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2024 International Conference on computer and Applications, ICCA 2024

作者： Saleh, Hager McCann, Michael El-Sappagh, Shaker Breslin, John G. Hurghada University Faculty of Computers and Artificial Intelligence Hurghada Egypt University of Galway Insight Research Ireland Centre for Data Analytics GalwayH91 TK33 Ireland Atlantic Technological University Letterkenny Ireland Atlantic Technological University Department of Computing Letterkenny Ireland Galala University Faculty of Computer Science and Engineering Suez Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt School of Engineering University of Galway GalwayH91 TK33 Ireland

ISBN: (纸本)9798350367560

This paper presents a real-time multimodal frame-work to enhance ICU mortality prediction for heart disease patients by integrating structured data, clinical notes, and big data streaming platforms. The proposed framework comprises two components: an offline multimodal model and a real-time pipeline. In the offline component, we proposed the Transformer-FusionNet model leverages BioBERT-Transformer for clinical notes and a Recurrent Neural Network (RNN) for structured data. The model integrates outputs through a concatenation layer to make predictions and is rigorously evaluated using the MIMIC-III dataset. It demonstrates superior performance across evaluation metrics and achieves an accuracy of 91.720%, precision of 91.85%, recall of 91.720%, and F1-score of 91.693%, outperforming other single and multimodal models. The real-time pipeline component incorporates Apache Spark and Apache Kafka for ingesting, preprocessing, and streaming structured data and clinical notes. This integration enables real-time mor-tality prediction with TransformerFusionNet, addressing critical gaps in the utilization of clinical notes in healthcare analytics. The framework significantly advances multimodal deep learning applications in healthcare by combining state-of-the-art models with scalable streaming technologies, offering a practical solution for improving clinical decision support systems and patient outcomes in real-time, especially in intensive care unit settings. © 2024 IEEE.

关键词： Recurrent neural networks

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X-ray simulations with gVXR as a useful tool for education, data analysis, set-up of CT scans, and scanner development 15

X-ray simulations with gVXR as a useful tool for education, ...

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15th SPIE Conference on Developments in X-Ray Tomography

作者： Vidal, Franck P. Afshari, Shaghayegh Ahmed, Sharif Atkins, Carolyn Béchet, Éric Bellot, Alberto Corbí Bosse, Stefan Chahid, Younes Chou, Cheng-Ying Culver, Robert Dixon, Lewis Friemann, Johan Garbout, Amin Hatton, Clémentine Henry, Audrey Leblanc, Christophe Leonardi, Alberto Létang, Jean Michel Lipscomb, Harry Manchester, Tristan Meere, Bas Middleburgh, Simon Mitchell, Iwan Perera, Liam Puig, Martí Tugwell-Allsup, Jenna Scientific Computing Ada Lovelace Centre Science Technology Facilities Council United Kingdom School of Computer Science & Engineering Bangor University United Kingdom Department of Biomechatronics Engineering National Taiwan University Taiwan DIAD Beamline Diamond Light Source United Kingdom UK Astronomy Technology Centre Royal Observatory Edinburgh United Kingdom Département d’Aérospatiale et Mécanique Université de Liège Belgium Escuela Superior de Ingeniería y Tecnología Universidad Internacional de La Rioja Spain Department of Computer Science University of Koblenz Koblenz Germany Department of Mechanical Engineering University of Siegen Siegen Germany The Manufacturing Technology Centre United Kingdom Department of Industrial and Materials Science Chalmers University of Technology Sweden Henry Royce Institute Henry Moseley X-Ray Imaging Facility Department of Materials The University of Manchester United Kingdom Scalian DS Rennes France INSA-Lyon Université Claude Bernard Lyon 1 CNRS Inserm CREATIS UMR 5220 U1294 Lyon France Department of Mechanical Engineering Eindhoven University of Technology Netherlands Department of Engineering Science University of Oxford United Kingdom Ysbyty Gwynedd United Kingdom

ISBN: (纸本)9781510679641

gVirtualXray (gVXR) is an open-source framework that relies on the Beer-Lambert law to simulate X-ray images in real time on a graphics processor unit (GPU) using triangular meshes. A wide range of programming languages is supported (C/C++, Python, R, Ruby, Tcl, C#, Java, and GNU Octave). Simulations generated with gVXR have been benchmarked with clinically realistic phantoms (i.e. complex structures and materials) using Monte Carlo (MC) simulations, real radiographs and real digitally reconstructed radiographs (DRRs), and X-ray computed tomography (CT). It has been used in a wide range of applications, including real-time medical simulators, proposing a new densitometric radiographic modality in clinical imaging, studying noise removal techniques in fluoroscopy, teaching particle physics and X-ray imaging to undergraduate students in engineering, and XCT to masters students, predicting image quality and artifacts in material science, etc. gVXR has also been used to produce a high number of realistic simulated images in optimization problems and to train machine learning algorithms. This paper presents applications of gVXR related to XCT. © 2024 SPIE.

