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3rd International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Lou, Haijie Ma, Ying Li, Jianmin Faculty of Computer and Information Engineering Xiamen University of Technology Xiamen China Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9781510671485

To forecast the condition of traffic networks in the future, it is crucial to model the spatial and temporal correlation of traffic series. The majority of current research has been on creating complicated graph neural networks that can capture common patterns using preconfigured graphs. In this paper, we claim that predefined graphs may be avoided and that adaptive graphs can be used to capture spatial correlations between traffic series and improve the performance of graph neural networks. In order to capture the temporal relationships of sequences, we also aggregated gated recurrent neural networks. Then, we encode the relative time position of the sequence in order to fully extract the characteristics of the traffic sequence. Finally, we add the values of the previous day and the same day of the previous week as a reference in the final prediction to improve the accuracy of our prediction. Experimental results on two sets of real-word traffic data (PeMSD4 and PeMSD8) demonstrate that our method is better than the existing methods. © 2023 SPIE.

关键词： Graph neural networks

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Carbon Dioxide Emission Forecasting using Machine Learning and Time Series Statistical Models 25

Carbon Dioxide Emission Forecasting using Machine Learning a...

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25th International Arab Conference on information technology, ACIT 2024

作者： Hassan, Tahir Mansour, Khalid Waseemullah, Waseemullah Memon, Muhammad Qasim Ned University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan Arab Open University Faculty of Computer Studies Bahrain Sindh Bhitshah Pakistan

ISBN: (纸本)9798331540012

Climate change is considered a global disaster that has wreaked havoc worldwide. Climate change conditions are primarily driven due to emission of carbon dioxide and other greenhouse gases. Around the globe, several countries are facing serious challenges related to global warming and climate change. The adverse effects of global warming and climate change are recently observed in the form heat waves, floods, extreme rainfall events and drought conditions. The escalating CO2 emission level causes threats of food security and water scarcity. To overcome this, timely and accurate CO2 emissions forecasting is essential for developing effective mitigation strategies in such a manner that CO2 emission impacts can be reduced. The aim of this study is to forecast the total CO2 emissions of upcoming years using various statistical, machine learning models such as ARIMA (Auto-Regressive Integrated Moving Average), SARIMA (Seasonal ARIMA), Linear Regression, and Decision tree regressor. We had forecasted CO2 emission on the basis of influencing factors, which highly contributes in CO2 emission. The performance of the models is validated using multiple evaluation metrics such as MAE, MSE RMSE,MAPE and R2 square. The results show that CO2 emission is highly affected by fossil fuel consumption. In particular, Linear regression performs better among all applied models, with R2 value 0.93 and MAE, RMSE and MAPE values 5.52, 6.47 and 0.03, respectively. Our research delineates the results and insights that will assist concerned authorities, policymakers and environmental agencies. The present study also aim to inform authorities, helping them to better understand the future of CO2 emission trajectories and make timely efforts and interventions to tackle the climate change and global warming challenges. © 2024 IEEE.

关键词： Low emission

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Coverage Probability of EH-enabled LoRa networks - A Deep Learning Approach

EAI Endorsed Transactions on Industrial Networks and Intelli...

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EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2025年第2期12卷 1-11页

作者： Nguyen, Thi-Tuyet-Hai Hung, Tran Cong Son, Nguyen Hong Hanh, Tan Duy, Tran Trung Tu, Lam-Thanh Faculty of Information Technology Posts and Telecommunications Institute of Technology Viet Nam School of Computer Science & Engineering Saigon International University Ho Chi Minh City Viet Nam Faculty of Telecommunications Posts and Telecommunications Institute of Technology Viet Nam Communication and Signal Processing Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam

The performance of energy harvesting (EH)-enabled long-range (LoRa) networks is analyzed in this work. Specifically, we employ deep learning (DL) to estimate the coverage probability (Pcov) of the considered networks. Our study incorporates a general fading distribution, specifically the Nakagami-m distribution, and utilizes tools from stochastic geometry (SG) to model the spatial distributions of all nodes and end-devices (EDs) with EH capability. The DL approach is employed to overcome the limitations of model-based methods that can only evaluate the Pcov under simplified network conditions. Therefore, we propose a deep neural network (DNN) that estimates the Pcov with high accuracy compared to the ground truth values. Additionally, we demonstrate that DL significantly outperforms the Monte Carlo simulation approach in terms of resource consumption, including time and memory. © (2025), (European Alliance for Innovation). All rights reserved.

