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

作者： Amjad Aldweesh Mohammad Alauthman Ahmad Al-Qerem Mohammad Al Khaldy Saad Alateef Someah Alangari College of computing and IT Shaqra University Shaqra Saudi Arabia Faculty of Information Technology University of Petra Amman Jordan Faculty of Information Technology Zarqa University Zarqa Jordan Department of Business Intelligence and Data Analytics University of Petra Amman Jordan School of Computing Science Newcastle University Newcastle UK College of Computing and IT Shaqra University Aldawadmi Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Urbanization and increasing vehicles in smart cities have created challenges in managing parking efficiently. Traditional systems face issues like lack of transparency, security vulnerabilities, and poor stakeholder coordination. This paper introduces Park4U, a blockchain-based parking reservation and management system for smart cities. It takes advantage of blockchain's decentralized, secure, and transparent nature to improve efficiency, accessibility, and reliability of parking services. Park4U integrates IoT sensors, smart contracts, and a mobile application facilitating seamless reservation, payment, and management. The system aims to enhance resource utilization, reduce congestion, and provide a secure and transparent platform for stakeholders: drivers, operators, and authorities. An evaluation of performance and comparison with traditional systems are presented, showcasing blockchain-based approach's benefits. It also explores potential challenges and future research directions.

关键词： Smart cities Smart contracts Blockchains Mobile applications Stakeholders Security Reliability Intelligent sensors Faces Vehicles

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Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms

arXiv

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arXiv 2025年

作者： Wang, Xiaoyu Kasprzak, Mikolaj J. Negrea, Jeffrey Bourguin, Solesne Huggins, Jonathan H. Department of Mathematics & Statistics Boston University United States Department of Information Systems Data Analytics & Operations ESSEC Business School France Department of Statistics & Actuarial Science University of Waterloo Canada Faculty of Computing & Data Sciences Boston University United States

Stochastic iterative algorithms, including stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD), are widely utilized for optimization and sampling in large-scale and high-dimensional problems in machine learning, statistics, and engineering. Numerous works have bounded the parameter error in, and characterized the uncertainty of, these approximations. One common approach has been to use scaling limit analyses to relate the distribution of algorithm sample paths to a continuous-time stochastic process approximation, particularly in asymptotic setups. Focusing on the univariate setting, in this paper, we build on previous work to derive non-asymptotic functional approximation error bounds between the algorithm sample paths and the Ornstein-Uhlenbeck approximation using an infinite-dimensional version of Stein's method of exchangeable pairs. We show that this bound implies weak convergence under modest additional assumptions and leads to a bound on the error of the variance of the iterate averages of the algorithm. Furthermore, we use our main result to construct error bounds in terms of two common metrics: the Lévy-Prokhorov and bounded Wasserstein distances. Our results provide a foundation for developing similar error bounds for the multivariate setting and for more sophisticated stochastic approximation *** Codes 60F17 (Primary) 60J60, 62-08, 68T05, 62E17 (Secondary) Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Safe and Reliable Diffusion Models via Subspace Projection

arXiv

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arXiv 2025年

作者： Chen, Huiqiang Zhu, Tianqing Wang, Linlin Yu, Xin Gao, Longxiang Zhou, Wanlei Faculty of Data Science City University of Macau China School of Computer Science University of Queensland QLD Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

Large-scale text-to-image (T2I) diffusion models have revolutionized image generation, enabling the synthesis of highly detailed visuals from textual descriptions. However, these models may inadvertently generate inappropriate content, such as copyrighted works or offensive images. While existing methods attempt to eliminate specific unwanted concepts, they often fail to ensure complete removal—allowing the concept to reappear in subtle forms. For instance, a model may successfully avoid generating images in Van Gogh’s style when explicitly prompted with "Van Gogh", yet still reproduce his signature artwork when given the prompt "Starry Night". In this paper, we propose SAFER, a novel and efficient approach for thoroughly removing target concepts from diffusion models. At a high level, SAFER is inspired by the observed low-dimensional structure of the text embedding space. The method first identifies a concept-specific subspace Sc associated with the target concept c. It then projects the prompt embeddings onto the complementary subspace of Sc, effectively erasing the concept from the generated images. Since concepts can be abstract and difficult to fully capture using natural language alone, we employ textual inversion to learn an optimized embedding of the target concept from a reference image. This enables more precise subspace estimation and enhances removal performance. Furthermore, we introduce a subspace expansion strategy to ensure comprehensive and robust concept erasure. Extensive experiments demonstrate that SAFER consistently and effectively erases unwanted concepts from diffusion models while preserving generation quality. © 2025, CC BY.

