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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Huang, Yangfan Lin, Yuling Du, Haizhou Chen, Yijian Song, Haohao Kong, Linghe Xiang, Qiao Li, Qiang Le, Franck Shu, Jiwu Institute of Artificial Intelligence Xiamen University China School of Informatics Xiamen University China School of Computer Science and Technology Shanghai University of Electric Power China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Alibaba China Ibm Research Minjiang University China

ISBN: (纸本)9798350399738

There has been a growing interest in applying machine learning to real-world tasks. However, due to the blackbox nature of machine learning models, it is crucial to (1) verify important properties of a model and (2) understand the reasons behind a model's prediction before deploying them in a production environment. Existing approaches typically handle them as two separate and sometimes orthogonal topics. In this paper, we show that the verification and interpretability of machine learning models are tightly related and can be unified by satisfiability modulo theories (SMT). Our key insight is: not only a wide range of properties of machine learning models can be formulated as SMT problems and verified accordingly, but many commonly studied interpretability questions can also be answered by iteratively checking the satisfiability and related properties of multiple SMT problems. Leveraging this insight, we design UINT, a general verification and interpretability framework for learning-based networking systems. UINT (1) allows operators to specify verification and interpretability problems as SMT formulas, (2) encodes the target machine learning models into SMT constraints, and (3) automatically solves the corresponding verification and interpretability problems using commodity SMT solvers. We implement a prototype of UINT and evaluate it on real-world learning-based networking systems. Results demonstrate the efficiency and efficacy of UINT in verifying and interpreting key questions for these systems. © 2023 IEEE.

关键词： Machine learning

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Early Prediction of Chronic Heart Disease Based on Electronic Triage Dataset by using Machine Learning

Early Prediction of Chronic Heart Disease Based on Electroni...

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Communication and Information Technology (AICCIT), Al-Sadiq International Conference on

作者： Saleh Mahdi Muhammed Ghassan Abdul-Majeed Mahmoud Shuker Mahmoud Department of Computer Science Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Department of Computer Science College of Engineering University of Baghdad Baghdad Iraq Department of Communications (of Affiliation) Al/Mansour University College Baghdad Iraq

Chronic Heart Disease (CHD) is the main reason for mortality around the world, constituting about 15% of all natural deaths, which confirms the importance of early detection to mitigate these rates. CHD can be detected by measuring multiple health parameters such as blood pressure, SpO2, ECG, and other parameters. Therefore, there is a persistent need for effective and precise prediction methods. Traditional techniques for prediction and analysis are limited due to the complexity of the data and correlations. First approach for developing these techniques is by using machine learning (ML) algorithms. ML can examine vast amounts of patient information, including medical records, test outcomes, and imaging scans to identify patterns and predict the possibility of developing heart disease or having a cardiac event. This study aims to improve the accuracy of prediction by utilizing ML techniques to predict CHD using medical data, reduce the incidence of misdiagnosis, and ultimately save lives. Supervised learning techniques, such as Support Vector Machine (SVM), Artificial Neural Networks (ANN), AdaBoost, Logistic Regression (LR), K-Nearest Neighbor (KNN) and others are employed to identify correlations in CHD data and enhance prediction rates. The results indicate that SVM-derived probabilistic models outperform other algorithms in predicting CHD and the accuracy of the model was 96.2%, as revealed through various performance evaluation metrics.

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Enhancing Sentiment Classification in Twitter Data Through Context-Driven Text Processing and Tweet Embeddings 11

Enhancing Sentiment Classification in Twitter Data Through C...

