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2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022

作者： Rajan, Karthick Kumar, K Sampath Kannapiran, Thirunavukkarasu Khan, Shahnawaz Al-Dmour, Ayman Sharef, Baraa T. School of Computing Science and Engineering Galgotias University Greater Noida India Unitedworld School of Computational Intelligence Karnavati University Gujarat India Bahrain Polytechnic Faculty of Engineering Design and Information and Communications Technology Isa Town Bahrain College of Arts and Science Applied Science University Al Eker Bahrain College of Information Technology Ahlia University Manama Bahrain

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

Traffic congestion on roadways or at intersections can occur for a variety of reasons, including increased vehicle queues, slow driving, and so on. When multiple emergencies occur at the same time, traffic gridlock may occur. An adaptive traffic control system is a traffic management method that allows signals to adapt to the current traffic demand in real time. Adaptive traffic signals work by synchronizing software and hardware. Q-learning necessitates the creation of accurate natural environment forms prior to taking action. Instead, a dynamic communication system was developed to explore the interplay between state, action, and rewards in that particular setting. The current traffic signal functions are based on predetermined traffic flow information to extract short time expectations, making it easier to calculate the signal management system's conclusion. If a certain model is used, then customizable traffic light management agents will have to collect photos of the current traffic situation and generate control signals at regular intervals. To improve the algorithm's stability, replay, occurrence, and ideal techniques have been implemented. The primary goal of the algorithm is to regulate congested traffic, which is accomplished by combining the dynamic network with the linear signal layout. © 2022 IEEE.

关键词： Traffic signals

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Blockchain as a decentralized communication tool for sustainable development 2

Blockchain as a decentralized communication tool for sustain...

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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Baker El-Ebiary, Yousef A. Hatamleh, Amer Aseh, Khairi Altrad, Abdullah Amayreh, Khalid Thaher Hilles, Shadi Mohamed, Rajina R. Ravindran Pathmanathan, P. Aledinat, Lowai Saleh UniSZA Faculty of Informatics and Computing Malaysia Irbid National University Faculty of Financial and Business Science Information System Department Irbid Jordan International Institute of Applied Science Swiss School of Management Netherlands Al Madinah International University Faculty of Computer and Info. Tech Malaysia Irbid National University Faculty of Financial and Business Science Administration Department Irbid Jordan Istanbul Okan University Department of Software Engineering Turkey Universiti Tenaga Nasional College of Computing Dan Informatics Malaysia UniSZA Faculty of Informatics and Computing Malaysia

ISBN: (纸本)9781665432221

This research introduces the distributed ledger technology (blockchain) in terms of sustainable development in the future. This study is based on private, public and global communications in various industries. The growing demands for secure and decentralized transactions around the world demonstrate the need for Blockchain technology as a medium of operations and communications. The blockchain system is completely decentralized and allows users to exchange messages in an efficient and secure manner. The highlight of the paper is the anticipated future use of blockchain networks as a communication tool in every business and digital economy for a safe and sustainable development, while presenting its advantages and limitations that have been confirmed based on the experiences of various experts. Moreover, the article provides recommendations for the use of blockchain to greatly complicate its identification in future communications. The paper also concludes that the use of the blockchain system could be beneficial and enhance future communications and digital technologies. © 2021 IEEE.

关键词： Blockchain

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Understanding Comparative Genomics Data Visualization for Biodiversity Domain Through Persona-Based Interviews

Understanding Comparative Genomics Data Visualization for Bi...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Chan, Vei Siang Mohamed, Farhan Bin Salleh, Faezah Binti Mohd Iglesias, Andres Jamaludin, Najwa Ayuni Binti Mustafa Albakri, Ikmal Faiq Albakri Media and Game Innovation Center of Excellence Institute of Human Centered Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Biosciences Faculty of Science Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Applied Mathematics and Computational Sciences Universidad de Cantabria Santander Spain Pervasive Computing & Educational Technology Department of Media Interactive Fakulti Teknologi Maklumat dan Komunikasi Universiti Teknikal Malaysia Melaka Melaka Malaysia

ISBN: (纸本)9798331530303

Understanding the needs of genomic data visualization techniques is important to help bioinformaticians to perform their comparative analytical tasks effectively. DNA sequencing datasets consist of a long sequential string of nucleotide base pairs combination and are often involved in multiple sequences alignment for comparative analysis. Therefore, existing challenges include designing suitable visualization layout and the need of user-centered approach to ensure optimal user experience. Existing works also featured heavily on the qualitative evaluation of comparative visualization tools, such as use cases and case studies, which are difficult to quantify and conduct follow up evaluation for tool improvement. Hence, this paper aims to understand the needs of visualization tools in comparative genomic analysis to identify visualization tasks and visual design requirements. The methodology includes the creation of semi-structured interview based on personas template as guiding questions and the synthesis of thematic analysis results. These results presented the requirements of the visualization tasks for the biodiversity domain based on comparative targets. For future work, this research will design, develop, and validate the comparative visualization tool based on the discovered requirements. ©2024 IEEE.

