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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Muhammad Umair Raza Jie Chen Li Wang Adil Nawaz Victor C.M. Leung Jianqiang Li College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The challenge of interpretability remains a significant barrier to adopting deep neural networks in healthcare domains. Although tree regularization aims to align a deep neural network’s decisions with a single axis-aligned decision tree, however, relying on one tree for all inputs often leads to sub-optimal performance and interoperability. To address this limitation, we propose an enhanced tree regularization method that integrates a post-hoc visual explainable model such as Grad-CAM. This approach guides the deep model to be well-approximated by decision trees tailored to the salient regions identified by Grad-CAM in the input space. We rigorously validate the effectiveness of this framework on two cancer cell datasets: CNMC, which focuses on acute lymphoblastic leukemia cells, and ISBI2016, which comprises benign and malignant skin lesions. The results demonstrate that the proposed method delivers simpler and more interpretable explanations without compromising accuracy, thereby advancing the interpretability of deep learning models in critical healthcare applications.

关键词： Visualization Accuracy Decision making Artificial neural networks Predictive models Skin Decision trees Lesions Biological cells Cancer

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Uncertainty for Active Learning on Graphs

arXiv

引用

arXiv 2024年

作者： Fuchsgruber, Dominik Wollschläger, Tom Charpentier, Bertrand Oroz, Antonio Günnemann, Stephan School of Computation Information and Technology Technical University of Munich Germany Munich Data Science Institute Germany

Uncertainty Sampling is an Active Learning strategy that aims to improve the data efficiency of machine learning models by iteratively acquiring labels of data points with the highest uncertainty. While it has proven effective for independent data its applicability to graphs remains under-explored. We propose the first extensive study of Uncertainty Sampling for node classification: (1) We benchmark Uncertainty Sampling beyond predictive uncertainty and highlight a significant performance gap to other Active Learning strategies. (2) We develop ground-truth Bayesian uncertainty estimates in terms of the data generating process and prove their effectiveness in guiding Uncertainty Sampling toward optimal queries. We confirm our results on synthetic data and design an approximate approach that consistently outperforms other uncertainty estimators on real datasets. (3) Based on this analysis, we relate pitfalls in modeling uncertainty to existing methods. Our analysis enables and informs the development of principled uncertainty estimation on graphs. © 2024, CC BY.

关键词： Active learning

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Multi-objective Reinforcement Learning - Concept, Approaches and Applications 10

Multi-objective Reinforcement Learning - Concept, Approaches...

引用

10th International Conference on Information technology and Quantitative Management, ITQM 2023

作者： Zhang, Linzi Qi, Zhiquan Shi, Yong School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Real-world decision-making tasks are generally complicated and require trade-offs between multiple, even conflicting, objectives. As the advent and great development of advanced information technology, it has evolved into using reinforcement learning (RL) algorithms to tackle the multi-objective decision making (MODM) problems. In this paper, we will first identify the basic concepts and factors when modelling the MODM tasks with reinforcement learning, and then review the traditional RL, such as Sarsa, Q-Learning, Policy Gradients, Actor-Critic, Monte-Carlo learning, and modern deep RL algorithms applied in this process. Furthermore, the specific practical scenarios described in MODM problems will be summarized through analyzing some typical articles. Finally, the future trends of multi-objective reinforcement learning will be discussed. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information technology and Quantitative Management.

关键词： Reinforcement learning

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Video-Based Detection of Epileptic Spasms in IESS:Modeling,Detection,and Evaluation

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Journal of Shanghai Jiaotong university(science) 2025年第1期30卷 1-9页

作者： DING Lihui FU Lijun YANG Guang WAN Lin CHANG Zhijun Shenyang Institute of Computing Technology Chinese Academy of SciencesShenyang110168China University of Chinese Academy of Sciences Beijing100049China Laboratory of Big Data and Artificial Intelligence Technology Shandong UniversityJinan250100China Senior Department of Pediatrics The Seventh Medical Center of Chinese PLA General HospitalBeijing100700China Department of Pediatrics The First Medical Center of Chinese PLA General HospitalBeijing100853China The Second School of Clinical Medicine Southern Medical UniversityGuangzhou510280China The National Science Library Chinese Academy of SciencesBeijing100190China

Behavioral scoring based on clinical observations remains the gold standard for screening,diagnosing,and evaluating infantile epileptic spasm syndrome(IESS).The accurate identification of seizures is crucial for clinical diagnosis and *** this study,we propose an innovative seizure detection method based on video feature recognition of patient *** capture the temporal characteristics of the spasm behavior presented in the videos effectively,we incorporate asymmetric convolutions and convolution–batch normalization–ReLU(CBR)*** within the 3D-ResNet residual blocks,we split the larger convolutional kernels into two asymmetric 3D convolutional *** kernels are connected in series to enhance the ability of the convolutional layers to extract key local features,both horizontally and *** addition,we introduce a 3D convolutional block attention module to enhance the spatial correlations between video frame channels *** improve the generalization ability,we design a composite loss function that combines cross-entropy loss with triplet loss to balance the classification and similarity *** train and evaluate our method using the PLA IESS-VIDEO dataset,achieving an average seizure recognition accuracy of 90.59%,precision of 90.94%,and recall of 87.64%.To validate its generalization capability further,we conducted external validation using six different patient monitoring videos compared with assessments by six human experts from various medical *** final test results demonstrate that our method achieved a recall of 0.6476,surpassing the average level achieved by human experts(0.5595),while attaining a high F1-score of *** findings have substantial significance for the long-term assessment of patients with IESS.

