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High-Performance Flow Classification of Big Data Using Hybrid CPU-GPU Clusters of Cloud Environments

Tsinghua Science and Technology 2024年第4期29卷 1118-1137页

作者： Azam Fazel-Najafabadi Mahdi Abbasi Hani H.Attar Ayman Amer Amir Taherkordi Azad Shokrollahi Mohammad R.Khosravi Ahmed A.Solyman Department of Computer Engineering Faculty of EngineeringBu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13132Jordan Department of Informatics University of OsloOslo 0316Norway Department of Computer Science MalmöUniversityMalmö20506Sweden Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261100China Department of Electrical and Electronics Engineering NişantaşıUniversityIstanbul 34481742Türkiye

The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.

关键词： OpenMP Compute Unified Device Architecture(CUDA) Message Passing Interface(MPI) packet classification medical data tuple space algorithm Graphics Processing Unit(GPU)cluster

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Ensemble based unsupervised anomaly detection with concept drift adaptation for time series data 10

Ensemble based unsupervised anomaly detection with concept d...

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10th IEEE Smart World Congress, SWC 2024

作者： Li, Danlei Nair, Nirmal Wang, Kevin I-Kai Sakurai, Kouichi University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Kyushu University Department of Informatics Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798331520861

Anomaly detection is the identification of instances that substantially deviate from the majority of the data and do not conform to a well-defined normal behavior. Investigating time series anomalies has become increasingly popular in recent years due to widely deployed IoT devices and sensors. Although most existing methods apply image anomaly detection algorithms directly on time series and commonly assume the data is IID (independent and identically distributed). However, in most realworld applications, the underlying data distribution alters over time. Changes in data over time are referred to as concept drift and can significantly degrade the efficacy of an anomaly detection model. Existing research also indicates that the majority of anomaly detection frameworks ignored concept drift, resulting in poor long-term model performance. This paper presents an ensemble-based, unsupervised anomaly detection framework that is adaptive to the concept drift in time series data. Both distribution and performance-based techniques are used to detect concept drift and update the model to ensure the framework's longterm performance reliability. The proposed method is evaluated using a real-world univariate time series data that captures sea surface temperature data over a 2 -year period. By comparing our proposed framework to other state-of-the-art algorithms, we demonstrate our proposed method has obvious benefits in accuracy and long-term stability. © 2024 IEEE.

关键词： Anomaly detection Concept drift adaptation Ensemble method Time series Unsupervised method

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Non-Invasive and Cost-Effective Monitoring of Street Lighting Photometric Performance 11

Non-Invasive and Cost-Effective Monitoring of Street Lightin...

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11th International Conference on electrical and Electronics engineering, ICEEE 2024

作者： Khan, Esrat Ba'ba, Leen Alshatri, Ruqaia Parvez, Saba Gad, Abdalla Jama, Mohamed College of Engineering Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350362541

The United Arab Emirates has a large and sophisticated street lighting infrastructure;therefore, the photometric performance of the street lighting systems must be prioritized. Locating the malfunctions in a large street lighting infrastructure can be time-consuming and arduous. In this paper, a low-cost street lighting performance monitoring system is designed, implemented, and tested. The proposed system comprises three main components: an illuminance sensor, a spectrometer, and a GNSS receiver. These components build a light and robust system to mount on vehicles' rooftops. Additionally, a comprehensive data analysis and inference engine is developed to determine the photometric performance of the lighting poles by identifying the number and the position of the defective luminaires, under-performing luminaires, poles obstructed by vegetation, and lighting poles that suffer from a complete power outage due to electrical faults in lighting control cabinets. Lastly, the field survey results are demonstrated in reports and interactive online visualization using Google Maps, in which the obtained information can be converted into performance metrics and indexes overlayed on urban maps. © 2024 IEEE.

