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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Vu-Thanh, Trung He, Jing Duong-Bao, Ninh Thi, Luong Nguyen Lee, Seon-Woo Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Mathematics and Informatics Dalat University Dalat Viet Nam Faculty of Information Technology Dalat University Dalat Viet Nam Division of Software Hallym University Chuncheon Korea Republic of Department of Electronics and Telecommunications Dalat University Dalat Viet Nam

ISBN: (纸本)9798400717161

Recently, WiFi Fingerprinting-based indoor positioning systems have gained significant attention due to the low cost of infrastructure deployment. In the data collection process, a higher density of reference points may lead to better positioning results. However, it makes this process time-consuming and laborious. To solve this problem, this study applies and compares different interpolation methods to enrich the fingerprint database without increasing the cost of the collection process. To evaluate the performance of the chosen methods, five interpolation methods are implemented in a multi-condition WiFi Fingerprinting dataset. Moreover, to check the robustness of the methods, different numbers of reference points are considered when creating the new interpolated fingerprint databases. According to the results, inverse distance weighting (IDW) is the most suitable interpolation method with low complexity and stable performance. The IDW-based database achieves an average interpolation error of less than 6 dBm compared to the original database. When applying this interpolated database to perform positioning tasks, it helps reduce the collection cost by 30% with slightly lower positioning results compared to the original database. This work demonstrates the feasibility of using interpolation to ease the data collection process of WiFi Fingerprinting, especially in complex and big areas such as airports or hospitals. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Wi-Fi

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Interpretable deep learning method to predict wound healing progress based on collagen fibers in wound tissue

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computers in Biology and Medicine 2025年 191卷 110110-110110页

作者： He, Juan Wang, Xiaoyan Wang, Zhengshan Xie, Ruitao Zhang, Zhiming Liu, Tzu-Ming Cai, Yunpeng Chen, Long Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute of Translational Medicine Faculty of Health Sciences & Ministry of Education Frontiers Science Center for Precision Oncology University of Macau 999078 China

Background and objective: The dynamic evolution of collagen fibers during wound healing is crucial for assessing repair progression, guiding clinical treatment, and drug screening. Current quantitative methods analyzing collagen spatial patterns (density, orientation variance) lack established criteria to both stratify distinct healing periods and detect delayed healing conditions, necessitating the establishment of a novel classification method for wound healing status based on collagen fibers. Methods: We propose a deep learning method to classify various time points of wound healing and delayed healing using histological images of skin tissue. We fine-tune a pre-trained VGG16 model and enhance it with an interpretable framework that combines LayerCAM and Guided Backpropagation, leveraging model gradients and features to visually identify the tissue regions driving model predictions. Results: Our model achieved 85 % accuracy in a five-class classification task (normal skin, wound skin at 0, 3, 7, and 10 days) and 78 % in a three-class task (normal skin, wound skin at 0 days, diabetic wound skin at 10 days). Our interpretable framework accurately localizes collagen fibers without pixel-level annotations, demonstrating that our model classifies healing periods and delayed healing based on collagen regions in histological images rather than other less relevant tissue structures. Conclusions: Our deep learning method leverages collagen fiber features to predict various time points of wound healing and delayed healing with high accuracy and visual interpretability, enhancing doctors' trust in model decisions. This could lead to more precise and effective wound treatment practices. © 2025

关键词： Deep learning

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CA-FedRC: Codebook Adaptation Via Federated Reservoir Computing in 5G NR

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IEEE Transactions on Vehicular technology 2025年

作者： Ye, Ziqiang Liao, Sikai Gao, Yulan Fang, Shu Xiao, Yue Xiao, Ming Zammit, Saviour University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China KTH Royal Institute of Technology Division of Information Science and Engineering Stockholm10044 Sweden University of Malta Department of Communications and Computer Engineering Msida2080 Malta

