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data science and Information System (ICDSIS), IEEE International Conference on

作者： K M Monica Mohammed Al-Farouni G K Madhura K. Ghamya M. Geethalakshmi School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial intelligence and Machine learning Nitte Meenakshi Institute of Technology Bengaluru India Dept. of CSE(AI&ML) School of Computing Mohan Babu University Tirupati India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Nowadays, Traffic density estimation can be utilized to control traffic light signals for managing the traffic effectively. Vehicle Type Classification (VTC) is used in this traffic density. Because of the factors like minimal interclass variance and maximum intraclass variation, the VTC is challenging which is handled by proposed Improved Relief Feature Selection Algorithm and Bi-directional Gated Recurrent Unit (IRFSA-Bi-GRU) method. Bi-directional Gated Recurrent Unit (Bi-GRU) model is proposed for enhancing Vehicle Type Classification (VTC) within Intelligent Transport Systems. Multiview In Out Traffic Complexity database (MIOTCD) is used for data collection then a camera response model is used for enhancing visual data quality. Kalman filtering and Gaussian mixture model techniques are utilized for vehicle recognition and tracking. Local Binary Pattern (LBP), Steerable Pyramid Transform (SPT), and Dual Tree Complex Wavelet Transform (DTCWT) are used for feature extraction and Improved Relief Feature Selection Algorithm (IRFSA) is used for feature selection. Finally, the Bi-directional Gated Recurrent Unit (Bi-GRU) model is used for classifying 11 vehicle categories on MIO-TCD. The obtained results exposed that the IRFSA-BiGRU method outperformed the existing methods by achieving an accuracy of 99.02%, 98.34% F-score, 98.78% MCC,99.93% specificity, 99.32% NPV, 98.75% precision, 98.14% recall on the MIO-TCD database.

关键词： Wavelet transforms Visualization Accuracy data integrity Bidirectional control Logic gates Feature extraction

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Medical Image Classification Using Thermal Images and Diagnosis of Carcinogen

Medical Image Classification Using Thermal Images and Diagno...

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Innovation and Novelty in Engineering and Technology (INNOVA), International Conference on

作者： Govinda Rajulu. G L. Sharmila D. Venkatesan K Srinivas Nallam Krishnaiah K. Laxman Kumar Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology. Avadi Chennai Tamil Nadu India Dept. of Artificial Intelligence and Machine Learning St. Martin's Engineering College Hyderabad India Deptarment of CSE(AI & ML) St. Martin's Engineering College Hyderabad India Department of IT St. Martin's Engineering College Dhulapally Secunderabad Telangana India Deptartment of Computer Science and Design St. Martin's Engineering College Hyderabad India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

Lung and Colon (L&C) tumors are lethal sicknesses that can foster in a few organs all the while and, in specific circumstances, jeopardize human existence. Even though it is highly unlikely that these two types of cancer will develop simultaneously, delaying diagnosis significantly increases the likelihood of metastasis between the organs that are affected. To really treat specific sorts of malignant growth, histological determination is fundamental. In the past, doctors had to go through a long and difficult process to look at thermal images and figure out if a patient had cancer; however, this procedure may now be completed much more quickly thanks to the new technology options. Histological images of L&C tumors were classified using a hybrid Deep learning (DL) model with an attention mechanism and a multipath network in this study. To zero in on the main attributes and negligence the less significant ones, a consideration component was utilized. In a multipath network, data travels over a number of channels before each channel is converted and the output from all of the branches is combined. The multipath network is similar to grouped convolution when simplified. The five thermal image categories of the LC25000 dataset were utilized. Among these classes were two for colon disease and three for cellular breakdown in the lungs. The proposed model was compared to a number of well-known DL models, such as ResNet-50, VGG-16, and AlexNet. The proposed approach showed the best exhibition concerning exactness (99.2%), particularity (99.12%), responsiveness (99.28%), accuracy (99.12%), and F1 score (99.2%), as indicated by the exploratory discoveries

关键词： Technological innovation Accuracy Lungs Subject matter experts Medical services Colon Medical diagnostic imaging Cancer Tumors Testing

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Bayesian modeling of effective and functional brain connectivity using hierarchical vector autoregressions

arXiv

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arXiv 2021年

作者： Wegmann, Bertil Lundquist, Anders Eklund, Anders Villani, Mattias Div. of Statistics and Machine Learning Dept. of Computer and Information Science Linköping University Sweden Umeå University Sweden Div. of Medical Informatics Dept. of Biomedical Engineering Linköping University Sweden Dept. of Statistics Stockholm University Sweden

Analysis of brain connectivity is important for understanding how information is processed by the brain. We propose a novel Bayesian vector autoregression (VAR) hierarchical model for analyzing brain connectivity in a resting-state fMRI data set with autism spectrum disorder (ASD) patients and healthy controls. Our approach models functional and effective connectivity simultaneously, which is new in the VAR literature for brain connectivity, and allows for both group- and single-subject inference as well as group comparisons. We combine analytical marginalization with Hamiltonian Monte Carlo (HMC) to obtain highly efficient posterior sampling. The results from more simplified covariance settings are, in general, overly optimistic about functional connectivity between regions compared to our results. In addition, our modeling of heterogeneous subject-specific covariance matrices is shown to give smaller differences in effective connectivity compared to models with a common covariance matrix to all subjects. © 2021, CC BY.