关键词： Poles

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Gkillas, Athanasios Simos, Michael Angelos Makris, Christos University of Patras Department of Electrical and Computer Engineering Patras Greece University of Patras Department of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350318067

YouTube is a popular online video-sharing and social media platform, hosting diverse content for all ages. The platform is gradually gaining ground against traditional media, with major turnover increasingly turning there due to videos and creators' influence, especially in younger audiences. Some of the videos may attain a large number of views often accompanied by many comments. Comments, often focus not only on the content of the video but also on several other aspects and topics. In this paper, we focus on the sentiment analysis of video comments, using a variety of methods we explore the correlation between user comments' sentiment polarity score and the popularity of the relevant video. Some adversities of machine learning or neural network model approach to the task include linguistic irregularities, typing errors, niche vocabulary, sarcastic and ambiguous content, or unrelated events influencing the commented themes. Unlike previous works [1], [2], our focus is centered on leveraging comment sentiment polarity for deriving predictions of the popularity and success of the respective videos. Our methods depend on lexicon-based sentiment analysis approaches like VADER and TextBlob but also on neural network architectures including BERT and RoBERTa. Our experimental analysis resulted in promising results, setting a baseline for this application theme. © 2023 IEEE.

关键词： Sentiment analysis

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of biomedical engineering 2025.

关键词： Decision trees

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Highly sensitive optical biosensor based on electromagnetically induced transparency in a graphene-dielectric metasurface

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Optical and Quantum Electronics 2025年第4期57卷 1-17页

作者： Bagheritabar, Mohsen Molaei Zarasvand, Mahdi Yazdani, Afsoon Molaei Zarasvand, Melika Salemizadeh Parizi, Mohammad Bahadori-Haghighi, Shahram Department of Electrical Engineering University of Cincinnati CincinnatiOH45221-0030 United States Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States Department of Electrical and Computer Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30332 United States Department of Communication and Electronics Engineering Shiraz University Shiraz Iran

In this paper, a graphene-dielectric metasurface is proposed for effective optical biosensing. The structure is composed of a periodic array of double-slit split-ring resonators (SRRs) adjacent to silicon bars over a sheet of graphene and silicon dioxide substrate. Interactions between electric dipoles (bright modes) in silicon bars and magnetic dipoles (dark modes) excited in SRRs lead to a high quality factor resonance based on the phenomenon of electromagnetically induced transparency (EIT). High electric field confinement is achieved within the two gaps of SRRs at the resonance frequency, which result in strong light-analyte interaction. The structural parameters of the proposed metasurface are optimized to achieve the best performance. The biosensor is analyzed using the finite-element method (FEM) and the results are presented. Owing to the high Q-factor EIT resonance and the enhanced light-matter interaction inside the gaps, a high sensitivity of 496 nm/RIU and figure-of-merit (FOM) of as high as 741 RIU−1 are achieved that are higher than those reported in recently published works. The resolution and the linearity R2 value of the proposed biosensor are calculated to be 2.02e-4 and 0.999, respectively. The reported characteristics could be promising for sensing of various biomolecules such as hemoglobin. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Q factor measurement

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A comprehensive evaluation of large language models in mining gene relations and pathway knowledge

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Quantitative Biology 2024年第4期12卷 360-374页

作者： Muhammad Azam Yibo Chen Micheal Olaolu Arowolo Haowang Liu Mihail Popescu Dong Xu Department of Electrical Engineering and Computer Science University of MissouriColumbiaMissouriUSA Bond Life Sciences Center University of MissouriColumbiaMissouriUSA Institute for Data Science and Informatics University of MissouriColumbiaMissouriUSA Department of Biomedical Informatics Biostatistics and Medical EpidemiologyUniversity of MissouriColumbiaMissouriUSA

