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A review of big data technology and its application in cancer care

Computers in Biology and Medicine 2024年 176卷 108577-108577页

作者： Xiao, Tianyun Kong, Shanshan Zhang, Zichen Hua, Dianbo Liu, Fengchun Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China Beijing Sitairui Cancer Data Analysis Joint Laboratory Beijing101149 China

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future. © 2024 Elsevier Ltd

关键词： Big data

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Actual Cause Guided Adaptive Gradient Scaling for Balanced Multimodal Sentiment Analysis

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ACM Transactions on Multimedia computing, Communications, and applications 1000年

作者： Jili Chen Qionghao Huang Changqin Huang Xiaodi Huang Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China College of Education Zhejiang University China School of Computing Mathematics and Engineering Charles Sturt University Australia

Multimodal sentiment analysis leverages information from multiple sensors to achieve a comprehensive interpretation of emotions. However, different modalities do not always boost each other as expected. They compete with each other, leading to some modalities being under-optimized during the training process. To address this issue, we propose Adaptive Gradient Scaling with Sparse Mixture-of-Experts (AGS-SMoE). We first discuss the issue of modal preemption in unified multimodal learning from the perspective of causal preemption. Driven by actual cause, we use the gradient norms from different encoders at two fusion stages as evidence, estimating the current modal preemption state using a parameter-free method. Then, based on the dynamic preemption factor, we design a gradient scaling method to balance optimization for different encoders. Furthermore, we use Mixture-of-Experts to sparsify and perceive multimodal tokens in different preemption states. As a result, our experiments on four multimodal sentiment analysis datasets have achieved state-of-the-art results. Moreover, our method improves modal representation learning at different stages. Extensive experiments confirm that our method can alleviate the modal preemption problem in a plug-and-play manner. Our code is available at https://***/TheShy-Dream/AGS-SMoE.

关键词： Multimodal Sentiment Analysis Causal Preemption Multimodal Competition

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Msnoa-Catboost: A Novel Pan-Cancer Diagnosis Model with Feature Subset Selection and Hyperparameter Optimization

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SSRN 2024年

作者： Xiao, Tianyun Zhang, Zichen Kong, Shanshan Liu, Fengchun Yang, Aimin Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China

Cancer, as a global epidemic disease, remains a significant threat to human life and health. Prevention and early diagnosis are widely recognized as effective ways to tackle cancer. With the development of big data technology, many data-driven cancer diagnosis models have been proposed. However, the accuracy and generalization ability of existing models often fail to meet practical application needs. In order to address these challenges, this paper proposes a novel and improved protocol for high-precision pan-cancer diagnosis based on five publicly available cancer datasets from the UCI database and Kaggle, covering different cancer types such as breast, lung, and glioma. To achieve feature subset selection and hyperparameter optimization, we first introduce the nutcracker optimization algorithm (NOA), and improve it by using a multi-strategy method (MSNOA) to enhance optimization performance and efficiency. In the comparative experiment phase, we compare several popular machine learning models and identify MSNOA-CatBoost as the proposed model. The experimental results show that MSNOA-CatBoost improves the accuracy of the five datasets by an average of 9.12% compared to the baseline optimal model, and outperforms existing models in terms of performance. In summary, this study presents a promising approach to achieving high-precision pan-cancer diagnosis by leveraging the power of big data and advanced machine learning techniques. The proposed MSNOA-CatBoost model shows remarkable performance improvement over existing models, and has the potential to be applied in clinical practice for cancer diagnosis and treatment planning. © 2024, The Authors. All rights reserved.

关键词： Optimization

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Reconstructing Missing Modalities in Multi-Modal Endoscopic Ultrasound Via Cross-Modal Feature Replacement Representation

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SSRN 2024年

作者： Zheng, Cenyang Gong, Xun Fan, Lin Li, Jiao School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China Department of Gastroenterology The Third People’s Hospital of Chendu Affiliated Hospital of Southwest Jiaotong University Chengdu610031 China

Endoscopic ultrasound (EUS) is the primary diagnostic imaging technique for gastrointestinal stromal tumors. However, accurately identifying these tumors through EUS imaging alone remains challenging, even for experienced clinicians. They often rely on additional data, such as white light endoscopy (WLE) and EUS text reports, to aid in the diagnosis. Unfortunately, in practice, these supplementary modalities are sometimes overlooked during patient evaluation. To address these issues, we propose the Cross-modal Feature Reconstruction Network (CFRN), which integrates multimodal data modeling for medical diagnostic processes and performs specific learning for different modalities with missing data. The network fuses the multi-modal features based on the clinical medical process, realizes the feature-level missing modality reconstruction, and learns the refined features of different modal true cases by decoding the mode-specific missing. We trained and tested the model on two publicly available datasets, Audiovision-MNIST, MM-IMDb, and data sets collected from real clinical scenarios at three hospitals. Our results show that CFRN outperforms state-of-the-art methods, achieving higher prediction accuracy than using EUS alone. By simultaneously incorporating multimodal or reconstructed modal data for disease diagnosis, our single-modal EUS model proves to be more suitable for clinical scenarios. © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning

