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A systematic literature review of machine learning application in COVID-19 medical image classification

Procedia computer science 2023年 216卷 749-756页

作者： Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia. Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia. Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia.

Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in this sector. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. According to the findings, chest X-ray were the most commonly used data to categorize COVID-19 and transfer learning techniques were the method used in this study. Researchers also concluded that lung segmentation and use of multimodal data could improve performance.

关键词： COVID-19 classification disease machine learning medical images

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A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection

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Plant Phenomics 2023年第2期5卷 209-225页

作者： Xiaojun Xie Fei Xia Yufeng Wu Shouyang Liu Ke Yan Huanliang Xu Zhiwei Ji College of Artificial Intelligence Nanjing Agricultural UniversityNanjingJiangsu 210095China Center for Data Science and Intelligent Computing Nanjing Agricultural UniversityNanjingJiangsu 210095China State Key Laboratory for Crop Genetics and Germplasm Enhancement Bioinformatics CenterAcademy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjingJiangsu 210095China Academy for Advanced Interdisciplinary Studies Nanjing Agricultural UniversityNanjingJiangsu 210095China Department of the Built Environment College of Design and EngineeringNational University of Singapore4 Architecture DriveSingapore 117566Singapore

Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern ***,it has limited interpretability for deep *** the transfer of expert knowledge,handcrafted features provide a new way for personalized diagnosis of plant ***,irrelevant and redundant features lead to high *** this study,we proposed a swarm intelligence algorithm for feature selection[salp swarm algorithm for feature selection(SSAFS)]in image-based plant disease *** is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of *** verify the effectiveness of the developed SSAFS algorithm,we conducted experimental studies using SSAFS and 5 metaheuristic *** evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from *** results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms,confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image *** computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.

关键词： Plant image redundant

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Short-term Weather Forecasting for Wind Energy Generation using A Deep Learning Technique 5

Short-term Weather Forecasting for Wind Energy Generation us...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Zim, Abid Hasan Zeyad, Mohammad Ahmed, S.M. Masum Hossain, Eftakhar Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh ABCD Laboratory Bangladesh Chattogram Bangladesh Heriot-Watt University Edinburgh United Kingdom University of Antwerp Faculty of Applied Engineering - Construction Antwerp Belgium

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

Inadequate or erroneous weather predictions have a great impact on wind turbine energy output. Certain weather fluctuations and interruptions affect the efficient operation of wind turbines. Short-term weather predictions that are calibrated and verified make it simple to reduce the threat presented by meteorological uncertainty. Conventional physics-based techniques for forecasting are relatively weaker than data-driven approaches since wind turbine components usually have a multipart nonlinear relationship. Compared to physical methods, non-parametric alternatives like deep learning are thought to be more stable and lead to fewer mistakes in analyst predictions. This research presents the state-of-the-art Temporal Convolutional Network (TCN), one of the deep learning methods, which is introduced as a means of enhancing the precision of short-term forecasting of weather variables. TCN is an architecture that makes use of casual convolutions and dilations in order to be adaptable for sequential data with its temporality and huge receptive fields. Seven significant meteorological parameters from the NASA POWER dataset of Chouchala Sea Beach in Chittagong, Bangladesh, over a period of four months were used in this research. The accuracy was measured using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results obtained from the suggested TCN model demonstrate that the predicted outcome is pretty accurate and consistent. © 2022 IEEE.

关键词： Wind turbines

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Bayesian Machine Learning for Estimating Optimal Dynamic Treatment Regimes with Ordinal Outcomes

arXiv

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arXiv 2025年

作者： Wang, Xinru Chakraborty, Tanujit Chakraborty, Bibhas Centre for Quantitative Medicine Duke-NUS Medical School Singapore School of Data Science and Engineering Sorbonne University Abu Dhabi Abu Dhabi United Arab Emirates Sorbonne Center For Artificial Intelligence Sorbonne University Paris France Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University Durham United States

Dynamic treatment regimes (DTRs) are sequences of decision rules designed to tailor treatment based on patients’ treatment history and evolving disease status. Ordinal outcomes frequently serve as primary endpoints in clinical trials and observational studies. However, constructing optimal DTRs for ordinal outcomes has been underexplored. This paper introduces a Bayesian machine learning (BML) framework to handle ordinal outcomes in the DTR setting. To deal with potential nonlinear associations between outcomes and predictors, we first introduce ordinal Bayesian additive regression trees (OBART), a new model that integrates the latent variable framework within the traditional Bayesian additive regression trees (BART). We then incorporate OBART into the BML to estimate optimal DTRs based on ordinal data and quantify the associated uncertainties. Extensive simulation studies are conducted to evaluate the performance of the proposed approach against existing methods. We demonstrate the application of the proposed approach using data from a smoking cessation trial and provide the OBART R package along with R code for implementation. © 2025, CC BY.