With the burgeon deployment of the fifth-generation new radio (5 G NR) networks, the codebook plays a crucial role in enabling the base station (BS) to acquire the channel state information (CSI). Different 5 G NR codebooks incur varying overheads and exhibit performance disparities under diverse channel conditions, necessitating codebook adaptation based on channel conditions to reduce feedback overhead while enhancing performance. However, existing methods of 5 G NR codebooks adaptation require significant overhead for model training and feedback or fall short in performance. To address these limitations, this letter introduces a federated reservoir computing framework designed for efficient codebook adaptation in computationally and feedback resource-constrained mobile devices. This framework utilizes a novel series of indicators as input training data, striking an effective balance between performance and feedback overhead. Compared to conventional models, the proposed codebook adaptation via federated reservoir computing (CA-FedRC), achieves rapid convergence and significant loss reduction in both speed and accuracy. Extensive simulations under various channel conditions demonstrate that our algorithm not only reduces resource consumption of users but also accurately identifies channel types, thereby optimizing the trade-off between spectrum efficiency, computational complexity, and feedback overhead. © 1967-2012 IEEE.

关键词： 5G mobile communication systems

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No Imputation Without Representation 35th

No Imputation Without Representation

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35th Benelux Conference on Artificial Intelligence and Machine Learning, BNAIC/Benelearn 2023

作者： Lenz, Oliver Urs Peralta, Daniel Cornelis, Chris Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Research Group for Computational Web Intelligence Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Information Technology Ghent University Ghent Belgium

ISBN: (纸本)9783031746499

By filling in missing values in datasets, imputation allows these datasets to be used with algorithms that cannot handle missing values by themselves. However, missing values may in principle contribute useful information that is lost through imputation. The missing-indicator approach can be used in combination with imputation to instead represent this information as a part of the dataset. There are several theoretical considerations why missing-indicators may or may not be beneficial, but there has not been any large-scale practical experiment on real-life datasets to test this question for machine learning predictions. We perform this experiment for three imputation strategies and a range of different classification algorithms, on the basis of twenty real-life datasets. In a follow-up experiment, we determine attribute-specific missingness thresholds for each classifier above which missing-indicators are more likely than not to increase classification performance. And in a second follow-up experiment, we evaluate numerical imputation of one-hot encoded categorical attributes. We reach the following conclusions. Firstly, missing-indicators generally increase classification performance. Secondly, with missing-indicators, nearest neighbour and iterative imputation do not lead to better performance than simple mean/mode imputation. Thirdly, for decision trees, pruning is necessary to prevent overfitting. Fourthly, the thresholds above which missing-indicators are more likely than not to improve performance are lower for categorical attributes than for numerical attributes. Lastly, mean imputation of numerical attributes preserves some of the information from missing values. Consequently, when not using missing-indicators it can be advantageous to apply mean imputation to one-hot encoded categorical attributes instead of mode imputation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Model-Driven Channel Estimation Network for Orthogonal Time-Frequency Space Systems

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IEEE Transactions on Vehicular technology 2025年

作者： Wang, Qian Chen, Xingke Tao, Qin Qian, Li Ping Kam, Pooi-Yuen Wu, Yuan Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China Hangzhou Normal University School of Information Science and Technology Hangzhou311121 China School of Science and Engineering Shenzhen518172 China Center for Advanced Microelectronic Devices Suzhou215123 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China

Orthogonal time-frequency space (OTFS) modulation has garnered significant attention in wireless communications, particularly in high-mobility scenarios, due to its exceptional resilience to Doppler shifts. While the delay- Doppler domain channel estimation is mostly considered for reliable communication, it is often hindered by fractional Doppler shifts in complex channel conditions. To overcome this challenge, this paper proposes a model-driven channel estimation network (CENet) for accurate channel estimation in the delay-time (DT) domain, with the initial least-squares (LS) solutions as prior information for training. Specifically, a LS-driven CENet with parallel structure is proposed to efficiently handle the high-dimensional DT domain channel matrix, which enables the row- wise training and estimation with fast convergence. Simulation results demonstrate that our proposed CENet significantly outperforms the conventional LS and deep neural network-based methods. © 1967-2012 IEEE.

