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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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PWG-IDS: An intrusion detection model for solving class imbalance in IIoT networks using generative adversarial networks

arXiv

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arXiv 2021年

作者： Zhang, Lei Jiang, Shuaimin Shen, Xiajiong Gupta, Brij B. Tian, Zhihong Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng47500 China Institute of Data and Knowledge Engineering Henan University Kaifeng47500 China School of Computer and Information Engineering Henan University Kaifeng47500 China National Institute of Technology Kurukshetra India Asia University Taichung Taiwan The Department of Cyberspace Insitute of Advanced Technology GuangZhou University GuangZhou510006 China

With the continuous development of industrial IoT (IIoT) technology, network security is becoming more and more important. And intrusion detection is an important part of its security. However, since the amount of attack traffic is very small compared to normal traffic, this imbalance makes intrusion detection in it very difficult. To address this imbalance, an intrusion detection system called pretraining Wasserstein generative adversarial network intrusion detection system (PWG-IDS) is proposed in this paper. This system is divided into two main modules: 1) In this module, we introduce the pretraining mechanism in the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) for the first time, firstly using the normal network traffic to train the WGAN-GP, and then inputting the imbalance data into the pre-trained WGAN-GP to retrain and generate the final required data. 2) Intrusion detection module: We use LightGBM as the classification algorithm to detect attack traffic in IIoT networks. The experimental results show that our proposed PWG-IDS outperforms other models, with F1-scores of 99% and 89% on the 2 datasets, respectively. And the pretraining mechanism we proposed can also be widely used in other GANs, providing a new way of thinking for the training of GANs. Copyright © 2021, The Authors. All rights reserved.

关键词： Intrusion detection

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Stratified subsampling for regression analysis of big count response data

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AIP Conference Proceedings 2023年第1期2756卷

作者： Yeh-Ching Low Seng-Huat Ong Department of Computing and Information Systems School of Engineering and Technology Sunway University 47500 Selangor Malaysia Institute of Actuarial Science and Data Analytics UCSI University 56000 Kuala Lumpur Malaysia

Count frequency data are found in many applications such as traffic accident analysis, car insurance claims, hospital admissions records and adverse vaccine reaction data. In some cases, these data have high zero counts and/or heavy tails due to cases such as no claim or no incidence in insurance and accident data, respectively. In regression modelling, the data size can be formidable due to either large sample size and/or large number of covariates or predictors which leads to computational challenges. Although many computer engineering solutions are available through supercomputers and parallel computing, there exists limitations due to cost and accessibility. As such, statistical solutions have been considered to ameliorate the challenges posed by regression modelling of big data. In general, these statistical solutions are classified as divide and conquer approach, fine-to-coarse method and subsampling methods. To address the problem of large data size in count regression modelling, we propose a stratified subsampling strategy according to frequency classes with shrinkage leveraging for statistical inference. An attractive feature of this strategy is in its ability to preserve the characteristics of data like over dispersion, high zero counts and/or heavy-tailed. A Monte Carlo simulation study is conducted to investigate the performance of the proposed stratified subsampling method in regression modelling with big count data. The regression analysis will be illustrated using a family of mixed Poisson regression models which have been shown to be flexible in its ability to model count data with high zero counts and/or a long tail.

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The Interrelationships between the length of stay, readmission, and post-acute care referral in cardiac surgery patients

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Healthcare Analytics 2022年 2卷 100062-100062页

作者： Sultana, Ineen Erraguntla, Madhav Kum, Hye-Chung Delen, Dursun Lawley, Mark Department of Industrial and Systems Engineering Texas A&M University College Station TX United States Data Scientist WVU Research Corporation West Virginia University United States School of Public Health Texas A&M University College Station TX United States Department of Management Science and Information Systems Spears School of Business Oklahoma State University United States Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey

Prolonged hospital stays, and readmission contribute to substantial healthcare cost. Hence, an assessment of the optimal inpatient length of stay (LOS) associated with lower readmission rate is important for healthcare providers. Post-acute care (PAC) facilities have promising potential to shorten the LOS;however, currently their influence on overall patient outcomes is not well understood. The primary goal of this study is to highlight the interrelated risk factors of LOS and readmission for cardiac patients. The study also examines the influence of PAC referral on LOS and readmission. In this paper, a cohort of 13,982 Coronary Artery Bypass Graft (CABG) and Valve Replacement (VR) patients from 49 hospitals in the U.S. were analyzed with respect to the association of healthcare delivery, demographics, PAC referral, and clinical conditions with LOS and readmission. A generalized linear mixed model and multinomial logistic regression model were developed to evaluate the readmission and LOS associative risk factors, respectively. Referral to PAC was included as a vital predictor in both models to examine its impact on optimal LOS and improving patient outcomes. The results indicate a non-uniform care distribution across census divisions and an inverse relationship between LOS and readmissions. The analytics showed that higher LOS patients were more often referred to PAC and yet they were more prone to readmission except for the patients who were referred to Long Term Care (LTC). This study identifies the effects of healthcare delivery, demographic, comorbidity, and PAC referral factors on readmission and LOS, so that personalized acute and post-acute care coordination can be achieved to improve overall patient outcomes. Interventions such as treating CABG and VR patients with multiple comorbidities for longer time in acute hospitals and increasing LTC referral can result in significant improvement. © 2022 The Author(s)