关键词： Genome

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Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification on health datasets

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Computers in Biology and Medicine 2025年 190卷 109985-109985页

作者： Hartoyo, Agus Argasiński, Jan Trenk, Aleksandra Przybylska, Kinga Blasiak, Anna Crimi, Alessandro Sano – Centre for Computational Personalised Medicine International Research Foundation Krakow Poland Department of Neurophysiology and Chronobiology Institute of Zoology and Biomedical Research Faculty of Biology Jagiellonian University Krakow Poland AGH University of Krakow Krakow Poland School of Computing Telkom University Bandung Indonesia Department of Human-Centered Artificial Intelligence Institute of Applied Computer Science Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland Doctoral School of Exact and Natural Sciences Jagiellonian University Krakow Poland

Covariance and Hessian matrices have been analyzed separately in the literature for classification problems. However, integrating these matrices has the potential to enhance their combined power in improving classification performance. We present a novel approach that combines the eigenanalysis of a covariance matrix evaluated on a training set with a Hessian matrix evaluated on a deep learning model to achieve optimal class separability in binary classification tasks. Our approach is substantiated by formal proofs that establish its capability to maximize between-class mean distance (the concept of separation) and minimize within-class variances (the concept of compactness), which together define the two linear discriminant analysis (LDA) criteria, particularly under ideal data conditions such as isotropy around class means and dominant leading eigenvalues. By projecting data into the combined space of the most relevant eigendirections from both matrices, we achieve optimal class separability as per these LDA criteria. Empirical validation across neural and health datasets consistently supports our theoretical framework and demonstrates that our method outperforms established methods. Our method stands out by addressing both separation and compactness criteria, unlike PCA and the Hessian method, which predominantly emphasize one criterion each. This comprehensive approach captures intricate patterns and relationships, enhancing classification performance. Furthermore, through the utilization of both LDA criteria, our method outperforms LDA itself by leveraging higher-dimensional feature spaces, in accordance with Cover's theorem, which favors linear separability in higher dimensions. Additionally, our approach sheds light on complex DNN decision-making, rendering them comprehensible within a 2D space. © 2025

关键词： Covariance matrix

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks

Swallowing Assessment using High-Resolution Cervical Auscult...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ayman Anwar Yassin Khalifa Erin Lucatorto James L. Coyle Ervin Sejdic Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto Toronto Canada Center for Research Computing and Information Technology Analytics University of Pittsburgh Pittsburgh PA USA Systems and Biomedical Engineering Department Faculty of Engineering Cairo University Giza Egypt Department of Communication Science and Disorders School of Health and Rehabilitation Sciences University of Pittsburgh Pittsburgh PA USA Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto and North York General Hospital Toronto Canada

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient’s neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics; specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers’ ability to generalize on unseen data.

关键词： Accuracy Sensitivity Recurrent neural networks Convolution Kinematics Transformers Feature extraction Solids Data models Signal resolution

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Feasibility-Guided Constraint-Handling Techniques for Engineering Optimization Problems

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Computers, Materials & Continua 2021年第6期67卷 2845-2862页

作者： Muhammad Asif Jan Yasir Mahmood Hidayat Ullah Khan Wali Khan Mashwani Muhammad Irfan Uddin Marwan Mahmoud Rashida Adeeb Khanum Ikramullah Noor Mast Institute of Numerical Sciences Kohat University of Science&TechnologyKohatPakistan Department of Economics Abbottabad University of Science&TechnologyAbbottabadPakistan Institute of Computing Kohat University of Science&TechnologyKohatPakistan Faculty of Applied Studies King Abdulaziz UniversityJeddahSaudi Arabia Jinnah College for Women University of PeshawarPeshawarPakistan Department of Physics Kohat University of Science&TechnologyKohatPakistan

