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International Conference on computer and Information Technology (ICCIT)

作者： Koushick Barua Tanjim Mahmud Anik Barua Nahed Sharmen Nanziba Basnin Dilshad Islam Mohammad Shahadat Hossain Karl Andersson Sazzad Hossain Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Obstetrics and Gynecology Chattogram Maa-O-Shishu Hospital Medical College Chittagong Bangladesh Department of Computer Science and Engineering Leeds Beckett University UK Dept. of Physical and Mathematical Sciences Chattogram Veterinary and Animal Sciences University Chittagong Bangladesh Department of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Pervasive and Mobile Computing Laboratory Luleå University of Technology Luleå Sweden Department of Computer Science and Engineering University of Liberal Arts Bangladesh (ULAB) Dhaka Bangladesh

The human skeletal framework relies heavily on bones, and one such crucial component is the “Humerus.” Positioned in the upper arm, extending from the shoulder to the elbow junction, the Humerus provides essential structural support for muscles and facilitates upper-body movement, particularly in the arms and hands. Consequently, Humerus fractures significantly impact daily life, causing disruptions and limitations. This paper presents a thorough exploration of an Explainable AI-based Humerus Fracture Detection and Classification system, employing various deep learning models. Leveraging a dataset of 1266 X-ray images, encompassing fractured and non-fractured humerus bones from the publicly available “MURA” dataset, our research evaluates the effectiveness of Convolutional Neural Networks (CNN), VGG16, VGG19, DenseNet121, and DenseNet169 in detecting fractures. After 30 epochs of training, we assessed their performance using critical metrics: accuracy, precision, recall, and F1 score. Notably, DenseNet121 and DenseNet169 exhibited superior accuracy, precision, and recall, laying a robust foundation for automated humerus fracture diagnosis. We also introduced two ensemble models, "Ensemble-1 (VGG16 and VGG19)" and "Ensemble-2 (DenseNet121 and DenseNet169)," which delivered substantial improvements in accuracy, precision, recall, and F1 score, showcasing the potential of ensemble techniques in clinical settings. Furthermore, we enhanced model interpretability and transparency by incorporating Saliency Maps and GRAD-CAM (Gradient-weighted Class Activation Mapping) for Explainable AI (XAI). This visualization allowed us to identify regions of interest in X-ray images contributing to the model’s predictions, providing valuable insights for medical practitioners.

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From Single to Multi-Functional RIS: Architecture, Key Technologies, Challenges, and Applications

arXiv

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arXiv 2024年

作者： Ni, Wanli Zheng, Ailing Wang, Wen Niyato, Dusit Al-Dhahir, Naofal Debbah, Mérouane The Department of Electronic Engineering Tsinghua University China The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Computer Engineering The University of Texas Dallas United States The KU 6G Research Center Khalifa University of Science and Technology United Arab Emirates

Although reconfigurable intelligent surfaces (RISs) have demonstrated the potential to boost network capacity and expand coverage by adjusting their electromagnetic properties, existing RIS architectures have certain limitations, such as double-fading attenuation and restricted half-space coverage. In this article, we delve into the progressive development from single to multi-functional RIS (MF-RIS) that enables simultaneous signal amplification, reflection, and refraction. We begin by detailing the hardware design and signal model that distinguish MF-RIS from traditional RISs. Subsequently, we introduce the key technologies underpinning MF-RIS-aided communications, along with the fundamental issues and challenges inherent to its deployment. We then outline the promising applications of MF-RIS in the realm of communication, sensing, and computation systems, highlighting its transformative impact on these domains. Lastly, we present simulation results to demonstrate the superiority of MF-RIS in enhancing network performance in terms of spectral efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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Finite-Size Analysis of Prepare-and-Measure and Decoy-State Quantum Key Distribution via Entropy Accumulation

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PRX Quantum 2025年第2期6卷 020342-020342页

作者： Lars Kamin Amir Arqand Ian George Norbert Lütkenhaus Ernest Y.-Z. Tan Institute for Quantum Computing and Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA

An important goal in quantum key distribution (QKD) is the task of providing a finite-size security proof without the assumption of collective attacks. For prepare-and-measure QKD, one approach for obtaining such proofs is the generalized entropy-accumulation theorem (GEAT) but thus far it has only been applied to study a small selection of protocols. In this work, we present techniques for applying the GEAT in finite-size analysis of generic prepare-and-measure protocols, with a focus on decoy-state protocols. In particular, we present an improved approach for computing entropy bounds for decoy-state protocols, which has the dual benefits of providing tighter bounds than previous approaches (even asymptotically) and being compatible with methods for computing min-trade-off functions in the GEAT. Furthermore, we develop methods to incorporate some improvements to the finite-size terms in the GEAT and implement techniques to automatically optimize the min-trade-off function. Our approach also addresses some numerical-stability challenges specific to prepare-and-measure protocols, which have not been addressed in previous works.