关键词： computerized tomography

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Enhanced Quantum Convolutional Neural Network for Signature Authentication in Consumer Products

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IEEE Transactions on Consumer Electronics 2024年

作者： Raghupathy, Bala Krishnan Vairavasundaram, Subramaniyaswamy Ganesan, Manikandan Namachivayam, Rajesh Kumar Kotecha, Ketan Herencsar, Norbert SASTRA Deemed University Department of CSE India Vellore Institute of Technology School of Computer Science and Engineering Vellore632 014 India SASTRA Deemed University School of Computing India Symbiosis Centre for Applied Artificial Intelligence Pune India Brno University of Technology Faculty of Electrical Engineering and Communication Department of Telecommunications Technicka 3082/12 Brno61600 Czech Republic

Product tracking applications utilize the Internet of Things and cyber-physical systems to identify permitted or unauthorized user intrusions into the system. Classical machine learning algorithms cannot detect every risk in an environment that evolves constantly and where new abnormalities are visible. This article investigates the potential of quantum machine learning (QML) for real-time product purchase monitoring and intrusion detection using an enhanced quantum convolutional neural network (EQCNN) with signature-based detection over a massive volume of search space data (qubits). We suggest a three-stage technique to effectively handle the sensitive content: Pre-processing, EQCNN-based feature extraction, and syntactic pattern recognition. Signature-based identification is a feature of the EQCNN architecture that helps detect particular patterns linked to goods purchases or invasions. The model can minimize product tracking mistakes by utilizing the QML-based EQCNN with signature-based detection, resulting in a more efficient supply chain. © 2024 IEEE.

关键词： Convolutional neural networks

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Donut Modes in Space Wavelength Division Multiplexing: Multimode Optical Fiber Transmission based on Electrical Feedback Equalizer 5

Donut Modes in Space Wavelength Division Multiplexing: Multi...

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5th International Conference on Electronic Design, ICED 2020

作者： Ghazi, Alaan Aljunid, S.A. Idrus, Syed Zulkarnain Syed Endut, R. Ali, N. Amphawan, Angela Fareed, Alaa Al-Dawoodi, Aras Noori, Awab School of Human Development and Techno-communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia Computer Technical Engineering Al-Qalam University College Iraq Centre of Excellence Universiti Malaysia Perlis Malaysia Universiti Malaysia Perlis Perlis Malaysia Lightwave Laboratory School of Engineering and Technology Sunway University Petaling Jaya Selangor47500 Malaysia School of Computing University Utara Malaysia Malaysia Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Data Science Research Lab School of Computing Universiti Utara Malaysia Malaysia

This study demonstrates the implementation of multi-input-multi-output (MIMO) transmission donut modes in space wavelength division multiplexing (SWDM) over multi-mode optical fiber (MMOF) and simulating the effect of mode coupling of the transmitted signal. However, during the data transmitted over MMF by using SWDM an issue is encountered, which is the inter symbol interference (ISI) due to mode coupling. ISI has been recognized as a major obstacle to high-speed data transmission. The signal processing in SDM of donut modes over MMOF with electrical feedback equalizer is used to improve the performance for increasing the bandwidth. A data rate 80 Gbit/s up for a distance of 1600 meters is achieved. SWDM topology was designed emitted from a spatial vertical cavity surface-emitting laser (VCSEL) through MMOF in conjunction with the configurations of electrical feedback equalizer. The parameters and values used in Opt-Sim simulation topology were listed. The performance measurement of this study based on bit-error rates (BER) and eye diagram before and after electrical feedback equalization were analysed. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Space division multiple access

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Bilateral Contract for Load Frequency and Renewable Energy Sources Using Advanced Controller

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computers, Materials & Continua 2022年第11期73卷 3165-3180页

作者： Krishan Arora Gyanendra Prasad Joshi Mahmoud Ragab Muhyaddin Rawa Ahmad H.Milyani Romany F.Mansour Eunmok Yang School of Electronics and Electrical Engineering Lovely Professional UniversityPhagwara144001PunjabIndia School of Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia King Abdullah City for Atomic and Renewable Energy(K.A.CARE) RiyadhSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoul02707Korea

