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

作者： Laith Al Shehab Rehab Abdulrahman Alsaidi Rana Haitham Alraqad Department of Computer Science Middle East University Amman Jordan Department of Computer Vision Optriment Computer Systems Amman Jordan Department of Data Science App Trainers Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Today, liver tumors have become a crucial and popular problem in our current era. Advances in artificial intelligence, especially computer vision, have enhanced diagnostic capabilities and provided deeper insights into tumor features. In this study, we leverage YOLOv8s for liver tumor detection, using MRI images to identify tumors with bounding box annotations and extract critical features such as shape, size, and diameter. These features are then used to determine the optimal nanoparticle size based on predefined rules, ensuring a tailored approach to treatment. Our framework demon- strates how AI can bridge the gap between tumor detection and nanoparticle design. The study revealed that tumor diameter significantly influences the optimal nanoparticle design. For tumor diameters of 257.99 µm, 297.47 µm, and 422.3 µm, the corresponding nanoparticle sizes were 729.0 nm, 535.0 nm, and 447.0 nm, respectively. Experimental results confirm the detection system's accuracy and effectiveness in recommending nanoparticle sizes, aiding liver tumor diagnosis and treatment.

关键词： Nanoparticles computer vision Liver cancer Targeted drug delivery Shape Magnetic resonance imaging Liver Feature extraction Artificial intelligence Tumors

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Channel-wise attention based binarization for CNN Architectures 5

Channel-wise attention based binarization for CNN Architectu...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Paul, Ipsita Bandyopadhyay, Mainak Dash, Satya Ranjan Aurpa, Tanjim Taharat Kiit D.U. School of Computer Engineering Bhubaneswar India Kiit D.U. School of Computer Applications Bhubaneswar India Bsmr Digital University Department of Data Science Engineering Bangladesh

ISBN: (纸本)9798331522100

Attention mechanisms are known to effectively increase the performance of a model by focusing on specific sections of the input while executing a task. Squeeze and Excitation module (SE-Net) and Efficient channel attention module (ECA-Net) are well-known Channel Attention networks that only involve a few parameters to achieve a clear performance gain. In order to further improve the model's accuracy, Binarized Neural Networks (BNNs) can be introduced in the same. BNNs are deep learning models that use binarized values for activation functions and training weights, instead of fully precised values that leads to much faster computation, occupying less memory and power. Motivated by this, a method of binarization of the convolutional network is proposed in this paper that can enhance the overall model accuracy. In our work, we focused on evaluating the performance of our binarized attention module over the pre-existing ones. Thus, the objective of this work is to design, implement, and evaluate efficient neural network models that balance accuracy and computational efficiency for image classification tasks on datasets with differing complexity levels, such as CIFAR-10 and CIFAR-100. By exploring various backbone architectures (e.g., MobileNetV2 and ResNet50), attention mechanisms (e.g., ECANet and SENet), and binarization techniques, this study aims to understand the impact of these components on model performance and resource requirements. Ultimately, the goal is to develop optimized models that achieve high accuracy with minimal computational overhead, making them suitable for deployment in resource-constrained environments. © 2025 IEEE.

关键词： Convolutional neural networks

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EIGENVALUE BOUNDS FOR THE QUANTUM CHROMATIC NUMBER OF GRAPH POWERS

arXiv

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arXiv 2025年

作者： Abiad, Aida Jany, Benjamin Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Mathematics and Data Science Vrije Universiteit Brussel Belgium

The quantum chromatic number, a generalization of the chromatic number, was first defined in relation to the non-local quantum coloring game. We generalize the former by defining the quantum k-distance chromatic number χkq(G) of a graph G, which can be seen as the quantum chromatic number of the k-th power graph, Gk, and as generalization of the classical k-distance chromatic number χk(G) of a graph. It can easily be shown that χkq(G) ≤ χk(G). In this paper, we strengthen three classical eigenvalue bounds for the k-distance chromatic number by showing they also hold for the quantum counterpart of this parameter. This shows that several bounds by Elphick et al. [J. Combinatorial Theory Ser. A 168, 2019, Electron. J. Comb. 27(4), 2020] hold in the more general setting of distance-k colorings. As a consequence we obtain several graph classes for which χkq(G) = χk(G), thus increasing the number of graphs for which the quantum parameter is known. © 2025, CC BY.

