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Informatics and Health 2024年第2期1卷 70-81页

作者： Temidayo Oluwatosin Omotehinwa David Opeoluwa Oyewola Ervin Gubin Moung Department of Mathematics and Computer Science Federal University of Health SciencesP.M.B.145OtukpoNigeria Department of Mathematics and Statistics Federal University KashereP.M.B.0182GombeNigeria Data Technologies and Applications(DaTA)Research Group Faculty of Computing and InformaticsUniversiti Malaysia SabahKota Kinabalu 88400SabahMalaysia

Background:Coronary heart disease(CHD)remains a prominent cause of mortality globally,necessitating early and accurate detection *** diagnostic approaches can be invasive,costly,and time-consuming,necessitating the need for more efficient *** aimed to optimize the Light Gradient-Boosting Machine(LightGBM)algorithm to enhance its performance and accuracy in the early detection of CHD,providing a reliable,cost-effective,and non-invasive diagnostic ***:The Framingham Heart Study(FHS)dataset publicly available on Kaggle was used in this *** Imputations by Chained Equations(MICE)were applied separately to the training and testing sets to handle missing ***-SMOTE(Synthetic Minority Over-sampling Technique)was used on the training set to balance the *** LightGBM algorithm was selected for its efficiency in classification tasks,and Bayesian Optimization with Tree-structured Parzen Estimator(TPE)was employed to fine-tune its *** optimized LightGBM model was trained and evaluated using metrics such as accuracy,precision,and AUC-ROC on the test set,with cross-validation to ensure robustness and ***:The optimized LightGBM model showed significant improvement in early CHD *** baseline LightGBM model with dropped missing values had an accuracy of 0.8333,sensitivity of 0.1081,precision of 0.3429,F1 score of 0.1644,and AUC of *** MICE imputation,performance improved to an accuracy of 0.9399,sensitivity of 0.6693,precision of 0.9043,F1 score of 0.7692,and AUC of *** combined approach of Borderline-SMOTE,MICE imputation,and TPE for LightGBM achieved an accuracy of 0.9882,sensitivity of 0.9370,precision of 0.9835,F1 score of 0.9597,and AUC of 0.9963,indicating a highly effective and robust ***:The optimized model demonstrated outstanding performance in early CHD *** study’s strengths include its comprehensive approach to addres

关键词： Clinical decision making Coronary heart disease Light gradient-boosting machine Machine learning MICE Tree-structured Parzen estimator

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EEG Sleep Stages Classification using Transformer-based Models: A Review

EEG Sleep Stages Classification using Transformer-based Mode...

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computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Yuin Torng Yew Jia Hong Puah Chow Khuen Chan Norita Mohd Zain Chean Khim Toa Sim Kuan Goh Department of Biomedical Engineering Faculty of Engineering Universiti Malaya School of Energy and Chemical Engineering Xiamen University Malaysia School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia School of Computing and Data Science Xiamen University Malaysia

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

Sleep research is vital for understanding the complexities of human physiology and promoting overall health and well-being. Despite its importance, sleep disorders are widespread and can significantly impact daily functioning and quality of life. A critical aspect of sleep research is the classification of sleep stages, traditionally performed manually but now increasingly automated using computational techniques. However, current classification models face challenges, particularly in accurately distinguishing between sleep stages such as N1, REM, and wakefulness, and in classifying different types of sleep, such as micro sleep and macro sleep. Addressing these research challenges can lead to significant advancements in automated sleep scoring and contribute to the development of clinically deployable sleep diagnostic tools. Recently, there is a growing interest in leveraging advanced deep learning models, especially transformers, for analyzing EEG signals, which are crucial for sleep stage classification due to their temporal dependency. In contrast to convolutional neural networks (CNNs), which have been commonly used for this purpose, transformer architectures offer promise in capturing long-range dependencies in time-series data. Furthermore, the integration of multimodal learning approaches, such as combining EEG signals with other physiological data, holds the potential for improving the accuracy and robustness of sleep stage classification. This review article focuses on transformer-based models for sleep stages classification, and also to clarify the current gaps in sleep classification.