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11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Gerogiannis, Vassilis C. Kanavos, Andreas Antonopoulos, Nikos Bhola, Amrita Acharya, Biswaranjan University of Thessaly Department of Digital Systems Larissa Greece Ionian University Department of Informatics Corfu Greece Ionian University Department of Digital Media and Communication Argostoli Greece Indira Gandhi Institute of Technology Odisha Sarang India Marwadi University Department of Computer Engineering- AI and BDA Rajkot India

ISBN: (纸本)9798350326147

Sentiment analysis and text classification tasks heav-ily rely on text processing techniques. However, existing approaches often neglect domain-specific factors and rely on generic routines and pre-built dictionaries. In this paper, we investigate the impact of text processing steps on sentiment classification using Twitter data. Our approach introduces skip gram-based word embeddings that effectively capture Twitter-specific fea-tures, such as informal language and emojis. Through rigorous experimentation, we identify the detrimental consequences of conventional text processing steps like stop word removal and simple averaging of term vectors for tweet representation. To optimize sentiment classification, we propose new effective steps, including the inclusion of emoji characters, measuring word importance from embeddings, aggregating term vectors into tweet embeddings, and creating a linearly separable feature space. Our results demonstrate the superiority of context-driven word embeddings in selecting important words for tweet clas-sification, outperforming pre-built word dictionaries. Moreover, the proposed tweet embedding reduces reliance on multiple text processing steps, resulting in more accurate sentiment analysis on Twitter data. © 2023 IEEE.

关键词： Embeddings

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Sentiment Polarity Analysis of Bangla Food Reviews Using Machine and Deep Learning Algorithms

arXiv

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arXiv 2024年

作者： Amin, Al Sarkar, Anik Islam, Md Mahamodul Miazee, Asif Ahammad Islam, Md Robiul Hoque, Md Mahmudul Department of Computer Science and Engineering Uttara University Dhaka Bangladesh National Institute of Technology Silchar India Department of Informatics University of Oslo Oslo Norway Department of Computer Science Maharishi International University Iowa United States Department of Computer Science and Engineering CCN University of Science & Technology Comilla Bangladesh

The Internet has become an essential tool for people in the modern world. Humans, like all living organisms, have essential requirements for survival. These include access to atmospheric oxygen, potable water, protective shelter, and sustenance. The constant flux of the world is making our existence less complicated. A significant portion of the population utilizes online food ordering services to have meals delivered to their residences. Although there are numerous methods for ordering food, customers sometimes experience disappointment with the food they receive. Our endeavor was to establish a model that could determine if food is of good or poor quality. We compiled an extensive dataset of over 1484 online reviews from prominent food ordering platforms, including Food Panda and HungryNaki. Leveraging the collected data, a rigorous assessment of various deep learning and machine learning techniques was performed to determine the most accurate approach for predicting food quality. Out of all the algorithms evaluated, logistic regression emerged as the most accurate, achieving an impressive 90.91% accuracy. The review offers valuable insights that will guide the user in deciding whether or not to order the food. © 2024, CC BY.

关键词： Sentiment analysis

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Weighting Heart Disease Criteria Using Multi-Criteria Decision-Making

Weighting Heart Disease Criteria Using Multi-Criteria Decisi...

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Communication and Information Technology (AICCIT), Al-Sadiq International Conference on

Heart disease describes a group of conditions that affect the heart and there are many different factors including lifestyle, medical history, family history, and so on that may cause this complex disease. It is therefore important to consider multiple criteria, such as smoking status, blood pressure, and cholesterol levels to make informed decisions about patient priority management and treatment of HD. Multicriteria Decision Making (MCDM) allows us to identify and weigh the importance of each criterion. This approach can enhance the quality of patient care and improve clinical outcomes. Therefore, weighting HD criteria using MCDM is an essential step in achieving better healthcare outcomes in the management of HD. The FWZIC method was used in this study to weigh 11 heart disease parameters with the assistance of four cardiologists. The results of the criterion weights were without any difference in the preferences of the respective expert. Therefore, this method is flexible, robust, and useful in complex real-world problems.

