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arXiv 2024年

作者： Hallee, Logan Kapur, Rohan Patel, Arjun Gleghorn, Jason P. Khomtchouk, Bohdan Center for Bioinformatics and Computational Biology University of Delaware United States Department of Physics University of Chicago United States The College The University of Chicago United States Department of Biomedical Engineering University of Delaware United States Department of BioHealth Informatics Luddy School of Informatics Computing and Engineering Indiana University United States

The advancement of transformer neural networks has significantly elevated the capabilities of sentence similarity models, but they still struggle with highly discriminative tasks and may produce sub-optimal representations of important documents like scientific literature. With the increased reliance on retrieval augmentation and search, representing diverse documents as concise and descriptive vectors is crucial. This paper improves upon the vectors embeddings of scientific text by assembling niche datasets using co-citations as a similarity metric, focusing on biomedical domains. We apply a novel Mixture of Experts (MoE) extension pipeline to pretrained BERT models, where every multi-layer perceptron section is enlarged and copied into multiple distinct experts. Our MoE variants perform well over N scientific domains with N dedicated experts, whereas standard BERT models excel in only one domain at a time. Notably, extending just a single transformer block to MoE captures 85% of the benefit seen from full MoE extension at every layer. This holds promise for versatile and efficient One-Size-Fits-All transformer networks for numerically representing diverse inputs. Our methodology marks advancements in representation learning and holds promise for enhancing vector database search and compilation. © 2024, CC BY.

关键词： Vectors

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Enhancing Diabetic Retinopathy Screening with Sequential Deep Learning Models

Enhancing Diabetic Retinopathy Screening with Sequential Dee...

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International Conference on Image Information Processing

作者： Vankadari Mohith Kalpana Raja I. R. Oviya Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven USA

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Diabetic retinopathy is a serious medical disorder that, if left untreated, can result in visual impairment or blindness. The precise and timely categorization of its severity is critical for appropriate medical management. This research describes an automated method for determining the severity of diabetic retinopathy using deep learning algorithms. The suggested methodology adopts a two-step process for identifying diabetic retinopathy and then categorizing its severity. In this technique, two pre-trained deep learning models, InceptionResNetV2 and DenseNet121, are used. For training and assessment purposes, the “APTOS 2019 Blindness Detection” dataset which consists of five severity classes is used as the foundation. To overcome class imbalance, data augmentation approaches are used to improve the model’s performance. The results of the experiment show that the strategy is effective, with an accuracy of 83.10 % and an F1 score of 82.77 %. This study contributes to the development of automated systems for assessing diabetic retinopathy, perhaps enhancing early detection and treatment.

关键词： Deep learning Training Diabetic retinopathy Visual impairment Transfer learning Blindness Information processing

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A systematic study on the role of SentiWordNet in opinion mining

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Frontiers of Computer Science 2021年第4期15卷 165-183页

作者： Mujtaba HUSNAIN Malik Muhammad Saad MISSEN Nadeem AKHTAR Mickael COUSTATY Shahzad MUMTAZ V.B.Surya PRASATH Department of IT Faculty of ComputingThe Islamia University of BahawalpurBahawalpur 63100Pakistan Laboratoire Informatique Image et Interaction(L3i)Facultés des Sciences et TechnologiesUniversity of La RochelleLa Rochelle 17000France Division of Biomedical Informatics Cincinnati Children’s Hospital Medical CenterCincinnati 45229USA Department of Pediatrics University of Cincinnati College of MedicineCincinnati 45229USA Department of Biomedical Informatics College of MedicineUniversity of CincinnatiCincinnati 45267USA Department of Electrical Engineering and Computer Science University of CincinnatiCincinnati 45221USA