关键词： Deep learning Accuracy Sleep Reviews Computational modeling Computer architecture Transformers Brain modeling Electroencephalography Physiology

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Sparse graph counting and Kelley–Meka bounds for binary systems

arXiv

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arXiv 2023年

作者： Filmus, Yuval Hatami, Hamed Hosseini, Kaave Kelman, Esty The Henry & Marilyn Taub Faculty of Computer Science Faculty of Mathematics Technion School of Computer Science McGill University Canada Department of Computer Science University of Rochester United States Department of Computer Science Department of Computing & Data Sciences Boston University CSAIL Massachusetts Institute of Technology United States

In a recent breakthrough, Kelley and Meka (FOCS 2023) obtained a strong upper bound on the density of sets of integers without non-trivial three-term arithmetic progressions. In this work, we extend their result, establishing similar bounds for all linear patterns defined by binary systems of linear forms, where "binary" indicates that every linear form depends on exactly two variables. Prior to our work, no strong bounds were known for such systems even in the finite field model setting. A key ingredient in our proof is a graph counting lemma. The classical graph counting lemma, developed by Thomason (Random Graphs 1985) and Chung, Graham, and Wilson (Combinatorica 1989), is a fundamental tool in combinatorics. For a fixed graph H, it states that the number of copies of H in a pseudorandom graph G is similar to the number of copies of H in a purely random graph with the same edge density as G. However, this lemma is only non-trivial when G is a dense graph. In this work, we prove a graph counting lemma that is also effective when G is sparse. Moreover, our lemma is well-suited for density increment arguments in additive number theory. As an immediate application, we obtain a strong bound for the Turán problem in abelian Cayley sum graphs: let Γ be a finite abelian group with odd order. If a Cayley sum graph on Γ does not contain any r-clique as a subgraph, it must have at most 2−Ωr(log1/16 |Γ|) · |Γ|2 edges. These results hinge on the technology developed by Kelley and Meka and the follow-up work by Kelley, Lovett, and Meka (STOC 2024). © 2023, CC BY.

关键词： Graph theory

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Graph Cross-Correlated Network for Recommendation

arXiv

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arXiv 2024年

作者： Chen, Hao Bei, Yuanchen Huang, Wenbing Chen, Shengyuan Huang, Feiran Huang, Xiao Faculty of Data Science City University of Macau China College of Computer Science and Technology Zhejiang University Hangzhou China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong College of Information Science and Technology Guangzhou China

Collaborative filtering (CF) models have demonstrated remarkable performance in recommender systems, which represent users and items as embedding vectors. Recently, due to the powerful modeling capability of graph neural networks for user-item interaction graphs, graph-based CF models have gained increasing attention. They encode each user/item and its subgraph into a single super vector by combining graph embeddings after each graph convolution. However, each hop of the neighbor in the user-item subgraphs carries a specific semantic meaning. Encoding all subgraph information into single vectors and inferring user-item relations with dot products can weaken the semantic information between user and item subgraphs, thus leaving untapped potential. Exploiting this untapped potential provides insight into improving performance for existing recommendation models. To this end, we propose the Graph Cross-correlated Network for Recommendation (GCR), which serves as a general recommendation paradigm that explicitly considers correlations between user/item subgraphs. GCR first introduces the Plain Graph Representation (PGR) to extract information directly from each hop of neighbors into corresponding PGR vectors. Then, GCR develops Cross-Correlated Aggregation (CCA) to construct possible cross-correlated terms between PGR vectors of user/item subgraphs. Finally, GCR comprehensively incorporates the cross-correlated terms for recommendations. Experimental results show that GCR outperforms state-of-the-art models on both interaction prediction and click-through rate prediction tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Collaborative filtering

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Deep learning for brain age estimation: A systematic review