Reestablishment in power system brings in significant transformation in the power sector by extinguishing the possession of sound consolidated ***,the collaboration of various manufacturing agencies,autonomous power manufacturers,and buyers have created complex installation *** regular active load and inefficiency of best measures among varied associates is a huge *** sudden load deviation will give rise to immediate amendment in frequency and tie-line power *** is essential to deal with every zone’s frequency and tie-line power within permitted confines followed by fluctuations within the ***,it can be proficient by implementing Load Frequency Control under the Bilateral case,stabilizing the power and frequency distinction within the interrelated power *** the net deviation in multiple areas is possible by minimizing the unbalance of Bilateral Contracts with the help of proportional integral and advanced controllers like Harris Hawks *** proposed the advanced controller Harris Hawk optimizer-based model and validated it on a test *** experiment results show that the delay time is 0.0029 s and the settling time of 20.86 s *** model can also be leveraged to examine the decision boundaries of the Bilateral case.

关键词： Bilateral contract load frequency control optimization harris hawks optimizer

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Entirety of Quantum Uncertainty and Its Experimental Verification

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Chinese Physics Letters 2021年第7期38卷 11-16页

作者： Jie Xie Li Zhou Aonan Zhang Huichao Xu Man-Hong Yung Ping Xu Nengkun Yu Lijian Zhang National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences and School of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Centre for Quantum Software and Information School of SoftwareFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNSWAustralia

As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we present a method for geometrically characterizing uncertainty relations as an entire area of variances of the observables,ranging over all possible input *** find that for the pair of position and momentum operators,Heisenberg's uncertainty principle points exactly to the attainable area of the variances of position and ***,for finite-dimensional systems,we prove that the corresponding area is necessarily semialgebraic;in other words,this set can be represented via finite polynomial equations and inequalities,or any finite union of such *** particular,we give the analytical characterization of the areas of variances of(a)a pair of one-qubit observables and(b)a pair of projective observables for arbitrary dimension,and give the first experimental observation of such areas in a photonic system.

关键词： observable union operators

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A hybrid flow shop scheduling optimization using puma optimizer: Addressing energy efficiency and workforce restrictions

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Alexandria engineering Journal 2025年 128卷 340-365页

作者： Sang-Woong Lee Jawad Tanveer Amir Masoud Rahmani Farhad Soleimanian Gharehchopogh Benyamin Abdollahzadeh Hamid Rokny Thantrira Porntaveetus Jan Lansky Mehdi Hosseinzadeh Pattern Recognition and Machine Learning Lab School of Computing Gachon University Seongnam 13120 Republic of Korea Department of Computer Science and Engineering Sejong University Seoul 05006 Republic of Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Center of Excellence in Precision Medicine and Digital Health Department of Physiology Faculty of Dentistry Chulalongkorn University Bangkok 10330 Thailand Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic School of Computer Science Duy Tan University Da Nang Vietnam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India

The Hybrid Flow Shop Scheduling Problem (HFSP) is a complex challenge in production and manufacturing, requiring efficient scheduling solutions that optimize productivity while considering real-world constraints. This study proposes an advanced optimization framework utilizing the Puma Optimizer, a metaheuristic algorithm enhanced with an adaptive control mechanism for intelligently selecting phases and operators. This feature makes it well-suited for discrete optimization problems such as HFSP. Our approach integrates heuristic decoding and adaptive local search, balancing exploration and exploitation to generate high-quality schedules. The proposed mathematical model optimizes makespan and total tardiness while energy consumption is incorporated as a constraint to enhance sustainability. Extensive computational experiments demonstrate the superior performance of the proposed algorithm compared to traditional methods, achieving shorter makespan and lower tardiness while ensuring workforce feasibility. These results highlight the Puma Optimizer’s adaptability and potential to set new benchmarks for HFSP, offering a scalable and effective solution for modern production systems.

关键词： Hybrid Flow Shop Scheduling Problem (HFSP) Puma optimizer Energy efficiency Worker constraints Metaheuristic optimization

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A Squeeze-Excitation ResNet Approach for Effective Classification of Parasitic Eggs

A Squeeze-Excitation ResNet Approach for Effective Classific...

