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Transforming Healthcare Diagnostics With Tensorized Attention and Continual Learning on Multi-Modal Data

IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Shabaz, Mohammad Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Algamdi, Shabbab Ali Li, Yang Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Model Institute of Engineering and Technology J and K Jammu India Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia Prince Sattam bin Abdulaziz University College of Computer Science and Engineering Department of Software Engineering Al Kharj Saudi Arabia Shihezi University College of Mechanical and Electrical Engineering Shihezi832003 China

Analyzing multi-modal medical data in the setting of uncertain healthcare situations continues to be a major topic in medical image analysis and healthcare big data. Traditional machine learning algorithms are severely hampered by inaccurate data fusion, a lack of adaptability to changing patient data, and challenges managing uncertainty. These difficulties are made worse by complicated medical images and diverse data sources, which results in less accurate diagnosis and worse-than-ideal healthcare choices. To tackle these urgent problems, this paper suggests two new approaches: Continual Learning using Progressive Neural Networks (PNNs) and Tensorized Attention Mechanism for Data Fusion. The Tensorized Attention Mechanism improves multi-modal data fusion by using dynamic, task-specific attention to improve feature alignment across modalities, and the PNNs framework uses continual learning, memory augmentation, and domain adaptation to ensure robust learning under data uncertainty. We test these methods on a variety of multi-modal datasets, such as MIMIC-IV, CheXpert, MOST, OAI, and Heart Murmur, which offer a comprehensive representation of medical data from clinical reports, chest X-rays, heart murmurs, and other heterogeneous data sources. Our experimental results show notable improvements in diagnostic performance, with notable results like a CFI of 0.10, a KR score of 90.4%, and an MMC score of 0.097, indicating superior generalization and robustness across domains. Healthcare AI applications could be revolutionized by the use of specialized losses, such as Conditional Variational Autoencoder (CVAE), Adversarial Contrastive Learning (ACL), Reciprocal Regularization, and domain adaptation losses, which are essential for preventing forgetting and guaranteeing learning stability across shifting data streams. © 1975-2011 IEEE.

关键词： Sensor data fusion

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A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

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IEEE Communications Surveys and Tutorials 2025年

作者： Boateng, Gordon Owusu Sami, Hani Alagha, Ahmed Elmekki, Hanae Hammoud, Ahmad Mizouni, Rabeb Mourad, Azzam Otrok, Hadi Bentahar, Jamal Muhaidat, Sami Talhi, Chamseddine Dziong, Zbigniew Guizani, Mohsen Khalifa University KU6G Research Center Department of Computer Science Abu Dhabi United Arab Emirates Ecole de Technologie Superieure Department of Software and IT Engineering Montreal Canada Lebanese American University Artificial Intelligence and Cyber Systems Research Center Department of CSM Beirut Lebanon Concordia University Concordia Institute for Information Systems Engineering MontrealQC Canada Ecole de Technologie Superieure Department of Electrical Engineering Montreal Canada Department of Computer Science Abu Dhabi United Arab Emirates Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Mohamed Bin Zayed University of Artificial Intelligence Department of ML Abu Dhabi United Arab Emirates

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM. © 1998-2012 IEEE.

关键词： Resource allocation

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Automating the Training and Deployment of Models in MLOps by Integrating systems with Machine Learning

arXiv

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arXiv 2024年

作者： Liang, Penghao Song, Bo Zhan, Xiaoan Chen, Zhou Yuan, Jiaqiang Information Systems Northeastern University San JoseCA United States Computer Science Northeastern University BostonMA United States Electrical Engineering New York University NY United States Software Engineering Zheiiang University Hangzhou China Information Studies Trine University PhoenixAZ United States

This article introduces the importance of machine learning in real-world applications and explores the rise of MLOps (Machine Learning Operations) and its importance for solving challenges such as model deployment and performance monitoring. By reviewing the evolution of MLOps and its relationship to traditional software development methods, the paper proposes ways to integrate the system into machine learning to solve the problems faced by existing MLOps and improve productivity. This paper focuses on the importance of automated model training, and the method to ensure the transparency and repeatability of the training process through version control system. In addition, the challenges of integrating machine learning components into traditional CI/CD pipelines are discussed, and solutions such as versioning environments and containerization are proposed. Finally, the paper emphasizes the importance of continuous monitoring and feedback loops after model deployment to maintain model performance and reliability. Using case studies and best practices from Netflix, the article presents key strategies and lessons learned for successful implementation of MLOps practices, providing valuable references for other organizations to build and optimize their own MLOps practices. Copyright © 2024, The Authors. All rights reserved.