关键词： Cardiac surgery Length of stay Logistic regression Post-acute care Readmission

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Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis (vol 14, 3750, 2023)

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NATURE COMMUNICATIONS 2023年第1期14卷 1-16页

作者： Takada, Hitomi Sasagawa, Yohei Yoshimura, Mika Tanaka, Kaori Iwayama, Yoshimi Hayashi, Tetsutaro Isomura-Matoba, Ayako Nikaido, Itoshi Kurisaki, Akira Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan Master’s/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

关键词： Adult stem cells Stem-cell differentiation Stem-cell niche

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Design and Comparison of Collaborative Filtering Technology for Product Suggestions in E-Commerce

Design and Comparison of Collaborative Filtering Technology ...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： P. Rajasekar B. Mohanraj S. N. Padhi N. Sivakumar Lavanya J Charles Prabu V Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology Karpagam College of Engineering Coimbatore Tamil Nadu India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Chennai Tamil Nadu India Department of Fashion Designing SRM Institute of Science and Technology CSH Kattankulathur Campus Chennai Tamil Nadu India Department of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (纸本)9781665460859

E-commerce websites and internet purchasing have never been more necessary. Product recommendations are a key tactic employed by online buying platforms. This study compares two different filtering algorithms that are used in product recommendationson websites like Amazon and Flipkart. Reviews of electrical devices, user data, and timestamps were all gathered from Kaggle and put into a dataset. These specifics then undergo several preprocessing steps. The steps are data visualization, duplicate value analysis, missing value analysis, and datatype analysis. The exhibited data is then used to train and test the machine learning models. For this purpose, two machine learning models are developed using two different algorithms. The algorithms K-Nearest Neighbor(KNN) and Support Vector Decomposition (SVD) are employed. The models are then trained to acquire used in the prediction procedure after that. The best models for the prediction process are then determined by scrutinizing the results of both models. The Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) values are located and compared because the findings of the analysis weren't *** erroneous data are plotted into a graph and evaluated to further demonstrate the point. As the SVD method is discovered to be the best of the group, the algorithm's greatest and worst predictions are discovered and examined. Even the poorest model constructed for the study's predictions turned out to be superior to several other prediction algorithms used for product suggestions. This algorithm can be deployed into the backend of any e-commerce website making it more effective.

关键词： Training Support vector machines Analytical models Machine learning algorithms data visualization Machine learning Predictive models

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Lesion2Vec: Deep metric learning for few-shots multiple lesions recognition in wireless capsule endoscopy video: A preprint

arXiv

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arXiv 2021年

作者： Adewole, Sodiq Fernandez, Philip Yeghyayan, Michelle Jablonski, James Copland, Andrew Porter, Michael Syed, Sana Brown, Donald Systems & Information Engineering University of Virginia CharlottesvilleVA22903 United States Department of Pediatrics School of Medicine University of Virginia CharlottesvilleVA United States Data Science Institute University of Virginia CharlottesvilleVA22903 United States

Effective and rapid detection of lesions in the Gastrointestinal tract is critical to how promptly gastroenterologist can respond to some life-threatening diseases. Wireless Capsule Endoscopy (WCE) has revolutionized traditional endoscopy procedure by allowing gastroenterologists visualize the entire GI tract non-invasively. Once the tiny capsule is swallowed, it sequentially capture images of the GI tract at about 2 to 6 frames per second (fps). A single video can last up to 8 hours producing between 30,000 to 100,000 images. Automating the detection of frames containing specific lesion in WCE video would relieve gastroenterologists the arduous task of reviewing the entire video before making diagnosis. While the WCE produces large volume of images, only about 5% of the frames contain lesions that aid the diagnosis process. Convolutional Neural Network (CNN) based models have been very successful in various image classification tasks. However, they suffer excessive parameters, are sample inefficient and rely on very large amount of training data. Deploying a CNN classifier for lesion detection task will require time-to-time fine-tuning to generalize to any unforeseen category. In this paper, we propose a metric-based learning framework followed by a few-shot lesion recognition in WCE data. Metric-based learning is a meta-learning framework designed to establish similarity or dissimilarity between concepts while few-shot learning (FSL) aims to identify new concepts from only a small number of examples. We train a feature extractor to learn a representation for different small bowel lesions using metric-based learning. At the testing stage, the category of an unseen sample is predicted from only a few support examples, thereby allowing the model to generalize to a new category that has never been seen before. We demonstrated the efficacy of this method on real patient capsule endoscopy data with additional category during testing that was not part of our training. Ex

关键词： Convolutional neural networks

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Detection of Pulsar Candidates using Bagging Method

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Procedia Computer Science 2020年 170卷 1096-1101页