关键词： Coverage probability deep learning energy harvesting long range power beacon

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A Cross Attention Transformer-Mixed Feedback Video Recommendation Algorithm Based on DIEN

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computers, Materials & Continua 2025年第1期82卷 977-996页

作者： Jianwei Zhang Zhishang Zhao Zengyu Cai Yuan Feng Liang Zhu Yahui Sun College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450000China Faculty of Information Engineering Xuchang Vocational Technical CollegeXuchang461000China College of Computer Science and Technology Zhengzhou University of Light IndustryZhengzhou450000China College of Electronics and Information Zhengzhou University of Light IndustryZhengzhou450000China

The rapid development of short video platforms poses new challenges for traditional recommendation *** systems typically depend on two types of user behavior feedback to construct user interest profiles:explicit feedback(interactive behavior),which significantly influences users’short-term interests,and implicit feedback(viewing time),which substantially affects their long-term ***,the previous model fails to distinguish between these two feedback methods,leading it to predict only the overall preferences of users based on extensive historical behavior ***,it cannot differentiate between users’long-term and shortterm interests,resulting in low accuracy in describing users’interest states and predicting the evolution of their *** paper introduces a video recommendationmodel calledCAT-MFRec(CrossAttention Transformer-Mixed Feedback Recommendation)designed to differentiate between explicit and implicit user feedback within the DIEN(Deep Interest Evolution Network)*** study emphasizes the separate learning of the two types of behavioral feedback,effectively integrating them through the cross-attention ***,it leverages the long sequence dependence capabilities of Transformer technology to accurately construct user interest profiles and predict the evolution of user *** results indicate that CAT-MF Rec significantly outperforms existing recommendation methods across various performance *** advancement offers new theoretical and practical insights for the development of video recommendations,particularly in addressing complex and dynamic user behavior patterns.

关键词： Video recommendation user interest cross-attention transformer

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Multi Label Classification Based on Ensemble Learning: Investigative Study 25

Multi Label Classification Based on Ensemble Learning: Inves...

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25th International Arab Conference on information technology, ACIT 2024

作者： Nasir, Ola Alazaidah, Raed Hadi, Wael Samara, Ghassan Al Daoud, Essam Owaida, Suhaila Abu Elrashidi, Ali Elsoud, Esraa Abu Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Petra University Faculty of Information Technology Department of Cyber Security Amman Jordan Zarqa University Faculty of Information Technolog Department of Computer Science Zarqa Jordan Al-al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Applied Science Private University Faculty of It Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

In the big data technology nowadays, activities associated with Multi-Label Classification (MLC) pose big and complex challenges, receiving terrific interest in diverse fields. Existing MLC algorithms suffer from low overall performance. This paper considers four well-known Problem Transformation Methods (PTMs). Two of these PTMs are based on the frequency of the labels (MFL and LFL) while the last two PTMs are based on the positive correlations among labels. These PTMs have been implemented and compared against four multi label datasets of various sizes to identify the best one that fits the nature of MLC datasets. Moreover, six popular ensemble learning models have been carried out and compared on the four datasets with respect to several evaluation metrics such as Accuracy, Hamming loss, and Exact Match and using SMO, DecisionStump, Random Forest, and Random Commission classifiers. The results revealed that MFL accomplished the quality performance amongst the four PTMs, and Stacking SVM is the best ensemble learning technique. © 2024 IEEE.

关键词： Contrastive Learning

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2D-DOA for a Monostatic ULA EMVS-MIMO Radar Based on RC-PARAFAC

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IEEE Sensors Journal 2025年第12期25卷 21763-21774页

作者： Wang, Jianlong Shi, Junpeng Wen, Fangqing China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hvdroelectric Engineering College of Computer and Information Technology Yichang443002 China National University of Defense Technology College of Electronic Countermeasure Hefei230037 China City University of Macau College of Computer and Information Technology China Three Gorges University Faculty of Data Science 999078 China