关键词： Pipelines

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant Security Controls

arXiv

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arXiv 2024年

作者： Gupta, Deepti Elluri, Lavanya Jain, Avi Moni, Shafika Showkat Aslan, Omer Dept. of Computer Information Systems Texas A&M University - Central Texas TX United States College of Business University of Tampa Florida United States Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida United States Department of Software Engineering Bandırma Onyedi Eylül University Bandırma Turkey

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamper-proof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls—such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture—this approach strengthens an organization’s defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth’s transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, enab

关键词： Blockchain

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An Incremental Learning Approach to Automatically Recognize Pulmonary Diseases from the Multi-vendor Chest Radiographs

arXiv

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arXiv 2022年

作者： Sirshar, Mehreen Hassan, Taimur Akram, Muhammad Usman Khan, Shoab Ahmed Department of Computer and Software Engineering National University of Sciences and Technology Islamabad44000 Pakistan Center of Cyber-Physical Systems Department of Electrical Engineering and Computer Sciences Khalifa University Abu Dhabi127788 United Arab Emirates

The human respiratory network is a vital system that provides oxygen supply and nourishment to the whole body. Pulmonary diseases can cause severe respiratory problems, leading to sudden death if not treated timely. Many researchers have utilized deep learning systems (in both transfer learning and fine-tuning modes) to diagnose pulmonary disorders using chest X-rays (CXRs). However, such systems require exhaustive training efforts on large-scale (and well-annotated) data to effectively diagnose chest abnormalities (at the inference stage). Furthermore, procuring such large-scale data (in a clinical setting) is often infeasible and impractical, especially for rare diseases. With the recent advances in incremental learning, researchers have periodically tuned deep neural networks to learn different classification tasks with few training examples. Although, such systems can resist catastrophic forgetting, they treat the knowledge representations (which the network learns periodically) independently of each other, and this limits their classification performance. Also, to the best of our knowledge, there is no incremental learning-driven image diagnostic framework (to date) that is specifically designed to screen pulmonary disorders from the CXRs. To address this, we present a novel framework that can learn to screen different chest abnormalities incrementally (via few-shot training). In addition to this, the proposed framework is penalized through an incremental learning loss function that infers Bayesian theory to recognize structural and semantic inter-dependencies between incrementally learned knowledge representations to diagnose the pulmonary diseases effectively (at the inference stage), regardless of the scanner specifications. We tested the proposed framework on five public CXR datasets containing different chest abnormalities, where it achieved an accuracy of 0.8405 and the F1 score of 0.8303, outperforming various state-of-the-art incremental learning scheme

关键词： Diagnosis

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ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA

arXiv

引用

arXiv 2022年

作者： Ramakrishna, Shreyas Luo, Baiting Kuhn, Christopher B. Karsai, Gabor Dubey, Abhishek Institute for Software Integrated Systems Vanderbilt University NashvilleTN37212 United States Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany

Despite recent advances in autonomous driving systems, accidents such as the fatal Uber crash in 2018 show these systems are still susceptible to edge cases. Such systems must be thoroughly tested and validated before being deployed in the real world to avoid such events. Testing in open-world scenarios can be difficult, time-consuming, and expensive. These challenges can be addressed by using driving simulators such as CARLA instead. A key part of such tests is adversarial testing, in which the goal is to find scenarios that lead to failures of the given system. While several independent efforts in testing have been made, a well-established testing framework that enables adversarial testing has yet to be made available for CARLA. We therefore propose ANTI-CARLA, an automated testing framework in CARLA for simulating adversarial weather conditions (e.g., heavy rain) and sensor faults (e.g., camera occlusion) that fail the system. The operating conditions in which a given system should be tested are specified in a scenario description language. The framework offers an efficient search mechanism that searches for adversarial operating conditions that will fail the tested system. In this way, ANTI-CARLA extends the CARLA simulator with the capability of performing adversarial testing on any given driving pipeline. We use ANTI-CARLA to test the driving pipeline trained with Learning By Cheating (LBC) approach. The simulation results demonstrate that ANTI-CARLA can effectively and automatically find a range of failure cases despite LBC reaching an accuracy of 100% in the CARLA benchmark. Copyright © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Automative Video Compression for Remote Driving via Safety Considerations

Automative Video Compression for Remote Driving via Safety C...

