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第41届中国控制会议

作者： Zhenfeng He Yin Zhang College of Computer and Data Science Fuzhou University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Subgroup discovery(SD) identifies disproportionally distributed subsets from a dataset according to a target *** features are often discretized before SD to avoid generating too many interval based patterns and aggravating the "pattern flooding" ***,early discretization greatly reduces the quality of *** addition of a few features,especially numerical features,often sharply prolongs the running time of SD,so removing irrelevant features may be a better ***,a recently proposed non-discretization SD approach for numerical features,uses an empirical method to select a subset of ***,the method ignores the labelling information,so it can not remove irrelevant features *** paper analyses Relief based feature selection for SD,and suggests using interval based local subgroups to evaluate the discrimination ability of a *** presents ReliefSD,a novel feature selection method for SD by updating *** interesting subgroups have many positive instances,ReliefSD only selects positive ***,for each feature ReliefSD constructs a single feature based local subgroup whose boundary is defined by the randomly selected instance and its neighbouring positive *** evaluating the purity of the subgroups,ReliefSD iteratively estimates the importance of *** results on 10 UCI datasets suggest ReliefSD is the best in selecting feature subsets for FSSD when compared with the empirical method and ReliefF.

关键词： Subgroup discovery Feature selection Relief Numerical feature

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Enhancing Progressive Diagnosis Prediction in Healthcare with Continuous Normalizing Flows 24

Enhancing Progressive Diagnosis Prediction in Healthcare wit...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Tan, Yanchao Zhang, Hengyu Zhou, Zihao Ma, Guofang Wang, Fan Liu, Weiming Liao, Xinting Hertzberg, Vicki S. Yang, Carl College of Computer and Data Science Fuzhou University China College of Computer Science Zhejiang University China School of Computer Science Zhejiang Gongshang University China Nell Hodgson Woodruff School of Nursing Emory University United States Department of Computer Science Emory University United States

ISBN: (纸本)9798400701726

Progressive diagnosis prediction in healthcare is a promising yet challenging task. Existing studies usually assume a pre-defined prior for generating patient distributions (e.g., Gaussian). However, the inferred approximate posterior can deviate from the real-world distribution, which further affects the modeling of continuous disease progression over time. To alleviate such inference bias, we propose an enhanced progressive diagnostic prediction model (i.e., ProCNF), which integrates continuous normalizing flows (CNF) and neural ordinary differential equations (ODEs) to achieve more accurate approximations of patient health trajectories while capturing the continuity underlying disease progression. We first learn patient embeddings with CNF to construct a complex posterior approximation of patient distributions. Then, we devise a CNF-enhanced neural ODE module for progressive diagnostic prediction, which aims to improve the modeling of disease progression for individual patients. Extensive experiments on two real-world longitudinal EHR datasets show significant performance gains brought by our method over state-of-the-art competitors. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Diagnosis

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A Machine Learning Approach to Model Counting 36

A Machine Learning Approach to Model Counting

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Dalla, Marco Visentin, Andrea O'Sullivan, Barry SFI Centre for Research Training in AI School of Computer Science & IT University College Cork Ireland Insight Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

ISBN: (纸本)9798331527235

Model counting (#SAT) is the problem of computing the number of satisfying assignments for a given Boolean formula. It has a significant theoretical and practical interest. Tackling it can be challenging since the number of potential solution grows exponentially with the number of variables. Due to the inherent complexity of the problem, approaches to approximate model counting have been developed as a practical alternative. These methods extract the number of solutions within user-specified tolerance and confidence levels and in a fraction of the time required by exact model counters. However, even these methods require extensive computations, restricting their applicability to relatively small instances. In this paper, we propose a new approximate machine learning model counter that overcome this limitation. Predicting the number of solutions can be seen as a regression problem. We deploy an array of machine learning techniques trained to infer the approximate number of solutions based on statistical features extracted from a SAT propositional formula. Extensive numerical experiments performed on synthetic crafted and benchmark datasets show that learning approaches can provide a good approximation of the number of solutions with a much lower computational time and resource cost than the state-of-the-art approximate and exact model counters, making it possible to approximate the model count of instances previously out of reach. We then investigated the structural factors that lead to a high model count using AI explainability approaches. © 2024 IEEE.