关键词： Least squares approximations

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AI-Driven Nutritional Intelligence: A Predictive Data Analytics Framework for Personalized Dietary Optimization

AI-Driven Nutritional Intelligence: A Predictive Data Analyt...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Jayabhaduri R Prabanjan U V Akey Sungheetha Gunashekara Reddy M R Santhosh G Yashas M R Department of Information Technology and Engineering Alliance School of Advanced Computing Alliance University - Central Campus Alliance University Bengaluru India Department of Information Technology and Engineering Alliance School of Advanced Computing Alliance University - Central Campus Alliance University Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Public health is critically dependent on nutrition, yet malnutrition and poor dietary habits persist globally. This study presents an AI-driven nutritional intelligence framework that leverages machine learning, behavioral insights, and real-time data analytics to enhance nutritional well-being. By integrating heterogeneous datasets-including dietary surveys, medical records, socioeconomic profiles, a nd g eospatial data-the proposed model identifies key nutritional deficiencies and risk factors for diet-related diseases. Predictive modeling is employed to generate personalized dietary recommendations, while wearable and mobile technologies facilitate real-time nutrient monitoring. Additionally, AI-powered learning platforms and community-driven interventions improve adherence to healthier eating habits. The study evaluates the framework through case studies and pilot programs, demonstrating measurable improvements in dietary outcomes and reduced nutritional disparities.

关键词： Surveys Technological innovation Data analysis System performance Machine learning Predictive models Real-time systems Sustainable development Wearable devices Socioeconomics

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Multi-modal Self-perception Enhanced Large Language Model for 3D Region-of-Interest Captioning with Limited Data

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IEEE Transactions on Multimedia 2025年 27卷 2935-2948页

作者： Shi, Lu Kan, Shichao Jin, Yi Zhang, Linna Cen, Yigang Bejing Jiaotong University and the Beijing Key Laboratory of Advanced Information Science and Network Technology Institute of Information Science Beijing100044 China Central South University School of Computer Science and Engineering Hunan Changsha0410083 China Beijing Jiaotong University Key Laboratory of Big Data and Artificial Intelligence in Transportation Ministry of Education and the School of Computer and lnformation Technology Beijing100044 China Guizhou University School of Mechanical Engineering Guiyang550025 China

3D Region-of-Interest (RoI) Captioning involves translating a model's understanding of specific objects within a complex 3D scene into descriptive captions. Recent advancements in Large Language Models (LLMs) have shown great potential in this area. Existing methods capture the visual information from RoIs as input tokens for LLMs. However, this approach may not provide enough detailed information for LLMs to generate accurate region-specific captions. In this paper, we introduce Self-RoI, a Large Language Model with multi-modal self-perception capabilities for 3D RoI captioning. To ensure LLMs receive more precise and sufficient information, Self-RoI incorporates Implicit Textual Info. Perception to construct a multi-modal vision-language information. This module utilizes a simple mapping network to generate textual information about basic properties of RoI from vision-following response of LLMs. This textual information is then integrated with the RoI's visual representation to form a comprehensive multi-modal instruction for LLMs. Given the limited availability of 3D RoI-captioning data, we propose a two-stage training strategy to optimize Self-RoI efficiently. In the first stage, we align 3D RoI vision and caption representations. In the second stage, we focus on 3D RoI vision-caption interaction, using a disparate contrastive embedding module to improve the reliability of the implicit textual information and employing language modeling loss to ensure accurate caption generation. Our experiments demonstrate that Self-RoI significantly outperforms previous 3D RoI captioning models. Moreover, the Implicit Textual Info. Perception can be integrated into other multi-modal LLMs for performance enhancement. We will make our code available for further research. © 1999-2012 IEEE.

关键词： Digital elevation model

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Coexisting Hyperchaos in a Memristive Neuromorphic Oscillator

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Zhang, Xin Li, Chunbiao Lei, Tengfei Iu, Herbert Ho-Ching Kapitaniak, Tomasz Nanjing University of Information Science and Technology School of Electronic and Information Engineering Jiangsu Nanjing210044 China Nanjing University of Information Science and Technology School of Artificial Intelligence Jiangsu Nanjing210044 China Shandong Jinan250200 China University of Western Australia School of Electrical Electronic and Computer Engineering CrawleyWA6009 Australia Lodz University of Technology Division of Dynamics Lodz90-924 Poland