关键词： Adversarial machine learning

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Uniform Consistency of Generalized Fréchet Means

arXiv

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arXiv 2024年

作者： Aveni, Andrea Mukherjee, Sayan Department of Statistical Science Duke University United States Center for Scalable Data Analytics and Artificial Intelligence Department of Computer Science Universität Liepzig Germany Max Planck Institute for Mathematics in the Natural Sciences Duke University Departments of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics United States

We study a generalization of the Fréchet mean on metric spaces, which we call φ-means. Our generalization is indexed by a convex function φ. We find necessary and sufficient conditions for φ-means to be finite and provide a tight bound for the diameter of the intrinsic mean set. We also provide sufficient conditions under which all the φ-means coincide in a single point. Then, we prove the consistency of the sample φ-mean to its population analogue. We also find conditions under which classes of φ-means converge uniformly, providing a Glivenko-Cantelli result. Finally, we illustrate applications of our results and provide algorithms for the computation of φ-*** Codes 0F05, 60F15, 60D05, 62E20, 62F10, 58K05 © 2024, CC BY.

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Cancer Informatics: Novel Methods and Applications of Artificial Intelligence in Cancer Care Delivery

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Yearbook of medical informatics 2024年第1期33卷 99-101页

作者： Aneja, Sanjay Parikh, Ravi B. Yale Cancer Center New Haven CT United States Department of Therapeutic Radiology Yale School of Medicine New Haven CT United States Department of Bioinformatics and Data Science Yale School of Medicine New Haven CT United States Abramson Cancer Center Philadelphia PA United States Department of Medicine Philadelphia PA United States Department of Medical Ethics and Health Policy Philadelphia PA United States

OBJECTIVES: To summarize significant research contributions on cancer informatics published in 2023, an extensive search using PubMed/MEDLINE was conducted to identify the scientific contributions published in 2023 that address topics in cancer. The selection process comprised three steps: (i) ten candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of three best papers was conducted by the editorial board of the Yearbook. RESULTS: The two selected papers demonstrate advances in the clinical implementation of cancer informatics methodologies. Both studies highlight translation of informatics methodologies to improve cancer outcomes. CONCLUSIONS: Cancer informatics is a maturing subfield of bioinformatics. As novel methodologies continue to emerge, further emphasis will be placed on rigorous clinical validation and real-world scalability of such solutions to positively impact patient outcomes. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://***/licenses/by/4.0/).

关键词： Neoplasms informatics health information technology, clinical implementation

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HARD: Hard Augmentations for Robust Distillation

arXiv

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arXiv 2023年

作者： Nix, Arne F. Burg, Max F. Sinz, Fabian H. Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Institute for Computer Science Campus Institute Data Science University of Göttingen Germany Institute for Theoretical Physics University of Tübingen Germany

Knowledge distillation (KD) is a simple and successful method to transfer knowledge from a teacher to a student model solely based on functional activity. However, current KD has a few shortcomings: it has recently been shown that this method is unsuitable to transfer simple inductive biases like shift equivariance, struggles to transfer out of domain generalization, and optimization time is magnitudes longer compared to default non-KD model training. To improve these aspects of KD, we propose Hard Augmentations for Robust Distillation (HARD), a generally applicable data augmentation framework, that generates synthetic data points for which the teacher and the student disagree. We show in a simple toy example that our augmentation framework solves the problem of transferring simple equivariances with KD. We then apply our framework in real-world tasks for a variety of augmentation models, ranging from simple spatial transformations to unconstrained image manipulations with a pretrained variational autoencoder. We find that our learned augmentations significantly improve KD performance on in-domain and out-of-domain evaluation. Moreover, our method outperforms even state-of-the-art data augmentations and since the augmented training inputs can be visualized, they offer a qualitative insight into the properties that are transferred from the teacher to the student. Thus HARD represents a generally applicable, dynamically optimized data augmentation technique tailored to improve the generalization and convergence speed of models trained with KD. © 2023, CC BY.

关键词： Distillation

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Society with Trust: A Scientometrics Review of Zero-Knowledge Proof Advanced Applications in Preserving Digital Privacy for Society 5.0 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Dominic, Nicholas Pratama, Naufal Rizqi Cornelius, Kenny Senewe, Shavellin Herliman Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Universitas Multimedia Nusantara Tangerang15111 Indonesia Psychology Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Society 5.0 focuses on human productivity in the midst of advanced technological services. While the concept has human trust at its core, technology development is now leading to zero-trust architecture. In this scientometrics review, 107 selected papers were analyzed to obtain the nature of the Zero-knowledge Proof (ZKP) theory and its advanced applications to favor digital privacy in greater society. With the common literature review strategy, it was found that the most citations in this field appeared in 2018, with IEEE, Springer, and Elsevier as the top three publishers. By affiliations, China and United States are both leading countries where the researcher comes from. And as the positive trend emerged, the ZKP mechanism will keep being leveraged as one of the cryptographic toolsets in the cloud services, Internet of Things (IoT), smart contracts, healthcare, electronic systems, and many other industrial needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Smart contract

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MassSpecGym: A benchmark for the discovery and identification of molecules 38

MassSpecGym: A benchmark for the discovery and identificatio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of computing science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. © 2024 Neural information processing systems foundation. All rights reserved.

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P522 Characterization of linkage disequilibrium between DRB1*01 and DRB5

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Human Immunology 2023年 84卷 145-146页

作者： Norvell, B. Williams, J. Norvell, B. Williams, J. Liu, H. Iyer, L. Riley, E. Coggins, M. Deters, J. Mone, C. Research and Development Labcorp Burlington North Carolina United States Data Science Artificial Intelligence and Bioinformatics Labcorp Westborough Massachusetts United States Consumer Genetics Labcorp Burlington North Carolina United States

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