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7th IEEE International Congress on Information science and Technology, CiSt 2023

作者： Alla, Hajar Moumoun, Lahcen Balouki, Youssef Hassan First University of Settat Laboratory of Mathematics Computer and Engineering Sciences Mathematics and Computer Science Department Faculty of Sciences and Techniques Settat26000 Morocco

ISBN: (纸本)9781665461337

Air traffic demand has recently expanded considerably, creating worries about saturation, congestion, and queuing. Flight delays impact transportation networks, resulting in operational errors, higher economic losses for airlines, and consumer complaints and discontent. As a result, aviation authorities, universities, and researchers are increasingly interested in developing preventive techniques to mitigate the impact of aircraft delays. In this study, we created a predictive model to estimate airline delays for domestic flights in the United States through a survey of pilots, air traffic controllers, airport leaders, airport personnel, and passengers. Data for all the year 2018 were utilized with powerful Machine Learning algorithms such as feed-forward Neural Network, Random Forest, Gradient Boosting and Decision Trees. The conventional attributes that influence on flight delays were taken from the database used. To increase the performance of the suggested model, we created and introduced more variables that, as far as we know, had never been considered in prior research. As a consequence, with the incorporation of the conventional and suggested features at the same time, the model proved capable to estimate flight delays with an R-squared of 98.33% for the neural network. © 2023 IEEE.

关键词： Air transportation

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Graph Attention Network-based Multimodal Fusion for Fake Food Reviews Detection 24

Graph Attention Network-based Multimodal Fusion for Fake Foo...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Yang, Jiajie Mao, Dianhui Zhang, Denghui Beijing Key Laboratory of Big Data Technology for Food Safety Computer Science Department Beijing Technology and Business University China

ISBN: (纸本)9798400718137

In the field of computational intelligence and data analytics, the detection of fake food reviews has emerged as a pressing challenge, exacerbated by the widespread use of social media. These fraudulent reviews, particularly prevalent in the food and beverage industry, distort consumer decision-making and disrupt market fairness. To address this, we propose GMF-R, an innovative multimodal fusion framework based on Graph Attention Network (GAT), designed to effectively detect fake food reviews. The framework features end-to-end trainability and introduces advanced multimodal feature processing mechanisms, including fine-grained cross-modal interactions, a shared feature space, and multimodal graph construction. By utilizing GAT, GMF-R can adaptively assign attention weights to key nodes and accurately capture complex inter-modal relationships. Experimental results demonstrate that GMF-R achieves a 97.4% accuracy rate and a 98.1% F1 score on the Yelp reviews website dataset, significantly outperforming previous state-of-the-art models. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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A UNIFIED FRAMEWORK FOR BAYESIAN OPTIMIZATION UNDER CONTEXTUAL UNCERTAINTY 12

A UNIFIED FRAMEWORK FOR BAYESIAN OPTIMIZATION UNDER CONTEXTU...

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12th International Conference on Learning Representations, ICLR 2024

作者： Tay, Sebastian Shenghong Foo, Chuan Sheng Urano, Daisuke Leong, Richalynn Chiu Xian Low, Bryan Kian Hsiang Department of Computer Science National University of Singapore Singapore A*STAR Singapore A*STAR Singapore Temasek Life Sciences Laboratory Singapore

Bayesian optimization under contextual uncertainty (BOCU) is a family of BO problems in which the learner makes a decision prior to observing the context and must manage the risks involved. Distributionally robust BO (DRBO) is a subset of BOCU that affords robustness against context distribution shift, and includes the optimization of expected values and worst-case values as special cases. By considering the first derivatives of the DRBO objective, we generalize DRBO to one that includes several other uncertainty objectives studied in the BOCU literature such as worst-case sensitivity (and thus notions of risk such as variance, range, and conditional value-at-risk) and mean-risk tradeoffs. We develop a general Thompson sampling algorithm that is able to optimize any objective within the BOCU framework, analyze its theoretical properties, and compare it to suitable baselines across different experimental settings and uncertainty objectives. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Value engineering

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Snowball Effect in Federated Learning: An Approach of Exponentially Expanding Structures for Optimizing the Training Efficiency

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1803-1817页

作者： Cheng, Guoliang Li, Peichun Tan, Beihai Yu, Rong Wu, Yuan Pan, Miao Guangdong University of Technology School of Automation Guangzhou China Guangdong Key Laboratory of IoT Information Technology Guangzhou China University of Macau State Key Laboratory of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China Guangdong University of Technology School of Integrated Circuits Guangzhou China University of Houston Department of Electrical and Computer Engineering Houston United States

Efficient federated learning (FL) in mobile edge networks faces challenges due to energy-consuming on-device training and wireless transmission. Optimizing the neural network structures is an effective approach to achieving energy savings. In this paper, we present a Snowball FL training with expanding neural network structure, which starts with a small-sized submodel and gradually progresses to a full-sized model. To achieve this, we first design the submodel and embedding extraction schemes for fine-grained model structure expansion. We then investigate the joint minimization problem of the global training loss and system-wise energy consumption. After that, we decompose the optimization problem into a long-term model structure expansion subproblem and a single-round local resource allocation subproblem. Specifically, the former subproblem is transformed into a variational calculus problem by leveraging theoretical analysis of the convergence bound. The Euler-Lagrange method is used to derive the solution, where the optimal evolution strategy for the model structure exponentially increases with the global round (i.e., the Snowball effect). Meanwhile, the latter subproblem is solved by convex optimization to acquire the optimal computing frequency and transmission power. Experiments indicate that the proposed framework can save about 50% of energy consumption to achieve on-par accuracy against state-of-the-art algorithms. © 2015 IEEE.

