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Gait Pattern Recognition through Force Sensor Platform based on XGBoost Model and Harris’ Hawks Optimization

IAENG International Journal of Applied Mathematics 2025年第1期55卷 118-125页

作者： Yu, Lie Mei, Pengzhi Ding, Lei School of Electronic and Electrical Engineering Wuhan Textile University Wuhan China School of Electronic and Electrical Engineering Wuhan Textile University Wuhan China School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China

This study developed a gait pattern classification system based on ground contact forces measured by six force sensors embedded inside the shoe sole. The data transmission is facilitated via the Bluetooth module integrated into an STM32 microcontroller. The extreme gradient boosting (XGBoost) algorithm is used to identify the gait patterns, and the basic idea of XGBoost is to use second-order derivatives to make the loss function more precise, incorporate regularization to prevent tree overfitting, and enable block storage for parallel computation. By optimizing the XGBoost algorithm with four algorithms, the exploration capabilities of these algorithms are effectively incorporated into the fusion model. Experimental results indicate that the XGBoost algrithm optimized by Harris’ hawks optimization (HHO) outperforms the other optimization algorithms. Specifically, the HHO-XGBoost achieved high values of 97.41%, 97.03%, and 97.22% severally in the metrics of precision, recall, and F1 score. This research illustrates the HHO-XGBoost method's superiority in gait phase recognition. © (2025), (International Association of Engineers). All rights reserved.

关键词： Gait analysis

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Intelligent Computing System for Breast Cancer Identification utilizing U-Net for Digital Image Segmentation 3

Intelligent Computing System for Breast Cancer Identificatio...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Goli, Ranadheer Reddy Jadala, Vijaya Chandra Jamalpur, Bhavana Department of Computer Science and Artificial Intelligene School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798331527549

As per WHO calculation of deaths, globally due to Breast Cancer is around one million. Breast cancer can be cured by early detection, in which abnormal breast cells proliferate uncontrollably, leading to tumor formation. Early detection of breast cancer may reduce deaths, as per World Health Organization records there are about one million deaths are caused by 2024 among them only 0.5 percent are of men. The different methodologies adopted in medical image processing related to breast cancer detection methods include mammography. We concentrated on medical image segmentation which helps to get visual information about breast tissue. This paper presents an intelligent computing system designed to support medical image processing through utilizing U-Net methodology for segmentation as part of deep learning methodologies. As part of Experimental Analysis, a BUSI dataset is used for the implementation of Deep Learning methodologies. A graphic analysis based on medical images, followed by an analysis of the results. This Intelligent Computing System based on artificial intelligence helps radiologists, medical professionals, and doctors with early and simplified breast cancer diagnosis. © 2025 IEEE.

关键词： Breast Cancer Intelligent System Segmentation U-Net

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SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis 31

SILC-EFSA: Self-aware In-context Learning Correction for Ent...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhu, Senbin He, Chenyuan Liu, Hongde Dong, Pengcheng Zhao, Hanjie Yan, Yuchen Jia, Yuxiang Zan, Hongying Peng, Min School of Computer and Artificial Intelligence Zhengzhou University China School of Computer Science Wuhan University China

ISBN: (纸本)9798891761964

In recent years, fine-grained sentiment analysis in finance has gained significant attention, but the scarcity of entity-level datasets remains a key challenge. To address this, we have constructed the largest English and Chinese financial entity-level sentiment analysis datasets to date. Building on this foundation, we propose a novel two-stage sentiment analysis approach called Self-aware In-context Learning Correction (SILC). The first stage involves fine-tuning a base large language model to generate pseudo-labeled data specific to our task. In the second stage, we train a correction model using a GNN-based example retriever, which is informed by the pseudo-labeled data. This two-stage strategy has allowed us to achieve state-of-the-art performance on the newly constructed datasets, advancing the field of financial sentiment analysis. In a case study, we demonstrate the enhanced practical utility of our data and methods in monitoring the cryptocurrency market. Our datasets and code are available at https://***/NLP-Bin/SILC-EFSA. © 2025 Association for Computational Linguistics.

关键词： Decentralized finance

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Cooperative UAV Clustering for Fair Coverage of Communication Regions

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Intelligent and Converged Networks 2025年第1期6卷 1-19页

作者： Wu, Jiehong Gu, Linpeng Jia, Zhongli Wu, Jinsong School of Computer Science Shenyang Aerospace University Shenyang110136 China School of Artificial Intelligence Guilin University of Electronic Technology Guilin541004 China

Cooperative unmanned aerial vehicles (UAVs) cluster technology is considered a prospective solution for area coverage problems, enabling network access and emergency communications in remote areas. In this paper, we investigate how to control UAV cluster to achieve long-term and stable regional coverage while maintaining link connectivity and minimizing energy consumption, given the limited communication range and energy consumption of the UAVs themselves. To this end, we propose a cooperative UAV cluster strategy based on multi-agent deep reinforcement learning (MADRL) to achieve fair coverage of communication regions, which we call MADRL-based cooperative UAV cluster strategy (MADRL-CUCS). Our solution is a centralized training distributed execution architecture and defines a cluster structure for leader UAVs and follower UAVs. Under the premise of comprehensively considering the maximum coverage, we use a new energy efficiency function to minimize energy consumption, so as to extend the network lifetime of the UAVs cluster networks. The new fairness index and collision avoidance factor are used to ensure that the UAV cluster achieve effective and secure regional coverage. We adopt depth first search algorithm to check the link connectivity of the UAVs during the coverage process. Experiments show that the MADRL-CUCS algorithm outperforms the benchmark algorithm. © 2020 Tsinghua University Press.

