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A novel pythonic paradigm for image encryption using axis-aligned bounding boxes

Scientific Reports 2025年第1期15卷 1-27页

作者： Banga, Abdulbasid Abbas, Akifa Ali, Danish Innab, Nisreen Alluhaidan, Ala Saleh Iqbal, Nadeem Diab, Hossam College of Computing and Informatics (CCI) Saudi Electronic University Riyadh 11673 Saudi Arabia School of Computing Bath Spa University Academic Center Ras Ul Khaima United Arab Emirates Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh 13713 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia Department of Computer Science & IT The University of Lahore Lahore 54000 Pakistan Math and Computer Science Department Faculty of Science Menoufia University Menoufia Shibin El Kom 32511 Egypt Computer Science Department Applied College Taibah University Madinah 41477 Saudi Arabia

In the digital age, images permeate every facet of our lives, often carrying critical information for organizations, institutions, and even nation-states. Ensuring their security against unauthorized access is paramount. This research introduces a novel image encryption algorithm designed to safeguard the integrity and confidentiality of sensitive gray-scale digital images. The algorithm leverages the theory of axis-aligned bounding boxes, translating the input image into a 3D representation. Within this 3D space, two identically sized boxes are generated and assessed for overlap. If the boxes do not overlap, the pixels within them are swapped. This pixel-swapping process, guided by random numbers generated from a 5D multi-wing hyper-chaotic map, is repeated numerous times to infuse confusion into the image. To further enhance security, diffusion effects are introduced by performing an XoR operation between the confused image and random numbers provided by the piece-wise linear chaotic map. This study employs private key cryptography and utilizes four gray-scale images to validate the feasibility and effectiveness of the proposed method. Simulation has been carried out in the Pythonic ecosystem. So, the algorithms presented in this study are designed to closely resemble Python code. Comprehensive validation metrics attest to the robustness of the cipher, achieving an information entropy of 7.99985 and a computational speed of 0.3987 seconds. These results underscore the potential of this encryption approach for practical, real-world applications. These applications span military, diplomacy & government, commerce, showbiz, industry, and social life etc. © The Author(s) 2025.

关键词： Chaotic map Non-linearity Random numbers S-Box Smart cities Wireless communication security

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A Survey on Decentralized Identifiers and Verifiable Credentials

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IEEE Communications Surveys and Tutorials 2025年

作者： Mazzocca, Carlo Acar, Abbas Uluagac, Selcuk Montanari, Rebecca Bellavista, Paolo Conti, Mauro University of Salerno Department of Information and Electrical Engineering and Applied Mathematics Fisciano84084 Italy Florida International University Cyber-Physical Systems Security Lab School of Computing and Information Science MiamiFL33174 United States University of Bologna Department of Computer Science and Engineering Bologna40136 Italy University of Padua Department of Mathematics Padua35131 Italy

Digital identity has always been one of the keystones for implementing secure and trustworthy communications among parties. The ever-evolving digital landscape has undergone numerous technological transformations that have profoundly reshaped digital identity management, leading to a major shift from centralized to decentralized identity models. The latest stage of this evolution is represented by the emerging paradigm of Self-Sovereign Identity (SSI), which gives identity owners full control over their data. SSI leverages Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), which have been recently standardized by theWorldWideWeb Consortium (W3C). These technologies have the potential to build more secure and decentralized digital identity systems, significantly strengthening communication security in scenarios involving many distributed participants. It is worth noting that use of DIDs and VCs is not limited to individuals but extends to a wide range of entities including cloud, edge, and Internet of Things (IoT) resources. However, due to their novelty, existing literature lacks a comprehensive survey on DIDs and VCs beyond the scope of SSI. This paper fills this gap by providing a comprehensive overview of DIDs and VCs from multiple perspectives. It identifies key security threats and mitigation strategies, analyzes available implementations to guide practitioners in making informed decisions, and reviews the adoption of these technologies across various application domains. Moreover, it also examines related regulations, projects, and consortiums emerging worldwide. Finally, it discusses the primary challenges hindering their real-world adoption and outlines future research directions. © 1998-2012 IEEE.