关键词： Covariance matrix

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Development of water flood model for oil production enhancement

Development of water flood model for oil production enhancem...

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2020 science and Artificial Intelligence Conference, S.A.I.ence 2020

作者： Tsvaki, Jetina J. Tailakov, Dmitry O. Pavlovskiy, Evgeniy N. Novosibirsk State University Dept. of Mathematics and Mechanics Novosibirsk Russia Kutateladze Institute of Thermophysics Sb Ras Laboratory for Fundamentals of Energy Technologies Novosibirsk Russia Novosibirsk State University Stream Data Analytics and Machine Learning Laboratory Novosibirsk Russia

ISBN: (纸本)9780738131115

Main goal of any industry is to increase productivity which in oil and gas field is to increase reservoir oil asset by producing oil in an effective and economically efficient manner. The objective of the study is to develop a water flood model for oil production enhancement using artificial neural networks and provide a model that maximizes oil production for a given water injection that in turn will extend mature fields life and decrease operational costs. Using the data comprising of daily water injection rates, oil production rates, water production, and gas production from the year 2004 to 2016 for 577 injection wells, 1344 production wells, and 36 events which had occurred during the course. Comparative analysis on the deep neural models such as Multi-Layer Perception, Convolutional Neural Networks, Long Short-Term Memory, and Gated Recurrent Neural Networks are used, and Gated Recurrent Neural Networks outperformed them. To minimize the loss and improve the performance of the water flood model tabular data mix-up was adopted on all the models above. The results showed that the data mixed up Gated Recurrent Neural Network outperformed all the other models. To maximize the oil production Nelder-Mead optimization method was adopted to find appropriate water injection rates. A simple two-layered multi-layer perceptron was used in modeling the nonlinear relationship between water injection and oil production to avoid function complexity. © 2020 IEEE.

关键词： Floods

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Real-Time Health data Analytics in IoT-Connected Wearable Devices

Real-Time Health Data Analytics in IoT-Connected Wearable De...

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Emerging Technology (INCET), International Conference for

作者： Jyostna Devi G S Venkatakiran V Nuthan Prasad Swathi Pai M KDV Prasad Sheetalrani Rukmaji Kawale Department of Electronics and Communication Engineering Sri Venkatesa Perumal College of Engineering (AUTONOMOUS) Andhra Pradesh India Department of Electronic and Communication MSRIT Bangalore Karnataka Dept. of Artificial Intelligence and Machine Learning NITTE (Deemed to be University) NMAM Institute of Technology Nitte Karnataka India Department of Research Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Department of Computer Science Karnataka State Akkamahadevi Women University Vijayapura Karnataka India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

This research paper focuses on the revolutionary effect of instant in-person health analytics by IoT -connected wearable medical devices on health results. By the exploration of currently available literature, technical systems and showing case studies, we provided strong evidence that real time monitoring improved patient outcome and made healthcare delivery better. The research unveils the remarkable sequestration of advancing wearable device technology that can capture and analyze essential health factors in the current state. Through our representation of the remote patient monitoring, chronic disease management, and fitness tracking trends, we show the effects of those advancements being clearly pronounced as early disease detection, personal interventions, and higher patient engagement. On the other hand we tackle the mentioned issues such as data privacy and security presenting new methods as possible options to minimize the dangers. Stakeholders in the healthcare and technology sector obtain useful information by analyzing the results of this study, as they can now explore the power of real-time health data analytics to completely change patient care and design upon healthcare delivery in the future.

关键词： data analysis Accuracy Medical services Real-time systems User experience Stakeholders Security

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Understanding the Impacts of ECG Signal Processing Techniques on Recognition of Cardiovascular Diseases

Understanding the Impacts of ECG Signal Processing Technique...

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Vanita Agrawal Bhavesh Raju Mudhivarthi Ketki Kshirsagar Jayant Jagtap Department of Computer Science and Information Tech. Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India Department of Electronics and Telecommunication Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India Artificial Intelligence & Machine Learning Dept. Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India

Cardiovascular diseases (CVD) are becoming the most common diseases that infect the heart's blood vessels. Building a fatty layer inside arteries causes more blood clots inside the heart, the most death-causing diseases from the past few decades. To sense heart condition and improve heart health, an Electrocardiogram (ECG) is used. ECG uses sensors and electronic signals for painless tests to find heart abnormalities and monitor heart health conditions. Digital signal processing (DSP) plays a crucial role in electrocardiograms in monitoring and detecting heart conditions. This paper describes the role of DSP in an ECG while detecting CVD kind congenital heart defects like aortic valve stenosis, ebstein's anomaly, ventricular and atrial septal defects, and valve stenosis of pulmonary with a detailed explanation of waves. It leads to information and analysis of existing works. There is a scope for detecting coarctation of the aorta, single ventricle defects, and patent ductus arteriosus using ECG.