关键词： infantile epileptic spasm syndrome video-based seizure analysis asymmetric convolution 3D-ResNet attention mechanism

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Transfer Learning for High-dimensional Quantile Regression with Distribution Shift

arXiv

引用

arXiv 2024年

作者： Bai, Ruiqi Zhang, Yijiao Yang, Hanbo Zhu, Zhongyi Department of Statistics and Data Science Fudan University China School of the Gifted Young University of Science and Technology of China China

Information from related source studies can often enhance the findings of a target study. However, the distribution shift between target and source studies can severely impact the efficiency of knowledge transfer. In the high-dimensional regression setting, existing transfer approaches mainly focus on the parameter shift. In this paper, we focus on the high-dimensional quantile regression with knowledge transfer under three types of distribution shift: parameter shift, covariate shift, and residual shift. We propose a novel transferable set and a new transfer framework to address the above three discrepancies. Non-asymptotic estimation error bounds and source detection consistency are established to validate the availability and superiority of our method in the presence of distribution shift. Additionally, an orthogonal debiased approach is proposed for statistical inference with knowledge transfer, leading to sharper asymptotic results. Extensive simulation results as well as real data applications further demonstrate the effectiveness of our proposed procedure. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Machine Anomalous Sound Detection Using Spectral-Temporal Modulation Representations Derived From Machine-Specific Filterbanks

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2059-2073页

作者： Kai Li Khalid Zaman Xingfeng Li Masato Akagi Jianwu Dang Masashi Unoki Graduate School of Advanced Science and Technology Japan Advanced Institute of Science and Technology Nomi Ishikawa Japan Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Faculty of Data Science City University of Macau Macau China

Early detection of factory machinery malfunctions is crucial in industrial applications. In machine anomalous sound detection (ASD), different machines exhibit unique vibration-frequency ranges based on their physical properties. Meanwhile, the human auditory system is adept at tracking both temporal and spectral dynamics of machine sounds. Consequently, integrating the computational auditory models of the human auditory system with machine-specific properties can be an effective approach to machine ASD. We first quantified the frequency importances of four types of machines using the Fisher ratio (F-ratio). The quantified frequency importances were then used to design machine-specific non-uniform filterbanks (NUFBs), which extract the log non-uniform spectrum (LNS) feature. The designed NUFBs have a narrower bandwidth and higher filter distribution density in frequency regions with relatively high F-ratios. Finally, spectral and temporal modulation representations derived from the LNS feature were proposed. These proposed LNS feature and modulation representations are input into an autoencoder neural-network-based detector for ASD. The quantification results from the training set of the Malfunctioning Industrial Machine Investigation and Inspection dataset with a signal-to-noise (SNR) of 6 dB reveal that the distinguishing information between normal and anomalous sounds of different machines is encoded non-uniformly in the frequency domain. By highlighting these important frequency regions using NUFBs, the LNS feature can significantly enhance performance using the metric of AUC (area under the receiver operating characteristic curve) under various SNR conditions. Furthermore, modulation representations can further improve performance. Specifically, temporal modulation is effective for fans, pumps, and sliders, while spectral modulation is particularly effective for valves.

关键词： Frequency modulation Ear Spectrogram Information filters Feature extraction Acoustics Computational modeling Psychoacoustic models Bandwidth Training

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Exploiting Structural and Temporal Influence for Dynamic Social-Aware Recommendation

引用

Journal of Computer science & technology 2020年第2期35卷 281-294页

作者： Yang Liu Zhi Li Wei Huang Tong Xu En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230027China

Recent years have witnessed the rapid development of online social platforms,which effectively support the business intelligence and provide services for massive *** this line,large efforts have been made on the socialaware recommendation task,i.e.,leveraging social contextual information to improve recommendation *** existing methods have treated social relations in a static way,but the dynamic influence of social contextual information on users'consumption choices has been largely *** that end,in this paper,we conduct a comprehensive study to reveal the dynamic social influence on users'preferences,and then we propose a deep model called Dynamic Social-Aware Recommender System(DSRS)to integrate the users'structural and temporal social contexts to address the dynamic socialaware recommendation *** consists of two main components,i.e.,the social influence learning(SIL)and dynamic preference learning(DPL).Specifically,in the SIL module,we arrange social graphs in a sequential order and borrow the power of graph convolution networks(GCNs)to learn social ***,we design a structural-temporal attention mechanism to discriminatively model the structural social influence and the temporal social ***,in the DPL part,users'individual preferences are learned dynamically by recurrent neural networks(RNNs).Finally,with a prediction layer,we combine the users'social context and dynamic preferences to generate *** conduct extensive experiments on two real-world datasets,and the experimental results demonstrate the superiority and effectiveness of our proposed model compared with the state-of-the-art methods.

