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arXiv 2023年

作者： Ukwandu, Ogechukwu Hindy, Hanan Ukwandu, Elochukwu Department of Computer Communication and Information Systems School of Engineering and Built Environment Glasgow Caledonian University School of Computer Science and Informatics Cardiff University United Kingdom Computer Science Department Faculty of Computer and Information Sciences Ain Shams University Cairo11566 Egypt Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University CardiffCF5 2YB United Kingdom

Timely and rapid diagnoses are core to informing on optimum interventions that curb the spread of COVID-19. The use of medical images such as chest X-rays and CTs has been advocated to supplement the Reverse-Transcription Polymerase Chain Reaction (RT-PCR) test, which in turn has stimulated the application of deep learning techniques in the development of automated systems for the detection of infections. Decision support systems relax the challenges inherent to the physical examination of images, which is both time consuming and requires interpretation by highly trained clinicians. A review of relevant reported studies to date shows that most deep learning algorithms utilised approaches are not amenable to implementation on resource-constrained devices. Given the rate of infections is increasing, rapid, trusted diagnoses are a central tool in the management of the spread, mandating a need for a low-cost and mobile point-of-care detection systems, especially for middle- and low-income nations. The paper presents the development and evaluation of the performance of lightweight deep learning technique for the detection of COVID-19 using the MobileNetV2 model. Results demonstrate that the performance of the lightweight deep learning model is competitive with respect to heavyweight models but delivers a significant increase in the efficiency of deployment, notably in the lowering of the cost and memory requirements of computing resources. © 2023, CC BY-NC-SA.

关键词： COVID-19

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Tensor-Based Multi-Scale Correlation Anomaly Detection for AIoT-Enabled Consumer Applications

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IEEE Transactions on Consumer Electronics 2024年

作者： Zeng, Jiuzhen Yang, Laurence T. Wang, Chao Deng, Xianjun Yang, Xiangli Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada University of South China School of Electrical Engineering Hengyang421001 China

Artificial Intelligence of Things (AIoT) is an innovative paradigm expected to enable various consumer applications that is transforming our lives. While enjoying benefits and services from these applications, we also face serious security issues due to malicious cyber attacks on the massive growth of AIoT consumer devices. Accurate anomaly detection is one of the critical tasks for the trustworthy AIoT removing those obstacles. However, limited by the vector-based data pattern and ill-considered factors for anomalous samples analysis, existing methods suffer from the low detection performance. In this paper, a multi-scale correlation tensor convolutional Gaussian mixture network (named as MTCGM) is presented for ameliorating this actuality. Specifically, MTCGM suggests to construct the multi-scale correlation tensor by stacking one self-correlation matrix and multiple surrounding-correlations of different scales, which well characterizes the network status of AIoT. Subsequently, a 3D-convolutional autoencoder (3DCA) is designed for capturing inter-feature correlations, and followed with a Gaussian mixture probability (GMP) network for the observations likelihood estimation. Moreover, low-dimensional space features, relative Euclidean distance and tensor cosine similarity (TCS) are adopted in MTCGM as the multi-factor to boost the likelihood estimation. Extensive experiments on public benchmark datasets verify the validity of MTCGM, and demonstrate its superiority over the state-of-the-art baselines even in presence of contaminated training samples and input noise. © 1975-2011 IEEE.

关键词： Tensors

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Online Examination and Evaluation System

Online Examination and Evaluation System

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IEEE Punecon

作者： Harshad Kumar Dandage Deepak S. Uplaonkar Ankita Shete Avani Shete Lavanya Bodele Shruti Jadhav Department of Computer Engineering Southern Taiwan University of Science and Technology Taiwan Department of Computer Engineering I2IT Pune India Department of IT I2IT Pune India Department of IT I2IT Pune India

ISBN: (纸本)9781665498982

Currently, online test systems have adapted easily to today's technologically advanced world. Examinations are an intrinsic part of the educational process. Even though the test are conducted online the teacher has to do manual evaluation. The examinations can be classified into two main types of evaluation, objective answer and subjective answer. As of now, online evaluation is available for the objective questions, hence the manual assessment of the theory answer, is a tedious task for the teacher. The teacher checks the answer manually and gives the marks. In this paper, the literature survey of existing solution is analyzed.

关键词： IEEE Sections Manuals Task analysis

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Contrastive Learning Driven Self-Supervised Framework for Segmentation of Biomarker of Diabetic Macular Edema

Contrastive Learning Driven Self-Supervised Framework for Se...