关键词： Resource allocation

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Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

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computers, Materials & Continua 2022年第7期72卷 561-576页

作者： Aymen Saad Israa SKamil Ahmed Alsayat Ahmed Elaraby Department of Information Technology Technical College of ManagementKufaAl Furat Alawsat UniversityIraq Department of Medical Laboratory Techniques Al-Mustaqbal University CollegeHillaIraq Department of Computer Science College of Computer and Information SciencesJouf UniversitySaudi Arabia Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt

COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world *** spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment.X-ray images are one of the most classifiable images that are used widely in diagnosing patients’data depending on radiographs due to their structures and tissues that could be *** Neural Networks(CNN)is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification *** using CNNs techniques requires a large number of images to learn and obtain satisfactory *** this paper,we used SqueezNet with a modified output layer to classify X-ray images into three groups:COVID-19,normal,and *** this study,we propose a deep learning method with enhance the features of X-ray images collected from Kaggle,Figshare to distinguish between COVID-19,Normal,and Pneumonia *** this regard,several techniques were used on the selected image samples which are Unsharp filter,Histogram equal,and Complement image to produce another view of the *** Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type(COVID-19,Normal and Pneumonia).In the first scenario,the model has been tested without any enhancement on the *** achieved an accuracy of 91%.But,in the second scenario,the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%.The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images.A comparison of the outcomes demonstrated the effectiveness of ourDLmethod for classifying COVID-19 based on enhanced X-ray images.

关键词： COVID-19 X-ray image enhancement classification CNN SqueezNet model

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A novel mixture of ensemble learning experts for the assessment of the quality of experience 28th

A novel mixture of ensemble learning experts for the assessm...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Elwerghemmi, Radhia Zaibi, Dorra Ksantini, Riadh Bouallegue, Ridha Innov'COM Laboratory University of Carthage SUP'COM Ariana Tunisia Department of Computer Science College of IT University of Bahrain Bahrain

The effective management of services based on Quality of Experience (QoE) is crucial for the successful deployment of multimedia services in advanced networks like 5G/6G. This necessitates the use of appropriate tools for monitoring, predicting, and managing quality, with Machine Learning (ML) playing a pivotal role. However, predicting the QoE of multimedia streams poses a challenge due to its dependence on numerous influencing factors, and it must adapt to dynamic environments with large-scale data. Machine learning approaches provide a method to quantify the complex relationships between these influencing factors and QoE. In this paper, we propose an innovative improved mixture of classification for assessing the QoE of video streaming. Our approach combines the predictive capabilities of ensemble learning models rather than single models, as experts. Also, we have employed a robust gating network trained by minimizing an improved error function that combines the Classification Loss and the gating loss. We assess our method through a comprehensive simulation model encompassing diverse wireless network environments and various video sequences. The experimental results reveal substantial enhancements in QoE, ensuring users have stable, high-quality video streaming sessions. © 2024 The Authors.

关键词： Multimedia services

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Hierarchical Clustering Based on Dendrogram in Sustainable Transportation Systems

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 15724-15739页

作者： Sangaiah, Arun Kumar Javadpour, Amir Ja'fari, Forough Zhang, Weizhe Khaniabadi, Shadi Mahmoodi International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon Instituto Politécnico de Viana Do Castelo ADiT-Laboratory Electrotechnics and Telecommunications Department Porto4900-347 Portugal Harbin Institute of Technology Department of Computer Science and Technology Shenzhen150001 China Sharif University of Technology Department of Computer Engineering TehranP932 FM4 Iran Usm Engineering Campus Department of Computer Science and Engineering George Pulau Pinang14300 Malaysia

Each group in a data-driven automobile network has its cluster head. A group can communicate with each other and members of other groups once it has been founded. Vehicles belonging to each group near the other group allow intergroup communication. Because nodes in automotive networks move so quickly, routing in these networks is a complex problem to solve. Each cluster in hierarchical clustering can be partitioned into multiple sub-clusters. Put another way, and the data is stored in a cluster, which is then divided into more clusters. The data is stored directly in separate clusters in non-hierarchical approaches. A dendrogram is a type of hierarchical tree. We anticipate increasing information sharing in clusters by properly clustering vehicles on the road and establishing clusters of the desired size in the relevant dendrogram. We can select clusters of the necessary extent and compare the Quality of Service (QoS) network's outcomes by breaking the dendrogram at different levels. The findings reveal that the suggested method outperforms AIVISN in delay, PDR, overhead, and Drooped packets compared to AIVISN, 7.12%, 12.21%,8.32%, and 7.34%, respectively. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes 10