关键词： recommender system social influence sequential recommendation

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Three-Dimensional Convolutional Neural Network for Wheat Rust Disease Classification 2

Three-Dimensional Convolutional Neural Network for Wheat Rus...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Jagadeeshan, N. Nagendar, Y. Swarnalatha, K. Al-Attabi, Kassem Ramachandra, A.C. Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Engineering Sr University Telangana Warangal India Maharaja Institute of Technology Department of Artificial Intelligence and Data Science Thandavapura India The Islamic University Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350382952

In worldwide, the wheat is a significant crop which generates main source of food for numerous peoples. Through, the development of crop productions is vulnerable through various diseases such as bacterial, viral and fungal infections. These type of disease can cause important damage in crops which leads to diminish the yield production and grain quality. This paper proposed a Three-Dimensional Convolutional Neural Network (3D-CNN) for wheat rust disease classification that learns to recognize the pattern and structure of rust disease using convolution filter layers. The CGIAR dataset is used which contains 1486 images and it is pre-processed by gaussian filter which reduces the noise and smoothens the image. Then, the Discrete Wavelet Transform (DWT) is used for feature extraction which works in discrete timespan that outputs in low computational cost. Then, 3D-CNN is used for the classification of wheat rust disease. The performance of 3D-CNN is estimated by accuracy, recall, f1score and precision. The 3D-CNN attains accuracy 99.83%, recall 98.89%, f1score 98.81 % and precision 98.79% when compared to existing techniques like GNet+FERSPNET-50 and Few-shot learning based EfficientNet. © 2024 IEEE.

关键词： Discrete wavelet transforms

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CTGGAN: Reliable Fetal Heart Rate Signal Generation Using GANs

CTGGAN: Reliable Fetal Heart Rate Signal Generation Using GA...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Zichang Yu Yating Hu Yu Lu Leya Li Huilin Ge Xianghua Fu College of Applied Science Shenzhen University Shenzhen China College of Big Data and Internet Shenzhen Technology University Shenzhen China College of Automation Jiangsu University Science and Technology Zhenjiang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Ensuring fetal health during pregnancy is critically dependent on precise Fetal Heart Rate (FHR) monitoring. A major challenge in this area is the limited availability of labeled FHR data, which poses a barrier to developing reliable automated analysis systems. To address this gap, our study introduces CTGGAN, a novel method employing Generative Adversarial Networks (GANs) to create synthetic, high-quality FHR signals. Specifically, CTGGAN integrates self-attention and residual modules within a Conditional GAN framework, fine-tuned to replicate the complex patterns characteristic of FHR data accurately. A notable feature of CTGGAN is its effective loss function, which combines Wasserstein distance with a gradient penalty to ensure training stability and enhance the authenticity of the generated signals. In performance metrics, Our method demonstrates the highest signal fidelity and distribution similarity, across five key measures: 0.215 maximum mean deviation (MMD), 0.012 sliced Wasserstein distance (SWD), 4.821 percent root mean square difference (PRD), 5.621 relative entropy (RE), and 0.614 Frechet distance (FD). This advancement in generating realistic FHR data with CTGGAN addresses critical issues like data insufficiency and class imbalance, thus advancing the field of prenatal healthcare technology. The code for CTGGAN is available at https://***/ijcnn2024/CTGGAN.

关键词： Training Pregnancy Fetal heart rate Neural networks Generative adversarial networks Stability analysis Reliability

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Self-Supervised Acoustic Word Embedding Learning via Correspondence Transformer Encoder

arXiv

引用

arXiv 2023年

作者： Lin, Jingru Yue, Xianghu Ao, Junyi Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China

Acoustic word embeddings (AWEs) aims to map a variable-length speech segment into a fixed-dimensional representation. High-quality AWEs should be invariant to variations, such as duration, pitch and speaker. In this paper, we introduce a novel self-supervised method to learn robust AWEs from a large-scale unlabelled speech corpus. Our model, named Correspondence Transformer Encoder (CTE), employs a teacher-student learning framework. We train the model based on the idea that different realisations of the same word should be close in the underlying embedding space. Specifically, we feed the teacher and student encoder with different acoustic instances of the same word and pre-train the model with a word-level loss. Our experiments show that the embeddings extracted from the proposed CTE model are robust to speech variations, e.g. speakers and domains. Additionally, when evaluated on Xitsonga, a low-resource cross-lingual setting, the CTE model achieves new state-of-the-art performance. © 2023, CC BY.

关键词： Embeddings

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