关键词： Eigenvalues and eigenfunctions

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Enhancing Brain Tumor Detection with YOLOv11: An Innovative Deep Learning Approach in Medical Imaging

Enhancing Brain Tumor Detection with YOLOv11: An Innovative ...

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

作者： Huthaifa Ziad Abuhammad Rehab Abdulrahman ALsaidi Rana Haitham Alraqad Department of Computer Science Middle East University Amman Jordan Department of Computer Vision OPTRIMENT COMPUTER SYSTEMS Amman Jordan Department of Data Science App Trainers Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This study presents the application of YOLOv11 for the detection of brain tumors in MRI scans, achieving groundbreaking performance with a mean Average Precision at 50% intersection over union (mAP50) of 0.993, a precision rate of 98.8%, and a recall rate of 98.2%. These results significantly surpass the performance of previous state-of-the-art models in the field. The proposed YOLOv11 architecture integrates advanced techniques such as multiscale feature fusion, channel attention mechanisms, and an optimized loss function, which collectively contribute to its enhanced ability to detect small and subtle tumors. This innovation not only improves the accuracy of brain tumor detection but also reduces the time required for manual interpretation, which is crucial in clinical settings. The results demonstrate that YOLOv11 can serve as a reliable tool for enhancing diagnostic efficiency and ensuring more consistent detection outcomes, thus improving the overall reliability of automated systems in the medical imaging domain.

关键词： Deep learning Technological innovation Accuracy Magnetic resonance imaging Brain tumors Medical treatment Brain modeling Real-time systems Reliability Biomedical imaging

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Privacy-Preserving Cyberattack Detection in Blockchain-Based IoT Systems Using AI and Homomorphic Encryption

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IEEE Internet of Things Journal 2025年第11期12卷 16478-16492页

作者： Manh, Bui Duc Nguyen, Chi-Hieu Hoang, Dinh Thai Nguyen, Diep N. Zeng, Ming Pham, Quoc-Viet University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia Université Laval Department of Electrical Engineering and Computer Engineering QuebecQCG1V 0A6 Canada University of Dublin School of Computer Science and Statistics Trinity College Dublin D02 PN40 Ireland

This work proposes a novel privacy-preserving cyberattack detection framework for blockchain-based Internet-of-Things (IoT) systems. In our approach, artificial intelligence (AI)-driven detection modules are strategically deployed at blockchain nodes to identify real-time attacks, ensuring high accuracy and minimal delay. To achieve this efficiency, the model training is conducted by a cloud service provider (CSP). Accordingly, blockchain nodes send their data to the CSP for training, but to safeguard privacy, the data is encrypted using homomorphic encryption (HE) before transmission. This encryption method allows the CSP to perform computations directly on encrypted data without the need for decryption, preserving data privacy throughout the learning process. To handle the substantial volume of encrypted data, we introduce an innovative packing algorithm in a Single-Instruction-Multiple-data (SIMD) manner, enabling efficient training on HE-encrypted data. Building on this, we develop a novel deep neural network training algorithm optimized for encrypted data. We further propose a privacy-preserving distributed learning approach based on the FedAvg algorithm, which parallelizes the training across multiple workers, significantly improving computation time. Upon completion, the CSP distributes the trained model to the blockchain nodes, enabling them to perform real-time, privacy-preserved detection. Our simulation results demonstrate that our proposed method can not only mitigate the training time but also achieve detection accuracy that is approximately identical to the approach without encryption, with a gap of around 0.01%. Additionally, our real implementations on various blockchain consensus algorithms and hardware configurations show that our proposed framework can also be effectively adapted to real-world systems. © 2025 IEEE.

关键词： Differential privacy

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A Blockchain-Based Secure and Transparent Global Radioactive Source Lifecycle Management System

A Blockchain-Based Secure and Transparent Global Radioactive...