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Information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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Enabling Technologies for Web 3.0: A Comprehensive Survey

arXiv

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arXiv 2023年

作者： Hassan, Md Arif Jamshidi, Mohammad Manh, Bui Duc Chu, Nam H. Nguyen, Chi-Hieu Hieu, Nguyen Quang Nguyen, Cong T. Hoang, Dinh Thai Nguyen, Diep N. Van Huynh, Nguyen Alsheikh, Mohammad Abu Dutkiewicz, Eryk School of Electrical and Data Engineering University of Technology Sydney Australia School of Computing Engineering and the Built Environment Edinburgh Napier University United Kingdom Faculty of Science and Technology University of Canberra Australia

Web 3.0 represents the next stage of Internet evolution, aiming to empower users with increased autonomy, efficiency, quality, security, and privacy. This evolution can potentially democratize content access by utilizing the latest developments in enabling technologies. In this paper, we conduct an in-depth survey of enabling technologies in the context of Web 3.0, such as blockchain, semantic web, 3D interactive web, Metaverse, Virtual reality/Augmented reality, Internet of Things technology, and their roles in shaping Web 3.0. We commence by providing a comprehensive background of Web 3.0, including its concept, basic architecture, potential applications, and industry adoption. Subsequently, we examine recent breakthroughs in IoT, 5G, and blockchain technologies that are pivotal to Web 3.0 development. Following that, other enabling technologies, including AI, semantic web, and 3D interactive web, are discussed. Utilizing these technologies can effectively address the critical challenges in realizing Web 3.0, such as ensuring decentralized identity, platform interoperability, data transparency, reducing latency, and enhancing the system's scalability. Finally, we highlight significant challenges associated with Web 3.0 implementation, emphasizing potential solutions and providing insights into future research directions in this field. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Web

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AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing

arXiv

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arXiv 2023年

作者： Chen, Zhaoliang Wu, Zhihao Lin, Zhenghong Wang, Shiping Plant, Claudia Guo, Wenzhong The College of Computer and Data Science Fuzhou University Fuzhou350116 China The Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China The Faculty of Computer Science Austria The research network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria

Graph Convolutional Network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-based models suffer from the notorious over-smoothing issue, owing to which shallow networks are extensively adopted. This may be problematic for complex graph datasets because a deeper GCN should be beneficial to propagating information across remote neighbors. Recent works have devoted effort to addressing over-smoothing problems, including establishing residual connection structure or fusing predictions from multi-layer models. Because of the indistinguishable embeddings from deep layers, it is reasonable to generate more reliable predictions before conducting the combination of outputs from various layers. In light of this, we propose an Alternating Graph-regularized Neural Network (AGNN) composed of Graph Convolutional Layer (GCL) and Graph Embedding Layer (GEL). GEL is derived from the graph-regularized optimization containing Laplacian embedding term, which can alleviate the over-smoothing problem by periodic projection from the low-order feature space onto the high-order space. With more distinguishable features of distinct layers, an improved Adaboost strategy is utilized to aggregate outputs from each layer, which explores integrated embeddings of multi-hop neighbors. The proposed model is evaluated via a large number of experiments including performance comparison with some multi-layer or multi-order graph neural networks, which reveals the superior performance improvement of AGNN compared with state-of-the-art models. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Vehicle Check-In data-driven POI Recommendation Based on Improved SVD and Graph Convolutional Network 19

Vehicle Check-In Data-driven POI Recommendation Based on Imp...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Yuwen Zhang, Jie Dou, Ruihan Zhou, Xiaokang Xu, Xiaolong Wang, Shoujin Qi, Lianyong College of Computer Science and Technology Qingdao China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology China Shanghai Meteorological Information and Technology Support Center China University of Waterloo Faculty of Mathematics Waterloo Canada Shiga University Faculty of Data Science Japan Riken Center for Advanced Intelligence Project Riken Japan Nanjing University of Information Science and Technology School of Computer and Software China Macquarie University School of Computing Australia Qufu Normal University School of Computer Science China

