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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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Intelligent Agriculture Robot for Tea Plantation Preservation - TeaBot

Intelligent Agriculture Robot for Tea Plantation Preservatio...

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IEEE International Conference on Multi Topic

作者： Imalka Gunawardana Varnavi Perera Thulya Premathilake Gayan Bamunusinghe Shashika Lokuliyana Narmada Gamage Rajitha de Silva Dept. of Software Engineering SLIIT Malabe Sri Lanka Dept. of IT SLIIT Malabe Sri Lanka Dept. of Computer Systems Engineering SLIIT Malabe Sri Lanka Lincoln Agri-Robotics University of Lincoln United Kingdom

Tea cultivation stands out as one of the most significant export products that contribute to the Gross Domestic Product (GDP). In the previous decades, the substantial work-force gradually transitioned to different occupations. Compared to other crops, tea cultivation demands costly and meticulous upkeep. With a shortage of labor, maintaining tea estates grew tougher, leading to decreased yields. In reaction, estate owners transitioned to cultivating low-maintenance crops. The TeaBot is an advanced robot designed to replace human labor for watering and fertilizing vast tea estates. The TeaBot distinguishes itself by operating in rough outdoor terrain and infrastructure-free navigation in real tea plantations. Given that tea plants demand continuous hydration and nutrients for optimal crop yield, TeaBot plays a pivotal role in enhancing efficiency while reducing water and fertilizer wastage. Mainly four motor-powered wheels move accurately by translating linear and angular velocities using a precise motor control algorithm. An autonomous navigation algorithm was developed using two distinct approaches, which include deep learning-based computer vision and classical computer vision. The selection of the classical computer vision method was predicated upon its notable attributes, including high precision, minimal resource utilization, and optimal efficiency. A deep learning-based stem identification model was trained based on MobileNetV2 architecture to detect where the plant stem meets the ground for efficient hydration of individual plants. This lightweight model achieved 90% detection accuracy. The precise results of stem detection have made the liquid fertilization process more efficient.

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EpiTESTER: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanism

arXiv

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arXiv 2023年

作者： Lu, Chengjie Ali, Shaukat Yue, Tao The Department of Engineering Complex Software Systems Simula Research Laboratory Oslo0258 Norway Simula Research Laboratory Norway Oslo Metropolitan University Oslo0258 Norway The School of Computer Science and Engineering Beihang University Beijing100191 China

Testing autonomous vehicles (AVs) under various environmental scenarios that lead the vehicles to unsafe situations is known to be challenging. Given the infinite possible environmental scenarios, it is essential to find critical scenarios efficiently. To this end, we propose a novel testing method, named EpiTESTER, by taking inspiration from epigenetics, which enables species to adapt to sudden environmental changes. In particular, EpiTESTER adopts gene silencing as its epigenetic mechanism, which regulates gene expression to prevent the expression of a certain gene, and the probability of gene expression is dynamically computed as the environment changes. Given different data modalities (e.g., images, lidar point clouds) in the context of AV, EpiTESTER benefits from a multi-model fusion transformer to extract high-level feature representations from environmental factors and then calculates probabilities based on these features with the attention mechanism. To assess the cost-effectiveness of EpiTESTER, we compare it with a classical genetic algorithm (GA) (i.e., without any epigenetic mechanism implemented) and EpiTESTER with equal probability for each gene. We evaluate EpiTESTER with four initial environments from CARLA, an open-source simulator for autonomous driving research, and an end-to-end AV controller, Interfuser. Our results show that EpiTESTER achieved a promising performance in identifying critical scenarios compared to the baselines, showing that applying epigenetic mechanisms is a good option for solving practical problems. © 2023, CC BY.

