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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

Procedia Computer Science 2023年 218卷 2786-2794页

作者： Seifedine Kadry Enrique Herrera-Viedma Rubén González Crespo Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Granada Andalusian Spain Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai 600119 Tamil Nadu India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases; (i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods.

关键词： Lung nodule CT scan CNN segmentation VGG-SegNet Accuracy Detection

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Residual2Vec: debiasing graph embedding with random graphs 21

Residual2Vec: debiasing graph embedding with random graphs

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Sadamori Kojaku Jisung Yoon Isabel Constantino Yong-Yeol Ahn Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Department of Industrial Management and Engineering Pohang University of Science and Technology South Korea Indiana University Network Science Institute Indiana University Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University and Indiana University Network Science Institute Indiana University and Connection Science Massachusetts Institute of Technology

ISBN: (纸本)9781713845393

Graph embedding maps a graph into a convenient vector-space representation for graph analysis and machine learning applications. Many graph embedding methods hinge on a sampling of context nodes based on random walks. However, random walks can be a biased sampler due to the structural properties of graphs. Most notably, random walks are biased by the degree of each node, where a node is sampled proportionally to its degree. The implication of such biases has not been clear, particularly in the context of graph representation learning. Here, we investigate the impact of the random walks' bias on graph embedding and propose residual2vec, a general graph embedding method that can debias various structural biases in graphs by using random graphs. We demonstrate that this debiasing not only improves link prediction and clustering performance but also allows us to explicitly model salient structural properties in graph embedding.

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Quantum computing quantum Monte Carlo with hybrid tensor network for electronic structure calculations

arXiv

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arXiv 2023年

作者： Kanno, Shu Nakamura, Hajime Kobayashi, Takao Gocho, Shigeki Hatanaka, Miho Yamamoto, Naoki Gao, Qi Mitsubishi Chemical Corporation Science & Innovation Center Yokohama227-8502 Japan Quantum Computing Center Keio University Yokohama223-8522 Japan IBM Quantum – IBM Research Tokyo Tokyo103-8510 Japan School of Fundamental Science and Technology Faculty of Science and Technology Keio University Yokohama223-8522 Japan Department of Applied Physics and Physico-Informatics Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan

Quantum computers have a potential for solving quantum chemistry problems with higher accuracy than classical computers. Quantum computing quantum Monte Carlo (QC-QMC) is a QMC with a trial state prepared in quantum circuit, which is employed to obtain the ground state with higher accuracy than QMC alone. We propose an algorithm combining QC-QMC with a hybrid tensor network to extend the applicability of QC-QMC beyond a single quantum device size. In a two-layer quantum-quantum tree tensor, our algorithm for the larger trial wave function can be executed than preparable wave function in a device. Our algorithm is evaluated on the Heisenberg chain model, graphite-based Hubbard model, hydrogen plane model, and MonoArylBiImidazole using full configuration interaction QMC. Our algorithm can achieve energy accuracy (specifically, variance) several orders of magnitude higher than QMC, and the hybrid tensor version of QMC gives the same energy accuracy as QC-QMC when the system is appropriately decomposed. Moreover, we develop a pseudo-Hadamard test technique that enables efficient overlap calculations between a trial wave function and an orthonormal basis state. In a real device experiment by using the technique, we obtained almost the same accuracy as the statevector simulator, indicating the noise robustness of our algorithm. These results suggests that the present approach will pave the way to electronic structure calculation for large systems with high accuracy on current quantum devices. © 2023, CC BY-NC-SA.

关键词： Tensors

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A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate

A Comparative Study of Gaussian Process Machine Learning and...

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International Conference on Computer and Automation Engineering, ICCAE

作者： Muhammad Naeim Mohd Aris Shalini Nagaratnam Nurul Nnadiah Zakaria Muhammad Fadhirul Anuar Mohd Azami Muhammad Afiq Ikram Samsudin Ernee Sazlinayati Othman Department of American Degree Transfer Program School of American Education Sunway University Bandar Sunway Malaysia Department of Econometrics and Business Statistics School of Business Monash University Malaysia Bandar Sunway Malaysia Department of Fundamental and Applied Sciences Faculty of Science and Information Technology Universiti Teknologi PETRONAS Seri Iskandar Malaysia Department of American Degree Transfer Programme School of American Education Sunway University Bandar Sunway Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Seremban 3 Malaysia Centre of Foundation Studies Universiti Teknologi MARA Cawangan Selangor Kampus Dengkil Dengkil Malaysia

ISBN: (数字)9798350370058

ISBN: (纸本)9798350370164

This study explores and compares the capability of Gaussian process machine learning (GPML) with time series analysis techniques, which are autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA) and cubic spline interpolation, in modeling unemployment rate in Malaysia over the period of 1991 to 2022. Six predictive models are developed based on the observations. The predictive performance of each model is quantified using mean absolute error (MAE) and mean squared error (MSE). GPML demonstrated the best predictive model, exhibiting minimal MAE and MSE compared to the time series analysis techniques. The statistical analysis also concluded no significant mean difference between the GPML model and the actual observations, implying a robust predictive model in predicting the unemployment rate in Malaysia.