Understanding complex biological pathways,including gene–gene interactions and gene regulatory networks,is critical for exploring disease mechanisms and drug *** literature curation of biological pathways cannot keep up with the exponential growth of new discoveries in the ***-scale language models(LLMs)trained on extensive text corpora contain rich biological information,and they can be mined as a biological knowledge *** study assesses 21 LLMs,including both application programming interface(API)-based models and open-source models in their capacities of retrieving biological *** evaluation focuses on predicting gene regulatory relations(activation,inhibition,and phosphorylation)and the Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway *** indicated a significant disparity in model ***-based models GPT-4 and Claude-Pro showed superior performance,with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction,and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction,***-source models lagged behind their API-based counterparts,whereas Falcon-180b and llama2-7b had the highest F1 scores of 0.2787 and 0.1923 in gene regulatory relations,*** KEGG pathway recognition had a Jaccard similarity index of 0.2237 for Falcon-180b and 0.2207 for *** study suggests that LLMs are informative in gene network analysis and pathway mapping,but their effectiveness varies,necessitating careful model *** work also provides a case study and insight into using LLMs das knowledge *** code is publicly available at the website of GitHub(Muh-aza).

关键词： biomedical text mining gene-gene interaction KEGG pathway large language model

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Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder

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IEEE Transactions on Neural Systems and Rehabilitation engineering 2024年 32卷 2096-2105页

作者： Zhang, Zhao Lin, Bor-Shyh Peng, Chih-Wei Lin, Bor-Shing Wuyi University College of Mechanical and Electrical Engineering Fujian Wuyishan354300 China National Taipei University College of Electrical Engineering and Computer Science Department of Computer Science and Information Engineering New Taipei City237303 Taiwan Institute of Imaging and Biomedical Photonics National Yang Ming Chiao Tung University Tainan71150 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering School of Gerontology and Long-Term Care College of Nursing Taipei11031 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan

Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorde

关键词： Sleep research

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Occupant-Centric Proactive Control Methodology for IEQ 18

Occupant-Centric Proactive Control Methodology for IEQ

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18th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2024

作者： Kakoulli, Christina Kyriacou, Alexis Michaelides, Michalis Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Lelantus Innovations Ltd Nicosia Cyprus

ISBN: (纸本)9798331306816

Indoor Environmental Quality (IEQ) is undeniably a crucial factor that affects the occupants’ overall health and well-being. However, there is a significant lack of available actionable and measurable real-time metrics for the impact of IEQ on occupants, restricting potential control solutions and the validation of their effectiveness. Even more, occupant-centric and energy concerned automatic control solutions with direct aim to decrease or even prevent any negative effects of poor IEQ on occupants are required for creating sustainable built environments. In this work, we propose a novel framework for an occupant-centric proactive air-purification and ventilation control methodology (PPV). The method utilizes state-space models along with what-if scenarios to generate the potential solution space. A multi-objective optimization extracts the optimal solution in terms of air-purification and ventilation operation points, while minimizing various computed IEQ impact metrics in terms of the occupants’ health, comfort and virus transmissibility risk, and also considering the energy expenditure. © 2024 18th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2024 - Conference Program and Proceedings. All rights reserved.

关键词： Indoor air pollution

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Detection and Counting Bacilli Tuberculosis Images using Classical Image Processing and Deep Learning 24

Detection and Counting Bacilli Tuberculosis Images using Cla...

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2nd International Conference on Communications, Computing and Artificial Intelligence, CCCAI 2024

作者： Yuniarti, Heny Fatichah, Chastine Sigit, Riyanto Department of Informatics Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Indonesia Department of Computer Engineering Politeknik Elektronika Negeri Surabaya Indonesia

ISBN: (纸本)9798400716898

Tuberculosis (TB) has been known as the top two murdering disease worldwide after HIV/AIDS. Indonesia is the second country with the highest number of tuberculosis. One of the reasons for the high number of cases is that the microscopic sputum smear image analysis process takes a long time. This study compares the detection and counting of bacilli images using classical image processing and deep learning to ascertain the performance of both methods. Steps in classical image processing consist of RGB to HSV converting, HSV thresholding, morphological operation and bacilli counting. YOLOv5 is used as deep learning method in this research to detect and count bacilli. While classical image processing accuracy is 59.59%, YOLOv5 can achieve 86.09% which is better than the previous research. By comparing classical image processing methods and deep learning, it is evident that deep learning techniques can detect and count overlapping bacilli. This result can be further improved by increasing the learning dataset especially the severe TB patient. © 2024 ACM.

关键词： Image analysis

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