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Advancements in Left Ventricular Myocardium Analysis: A Comprehensive Review of Myocardium Segmentation, Fibrosis Detection and Wall Motion Estimation Using Cardiac Magnetic Resonance Imaging

Advancements in Left Ventricular Myocardium Analysis: A Comp...

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Emerging Technologies in computer science for Interdisciplinary Applications (ICETCS), International Conference on

作者： H G Sunanda K S Kavitha Dept. of Computer Science and Engineering Bangalore Institute of Technology Bengaluru Visvesvaraya Technological University Belagavi India Dept. of Computer Science and Engineering Dayananda Sagar College of Engineering Bengaluru India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

Cardiac Magnetic Resonance Imaging (CMRI) has established itself as a cornerstone in cardiovascular diagnostics, enabling precise assessment of left ventricular (LV) myocardium. This review meticulously examines the multifaceted aspects of LV myocardium analysis, focusing on three pivotal components: myocardium segmentation, fibrosis detection, and wall motion estimation. By exploring cutting-edge techniques and methodologies in these domains, this article sheds light on the profound impact CMR has had on the accurate diagnosis, prognosis and structuring treatment plans for diseases of the heart. In the realm of cardiac diagnostics, Left Ventricular (LV) myocardium analysis utilizing CMRI has undergone a transformation advancement. This review encapsulates the comprehensive exploration of LV myocardium analysis, Myocardium segmentation techniques, ranging from traditional methods to contemporary machine learning-driven approaches, are scrutinized for their accuracy and efficiency. The detection and characterization of myocardial fibrosis, crucial in diagnosing various cardiac conditions, are dissected through cutting-edge CMR techniques such as Late Gadolinium Enhancement (LGE) imaging and quantitative mapping methods. Additionally, advanced methods of wall motion estimation, including feature tracking and strain imaging, highlighting their applications in assessing myocardial function. Real-world clinical scenarios underscore the transformation impact of these techniques on diagnosing heart diseases, guiding treatment decisions, and improving patient outcomes. The current landscape of LV myocardium analysis, emphasizing the pivotal role of CMR in revolutionizing cardiac healthcare.

关键词： Image segmentation Reviews Tracking Motion estimation Motion segmentation Magnetic resonance imaging Myocardium

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ADN-BERT: Attention-Based Deep Network Model Using BERT for Sarcasm Classification 11th

ADN-BERT: Attention-Based Deep Network Model Using BERT for...

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Mishra, Pallavi Sharma, Omisha Panda, Sandeep Kumar Dept. of Computer Science and Engineering ICFAI Foundation for Higher Education Telangana Hyderabad India Dept. of Artificial Intelligence and Data Science ICFAI Foundation for Higher Education Telangana Hyderabad India

ISBN: (纸本)9789819967018

Sarcasm classification has gained popularity among researchers due to its complexity and implicit form of context representation. The most challenging aspect of sarcasm identification is to understand the exact context behind the statement. Therefore, a context-based model helps solve this critical task in the research domain. The importance of sarcasm detection and classification is primarily helpful for society to avoid misinterpreting statements or reviews that could affect one’s mental condition and perception. Unfortunately, to increase the retention level of audience toward media news, often the media incorporate sarcasm in their news headlines. However, people find it difficult to detect sarcasm in news headlines, resulting in them having a false impression of the news and spreading it to their surroundings. Therefore, it has become a significant concern among the society to develop a sense of understanding of the hidden context behind the statement before making any type of impression and judgment. In this paper, we have focused on this problem statement and developed a novel technique called an Attention-based deep network model using Bidirectional Encoder Representations from Transformers (BERT) i.e., ADN-BERT to classify news headlines into sarcastic and non-sarcastic categories. The News Headlines dataset is collected from Kaggle, and we have incorporated text augmentation to increase the size of the dataset to yield better results and accuracy. The evaluation metrics used in our study include accuracy, recall, precision, and F1-score. Our proposed model (ADN-BERT) has outperformed the recent state-of-the-art techniques with 94.1% accuracy, 95.2% recall, 94.5% precision, and 94.8% *** check and approve the edits made in the chapter title and running ***. ApprovedPlease check and confirm if the author names and initials are ***, correct. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Detection of Mid Mesial Canal in Dental CBCT Images Using Combined YOLO and 2D U-Net Model

Detection of Mid Mesial Canal in Dental CBCT Images Using Co...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Madhuri, J. Mohan, Shreya Praveen, Sneha Chandan, Tanvi Reddy Gurram, Tanusha Anitha Kumari, R. Krishna, Soumya Bangalore Institute of Technology Dept. of Computer Science and Engineering Bangalore India V. S. Dental College and Hospitals Bangalore India

ISBN: (纸本)9798350300826

Failures in root canal treatments often arise from undetected canals and a limited comprehension of the intricate structure of the root canal system. An illustrative instance is the Middle Mesial Canal (MMC), situated between the mesiobuccal and mesiolingual canals of the mesial root of the first mandibular molar. Neglecting to identify and address these canals in the molar tooth can lead to subsequent treatment complications, resulting in persistent infection. The primary objective of this study is to evaluate the potential of three-dimensional dental Cone Beam Computed Tomography (CBCT) scans in detecting the presence of mid mesial canals through a deep learning approach. The 3D dental CBCT images are segmented into two-dimensional cross sections and processed to ascertain the occurrence of MMC. To achieve this, a deep learning Convolutional Neural Network (CNN) algorithm, known as YOLO (You Only Look Once), an object detection algorithm, and a segmentation algorithm called 2D U-Net are employed. The results obtained from YOLO reveal a confidence score of 0.89, a precision of 0.945, a recall of 0.902, and a Mean Average Precision (MAP) of 0.988, while the 2D U-Net achieves an accuracy of 0.9772. © 2023 IEEE.