关键词： Autonomous vehicles

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Realizing Emotional Interactions to Learn User Experience and Guide Energy Optimization for Mobile Architectures 22

Realizing Emotional Interactions to Learn User Experience an...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Xueliang Li Zhuobin Shi Junyang Chen Yepang Liu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation and Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9781665462723

In the age of AI, mobile architectures such as smartphones are still "cold machines"; machines do not feel. If the architecture is able to feel users' feelings and runtime user experience (UX), it will accordingly adapt performance/energy to find the optimal system-operating state that consumes the least energy to satisfy users. In this paper, we will utilize users' facial expressions (FEs) to learn their runtime UX. We know that FEs are the natural and direct way for humans to convey their emotions and feelings. Our study reveals that FEs also reflect UX. Our research for the first time quantifies the link between FEs and UX. Leveraging this link, the architecture will be able to use the front camera to see FEs and feel users' UX. Based on UX, the architecture can appropriately provision computing resources. We propose Vi-energy system to realize the above idea. Our evaluation shows that Vi-energy reduces energy consumption by 52.9% at maximum and secures UX.

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An Intelligent Approach to Segment the Liver Cancer using Machine Learning Method 4

An Intelligent Approach to Segment the Liver Cancer using Ma...

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4th International Conference on Electronics and Sustainable Communication systems, ICESC 2023

作者： Anand, L. Maurya, Mahesh Seetha, J. Nagaraju, D. Ravuri, Ananda Vidhya, R.G. School of Computing Srm Institute of Science and Technology Department of Networking and Communications India University of Mumbai K. C. College of Engineering Management and Research Mith Bunder Road Near Hume Pipe Maharashtra Thane400603 India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Sri Venkatesa Perumal College of Engineering and Technology Department of CSE Andhra Pradesh Puttur India Senior Software Engineer Intel Corporation HillsboroOR97124 United States Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350300093

Cancer Liver cancer is difficult to detect and diagnose early. Machine learning has improved liver cancer diagnosis in recent years. This abstract describes a novel liver cancer detection and classification method using Autoencoder-Extreme Learning Machine (AE-ELM) and Convolutional Neural Network (CNN) technology. AE-ELM models extract features in the suggested method. The autoencoder-extreme learning machine AE-ELM technique learns high-level abstract characteristics from incoming data. AE-ELM extracts liver cancer-specific features by reducing the dimensionality of input data. A CNN model classifies the extracted features. CNNs are good at detecting spatial patterns in medical pictures like CT and MRI scans. The CNN model learns hierarchical representations of input information and classifies them into liver cancer subtypes or stages using various convolutional and pooling processes. AE-ELM and CNN are advantageous. First, AE-ELM decreases input data dimensionality to extract critical features without manual feature engineering. This simplifies feature selection and improves the model's capacity to handle big data. Second, the CNN model uses hierarchical convolutional processes to capture subtle patterns and spatial correlations in input data, enhancing liver cancer classification accuracy. The proposed liver cancer diagnostic method works in experiments. AE-ELM and CNN models outperform classic machine learning approaches and standalone CNN models. Improved liver cancer detection accuracy, sensitivity, and specificity help healthcare practitioners make quick and precise diagnosis. Finally, AE-ELM and CNN technologies can identify and classify liver cancer. This unique strategy uses both models to extract relevant characteristics and capture complicated patterns, improving liver cancer diagnosis accuracy and efficiency. To evaluate this approach's therapeutic promise and application in real-world healthcare settings, additional datasets are needed. © 2023 IEEE.

关键词： Convolutional neural networks

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TransOff: Towards Fast Transferable Computation Offloading in MEC via Embedded Reinforcement Learning

TransOff: Towards Fast Transferable Computation Offloading i...

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International Conference on Distributed Computing systems

作者： Zheyuan Hu Jianwei Niu Tao Ren State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing China