Sentiment lexicons(SL)(aka lexical resources)are the repositories of one or several dictionaries that consist of known and precompiled sentiment *** lexicons play an important role in performing several different opinion mining *** efficacy of the lexicon-based approaches in performing opinion mining(OM)tasks solely depends on selecting an appropriate opinion lexicon to analyze the ***,one has to explore the available sentiment lexicons and then select the most suitable *** available resources,SentiWordNet(SWN)is the most widely used lexicon to perform tasks related to opinion *** SWN,each synset of WordNet is being assigned the three sentiment numerical scores;positive,negative and objective that are calculated using by a set of *** this paper,a detailed and comprehensive review of the work related to opinion mining using Senti-WordNet is provided in a very distinctive *** survey will be useful for the researchers contributing to the field of opinion *** features make our contribution worthwhile and unique among the reviews of similar kind:(i)our review classifies the existing literature with respect to opinion mining tasks and subtasks(ii)it covers a very different outlook of the opinion mining field by providing in-depth discussions of the existing works at different granularity levels(word,sentences,document,aspect,clause,and concept levels)(iii)this state-ofart review covers each article in the following dimensions:the designated task performed,granularity level of the task completed,results obtained,and feature dimensions,and(iv)lastly it concludes the summary of the related articles according to the granularity levels,publishing years,related tasks(or subtasks),and types of classifiers *** the end,major challenges and tasks related to lexicon-based approaches towards opinion mining are also discussed.

关键词： opinion mining lexical based resources Senti-WordNet opinion strength

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Hypothyroidism Prediction and Detection Using Machine Learning 5

Hypothyroidism Prediction and Detection Using Machine Learni...

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5th International Conference on Engineering Technology and its Applications, IICETA 2022

作者： Almahshi, Hebatullah Mohammad Almasri, Esraa Abdallah Alquran, Hiam Mustafa, Wan Azani Alkhayyat, Ahmed Jordan University of Science and Technology Department of Biomedical Engineering Irbid21163 Jordan Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid21163 Jordan Faculty of Electrical Engineering Technology Pauh Putra Campus Perlis Arau02600 Malaysia Advanced Computing Centre of Excellence Pauh Putra Campus Perlis Arau02600 Malaysia The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9781665472159

The world is facing an increase in the incidence of thyroid diseases, including in Jordan as well, especially hypothyroidism. This condition can be diagnosed through blood samples to know the percentage of hormones (TSH, T3, and T4) and take the appropriate decision after that. The major goal of this study is to make diagnosis easier and reduce the risk of misdiagnosis, which happens all too often. Methods: The use of machine learning in the prediction of disease is a crucial step in the process of data analysis. This paper presents an analysis and classification model that takes into account the various factors involved in the prediction of disease. We applied a number of machine learning algorithms like support vector machine (SVM), Nave Bayes, decision trees, and ensemble to get the best prediction. The decision tree algorithm showed the highest accuracy and lowest cost with a percentage of 97.6 percent and 90, respectively. The results appeared in the form of three classes (compensated hypothyroidism, primary hypothyroidism, and negative). Conclusion: The best results were obtained at the milestone point in computer-aided diagnosis of hypothyroidism. Future work is considered to increase the number of cases that can be detected and diagnosed using machine learning. © 2022 IEEE.

关键词： Decision trees

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Comfort Shoulder (Rhomboid Major) Muscles Chair Identifier System

Comfort Shoulder (Rhomboid Major) Muscles Chair Identifier S...

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International Conference on Communications, Information System and Computer Engineering (CISCE)

作者： Ata Jahangir Moshayedi Yuexin Yu Kimia Savoji Mehran Emadi Andani School of Information Engineering Jiangxi University of Science and Technology Ganzhou Jiangxi China Biomedical Data Science and Informatics School of computing Clemson University Clemson South Carolina USA Department of Neurosciences Biomedicine and Movement Sciences University of Verona Verona Italy

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Nowadays, Sitting is one of the frequent activities of humans. particularly with the rise of office work, which is leading to an increase in the sitting periods for many individuals. Research highlights the importance of studying sitting posture to enhance seat quality, correct sitting methods, and prevent various health issues like neck and shoulder strains, etc. This article focuses on designing an IoT-based system that enables web-based data visualization without specialized software that can enable distance sampling. Through temperature sensors (DS18b20 and SHT11) and an FSR sensor, the system can identify distinct patterns in the main rhomboid muscles (the most important muscles of humans). The results show a significant relationship between individuals' body mass (BMI) and the observed muscle patterns, highlighting the importance of this correlation for researchers and industry designers alike.