关键词： Decentralized control

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MOAC: Multi-Objective Adaptive Clustering Optimization Algorithm Based on Cluster Adaptation

MOAC: Multi-Objective Adaptive Clustering Optimization Algor...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hossam M. J. Mustafa Dheeb Albashish Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper presents a novel Multi-Objective Adaptive Clustering (MOAC) algorithm that addresses current limitations in existing multi-objective optimization methods. While current algorithms often struggle with premature convergence and diversity preservation, the MOAC algorithm proposes a novel clustering-based approach to search space by partitioning. The algorithm combines cluster evolution with an adaptive parameter control mechanism to achieve significant improvements in balancing exploration-exploitation and diversity maintenance. MOAC employs k-means clustering for population partitioning, combined with cluster-based genetic operators, and an efficient diversity preservation scheme. Extensive experiments across different ZDT, MMF, and UF test suites demonstrate that MOAC's performance is superior, which achieves outstanding hypervolume and IGD values in complex multi-modal problems. The algorithm shows strength in handling complex Pareto fronts, and comprehensive statistical analysis through the Friedman rankings test with a 2.21 rank. Moreover. The results reveal that MOAC is a robust approach for complex multi-objective optimization problems, particularly maintaining solution diversity and achieving consistent convergence across different problems.

关键词： Ranking (statistics) Statistical analysis Metaheuristics Clustering algorithms Optimization methods Evolutionary computation Partitioning algorithms Maintenance Convergence Genetic operators

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Quantum Hamiltonian Descent for Non-smooth Optimization

arXiv

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arXiv 2025年

作者： Leng, Jiaqi Zheng, Yufan Jia, Zhiyuan Fan, Lei Zhao, Chaoyue Peng, Yuxiang Wu, Xiaodi Simons Institute for the Theory of Computing University of California Berkeley United States Department of Mathematics University of California Berkeley United States Department of Computer Science University of Maryland College Park United States Joint Center for Quantum Information and Computer Science University of Maryland United States Department of Applied Mathematics University of Washington United States Department of Engineering Technology University of Houston United States Department of Electrical and Computer Engineering University of Houston United States Department of Industrial and Systems Engineering University of Washington United States Artephi Computing Inc United States

Non-smooth optimization models play a fundamental role in various disciplines, including engineering, science, management, and finance. However, classical algorithms for solving such models often struggle with convergence speed, scalability, and parameter tuning, particularly in high-dimensional and non-convex settings. In this paper, we explore how quantum mechanics can be leveraged to overcome these limitations. Specifically, we investigate the theoretical properties of the Quantum Hamiltonian Descent (QHD) algorithm for non-smooth optimization in both continuous and discrete time. First, we propose continuous-time variants of the general QHD algorithm and establish their global convergence and convergence rate for non-smooth convex and strongly convex problems through a novel Lyapunov function design. Furthermore, we prove the finite-time global convergence of continuous-time QHD for non-smooth non-convex problems under mild conditions (i.e., locally Lipschitz). In addition, we propose discrete-time QHD, a fully digitized implementation of QHD via operator splitting (i.e., product formula). We find that discrete-time QHD exhibits similar convergence properties even with large time steps. Finally, numerical experiments validate our theoretical findings and demonstrate the computational advantages of QHD over classical non-smooth non-convex optimization algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Implicit U-KAN2.0: Dynamic, Efficient and Interpretable Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Cheng, Chun-Wun Zhao, Yining Cheng, Yanqi Montoya, Javier Schönlieb, Carola-Bibiane Aviles-Rivero, Angelica I. Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Siebel School of Computing and Data Science University of Illinois Urbana-Champaign United States Lucerne School of Computer Science and Information Technology Lucerne University of Applied Sciences and Arts United States Yau Mathematical Sciences Center Tsinghua University China