关键词： Embeddings

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Early Detection of Diabetic Foot Ulcers through CoAtnet Model: A Scalable Solution for Healthcare Integration

Early Detection of Diabetic Foot Ulcers through CoAtnet Mode...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Muhammad Abdullah Khan Muhammad Zaman Tanzila Kehkashan Nikola Ivković Vedran Uroš Adnan Akhunzada Faculty of Computer Science The University Of Lahore Sargodha Pakistan Department of Computer Science & IT Superior Univerity Lahore Pakistan Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia Faculty of Organization and Informatics University of Zagreb Varaždin Croatia Polytechnic Marko Marulić in Knin Knin Croatia Department of Data and Cybersecurity University of Doha for Science and Technology Doha Qatar

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Diabetic foot ulcers are one of the serious complications of diabetes mellitus affecting millions of patients across the globe and could lead to infections and amputations if diagnosed late. The recent trends in machine learning, with high promise, have been embodied in deep learning to solve the detection task for diabetic foot ulcers accurately. However, existing methods, such as the traditional CNN, faced the challenge of focusing on the critical ulcerous regions and thus were not productive in the real-time performances. Thus, this present research focuses on improving a deep learning-based model by employing CoAtNet, which is a customized attention mechanism to develop convolutional mechanisms for enhancing the outcomes related to DFU detection from foot images. The model was trained on the DFUC dataset, along with data augmentation techniques such as rotation, flipping, and contrast adjustment to enhance robustness. Therefore, accuracy was around 98% and F1-score stood at 90%. The suggested solution has outstanding potential for the early detection of DFUs, especially in resource-poor health environments, thus improving the outcome of patients and reducing the occurrence of severe complications like amputation.

关键词： Deep learning Accuracy Attention mechanisms Computational modeling Amputation Wounds data models Robustness Diabetes Medical diagnostic imaging

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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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Back to the Future: Challenges of Sparse and Irregular Medical Image Time Series

Back to the Future: Challenges of Sparse and Irregular M...

引用

Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

作者： Disch, Nico Albert Peretzke, Robin Roy, Saikat Ulrich, Constantin Zimmerer, David Stiefelhagen, Rainer Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Karlsruhe Institute of Technology Karlsruhe Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany

ISBN: (纸本)9783031845246

In longitudinal medical image analysis, most work focuses on regularly sampled images, or on tasks like regression or classification. However, in the clinical context, images are frequently generated irregularly due to factors such as cost constraints. This work tackles the problem of irregularly sampled longitudinal medical imaging by evaluating methods in segmentation and reconstruction tasks. We examine the reconstruction for real-life MRI from patients with Alzheimer’s Disease (AD), where the model predicts future MRI scans from an arbitrary number of input images sampled at irregular intervals. However, experiments show that most models cannot surpass the performance of using an image from the same patient at a different time point as a baseline. Therefore, we conducted experiments on a synthetic dataset to better isolate effects in temporal learning. These experiments offer insights into model behavior and serve as a benchmark for validating models’ longitudinal learning capability, thus providing a straightforward method to assess temporal understanding in a controlled environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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6G communications through sub-Terahertz CMOS power amplifiers: Design challenges and trends

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Heliyon 2025年第10期11卷

作者： Lee, Jun Yan Wu, Duo Guo, Xuanrui Tan, Jian Ding Yew, Teh Jia Ng, Zi Neng Bhuiyan, Mohammad Arif Sobhan Miraz, Mahdi H. School of Artificial Intelligence and Robotics Xiamen University Malaysia Selangor Malaysia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of Computing Faculty of Arts Science and Technology Wrexham University Wrexham United Kingdom Faculty of Computing Engineering and Science University of South Wales Pontypridd United Kingdom

The fifth-generation (5G) network faces limitations in supporting emerging applications, such as artificial intelligence (AI), virtual reality (VR) and digital twins. To overcome these confines, sub-Terahertz (sub-THz) and Terahertz (THz) technologies are considered to be key enablers of effective 6G wireless communications, offering higher transmission speeds, longer range and wider bandwidth. Achieving these capabilities requires careful engineering of 6G transceivers, with a focus on efficient power amplifiers (PAs) in the front-end, which play a critical role in effectively amplifying and transmitting signals over long distances. Complimentary metal-oxide-semiconductor (CMOS) technology-based PA in sub-THz suffers severe parasitic and limited maximum frequency, however, this has eventually been solved by different design architectures and scaling down of CMOS technology to break through the frequency limitations. In this article, we reviewed the potentials and capabilities of CMOS technology for designing 6G hardware, identified the state-of-art PA designs in the sub-THz band and then examined as well as compared the designs to identify the suitable design strategies for better performance. The circuit optimisation techniques, such as coupled-line, passive gain boosting method, zero-degree power splitting, load-pull matching, diode and capacitor linearisation for better gain, saturated output power and power added efficiency, are considered for the PA design architectures at different sub-THz bands. Furthermore, these methods are summarised and discussed with their advantages and disadvantages in lieu with their performances. The PA design trends, challenges and future perspectives are also presented and discussed. Therefore, this comprehensive review article will serve as a comparative study and reference for future PA designs for radio frequency integrated circuits (RFIC). © 2025 The Authors

关键词： 6G CMOS Front-end Power amplifier Sub-THz Transceiver Transmitter

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UNeLF: Unconstrained Neural Light Field for Self-Supervised Angular Super-Resolution