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Improving Wideband Massive MIMO Channel Estimation with UAV State-Space Information

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IEEE Transactions on Vehicular Technology 2025年

作者： Vlachos, Evangelos Mavrokefalidis, Christos Berberidis, Kostas Alexandropoulos, George C. ATHENA Research and Innovation Centre Industrial Systems Institute Rio-Patras 26504 Greece University of Patras Department of Computer Engineering and Informatics Rio-Patras 26504 Greece National and Kapodistrian University of Athens Department of Informatics and Telecommunications Panepistimiopolis Ilissia Athens15784 Greece

In this paper, we focus on the estimation of massive Multiple-Input Multiple-Output (mMIMO) channels in high-frequency point-to-point wireless communications between a terrestrial multi-antenna base station, realizing fully digital beamforming, and a multi-antenna Unmanned Aerial Vehicle (UAV), capable of only analog combining via a single reception Radio-Frequency (RF) chain. A wideband channel model, incorporating selectivity which is usually referred to as beam squint and is particularly relevant in high frequency bands, such as millimeter waves and terahertz, is considered. To account for the UAV's limited capability to collect long training sequences, due to its single-RF reception architecture, and the beam-squint effect at both communication ends, we present a novel channel estimation approach that exploits information provided by the control module of the UAV, namely its state-space vector. The proposed approach comprises a hybrid parametric and non-parametric estimation optimization framework that is efficiently solved through an iterative algorithm using the alternating direction method of multipliers. Our extensive performance investigations, including comparisons with benchmark schemes, showcase that considerable gains can be achieved if state-space information from the UAV's control module is appropriately incorporated in the channel estimation process. © 1967-2012 IEEE.

关键词： Drones

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Radiance Field Delta Video Compression in Edge-Enabled Vehicular Metaverse

arXiv

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arXiv 2024年

作者： Dopiriak, Matúš Šlapak, Eugen Gazda, Juraj Gurjar, Devendra S. Faruque, Mohammad Abdullah Al Levorato, Marco Department of Computers and Informatics Technical University of Košice Slovakia Department of Electronics and Communications Engineering National Institute of Technology Silchar India Department of Electrical Engineering and Computer Science University of California Irvine United States

Connected and autonomous vehicles (CAVs) offload computationally intensive tasks to multi-access edge computing (MEC) servers via vehicle-to-infrastructure (V2I) communication, enabling applications within the vehicular metaverse, which transforms physical environment into the digital space enabling advanced analysis or predictive modeling. A core challenge is physical-to-virtual (P2V) synchronization through digital twins (DTs), reliant on MEC networks and ultra-reliable low-latency communication (URLLC). To address this, we introduce radiance field (RF) delta video compression (RFDVC), which uses RF-encoder and RF-decoder architecture using distributed RFs as DTs storing photorealistic 3D urban scenes in compressed form. This method extracts differences between CAV-frame capturing actual traffic and RF-frame capturing empty scene from the same camera pose in batches encoded and transmitted over the MEC network. Experiments show data savings up to 71% against H.264 codec and 44% against H.265 codec under different conditions as lighting changes, and rain. RFDVC also demonstrates resilience to transmission errors, achieving up to +0.29 structural similarity index measure (SSIM) improvement at block error rate (BLER) = 0.35 in non-rainy and +0.25 at BLER = 0.2 in rainy conditions, ensuring superior visual quality compared to standard video coding (VC) methods across various conditions. Copyright © 2024, The Authors. All rights reserved.

关键词： Image compression

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Enhancing Cryptocurrency Value Prediction: A Comparative Study of Novel Random Forest and K-Nearest Neighbor Algorithms for Improved Accuracy

Enhancing Cryptocurrency Value Prediction: A Comparative Stu...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： S. Kiruthiga R. Balamanigandan R. Mahaveerakannan A. Mary Jenifer Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Spatial Informatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The effectiveness of the Novel Random Forest (RF) Algorithm for predicting cryptocurrency prices was evaluated and compared to the K-Nearest Neighbor (KNN) Algorithm. Machine learning methods were used to develop the two algorithms, and a pretest power analysis was conducted using two groups with the iteration of 10 at 85% of G-power and the setup parameters are alpha = 0.05 and beta = 0.85. Hence the P value is less than 0.005 (P<0.05) there is a statistical significance (p=0.007) between these two algorithms. The Novel RF Algorithm achieved an accuracy of 87.7330%, while the KNN Algorithm achieved an accuracy of 72.1250%. When the two algorithms were compared using an independent sample t-test, the difference in accuracy was found to be statistically significant at 0.760.