关键词： Temperature sensors Pediatrics Shape Ergonomics Muscles Software Neck

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Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data

arXiv

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arXiv 2022年

作者： Leng, Zikang Jain, Yash Kwon, Hyeokhyen Plötz, Thomas College of Computing Georgia Institute of Technology Atlanta United States School of Interactive Computing Georgia Institute of Technology Atlanta United States Department of Biomedical Informatics Emory University Atlanta United States

Previous work has demonstrated that virtual accelerometry data, extracted from videos using cross-modality transfer approaches like IMUTube, is beneficial for training complex and effective human activity recognition (HAR) models. Systems like IMUTube were originally designed to cover activities that are based on substantial body (part) movements. Yet, life is complex, and a range of activities of daily living is based on only rather subtle movements, which bears the question to what extent systems like IMUTube are of value also for finegrained HAR, i.e., When does IMUTube break? In this work we first introduce a measure to quantitatively assess the subtlety of human movements that are underlying activities of interest-the motion subtlety index (MSI)-which captures local pixel movements and pose changes in the vicinity of target virtual sensor locations, and correlate it to the eventual activity recognition accuracy. We then perform a "stress-test" on IMUTube and explore for which activities with underlying subtle movements a cross-modality transfer approach works, and for which not. As such, the work presented in this paper allows us to map out the landscape for IMUTube applications in practical scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Mutually-Constrained Cross-Sectional and Longitudinal Non-Negative Matrix Factorization: Application to Modeling Brain Aging Trajectories

Mutually-Constrained Cross-Sectional and Longitudinal Non-Ne...

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IEEE International Symposium on biomedical Imaging

作者： Ioanna Skampardoni Junhao Wen Erus Guray Haochang Shou Konstantina Nikita Christos Davatzikos Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Laboratory of AI and Biomedical Science (LABS) Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University of Southern California Los Angeles CA USA Department of Biostatistics Epidemiology & Informatics Penn Statistics in Imaging and Visualization Center University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Brain aging is a multifaceted and highly heterogeneous process accompanied by several pathologies. Here, we propose a method for dissecting the heterogeneity of neuropathologic processes occurring with aging using machine learning and leveraging information from cross-sectional and longitudinal data. Specifically, we hypothesize that the heterogeneity observed in brain aging can be captured by a set of patterns consistent with longitudinal trajectories of brain change, the latter directly capturing evolving neuropathologic processes on an individual basis. Applying the method to structural magnetic resonance imaging data from the BLSA study, we derived five distinct, reproducible, and clinically informative components of neuroanatomical brain change, highlighting the method’s potential as a tool for precision medicine.

关键词： Pathology Magnetic resonance imaging Precision medicine Sociology Aging Hazards Trajectory

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A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Model for Classifying DNA Barcodes

A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Mo...

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Advances in Electrical, Electronics and Computational Intelligence (ICAEECI), International Conference on

作者： Abhivyakti Yadav Divya S I R Oviya Kalpana Raja Department of Computer Science and Engineering (Artificial Intelligence) Amrita School of Computing Amrita Vishwa Vidyapetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

DNA Barcodes, which are particular fragments derived from brief sections of DNA (such as mitochondrial, nuclear, and plastid sequences), can be used to identify organisms from the major life kingdoms. In addition to supporting conventional taxonomic techniques, DNA barcoding is a potent tool that advances our knowledge of species diversity and their ecological functions. On a variety of organisms, the use of this approach for species categorization has been successful. In this paper, we examine how DNA barcoding has been used to classify species based on DNA barcodes as well as other related research that has been done over the years on the subject. After experimenting with a number of deep learning models, we have propose a Variational Auto Encoder + Feed Forward Neural Network workflow for classifiying species using DNA barcodes. The models have been assessed on the basis of performance factors including accuracy, recall, and precision. COI, rbcL, matK, and ITS are the specific gene sections that have been identified as barcodes. For both simulated and real datasets, the model can attain an average accuracy of greater than 95 percent. This DNA barcoding approach has the ability to simplify DNA barcode-based species identification and serve as a tool for species categorization.

