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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

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

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

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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

Retraction Note: Advanced artificial intelligence in heart rate and blood pressure monitoring for stress management

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Journal of Ambient Intelligence and Humanized computing 2024年第1期15卷 255-255页

作者： Lin, Qiang Li, Tongtong Shakeel, P. Mohamed Samuel, R. Dinesh Jackson School of Mathematics and Computer Science Northwest Minzu University LanzhouGansu China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University LanzhouGansu China Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University LanzhouGansu China Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka (UTeM) Durian Tunggal Malaysia Faculty of Technology Design and Environment Visual Artificial Intelligence Lab Oxford Brookes University Oxford UK

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)

The First GECAM Observation Results on Terrestrial Gamma-ray Flashes and Terrestrial Electron Beams

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arXiv

arXiv 2023年

作者： Zhao, Y. Liu, J.C. Xiong, S.L. Xue, W.C. Yi, Q.B. Lu, G.P. Xu, W. Lyu, F.C. Sun, J.C. Peng, W.X. Zheng, C. Zhang, Y.Q. Cai, C. Xiao, S. Xie, S.L. Wang, C.W. Tan, W.J. An, Z.H. Chen, G. Du, Y.Q. Huang, Y. Gao, M. Gong, K. Guo, D.Y. He, J.J. Li, B. Li, G. Li, X.Q. Li, X.B. Liao, J.Y. Liang, J. Liang, X.H. Liu, Y.Q. Ma, X. Qiao, R. Song, L.M. Song, X.Y. Sun, X.L. Wang, J. Wang, J.Z. Wang, P. Wen, X.Y. Wu, H. Xu, Y.B. Yang, S. Zhang, B.X. Zhang, D.L. Zhang, F. Zhang, P. Zhang, H.M. Zhang, Z. Zhao, X.Y. Zheng, S.J. Zhang, K.K. Han, X.B. Wu, H.Y. Hu, T. Geng, H. Zhang, H.B. Lu, F.J. Zhang, S.N. Yu, H. Department of Astronomy Beijing Normal University Beijing Beijing100875 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing Beijing100049 China University of Chinese Academy of Sciences Beijing Beijing100049 China School of Physics and Optoelectronics Xiangtan University Hunan Xiangtan411105 China School of Earth and Space Sciences University of Science and Technology of China Anhui Hefei230026 China Electronic Information School Wuhan University Hubei Wuhan430072 China Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric Sciences Jiangsu Nanjing210000 China College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University GuiZhou Guiyang550001 China School of Physics and Electronic Science Guizhou Normal University GuiZhou Guiyang550001 China Institute of Astrophysics Central China Normal University HuBei Wuhan430079 China School of Computing and Artificial Intelligence Southwest Jiaotong University SiChuan Chengdu611756 China College of Electronic and Information Engineering Tongji University Shanghai Shanghai201804 China Innovation Academy for Microsatellites Chinese Academy of Sciences Shanghai Shanghai201304 China National Space Science Center Chinese Academy of Sciences Beijing Beijing100190 China Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing Beijing100029 China

Gravitational-wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is a space-borne instrument dedicated to monitoring high-energy transients, including Terrestrial Gamma-ray Flashes (TGFs) and Terrestrial Electron Beams (TEBs). We implemented a TGF/TEB search algorithm for GECAM, with which 147 bright TGFs, 2 typical TEBs and 2 special TEB-like events are identified during an effective observation time of ∼9 months. We show that, with gamma-ray and charged particle detectors, GECAM can effectively identify and distinguish TGFs and TEBs, and measure their temporal and spectral properties in detail. A very high TGF-lightning association rate of ∼80% is obtained between GECAM and GLD360 in east Asia region. Copyright © 2023, The Authors. All rights reserved.