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Visualization, Informatics and Technology Conference (IVIT), International

作者： Muthulakshm M K Venkatesan Syarifah Bahiyah Rahayu Karthickeien Elangovan Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Faculty of Defense Science and Technology National Defence University Malaysia Kuala Lumpur Malaysia Cyber Security and Digital Industrial Revolution Centre National Defence University Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9798350354447

ISBN: (纸本)9798350354454

The identification and classification of different kinds of parasite eggs in microscopic samples represent a critical challenge in the field of Soil-transmitted helminth infection diagnosis. Traditional methods are often labor-intensive and time-consuming. The emergence of deep learning models has shown promising results in automating this process by extracting intricate features from complex images. This study aims to develop an automated system for accurately classifying parasite egg types in microscopic images by leveraging the ability of squeeze excitation layers to learn the global information from the input. The proposed system employs features extracted by ResNet50 and ResNet101 with Squeeze Excitation (SE) layers for analysis. The extracted features are then input into a Support Vector Classifier. The study systematically evaluates the features extracted from ResNet50+SE and ResNet101+SE. Results from the evaluation demonstrate the efficacy of the ResNet50+SE in accurately classifying parasite egg types in microscopic images with an accuracy of 0.94. The study provides valuable insights into the choice of squeeze-excitation block added Resnet in the context of parasitology, contributing to the advancement of automated medical image analysis. The findings hold great potential for improving diagnostic processes and supporting epidemiological studies through efficient and accurate parasite detection.

关键词： Measurement Visualization Accuracy Microscopy Static VAr compensators Feature extraction Vectors Informatics Medical diagnostic imaging Residual neural networks

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ZETA: ZEro-Trust Attack Framework with Split Learning for Autonomous Vehicles in 6G Networks

ZETA: ZEro-Trust Attack Framework with Split Learning for Au...

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IEEE Conference on Wireless Communications and Networking

作者： Sunder Ali Khowaja Parus Khuwaja Kapal Dev Keshav Singh Lewis Nkenyereye Dan Kilper Faculty of Computing Digital and Data Technological University Dublin Ireland Institute of Business Administration University of Sindh Pakistan Department of Computer Science CONNECT Centre Munster Technological University Ireland Institute of Communications Engineering (ICE) National Sun Yat-sen University Taiwan Department of Computer and Information Security Sejong University South Korea CONNECT Centre School of Engineering Trinity College Dublin Ireland

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In past, due to data and model security concerns, modern communication systems mainly focus on the use of edge computing devices for enabling immersive applications and services. Federated learning is one of the preferred solutions but it stresses the computation capability of the edge devices for immersive applications. Much research is now focusing on split learning as an alternative due to its ability of performing joint training with limited computing resources. However, split learning is also vulnerable to data reconstruction, feature space hijacking, and model inversion attacks, which are quite common concerning immersive applications such as Metaverse. In this regard, we propose a ZEro-Trust Attack (ZETA) framework for data reconstruction and model inversion attacks for autonomous vehicles opting for split learning strategies. We propose the joint training of client, server, and shadow models for both the reconstruction and main task to fool existing methods. Our experimental results demonstrate that the proposed method is capable of reconstructing client's data with an error of 0.0032. This study is proposed as a basis to design more sophisticated defense mechanisms for autonomous vehicles to protect user services in 5G/6G networks.

关键词： Training Performance evaluation Sensitivity analysis Computational modeling Data models Zero Trust Servers

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Enhancing Ieee 1588 Ptp Security for Iiot Networks: A Lightweight Attack Detection and Mitigation Framework

SSRN

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SSRN 2025年

作者： Idrees, Zeba Latif, Shahid Tahir, Hira Zheng, Liron Electrical and Computer Engineering Department University of Alberta Canada School of Computing and Creative Technologies University of the West of England United Kingdom Interdisciplinary Research Centre for Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia School of Information Science and Technology Fudan University China

Highly precise clock synchronization is an important aspect of the Industrial Internet of Things (IIoT) network because desynchronized clocks among nodes in IIoT can degrade system performance. IEEE 1588 Precision Time Protocol (PTP) is widely used in such time-sensitive networks. Resource efficiency and security have become the most important concerns in designing PTP for IIoT applications. PTP provides unified and high-precision time, whereas it is resource inefficient and insecure in its current form, particularly for resource-constrained IoT devices, such as battery powered sensing nodes. This paper aims to advance the existing PTP to improve security for IIoT networks without involving complex and power-consuming cryptographic algorithms. We study and analyze the potential cyber-attacks that can affect the security and synchronization of the PTP network. Considering the limitations of the PTP security defined by IEEE 1588 in its Annex K, we propose a security extension to the PTP algorithm. This security model covers the full PTP attack surface and allows the detection of attacks on all the PTP nodes in a timely manner. Along with the attack detection, we establish an attack mitigation model to mitigate the attack effects on Master PTP nodes. The proposed secure PTP model was evaluated under different network conditions and with varying important parameters. It was observed that newly introduced functions do not compromise synchronization accuracy. All the experimental evaluations demonstrate that the proposed approach is more secure and robust to cyber-attacks and does not affect the operation of PTP devices in all considered network configurations. © 2025, The Authors. All rights reserved.

关键词： Cyber attacks

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