关键词： Analytical models Interpolation Statistical analysis Time series analysis Gaussian processes Machine learning Predictive models

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Analysis and mean-field derivation of a porous-medium equation with fractional diffusion

arXiv

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arXiv 2021年

作者： Chen, Li Holzinger, Alexandra Jüngel, Ansgar Zamponi, Nicola University of Mannheim School of Business Informatics and Mathematics Mannheim68131 Germany Institute for Analysis and Scientific Computing Vienna University of Technology Wiedner Hauptstraße 8-10 Wien1040 Austria

MSC Codes 35K65, 35R11, 60H10, 60H30A mean-field-type limit from stochastic moderately interacting many-particle systems with singular Riesz potential is performed, leading to nonlocal porous-medium equations in the whole space. The nonlocality is given by the inverse of a fractional Laplacian, and the limit equation can be interpreted as a transport equation with a fractional pressure. The proof is based on Oelschläger's approach and a priori estimates for the associated diffusion equations, coming from energy-type and entropy inequalities as well as parabolic regularity. An existence analysis of the fractional porous-medium equation is also provided, based on a careful regularization procedure, new variants of fractional Gagliardo-Nirenberg inequalities, and the div-curl lemma. A consequence of the mean-field limit estimates is the propagation of chaos property. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

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Non-Linear Model Predictive Speed Control of Six Phase Squirrel Cage Generator in Wind Energy System

Non-Linear Model Predictive Speed Control of Six Phase Squir...

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International Conference on Electrical, Electronics and System Engineering (ICEESE)

作者： Ibrahim Abdulwahab Umar Musa Shamsul Sarip Abdullahi Abubakar Mas'ud Firdaus Muhammad-Sukki Shehu A. Faskari Abubakar T. Mahmud Electical Engineering Ahmadu Bello University Zaria Nigeria Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Electical Engineering Jubail Industrial College Jubail-KSA School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh Scotland UK Electrical Engineering Baze University Abuja Nigeria Hydraulic Equipment Development Institute NASENI Abuja Nigeria

Due to the intermittent nature of wind, varying wind speed causes imbalance in the current of the six phase squirrel cage induction generator (SCIG). These changes in the input signal create inbalance in the current and therefore causes deviations in the torque and flux of the generator. Therefore, this work develops non-linear model predictive control (NMPC) scheme for the generator to ensure that uncertainties in the input of the wind energy conversion system (WECS) have little effect in the quality of output power supplied to the grid. Simulation results obtained when NMPC technique was used in controlling the generator in the WECS showed the effectiveness of the aforementioned technique. The results obtained from the developed scheme were compared with those obtained when backstepping controller was used in controlling the six phase SCIG. It was observed that the developed scheme outperformed the backstepping technique in terms of electromagnetic torque by 16.844%, while a 61.1% improvement was observed in terms of settling time for the rotor flux of the developed technique over that of the backstepping. Finally, the results obtained for the DC link voltage output showed an improvement in terms of overshoot and settling time respectively.

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Towards the development of data migration method for MongoDB and Cassandra databases

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AIP Conference Proceedings 2024年第1期2991卷

作者： Lim Fung Ji Nurulhuda Firdaus Mohd Azmi Sarina Sulaiman Faculty of Computing and Information Technology Tunku Abdul Rahman University College 53100 Kuala Lumpur Malaysia Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia 54100 Kuala Lumpur Malaysia UTM Big Data Centre Soft Computing Research Group School of Computing Universiti Teknologi Malaysia 81310 Johor Bahru Johor Malaysia

The migration of data between NoSQL databases faces challenges due to the heterogeneity of the data schema and storage paradigm between the data-bases. In addition, the flexibility of storage paradigm allows non-standardized schema between records in NoSQL database. For example, in MongoDB, documents within the same collection could have different schema. However, this flexi-schema may cause difficulties in data migration as there is without any standard data schema to follow by for the target data-base during data migration. This paper proposed a migration method ap-plied to suit the different data model or schema for different NoSQL databases. The paper focus on the migration between document-based database and column-based database. The paper also discusses on the database schema of the target database that need to store and support the data that is with different schema.