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SSRN 2024年

作者： Hu, Fuyuan Zhang, Jian Lyu, Fan Zhao, Linglan Liu, Chengyan Zhang, Tao Yu, Baoqing School of Electronics and Information Engineering Suzhou University of Science and Technology No.99 Xuefu Road Jiangsu Suzhou215000 China Jiangsu Industrial Intelligent Low Carbon Technology Engineering Center Jiangsu Suzhou215009 China Suzhou Key Laboratory of Intelligent Low Carbon Technology Application Jiangsu Suzhou215009 China New Laboratory of Pattemn Recognition Institute of Automation Chinese Academy of Sciences College of Intelligence and Computing No.95 Zhongguancun East Road Beijing100190 China Tencent Youtu Lab Shanghai201600 China Seventh People’s Hospital Shanghai University of Traditional Chinese Medicine No.358 Datong Road Pudong New District Shanghai200137 China

Few-shot class-incremental learning (FSCIL) aims to build machine learning models that can continually learn novel classes from a few training samples, without forgetting knowledge of old classes. The challenges of FSCIL lie in the limited data of novel classes, which not only lead to significant overfitting issues but also exacerbate the notorious catastrophic forgetting problems. As proved in early studies, building sample relationships is beneficial for learning from few-shot classes. In this paper, we advance the idea to the incremental scenario and propose a Sample-to-Class (S2C) graph learning method for FSCIL. Concretely, we propose a Sample-level Graph Network (SGN) that focuses on analyzing sample relationships within a single session. This network helps aggregate similar samples, ultimately leading to the extraction of more refined class-level features. Then, we present a Class-level Graph Network (CGN) that establishes connections across class-level features of both novel and old classes. This network plays a crucial role in linking the knowledge between different sessions and helps improve overall learning performance in the FSCIL scenario. Moreover, we design a multi-stage strategy for training the S2C model, which mitigates the training challenges posed by limited data in the incremental learning process. The multi-stage training strategy is designed to build an S2C graph from base to incremental sessions and improve the capacity via an extra pseudo-incremental stage. Extensive experiments on three popular benchmark datasets show that our method outperforms the baselines and sets new state-of-the-art results in FSCIL. Our codes will be available upon acceptance. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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The research of springback compensation design method based on reverse modeling

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AIP Conference Proceedings 2017年第1期1864卷

作者： Ru Wang Fenlou Zhai Bing Wu Yutong Ji 1Beijing Computing Center Beijing 100094 China 2Beijing Engineering Laboratory on Cloud Computing Service of Internet Plus Intelligent Equipment Beijing 100094 China 3Beijing Key Laboratory of Cloud Computing Technology and Application Beijing 100094 China 4Gold-Key motor die company droup Hebei 062150 China

To solve the springback problem of automotive covering parts, a springback compensation design method of Stamping die based on reverse modeling is researched. By the Alignment module in Poly Works software, springback analysis is quickly implemented. Then, According to the results of springback analysis, the springback compensation design method which used the smooth displacement adjustment theory is established. At last, Set an inner plate of wheel housing as example, the methods has been applied in springback analysis of the part and springback compensation design of the die-face, which effectively reduces the times of compensation in try pressing stage and improves productivity.

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Fs-Woa-Stacking: A Novel Ensemble Model for Early Diagnosis of Breast Cancer

SSRN

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SSRN 2024年

作者： Xiao, Tianyun Kong, Shanshan Zhang, Zichen Liu, Fengchun Yang, Aimin Hua, Dianbo Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China Beijing Sitairui Cancer Data Analysis Joint Laboratory Beijing101149 China

In this paper, a new integrated scheme is proposed to accurately predict breast cancer, help doctors make early diagnosis and treatment plans, and improve the prognosis of patients. We selects five mainstream machine learning models: support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost). The Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) are used as datasets to investigate the predictive performance of these single and ensemble models. Then, we use multiple linear regression method for feature selection (FS), the experimental results show that the change of feature subset will significantly affect the performance of the model. After that, we apply whale optimization algorithm (WOA) to optimize the hyperparameters of the model to improve their prediction performance. In addition, we ensemble these models by stacking, investigate the performance changes of the ensemble model when different models are used as meta learners. Finally, the FS-WOA-Stacking model achieves 99.56% accuracy on WBCD and 99.65% accuracy on WDBC. Compared with the existing breast cancer prediction models, the performance of the proposed model is at an excellent level. © 2024, The Authors. All rights reserved.