Mobile edge computing (MEC) has been proposed as a promising paradigm to provide mobile devices with both satisfactory computing capacity and task latency. One key issue in MEC is computation offloading (CompOff), which has attracted numerous research interests. Most existing CompOff approaches are developed based on iterative programming (IterProg), that calculates a CompOff action based on system dynamics each time mobile tasks arrive. Due to the heavy dependency of IterProg on reliable system dynamics, as well as the online computational burden, recent years have seen a popular trend to develop CompOff approaches based on deep reinforcement learning (DRL), which could generate real-time model-free CompOff actions. However, due to the intrinsic poor generalization of DRL, it is hard to directly apply DRL-based policies in new MEC environments, and long-time fine-tuning is often required. To address the challenge, this paper proposes a fast transferable CompOff framework (named TransOff), based on the idea of embedded reinforcement learning. Specifically, TransOff is composed of multiple primitive CompOff policies (pCOPs) and a multiplicative composition function (MCF). The pCOPs and MCF are pre-trained in a diverse variety of MEC environments. When encountering new MEC environments, pCOPs are kept fixed to prevent catastrophic forgetting of pre-trained CompOff skills, while only MCF is fine-tuned to produce new compositions of pCOPs to achieve fast transfer. We conduct extensive experiments via both numerical simulation and real testbed, indicating the fast transfer ability of TransOff compared to the state-of-the-art DRL-based and meta learning-based CompOff approaches.

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Automatic Vehicle Trajectory Data Reconstruction at Scale

SSRN

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SSRN 2023年

作者： Wang, Yanbing Gloudemans, Derek Ji, Junyi Teoh, Zi Nean Liu, Lisa Zachár, Gergely Barbour, William Work, Daniel Department of Civil and Environmental Engineering Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University United States Department of Computer Science Vanderbilt University United States Department of Electrical Engineering Vanderbilt University United States

Vehicle trajectory data has received increasing research attention over the past decades. With the technological sensing improvements such as high-resolution video cameras, in-vehicle radars and lidars, abundant individual and contextual traffic data is now available. However, though the data quantity is massive, it is by itself of limited utility for traffic research because of noise and systematic sensing errors, thus necessitates proper processing to ensure data quality. We draw particular attention to extracting high-resolution vehicle trajectory data from video cameras as traffic monitoring cameras are becoming increasingly ubiquitous. We explore methods for automatic trajectory data reconciliation, given "raw" vehicle detection and tracking information from automatic video processing algorithms. We propose a pipeline including (a) an online data association algorithm to match fragments that are associated to the same object (vehicle), which is formulated as a min-cost network flow problem of a graph, and (b) a trajectory reconciliation method formulated as a quadratic program to enhance raw detection data. The pipeline leverages vehicle dynamics and physical constraints to associate tracked objects when they become fragmented, remove measurement noise on trajectories and impute missing data due to fragmentations. The accuracy is benchmarked on a set of microsimulation datasets as well as two samples of manually-labeled data, which shows that the reconciled trajectories improve the accuracy on all the tested input data for a wide range of measures. A real-time version of the reconciliation pipeline is implemented and will be applied in a continuous video processing system running on a camera network covering a 4-mile stretch of Interstate-24 near Nashville, Tennessee. © 2023, The Authors. All rights reserved.

关键词： Pipelines

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Family of Mutually Uncorrelated Codes for DNA Storage Address Design

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年 PP卷 PP页

作者： Liu, Zhenlu Cao, Ben Shao, Qi Zheng, Yanfen Wang, Bin Zhou, Shihua Zheng, Pan Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China University of Canterbury Upper Riccarton Department of Accounting and Information Systems Christchurch8140 New Zealand

Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA storage, especially the lack of high-quality random access address sequences. Therefore, in this paper, we report a series of approaches based on k-weakly mutually uncorrelated (k-WMU) codes to design the address sequence to improve the access efficiency of DNA storage. To address the problem of DNA sequences that are poorly scalable at the base level, we propose a 0-m-ruling coding scheme combined with k-WMU codes that can make address sequences avoid generating secondary structure with stem lengths ranging from 3 to 9. Based on the decoupled structure, We further extend the k-WMU codes with error correction function while satisfying combinatorial biological constraints. In order to investigate the performance of the designed address sequences for real-world applications, we perform simulation experiments based on thermodynamic properties and error correction capability as well as compared the minimum free energy (MFE), melting temperature (TM), and average decoding success rate (ADSR) with previous work. The results show that designed address sequences have a high MFE value and ADSR and a substantial reduction in TM-variance while satisfying the combinatorial biological constraints. As the quality of address sequences improves, this will help to achieve accurate random access as well as enhance the robustness of the DNA storage system. © 2002-2011 IEEE.