关键词： Convolutional neural networks

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Database Matching Service for Stable Marriage Problem 25

Database Matching Service for Stable Marriage Problem

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25th International Electronics Symposium, IES 2023

作者： Alimudin, Akhmad Ishida, Yoshiteru Politeknik Elektronika Negeri Surabaya Dept. of Multimedia Creative Technology Surabaya Indonesia Toyohashi University of Technology Dept. of Computer Science and Engineering Toyohashi Japan

ISBN: (纸本)9798350314731

This study revolves around delving into the realm of dynamic preferences within the context of the Stable Marriage Problem (SMP). While the Gale-Shapley algorithm necessitates a time complexity of O(n^{2}) to solve an SMP with static preferences, the pursuit of identifying stable matchings under dynamic preferences becomes significantly more time-intensive, entailing a time complexity of O(k\cdot n!\cdot n^{2}). The majority of the process involved in discovering stable matchings under dynamic preferences revolves around iterative searching. This circumstance motivates us to develop a database comprising SMP instances and their corresponding stable matchings. This database facilitates a reduction in the time required to uncover static stable matchings from O(n^{2}) to O(n). Additionally, our application incorporates several features that enhance users' comprehension of stable matching structures within SMP instances, including visualization through a lattice structure. © 2023 IEEE.

关键词： Database systems

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Enhancing Autism Spectrum Disorder Identification: A Machine Learning Approach Using CatBoost

Enhancing Autism Spectrum Disorder Identification: A Machine...

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

作者： Vijayalaxmi N Rathod R.H. Goudar Dhananjaya. G. M Minal Patil Geetabai S Hukkeri Rohit B Kaliwal Dept of Computer Science and Engineering Visvesvaraya Technological University Belagavi Karnataka India Dept of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

Autism is a complex neurodevelopment condition that affects an individual's behavior, communication, and social interaction. Identification is a critical endeavor in health care, necessitating accurate and efficient diagnostic methodologies, early identification is pivotal for timely intervention and improved outcomes in affected individuals. This paper investigates the use of machine learning algorithms, specifically CatBoost, for Autism trait identification using heterogeneous datasets from toddlers, children, adolescents, and adults. The research investigates the performance of CatBoost in handling mixed data types, including categorical features and missing values, without extensive preprocessing. Utilizing gradient boosting on decision trees, CatBoost demonstrates its efficacy in capturing complex relationships between features, facilitating high predictive accuracy in autism identification. Through rigorous evaluation metrics such as accuracy, precision, recall, and Fl score, the designed system achieves a precise accuracy of 92% for adult datasets and 88% for child and adolescent datasets. This study delineates CatBoost's robustness across diverse age groups, providing insightful information on its applicability for Autism Spectrum Disorder diagnosis in the healthcare domain.

关键词： Autism Pediatrics Technological innovation Accuracy Machine learning algorithms Boosting Robustness Data models Decision trees Overfitting

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Artificial Intelligence and Blockchain-enabled Secure Birth Certificate Management System

Artificial Intelligence and Blockchain-enabled Secure Birth ...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Aaryaveer Raiput Kalp Prajapati Jigna J. Hathaliya Nilesh Kumar Jadav Sudeep Tanwar Deepak Garg Dept.of CSE Institute of Technology Nirma University Ahmedabad India School of Computer Science & AI SR University Warangal Telangana India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

With the recent advancement in technology, forgery activities have become more sophisticated and challenging to detect. This necessitates the development of advanced technologies that can counteract the illicit practices over digital platforms to streamline their operation. This article confronts the birth certificate forgery activities to strengthen the document verification system. In that context, we proposed an artificial intelligence (AI) and blockchain-based reliable framework for securing an individual's birth records. For that, we utilized a standard birth record dataset comprised of tampered and non-tampered data, which is trained on different machine learning (ML) algorithms, such as K-nearest neighbours (KNN), decision tree (DT), logistic regression (LR), and perceptron (PT) for binary classification tasks. Further, the predicted data from the AI models are forwarded to the blockchain network for secure data storage. In that context, we first designed a smart contract that validates the legitimacy of the predicted data; upon validation, it is forwarded to the interplanetary file system (IPFS) for hash computation. Integrating IPFS improves the scalability and response time of the blockchain network. Lastly, the proposed work is evaluated by considering different evaluation metrics, such as training accuracy, statistics measures (precision, recall, and F1 score), blockchain transaction and execution cost. Integrating blockchain and AI addresses data security, confidentiality, and accuracy in birth registration, providing a robust solution against potential digital and offline threats.