关键词： Adversarial machine learning

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Integrating Multimodal AI Models for Enhanced Oral Cancer Detection and Diagnosis 2

Integrating Multimodal AI Models for Enhanced Oral Cancer De...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Shantha Kumari, K. Udhya shree, S. Calarany, C. Department of Data Science and Business Systems SRM Institue of Science and Technology Kattankulathur India Department of Computer Science and Engineering Rajiv Gandhi College of Engineering and Technology Pondicherry India Department of Computer Science and Engineering Tagore Government Arts and Science College Pondicherry India

ISBN: (纸本)9798331504960

Oral cancer is a major global health issue marked by high morbidity and mortality rates, primarily due to late-stage diagnosis. This work proposes the Advanced AI models that use multimodal data for early detection and diagnosis of oral cancer. Currently, traditional machine learning algorithms coupled with deep learning algorithms such as Convolutional neural networks (CNNs), focus on image-based analysis for detecting cancer. These works are delimited with minimal robustness and input data efficacy. Through the proposed multimodal AI model, histopathological images and clinical notes of the oral cancer patients are combinedly analyzed by which diagnostic accuracy is enhanced. This framework integrates CNNs for image analysis and Transformer-based models for text analysis, with a sophisticated fusion layer for data integration. Enhanced preprocessing techniques and performance optimization through Vision Transformers (ViTs) and BERT models significantly improve sensitivity, specificity, and F1-score. The multimodal AI model achieved a sensitivity of 0.90, specificity of 0.85, and an F1-score of 0.89, outperforming previous models like SVMs, RF, and CNNs. This indicates a high accuracy in identifying both positive and negative cases, ensuring reliable diagnoses. The results demonstrate a substantial improvement over traditional methods, that lays foundations for early diagnosis. © 2024 IEEE.

关键词： Lung cancer

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Gloss-Free Sign Language Translation using Sign2gpt-Next Technique

Gloss-Free Sign Language Translation using Sign2gpt-Next Tec...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Babisha, A. Umarani Srikanth, G. Angel Kiruba, D.R. Sundar, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510299

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing. © 2024 IEEE.

关键词： Audition

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MSTMN: a novel meta-attention-based multi-task spatiotemporal network for traffic flow prediction

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Neural Computing and Applications 2024年第36期36卷 23195-23222页

作者： Zhou, Qianqian Chen, Nan College of Computer and Data Science Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China Fuzhou University Fuzhou350108 China

Traffic flow prediction in a given area is often influenced by the interactions with complex dependencies among multiple areas. By far, it remains unexplored to obtain interactive information. To address the issue, MSTMN was proposed, a multi-task learning framework that jointly learns interactive information and spatiotemporal dependencies across tasks. MSTMN consists of a node network, an edge network, and a prediction network. The node network and edge network were trained using the proposed meta-fully convolutional blocks to extract interactive features and generalizable features. The prediction network employed the meta-gated fusion and the recalibration block to both integrate these learned features and external factors. This ensures that the features capture optimal interaction information during the training phase. The proposed model was validated on two real-world movement-on-demand traffic datasets collected in Xiamen, China. Experimental results showed that MSTMN improved performance by 38.42% and 31.77% for one-step and multi-step prediction compared to the state-of-the-art baseline. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Multi-task learning

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Traffic flow prediction based on graph convolutional networks with a parallel attention network and stacked gate recurrent units

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Multimedia Tools and Applications 2025年第15期84卷 14329-14358页

作者： Xia, Dawen Ao, Yuce Wei, Xiaoduo Li, Yunsong Chen, Yan Hu, Yang Li, Yantao Li, Huaqing College of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China Department of Automotive Engineering Guizhou Traffic Technician and Transportation College Guiyang550008 China College of Computer Science Chongqing University Chongqing400044 China College of Electronic and Information Engineering Southwest University Chongqing400715 China

Accurate traffic flow prediction is essential to address traffic issues and assist traffic managers make informed decisions in intelligent transportation systems. Extracting potential features from traffic data is challenging due to the complex topology of urban road networks and the time-varying traffic flow. To capture the global spatiotemporal characteristics of traffic flow, we propose a novel model based on graph convolutional networks with a parallel attention network and stacked gated recurrent units (PAGCN-SGRU). First, the parallel attention (PA) network enhances the feature representation of global traffic road nodes and road segments. Then, the graph convolutional networks (GCN) are designed to extract spatial characteristics. Next, the stacked gate recurrent units (SGRU) are employed to capture temporal features. Finally, PAGCN-SGRU discovers global spatiotemporal features for traffic flow prediction. The experimental results demonstrate that the accuracy of PAGCN-SGRU under the SZ-dataset is improved by 9.76%, 72.54%, 5.76%, 16.07%, 2.07%, 1.82%, 3.35%, and 6.59%, respectively, compared to that of HA, ARIMA, SVR, GCN, T-GCN, A3T-GCN, ST-GCN, and DCRNN. In the Los-dataset, the accuracy values increase by 7.11%, 8.94%, 6.66%, 7.77%, 3.43%, 2.45%, 2.28%, and 4.09%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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A novel framework integrating ensemble transfer learning and Ant Colony Optimization for Knee Osteoarthritis severity classification