The electromagnetic vector sensor (EMVS) embedded multiple-input multiple-output (MIMO) radar is an emerging technology capable of two-dimensional (2D) direction of arrival (DOA) estimation. In this paper, we propose a low-complexity algorithm for monostatic uniform linear array (ULA) EMVS-MIMO radar to rapidly estimate 2D-DOA. First, we decompose the received signal into a third-order tensor using parallel factor analysis (PARAFAC). Next, we eliminate data redundancy through a reduced complexity (RC) process. Then, we use the least squares method to extract ambiguous elevation angle information from the spatial response matrix and apply the vector cross-product (VCP) technique to the electromagnetic vector to achieve unambiguous 2D-DOA estimation. Finally, we combine these estimates to achieve high-resolution, unambiguous elevation angle estimation. The proposed algorithm is suitable for large-scale and sparse EMVS-MIMO radar systems. © 2001-2012 IEEE.

关键词： Direction of arrival

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DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

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computers, Materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi Mohd.Hasbullah Omar Abdullah Almogahed Noradila Nordin Salman A.Alqahtani Atif M.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of Medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

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Handling Imbalanced Datasets in Researcher Scientific Classification Using Oversampling and Undersampling Approaches 11

Handling Imbalanced Datasets in Researcher Scientific Classi...

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11th International Conference on Electrical engineering, computer Science and Informatics, EECSI 2024

作者： Zulius, Antoni Defit, Sarjon Stiawan, Deris Abdiansah, Abdiansah Samsuryadi, Samsuryadi Rahmi, Nadya Alinda Universitas Bina Insan Lubuklinggau Universitas Sriwijaya Faculty of Engineering Sciences Faculty of Engineering Palembang Indonesia Universitas Putra Indonesia YPTK Department of Information Technology Faculty of Computer Science Padang Indonesia Universitas Sriwijaya Department of Computer Engineering Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Department of Informatic Engineering Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350355314

The challenge of class imbalance in datasets is a prevalent issue in machine learning, often resulting in biased and inaccurate predictions. This study investigates the effectiveness of different data balancing techniques in enhancing classifier accuracy when applied to an imbalanced Indonesian National Scientific Manuscript dataset. Specifically, we examine how various classifiers respond to three widely used data balancing methods: Synthetic Minority Over-sampling Technique (SMOTE), Random Under-sampling, and Random Over-sampling. We assess the performance of six classifiers-Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Naïve Bayes (NB), and Gradient Boosting (GB)-when these techniques are applied. The analysis reveals diverse outcomes, with some classifiers showing substantial accuracy gains, while others experience moderate or even adverse effects. The findings underscore the importance of selecting an appropriate balancing method based on the dataset's characteristics and the specific classification algorithm employed in the knowledge classification process. © 2024 IEEE.

关键词： Adversarial machine learning

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Multi Label Classification Algorithm Based on Conditional Local Correlations among Labels and Ensemble Learning 25

Multi Label Classification Algorithm Based on Conditional Lo...

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25th International Arab Conference on information technology, ACIT 2024

作者： Alazaidah, Raed Nasir, Ola Hadi, Wael Samara, Ghassan Al Daoud, Essam Aljaidi, Mohammad Khouj, Mohammed Talat Elsoud, Esraa Abu Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Petra University Faculty of Information Technology Department of Cyber Security Amman Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Multi-Label Classification (MLC) is an advanced form of traditional classification, where each data instance is simultaneously associated with multiple labels. The importance of MLC has increased significantly in recent years, due to its wide range of application areas. Modern algorithms, when faced with the challenge of multiple labels, continue to show suboptimal performance, conversely, they show significantly superior performance when processing single-label datasets. This paper proposes a new algorithm to deal with the problem of transforming datasets from multiple labels to single labels by exploiting conditional and local correlations between labels after applying the Apriori predictive algorithm to extract relationships. Six ensemble learning models: Voting Hard, Voting Soft, Voting Weighted, Stacking LR, Stacking RF, and Stacking SVM, along with four classification models: SMO, DecisionStump, Random Forest, and Random Committee, were applied to three datasets. The algorithm showed effective and good performance by comparing the results of the proposed Multi Label Classification algorithm based on Conditional Local Correlations among Labels (MLC-CLCL) with several popular algorithms for dealing with multiple labels. © 2024 IEEE.

关键词： Contrastive Learning

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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