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Lin Lin

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21 papers

1 author row

EAAI Journal 2026 Journal Article

A novel fractional order partial grey prediction model with conformable fractional derivative and its application to energy prediction

Qiong Wang
Lin Lin
Guan Wang
Wei Chen
Guoping Zhan

Precise regional energy output prediction is key to optimizing the energy structure, promoting clean energy, lessening fossil fuel reliance, and providing an important reference for formulating energy policies and achieving sustainable development. This paper constructs a fractional order partial grey model incorporating a control matrix by combining a partial differential equation, which predicts energy production. First, the new model effectively reduces the random fluctuations in the data by introducing a fractional order accumulation operator, and enhances the ability to handle nonlinear data by leveraging conformable fractional derivatives. At the same time, a control matrix containing exponential and trigonometric functions is used to dynamically adjust parameters, allowing the model to better adapt to various oscillatory data, thereby improving its generalizability. Additionally, the model’s more accurate time response function is obtained through the characteristic curve method, and the optimal parameters of the model are determined using the particle swarm optimization algorithm. Finally, this paper evaluates the effectiveness of the new model from different angles using seven evaluation indicators by simulating and predicting the output of raw coal, gasoline, coalbed methane, and natural gas in nine provinces across China. The results show that the performance of the new model is superior to that of the comparison models, demonstrating its efficacy in forecasting energy production. Ultimately, this novel model is employed to project and assess crude oil production in Jiangsu Province, offering theoretical insights and technical assistance for energy management, economic planning, and environmental conservation.

AAAI Conference 2026 Conference Paper

K-ProtoDiff: Key Prototypes-Guided Diffusion for Time Series Generation

Yuhang Duan
Lin Lin
Xiaoshuai Wu

Time series generation is essential for advancing data-driven modeling and decision-making across a wide range of domains. However, existing approaches primarily focus on global patterns, often failing to capture local key patterns such as abrupt changes or anomalies. These key patterns are crucial for interpretability and operational decision making, as they frequently represent intervention points with significant real-world impact. To bridge this gap, we propose Key Prototypes-Guided Diffusion (K-ProtoDiff) for time series generation, a new model that learns the global data distribution while preserving localized key patterns critical for temporal dynamics. In K-ProtoDiff, we first derive time series prototype representations through adaptive self-supervised learning. Then, a key prototype assignment module is used to extract prototype weights, forming key prototype-aware representations that serve as conditional guidance for generation. During sampling, to further enhance the fidelity of key patterns during the denoising process, we propose Reflection Sampling (R-Sampling), a step-wise refinement strategy that encourages the reverse trajectory to better align with key prototype constraints. Experiments on nine real-world datasets demonstrate that K-ProtoDiff significantly outperforms state-of-the-art baselines in key pattern retention, achieving an average 77.6% improvement in key pattern preservation.

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JBHI Journal 2025 Journal Article

A Deep Learning-Based Approach for the Diagnostic of Brucellar Spondylitis in Magnetic Resonance Images

Dan Shao
Jinquan Wei
Binyang Wang
Zhijun Wang
Pengying Niu
Lvlin Yang
Guangzhao Zhang
Pu Chen

Brucellar spondylitis (BS), a prevalent zoonotic disease caused by Brucella, poses a significant global health threat. Accurate and timely diagnosis of BS is crucial for effective treatment; however, no specialized deep learning model has been developed for detecting BS in MR images. In this study, we proposed Brucella Spondylitis MRI Diagnosis Network (BSMRINet), a fully automated diagnostic framework designed for the detection of BS from T2-weighted (T2W) MR images. The model was developed and validated using 582 cohorts collected from four hospitals between January 2018 and August 2023. The BSMRINet architecture comprised two key modules. The vertebral body lesion detection module was designed to detect BS in intact vertebral bodies by integrating a corner detection algorithm with a ResNet-based deep learning model. This module provided accurate identification and localization of potential lesions of Brucella and calculated intervertebral disc height (DH) values. The spine lesion detection module was specifically designed to detect BS in damaged vertebral bodies by utilizing a DenseNet architecture with modified squeeze-and-excitation (scSE) networks. This module further evaluated paravertebral injuries, including abscess formation, soft tissue swelling, and joint involvement. BSMRINet demonstrated strong robustness and generalization across both internal and external validation phases. Additionally, it outperformed two radiologists with 10 to 15 years of experience in diagnosing spinal MR images. The results suggested that BSMRINet can assist in the diagnostic process of BS and enhance the diagnostic capabilities of radiologists.