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Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep learning Models: A Review

arXiv

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

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Agreement of Image Quality Metrics with Radiological Evaluation in the Presence of Motion Artifacts

arXiv

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arXiv 2024年

作者： Marchetto, Elisa Eichhorn, Hannah Gallichan, Daniel Schnabel, Julia A. Ganz, Melanie Bernard and Irene Schwartz Center for Biomedical Imaging Dept. of Radiology NYU School of Medicine NY United States Dept. of Radiology NYU School of Medicine NY United States CUBRIC School of Engineering Cardiff University Cardiff United Kingdom Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Neuherberg Germany School of Computation Information and Technology Technical University of Munich Munich Germany School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Department of Computer Science University of Copenhagen Copenhagen Denmark Neurobiology Research Unit Copenhagen University Hospital Copenhagen Denmark

Purpose: Reliable image quality assessment is crucial for evaluating new motion correction methods for magnetic resonance imaging. In this work, we compare the performance of commonly used reference-based and reference-free image quality metrics on a unique dataset with real motion artifacts. We further analyze the image quality metrics’ robustness to typical pre-processing techniques. Methods: We compared five reference-based and five reference-free image quality metrics on data acquired with and without intentional motion (2D and 3D sequences). The metrics were recalculated seven times with varying pre-processing steps. The anonymized images were rated by radiologists and radiographers on a 1-5 Likert scale. Spearman correlation coefficients were computed to assess the relationship between image quality metrics and observer scores. Results: All reference-based image quality metrics showed strong correlation with observer assessments, with minor performance variations across sequences. Among reference-free metrics, Average Edge Strength offers the most promising results, as it consistently displayed stronger correlations across all sequences compared to the other reference-free metrics. Overall, the strongest correlation was achieved with percentile normalization and restricting the metric values to the skull-stripped brain region. In contrast, correlations were weaker when not applying any brain mask and using min-max or no normalization. Conclusion: Reference-based metrics reliably correlate with radiological evaluation across different sequences and datasets. Pre-processing steps, particularly normalization and brain masking, significantly influence the correlation values. Future research should focus on refining pre-processing techniques and exploring machine learning approaches for automated image quality evaluation. © 2024, CC BY.

关键词： Image quality

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Improving language generation from feature-rich tree-structured data with relational graph convolutional encoders 2

Improving language generation from feature-rich tree-structu...

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2nd Workshop on Multilingual Surface Realisation, MSR@EMNLP-IJCNLP 2019

作者： Hong, Xudong Chang, Ernie Demberg, Vera Dept. of Computer Vision and Machine Learning MPI Informatics Germany Dept. of Language Science and Technology Saarland University Germany

ISBN: (纸本)9781950737765

The Multilingual Surface Realization Shared Task 2019 focuses on generating sentences from lemmatized sets of universal dependency parses with rich features. This paper describes the system design and the results of our participation in the deep track. The core innovation in our approach is to use a graph convolutional network to encode the dependency trees given as input. Upon adding morphological features, our system achieves the second rank in the deep track without using data augmentation techniques or additional components (such as a re-ranker). © 2019 Association for Computational Linguistics.

关键词： Forestry

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Evaluating deep transfer learning for whole-brain cognitive decoding

arXiv

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arXiv 2021年

作者： Thomas, Armin W. Lindenberger, Ulman Samek, Wojciech Müller, Klaus-Robert Machine Learning Group Dept. of Computer Science and Electrical Engineering Technische Universität Berlin Berlin Germany Center for Lifespan Psychology Max Planck Institute for Human Development Berlin Germany Stanford Data Science Stanford University StanfordCA United States Dept. of Psychology Stanford University StanfordCA United States Max Planck UCL Centre for Computational Psychiatry and Ageing Research Berlin Germany Dept. of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Max Planck Institute for Informatics Saarbrücken Germany

Research in many fields has shown that transfer learning (TL) is well-suited to improve the performance of deep learning (DL) models in datasets with small numbers of samples. This empirical success has triggered interest in the application of TL to cognitive decoding analyses with functional neuroimaging data. Here, we systematically evaluate TL for the application of DL models to the decoding of cognitive states (e.g., viewing images of faces or houses) from whole-brain functional Magnetic Resonance Imaging (fMRI) data. We first pre-train two DL architectures on a large, public fMRI dataset and subsequently evaluate their performance in an independent experimental task and a fully independent dataset. The pre-trained models consistently achieve higher decoding accuracies and generally require less training time and data than model variants that were not pre-trained, clearly underlining the benefits of pre-training. We demonstrate that these benefits arise from the ability of the pre-trained models to reuse many of their learned features when training with new data, providing deeper insights into the mechanisms giving rise to the benefits of pre-training. Yet, we also surface nuanced challenges for whole-brain cognitive decoding with DL models when interpreting the decoding decisions of the pre-trained models, as these have learned to utilize the fMRI data in unforeseen and counterintuitive ways to identify individual cognitive states. © 2021, CC BY.

关键词： Decoding

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