引用

IEEE Conference on Standards for Communications and Networking (CSCN)

作者： Dan Peled Armin Shmilovici Ofer Hadar The Department of Software and Information Systems Engineering Ben-Gurion University of the Negev P.O.Box 653 Beer Sheva Israel School of Electrical and Computer Engineering Ben-Gurion University of the Negev P.O.Box 653 Beer Sheva Israel

Remote driving serves as a viable solution in situations where fully autonomous vehicles encounter critical events, such as sensor failures. However, implementing remote driving poses certain technical challenges, including the need to ensure high-quality video transmission to the remote driver. Additionally, in scenarios involving poor road conditions, multiple autonomous vehicles may simultaneously require remote driving assistance at specific locations, straining the communication infrastructure. To address these challenges, we propose a novel approach that involves compression of the driving video using a driving safety model. This model intelligently prioritizes key objects within the frame, resulting in improved compression quality. An initial experiment demonstrated that 60% of the required bitrate can be reduced while retaining 90% of the perceived quality.

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A Proof of Kirchhoff's First Law for Hyperbolic Conservation Laws on Networks

arXiv

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arXiv 2022年

作者： Bayen, Alexandre M. Keimer, Alexander Müller, Nils Department of Electrical Engineering and Computer Sciences Department of Civil and Environmental Engineering University of California Berkeley United States Germany Max Planck Institute for Software Systems Saarland Informatics Campus Germany

Networks are essential models in many applications such as information technology, chemistry, power systems, transportation, neuroscience, and social sciences. In light of such broad applicability, a general theory of dynamical systems on networks may capture shared concepts, and provide a setting for deriving abstract properties. To this end, we develop a calculus for networks modeled as abstract metric spaces and derive an analog of Kirchhoff’s first law for hyperbolic conservation laws. In dynamical systems on networks, Kirchhoff’s first law connects the study of abstract global objects, and that of a computationally-beneficial edgewise-Euclidean perspective by stating its equivalence. In particular, our results show that hyperbolic conservation laws on networks can be stated without explicit Kirchhoff-type boundary *** Codes 35R02, 35C99 (Primary) 35L65, 00A71 (Secondary) © 2022, CC BY.

关键词： Dynamical systems

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Feasibility Study of a DC House Connected to a Conventional AC Distribution Network

Feasibility Study of a DC House Connected to a Conventional ...

引用

Compatibility in Power Electronics (CPE)

作者： Hossein Nourollahi Hokmabad Noman Shabir Victor Astapov Eduard Petlenkov Oleksandr Husev Juri Belikov Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia FinEST Centre for Smart Cities Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia

ISBN: (数字)9798350318265

ISBN: (纸本)9798350318272

Leveraging advancements in power electronics, the adoption of Direct Current (dc) technology in net-Zero Energy Buildings (nZEBs) is seen as a promising approach to boost energy efficiency. Emerging dc technology aims to reduce power losses by eliminating unnecessary conversions between dc and Alternating Current (ac). This paper thoroughly assesses the effectiveness of dc and hybrid dc (partial dc topologies in comparison to conventional ac nZEBs. The hybrid solution integrates ac and dc networks and loads within the building, while the dc solution benefits from a purely dc internal electricity distribution network and exclusively dc loads. The study involves analyzing annual load profile data from 16 neighboring houses in Estonia, simulating their transition to nZEBs with dc, hybrid dc, and pure dctopologies. The study examines power conversion losses, the operational periods of power converters in relation to their maximum power capacity, and energy exchanges with the utility grid. Additionally, it explores the potential for creating energy communities based on the consumption patterns of these houses. The results indicate that pure dc nZEBs might not be as efficient as initially thought, especially when renewable resources are limited.

关键词： Measurement Renewable energy sources Power engineering Network topology Net zero Buildings Hybrid power systems

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A Review of Algorithms’s Complexities on Different Valued Sorted and Unsorted Data

A Review of Algorithms’s Complexities on Different Valued S...

引用

IT and Industrial Technologies (ICIT), International Conference on

作者： Attia Shabbir Asad Majeed Mahnoor Iftikhar Raja Hashim Ali Usama Arshad Muhammad Zeeshan Shabbir Ali Zeeshan Ijaz Nisar Ali Ali Aftab Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Department of Electrical Engineering University of Engineering & Technology (UET) Lahore Pakistan Faculty of Electronic Systems Engineering University of Regina Regina Canada

Sorting and searching are critical processes for effecttive data analysis. In this paper, we evaluate the performance of various sorting and searching algorithms and compare their time and space complexities on both sorted and unsorted data. The algorithms we analyzed include six common sorting algorithms (insertion, radix, bucket, merge, bubble, and quick sort) and three search algorithms (linear, binary, and jump search). The results of our study provide insights into the best algorithms to use for different input sizes and types of data. It was found that for small input sizes, all algorithms perform similarly, but for larger input sizes, insertion and radix sorts are better for time complexity while bubble sort is better for space complexity. Additionally, jump search outperformed linear and binary search algorithms in both time and space complexity. Besides, difference between time and space complexity of sorted and unsorted data was significant.

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