作者： Mourad Azhari Abdallah Abarda Altaf Alaoui Badia Ettaki Jamal Zerouaoui Laboratory of Engineering Sciences and Modeling Faculty of Sciences- Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and knowledge Engineering School of Information Sciences Rabat Morocco

The pulsar classification represents a major issue in the astrophysical area. The Bagging Algorithm is an ensemble method widely used to improve the performance of classification algorithms, especially in the case of pulsar search. In this way, our paper tries to prove how the Bagging Method can improve the performance of pulsar candidate detection in connection with four basic classifiers: Core Vector Machines (CVM), the K-Nearest-Neighbors (KNN), the Artificial Neural Network (ANN), and Cart Decision Tree (CDT). The Error Rate, Area Under the Curve (AUC), and Computation Time (CT) are measured to compare the performance of different classifiers. The High Time Resolution Universe (HTRU2) dataset, collected from the UCI Machine Learning Repository, is used in the experimentation phase.

关键词： Bagging CVM ANN KNN CDT AUC Error Rate Computation Time(CT)

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data augmentation in microscopic images for material data mining

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npj Computational Materials 2020年第1期6卷 601-609页

作者： Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China School of Materials Science and Technology Liaoning Technical UniversityLiaoning 114051China The Institute of Statistical Mathematics Research Organization of Information and SystemsTachikawaTokyo 190-8562Japan National Intellectual Property Administration Beijing 100088China Faculty of Engineering Sciences University of BurundiBujumburaBurundi Faculty of Engineering and Technology Palestine Technical University–KadoorieTulkaremPalestine College of Information Science and Engineering China University of PetroleumBeijingChina Key Lab of Petroleum Data Mining China University of PetroleumBeijingChina

Recent progress in material data mining has been driven by high-capacity models trained on large ***,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise ***,we develop a novel transfer learning strategy to address problems of small or insufficient *** strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining *** a specific task of grain instance image segmentation,this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the“image style”information in real *** results show that the model trained with the acquired synthetic data and only 35%of the real data can already achieve competitive segmentation performance of a model trained on all of the real *** the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data,our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation.

关键词： mining instance competitive

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Crowdsourcing Geospatial data for Earth and Human Observations:A Review

Journal of Remote Sensing

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Journal of Remote Sensing 2024年第1期4卷 766-790页

作者： Xiao Huang Siqin Wang Di Yang Tao Hu Meixu Chen Mengxi Zhang Guiming Zhang Filip Biljecki Tianjun Lu Lei Zou Connor Y.H.Wu Yoo Min Park Xiao Li Yunzhe Liu Hongchao Fan Jessica Mitchell Zhenlong Li Alexer Hohl Department of Environmental Sciences Emory UniversityAtlantaGAUSA Spatial Sciences Institute University of Southern CaliforniaLos AngelesCAUSA Wyoming Geographic Information Science Center University of WyomingLaramieWYUSA Department of Geography Oklahoma State UniversityStillwaterOKUSA Department of Geography and Planning University of LiverpoolLiverpoolUK Carilion School of Medicine Virginia TechBlacksburgVAUSA Department of Geography&the Environment University of DenverDenverCOUSA Department of Architecture National University of SingaporeSingaporeSingapore Department of Epidemiology and Environmental Health College of Public HealthUniversity of KentuckyLexingtonKYUSA Department of Geography Texas A&M UniversityCollege StationTXUSA Department of Management Science and Information Systems Oklahoma State UniversityStillwaterOKUSA Department of Geography University of ConnecticutStorrsCTUSA Transport Studies Unit University of OxfordOxfordUK The MRC Centre for Environment and Health Imperial College LondonLondonUK Department of Civil and Environmental Engineering Norwegian University of Science and TechnologyTrondheimNorway Spatial Analysis Lab University of MontanaMissoulaMTUSA Geoinformation and Big Data Research Laboratory Department of GeographyThe Pennsylvania State UniversityUniversity ParkPAUSA Department of Geography The University of UtahSalt Lake CityUTUSA.

The transformation from authoritative to user-generated data landscapes has garnered considerable attention,notably with the proliferation of crowdsourced geospatial *** by advancements in digital technology and high-speed communication,this paradigm shift has democratized data collection,obliterating traditional barriers between data producers and *** previous literature has compartmentalized this subject into distinct platforms and application domains,this review offers a holistic examination of crowdsourced geospatial *** a narrative review approach due to the interdisciplinary nature of the topic,we investigate both human and Earth observations through crowdsourced *** review categorizes the diverse applications of these data and rigorously examines specific platforms and paradigms pertinent to data ***,it addresses salient challenges,encompassing data quality,inherent biases,and ethical *** contend that this thorough analysis will serve as an invaluable scholarly resource,encapsulating the current state-of-the-art in crowdsourced geospatial data,and offering strategic directions for future interdisciplinary research and applications across various sectors.

关键词： crowdsourced geospatial datafacilitated crowdsourcing human observations geospatial data high speed communication paradigm shift earth observations digital technology

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