Summit Vitals: Multi-Camera and Multi-Signal Biosensing at H...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Ke Tang, Jiankai Jiang, Zhang Wang, Yuntao Liu, Xiaojing Li, Dong Shi, Yuanchun Qinghai University Department of Computer Technology and Applications Qinghai810016 China National Key Laboratory of Human Factors Engineering Beijing100094 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

ISBN: (纸本)9798331520861

Video photoplethysmography (vPPG) is an emerging method for non-invasive and convenient measurement of physiological signals, utilizing two primary approaches: remote video PPG (rPPG) and contact video PPG (cPPG). Monitoring vitals in high-altitude environments, where heart rates tend to increase and blood oxygen levels often decrease, presents significant challenges. To address these issues, we introduce the SUMS dataset comprising 80 synchronized non-contact facial and contact finger videos from 10 subjects during exercise and oxygen recovery scenarios, capturing PPG, respiration rate (RR), and SpO2. This dataset is designed to validate video vitals estimation algorithms and compare facial rPPG with finger cPPG. Additionally, fusing videos from different positions (i.e., face and finger) reduces the mean absolute error (MAE) of SpO 2 predictions by 7. 6 % and 1 0. 6 % compared to only face and only finger, respectively. In cross-subject evaluation, we achieve an MAE of less than 0.5 BPM for HR estimation and 2. 5 % for SpO 2 estimation, demonstrating the precision of our multi-camera fusion techniques. Our findings suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO 2 estimation by 1 7. 8 %. © 2024 IEEE.

关键词： Photoplethysmography

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Impact of PON network range and laser power on GPON and XGSPON coexistence system 8

Impact of PON network range and laser power on GPON and XGSP...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Kherici, Cheikh Kandouci, Malika Bechikh, Amel Benhamidi, Aicha Laboratory of Electromagnetism Photonics and Optronics L.E.P.O Faculty of Mathematics and Computer Science Computer Science Department Sidi Bel Abbes Algeria Ibn Khaldoun University of Tiaret Tiaret Algeria Laboratory of Electromagnetism Photonics and Optronics L.E.P.O Faculty of Electrical Engineering Electronics Department Sidi Bel Abbes Algeria Djillali Liabes University of Sidi Bel Abbes Sidi Bel Abbes Algeria Ibn Khaldoun University of Tiaret Faculty of Mathematics and Computer Science Computer Science Department Tiaret Algeria

ISBN: (纸本)9798350309249

This article discusses the effect of laser emission power and PON network range when GPON and XGSPON systems coexist. The analysis aims to understand how these two factors influence the overall performance and stability of the coexistence between the two optical network technologies. In order to also meet the increasing demand for data rates and new services such as videoconferencing and very high-quality television (4K, 6K,...etc). the coexistence system is preferred, allowing effective collaboration between GPON and XGSPON *** work deals with the performance evaluation of GPON and XGSPON systems, focusing on reconciliations and adjustments related to BER error rates and Q factor. The study explores different scenarios by adjusting the laser power from -12 dBm to 12 dBm and varying the array range from 10 km to 120 km. The main objective is to determine the best combination of laser power and network range to ensure optimal performance for coexistence systems. © 2024 IEEE.

关键词： Q factor measurement

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Near-field Source Localization in 3-D Using Two Parallel Centrally Symmetric Unfold Coprime Array

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4738-4749页

作者： Chen, Hua Li, Junjie Yang, Songjie Liu, Wei Eldar, Yonina C. Yuen, Chau Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu610054 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Kowloon Hong Kong Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel Nanyang Technological University School of Electrical and Electronics Engineering Singapore639798 Singapore

Most near-field (NF) localization algorithms cannot deal with the underdetermined case, while those which can are computationally expensive due to employment of fourth-order cumulants. In this work, a low-complexity solution is provided for underdetermined three-dimensional (3-D) NF localization, by employing second-order statistics with a tailored array configuration named two parallel centrally symmetric unfold coprime (TPSC) array. Its implementation can be divided into three stages. Firstly, the proposed algorithm constructs two cross-correlation matrices based on the received array data, which eliminates the non-linear range-related information of NF signals. Secondly, covariance and vectorization operations are applied to these two cross-correlation matrices to form a virtual array with extended aperture. Finally, the two-dimensional (2-D) angle parameters are estimated by the sparse and parametric approach (SPA) and a phase retrieval operation, and then the one-dimensional (1-D) range parameter is achieved by the multiple signal classification (MUSIC) algorithm. One specific feature is that the estimated angle and range parameters are matched automatically. An analysis of the properties of the TPSC array is provided, and an optimal parameter configuration is derived, given that the total number of array elements is fixed. Simulation results demonstrate that the designed TPSC array can achieve underdetermined 3-D NF localization, and deliver enhanced estimation capabilities, surpassing those of established algorithms. © 2002-2012 IEEE.

关键词： Multiple signal classification

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