EAAI Journal 2025 Journal Article

An adaptive integrated learning-based virtual sensing framework for temperature prediction in aircraft brake monitoring engineering

Lin Lin
Yin Chen
Song Fu
Hao Zhang
Jinlei Wu

In commercial aviation, effective deceleration relies on accurate brake temperature trends. Traditional thermocouple sensors exhibit discontinuous responses, while machine learning-based methods often struggle with abnormal fluctuations due to high-dimensional signal overlap during low-speed braking. This study proposes an Adaptive Thermal Damping Integrated (ATDI) virtual sensor based on integrated learning to enhance brake temperature monitoring accuracy and address these challenges. A novel feature selection method is proposed that includes extracting baseline variables from five brake-related physical models and identifying significant latent variables using Shapley values, which are calculated by integrating three correlation indices based on linear, nonlinear, and rank consistency differences from aircraft flight records. The ATDI framework employs a time-lagged loss function and self-attention mechanism for dynamic weight assignment in the feature space, capturing critical temporal correlations to determine the thermal trend. A digital approach, adaptive to aircraft kinetic energy and called virtual thermal damping, is incorporated at the end of the prediction to mitigate fluctuations and ensure compliance with thermal conduction principles in braking systems. Experimental results demonstrate that the ATDI framework outperforms comparison networks in temperature value continuity and anomaly fluctuation suppression, especially with Long Short-Term Memory (LSTM) as the meta-learner, across three evaluation metrics. Additionally, application strategies for the ATDI framework in aircraft operation and maintenance are proposed.

EAAI Journal 2025 Journal Article

Deep reinforcement learning for optimization of spiral shaft design in shield machine

Lin Lin
Lingyu Yue
Song Fu
Dan Liu
Yancheng Lv
Yikun Liu
Sihao Zhang
Shiwei Suo

The increasing demand for highly customized shield machines imposes greater efficiency and accuracy requirements on the optimization. To address the inefficiencies and susceptibility to local optima in existing structural design methods, a novel approach to construct a simulation analysis surrogate model for creating an environment for the optimization of spiral shaft is introduced. It also improves the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize the structural parameters. In the surrogate model, a Goal-oriented autoencoder (GOAE)-classifier model is employed to discriminate the feasibility of samples, and a hybrid surrogate model, utilizing a Self-attention Artificial Neural Network (Self-attention ANN) weight allocation mechanism, makes precise predictions for feasible samples. This model automatically assigns adaptive weights to sub-surrogate models. Within the DDPG framework, a novel serial-parallel hybrid structure for the Actor network is proposed, harnessing the specialized feature representation capabilities of multiple networks to enhance optimization policy accuracy. Additionally, an experience replay filtering mechanism based on sample similarity is introduced to ensure sample diversity and boost the performance of the optimization policy. A simulation analysis surrogate model is constructed on a generated dataset, and the improved DDPG algorithm is leveraged for the optimization of spiral shafts based on this surrogate model. Experimental results demonstrate that the constructed simulation analysis surrogate model facilitates rapid and precise analysis of spiral shaft, while the improved DDPG algorithm successfully optimizes spiral shaft structural parameters, ultimately improving spiral shaft performance.

JBHI Journal 2025 Journal Article

Feature Separation in Diffuse Lung Disease Image Classification by Using Evolutionary Algorithm-Based NAS

Qing Zhang
Dan Shao
Lin Lin
Guoliang Gong
Rui Xu
Shoji Kido
HongWei Cui

In the field of diagnosing lung diseases, the application of neural networks (NNs) in image classification exhibits significant potential. However, NNs are considered “black boxes, ” making it difficult to discern their decision-making processes, thereby leading to skepticism and concern regarding NNs. This compromises model reliability and hampers intelligent medicine's development. To tackle this issue, we introduce the Evolutionary Neural Architecture Search (EvoNAS). In image classification tasks, EvoNAS initially utilizes an Evolutionary Algorithm to explore various Convolutional Neural Networks, ultimately yielding an optimized network that excels at separating between redundant texture features and the most discriminative ones. Retaining the most discriminative features improves classification accuracy, particularly in distinguishing similar features. This approach illuminates the intrinsic mechanics of classification, thereby enhancing the accuracy of the results. Subsequently, we incorporate a Differential Evolution algorithm based on distribution estimation, significantly enhancing search efficiency. Employing visualization techniques, we demonstrate the effectiveness of EvoNAS, endowing the model with interpretability. Finally, we conduct experiments on the diffuse lung disease texture dataset using EvoNAS. Compared to the original network, the classification accuracy increases by 0. 56%. Moreover, our EvoNAS approach demonstrates significant advantages over existing methods in the same dataset.

