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5th International Seminar on artificial intelligence, Networking and Information technology, AINIT 2024

作者： Ma, Zewei Wu, Feier School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China

ISBN: (纸本)9798350385557

This paper presents a method for pulmonary nodule detection in CT images based on an improved YOLOv8 framework. Against the backdrop of current deep learning technologies, automated detection and segmentation of medical images are crucial for enhancing diagnostic speed and accuracy. We combine the YOLOv8 object detection framework with Receptive Fields Block (RFB) modules to enhance feature extraction capabilities. Additionally, we introduce Inner-IoU as a loss function to optimize the detector's localization accuracy. Experimental results demonstrate significant performance improvement in pulmonary nodule detection tasks on CT images, thereby providing substantial support for the automation and intelligence of medical image processing. © 2024 IEEE.

关键词： Object detection

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Temporal Knowledge Extrapolation Based on Fine-Grained Tensor Graph Attention Network for Responsible AI

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2025年第2期6卷 448-457页

作者： Yang, Jing Huang, Chengxuan Yang, Xiangli Yang, Laurence T. Gao, Yuan Liu, Cong Zhengzhou University School of Computer Science and Artificial Intelligence Zhengzhou570001 China Duke University Graduate School Department of Computer Science DurhamNC27708 United States Dublin Institute of Technology School of Tourism and Hospitality Management Dublin9P3C+W7 Ireland Hainan University School of Computer Science and Technology Haikou570228 China St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China

Knowledge guidance is crucial for bridging the gap between high-level artificial intelligence (AI) ethics principles and the practical implementation of responsible AI systems. Diverging from static knowledge inference and temporal interpolation, the task of temporal knowledge extrapolation, which entails predicting future facts based on the evolution of historical facts, poses formidable challenges that remain largely unsolved. In existing temporal extrapolation methods, the structural learning of concurrent facts is primarily addressed by utilizing relation-aware graph neural networks. However, the temporal validity of facts leads to the sparsity of temporal subgraphs, where inherent coarse-level learning mechanisms hinder the capture of nuanced and scarce local semantics. Thus, this article proposes a fine-grained tensor graph attention network (F-GAT) that promotes representation learning of concurrent facts on sparse subgraphs by effectively distinguishing the significance of entities and relations within triplets and acquiring self-attention across diverse triplet contexts. Based on F-GAT, this article proposes a recurrent evolution network [recurrent evolution graph attention network (RE-GAT)] for temporal knowledge extrapolation. RE-GAT employed gated recurrent units to iteratively capture the sequential patterns between adjacent temporal facts, while simultaneously learning enriched embedding through the dual influence of historical factors and structural factors. The competitive results achieved by comparing the entity and relationship prediction performance of the proposed RE-GAT model with advanced methods on six public benchmarks demonstrate the effectiveness of RE-GAT for temporal extrapolation. © 2020 IEEE.

关键词： Extrapolation

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LAMNet: A lightweight attention mechanism dehazing network 3

LAMNet: A lightweight attention mechanism dehazing network

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3rd International Conference on Optics and Machine Vision, ICOMV 2024

作者： Xiong, Qian School of Computer Science and Artificial Intelligence Wuhan University of Technology Hubei Wuhan430070 China

ISBN: (纸本)9781510680319

Image dehazing is an important task in computer vision, as it can transform unclear images taken in haze weather into clear ones. Some deep-learning based dehazing models struggle to balance the relationship between the convolutional kernel receptive field and the number of parameters when improving model performance. We propose dilated convolutional kernels with a size of 13 to balance between the receptive field and the number of parameters. Furthermore, some deep-learning-based dehazing methods employ a significant number of channel attention mechanisms and spatial attention mechanisms, both of which can share a common fully connected layer. Our proposed fused attention mechanism incorporates the effects of both channel attention and spatial attention mechanisms, while reducing the number of parameters. Combining these two modules, we introduce a lightweight attention mechanism dehazing network (LAMNet). This network demonstrates effective dehazing results while maintaining a relatively low number of parameters. © 2024 SPIE ·

关键词： computer vision

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Phishing Detection Model Integrating URL Characters and HTML Word Semantic Deep Features 4

Phishing Detection Model Integrating URL Characters and HTML...