关键词： Radio frequency Accuracy Machine learning algorithms Software algorithms Estimation Nearest neighbor methods Prediction algorithms Software Cryptocurrency Random forests

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Joint estimation and inference for data integration problems based on multiple multi-layered Gaussian graphical models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 1-53页

作者： Subhabrata Majumdar George Michailidis Splunk and University of Florida Informatics Institute Gainesville FL Department of Statistics and Computer & Information Science & Engineering University of Florida Gainesville FL

The rapid development of high-throughput technologies has enabled the generation of data from biological or disease processes that span multiple layers, like genomic, proteomic or metabolomic data, and further pertain to multiple sources, like disease subtypes or experimental conditions. In this work, we propose a general statistical framework based on Gaussian graphical models for horizontal (i.e. across conditions or subtypes) and vertical (i.e. across different layers containing data on molecular compartments) integration of information in such datasets. We start with decomposing the multi-layer problem into a series of two-layer problems. For each two-layer problem, we model the outcomes at a node in the lower layer as dependent on those of other nodes in that layer, as well as all nodes in the upper layer. We use a combination of neighborhood selection and group-penalized regression to obtain sparse estimates of all model parameters. Following this, we develop a debiasing technique and asymptotic distributions of inter-layer directed edge weights that utilize already computed neighborhood selection coefficients for nodes in the upper layer. Subsequently, we establish global and simultaneous testing procedures for these edge weights. Performance of the proposed methodology is evaluated on synthetic and real data.

关键词： data integration Gaussian graphical models neighborhood selection group lasso high-dimensional asymptotics multiple testing false discovery rate

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WearMask: Fast In browser Face Mask Detection with Serverless Edge Computing for COVID 19

WearMask: Fast In browser Face Mask Detection with Serverles...

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IS and T International Symposium on Electronic Imaging: High Performance Computing for Imaging, HPCI 2023

作者： Wang, Zekun Wang, Pengwei Louis, Peter C. Wheless, Lee E. Huo, Yuankai Data Science Institute Vanderbilt University NashvilleTN United States Department of Information Science and Engineering Shandong University Qingdao China Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States

The COVID-19 epidemic has been a significant healthcare challenge across the world. COVID-19 is transmitted predominately by respiratory droplets generated when people breathe, talk, cough, or sneeze. Wearing a mask is the primary, effective, and convenient method of blocking 80% of respiratory infections. Therefore, many face mask detection systems have been developed to supervise hospitals, airports, publication transportation, sports venues, and retail locations. However, the current commercial solutions are typically bundled with software or hardware, impeding public accessibility. In this paper, we propose an in-browser serverless edge-computing-based face mask detection solution, called Web-based efficient AI recognition of masks (WearMask), which can be deployed on common devices (e.g., cell phones, tablets, computers) with internet connections using web browsers. The serverless edge-computing design minimizes hardware costs (e.g., specific devices or cloud computing servers). It provides a holistic edge-computing framework for integrating (1) deep learning models (YOLO), (2) high-performance neural network inference computing framework (NCNN), and (3) a stack-based virtual machine (WebAssembly). For end-users, our solution has the advantages of (1) serverless edge-computing design with minimal device limitation and privacy risk, (2) installation-free deployment, (3) low computing requirements, and (4) high detection speed. Our application has been launched with public access at ***. © 2023, Society for Imaging Science and Technology.

关键词： Edge computing

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