关键词： Numerical methods

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xuefei Li Mingzhe Chen Danpu Liu Zhilong Zhang Tony Q. S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs).

关键词： Base stations Correlation Network topology Simulation Collaboration Reinforcement learning Vectors Sensors Global communication Optimization

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3D PET/CT Tumor Lesion Segmentation Based on nnUNet with GCN Refinement

arXiv

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arXiv 2023年

作者： Xue, Hengzhi Fang, Qingqing Yao, Yudong Teng, Yueyang College of Medicine and Biological Information Engineering Northeastern University Shenyang110004 China Department of Electrical and Computer Engineering Steven Institute of Technology HobokenNJ07102 United States Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110169 China

Whole-body PET/CT scan is an important tool for diagnosing various malignancies (e.g., malignant melanoma, lymphoma, or lung cancer), and accurate segmentation of tumors is a key part for subsequent treatment. In recent years, CNN-based segmentation methods have been extensively investigated. However, these methods often give inaccurate segmentation results, such as over-segmentation and under-segmentation. Therefore, to address such issues, we propose a post-processing method based on a graph convolutional neural network (GCN) to refine inaccurate segmentation parts and improve the overall segmentation accuracy. Firstly, nnUNet is used as an initial segmentation framework, and the uncertainty in the segmentation results is analyzed. Certainty and uncertainty nodes establish the nodes of a graph neural network. Each node and its 6 neighbors form an edge, and 32 nodes are randomly selected for uncertain nodes to form edges. The highly uncertain nodes are taken as the subsequent refinement targets. Secondly, the nnUNet result of the certainty nodes is used as label to form a semi-supervised graph network problem, and the uncertainty part is optimized through training the GCN network to improve the segmentation performance. This describes our proposed nnUNet-GCN segmentation framework. We perform tumor segmentation experiments on the PET/CT dataset in the MICCIA2022 autoPET challenge. Among them, 30 cases are randomly selected for testing, and the experimental results show that the false positive rate is effectively reduced with nnUNet-GCN refinement. In quantitative analysis, there is an improvement of 2.12 % on the average Dice score, 6.34 on 95 % Hausdorff Distance (HD95), and 1.72 on average symmetric surface distance (ASSD). The quantitative and qualitative evaluation results show that GCN post-processing methods can effectively improve tumor segmentation performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Smart Last-Mile Delivery with IoT-Connected Robot Vehicles

Smart Last-Mile Delivery with IoT-Connected Robot Vehicles

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International Conference on Smart computing & Communications (ICSCC)

作者： M Sujatha Rajesh Kambattan Kovarasan K. Sudha Nilamadhab Mishra G Thiyagarajan C. Srinivasan Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Business Administration STET Women's College (Autonomous) Mannargudi Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

This research discusses using IoT technologies with robot vehicles to improve last-mile deliveries. This novel method optimizes the last stage of the distribution chain, which sometimes presents logistical issues. The system examines robot vehicles' last-mile delivery monitoring, data collecting, and communication capabilities via an Internet of Things connection. These trucks collect and communicate traffic, packaging, and environmental data using sensors and communication equipment. Analyzing and processing this data improves route planning, delivery timing, and performance. Through the IoT, autonomous delivery trucks, a centralized control center, and other components of the delivery ecosystem could operate in combination. This collaborative network helps respond to changing situations such as highway congestion and package urgency by rerouting or raising delivery priority. The potential for delivery trucks to improve productivity, intelligence, and focus on the customer in the last mile of a package's journey. The IoT-driven solutions may be highlighted as major results, advantages, and implications for the logistics sector, addressing urbanization, consumer demands, and environmental concerns in modern delivery systems.

关键词： Accuracy Urban areas Transforms medical services Robot sensing systems Timing Electronic commerce Sustainable development Monitoring Logistics

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Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

arXiv

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arXiv 2025年

作者： Ma, Chenxiang Chen, Xinyi Li, Yanchen Yang, Qu Wu, Yujie Li, Guoqi Pan, Gang Tang, Huajin Tan, Kay Chen Wu, Jibin Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Institute of Automation Chinese Academy of Sciences Beijing100045 China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou310027 China

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Advancing Predictive Maintenance in Smart Grids with Machine Learning Techniques Through Comparative Analysis of SVM and CNN Models

Advancing Predictive Maintenance in Smart Grids with Machine...

