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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xu, Jinpeng Zhao, Chunna Yang, Jing Huang, Yaqun Yang, Yaoyuan Yee, Por Lip School of Information Science & Engineering Yunnan University Kunming China Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350368741

Urban traffic flow management faces increasing challenges due to accelerating urbanization. Traffic data collected from roadside sensors contain complex temporal and spatial dependencies that interact simultaneously. Although Graph Neural Networks and Recurrent Neural Networks have been successful in capturing these dependencies, two critical issues remain: 1) Treating all traffic signals equally fails to capture the nuanced spatiotemporal dependencies hidden in time series data;2) Dynamic traffic conditions hinder the accurate capture of local spatial dependencies, thereby limiting prediction accuracy and reliability. To address these challenges, we propose an innovative model, FDDSGCN. To resolve the first issue, we introduce Fractional Residual Decomposition, which effectively separates traffic data into spatial and temporal signals. For the second issue, we employ Dynamic Spatial-Temporal Graph Convolution with fractional-order weight adjustments to dynamically capture local dependencies. Additionally, the Long Short-Term Dependency module analyzes both long-term and short-term dependencies. Extensive experiments on three public datasets demonstrate the superior performance and practical value of our model. © 2025 IEEE.

关键词： dynamic graph learning Fractional Residual decomposition spatial-temporal graph Traffic prediction

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Engagement Level Detection Using Facial Extraction and Multi-Stacked Convolutional Neural Network in e-Learning Settings 12

Engagement Level Detection Using Facial Extraction and Multi...

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12th Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2024

作者： Bachtiar, Fitra Abdurrachman Mutiar Mahesa, Al'Ravie Cooper, Eric Wallace Intelligent System Laboratory Universitas Brawijaya Computer Science Faculty Malang Indonesia Affective Engineering and Arts Laboratory Ritsumeikan University College of Information Science and Engineering Ibaraki Japan

ISBN: (纸本)9798350379914

Engagement in learning is crucial in maintaining student eagerness as fuel for student learning to achieve learning success. In e-Learning settings, engagement also plays an im-portant role as a factor influencing student learning success as students are regulating themselves during learning one-on-one using computer devices. Failing to detect student engagement may result in unsuccessful learning. In extreme cases, students may disengage and learning purpose cannot be achieved. This research aims to detect user engagement in e-Learning settings by using a deep learning approach with a feature extraction method. The dataset used in this study is the DAiSEE dataset. The dataset is in the form of videos and is extracted into frames and specific facial features. The facial area was extracted using the Mediapipe Library. The features include 1434 3D landmark features, 52 action unit features, one depth estimation feature, and three head pose estimation features. This research also conducts image selection, which selecting frames with an interval of 15 in the train, validation, and test folders. The multi-stacked Convolutional Neural Network model was used to classify four levels of engagements. The results were compared to the existing approach in engagement detection. The untuned proposed model achieved an accuracy of 48.95%, while the tuned proposed model achieved an accuracy of 52.94%. The proposed model is able to compete with state-of-the-art methods using a less complex model. Even so, the model still cannot learn labels perfectly due to a very imbalanced dataset and a possibility of model overfitting. This research is expected to be useful for educational institutions and the e-Learning field in the future. © 2024 IEEE.

关键词： Convolutional neural networks

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Securing Data in Multimode Fibers by Exploiting Mode-Dependent Light Propagation Effects

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Research 2023年第3期2023卷 669-682页

作者： Stefan Rothe Karl-Ludwig Besser David Krause Robert Kuschmierz Nektarios Koukourakis Eduard Jorswieck Jürgen W.Czarske TU Dresden Faculty of Electrical and Computer EngineeringLaboratory of Measurement and Sensor System Technique01062 DresdenGermany TU Braunschweig Faculty of Electrical EngineeringInformation TechnologyPhysicsInstitute for Communications Technology38106 BraunschweigGermany TU Dresden Faculty of Physics01062 DresdenGermany

Multimode fibers hold great promise to advance data rates in optical communications but come with the challenge to compensate for modal crosstalk and mode-dependent losses,resulting in strong *** holographic measurement of the transmission matrix enables not only correcting distortions but also harnessing these effects for creating a confidential data connection between legitimate communication parties,Alice and *** feasibility of this physical-layer-security-based approach is demonstrated experimentally for the first time on a multimode fiber link to which the eavesdropper Eve is physically *** the proper structured light field is launched at Alice’s side,the message can be delivered to Bob,and,simultaneously,the decipherment for an illegitimate wiretapper Eve is *** a real communication scenario,we implement wiretap codes and demonstrate confidentiality by quantifying the level of *** to an uncoded data transmission,the amount of securely exchanged data is enhanced by a factor of *** complex light transportation phenomena that have long been considered limiting and have restricted the widespread use of multimode fiber are exploited for opening new perspectives on information security in spatial multiplexing communication systems.