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhao, Mingyuan Sheng, Hao Chen, Rongshan Cong, Ruixuan Cui, Zhenglong Yang, Da Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Data Science and Intelligent Computing Laboratory Hangzhou International Innovation Institute Hangzhou311115 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

Compared to supervised learning methods, self-supervised learning methods address the domain gap problem between light field (LF) datasets collected under varying acquisition conditions, which typically leads to decreased performance when differences exist in the distribution between the training and test sets. However, current self-supervised light field angular super-resolution (LFASR) techniques primarily focus on exploiting discrete spatial-angular features while neglecting continuous LF information. In contrast to previous work, we propose a self-supervised unconstrained neural light field (UNeLF) to continuously represent LF for LFASR. Specifically, any LF can be described as the camera pose for each sub-aperture image (SAI) and the two-plane that captures these SAIs. To describe the former, we introduce a SAIs-dependent pose optimization method to solve the issue that arises from the narrow baseline of most LF data, which hinders robust camera pose estimation. This mechanism reduces the number of trainable camera parameters from a quadratic to a constant scale, thereby alleviating the complexity of joint optimization. For the latter, we propose a novel adaptive two-plane parameterization strategy to determine the two-plane that captures these SAIs, facilitating refocusing. Finally, we jointly optimize the camera parameters, near-far planes and neural light field, efficiently mapping each adaptive two-plane parameterized ray to its correspondence color in a continuous manner. Comprehensive experiments demonstrate that UNeLF achieves faster training and inference with fewer computational resources while exhibiting superior performance on both synthetic and real-world datasets. © 1991-2012 IEEE.

关键词： Self-supervised learning

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A Hybrid Deep Learning Algorithm for Forecasting of Global Currency Market 2nd

A Hybrid Deep Learning Algorithm for Forecasting of Global C...

引用

2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： San, Tan Jun Ajibade, Samuel-Soma M. Yong, Yoke Leng Ajibade, Temiloluwa Iyanuoluwa Ayodele, Olamide Emmanuel Oyewopo, Abdulgafar Olorunleke Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Faculty of Computing and Digital Technology Help University Bukit Damansara Kuala Lumpur Malaysia Department of Business Management Scholar School System Birmingham United Kingdom Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Administration Kwara State University Malete Nigeria Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The global currency market, which has millions of active participants and is the largest financial market in the world, has been greatly affected by the emergence of deep learning techniques. There is widespread recognition of deep learning’s effectiveness for price movement predictions, particularly in financial markets where its effectiveness has been shown. When compared to other machine learning methods, hybrid models like CNN-LSTM have shown to be the most effective and efficient. Nevertheless, there is a lack of thorough comparative research across deep learning hybrids, which makes it difficult to validate these claims empirically, especially in the diversified and extremely unpredictable FX market. To fill that void, this research builds a generic CNN-LSTM model to predict key currency pairs and compares it to another hybrid deep learning model. The objective is to evaluate and strengthen the reliability of deep learning techniques for FOREX forecasting. In order to maximize the generalizability of the model, this study mimics a categorization scenario using stringent cross-validation procedures. Important measures, including accuracy, precision, recall, and F1-score, were used to assess the models’ performance. The results show that there is a performance hierarchy, with CNN-LSTM, CNN-RNN. These findings highlight the need to evaluate deep learning models in different real-world contexts. In conclusion, this study highlights the difficulties of FOREX forecasting and the necessity for further evaluations of the predictive power of machine learning models in an ever-changing market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Empowering autonomous IoT devices in blockchain through gasless transactions

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Blockchain: Research and Applications 2025年第2期6卷

作者： Madhwal, Yash Yanovich, Yury Korotkevich, Aleksandra Parshina, Daria Seropian, Nshteh Gavrilov, Stepan Nikolaev, Alex Balachander, S. Murugan, A. Skolkovo Institute of Science and Technology Moscow121205 Russia Faculty of Computer Science HSE University Moscow109028 Russia Faculty of Information and Communication Technologies ITMO University St. Petersburg197101 Russia Moscow Institute of Physics and Technology Moscow141701 Russia Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur603203 India

The article introduces a proof-of-concept (PoC) that demonstrates the management of Internet of Things (IoT) devices' infrastructure via smart contracts, facilitating their interaction with the blockchain through gasless transactions. The focus is empowering IoT devices to autonomously sign transactions using their verified private keys, eliminating the necessity for external wallets and enabling blockchain interaction using Biconomy without incurring gas fees. In this PoC, managers can validate IoT devices, permitting them to transmit transactions securely without being able to manipulate measurements or risking losing crypto assets in case of hardware malfunctions. This innovative method ensures that devices with minimal funds can access sensor data and communicate with a smart contract on the blockchain to update information utilizing account abstraction. Detailed workflow and simulation results are provided to showcase the practicality and advantages of this approach in scenarios demanding seamless automated blockchain engagement through IoT devices. The PoC code is openly accessible on GitHub, enhancing the transparency and accessibility of our research outcomes. © 2025 The Author(s)

关键词： Account abstraction Blockchain Gasless transaction Internet of Things Mobile application Smart contract

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