JBHI Journal 2025 Journal Article

Global Habitat Analysis with Multi-graph Fusion Framework of Postoperative MRI for Predicting Radiotherapy Treatment Response in Glioma Patients

Yixin Wang
Lin Lin
Zongtao Hu
Hongzhi Wang
Qiupu Chen

Traditional methods for predicting treatment response often rely on readily available clinical factors. However, these methods often lack the granularity to capture the complex interplay between tumor heterogeneity and treatment efficacy. A Multi-graph Fusion (MGF) model that uses habitat subregion-derived radiomic features may help predicting the response to radiotherapy in glioma patients. Firstly, three structural and three physiological habitat regions were delineated using multi-parametric magnetic resonance imaging sequences. Then radiomic features derived from these habitat subregions were used to construct MGF model, which were trained on different combinations of habitat subregions. Each view corresponded to a graph constructed from a specific tumor habitat subregion. Lastly, proposed multi-view fusion module was employed to interpret critical views and interactions for predicting treatment response, while GNNExplainer was used to elucidate the contributions of each view. The MGF model incorporating all habitats achieved the highest area under the curve values of 0. 848 (95% CI: 0. 832–0. 863) for the training cohort and 0. 792 (95% CI: 0. 767–0. 818) for the validation cohort in predicting treatment response. The attention values indicated that physiological habitat 3 held the highest significance. The GNNExplainer revealed key nodes and radiomic features in each view. The MGF model utilizing all habitats-derived radiomics demonstrated the best performance in predicting treatment response. The combination of multi-view fusion module and GNNExplainer enables the framework to capture complex contextual information across six habitat subregions and provides interpretability regarding the factors influencing treatment response predictions.

AAAI Conference 2025 System Paper

MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs

Tianyang Zhang
Zhuoxuan Jiang
Haotian Zhang
Lin Lin
Shaohua Zhang

We propose a novel system, MathMistake Checker, designed to automate step-by-step mistake finding in mathematical problems with lengthy answers through a two-stage process. The system aims to simplify grading, increase efficiency, and enhance learning experiences from a pedagogical perspective. It integrates advanced technologies, including computer vision and the chain-of-thought capabilities of the latest large language models (LLMs). Our system supports open-ended grading without reference answers and promotes personalized learning by providing targeted feedback. We demonstrate its effectiveness across various types of math problems, such as calculation and word problems.

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JBHI Journal 2025 Journal Article

SecProGNN: Predicting Bronchoalveolar Lavage Fluid Secreted Protein Using Graph Neural Network

Dan Shao
Guangzhao Zhang
Lin Lin
Yucong Xiong
Kai He
Liyan Sun

Bronchoalveolar lavage fluid (BALF) is a liquid obtained from the alveoli and bronchi, often used to study pulmonary diseases. So far, proteomic analyses have identified over three thousand proteins in BALF. However, the comprehensive characterization of these proteins remains challenging due to their complexity and technological limitations. This paper presented a novel deep learning framework called SecProGNN, designed to predict secretory proteins in BALF. Firstly, SecProGNN represented proteins as graph-structured data, with amino acids connected based on their interactions. Then, these graphs were processed through graph neural networks (GNNs) model to extract graph features. Finally, the extracted feature vectors were fed into a multi-layer perceptron (MLP) module to predict BALF secreted proteins. Additionally, by utilizing SecProGNN, we investigated potential biomarkers for lung adenocarcinoma and identified 16 promising candidates that may be secreted into BALF.

AIIM Journal 2024 Journal Article

A clinically actionable and explainable real-time risk assessment framework for stroke-associated pneumonia

Lutao Dai
Xin Yang
Hao Li
Xingquan Zhao
Lin Lin
Yong Jiang
Yongjun Wang
Zixiao Li

The current medical practice is more responsive rather than proactive, despite the widely recognized value of early disease detection, including improving the quality of care and reducing medical costs. One of the cornerstones of early disease detection is clinically actionable predictions, where predictions are expected to be accurate, stable, real-time and interpretable. As an example, we used stroke-associated pneumonia (SAP), setting up a transformer-encoder-based model that analyzes highly heterogeneous electronic health records in real-time. The model was proven accurate and stable on an independent test set. In addition, it issued at least one warning for 98. 6 % of SAP patients, and on average, its alerts were ahead of physician diagnoses by 2. 71 days. We applied Integrated Gradient to glean the model's reasoning process. Supplementing the risk scores, the model highlighted critical historical events on patients' trajectories, which were shown to have high clinical relevance.