关键词： Adaptive boosting

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Ed-Tgan: A Methodology on Temporal Generative Gan for Imbalanced Data for Predicting Academic Performance of Students

SSRN

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SSRN 2022年

作者： Waheed, Hajra Zhou, Jia Aljohani, Naif R. Tuarob, Suppawong Nawaz, Raheel Herrera, Francisco Hassan, Saeed-Ul Information Technology University 346-B Ferozepur Road Lahore Pakistan Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Zhejiang Jinhua321004 China Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Faculty of Information and Communication Technology Mahidol University Thailand Staffordshire University Stoke-on-Trent United Kingdom Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Manchester Metropolitan University Manchester United Kingdom

With the increase in the self-paced learning phenomenon, class imbalance in the learning analytics community has become more prevalent, with lesser students dropping out than students who intend to pursue. Similarly, an imbalance in the behavioral performances of students is also observed. This imbalance problem becomes more complicated for temporal data, where each time sequence represents a particular student, and several sequences formulate the overall engagement of students throughout a course. The conventional methods for generating synthetic data do not account for this association of sequences, where each synthetically generated student is associated with its previous sequence. Generative Adversarial Networks (GANs) have been rapidly adopted in the machine learning discipline for synthetic data generation to reduce the class imbalance. In this study, we propose a deep learning-powered methodology called Educational Temporal Generative Adversarial Networks (Ed-TGANs) methodology comprising the following steps: a) Smart Data Processing for Actionable Data, b) GANs for Temporal Data Generation, and c) Learning Approach. Furthermore, the Open University Learning Analytics dataset was deployed as a case study comprising 69% pass and 31% fail students to validate the effectiveness of our proposed methodology, encompassing discrete and continuous features. The results are compared with GAN-based and non-GAN upsampling approaches to demonstrate the proposed methodology's effectiveness, which shows an increase in accuracy of up to 5.33% compared to classic GANs, resulting in improved understating of student behavior. We believe that the presented methodology can be used as a starting point for various areas for upsampling jobs with temporal longitudinal data. © 2022, The Authors. All rights reserved.

关键词： Students

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Consensus algorithms for biased labeling in crowdsourcing

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Information Sciences 2017年 382-383卷 254-273页

作者： Jing Zhang Victor S. Sheng Qianmu Li Jian Wu Xindong Wu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 P. R. China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 P. R. China Department of Computer Science University of Central Arkansas Conway AR 72035 USA Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 P. R. China School of Computing and Informatics University of Louisiana at Lafayette LA 70504 USA

Although it has become an accepted lay view that when labeling objects through crowdsourcing systems, non-expert annotators often exhibit biases, this argument lacks sufficient evidential observation and systematic empirical study. This paper initially analyzes eight real-world datasets from different domains whose class labels were collected from crowdsourcing systems. Our analyses show that biased labeling is a systematic tendency for binary categorization; in other words, for a large number of annotators, their labeling qualities on the negative class (supposed to be the majority) are significantly greater than are those on the positive class (minority). Therefore, the paper empirically studies the performance of four existing EM-based consensus algorithms , DS, GLAD, RY, and ZenCrowd, on these datasets. Our investigation shows that all of these state-of-the-art algorithms ignore the potential bias characteristics of datasets and perform badly although they model the complexity of the systems. To address the issue of handling biased labeling, the paper further proposes a novel consensus algorithm, namely adaptive weighted majority voting (AWMV), based on the statistical difference between the labeling qualities of the two classes. AWMV utilizes the frequency of positive labels in the multiple noisy label set of each example to obtain a bias rate and then assigns weights derived from the bias rate to negative and positive labels. Comparison results among the five consensus algorithms (AWMV and the four existing) show that the proposed AWMV algorithm has the best overall performance. Finally, this paper notes some potential related topics for future study.

关键词：

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Fractional Modeling and Dynamic Analysis of Two Kinds of Viscoelastic Thin Plates with Gaussian White Noise

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SSRN 2023年

作者： Deng, Jinna Liu, Qiumei Yang, Aimin Chen, Yiming College of Science North China University of Science and Technology Tangshan063210 China Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Tangshan063210 China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Tangshan063210 China School of Science Yanshan University Qinhuangdao066004 China LE STUDIUM Loire Valley Institute for Advanced Studies Orléans & Tours Orléans45000 France

According to fractional order model, the governing equation of viscoelastic plate is established. Using the properties of the shifted Bernstein polynomials, the fractional order equation of viscoelastic plate is resolved, the conversion of fractional differential equations to algebraic equations, and operator matrices represented by Kronecker products are solved in the domain. The convergence study validates the algorithm's efficacy and precision in solving differential equations. The displacement of viscoelastic metal thin plate under different external loads (uniform distribution and linear distribution), the effect of white Gaussian noise on the thin metal plate displacement, the effect of different boundary conditions on thin metal plate displacement under harmonic load are studied, the displacements of metal and polymer plates under different loads are contrasted and stress and strain relationship of two kinds of plates are discussed. Based on the outcomes, it can be concluded that the shift Bernstein polynomial approach is useful for numerically simulating viscoelastic plates. © 2023, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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