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2023 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2023

作者： Raja, Hina Hassan, Taimur Hassan, Bilal Werghi, Naoufel Raja, Hira Yousefi, Siamak University of Tennessee Health Science Center Department of Ophthalmology Memphis United States Abu Dhabi University Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates Khalifa University KUCARS and C2PS Abu Dhabi United Arab Emirates Margalla Institute of Health Sciences Rawalpindi Pakistan

ISBN: (纸本)9781665434454

Optical coherence tomography (OCT) has been widely used to investigate the pathological changes due to Diabetic Macular Edema (DME). In this paper, we developed a two-stage self-supervised learning approach to extract DME biomarkers. The backbone of the proposed scheme is the RAG-Net model trained in the first stage to extract different DME biomarkers, such as intra-retinal fluid, sub-retinal fluid, and hard exudates, using the low-shot supervision from the Zhang dataset. In the second training stage, it learns to extract the same biomarkers across the Duke-II dataset in a self-supervised manner (i.e., without using any ground-Truth annotations) via the triplet loss function. We validated the proposed approach across both datasets at the inference stage, where it achieved the mean \mathbf{IoU} score of 0.7610, and 0.7232, outperforming the state-of-The-Art by 1.734%, and 4.839% for extracting DME biomarkers irrespective of the scanner specifications, and vendor artifacts across the Zhang and Duke-II datasets, respectively. In addition, we employed an external BIOMISA dataset to evaluate the proposed model and obtained an IOU of 0.752. The suggested model has the potential to improve clinical practice by allowing more objective assessment of DME patients. In the future, we plan to expand the application of our self-supervised model to other ocular diseases, such as glaucoma and AMD, and to incorporate more imaging modalities, such as fundus photographs and OCT-Angiography (OCTA). © 2023 IEEE.

关键词： Optical tomography

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Guest Editorial Special Issue on Near-Field Communications (NFCs) in Internet of Everything

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IEEE Internet of Things Journal 2025年第12期12卷 18459-18460页

作者： Pin-Han Ho Miaowen Wen Zhiguo Ding Marco Di Renzo Wei Duan Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada School of Electronic and Information Engineering South China University of Technology Guangzhou China Department of Computer Communication Engineering Khalifa University Abu Dhabi UAE Intelligent Physical Communications group in the Laboratory of Signals and Systems (L2S) Paris-Saclay University Gif-sur-Yvette France School of Information Science and Technology Nantong University Nantong China

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Estimating Complexity for Perception-based ADAS in Unstructured Road Environments

Estimating Complexity for Perception-based ADAS in Unstructu...

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IEEE Symposium on Intelligent Vehicle

作者： Imane Taourarti Ayesha Choudhary Vivek Kumar Paswan Aditya Kumar Arunkumar Ramaswamy Javier Ibanez-Guzman Bruno Monsuez Adriana Tapus Autonomous Systems and Robotics Lab/U2IS Computer Science and System Engineering (U2IS) ENSTA Paris Institut Polytechnique de Paris France Research Department Renault Group France School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Advanced Driver Assistance Systems (ADAS) are rapidly becoming a standard feature in modern road vehicles, enhancing safety and driver comfort. As ADAS adoption expands across diverse geographical and cultural regions, the performance of camera-based perception systems may vary significantly due to environmental and expected social behaviour of the different actors. This paper explores the referred factors and evaluates the traffic environment complexity for vehicles with different levels of automation. In particular, we propose a novel modeling and quantitative assessment approach for environment complexity. Specifically, we compare a perception model trained on United States dataset with a dataset from India, a nation characterized by unique traffic patterns, signage conventions, and cultural norms to assess its performance variation, and to lay the basis for proposing influencing factors of traffic environment complexity. We establish a scheme of referential and additional static factors and based on an expert evaluation, environment complexity is established. The effectiveness of the proposed approach is testified by naturalistic driving data. These findings pave the way for future research in intelligent driving and emphasize the importance of addressing cultural nuances as vehicle automation levels increase.

关键词： Measurement Automation Roads Road vehicles Globalization Complexity theory Safety

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Comparative Study of Embedded Energy Management Methods based on Machine Learning for Dual-Source Electric Vehicles

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IEEE Transactions on Transportation Electrification 2025年

作者： Adnane, Marouane Nguyen, Bao-Huy Khoumsi, Ahmed Trovao, Joao Pedro F. Université de Sherbrooke Department of Electrical Engineering and Computer Engineering SherbrookeQCJ1K 2R1 Canada Hanoi University of Science and Technology CTI Lab4EV School of Electrical and Electronic Engineering Hanoi Viet Nam

In the actual context of dual-source electric vehicles (DSEVs), efficient energy management strategies (EMSs) are essential to optimize energy distribution between batteries and supercapacitors. However, achieving real-time decision-making and adaptability in resource-constrained environments remains a significant challenge. This study addresses these challenges by comparing two advanced machine learning (ML)-based EMSs: Driving Mode Prediction-based EMS (DMP-EMS) and Multi-Ensemble Learning-based EMS (MEL-EMS). Using Signal Hardware-In-the-Loop simulation and a Raspberry Pi 4 platform, the two EMSs are evaluated based on various criteria, including performance accuracy, model complexity, training and prediction times, required computing resources, scalability, and energy consumption. The findings highlight the trade-offs between these EMSs, offering insights into designing tailored EMSs for DSEVs under varying operational demands. While MEL-EMS excels in environments where prediction accuracy is paramount since it offers superior predictive performance suitable for applications demanding high accuracy and scalability, DMP-EMS provides a more efficient and practical solution for real-time energy management in resource-constrained settings. © 2015 IEEE.