JMLR Journal 2024 Journal Article

A Semi-parametric Estimation of Personalized Dose-response Function Using Instrumental Variables

Wei Luo
Yeying Zhu
Xuekui Zhang
Lin Lin

In the application of instrumental variable analysis that conducts causal inference in the presence of unmeasured confounding, invalid instrumental variables and weak instrumental variables often exist which complicate the analysis. In this paper, we propose a model-free dimension reduction procedure to select the invalid instrumental variables and refine them into lower-dimensional linear combinations. The procedure also combines the weak instrumental variables into a few stronger instrumental variables that best condense their information. We then introduce the personalized dose-response function that incorporates the subject's personal characteristics into the conventional dose-response function, and use the reduced data from dimension reduction to propose a novel and easily implementable nonparametric estimator of this function. The proposed approach is suitable for both discrete and continuous treatment variables, and is robust to the dimensionality of data. Its effectiveness is illustrated by the simulation studies and the data analysis of ADNI-DoD study, where the causal relationship between depression and dementia is investigated. [abs] [ pdf ][ bib ] &copy JMLR 2024. ( edit, beta )

EAAI Journal 2024 Journal Article

An adaptive hybrid surrogate model for FEA of telescopic boom of rock drilling jumbo

Yancheng Lv
Lin Lin
Hao Guo
Changsheng Tong
Yikun Liu
Sihao Zhang
Shiwei Suo

The rapid collaborative optimization (CO) has an increasing demand for high-fidelity surrogate models. However, the traditional surrogate model cannot be applied to all working conditions due to the limitations of model applicability. A hybrid surrogate model is proposed, which uses the attention mechanism to automatically decide the weights of the sub models according to the working conditions to ensure its approximation ability under all working conditions. First, according to the characteristics of the finite element analysis (FEA) parameters, a comprehensive design of experiment (DOE) is proposed, which ensures the space-filling property of the samples. Secondly, a Self-attention artificial neural network (ANN) is proposed to automatically adjust the weights of sub-surrogate models, improving the attention to the working condition-related features. The proposed Self-attention ANN is a general framework that can provide support for the adaptive weight decision in other equipment simulation hybrid surrogate models. The experiment on the database shows that the error of the two hybrid surrogate models established by the proposed method is 36. 04% and 33. 31% lower than that of the advanced model, respectively, and is significantly superior to other methods. This achievement not only combines the spatial approximation ability of sub models to establish nonlinear model, achieving the purpose of high-fidelity simulation of FEA systems, but also enables surrogate model covering complete space using limited samples, making the model suitable for various practical engineering problems. In summary, the proposed method has obvious advantages in solving existing problems, provides strong support for research and practical applications.

EAAI Journal 2024 Journal Article

Integrating adversarial training strategies into deep autoencoders: A novel aeroengine anomaly detection framework

Lin Lin
Lizheng Zu
Song Fu
Yikun Liu
Sihao Zhang
Shiwei Suo
Changsheng Tong

The anomaly detection of aeroengines faces significant challenges, including high noise, complex parameter correlations, and imbalanced data. Current methods primarily rely on the reconstruction error of autoencoders to isolate anomalies. However, such methods are ineffective in detecting minor anomalies, as the reconstruction error for minor anomaly samples closely resembles that of normal samples. To address this problem, this paper proposes an innovative Deep Autoencoder Anomaly Detection Model (DAADM) for the anomaly detection of aeroengines. DAADM consists of two sub-networks: Anomaly Score Calculation Network (ASCN) and Deep Feature Extraction Network (DFEN). Firstly, ASCN introduces the adversarial training strategy into deep autoencoders, ensuring training stability while effectively isolating minor anomalies. Secondly, DFEN extracts deep-level features from input samples, providing a different perspective from ASCN to enhance sample information extraction. The combination of ASCN and DFEN compensates for their respective deficiencies, comprehensively considering input sample information, thereby promoting the capability of anomaly detection. Finally, the proposed DAADM is validated on real an aeroengine dataset and a public dataset. On the aeroengine dataset, the accuracy, recall, and F1 score of DAADM exceed the state-of-the-art method by 4. 73%, 4. 79%, and 5. 76%, respectively. It is noteworthy that experimental results have proven that DAADM has strong anti-noise capabilities.