Image segmentation is a fundamental task in both image analysis and medical applications. State-of-the-art methods predominantly rely on encoder-decoder architectures with a U-shaped design, commonly referred to as U-Net. Recent advancements integrating transformers and MLPs improve performance but still face key limitations, such as poor interpretability, difficulty handling intrinsic noise, and constrained expressiveness due to discrete layer structures, often lacking a solid theoretical *** this work, we introduce Implicit U-KAN 2.0, a novel U-Net variant that adopts a two-phase encoder-decoder structure. In the SONO phase, we use a second-order neural ordinary differential equation (NODEs), called the SONO block, for a more efficient, expressive, and theoretically grounded modeling approach. In the SONO-MultiKAN phase, we integrate the second-order NODEs and MultiKAN layer as the core computational block to enhance interpretability and representation power. Our contributions are threefold. First, U-KAN 2.0 is an implicit deep neural network incorporating MultiKAN and second order NODEs, improving interpretability and performance while reducing computational costs. Second, we provide a theoretical analysis demonstrating that the approximation ability of the MultiKAN block is independent of the input dimension. Third, we conduct extensive experiments on a variety of 2D and a single 3D dataset, demonstrating that our model consistently outperforms existing segmentation networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

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Deep Neural Cellular Potts Models

arXiv

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arXiv 2025年

作者： Minartz, Koen d’Hondt, Tim Hillmann, Leon Starruß, Jörn Brusch, Lutz Menkovski, Vlado Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Department of Applied Physics and Science Education Eindhoven University of Technology Eindhoven Netherlands Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands Center for Information Services and High Performance Computing TUD Dresden University of Technology Dresden Germany

The cellular Potts model (CPM) is a powerful computational method for simulating collective spatiotemporal dynamics of biological cells. To drive the dynamics, CPMs rely on physics-inspired Hamiltonians. However, as first principles remain elusive in biology, these Hamiltonians only approximate the full complexity of real multicellular systems. To address this limitation, we propose NeuralCPM, a more expressive cellular Potts model that can be trained directly on observational data. At the core of NeuralCPM lies the Neural Hamiltonian, a neural network architecture that respects universal symmetries in collective cellular dynamics. Moreover, this approach enables seamless integration of domain knowledge by combining known biological mechanisms and the expressive Neural Hamiltonian into a hybrid model. Our evaluation with synthetic and real-world multicellular systems demonstrates that NeuralCPM is able to model cellular dynamics that cannot be accounted for by traditional analytical Hamiltonians. Copyright © 2025, The Authors. All rights reserved.

关键词： Potts model

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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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Two-stage CNN-based framework for leukocytes classification

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computers in Biology and Medicine 2025年 187卷 109616-109616页

作者： Khan, Siraj Sajjad, Muhammad Escorcia-Gutierrez, José Dhahbi, Sami Hijji, Mohammad Muhammad, Khan Department of Computer Science Islamia College University Peshawar25120 Pakistan Department of Computational Science and Electronics Universidad de la Costa CUC Barranquilla080002 Colombia Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Leukocytes are pivotal markers in health, crucial for diagnosing diseases like malaria and viral infections. Peripheral blood smear tests provide pathologists with vital insights into various medical conditions. Manual leukocyte counting is challenging and error-prone due to their complex structure. Accurate segmentation and classification of leukocytes remain challenging, impacting both accuracy and efficiency in blood microscopic image analysis. To overcome these limitations, we propose a robust two-stage CNN framework that integrates YOLOv8 for precise segmentation and MobileNetV3 for effective classification. Initially, WBCs are segmented using YOLOv8m-seg, extracting ROIs for subsequent analysis. Then, features from segmented ROIs are used to train MobileNetV3, classifying WBCs into lymphocytes, monocytes, basophils, eosinophils, and neutrophils. This framework significantly advances leukocyte categorization, enhancing diagnostic performance and patient outcomes. The proposed technique achieved impressive accuracy rates of 99.56 %, 99.19 % and 98.89 % during segmentation and 99.28 %, 99.63 % and 98.49 % during classification on Raabin-WBC, PBC and LISC datasets, respectively, outperforming state-of-the-art methods. © 2024 Elsevier Ltd