Memristors have been widely integrated into neurons as the bridge for introducing external magnetic induction currents. The complex oscillation induced by the external magnetic stimulation is a hot topic in neuron dynamics. When a memristor is introduced into the Hindmarsh-Rose (HR) neuron to simulate the external magnetic field, a novel memristive neuromorphic hyperchaotic oscillator is constructed. The memristor weight can trigger complex neuronal firing dynamics, including the rare hyperchaotic bursting. Furthermore, when the technology of offset boosting-oriented attractor doubling is employed, a double-scroll hyperchaotic attractor can be generated, which could split into three independent coexisting attractors under some specific offsets. More interesting, two symmetric periodic attractors and two symmetric hyperchaotic attractors can coexist under certain conditions. In this work, a neuron with coexisting hyperchaotic attractors is constructed and exhaustively explored, which provides a good candidate for constituting large-scale brain-like neuromorphic oscillator. A PCB-based hardware circuit produces the oscillations validating the numerical simulations and theoretical analyses. © 1982-2012 IEEE.

关键词： Neurons

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NeuroSpectrum Deep Learning Architecture for Automated Spectrum Analysis Resource Optimization

NeuroSpectrum Deep Learning Architecture for Automated Spect...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： K. Tamilselvan T. Senthil B. Buvaneswari Santhiya S Mukesh Soni Department of Information Technology AVS Engineering College Salem India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies chittoor India Department of Information Technology Panimalar Engineering College Chennai India Department of Computer science and Engineering Excel Engineering College Namakkal India Department of Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Reducing the availability of spectrum, as well as the nature of allocating channels in the present wireless telecommunication networks, is one of the critical issues facing modern wireless technology. Such spectrum sensing methods may not be highly flexible or efficient in environments where the radio spectrum undergoes frequent changes. This work proposes an improved deep learning spectrum sensing model based on CNN with reinforcement learning. The proposed system design calls for a pioneering dual mechanism, incorporating both spectrum analysis and a resource allocation learning model. The framework achieved remarkable performance metrics: 96 The spectrum sensing method achieved a 3% occurrence rate, a 94.8% precision rate for resource allocation, and a 92.7% efficiency in dynamic spectrum use. In the present investigation, we have employed it for different topologies and got 45% improvement in spectrum efficiency and 87% less interference than the conventional techniques. The work challenges deep learning-based spectrum sensing as well as management and indicates that they are prepared for the following generation of the wireless communication network.

关键词： Deep learning Wireless sensor networks Wireless networks Transfer learning Training data Sensors Resource management Optimization Spectral analysis Radio spectrum management

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Facial Depression Estimation via Multi-Cue Contrastive Learning

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wang, Xinke Xu, Jingyuan Sun, Xiao Li, Mingzheng Hu, Bin Qian, Wei Guo, Dan Wang, Meng Anhui University School of Artificial Intelligence Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230031 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China University of Science and Technology of China Institute of Advanced Technology Hefei230094 China Beijing Institute of Technology School of Medical Technology Beijing100081 China

Vision-based depression estimation is an emerging yet impactful task, whose challenge lies in predicting the severity of depression from facial videos lasting at least several minutes. Existing methods primarily focus on fusing frame-level features to create comprehensive representations. However, they often overlook two crucial aspects: 1) inter- and intra-cue correlations, and 2) variations among samples. Hence, simply characterizing sample embeddings while ignoring to mine the relation among multiple cues leads to limitations. To address this problem, we propose a novel Multi-Cue Contrastive Learning (MCCL) framework to mine the relation among multiple cues for discriminative representation. Specifically, we first introduce a novel cross-characteristic attentive interaction module to model the relationship among multiple cues from four facial features (e.g., 3D landmarks, head poses, gazes, FAUs). Then, we propose a temporal segment attentive interaction module to capture the temporal relationships within each facial feature over time intervals. Moreover, we integrate contrastive learning to leverage the variations among samples by regarding the embeddings of inter-cue and intra-cue as positive pairs while considering embeddings from other samples as negative. In this way, the proposed MCCL framework leverages the relationships among the facial features and the variations among samples to enhance the process of multi-cue mining, thereby achieving more accurate facial depression estimation. Extensive experiments on public datasets, DAIC-WOZ, CMDC, and E-DAIC, demonstrate that our model not only outperforms the advanced depression methods but that the discriminative representations of facial behaviors provide potential insights about depression. Our code is available at:https://***/xkwangcn/***. © 1991-2012 IEEE.

关键词： Face recognition

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