关键词： distortion destroyed holographic

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Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

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computers, Materials & Continua 2024年第6期79卷 4725-4740页

作者： Djeldjli Halima Benatiallah Djelloul Ghasri Mehdi Tanougast Camel Benatiallah Ali Benabdelkrim Bouchra Material Sciences Department Faculty of Material SciencesMathematics and Computer ScienceLaboratory of Sustainable Development and Computer Science(LSDCS)University of AdrarAdrar01000Algeria Department of Civil Engineering University of Sistan and BaluchestanZahedan98167-45845Iran Laboratory of Computer Production and Maintenance Engineering(LGIPM)University of LorraineChieulles57070France Laboratory of Energy Environment and Information System(LEEIS) University of AdrarAdrar01000Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first *** study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and *** proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN *** GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were *** model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

关键词： Solar energy systems genetic algorithm neural networks hybrid adaptive neuro fuzzy inference system solar radiation

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Data Science based Recommendation system - An Application of computer Science 14

Data Science based Recommendation System - An Application of...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Khan, Zeba Rahman, Abdul Applied College Jazan University Department of Computer and Information System Jazan Saudi Arabia Presidency University School of Computer Science and Engineering & Information Science Rajanukunte Yelahanka Bangalore India

Recommender system (RS) has emanated as the most popular application of e-commerce websites. In the e-commerce business, collaborative filtering based RS systems suggest products to the customers and find the interesting things for the users which they may wish to purchase. The success rate of any recommendation system depends upon, information reliability as well as expression of daily life behavior in the consolidated format. The main task is to produce the best ranked list of ‘n-number’ of items for the user’s need. Due to the natural structure, Z-Numbers are more consistent to producing a recommendation list. To solve real life problem, Z-Numbers should be incorporated into decision-making models. However, Online shopping involved multicriteria group decision-making (MCGDM). Z-information persisting some difficulties with MCGDM. Therefore, to enhancing the ability of Z-numbers Complex Fuzzy Sets (CFSs), are employed. Besides this entropy, distance measure, and aggregation operator are combined to produced MCGDM based ranking of customer preference. © Grenze Scientific Society, 2023.

关键词： Recommender systems

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Cluster-preserving sampling algorithm for large-scale graphs

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Science China(information Sciences) 2023年第1期66卷 60-76页

作者： Jianpeng ZHANG Hongchang CHEN Dingjiu YU Yulong PEI Yingjun DENG National Digital Switching System E&T Research Center Information Engineering University Network Systems Department of the Strategic Support Force School of Computer Science and Technology Eindhoven University of Technology Center for Applied Mathematics Tianjin University

Graph sampling is a very effective method to deal with scalability issues when analyzing largescale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using explicit properties(e.g., degree distribution) of the sample. However, the existing sampling techniques are inadequate for the current sampling task: sampling the clustering structure, which is a crucial property of the current networks. In this paper, using different expansion strategies, two novel top-leader sampling methods(i.e., TLS-e and TLS-i) are proposed to obtain representative samples, and they are capable of effectively preserving the clustering structure. The rationale behind them is to select top-leader nodes of most clusters into the sample and then heuristically incorporate peripheral nodes into the sample using specific expansion strategies. Extensive experiments are conducted to investigate how well sampling techniques preserve the clustering structure of graphs. Our empirical results show that the proposed sampling algorithms can preserve the population's clustering structure well and provide feasible solutions to sample the clustering structure from large-scale graphs.

关键词： graph sampling clustering structure top-leader nodes expansion strategies large-scale graphs

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Brain Tumor Detection Using Image Processing 5

Brain Tumor Detection Using Image Processing

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Wijerathna, Udari Dissanayake, Nimavi Nimasha, Dinithi Senarathne, Udara Weerasinghe, Lokeesha Kahandawaarachchi, Chathurangika Sri Lanka Institute of Information Technology Faculty of Computing Dept of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Dept of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Dept of Computer System Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Abnormal formation of cells in a layer. The brain is known as the brain cancer complex, and a brain cancer detection system is needed to detect cancer in its early stages Phase. As a result, the primary goal of this initiative is to assist existing doctors in their training for the early identification o f brain cancer by utilizing magnetic resonance imaging (MRI), which is the most effective approach to identifying brain cancer. This system is done through four components, and it has made it easier to detect brain tumors. These techniques with different image processing techniques include noise removal, segmentation, classification, a nd u se o fa nalysis segments f or detection. Pre-processing techniques prepare the image for further processing by removing image noise. During segmentation, the brain tumor system determines the size of the brain tumor and estimates its size. Specialists should examine them and classify brain tumors as meningiomas, gliomas, and pituitary tumors. In analysis, this component is expected to be designed so that users or medical experts can know the future through a dashboard. We explored the methodology of using generative adversarial networks (GANs) implemented in TensorFlow to denominate brain tumor images. Analyses the outcomes of various types of established deep learning models on the brain tumor MR image datasets, such as classification methods ResNet-50, VGG 16, CNN, and Inception V3. The validation precision, AUC, recall, and validation loss for CNN are 95.3%, 99.43%, 92.1 %, and 0.360 adjustments respectively. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Cloud-edge-based We-Market:Autonomous Bidding and Peer-to-peer Energy Sharing Among Prosumers