关键词： Deep reinforcement learning

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Importance-Based Pruning for Genetic Programming Based Symbolic Regression 37th

Importance-Based Pruning for Genetic Programming Based Symb...

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37th Australasian Joint Conference on artificial intelligence, AJCAI 2024

作者： Rimas, Mohamad Chen, Qi Zhang, Mengjie Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9789819603503

Pruning is a major research field in neural networks, enhancing their efficiency and generalization. The field of pruning approaches in genetic programming (GP) is continually evolving, with researchers actively exploring new techniques and approaches to optimise the performance of GP models. This research introduces a novel pruning algorithm for Genetic Programming-based Symbolic Regression (GPSR). The proposed method employs a weighting mechanism to identify and filter out unimportant subtrees in each generation. To achieve this, the method arranges all subtrees linearly and assigns weights to each subtree and terminal. It then uses Ordinary Least Squares (OLS) to optimize these weights, enabling the identification of unimportant subtrees and terminals for effective pruning. The algorithm’s effectiveness was evaluated using ten regression datasets, including high-dimensional and complex feature sets. Furthermore, comparisons were made with two other algorithms to evaluate its performance. The results indicate that the proposed approach not only achieves better learning and generalisation performance but also generates smaller trees compared to standard GP, thereby improving interpretability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic Programming Ordinary Least Squares Pruning Regression Subtrees

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S3F2Net: Spatial-spectral-structural Feature Fusion Network for Hyperspectral Image and LiDAR Data Classification

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4801-4815页

作者： Wang, Xianghai Song, Liyang Feng, Yining Zhu, Junheng Liaoning Normal University School of Computer Science and Artificial Intelligence Dalian116029 China Liaoning Normal University School of Geography Dalian116029 China

The continuous development of Earth observation (EO) technology has significantly increased the availability of multi-sensor remote sensing (RS) data. The fusion of hyperspectral image (HSI) and light detection and ranging (LiDAR) data has become a research hotspot. Current mainstream convolutional neural networks (CNNs) excel at extracting local features from images but have limitations in modeling global information, which may affect the performance of classification tasks. In contrast, modern graph convolutional networks (GCNs) excel at capturing global information, particularly demonstrating significant advantages when processing RS images with irregular topological structures. By integrating these two frameworks, features can be fused from multiple perspectives, enabling a more comprehensive capture of multimodal data attributes and improving classification performance. The paper proposes a spatial-spectral-structural feature fusion network (S3F2Net) for HSI and LiDAR data classification. S3F2Net utilizes multiple architectures to extract rich features of multimodal data from different perspectives. On one hand, local spatial and spectral features of multimodal data are extracted using CNN, enhancing interactions among heterogeneous data through shared-weight convolution to achieve detailed representations of land cover. On the other hand, the global topological structure is learned using GCN, which models the spatial relationships between land cover types through graph structure constructed from LiDAR data, thereby enhancing the model's understanding of scene content. Furthermore, the dynamic node updating strategy within the GCN enhances the model's ability to identify representative nodes for specific land cover types while facilitating information aggregation among remote nodes, thereby strengthening adaptability to complex topological structures. By employing a multi-level information fusion strategy to integrate data representations from both global and l

关键词： Remote sensing

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DialogueMMT: Dialogue Scenes Understanding Enhanced Multi-modal Multi-task Tuning for Emotion Recognition in Conversations 31

DialogueMMT: Dialogue Scenes Understanding Enhanced Multi-mo...

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31st International Conference on Computational Linguistics, COLING 2025

作者： He, Chenyuan Zhu, Senbin Liu, Hongde Gao, Fei Jia, Yuxiang Zan, Hongying Peng, Min School of Computer and Artificial Intelligence Zhengzhou University China School of Computer Science Wuhan University China

ISBN: (纸本)9798891761964

Emotion recognition in conversations (ERC) has garnered significant attention from the research community. However, due to the complexity of visual scenes and dialogue contextual dependencies in conversations, previous ERC methods fail to handle emotional cues from both visual sources and discourse structures. Furthermore, existing state-of-the-art ERC models are trained and tested separately on each single ERC dataset, not verifying their effectiveness across multiple datasets simultaneously. To address these challenges, this paper proposes an innovative framework for ERC, called Dialogue Scenes Understanding Enhanced Multi-modal Multi-task Tuning (DialogueMMT). More concretely, a novel video-language connector is applied within the large vision-language model for capturing video features effectively. Additionally, we utilize multi-task instruction tuning with a unified ERC dataset to enhance the model's understanding of multimodal dialogue scenes and employ a chain-of-thought strategy to improve emotion classification performance. Extensive experimental results on three benchmark ERC datasets indicate that the proposed DialogueMMT framework consistently outperforms existing state-of-the-art approaches in terms of overall performance. Our code is available at https://***/he2720/DialogueMMT. © 2025 Association for Computational Linguistics.