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4th International Conference on Communication technology and Information technology, ICCTIT 2024

作者： Meng, Lihui Ma, Zhujuan Zhu, Erzhou Anhui University School of Computer Science and Technology Hefei230601 China Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei230088 China

ISBN: (纸本)9798331528973

Deep learning methods, known for their powerful feature learning and classification capabilities, are widely used in phishing detection. To improve accuracy, this study proposes DPMLF (Deep Learning Phishing Detection Model with Multi-Level Features), which integrates URL character-level and HTML word-level semantic features. DPMLF utilizes character embeddings and parallel convolutional kernels to precisely extract local URL features. For HTML text, it employs word-level embeddings and stacked convolutional layers with dense connections to capture both local and long-range text information. The fully connected layer then fuses these features into a multi-level, fine-grained feature vector for classification. The results of experiments conducted on two public datasets with different scales show that DPMLF is accurate in phishing attack detection. © 2024 IEEE.

关键词： Phishing

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Hyperspectral Image Classification Based on A Multi-Scale Weighted Kernel Network

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Chinese Journal of Electronics 2022年第5期31卷 832-843页

作者： SUN Le XU Bin LU Zhenyu School of Computer Science Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing University of Information Science and Technology

Recently, many deep learning models have shown excellent performance in hyperspectral image(HSI) classification. Among them, networks with multiple convolution kernels of different sizes have been proved to achieve richer receptive fields and extract more representative features than those with a single convolution kernel. However, in most networks, different-sized convolution kernels are usually used directly on multibranch structures, and the image features extracted from them are fused directly and simply. In this paper, to fully and adaptively explore the multiscale information in both spectral and spatial domains of HSI, a novel multi-scale weighted kernel network(MSWKNet) based on an adaptive receptive field is proposed. First, the original HSI cubic patches are transformed to the input features by combining the principal component analysis and one-dimensional spectral convolution. Then, a three-branch network with different convolution kernels is designed to convolve the input features, and adaptively adjust the size of the receptive field through the attention mechanism of each branch. Finally, the features extracted from each branch are fused together for the task of *** on three well-known hyperspectral data sets show that MSWKNet outperforms many deep learning networks in HSI classification.

关键词： multibranch structures image features convolution kernels multiple convolution kernels adaptive receptive field image classification feature extraction HSI classification single convolution kernel convolutional neural nets hyperspectral image classification representative features hyperspectral imaging geophysical image processing deep learning (artificial intelligence) multiscale weighted kernel network deep learning networks hyperspectral data sets three-branch network HSI cubic patches principal component analysis 1D spectral convolution different-sized convolution kernels

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Complex Network Community Discovery Algorithms Based on Node Similarity and Network Embeddings 1

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13th International Conference on Frontier Computing, FC 2023

作者： Zhang, Zhixun Wang, Juan Xu, Yanqiang School of Computer Science and Artificial Intelligence Lanzhou Institute of Technology Gansu Lanzhou730050 China

ISBN: (数字)9789819994168

ISBN: (纸本)9789819994151

This paper briefly analyzes the summary of network embedding and node similarity, emphasizes the discovery, and takes numerical simulation as the entry point to study the evaluation index, real network and artificial network, which is expected to provide reference for relevant personnel. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network embeddings

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Fixed-Time Stability in Stochastic Nonlinear System 2

Fixed-Time Stability in Stochastic Nonlinear System

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2nd International Conference on artificial intelligence and Automation Control, AIAC 2024

作者： Liu, Bin School of Computer and Artificial Intelligence Yinchuan Institute of Science and Technology Yinchuan Ningxia750001 China

ISBN: (纸本)9798331529147

This article examines fixed-time stability (FTS) in stochastic nonlinear systems. A new theorem is presented for accurately estimating settling time, and its estimate is theoretically compared with the existing timed stability theorem. The findings indicate that our estimate surpasses the accuracy of previous results. © 2024 IEEE.