关键词： White blood cells Blood smear images Image segmentation Deep learning MobileNetV3 YOLOv8 Image classification

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Deep gradient reinforcement learning for music improvisation in cloud computing framework

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PeerJ computer science 2025年 11卷 e2265-e2265页

作者： Alrowais, Fadwa Arasi, Munya A. Alotaibi, Saud S. Alonazi, Mohammed Marzouk, Radwa Salama, Ahmed S. Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science Applied College King Khalid University RijalAlmaa Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Mecca Saudi Arabia Department of Information Systems College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Electrical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo New Cairo Egypt

Artificial intelligence (AI) in music improvisation offers promising new avenues for developing human creativity. The difficulty of writing dynamic, flexible musical compositions in real time is discussed in this article. We explore using reinforcement learning (RL) techniques to create more interactive and responsive music creation systems. Here, the musical structures train an RL agent to navigate the complex space of musical possibilities to provide improvisations. The melodic framework in the input musical data is initially identified using bi-directional gated recurrent units. The lyrical concepts such as notes, chords, and rhythms from the recognised framework are transformed into a format suitable for RL input. The deep gradient-based reinforcement learning technique used in this research formulates a reward system that directs the agent to compose aesthetically intriguing and harmonically cohesive musical improvisations. The improvised music is further rendered in the MIDI format. The Bach Chorales dataset with six different attributes relevant to musical compositions is employed in implementing the present research. The model was set up in a containerised cloud environment and controlled for smooth load distribution. Five different parameters, such as pitch frequency (PF), standard pitch delay (SPD), average distance between peaks (ADP), note duration gradient (NDG) and pitch class gradient (PCG), are leveraged to assess the quality of the improvised music. The proposed model obtains +0.15 of PF, -0.43 of SPD, -0.07 of ADP and 0.0041 NDG, which is a better value than other improvisation methods. © 2025 Alrowais et al.

关键词： Reinforcement learning

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Hybrid BiGRU-CNN Model for Load Forecasting in Smart Grids with High Renewable Energy Integration

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Ullah, Kaleem Shakir, Daniyal Abid, Usama Alahmari, Saad Aslam, Sheraz Ullah, Zahid US-Pakistan Centre for Advanced Studies in Energy University of Engineering and Technology Peshawar Peshawar Pakistan Sr. Business Analyst S&P Global Islamabad Islamabad Pakistan Department of Computer Science and Information Technology School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC Australia Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science CTL eurocolege Limassol Cyprus Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Lombardia Italy

Integrating renewable energy sources into smart grids increases supply and demand management because renewable energy sources are intermittent and variable. To overcome this type of challenge, short-term load forecasting (STLF) is essential for managing energy, demand-side flexibility, and the stability of smart grids with renewable energy integration. This paper presents a new model called BiGRU-CNN to improve the operation of STLF in smart grids. The BiGRU-CNN model integrates bidirectional gated recurrent units (BiGRUs) to model temporal dependencies and convolutional neural networks (CNNs) to extract spatial patterns from energy consumption data. The newly developed BiGRU captures past and future contexts through bidirectional processing, and the CNN component extracts high-level features to enhance the accuracy of load demand prediction. The BiGRU-CNN model is compared with two other hybrid models, CNN-LSTM and CNN-GRU, on real-world data from American electric power (AEP) and ISONE datasets. Simulation results show that the proposed BiGRU-CNN outperforms other models, with single-step forecasting yielding root mean square error (RMSE) of 121.43 (AEP) and 123.57 (ISONE), mean absolute error (MAE) of 90.95 (AEP) and 62.97 (ISONE), and mean absolute percentage error (MAPE) of 0.61% (AEP) and 0.41% (ISONE). For multi-step forecasting, the model yields RMSE of 680.02 (AEP) and 581.12 (ISONE), MAE of 481.12 (AEP) and 411.20 (ISONE), and MAPE of 3.27% (AEP) and 2.91% (ISONE). The results show that the BiGRU-CNN model can generate accurate and reliable STLF, which is useful in managing massive renewable energy-integrated smart grids. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Convolutional neural networks

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