关键词： Emotion Recognition

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Unsupervised Outlier Detection with Reinforced Noise Discriminator

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ACM Transactions on Intelligent Systems and Technology 2025年第2期16卷 1-26页

作者： Zhang, Zhongping Liu, Daoheng Zhu, Jinwei Wu, Youxi Hebei Key Laboratory of Computer Virtual Technology and System Integration School of Information Science and Engineering Yanshan University Qinhuangdao China School of Information Science and Engineering Yanshan University Qinhuangdao China School of Artificial Intelligence Hebei University of Technology Tianjin China

Outlier detection is one of the hot topics in the field of machine learning and data mining. At present, there are many kinds of outlier detection algorithms. The accuracies of traditional outlier detection algorithms are often affected by unique parameters, and an increase in the amount of data and the dimensions of the data can seriously affect their efficiency and effectiveness. Methods based on generative adversarial networks (GANs) can solve the above problems, but they are unacceptable since the model often collapses during the training period. In this article, to solve the problems of curse of dimensionality and model collapse, we propose a novel reinforced noise discriminator (RND) method for unsupervised outlier detection in tabular data. We consider outlier detection as a binary classification problem. Thus, we apply a learnable reinforced discriminator and generate a large number of potential outliers with a uniform distribution and potential outliers that are close to the original data that are used as a negative sample to train the discriminator, which learns the distribution of the original data to detect outliers. We empirically compare the proposed approach with ten state-of-the-art outlier detection methods on both synthetic and real-world tabular datasets. The experimental results show that RND outperforms its competitors in the majority of cases. The codes used to perform the experiments described in this article are available at https://***/urlhearts/r-n-d. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Generative adversarial networks

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GenWebNovel: A Genre-oriented Corpus of Entities in Chinese Web Novels 31

GenWebNovel: A Genre-oriented Corpus of Entities in Chinese ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhao, Hanjie Yan, Yuchen Zhu, Senbin Liu, Hongde Jia, Yuxiang Zan, Hongying Peng, Min School of Computer and Artificial Intelligence Zhengzhou University China School of Computer Science Wuhan University China

ISBN: (纸本)9798891761964

Entities are important to understanding literary works, which emphasize characters, plots and environment. The research on entity recognition, especially nested entity recognition in the literary domain is still insufficient partly due to insufficient annotated data. To address this issue, we construct the first Genre-oriented Corpus for Entity Recognition in Chinese Web Novels, namely GenWebNovel, comprising 400 chapters totaling 1,214,283 tokens under two genres, XuanHuan (Eastern Fantasy) and History. Based on the corpus, we analyze the distribution of different types of entities, including person, location, and organization. We also compare the nesting patterns of nested entities between GenWebNovel and the English corpus LitBank. Even though both belong to the literary domain, entities in different genres share few overlaps, making genre adaptation of NER (Named Entity Recognition) a hard problem. We propose a novel method that utilizes a pre-trained language model as an In-context learning example retriever to boost the performance of large language models. Our experiments show that this approach significantly enhances entity recognition, matching state-of-the-art (SOTA) models without requiring additional training data. Our code, dataset, and model are available at https://***/hjzhao73/GenWebNovel. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Network Coding for Efficient Molecular Data Transmission in Cancer Research

Network Coding for Efficient Molecular Data Transmission in ...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Alam, Sultan Yadav, Aniket Shahnawaz, Khan Mohammad Zaid Makhija, Piyush Jena, Birendra School of Computer Science and Engineering Vit Bhopal University Sehore466114 India School of Computer Science Engineering and Artificial Intelligence Vit Bhopal University Sehore466114 India

ISBN: (纸本)9798331527495

Efficient transmission of molecular data among labs is crucial for advancing cancer research and fostering collaboration. However, traditional data-sharing methods often cause delays, impeding research progress. This paper examines the role of network coding in improving the speed and efficiency of data transmission within cancer research. We analyze the limitations of current approaches, emphasizing the benefits of network coding as a transformative solution. The methodology of network coding is detailed, supported by a mathematical model demonstrating its effectiveness in optimizing data transfer processes. Furthermore, practical challenges and potential solutions for implementing network coding in real-world scenarios are discussed. By addressing these barriers, this paper highlights the potential of network coding to significantly accelerate data sharing, thereby enhancing the pace of discovery and innovation in cancer research. © 2025 IEEE.

关键词： cancer research data transmission efficiency Network coding

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