EAAI Journal 2023 Journal Article

Novel aeroengine fault diagnosis method based on feature amplification

Lin Lin
Wenhui He
Song Fu
Changsheng Tong
Lizheng Zu

Data-driven fault diagnosis methods have high requirements for data samples. The ideal state is that the input samples have good separability. However, because of the harsh working environment and complex operating conditions, the collected data are highly nonlinear inseparability. The existing methods often cannot achieve a good classification effect. To solve this problem, two feature amplification methods that make full use of the useful information from the aeroengine service data are proposed. One is a high-dimensional mapping method based on explicit mapping of kernel function to amplify features. The other is an experiential method to amplify features. Both methods map the input samples to high-dimensional space to make them more separable in high-dimensional space and retain the useful information of raw data. Each feature after feature amplification still covers strong independent information that can describe fault feature information from different dimensions. Then, four deep learning algorithms that have a good effect on processing time-series data are selected as the classifier. The aeroengine service dataset and the bearing vibration dataset from Case Western University are used to verify the effectiveness of the two feature amplification methods. The experimental results show that the fault diagnosis accuracy can be improved if sample features after high-dimensional mapping possess good orthogonality, otherwise, the accuracy will be reduced.

EAAI Journal 2021 Journal Article

A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection

Song Fu
Shisheng Zhong
Lin Lin
Minghang Zhao

The use of hidden features or reconstruction errors extracted by deep auto-encoder (DAE) is becoming popular to discriminate anomalies from normal. Nevertheless, the fact that the existing methods only involving one of these two aspects loss the useful information from the other one motivates this investigation of method to combine reconstruction errors with hidden features. More importantly, anomalies are not removed in the training set when optimizing the traditional DAE, which weakens the discrimination of the reconstruction error. Aiming at these two problem, a new deep learning method, the so-called Re-optimized Deep Auto-Encoder (R-DAE), is developed to improve the detective performance for gas turbine unsupervised anomaly detection. First, the developed R-DAE takes the hidden features and the induced reconstruction errors as the final features. Second, to make the reconstruction error more discriminative, a sample selection mechanism is designed to attempt to remove anomalies from original unannotated training set, which makes the R-DAE almost unaffected by abnormal samples during training. Third, to effectively process time series, isolation forest is used to detect the obtained final features. Experiments on the real-life operation data of a gas turbine sample fleet validate the excellent detective performance of the proposed method.

JBHI Journal 2021 Journal Article

Joint Extraction of Retinal Vessels and Centerlines Based on Deep Semantics and Multi-Scaled Cross-Task Aggregation

Rui Xu
Tiantian Liu
Xinchen Ye
Fei Liu
Lin Lin
Liang Li
Satoshi Tanaka
Yen-Wei Chen

Retinal vessel segmentation and centerline extraction are crucial steps in building a computer-aided diagnosis system on retinal images. Previous works treat them as two isolated tasks, while ignoring their tight association. In this paper, we propose a deep semantics and multi-scaled cross-task aggregation network that takes advantage of the association to jointly improve their performances. Our network is featured by two sub-networks. The forepart is a deep semantics aggregation sub-network that aggregates strong semantic information to produce more powerful features for both tasks, and the tail is a multi-scaled cross-task aggregation sub-network that explores complementary information to refine the results. We evaluate the proposed method on three public databases, which are DRIVE, STARE and CHASE_DB1. Experimental results show that our method can not only simultaneously extract retinal vessels and their centerlines but also achieve the state-of-the-art performances on both tasks.

NeurIPS Conference 2018 Conference Paper

Explaining Deep Learning Models -- A Bayesian Non-parametric Approach

Wenbo Guo
Sui Huang
Yunzhe Tao
Xinyu Xing
Lin Lin

Understanding and interpreting how machine learning (ML) models make decisions have been a big challenge. While recent research has proposed various technical approaches to provide some clues as to how an ML model makes individual predictions, they cannot provide users with an ability to inspect a model as a complete entity. In this work, we propose a novel technical approach that augments a Bayesian non-parametric regression mixture model with multiple elastic nets. Using the enhanced mixture model, we can extract generalizable insights for a target model through a global approximation. To demonstrate the utility of our approach, we evaluate it on different ML models in the context of image recognition. The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.