关键词： Training Accuracy Costs Forgery Blockchains InterPlanetary File System Decision trees

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Performance Evaluation of Data Imputation Methods for Graph Deep Learning-Based Traffic Prediction

Performance Evaluation of Data Imputation Methods for Graph ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Kim, Jeongseon Kim, Sungsoo Lim, Sungsu Chungnam National University Dept. of Computer Science & Engineering Daejeon Korea Republic of Smart Data Research Section Etri Daejeon Korea Republic of

ISBN: (纸本)9798350324457

We present five approaches for interpolating missing data in real-world traffic datasets using basic, statistical, and generative model methods. The effectiveness of these approaches is evaluated using graph deep learning models, and the results show that imputing missing data improves the performance of traffic prediction models, especially when dealing with a higher proportion of missing data. The experiments are conducted on two real-world datasets, METR-LA and PEMS-BAY, to evaluate the proposed methods comprehensively. The results demonstrate that imputing missing data significantly enhances the performance of traffic prediction models, particularly when dealing with a higher proportion of missing data. © 2023 IEEE.

关键词： Deep Learning on Graphs Missing Data Imputation Spatio-Temporal Time-Series

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Composition of Motion from Video Animation Through Learning Local Transformations 48

Composition of Motion from Video Animation Through Learning ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Vrigkas, Michalis Tagka, Virginia Plissiti, Marina E. Nikou, Christophoros University of Western Macedonia Dept. Communication and Digital Media Kastoria52100 Greece University of Ioannina Dept. Computer Science and Engineering Ioannina45110 Greece

ISBN: (纸本)9781728163277

In this work, we solve the problem of motion representation in videos, according to local transformations applied to specific keypoints extracted from static the images. First, we compute the co-ordinates of the keypoints of the body or face through a pre-trained model, and then we introduce a convolutional neural network to estimate a dense motion field through optical flow. Next, we train a generative adversarial network that exploits the previous information to generate new images that resemble as much as possible the target frames. To reduce trembling and extract smooth movements, our model incorporates a low-pass spatio-temporal Gaussian filter. Results indicate that our method provides high performance and the movement of objects is accurate and robust. © 2023 IEEE.

关键词： Animation Dense motion GANs Gaussian filter Video-synthesis

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The Role of Medical Expert Systems in Diagnosis of Chronic Kidney Diseases

The Role of Medical Expert Systems in Diagnosis of Chronic K...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Kuchipudi Prasanth Kumar K. F. Bharati Dept. of Computer Science and Engineering Jawaharlal Nehru Technological University Ananthapuramu Andhra Pradesh India Dept. of Computer Science and Engineering JNTUA College of Engineering Ananthapuramu Andhra Pradesh India

The ability to forecast kidney diseases in real-time is critical for preserving patients' lives. Early detection of these diseases not only extends the patient's longevity but also reduces treatment costs. It is very advised to develop a reliable expertise system for detecting these problems before they worsen. These smart solutions have the potential to save many lives. This review study assesses the Dice score efficacy characteristics obtained from various methodologies utilized in previous studies for detecting renal abnormalities or chronic kidney diseases. It gives a complete overview of medical expert systems in proposed methodology and emphasizes the most effective methods for identifying chronic kidney disease (CKD).

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Protecting Data Confidentiality and Assuring Integrity, and Preventing Rogue Devices in Medical Body Area Network

Protecting Data Confidentiality and Assuring Integrity, and ...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Rakibul Hasan Md Al Amin Sheikh Dobir Hossain Md. Ibrahim Abdullah Dept of Computer Science and Engineering Islamic University Bangladesh Dept of Computer Science Colorado State University USA Dept of Electrical and Computer Engineering The University of Texas at El Paso USA

The Internet of Things (IoT) is a modern technology that connects physical objects in a given environment over the Internet to make their information available from everywhere and at any time. The popularity of IoT technologies is rapidly increasing in most areas, such as education, health, agriculture, transportation, etc. Medical Body Area Network (MBAN) is an emerging healthcare technology that allows remote monitoring of patient's vital signs, providing physicians with real-time data and facilitating better treatment decisions. However, security concerns are one of the hardest things to solve in IoT systems because they have limited computing power, memory, and energy, among other things. Moreover, all the objects in the IoT system are communicated with, controlled, and monitored over the Internet, and most communications are established via wireless networks. Due to the weakness of the wireless network, it is essential to protect data so that a bad guy can't get to it. This article proposes a model based on a lightweight cryptographic protocol to ensure data confidentiality, integrity, and device authentication for MBAN patients. Experimental evaluations show that the proposed approach is functional with the currently available hardware resources to provide complete cryptographic functionalities, like ensuring patient physiological data confidentiality and upholding data integrity during the exchange between sensor devices and the central coordinator. Also, unauthorized access attempts from malicious devices or attackers are detected through mutual authentication to maintain the integrity of data sources.

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