The particle swarm optimization(PSO)algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and *** is essentially an unconstrained algorithm and requires constraint handling techniques(CHTs)to solve constrained optimization problems(COPs).For this purpose,we integrate two CHTs,the superiority of feasibility(SF)and the violation constraint-handling(VCH),with a *** CHTs distinguish feasible solutions from infeasible ***,in SF,the selection of infeasible solutions is based on their degree of constraint violations,whereas in VCH,the number of constraint violations by an infeasible solution is of more ***,a PSO is adapted for constrained optimization,yielding two constrained variants,denoted SF-PSO and *** SF-PSO and VCH-PSO are evaluated with respect to ve engineering problems:the Himmelblau’s nonlinear optimization,the welded beam design,the spring design,the pressure vessel design,and the three-bar truss *** simulation results show that both algorithms are consistent in terms of their solutions to these problems,including their different available *** of the SF-PSO and the VCHPSO with other existing algorithms on the tested problems shows that the proposed algorithms have lower computational cost in terms of the number of function evaluations *** also report our disagreement with some unjust comparisons made by other researchers regarding the tested problems and their different variants.

关键词： Constrained evolutionary optimization constraint handling techniques superiority of feasibility violation constraint-handling technique swarm based evolutionary algorithms particle swarm optimization engineering optimization proble

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3D Semantic Deep Learning Networks for Leukemia Detection

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Computers, Materials & Continua 2021年第10期69卷 785-799页

作者： Javaria Amin Muhammad Sharif Muhammad Almas Anjum Ayesha Siddiqa Seifedine Kadry Yunyoung Nam Mudassar Raza University of Wah Wah CanttPakistan COMSATS University Islamabad Wah CampusPakistan National University of Technology(NUTECH) IJP Road IslamabadPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

White blood cells(WBCs)are a vital part of the immune system that protect the body from different types of bacteria and *** cell growth destroys the body’s immune system,and computerized methods play a vital role in detecting abnormalities at the initial *** this research,a deep learning technique is proposed for the detection of *** proposed methodology consists of three *** I uses an open neural network exchange(ONNX)and YOLOv2 to localize *** localized images are passed to Phase II,in which 3D-segmentation is performed using deeplabv3 as a base network of the pre-trained Xception *** segmented images are used in Phase III,in which features are extracted using the darknet-53 model and optimized using Bhattacharyya separately criteria to classify *** proposed methodology is validated on three publically available benchmark datasets,namely ALL-IDB1,ALL-IDB2,and LISC,in terms of different metrics,such as precision,accuracy,sensitivity,and dice *** results of the proposed method are comparable to those of recent existing methodologies,thus proving its effectiveness.

关键词： YOLOv2 darknet53 Bhattacharyya separately criteria ONNX

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Explainable Artificial Intelligence in Malignant Lymphoma Classification: Optimized DenseNet121 Deep Learning Approach with Particle Swarm Optimization and Genetic Algorithm.

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IEEE Access 2025年 13卷 98639-98655页

作者： Elwahsh, Haitham Bakhiet, Ali Alirr, Omar Ibrahim Khalifa, Tarek Alsabaan, Maazen Ibrahem, Mohamed I. El-Shafeiy, Engy Applied Science Private University Faculty of Information Technology Amman 11931 Jordan Kafrelsheikh University Faculty of Computers and Information Department of Computer Science Kafrelsheikh 33516 Egypt Badya University Faculty of Computing and Data Science Egypt American University of the Middle East College of Engineering and Technology Egaila 54200 Kuwait King Saud University College of Computer and Information Sciences Department of Computer Engineering P.O.Box 51178 Riyadh 11543 Saudi Arabia Augusta University School of Computer and Cyber Sciences Augusta 30912 GA United States University of Sadat City Faculty of Computers and Artificial Intelligence Department of Computer Science Sadat City 32897 Egypt