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Multimedia Tools and Applications 2024年第39期83卷 86923-86954页

作者： Malik, Isra Yasmin, Mussarat Iqbal, Ahmed Raza, Mudassar Chun, Chang-Jae Al-antari, Mugahed A. Department of Computer Science COMSATS University Islamabad Wah Campus Wah Cantt44000 Pakistan Department of Computer Science Sir Syed Case Institute of Technology Islamabad45230 Pakistan Department of Computer Science HITEC University Taxila Taxila47080 Pakistan Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul05006 Korea Republic of

Knee Osteoarthritis (KOA), the most prevalent joint disease, significantly impacts elderly mobility due to progressive cartilage degeneration. Early prediction is crucial for preventing disease progression and guiding effective treatment plans. This paper proposes an EnsembleTL-ACO, fully automated, computer-aided diagnosis (CAD) system for accurate and rapid KOA severity grading. The proposed CAD system leverages an ensemble transfer learning strategy to extract robust deep features by fusing multiple deep learning models. It combines features from two consecutive AI models: (1) AlexNet for implicit class-wise deep feature extraction from preprocessed data, and (2) a custom IsrNet for further feature depth. Unsupervised k-means clustering based on PCA dimensionality reduction decomposes each class into subgroups, further refining features. Finally, Ant Colony Optimization (ACO) selects the most informative features. Evaluated on the Osteoarthritis Initiative (OAI) dataset, the proposed system achieves high accuracy in classifying the five KOA severity grades. With 1000 optimized features, it reaches average overall accuracies of 89.89% and 85.44% using Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers, respectively. This surpasses recent deep learning methods, demonstrating significant improvement. This novel CAD system presents a promising solution for practical applications, offering an accurate AI-powered tool for KOA diagnosis and management. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Ant colony optimization

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Context-Aware Quaternion Embedding for Knowledge Graph Completion 17th

Context-Aware Quaternion Embedding for Knowledge Graph Comp...

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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Wang, Jingbin Yang, Xinyi Ke, Xifan Wu, Renfei Guo, Kun College of Computer and Data Science Fuzhou University Fujian350108 China

ISBN: (纸本)9789819923847

In this paper, we study the learning representation of entities and relationships in the link prediction task of knowledge graph. The knowledge graph is a collection of factual triples, but most of them are incomplete. At present, some models use complex rotation to model triples, and obtain more effective results. However, such models generally use specific structures to learn the representation of entities or relationships, and do not make full use of the context information of entities and relations. In addition, in order to achieve high performance, models often need larger embedding dimensions and more epoches, which will cause large time and space cost. To systematically tackle these problems, we develop a novel knowledge graph embedding method, named CAQuatE. We propose two concepts to select valuable context information, then design a context information encoder to enhance the original embedding, and finally use quaternion multiplication to model triples. The experiment and results on two common benchmark datasets show that CAQuatE can significantly outperform the existing state-of-the-art model in the knowledge graph completion task by obtaining lower dimensional representation vectors with fewer epoches and no additional parameters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Knowledge graph

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Artificial Intelligence Methods Applied to Catalytic Cracking Processes

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Big data Mining and Analytics 2023年第3期6卷 361-380页

作者： Fan Yang Mao Xu Wenqiang Lei Jiancheng Lv College of Computer Science Sichuan UniversityChengdu 610041China Algorithm and Big Data Center New Hope Liuhe Co LtdChengdu 610000China. Data Intelligence Lab New Hope Liuhe Co LtdChengdu 610000China. College of Computer Science Sichuan UniversityChengdu 610041China.

Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated *** yield analysis,flue gas desulfurization prediction,and abnormal condition warning are several key research directions in *** paper will sort out the relevant research results of the existing Artificial Intelligence(AI)algorithms applied to the analysis and optimization of catalytic cracking processes,with a view to providing help for the follow-up *** with the traditional mathematical mechanism method,the AI method can effectively solve the difficulties in FCC process modeling,such as high-dimensional,nonlinear,strong correlation,and large *** methods applied in product yield analysis build models based on massive *** fitting the functional relationship between operating variables and products,the excessive simplification of mechanism model can be avoided,resulting in high model *** methods applied in flue gas desulfurization can be usually divided into two stages:modeling and *** the modeling stage,data-driven methods are often used to build the system model or rule base;In the optimization stage,heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule *** methods,including data-driven and knowledge-driven algorithms,are widely used in the abnormal condition ***-driven methods have advantages in interpretability and generalization,but disadvantages in construction difficulty and prediction *** the data-driven methods are just the ***,some studies combine these two methods to obtain better results.

关键词： neural networks intelligent optimization algorithm catalytic cracking lumped kinetics

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