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IEEE Southeastcon

作者： Susan Poudel Firdous Kausar Debashis Das Pushpita Chatterjee Sajid Hussain Uttam Ghosh Dept. of Mathematics and Computer Science Fisk University Nashville TN USA Department of Computer Science and Data Science Meharry Medical College Nashville TN USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The management of radioactive sources is a critical process that ensures the safe and responsible handling of radioactive materials throughout their lifecycle. These sources require careful management from production to disposal, such as real-time tracking and radiation monitoring to follow everyone's safety rules and protect people and the environment. However, these sources present significant global challenges, especially regarding safety, security, and transparency. Current systems face limitations such as fragmented oversight, lack of accountability, and risks of unauthorized access. To address these limitations, this paper proposes a blockchain-based radioactive source lifecycle management system to manage the lifecycle of radioactive sources. Blockchain's decentralized and tamper-proof ledger secures data throughout the entire lifecycle of radioactive materials. By using smart contracts and access controls, the system ensures that only authorized parties can monitor and verify transactions in real-time, which reduces human error and prevents unauthorized changes to the data. Users can perform key operations such as retrieving source details, transferring ownership, updating source locations, and adding observers to our proposed system. Our experiment in designing and testing a blockchain application has proven the potential for a secure and transparent system that enhances global cooperation in managing radioactive sources. Overall, the proposed system not only addresses current challenges in radioactive source management but also enhances the tracking and monitoring of radioactive materials.

关键词： Access control Radioactive materials Smart contracts Supply chains Production Position measurement Real-time systems Blockchains Safety Testing

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Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios

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Journal of Electronic science and Technology 2025年第2期23卷

作者： Kutpanova, Zarina Kadhim, Mustafa Zheng, Xu Zhakiyev, Nurkhat Department of Computer Engineering Astana IT University Astana010000 Kazakhstan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China Department of Computational and Data Science Astana IT University Astana010000 Kazakhstan

Unmanned aerial vehicles (UAVs) are widely used in situations with uncertain and risky areas lacking network coverage. In natural disasters, timely delivery of first aid supplies is crucial. Current UAVs face risks such as crashing into birds or unexpected structures. Airdrop systems with parachutes risk dispersing payloads away from target locations. The objective here is to use multiple UAVs to distribute payloads cooperatively to assigned locations. The civil defense department must balance coverage, accurate landing, and flight safety while considering battery power and capability. Deep Q-network (DQN) models are commonly used in multi-UAV path planning to effectively represent the surroundings and action spaces. Earlier strategies focused on advanced DQNs for UAV path planning in different configurations, but rarely addressed non-cooperative scenarios and disaster environments. This paper introduces a new DQN framework to tackle challenges in disaster environments. It considers unforeseen structures and birds that could cause UAV crashes and assumes urgent landing zones and winch-based airdrop systems for precise delivery and return. A new DQN model is developed, which incorporates the battery life, safe flying distance between UAVs, and remaining delivery points to encode surrounding hazards into the state space and Q-networks. Additionally, a unique reward system is created to improve UAV action sequences for better delivery coverage and safe landings. The experimental results demonstrate that multi-UAV first aid delivery in disaster environments can achieve advanced performance. © 2025 The Authors

关键词： Unmanned aerial vehicles (UAV)

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Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via Tensorization

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IEEE Transactions on Evolutionary Computation 2025年

作者： Liang, Zhenyu Li, Hao Yu, Naiwei Sun, Kebin Cheng, Ran Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China The Hong Kong Polytechnic University Department of Data Science and Artificial Intelligence The Department of Computing Hong Kong Hong Kong