One of the forms of cancerous tumors that can be fatal is malignant lymphoma. Histopathological examination of lymphoma tissue images is a diagnostic technique for detecting malignant lymphomas. Differentiating lymphoma subtypes manually is challenging due to their similar morphological features. This paper introduces a cutting-edge methodology for classifying malignant lymphoma using Hematoxylin and Eosin (H&E)-stained biopsy images. It integrates feature reduction using Particle Swarm Optimization (PSO) and model optimization through a Genetic Algorithm (GA) in conjunction with DenseNet121 architecture. The DenseNet121 model, which employs four distinct freezing strategies, extracts features from the images, generating an initial 4096 features per image. These features are subsequently reduced by more than 54.4% using PSO, thereby enhancing the computational efficiency without sacrificing classification accuracy. The trained GA Deep Neural Network (DNN) model achieved a significant score of 96.77% testing accuracy with high precision, recall and F1 scores as well. The results after comparison with other existing state-of-the-art models showed the usefulness of the proposed approach in terms of performance as well as the consumption of resources for medical imaging classification. It is recommended that the combined application of PSO for feature reduction and GA for model optimization can be successfully used for improving accuracy rate of such algorithms while reducing computation time. This technique is expected to be further developed and applied in different tasks of computer aided diagnosis of malignant lymphoma in particular the tasks with requirements for explainable artificial intelligence. © 2013 IEEE.

关键词： Artificial intelligent Deep Learning Genetic Algorithm malignant lymphoma Optimized DenseNet121 Particle Swarm Optimization

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SWAP Attack: Stealthy Side-Channel Attack on Multi-Tenant Quantum Cloud System

arXiv

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arXiv 2025年

作者： Lee, Wei Jie Bryan Wang, Siyi Dutta, Suman Maouaki, Walid El Chattopadhyay, Anupam College of Computing and Data Science Nanyang Technological University Singapore Applied Statistics Unit Indian Statistical Institute Kolkata India Quantum Physics and Spintronics Team LPMC Faculty of Sciences Ben M’Sik Hassan II University of Casablanca Morocco

The rapid advancement of quantum computing has spurred widespread adoption, with cloud-based quantum devices gaining traction in academia and industry. This shift raises critical concerns about the privacy and security of computations on shared, multi-tenant quantum platforms accessed remotely. In several recent works, crosstalk has been exploited on shared quantum devices, enabling adversaries to interfere with the victim circuits within a neighborhood. Though illuminating, these studies left several unresolved questions regarding the fundamental cause of crosstalk, effective countermeasures, and replicability across different circuits. In this paper, we revisit the crosstalk effect, tracing its origins to the SWAP path between various qubits in a circuit and demonstrating its effectiveness even over long distances. The results significantly improve our understanding of the phenomenon beyond prior works. Our proposed SWAP-based side-channel attack can be executed in both active and passive modes, as verified on real shared IBM quantum devices. In the active attack, an adversary executing a single CNOT gate can perturb victim circuits running Grover’s Algorithm from afar, reducing the expected output accuracy through 81.62% by strategically positioning qubits. Moreover, this phenomenon can be clearly explained through modeling, where specific qubits in a platform can be identified as more susceptible to an attacker. The passive attack, leveraging a stealthy circuit as small as 6.25% of the victim’s, achieves 100% accuracy in predicting the circuit size of the victim, running Simon’s Algorithm. These findings challenge the existing defense strategy of maximizing topological distance between circuits, demonstrating that attackers can still extract sensitive information or manipulate results remotely. Our work highlights the need for more robust security measures to safeguard quantum computations against such emerging threats. © 2025, CC BY.

关键词： Qubits

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Hybrid Migrating Birds Optimization Strategy for t-way Test Suite Generation 3

Hybrid Migrating Birds Optimization Strategy for t-way Test ...

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3rd International Conference on Computational science and Information Management, ICoCSIM 2019

作者： Zakaria, H.L. Zamli, K.Z. Din, Fakhrud Faculty of Computing and Applied Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Pekan Pekan Pahang26600 Malaysia School of Computer and Communication Engineering Universiti Malaysia Perlis Kampus Pauh Putra Perlis Arau02600 Malaysia Department of Computer Science and IT University of Malakand KPK Pakistan

Hybrid meta-heuristics algorithms have gained popularity in recent years to solve t-way test suite generation problems due to better exploration and exploitation capabilities of the hybridization. This paper presents the implementation of meta-heuristic search algorithms that are Migrating Birds Optimization (MBO) algorithm and Genetic Algorithm (GA) hybrid to a t-way test data generation strategy. The proposed strategy is called Elitist Hybrid MBO-GA Strategy (EMBO-GA). Based on the published benchmarking results, the result of these strategies are competitive with most existing strategies in terms of the generated test size in many of the parameter configurations. In the case where this strategy is not the most optimal, the resulting test size is sufficiently competitive. © Published under licence by IOP Publishing Ltd.

关键词： Genetic algorithms

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