关键词： fixed-time stability settling time stochastic nonlinear system

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Surrogate Modeling in Efficient Evolutionary Multiobjective Clustering Algorithm

Surrogate Modeling in Efficient Evolutionary Multiobjective ...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Sun, Nan Zhu, Shuwei School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China

ISBN: (纸本)9798400704956

Evolutionary multiobjective clustering (MOC) algorithms have shown powerful capability in generating a set of clusterings for decision making when the number of clusters k is not given. However, the quantities of data in many fields are increasing dramatically, which consequently challenges existing MOC methods in the computational burden. In this study, we explore surrogate-assisted techniques to reduce the cost of function evaluations, so as to develop an efficient framework of evolutionary MOC. Designed around a graph-based solution encoding based on the trained seed nodes of the dataset, the cluster representation is not only flexible and unbiased, but also keeps a fixed-length reduced decision space that is independent of different k. During optimization, various widely-used surrogate models and a novel surrogate model based on graph convolutional neural-network (GCN) is adopted to predict the clustering objective functions. Hence, this work aims to fill the important research gap in surrogate modeling of efficient MOC algorithm. We compare the performance of different surrogate models on several datasets, showing encouraging results, especially the newly-developed GCN-based model. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Conformal Prediction Techniques for Electricity Price Forecasting 9th

Conformal Prediction Techniques for Electricity Price Forec...

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9th International Workshop on Advanced Analytics and Learning on Temporal data, AALTD 2024

作者： O’Connor, Ciaran Prestwich, Steven Visentin, Andrea SFI CRT in Artificial Intelligence School of Computer Science & IT University College Cork Cork Ireland Insight Centre for Data Analytics School of Computer Science & IT University College Cork Cork Ireland

ISBN: (纸本)9783031770654

Integrating the erratic production of renewable energy into the electricity grid poses numerous challenges. One approach to stabilising market prices and reducing energy losses due to curtailments is the deployment of batteries. Efficient electricity arbitrage is crucial to make investments in storage systems financially viable;trading solutions to achieve this rely on price forecasting techniques. This study delves into the application of Conformal Prediction (CP) techniques, including Ensemble Batch Prediction Intervals (EnbPI) and Sequential Predictive Conformal Inference for Time Series (SPCI), for generating probabilistic forecasts in the Irish electricity market. Recent advancements in CP have addressed temporal considerations inherent in time series forecasting, eliminating the need for exchangeability assumptions. Our study demonstrates that despite potential efficiency trade-offs, CP methods consistently yield precise and reliable prediction intervals, ensuring comprehensive coverage. We assess the impact of CP on the financial results of a simulated trading algorithm. Monetary outcomes achieved with EnbPI and SPCI outperform those of both split CP and traditional quantile regression models, highlighting the practical superiority of CP in electricity price forecasting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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X-ray Flaw Detection of Welds Based on Slice Inference 8

X-ray Flaw Detection of Welds Based on Slice Inference

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Wang, Rui Gao, Shaoze Wang, Chen Zhou, Peng Hebei University of Technology School of Artificial Intelligence and Data Science Tianjin China

ISBN: (纸本)9798350302455

Aiming at the problems of high detection difficulty and low recognition rate due to the large length-to-width ratio of the weld image and complex defect imaging, this paper proposes a YOLO-SD model with a slight increase in the number of parameters. The model adds a convolutional attention mechanism module CBAM(Convolutional Block Attention Module) to the YOLOv5s (You Only Look Once) backbone network, which improves the accuracy of the model by enhancing effective features and suppressing invalid features;for large-size images with a fixed step size Slice the image, perform detection on the smaller image after the image is taken, and merge the prediction results on the image after the full image detection is completed. The experimental results show that the mAP value of the improved detection model in this paper is increased from 89.73% to 93.46%,and the YOLO-SD model has a stronger ability to identify weld defects. This method has higher accuracy and recognition rate under the premise of controlling the cost of weld defect detection. © 2023 IEEE.

关键词： Welds

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