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Journal of Modern Power systems and Clean Energy 2023年第4期11卷 1282-1293页

作者： Fangyuan Si Yinghua Han Qinqin Xu Jinkuan Wang Qiang Zhao College of Information Science and Engineering Northeastern UniversityShenyangChinaand F.Si is also with the State Key Laboratory of Power System and Generation EquipmentDepartment of Electrical EngineeringTsinghua UniversityBeijingChina School of Computer and Communication Engineering Northeastern University at QinhuangdaoQinhuangdaoChina School of Control Engineering Northeastern University at QinhuangdaoQinhuangdaoChina

With the extensive penetration of distributed renewable energy and self-interested prosumers,the emerging power market tends to enable user autonomy by bottom-up control and distributed *** paper is devoted to solving the specific problems of distributed energy management and autonomous bidding and peer-to-peer(P2P)energy sharing among prosumers.A novel cloud-edge-based We-Market is presented,where the prosumers,as edge nodes with independent control,balance the electricity cost and thermal comfort by formulating a dynamic household energy management system(HEMS).Meanwhile,the autonomous bidding is initiated by prosumers via the modified Stone-Geary utility *** the cloud center,a distributed convergence bidding(CB)algorithm based on consistency criterion is developed,which promotes faster and fairer bidding through the interactive iteration with the edge ***,the proposed scheme is built on top of the commercial cloud platform with sufficiently secure and scalable computing *** results show the effectiveness and practicability of the proposed We-Market,which achieves 15%cost reduction with shorter running *** analysis indicates better scalability,which is more suitable for largerscale We-Market implementation.

关键词： We-Market bidding energy sharing prosumer peer-to-peer cloud-edge

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Decision Support system For The Coaching Staff in the Process of Preliminary Selection of Players 18

Decision Support System For The Coaching Staff in the Proces...

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18th IEEE International Conference on computer Science and information Technologies, CSIT 2023

作者： Hovorushchenko, Tetiana Hnatchuk, Yaroslav Hnatchuk, Alina Fehyr, Oleksandr Computer Engineering and Information System Department Khmelnytskyi Ukraine Theory and Methods of Physical Education and Sports Department Khmelnytskyi Ukraine

ISBN: (纸本)9798350360462

Nowadays in Ukraine, there is a need to automate the routine work of coaches and sports managers in the preliminary selection of athletes. In the process of selecting athletes for competitions of various levels, the head coach and coaching staff need to process large amounts of information about the applicants. This is especially important at the stages of preliminary selection, when the number of players to be selected for preliminary training camps is quite large, and the amount of information about each individual athlete increases accordingly. Our research has shown that despite the rapid development and application of information technology in the sports industry, there is currently no intelligent sports-specific DSS in Ukraine that helps coaching staff and sports managers automate the routine and time-consuming process of preliminary selection of athletes. Taking into account the large amount and heterogeneity of data that must be processed for decision-making by the coaching staff during the preliminary selection of players, a decision support system for preliminary selection of players is proposed. The system is based on the method of supporting decision-making by the coaching staff in the process of preliminary selection of athletes. The result of the decision support system is a recommended list of 18 players who have been selected according to the specified criteria out of a total of 24 players. If the coaching staff is not satisfied with the recommended list, it is possible to set additional criteria and use the decision support method to obtain a more accurate result. The results can also be implemented in other team sports. © 2023 IEEE.

关键词： Decision support systems

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Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption

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Chinese Journal of Electronics 2024年第6期33卷 1435-1446页

作者： Zhiwei WANG Qiuchi ZHU Zhenqi ZHANG School of Computer Nanjing University of Posts and Telecommunications State Key Laboratory of Public Big Data Guizhou University Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Engineering Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University

The WiFi fingerprint-based localization method is considered one of the most popular techniques for indoor localization. In INFOCOM'14, Li et al. proposed a wireless fidelity(WiFi) fingerprint localization system based on Paillier encryption, which is claimed to protect both client C 's location privacy and service provider S's database privacy. However, Yang et al. presented a practical data privacy attack in INFOCOM'18, which allows a polynomial time attacker to obtain S's database. We propose a novel WiFi fingerprint localization system based on CastagnosLaguillaumie(CL) encryption, which has a trustless setup and is efficient due to the excellent properties of CL encryption. To prevent Yang et al.'s attack, the system requires that S selects only the locations from its database that can receive the nonzero signals from all the available access points in C 's nonzero fingerprint in order to determine C's location. Security analysis shows that our scheme is secure under Li et al.'s threat model. Furthermore, to enhance the security level of privacy-preserving WiFi fingerprint localization scheme based on CL encryption, we propose a secure and efficient zero-knowledge proof protocol for the discrete logarithm relations in C's encrypted localization queries.

关键词： Location awareness Threat modeling Data privacy Protocols Databases Fingerprint recognition Trustless services Encryption Servers Wireless fidelity

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