关键词： DNA sequences

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Stop-and-go wave super-resolution reconstruction via iterative refinement

arXiv

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arXiv 2024年

作者： Ji, Junyi Richardson, Alex Gloudemans, Derek Zachár, Gergely Nice, Matthew Barbour, William Sprinkle, Jonathan Piccoli, Benedetto Work, Daniel B. Department of Civil and Environmental Engineering Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University United States Department of Computer Science Vanderbilt University United States Department of Mathematical Sciences Rutgers University Camden United States

Stop-and-go waves are a fundamental phenomenon in freeway traffic flow, contributing to inefficiencies, crashes, and emissions. Recent advancements in high-fidelity sensor technologies have improved the ability to capture detailed traffic dynamics, yet such systems remain scarce and costly. In contrast, conventional traffic sensors are widely deployed but suffer from relatively coarse-grain data resolution, potentially impeding accurate analysis of stop-and-go waves. This article explores whether generative AI models can enhance the resolution of conventional traffic sensor to approximate the quality of high-fidelity observations. We present a novel approach using a conditional diffusion denoising model, designed to reconstruct fine-grained traffic speed field from radar-based conventional sensors via iterative refinement. We introduce a new dataset, WaveX, comprising 132 hours of data from both low and high-fidelity sensor systems, totaling over 2 million vehicle miles traveled. Our approach leverages this dataset to formulate the traffic measurement refinement problem as a spatio-temporal super-resolution task. We demonstrate that our model can effectively reproduce the patterns of stop-and-go waves, achieving high accuracy in capturing these critical traffic dynamics. Our results show promising advancements in traffic data refinement, offering a cost-effective way to leverage existing low spatio-temporal resolution sensor networks for improved traffic analysis and management. We also open-source our dataset, trained model and code to enable further research and applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Highway administration

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Secure Password Using EEG-based BrainPrint System: Unlock Smartphone Password Using Brain-computer Interface Technology

Secure Password Using EEG-based BrainPrint System: Unlock Sm...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Alkhyeli, Zuwaina Alshehhi, Ayesha Alhemeiri, Mazna Aldhanhani, Salma Albalushi, Khalil Alnuaimi, Fatima Ali Belkacem, Abdelkader Nasreddine UAE University CIT Dep. Computer Science and Software Engineering Al Ain United Arab Emirates UAE University CIT Dep. Information Systems and Security Al Ain United Arab Emirates United Arab Emirates University CIT Dep. Computer & Network Engineering Al Ain United Arab Emirates Autonomous Robotics Research Center TII Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665468190

As security becomes a strong factor in daily activities, finding secure ways to unlock machines and smartphones is a challenge due to hardware limitations and the high risk of hacking. Considering the level of security and privacy in the digital world, attackers tend to be one step ahead. Therefore, this technical paper introduces a brain-computer interface (BCI) for increasing subject-based security using unique biometric features as a solution to build complex passwords. The BCI measures brain changes and extracts relevant bio-features from each subject using non-invasive electroencephalogram (EEG) tests. The proposed system allows users to gain access to their devices using brain waves (bypass) instead of inserting their password manually (normal path), which saves the user time and upgrades the level of privacy as no physical actions are required during this process. This system is also well suited for individuals with mobility impairments. We used the P300-based BCI controlling paradigm which depends on reading the electrical brain activity of the user when observing a particular object. The other feature of the system is that it can extract unique features of each individual brain to produce a network that uniquely identifies them, which is used as a security layer. Users need to enter their unique network to access their device with failed attempts requiring an EEG test to identify the user. The system plays an active role in facilitating user processes for authentication while accessing devices. The system establishes an urgent call whenever the user's brain currents command it to. The project outcomes were assessed by simulating the BCI before real-time implementation to determine errors and resolve the validity of the project scope. © 2022 IEEE.

关键词： Brain computer interface

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