关键词： Electron beams

Insight-HXMT and GECAM-C observations of the brightest-of-all-time GRB 221009A

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arXiv

arXiv 2023年

作者： An, Zheng-Hua Antier, S. Bi, Xing-Zi Bu, Qing-Cui Cai, Ce Cao, Xue-Lei Camisasca, Anna-Elisa Chang, Zhi Chen, Gang Chen, Li Chen, Tian-Xiang Chen, Wen Chen, Yi-Bao Chen, Yong Chen, Yu-Peng Coughlin, Michael W. Cui, Wei-Wei Dai, Zi-Gao Hussenot-Desenonges, T. Du, Yan-Qi Du, Yuan-Yuan Du, Yun-Fei Fan, Cheng-Cheng Frontera, Filippo Gao, He Gao, Min Ge, Ming-Yu Gong, Ke Gu, Yu-Dong Guan, Ju Guo, Dong-Ya Guo, Zhi-Wei Guidorzi, Cristiano Han, Da-Wei He, Jian-Jian He, Jun-Wang Hou, Dong-Jie Huang, Yue Huo, Jia Ji, Zhen Jia, Shu-Mei Jiang, Wei-Chun Kann, David Alexander Klotz, A. Kong, Ling-Da Lan, Lin Li, An Li, Bing Li, Chao-Yang Li, Cheng-Kui Li, Gang Li, Mao-Shun Li, Ti-Pei Li, Wei Li, Xiao-Bo Li, Xin-Qiao Li, Xu-Fang Li, Yan-Guo Li, Zheng-Wei Liang, Jing Liang, Xiao-Hua Liao, Jin-Yuan Lin, Lin Liu, Cong-Zhan Liu, He-Xin Liu, Hong-Wei Liu, Jia-Cong Liu, Xiao-Jing Liu, Ya-Qing Liu, Yu-Rong Lu, Fang-Jun Lu, Hong Lu, Xue-Feng Luo, Qi Luo, Tao Ma, Bin-Yuan Ma, Fu-Li Ma, Rui-Can Ma, Xiang Maccary, Romain Mao, Ji-Rong Meng, Bin Nie, Jian-Yin Orlandini, Mauro Ou, Ge Peng, Jing-Qiang Peng, Wen-Xi Qiao, Rui Qu, Jin-Lu Ren, Xiao-Qin Shi, Jing-Yan Shi, Qi Song, Li-Ming Song, Xin-Ying Su, Ju Sun, Gong-Xing Sun, Liang Sun, Xi-Lei Tan, Wen-Jun Tan, Ying Tao, Lian Tuo, You-Li Turpin, Damien Wang, Jin-Zhou Wang, Chen Wang, Chen-Wei Wang, Hong-Jun Wang, Hui Wang, Jin Wang, Ling-Jun Wang, Peng-Ju Wang, Ping Wang, Wen-Shuai Wang, Xiang-Yu Wang, Xi-Lu Wang, Yu-Sa Wang, Yue Wen, Xiang-Yang Wu, Bo-Bing Wu, Bai-Yang Wu, Hong Xiao, Sheng-Hui Xiao, Shuo Xiao, Yun-Xiang Xie, Sheng-Lun Xiong, Shao-Lin Xiong, Sen-Lin Xu, Dong Xu, He Xu, Yan-Jun Xu, Yan-Bing Xu, Ying-Chen Xu, Yu-Peng Xue, Wang-Chen Yang, Sheng Yang, Yan-Ji Yang, Zi-Xu Ye, Wen-Tao Yi, Qi-Bin Yi, Shu-Xu Yin, Qian-Qing You, Yuan Yu, Yun-Wei Yu, Wei Yu, Wen-Hui Zeng, Ming Zhang, Bing Zhang, Bin-Bin Zhang, Da-Li Zhang, Fan Zhang, Hong-Mei Zhang, Juan Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences 19B Yuquan Road Beijing100049 China Institut für Astronomie und Astrophysik Kepler Center for Astro and Particle Physics Eberhard Karls Universität Sand 1 Tübingen72076 Germany College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China Department of Physics and Earth Science University of Ferrara Via Saragat 1 Ferrara44122 Italy Department of Astronomy Beijing Normal University Beijing100875 China CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Hefei230026 China Department of Astronomy Tsinghua University Beijing100084 China Southwest Jiaotong University Chengdu610092 China INAF Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 101 Bologna40129 Italy University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China College of physics Sciences Technology Hebei University No. 180 Wusi Dong Road Lian Chi District Hebei Baoding071002 China INFN - Sezione di Ferrara Via Saragat 1 Ferrara44122 Italy Physics and Space Science College China West Normal University China Yunnan Observatories Chinese Academy of Sciences Yunnan Province Kunming650011 China Key Laboratory of Space Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China School of Astronomy and Space Science Nanjing University Nanjing210093 China School of Physics and Optoelectronics Xiangtan University Yuhu District Hunan Xiangtan411105 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China Institute of Astrophysics Central China Normal University Wuhan430079 China Nevada Center for Astrophysics Department of Physics and Astronomy University of

GRB 221009A is the brightest gamma-ray burst ever detected since the discovery of this kind of energetic explosions. However, an accurate measurement of the prompt emission properties of this burst is very challenging due to its exceptional brightness. With joint observations of Insight-HXMT and GECAM-C, we made an unprecedentedly accurate measurement of the emission during the first ∼1800 s of GRB 221009A, including its precursor, main emission (ME, which dominates the burst in flux), flaring emission and early afterglow, in the hard X-ray to soft gamma-ray band from ∼ 10 keV to ∼ 6 MeV. Based on the GECAM-C unsaturated data of the ME, we measure a record-breaking isotropic equivalent energy (Eiso) of ∼ 1.5 × 1055 erg, which is about eight times the total rest-mass energy of the Sun. The early afterglow data require a significant jet break between 650 s and 1100 s, most likely at ∼ 950 s from the afterglow starting time TAG, which corresponds to a jet opening angle of ∼ 0.7◦ (ηγn)1/8, where n is the ambient medium density in units of cm−3 and ηγ is the ratio between γ-ray energy and afterglow kinetic energy. The beaming-corrected total γ-ray energy Eγ is ∼ 1.15 × 1051 (ηγn)1/4 erg, which is typical for long GRBs. These results suggest that this GRB may have a special central engine, which could launch and collimate a very narrowly beamed jet with an ordinary energy budget, leading to exceptionally luminous gamma-ray radiation per unit solid angle. Alternatively, more GRBs might have such a narrow and bright beam, which are missed by an unfavorable viewing angle or have been detected without distance measurement. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays