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Optimized isochronal late activation methods in the visualization and isthmus identification of ventricular tachycardia

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Heart Rhythm 2025年第5期22卷 1238-1248页

作者： Zeldin, Lawrence Majumder, Jonah A. Yang, Haiqiu Yarmohammadi, Hirad Biviano, Angelo Hennessey, Jessica Wan, Elaine Y. Ciaccio, Edward J. Garan, Hasan Hendon, Christine P. Saluja, Deepak Department of Medicine Columbia University Irving Medical Center New York New York United States Department of Biomedical Engineering Fu Foundation School of Engineering and Applied Science Columbia University New York New York United States Department of Electrical Engineering Fu Foundation School of Engineering and Applied Science Columbia University New York New York United States Division of Cardiology Department of Medicine Columbia University Irving Medical Center New York New York United States

Background: Catheter ablation of ventricular tachycardia (VT) during sinus rhythm often relies on isochronal late activation mapping (ILAM), a validated means of isthmus identification, whereby points are binned by local activation time (LAT) into 8 isochrones, and isthmus regions are identified as regions with isochronal crowding. The resulting output, however, is inherently discretized, and loss of LAT data occurs. To improve the precision of isthmus identification, we quantify isochronal density and assess the effect of increasing the number of isochrones used and the effectiveness of continuous metrics analogous to ILAM. Objective: The objective of this study was to determine whether current practices in ILAM calculation are optimized for isthmus detection. Methods: Patients undergoing VT ablation were included if high-density maps in both VT and sinus or paced rhythms were available. Isochronal density was assessed at differing numbers of isochrones, and isthmus discrimination was assessed. Continuous metrics that mimicked ILAM by assessing the local distribution of LAT values—using interquartile range or the median absolute deviation—were also defined and assessed. Results: Eight electroanatomic maps (EAMs) were included. Isthmus discrimination improved progressively from a minimum area under the curve (AUC) of 0.600 when assessing the isochronal density of 8-isochrone EAMs to a maximum of 0.776 when assessing the density of 1000-isochrone EAMs (DeLong test, P < .0001). On logistic regression, the continuous metrics using interquartile range (AUC = 0.714) and median absolute deviation (AUC = 0.721) better discriminated isthmus identity than ILAM. Conclusion: Using more isochrones or using continuous ILAM analogues improves isthmus identification. By retaining more data, these metrics could increase the precision of VT ablation. © 2024 Heart Rhythm Society

关键词： Ablation Isochronal late activation mapping Isthmus Velocity Ventricular tachycardia

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Learned, uncertainty-driven adaptive acquisition for photon-efficient scanning microscopy

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Optics Express 2025年第6期33卷 12269-12287页

作者： Ye, Cassandra Tong Han, Jiashu Liu, Kunzan Angelopoulos, Anastasios Griffith, Linda Monakhova, Kristina You, Sixian The Department of Electrical Engineering and Computer Science CambridgeMA02139 United States Fu Foundation School of Engineering and Applied Science Columbia University in The City of New York New YorkNY10027 United States University of California Berkeley Department of Electrical Engineering and Computer Science BerkeleyCA94720 United States Department of Biological Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Scanning microscopy systems, such as confocal and multiphoton microscopy, are powerful imaging tools for probing deep into biological tissue. However, scanning systems have an inherent trade-off between acquisition time, field of view, phototoxicity, and image quality. This often results in noisy measurements when fast, large field of view, and/or gentle imaging is needed. Deep learning could be used to denoise noisy microscopy measurements, but these algorithms can be prone to hallucination, which can be disastrous for medical and scientific applications. We propose a method to simultaneously denoise and predict pixel-wise uncertainty for scanning microscopy systems, improving algorithm trustworthiness and providing statistical guarantees for deep learning predictions. furthermore, we propose to leverage this learned, pixel-wise uncertainty to drive an adaptive acquisition technique that rescans only the most uncertain regions of a sample, saving time and reducing the total light dose to the sample. We demonstrate our method on experimental confocal and multiphoton microscopy systems, showing that our uncertainty maps can pinpoint hallucinations in the deep learned predictions. Finally, with our adaptive acquisition technique, we demonstrate up to 16× reduction in acquisition time and total light dose while successfully recovering fine features in the sample and reducing hallucinations. We are the first to demonstrate distribution-free uncertainty quantification for a denoising task with real experimental data and the first to propose adaptive acquisition based on reconstruction uncertainty. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Pixels

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The Role of Machine Learning in Reducing Healthcare Costs: The Impact of Medication Adherence and Preventive Care on Hospitalization Expenses

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arXiv 2025年

作者： Zhang, Yixin Chen, Yisong The Fu Foundation School of Engineering and Applied Science Columbia University United States College of Computing Georgia Institute of Technology United States

This study reveals the important role of prevention care and medication adherence in reducing hospitalizations. By using a structured dataset of 1,171 patients, four machine learning models Logistic Regression, Gradient Boosting, Random Forest, and Artificial Neural Networks are applied to predict five-year hospitalization risk, with the Gradient Boosting model achieving the highest accuracy of 81.2%. The result demonstrated that patients with high medication adherence and consistent preventive care can reduce 38.3% and 37.7% in hospitalization risk. The finding also suggests that targeted preventive care can have positive Return on Investment (ROI), and therefore ML models can effectively direct personalized interventions and contribute to long-term medical *** Codes 68T05, 68T09, 68U03, 62P10 Copyright © 2025, The Authors. All rights reserved.

关键词： mHealth

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Methodology for Evaluating the Impact of DevOps Principles

Methodology for Evaluating the Impact of DevOps Principles

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Aldin Alić Aldin Traljić Dženana Đonko Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina Fu Foundation School of Engineering and Applied Science Columbia University New York United States

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

The evolution of software development has led to the emergence of DevOps principles. DevOps principles enhance the integration between development and operations to achieve an uninterruptible and consistent flow of software delivery. The benefits of DevOps include the achievement of shared ownership of different parts of the process, automation of software development and testing, alongside the automation of infrastructure creation. Integrating DevOps, however, brings some challenges, primarily regarding its practical implementation. These challenges include cultural resistance, selection of imperfect tools and techniques, and the need for constant learning and improvement. Additionally, there is often lack of clarification and unity within an organization on if the implementation is successful, with differing views from different stakeholders. This compounds the practical challenges and makes it harder to improve over time. This paper aims to establish a new methodology for assessing the implementation of DevOps. Previous methodologies covered a wide range of questions, and this paper aims to improve on and complement areas covering adherence and understanding of DevOps principles by engineers and managers in companies. A study was conducted on whether our new methodology was able to evaluate the impact of DevOps across different teams, alongside whether its results conveyed a unison and unbiased representation from within the organization.

关键词： Surveys Resistance Measurement DevOps Automation Design methodology Software Stakeholders Faces Testing

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Methodology for Evaluating the Impact of DevOps Principles

Methodology for Evaluating the Impact of DevOps Principles

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Alić, Aldin Traljić, Aldin Donko, Dženana University of Sarajevo Faculty of Electrical Engineering Sarajevo Bosnia and Herzegovina Columbia University Fu Foundation School of Engineering and Applied Science New York United States

ISBN: (纸本)9798331515799

关键词： Software design

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Global Trends in the Application of Extended Reality Technology to Autism Research: Based on the Web of Bibliometric Analysis of science (1999.1.1–2023.3.4) 10th

Global Trends in the Application of Extended Reality Technol...

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10th China Health Information Processing Conference, CHIP 2024

作者： Zhao, Wang Nie, Xuehan Zhu, Wangshu Cai, Ziyi School of Management Engineering Henan University of Engineering Zhengzhou China School of Mechanical Engineering Henan University of Engineering Zhengzhou China Fu Foundation School of Engineering and Applied Science Columbia University New York United States School of Business Henan University of Engineering Zhengzhou China

ISBN: (纸本)9789819637546

In order to deeply analyze the research status and frontier trends of extended reality technology and autism rehabilitation, and to accurately understand the development stage and characteristics of research in this field worldwide, through the bibliometric tools CiteSpace and VOS viewer, the relevant literature published in the Web of science database from 1999 to March 4, 2023 was visually analyzed from six dimensions: literature itself, country, institution, journal, author, and keyword. The research found that the journals and institutions in the field of extended reality technology and autism rehabilitation are mainly concentrated in the United States, and the research hotspots mainly focus on the application of virtual reality in the rehabilitation intervention of autistic social skills. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autism CiteSpace Extended Reality Quantitative Analysis VOS viewer

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Adaptive AI-Based Personalized Learning for Accelerated Vocabulary and Syntax Mastery in Young English Learners

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 678-687页

作者： Aravind, Angalakuduru Durairaj, M. Chitkara, Preeti Baker El-Ebiary, Yousef A. Muniyandy, Elangovan Ushasree, Linginedi Ammar, Mohamed Ben Kodad Telangana 508206 India Dept.of English Panimalar Engineering College Poonamallee Chennai600123 India Head PR & International Relations Department of Applied Sciences KIET Group of Institutions Delhi-NCR Ghaziabad India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur Dist. 522302 India Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching may vary according to different learning speeds and it leads to unbalanced levels of proficiency among students and possibly leading to disengagement among slow learners. The present computer-assisted learning aids provide practice interactively but without real-time adaptation and personalized feedback, limiting their capacity to address learners’ unique problems. To overcome these constraints, this study suggests an Artificial Intelligence based personalized learning system that supports vocabulary and syntax learning via adaptive learning models, NLP-based chatbots and gamified interactive lessons. The system dynamically adapts content according to students’ most recent performance in real time to enable a personalized learning experience, which results in efficient Learning. The research has experimental study design, and two groups are considered, an AI-supported learning group and a traditional learning group. Pre-test and post-test design measures the effects of the system on vocabulary recall and syntax correctness. Other learner engagement rates like survey results and qualitative feedback inform learner experience and learning efficacy. Initial results indicate that learners working with the Artificial Intelligence powered learning system gained 25percent in recalling vocabulary and 30percent in syntax accuracy over the control group. further, learner engagement rates are elevated because of real-time feedback and gamification components. These results emphasize the promise of AI-based personalized learning to boost language acquisition and lay the basis for further effective innovations in adaptive education technologies. © (2025), (science and Information Organization). All rights reserved.

关键词： Students

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Disaster-Based Geographical Region Analysis Using Climate Change Detection Using Deep Learning Algorithm

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 555-565页

作者： Swapna, P.K. Mapari, Rahul Ganpat Muniyandy, Elangovan Tham, Jacquline Sophia, M. Sandra Carmel Haldar, Ritwik Department of English Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522502 India Electronics and Telecommunication Engineering Pimpri Chinchwad College of Engineering and Research Haveli Pune 412101 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Applied Science Research Center Applied Science Private University Amman Jordan Management and Science University Shah Alam Selangor Malaysia Department of Electronics and Communication Engineering Haldia Institute of Technology West Bengal Haldia 721657 India

An increasingly important field of research has emerged in recent decades: the interaction between urban infrastructure, human lives, and natural disasters. Analysis and reaction to natural disasters have made extensive use of deep learning (DL) techniques using semantic segmentation networks. The aim of this research is to develop novel method in disaster-based geographical region changes and climate change detection utilising DL model. Here, input is collected as disaster-based geographical region and climate change dataset as well as processed for noise removal and smoothening. Then, this image feature has been extracted and classified using watershed U-net component analysis with Hopfield variational fuzzy encoder neural networks. The fundamental component of the suggested approach is a deep learning procedure that makes it possible to identify soccer fields and analyse helicopter landing sites. The technique uses satellite photos and provided geographic data to train a deep learning autoencoder. Experimental analysis has been carried out in terms of classification accuracy, specificity, sensitivity, ROC, and F-measure. The proposed technique obtained 96% of classification accuracy, 94% of sensitivity, 98% of specificity, 95% of ROC, and 97% of F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change detection Deep learning model fuzzy encoder Geographical region changes U-net component analysis

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Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 564-576页

作者： Ramesh, Komminni Thomas, Christine Ann Osei-Asiamah, Joel Pagidipati, Bhuvaneswari Muniyandy, Elangovan Reddy, B. V. Suresh El-Ebiary, Yousef A. Baker BoS Anurag Engineering College Suryapet District Telangana Kodad India Department of English and Cultural Studies Christ University Bengaluru India Gauteng Province Pretoria South Africa West Godavari Dt Andhra Pradesh Bhimavaram534204 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Faculty of Informatics and Computing UniSZA University Malaysia

Traditional English as a Secondary Language (ESL) learning platform rely on static content delivery, often failing to adapt to individual learners’ cognitive capacities, leading to inefficient comprehension and increased cognitive load. A novel hybrid Feedforward Neural Network and Bidirectional Encoder Representation Transformer (FNN-BERT) framework stands as our solution because it performs dynamic content personalization through predictions of real-time cognitive load. The proposed approach incorporates Feedforward Neural Networks (FNN) alongside Bidirectional Encoder Representations from Transformers (BERT) to process behavioral analytics for optimized content complexity adjustment and adaptive and scalable learning delivery. Real-time adaptability, scalability and high computational needs of current models reduce their effectiveness in personalized learning environments. Through the application of Test of English for International Communication (TOEIC), International English Language Testing System (IELTS) and Test of English as a Foreign Language (TOEFL) datasets, our methodology uses Feedforward Neural Network (FNN) to forecast cognitive load based on student engagement behaviors and application errors then Bidirectional Encoders Representations from Transformer (BERT) processes content difficulty adjustments automatically. The proposed model delivers a 95.3% accuracy rate, 96.22% precision level, 96.1% recall capability and 97.2% F1-score which surpasses conventional Artificial Intelligence-based English as a Secondary Language (ESL) learning systems. The system makes use of Python for its implementation to improve understanding as well as student focus and mental processing speed. Personalized content presentation methods lead to lower cognitive strain which simultaneously advances student achievement numbers. The research adds value to smart educational frameworks through its introduction of a scalable framework that allows adaptable learning systems for E

关键词： Feedforward neural networks

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