paper uses KNN, SVM, Naïve Bayes and narrow neural network (NNN) classifiers. KNN showed 85.87% accuracy. SVM, Naïve Bayes and NNN showed 92.6%, 90.3% and 90% accuracy respectively. Results came after the fact that among the four classifiers SVM was the most accurate and much better to classify our data.

关键词： Support vector machines Machine learning algorithms Lung cancer Medical treatment Artificial neural networks

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Performance of a Superconducting Quantum Battery

arXiv

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arXiv 2024年

作者： Elghaayda, Samira Ali, Asad Al-Kuwari, Saif Czerwinski, Artur Mansour, Mostafa Haddadi, Saeed Laboratory of High Energy Physics and Condensed Matter Department of Physics Faculty of Sciences of Aïn Chock Hassan II University P.O. Box 5366 Maarif Casablanca20100 Morocco Qatar Centre for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Torun ul. Grudziadzka 5 Torun87-100 Poland Faculty of Physics Semnan University P.O. Box 35195-363 Semnan Iran Saeed's Quantum Information Group P.O. Box 19395-0560 Tehran Iran

Finding a quantum battery model that demonstrates a quantum advantage while remaining feasible for experimental production is a considerable challenge. In this paper, we introduce a superconducting quantum battery (SQB) model that exhibits such an advantage. The model consists of two coupled superconducting qubits that interact during the unitary charging process while remaining in equilibrium with a thermal reservoir. We first describe the model, provide evidence of the quantum advantage, and then discuss the fabrication process of the battery using superconducting qubits. Importantly, we derive analytical expressions for the ergotropy, instantaneous power, and capacity of the SQB, as well as their connection to quantum coherence. We demonstrate that leveraging the collective effects of Josephson energies and the coupling energy between qubits allows for optimization, resulting in improved energy redistribution and a significant enhancement in charging efficiency. This work highlights the complexities of tuning system parameters, which increase the potential for work extraction from the quantum battery, thereby providing a deeper understanding of the charging mechanisms involved. These findings can be applied to superconducting quantum circuit battery architectures, underscoring the feasibility of efficient energy storage in these systems. Our results pave the way for proposals of new superconducting devices capable of storing extractable work, emphasizing their potential for efficient energy storage. © 2024, CC BY.

关键词： Qubits

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Predicting risk of cardiovascular disease using retinal optical coherence tomography imaging

arXiv

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arXiv 2024年

作者： Maldonado-Garcia, Cynthia Bonazzola, Rodrigo Ferrante, Enzo Julian, Thomas H. Sergouniotis, Panagiotis I. Ravikumar, Nishant Frangi, Alejandro F. Centre for Computational Imaging and Simulation Technologies in Biomedicine School of Computing University of Leeds Leeds United Kingdom CONICET-UNL Santa Fe Argentina Division of Evolution Infection and Genomics School of Biological Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom Manchester Royal Eye Hospital Manchester University NHS Foundation Trust Manchester United Kingdom Manchester Centre for Genomic Medicine Saint Mary’s Hospital Manchester University NHS Foundation Trust Manchester United Kingdom Wellcome Genome Campus Cambridge United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom School of Computer Science Faculty of Science and Engineering University of Manchester Kilburn Building Manchester United Kingdom Christabel Pankhurst Institute University of Manchester Manchester United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom

Cardiovascular diseases (CVD) are the leading cause of death globally. Non-invasive, cost-effective imaging techniques play a crucial role in early detection and prevention of CVD. Optical coherence tomography (OCT) has gained recognition as a potential tool for early CVD risk prediction, though its use remains underexplored. In this study, we investigated the potential of OCT as an additional imaging technique to predict future CVD events. We analysed retinal OCT data from the UK Biobank. The dataset included 612 patients who suffered a myocardial infarction (MI) or stroke within five years of imaging and 2,234 controls without CVD (total: 2,846 participants). A self-supervised deep learning approach based on Variational Autoencoders (VAE) was used to extract low-dimensional latent representations from high-dimensional 3D OCT images, capturing distinct features of retinal layers. These latent features, along with clinical data, were used to train a Random Forest (RF) classifier to differentiate between patients at risk of future CVD events (MI or stroke) and healthy controls. Our model achieved an AUC of 0.75, sensitivity of 0.70, specificity of 0.70, and accuracy of 0.70, outperforming the QRISK3 score (the third version of the QRISK cardiovascular disease risk prediction algorithm;AUC = 0.60, sensitivity = 0.60, specificity = 0.55, accuracy = 0.55). The choroidal layer in OCT images was identified as a key predictor of future CVD events, revealed through a novel model explainability approach. This study demonstrates that retinal OCT imaging is a cost-effective, non-invasive alternative for predicting CVD risk, offering potential for widespread application in optometry practices and hospitals. © 2024, CC BY-NC-ND.