关键词： Hardware-in-the-loop simulation

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OSμS: An Open-Source Microservice Prototyping Platform

OSμS: An Open-Source Microservice Prototyping Platform

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IEEE International Conference on Cloud Computing Technology and science (CloudCom)

作者： Lamees M. Al Qassem Thanos Stouraitis Ernesto Damiani Ibrahim Abe M. Elfadel Center for Cyber Physical Systems (C2PS) and Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

One major advantage of microservice cloud architectures is the agility with which microservices can be replicated to help improve the overall quality of service and meet service-level contracts. Their challenge is to carefully balance the horizontal microservice replicas with the vertical resources of CPU, memory, and IO that are allocated to each microservice. The objective of such balancing act is, of course, to avoid both service bottlenecks and resource wastage. In this paper, we present OSμS, a new open-source microservice prototyping platform that has been developed and instrumented from the ground up with the objective of collecting fine-grained, non-proprietary metrology on microservice mesh performance. We will illustrate the use of OSμS for developing and evaluating machine-learning algorithms for the horizontal and vertical autoscaling of microservice architectures. A hybrid algorithm based on decision-tree learning will be implemented on OSμS and compared with the academic state of the art and existing cloud-provider solutions. The advantages of such algorithm in improving horizontal and vertical resource utilization will be highlighted.

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Hermitian {P,k+1}-(anti-)reflexive solutions to a quaternion matrix equation

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Computational and Applied Mathematics 2025年第6期44卷

作者： Liu, Xin Li, Wanqi Yuan, Shifang Zhang, Yang Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology Avenida Wai Long TaiPa999078 China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Avenida Wai Long TaiPa999078 China School of Mathematics and Computational Science Wuyi University Jiangmen529020 China Department of Mathematics University of Manitoba WinnipegMBR3T 2N2 Canada

In this paper, we consider the Hermitian {P,k+1}-(anti-)reflexive solutions to the quaternion matrix equation AXB+CXD=E and AX=E, respectively. We use the complex representation method to obtain the necessary and sufficient conditions for the existence of the Hermitian {P,k+1}-reflexive solution (resp. Hermitian {P,k+1}-anti-reflexive solution), respectively, and derive their solutions when the matrix equations have the Hermitian {P,k+1}-reflexive solution (resp. Hermitian {P,k+1}-anti-reflexive solution). Finally, two examples are provided to verify the effectiveness of our method. © The Author(s) under exclusive licence to Sociedade Brasileira de Matemática Aplicada e Computacional 2025.

关键词： Polynomials

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Deep Learning with Recurrent Expansion for Electricity Theft Detection in Smart Grids 48

Deep Learning with Recurrent Expansion for Electricity Theft...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Berghout, Tarek Benbouzid, Mohamed Ferrag, Mohamed Amine University of Batna 2 Laboratory of Automation and Manufacturing Engineering Batna Algeria University of Brest Shanghai Maritime University UMR CNRS 6027 IRDL Brest France University of Guelma Department of Computer Science Guelma Algeria

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

The increase in electricity theft has become one of the main concerns of power distribution networks. Indeed, electricity theft could not only lead to financial losses, but also leads to reputation damage by reducing the quality of supply. With advanced sensing technologies of metering infrastructures, data collection of electricity consumption enables data-driven methods to emerge in such non-technical loss detections as an alternative to traditional experience-based human-centric approaches. In this context, such fraud prediction problems are generally a thematic of missing patterns, class imbalance, and higher level of cardinality where there are many possibilities that a single feature can assume. Therefore, this article is introduced specifically to solve data representation problem and increase the sparseness between different data classes. As a result, deeper representations than deep learning networks are introduced to repeatedly merge the learning models themselves into a more complex architecture in a sort of recurrent expansion. To verify the effectiveness of the proposed recurrent expansion of deep learning (REDL) approach, a realistic dataset of electricity theft is involved. Consequently, REDL has achieved excellent data mapping results proven by both visualization and numerical metrics and shows the ability of separating different classes with higher performance. Another important REDL feature of outliers correction has been also discovered in this study. Finally, comparison to some recent works also proved superiority of REDL model. © 2022 IEEE.

关键词： Smart power grids

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