引用

Cognitive computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Hemanth Raju Shubhra Amandeep Nagpal J. Nagaraju Taqi Al-Rubaye Preeti Tewari New Horizon College of Engineering Bangalore India Department of Electrical Electronics & Communication Engineering Galgotias University Gerater Noida Uttar Pradesh Lovely Professional University Phagwara India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq Lloyd Law College Greater Noida Uttar Pradesh

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Applying ML techniques into smart grid architectures has transformed the predictive maintenance to a level that can predict equipment failures before they occur. This approach reduces the power system downtimes and also results to a very low maintenance cost hence make the power systems more reliable and efficient. The paper examines a variety of the supervised, unsupervised, and reinforcement learning approaches appropriate for processing petabytes of data collected through smart grid sensors and monitoring systems. These algorithms enable the study to find out such patterns and abnormal signs that signify that a piece of equipment is likely to break down soon thus preventative measures are taken. They include: higher models that can predict the failures with higher accuracy and also the causes of the failures that can enable the right approach to be made. These studies reveal that for the improvement of the operational reliability of smart grids, it is indispensable to use ML, thereby signalling a paradigm shift from reliability-centred maintenance to reliability enhancement centring on smart maintenance. In this study, we provide the further understanding of how advanced analytical tools can enhance the practical implementation of smart grid, and general aspect of energy industry.

关键词： Support vector machines Costs Predictive models Transformers Smart grids Maintenance Power system reliability Convolutional neural networks Integrated circuit modeling Predictive maintenance

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Multi-label advertising image classification using traditional deep neural networks and vision language models: dataset and annotation agreement method

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Multimedia Tools and Applications 2025年 1-30页

作者： Mir, Tatheer Hussain Wei, Jui-Cheng Simanjuntak, Mutiara Cheng, Wan-Shu Lee, Yi-Hsun Zhang, You-Chen Chen, Jia-Bin Dai, Hong-Jie Intelligent System Laboratory Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Computer Science and Information Management Providence University Taichung Taiwan Research and Development Department Linker AI Co. Ltd Taipei Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan

Effectively classifying advertising images is crucial in targeting the right audience and maximizing marketing performance. To address this problem, this paper presents a multi-label advertising image classification study using popular deep-learning architectures. First, we compile a dedicated dataset for this task and evaluate the performance of traditional deep learning-based models based on the convolutional neural network (CNN) and vision transformer architectures. To ensure the quality of dataset annotations, we introduce an extended Krippendorf’s Alpha (α) method based on the Jaccard index to provide a reliable measure of inter-annotation agreement which can address the missing annotations and multiple labels to establish the dataset’s annotation consistency. Our results demonstrate that transformer-based architectures like ViT and Swin outperform the CNN-based model’s baseline and differential learning rate settings. Through the visualization analysis of saliency maps, we gain insights into the model’s decision-making processes and identify the factors influencing their predictions. Furthermore, we assess the impact of annotation quality on model performance, comparing models trained on different annotation reliability levels. Our results indicate that higher annotation consistency, as quantified by α-Jaccard, leads to improved model performance, emphasizing the importance of high-quality datasets in advertising image classification. Beyond traditional deep learning models, we explore the effectiveness of vision language models (VLMs) in this task by employing prompt engineering and comparing their performance with fine-tuned deep learning models. Our findings indicate that while VLMs provide richer contextual annotations, they suffer from over-classification tendencies, subjective biases, and significantly higher computational costs. In contrast, deep learning models remain a more efficient and scalable solution for structured, large-scale advertising classi

关键词： Advertising image Convolution neural network Krippendorf’s Alpha Learning rate configuration Marketing effectiveness Multi-label classification Vision Language Models Vision transformer

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Watermarking and Encryption for Electronic medical Records (EMR) Security Based on mCloud

Watermarking and Encryption for Electronic Medical Records (...

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Cyber Resilience (ICCR), International Conference on

作者： Haider Rasheed Abdulshaheed Yaseen Saadi Abbas Jamal F. Tawfeq Karamath Ateeq Saif. E. A. Alnawayseh Taher M. Ghazal Computer Engineering Techniques Department Baghdad College of Economic Sciences University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Al-Nahrain University Baghdad Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq School of Computing Skyline University College University City Sharjah Sharjah UAE Department of Electrical Engineering College of Engineering Mutah University Al-Karak Jordan Computer Science Department Centre for Cyber Physical Systems Khalifa University Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Providing quality healthcare services and promoting collaborative clinical research are easier and more efficient with electronic medical record (EMR) systems. All aspects of care are included in electronic health records. Data characterized by volume, variety, and velocity is what IBM defines as big data. Therefore, we evidently have a significant problem managing “big EMR data” from a volume, accuracy, and velocity perspective. Data misuse and patient safety are significant risks associated with poorly implemented EMR-based systems. Storage, access, and retrieval of big data are all aspects of security to consider. Location is another important aspect of secure data use, in addition to strong access control mechanisms. Researchers are investigating secure mobile data usage based on location after recent reports of illegal trade and theft of patient data over mobile devices. To improve work flow dynamics and efficiency, healthcare facilities are increasingly using mobile devices. In spite of this, mobile devices are becoming more and more common. A new technique for preventing EMR infiltration and manipulation by hackers using the cloud is presented in this article.

关键词： Cloud computing Hospitals Watermarking Big Data Mobile handsets Robustness Safety

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