关键词： Optical coherence tomography

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Implementation of advanced techniques in production and manufacturing sectors through support vector machine algorithm with embedded system

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Measurement: Sensors 2024年 33卷

作者： Alfred, Rayner Chinthamu, Narender Jayanthy, T. Muniyandy, Elangovan Dhiman, Tarun Kumar Nimmy John, T. Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia Forbes Executive Technology Council Member BostonMA United States Panimalar Institute of Technology Tamilnadu Chennai India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai602 105 India Graphic Era Hill University Dehradun R/S Graphic Era Deemed to Be University Dehradun India Department of Instrumentation Kerala Kochi-22 India

Our study set out to explore the integration of digital twins into manufacturing and production sectors with the aim of enhancing sustainability and operational efficiency. Employing a structured methodology, we utilized advanced 3D modeling techniques to create virtual representations of physical environments and products, forming the basis of our digital twin ecosystem. Through virtual product implementation, we simulated product behavior and performance, facilitating comprehensive testing and analysis before physical manufacturing. Leveraging artificial intelligence, particularly Support Vector Machine (SVM) optimization, we conducted performance analysis by processing data from interconnected sources, including social media platforms, to optimize digital transformation processes. Our results indicate that digital twins significantly enhance operational efficiency by detecting inefficiencies early, reducing production costs, and expediting time-to-market. Moreover, they contribute to sustainability efforts by minimizing resource wastage and environmental impact during manufacturing processes. The adoption of digital twins also fosters a culture of innovation and continuous improvement, enabling technological advancements and adaptability in corporate and industrial settings. In conclusion, our findings underscore the transformative potential of digital twins in driving positive change and optimizing industrial processes. © 2024 The Authors

关键词： Efficiency

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Deep Learning Methods for Calibrated Photometric Stereo and Beyond

arXiv

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arXiv 2022年

作者： Ju, Yakun Lam, Kin-Man Xie, Wuyuan Zhou, Huiyu Dong, Junyu Shi, Boxin School of Electrical and Electronic Engineering Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong The Research Institute for Future Media Computing Shenzhen University Shenzhen China The Department of Informatics University of Leicester Leicester United Kingdom The Faculty of Information Science and Engineering The Institute for Advanced Ocean Study Ocean University of China Qingdao China The National Key Laboratory for Multimedia Information Processing National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models. Copyright © 2022, The Authors. All rights reserved.

关键词： Light sources

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Automatic Classification of Low-Angle Fuze-Quick Craters Using Deep Learning 6th

Automatic Classification of Low-Angle Fuze-Quick Craters Usi...

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6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020

作者： Aji, Sani Kumam, Poom Siricharoen, Punnarai Bukar, Ali Maina 126 Pracha-Uthit Road Bang Mod Thrung Khru Bangkok10140 Thailand 126 Pracha-Uthit Road Bang Mod Thrung Khru Bangkok10140 Thailand Department of Mathematics Faculty of Science Gombe State University Gombe Nigeria 126 Pracha-Uthit Road Bang Mod Thrung Khru Bangkok10140 Thailand Centre for Visual Computing Faculty of Engineering and Informatics University of Bradford Bradford West Yorkshire United Kingdom

ISBN: (纸本)9783030645793

Crater Analysis is a technique used for precautionary and investigative purpose, especially in confirming suspected locations of hostile fire and detecting the presence of enemy ammunition. Specifically, investigators have manually examined terrain disturbances due to blast craters, which are evidently caused by artillery fire. Although this manual process has been helpful in the identification of possible origins of fire, it is tedious and prone to errors. With the recent advancements in the fields of computer vision and machine learning (ML), this work investigates the automation of low-angle fuze-quick craters (LaFCs) classification using satellite imagery of the Ukranian border obtained from google earth. Due to limited volume of available satellite images (of LaFCs), we hereby propose the use of off-the-shelf ImageNet pretrained models (Resnet50, Resnet101 and ResNet152) for the automatic classification of the LaFCs. Thus, learned filters from these deep neural networks are used to extract features from the satellite images. These features are then fed into support vector machine (SVM) for classification. Promising results are obtained from our experiments, showing that it is possible to use ML to identify LaFCs. To the best of our knowledge, this is the first time classification of these type of craters has been investigated. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