ISBN: (纸本)9798350346558

The application of automobile tool software is becoming increasingly widespread, and its functions are becoming increasingly diverse. Using vehicle traffic data to give users with recommendations for Points-of-interest (POI) is becoming an intriguing research challenge. However, the current POI recommendation is either a sequence-based method, ignoring the interaction information between users and POIs. Or it needs a lot of model training time to use graph correlation method to mine the interaction between users or vehicles and POIs. In order to address these issues, we propose a method for vehicle check-in data-driven POI recommendation based on improved Singular Value Decomposition (SVD) and Graph Convolutional Network (GCN). First, we apply SVD to replace the neighborhood aggregation update method in traditional GCN, update the node embedding with fewer parameters and faster speed. Second, we propose an information enhancement strategy to make up for the information loss caused by the SVD. Experiments on large vehicle check-in datasets demonstrate that our method is both faster and more effective when making POI recommendations. © 2022 IEEE.

关键词： Singular value decomposition

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Chernobyl Disaster Optimizer Algorithm for Feature Selection Problems

Chernobyl Disaster Optimizer Algorithm for Feature Selection...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hossam M. J. Mustafa Hisham A. Shehadeh Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Department of Information Technology Al-Huson University College Al-Balqa Applied University Al-Huson Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The rapid growth of high-dimensional data in Machine Learning (ML) applications presents essential computational challenges. This phenomenon manifests primarily through redundant feature space dimensions, requiring robust dimensionality reduction approaches such as Feature Selection (FS). This research introduces an investigation using the Chernobyl Disaster Optimizer (CDO), a physics-inspired metaheuristic optimization algorithm for solving feature selection problems. We propose a CDO algorithm that utilizes dynamic radiation dispersion mechanisms for wrapper-based feature selection, where solution fitness is evaluated through accuracy metrics obtained from a K-nearest-neighbor (KNN) classifier. Comprehensive empirical analysis across eight UCI repository benchmark datasets shows a satisfactory performance of CDO in classification accuracy and feature selection efficiency with an average of 45.8% compared to competing metaheuristic approaches. Friedman tests, with p-value = 0.0302, confirm the algorithm's significant performance advantages. The results demonstrate that CDO is a reliable optimization algorithm for feature selection tasks, particularly effective in high-dimensional classification problems.

关键词： Dimensionality reduction Measurement Accuracy Heuristic algorithms Disasters Metaheuristics Nearest neighbor methods Feature extraction Classification algorithms Reliability

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Ontology-Based data Federation 35

Ontology-Based Data Federation

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35th International Workshop on Description Logics, DL 2022

作者： Gu, Zhenzhen Lanti, Davide Mosca, Alessandro Xiao, Guohui Xiong, Jing Calvanese, Diego KRDB Research Centre for Knowledge and Data Free University of Bozen-Bolzano Bolzano39100 Italy Faculty of Social Sciences University of Bergen Bergen Norway Ontopic S.R.L. Bolzano39100 Italy Department of Computing Science Umeå University Umeå901 87 Sweden

We formally introduce ontology-based data federation (OBDF), to denote a framework combining ontology-based data access (OBDA) with a data federation layer, which virtually exposes multiple heterogeneous sources as a single relational database. In this setting, the SQL queries generated by the OBDA component by translating user SPARQL queries are further transformed by the data federation layer so as to be efficiently executed over the data sources. The structure of these SQL queries directly affects their execution time in the data federation layer and their optimization is crucial for performance. We propose here novel optimizations specific for OBDF, which are based on "hints" about existing data redundancies in the sources, empty join operations, and the need for materialized views. Such hints can be systematically inferred by analyzing the OBDA mappings and ontology and exploited to simplify the query structure. We also carry out an experimental evaluation in which we show the effectiveness of our optimizations. © 2022 Copyright for this paper by its authors.

关键词： Ontology

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