JMLR Journal 2017 Journal Article

Clustering with Hidden Markov Model on Variable Blocks

Lin Lin
Jia Li

Large-scale data containing multiple important rare clusters, even at moderately high dimensions, pose challenges for existing clustering methods. To address this issue, we propose a new mixture model called Hidden Markov Model on Variable Blocks (HMM-VB) and a new mode search algorithm called Modal Baum-Welch (MBW) for mode-association clustering. HMM-VB leverages prior information about chain-like dependence among groups of variables to achieve the effect of dimension reduction. In case such a dependence structure is unknown or assumed merely for the sake of parsimonious modeling, we develop a recursive search algorithm based on BIC to optimize the formation of ordered variable blocks. The MBW algorithm ensures the feasibility of clustering via mode association, achieving linear complexity in terms of the number of variable blocks despite the exponentially growing number of possible state sequences in HMM-VB. In addition, we provide theoretical investigations about the identifiability of HMM-VB as well as the consistency of our approach to search for the block partition of variables in a special case. Experiments on simulated and real data show that our proposed method outperforms other widely used methods. [abs] [ pdf ][ bib ] &copy JMLR 2017. ( edit, beta )

EAAI Journal 2016 Journal Article

Novel continuous function prediction model using an improved Takagi–Sugeno fuzzy rule and its application based on chaotic time series

Feng Guo
Lin Lin
Chen Wang

A novel continuous function prediction model (CFPM) is proposed to resolve prediction problem whose input and output are both continuous functions (CFs). CFPM can simplify sample space reconstruction by using the coefficients of CFs, and use an improved Takagi–Sugeno (TS) fuzzy rule to predict output CF by optimizing the tendency of input CFs. The improved TS fuzzy rule handles each input CF as a consequent parameter and can obtain the nonlinear tendency. After learning process by using opinion-leader-based particle swarm optimization, output CF is determined. In the data prediction based on chaotic time series, CF can either be obtained directly or be fitted by discrete data points, thus the prediction range is enlarged because more discrete data points can be generated once output CF is determined. Two experiments and three cases based on chaotic time series are performed to validate CFPM. The Mackey–Glass chaotic time series is used to prove CFPM validation, while the NN3 time series is used to evaluate CFPM performance. The cases on exhaust gas temperature (EGT), EGT margin and delta EGT are used to show that CFPM is valuable in health status prediction for a particular aircraft engine in the practical engineering field.

EAAI Journal 2015 Journal Article

Novel informative feature samples extraction model using cell nuclear pore optimization

Lin Lin
Feng Guo
Xiaolong Xie

A novel informative feature samples extraction model is proposed to approximate massive original samples (OSs) by using a small number of informative feature samples (IFSs). In this model, (1) the feature samples (FSs) are identified using Support Vector Regression and Quantum-behaved Particle Swarm Optimization and (2) the IFSs space is established based on the Cell Nuclear Pore Optimization (CNPO) algorithm. CNPO uses a pore vector containing 0 or 1 to extract the essential FSs with high contribution based on the thought of cell nuclear pore selection mechanism. This model can be used to identify the continuous parameter based on the IFSs without massive OSs and time-consuming work. Two experiments are used to validate the proposed model, and one case is used to illustrate the practical value in the real engineer field. The experiments show that the IFSs could approximately represent the massive OSs, and the case shows that the model is helpful to identify the continuous parameters for the hydraulic turbine type design.

TIST Journal 2013 Journal Article

Web media semantic concept retrieval via tag removal and model fusion

Chao Chen
Qiusha Zhu
Lin Lin
Mei-Ling Shyu

Multimedia data on social websites contain rich semantics and are often accompanied with user-defined tags. To enhance Web media semantic concept retrieval, the fusion of tag-based and content-based models can be used, though it is very challenging. In this article, a novel semantic concept retrieval framework that incorporates tag removal and model fusion is proposed to tackle such a challenge. Tags with useful information can facilitate media search, but they are often imprecise, which makes it important to apply noisy tag removal (by deleting uncorrelated tags) to improve the performance of semantic concept retrieval. Therefore, a multiple correspondence analysis (MCA)-based tag removal algorithm is proposed, which utilizes MCA's ability to capture the relationships among nominal features and identify representative and discriminative tags holding strong correlations with the target semantic concepts. To further improve the retrieval performance, a novel model fusion method is also proposed to combine ranking scores from both tag-based and content-based models, where the adjustment of ranking scores, the reliability of models, and the correlations between the intervals divided on the ranking scores and the semantic concepts are all considered. Comparative results with extensive experiments on the NUS-WIDE-LITE as well as the NUS-WIDE-270K benchmark datasets with 81 semantic concepts show that the proposed framework outperforms baseline results and the other comparison methods with each component being evaluated separately.