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Large AI Models in Health informatics: Applications, Challenges, and the Future

arXiv

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arXiv 2023年

作者： Qiu, Jianing Li, Lin Sun, Jiankai Peng, Jiachuan Shi, Peilun Zhang, Ruiyang Dong, Yinzhao Lam, Kyle Lo, Frank P.-W. Xiao, Bo Yuan, Wu Wang, Ningli Xu, Dong Lo, Benny The Department of Computing Imperial College London United Kingdom The Department of Biomedical Engineering The Chinese University of Hong Kong Hong Kong Ltd. Hong Kong The Department of Informatics King’s College London United Kingdom The School of Engineering Stanford University United States The Department of Engineering Science University of Oxford United Kingdom The Faculty of Engineering The University of Hong Kong Hong Kong The Department of Surgery and Cancer Imperial College London United Kingdom The Hamlyn Centre for Robotic Surgery Imperial College London United Kingdom Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University China Beijing Ophthalmology & Visual Sciences Key Laboratory Beijing China The Department of Electrical Engineering and Computer Science United States The Christopher S. Bond Life Sciences Center University of Missouri United States The Facualty of Medicine Imperial College London United Kingdom

Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A prime example is ChatGPT, whose capability has compelled people’s imagination about the far-reaching influence that large AI models can have and their potential to transform different domains of our lives. In health informatics, the advent of large AI models has brought new paradigms for the design of methodologies. The scale of multi-modal data in the biomedical and health domain has been ever-expanding especially since the community embraced the era of deep learning, which provides the ground to develop, validate, and advance large AI models for breakthroughs in health-related areas. This article presents a comprehensive review of large AI models, from background to their applications. We identify seven key sectors in which large AI models are applicable and might have substantial influence, including 1) bioinformatics;2) medical diagnosis;3) medical imaging;4) medical © 2023, CC BY-NC-ND.

关键词： Medical imaging

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Optimization using RSM-CCD in the fabrication of magnesium-doped bismuth ferrite nanoparticles via sol–gel auto-combustion method for enhanced photocatalytic performance

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Results in Physics 2025年 69卷

作者： Sulaiman, Nur Fatin Mubin, Mohamad Helmi Abd Gunasekaran, Saraswathy Shamini Sofiah, A.G.N. Chaudhary, Kashif Tufail Mohamad, Saiful Najmee Ali, Jalil Institute of Informatics and Computing in Energy (IICE) College of Engineering Universiti Tenaga Nasional Selangor Kajang 43000 Malaysia Centre for Pre-University Studies MAHSA University Bandar Saujana Putra Selangor Jenjarom 42610 Malaysia Institute of Sustainable Energy (ISE) Universiti Tenaga Nasional Selangor Kajang 43000 Malaysia Physics Department COMSATS University Islamabad Lahore Campus Islamabad Capital Territory 45550 Pakistan Faculty of Applied Sciences Universiti Teknologi MARA Cawangan Johor Masai 81750 Malaysia Department of Physics Faculty of Science Universiti Teknologi Malaysia Johor Bahru 81310 Malaysia

Magnesium-doped bismuth ferrite (BiFeO3 or BFO) nanoparticles were fabricated through the sol–gel auto-combustion method to increase their photocatalytic performance. The synthesis parameters, specifically annealing ... 详细信息

Magnesium-doped bismuth ferrite (BiFeO₃ or BFO) nanoparticles were fabricated through the sol–gel auto-combustion method to increase their photocatalytic performance. The synthesis parameters, specifically annealing temperature (400, 500, and 600°C) and Mg doping concentration (5, 10, 15, 20, and 25 %), were varied to investigate their effects on the photocatalytic degradation of Rhodamine B under simulated solar irradiation. The nanoparticles were then characterized using different analytical techniques to assess their structural, optical, and morphological properties. X-ray diffraction (XRD) results found that the existence of high intensity cubic sillenite phase, Bi₂₅FeO₄₀ as a secondary phase in 25 % Mg-doped bismuth ferrite nanoparticles with a crystallite size of 33.9 nm, which would enhance the photocatalytic performance. Besides, the vibrating sample magnetometer (VSM) analysis showed that the doping of Mg in the bismuth ferrite host structure significantly enhances superparamagnetic and soft ferromagnetic behavior as the dopant concentration increases from 5 to 25 % due to increase in saturation magnetization (from 2.28 emu/g to 6.19 emu/g) and reduction in coercivity (from 88.35 Oe to 49.81 Oe). Field emission scanning electron microscopy-energy dispersive X-ray spectroscopy (FESEM-EDX) micrograph shows the particles were fully crystallized with regular shapes, suggesting a uniform surface morphology. Ultraviolet–visible diffuse reflectance spectroscopy (UV–Vis DRS) found that the band gap narrows to 2.0 eV when introducing Mg doping. Based on the photocatalytic degradation activity of Rhodamine B using Mg-doped bismuth ferrite nanoparticle (annealing temperature of 600 °C, 25 % Mg concentration) successfully achieved 80 % at 60 min, which was verified using response surface methodology (RSM)-central composite design (CCD). The photodegradation reaction followed pseudo-first-order kinetics, with the rate constant for the 25

关键词： Magnesium-doped bismuth ferrite Optimization Photocatalytic degradation Sillenite phase Sol-gel auto-combustion

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