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Measuring fairness preferences is important for artificial intelligence in health care

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The Lancet Digital Health 2024年第5期6卷 e302-e304页

作者： Näher, Anatol-Fiete Krumpal, Ivar Antão, Esther-Maria Ong, Erika Rojo, Marina Kaggwa, Fred Balzer, Felix Celi, Leo Anthony Braune, Katarina Wieler, Lothar H Agha-Mir-Salim, Louis Digital Global Public Health Hasso Plattner Institute for Digital Engineering University of Potsdam Potsdam14482 Germany Institute of Medical Informatics Charité–Universitätsmedizin Berlin Berlin Germany Faculty of Social Science and Philosophy University of Leipzig Leipzig Germany College of Medicine University of the Philippines Manila Philippines Department of Public Health School of Medicine University of Buenos Aires Buenos Aires Argentina Department of Computer Science Faculty of Computing and Informatics Mbarara University of Science and Technology Mbarara Uganda Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA United States Department of Biostatistics Harvard T H Chan School of Public Health BostonMA United States

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Generalization error bound for hyperbolic ordinal embedding

arXiv

引用

arXiv 2021年

作者： Suzuki, Atsushi Nitanda, Atsushi Wang, Jing Xu, Linchuan Cavazza, Marc Yamanishi, Kenji School of Computing and Mathematical Sciences Faculty of Liberal Arts and Sciences University of Greenwich United Kingdom Department of Artificial Intelligence Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Japan Department of Computing Hong Kong Polytechnic University Hong Kong Department of Mathematical Informatics Graduate School of Information Science and Technology University of Tokyo Japan

Hyperbolic ordinal embedding (HOE) represents entities as points in hyperbolic space so that they agree as well as possible with given constraints in the formof entity i ismore similar to entity j than to entity k. It has been experimentally shown that HOE can obtain representations of hierarchical data such as a knowledge base and a citation network effectively, owing to hyperbolic space's exponential growth property. However, its theoretical analysis has been limited to ideal noiseless settings, and its generalization error in compensation for hyperbolic space's exponential representation ability has not been guaranteed. The difficulty is that existing generalization error bound derivations for ordinal embedding based on the Gramian matrix do not work in HOE, since hyperbolic space is not inner-product space. In this paper, through our novel characterization of HOE with decomposed Lorentz Gramian matrices, we provide a generalization error bound of HOE for the first time, which is at most exponential with respect to the embedding space's radius. Our comparison between the bounds of HOE and Euclidean ordinal embedding shows that HOE's generalization error is reasonable as a cost for its exponential representation ability. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge based systems

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DB-GNN: Dual-Branch Graph Neural Network with Multi-Level Contrastive Learning for Jointly Identifying Within- and Cross-Frequency Coupled Brain Networks

arXiv

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arXiv 2025年

作者： Wang, Xiang Xu, Hui Yang, Xibei Cai, Jing Xue, Wei Zhou, Ta ACS Lab Huawei Technologies Shanghai China Joint Laboratory of Bioimaging Technology and Applications SAS-SIM-IT & MEDI Shanghai China School of Computing Jiangsu University of Science and Technology Zhenjiang China School of Health Technology and Informatics the Hong Kong Polytechnic University Hong Kong Academy for Engineering & Technology Fudan University Shanghai China

Within-frequency coupling (WFC) and cross-frequency coupling (CFC) in brain networks reflect neural synchronization within the same frequency band and cross-band oscillatory interactions, respectively. Their synergy provides a comprehensive understanding of neural mechanisms underlying cognitive states such as emotion. However, existing multi-channel EEG studies often analyze WFC or CFC separately, failing to fully leverage their complementary properties. This study proposes a dual-branch graph neural network (DB-GNN) to jointly identify within- and cross-frequency coupled brain networks. Firstly, DB-GNN leverages its unique dual-branch learning architecture to efficiently mine global collaborative information and local cross-frequency and within-frequency coupling information. Secondly, to more fully perceive the global information of cross-frequency and within-frequency coupling, the global perception branch of DB-GNN adopts a Transformer architecture. To prevent overfitting of the Transformer architecture, this study integrates prior within- and cross-frequency coupling information into the Transformer inference process, thereby enhancing the generalization capability of DB-GNN. Finally, a multi-scale graph contrastive learning regularization term is introduced to constrain the global and local perception branches of DB-GNN at both graph-level and node-level, enhancing its joint perception ability and further improving its generalization performance. Experimental validation on the emotion recognition dataset shows that DB-GNN achieves a testing accuracy of 97.88% and an F1-score of 97.87%, reaching the state-of-the-art performance. © 2025, CC BY.

关键词： Graph neural networks

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