Evolutionary multiobjective optimization (EMO) has made significant strides over the past two decades. However, as problem scales and complexities increase, traditional EMO algorithms face substantial performance limitations due to insufficient parallelism and scalability. While most work has focused on algorithm design to address these challenges, little attention has been given to hardware acceleration, thereby leaving a clear gap between EMO algorithms and advanced computing devices, such as GPUs. To bridge the gap, we propose to parallelize EMO algorithms on GPUs via the tensorization methodology. By employing tensorization, the data structures and operations of EMO algorithms are transformed into concise tensor representations, which seamlessly enables automatic utilization of GPU computing. We demonstrate the effectiveness of our approach by applying it to three representative EMO algorithms: NSGA-III, MOEA/D, and HypE. To comprehensively assess our methodology, we introduce a multiobjective robot control benchmark using a GPU-accelerated physics engine. Our experiments show that the tensorized EMO algorithms achieve speedups of up to 1113× compared to their CPU-based counterparts, while maintaining solution quality and effectively scaling population sizes to hundreds of thousands. Furthermore, the tensorized EMO algorithms efficiently tackle complex multiobjective robot control tasks, producing high-quality solutions with diverse behaviors. Source codes are available at https://***/EMI-Group/evomo. © 2025 IEEE.

关键词： Tensors

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Fusing Image and Text Features for Scene Sentiment Analysis Using Whale-Honey Badger Optimization Algorithm (WHBOA) 27th

Fusing Image and Text Features for Scene Sentiment Analysi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yadav, Prem Shanker Tyagi, Dinesh Kumar Vipparthi, Santosh Kumar Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India School of Artificial Intelligence and Data Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India

ISBN: (纸本)9783031781650

Developing a real-time sentiment analysis application that relies solely on features extracted from images or textual content falls short of capturing human emotions’ nuanced and multifaceted nature. The unlabeled dataset, though useful, has limitations for sentiment analysis due to its general image descriptions, which lack emotional depth and do not include direct sentiment labels. Finding scene sentiment is a challenging task. To address this, combining textual descriptions with visual features is crucial. Important parameters include entropy, bag of words, and parts of speech (nouns, adjectives, and verbs) for textual analysis, alongside visual features like SIFT, SURF, and color histograms. These features are integrated to capture a comprehensive range of sentiment cues, enhancing the accuracy and depth of sentiment insights. This paper proposes an optimized adaptive neuro-fuzzy inference system for a compelling feature enhancement using the Whale-Honey Badger Optimization Algorithm (WHBOA). The proposed method identifies the most relevant and effective features from both textual and visual data. It captures visual-specific attributes to provide a richer and more detailed representation of visual content, addressing the limitations of general image descriptions and paving the way for the development of predictive models. Additionally, text pre-processing cleans and normalizes the textual data. We conducted an extensive comparative performance evaluation to assess the effectiveness and accuracy of the proposed model. The model is compared with the Nearest Neighbor, Support Vector Machine (SVM), and Decision Tree classification algorithms for the performance *** results demonstrate that the optimized model performs better, achieving an accuracy of approximately 91.2%, compared to the other models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing 24

Octopus: Experiences with a Hybrid Event-Driven Architecture...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis

作者： Haochen Pan Ryan Chard Sicheng Zhou Alok Kamatar Rafael Vescovi Valérie Hayot-Sasson André Bauer Maxime Gonthier Kyle Chard Ian Foster Department of Computer Science University of Chicago Chicago IL USA Data Science and Learning Division Argonne National Laboratory Lemont IL USA Department of Computer Science Southern University of Science and Technology Guangdong China Department of Computer Science Illinois Institute of Technology Chicago IL USA Department of Computer Science University of Chicago Chicago IL USA and Data Science and Learning Division Argonne National Laboratory Lemont IL USA

ISBN: (纸本)9798350355543

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distributed research infrastructures by enabling the organization, communication, processing, reliability, and security of events generated from many sources. To support the development of scientific EDA, we introduce Octopus, a hybrid, cloud-to-edge event fabric designed to link many local event producers and consumers with cloud-hosted brokers, and to provide a fabric for developing resilient applications. Octopus can be scaled to meet demand, permits the deployment of highly available Triggers for automatic event processing, and enforces fine-grained access control. We identify requirements in self-driving laboratories, scientific data automation, online task scheduling, epidemic modeling, and dynamic workflow management use cases, and present results demonstrating Octopus' ability to meet those requirements. Octopus supports producing and consuming events at a rate of over 4.2 M and 9.6 M events per second, respectively, from distributed clients.

关键词： Event-driven architecture

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