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A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression

arXiv

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arXiv 2023年

作者： Bintsi, Kyriaki-Margarita Mueller, Tamara T. Starck, Sophie Baltatzis, Vasileios Hammers, Alexander Rueckert, Daniel BioMedIA Department of Computing Imperial College London United Kingdom Lab for AI in Medicine and Healthcare Faculty of Informatics Technical University of Munich Germany Biomedical Engineering and Imaging Sciences King’s College London United Kingdom

The difference between the chronological and biological brain age of a subject can be an important biomarker for neurodegenerative diseases, thus brain age estimation can be crucial in clinical settings. One way to incorporate multimodal information into this estimation is through population graphs, which combine various types of imaging data and capture the associations among individuals within a population. In medical imaging, population graphs have demonstrated promising results, mostly for classification tasks. In most cases, the graph structure is pre-defined and remains static during training. However, extracting population graphs is a non-trivial task and can significantly impact the performance of Graph Neural Networks (GNNs), which are sensitive to the graph structure. In this work, we highlight the importance of a meaningful graph construction and experiment with different population-graph construction methods and their effect on GNN performance on brain age estimation. We use the homophily metric and graph visualizations to gain valuable quantitative and qualitative insights on the extracted graph structures. For the experimental evaluation, we leverage the UK Biobank dataset, which offers many imaging and non-imaging phenotypes. Our results indicate that architectures highly sensitive to the graph structure, such as Graph Convolutional Network (GCN) and Graph Attention Network (GAT), struggle with low homophily graphs, while other architectures, such as GraphSage and Chebyshev, are more robust across different homophily ratios. We conclude that static graph construction approaches are potentially insufficient for the task of brain age estimation and make recommendations for alternative research directions. Copyright © 2023, The Authors. All rights reserved.

关键词： Population statistics

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CWT IoT Device for Detecting Rare Events of Orchid Disease

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IEEE Internet of Things Journal 2024年第12期11卷 22830-22842页

作者： Chen, Claire Y. T. Lin, Yi-Bing Chen, Wen-Liang Wu, Kuan-Chieh Lin, Yun-Wei Sun, Edward W. Liu, Chun-You Montpellier Business School Montpellier34090 France Department Of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan China Medical University Taichung 404 China Miin Wu School Of Computing National Cheng Kung University Tainan70101 Taiwan The Research Center For Information Technology Innovation Academia Sinica Taipei115 Taiwan College Of Biological Science And Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan College Of Artificial Intelligence National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Talence33405 France Department Of Biomedical Informatics China Medical University Taichung404 Taiwan

Existing non-image-based solutions for detecting Phalaenopsis orchid diseases rely on time-domain analysis of environmental conditions. These solutions are suitable for loosely controlled open farm fields. Conversely, the diseases are infrequent events in Internet of Things (IoT)-based smart orchid greenhouses, where environmental conditions are well-controlled. In tightly controlled settings, standard time domain methods fall short in detecting subtle changes in environmental conditions during rare orchid disease events. We observed that the influence of the time series of temperature and humidity on orchid fungus within the well-controlled greenhouse is not readily apparent, and the time-frequency coefficients of temperature and humidity must be used to detect orchid disease in this rare event case. In order to address this challenge, we propose OrchidTalk, an IoT-based deep learning platform, which employs a time-frequency domain analysis using the continuous wavelet transform (CWT) filter, for the purpose of detecting rare events of orchid diseases. Through a nontrivial process, we select the Gaussian wavelet with four derivatives to serve as the mother wavelet of the CWT in the data extraction phase, and invent the CWT IoT device to translate the time-domain IoT raw data to the time-frequency domain IoT data. The resulting CWT features are used as inputs to the Orchid-3D model (3-D ConvLSTM), which significantly outperforms the previously proposed Orchid-1D (CNN and LSTM) and Orchid-2D (2-D ConvLSTM) models. For the rare event detection of 0.2% of sick orchid plants, OrchidTalk achieves a recall of 0.902, precision of 0.92, accuracy of 0.99, and an F1-score of 0.911. To the best of our knowledge, this represents the highest prediction performance achieved for a well-controlled orchid greenhouse. © 2014 IEEE.

关键词： Time domain analysis

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