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Advances in Transportation Studies 2025年 66卷 19-38页

作者： Khalfi, A. Guerroumi, M. Haid, T. Laradji, N. Faculty of Computer Science Artificial Intelligence and Data Science Department University of Science and Technology Houari Boumediene Algiers Algeria Matter Sciences and Computer Science Faculty Department of Computer Science University of Djilali Bounaama Khemis Miliana Algeria

Leveraging advanced computer vision techniques, we present a novel method for analyzing and categorizing traffic videos by utilizing an image mapping technique combined with a UniVit (Unit Vision Transformer) model. First, video data is transformed into an image dataset, where each frame undergoes a mapping process based on vehicle detection using the YOLOv8s algorithm. These mapped images are then input into a vision transformer, which utilizes attention mechanisms to identify and analyze complex patterns across frames. The approach is evaluated on the UCSD (University of California San Diego) dataset, which features diverse weather conditions such as clear, overcast, and rainy scenarios, enabling a rigorous assessment under varied environments. Our method achieves a notable accuracy score of 99.89%, surpassing previous approaches on the UCSD benchmark. This improvement underscores the effectiveness of combining image mapping with advanced vision transformers, suggesting promising applications for intelligent traffic management systems aimed at mitigating traffic issues. © 2025, Aracne Editrice. All rights reserved.

关键词： Photomapping

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Federated Learning With Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach

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IEEE Transactions on Services Computing 2025年第3期18卷 1428-1444页

作者： Zuo, Xuhan Wang, Minghao Zhu, Tianqing Zhang, Lefeng Yu, Shui Zhou, Wanlei City University of Macau Faculty of Data Science Taipa999078 China University of Technology Sydney School of Computer Science UltimoNSW2007 Australia

With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative. This article introduces an innovative framework that melds blockchain with federated learning, ensuring an immutable record of unlearning requests and actions. Our approach not only bolsters the trustworthiness and integrity of the federated learning model but also adeptly addresses efficiency and security challenges typical in IoT environments. Key contributions include a certification mechanism for the unlearning process, enhancement of data security and privacy, and optimization of data management. Experimental results on MNIST and CIFAR-10 datasets demonstrate the effectiveness of our approach, achieving 0% accuracy for unlearned classes while maintaining 77.74% and 42.65% overall model accuracy for MNIST and CIFAR-10, respectively. Our time complexity analysis shows that the blockchain integration introduces only 2 seconds of overhead per epoch, highlighting the practicality of our solution for IoT applications. © 2008-2012 IEEE.

关键词： Federated learning

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Kalman filter based on a fractional discrete-time stochastic augmented CoVid-19 model

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Journal of Biosafety and Biosecurity 2024年第2期6卷 76-98页

作者： Mohammad Ghani Dwi Rantini Maryamah Data Science Technology Faculty of Advanced Technology and MultidisciplineUniversitas AirlanggaSurabaya 60115Indonesia

In this paper,we study the dynamics of the CoVid-19 outbreak in Semarang,Indonesia,using a fractional CoVid-19 *** first determine the effects of the isolation rateand infection rate b on the reproduction number R0 and infected number *** find that R0 is directly proportional to b and inversely proportional to.For V,the effect of physical distancing is not as significant as changing.Asincreases,V decreases,the number of susceptible individuals increases,the number of quarantined individuals decreases sharply,and the number of recovered individuals ***,the effect of vaccination is also *** combination of physical distancing,isolation,and vaccination has a significant impact on reducing the number of infected *** of dynamical systems allows us to understand the characteristics of our model,such as its boundedness and non-negativity,the existence of equilibrium points,the existence and uniqueness of solutions,and the local and global *** validate our fractional CoVid-19 model,we introduce the fractional extended Kalman filter(FracEKF)as a prediction method and compare the results against reported CoVid-19 *** is a modified version of the basic extended Kalman filter with a time-fractional memory *** prediction results illustrate the accuracy of this model in terms of the root mean square error(RMSE),normalized root mean square error(NRMSE),and mean absolute percentage error(MAPE)for each ***reproduces the trends observed in the reported data for the number of infected individuals,i.e.,whenincreases,the infected number ***,a higher fractional-order results in higher model ***,higher values of the process noise Qf give smaller errors,whereas higher values of the observation noise Rf produce higher *** and the fractional-order a are inversely proportional to RMSE;NRMSE,and MAPE,whereas Rf is directly proportional to RMSE;NRMSE,and MAPE

关键词： stability. fractional inverse

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Multi-UAV Cooperative Task Offloading in Blockchain-Enabled MEC for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 2271-2284页

作者： Jing, Jinfeng Yang, Yaozong Zhou, Xiaokang Huang, Jiwei Qi, Lianyong Chen, Ying Beijing Information Science and Technology University School of Computer Science Beijing100101 China Kansai University Faculty of Business and Data Science Japan China University of Petroleum Beijing Key Laboratory of Petroleum Data Mining Beijing102249 China College of Computer Science and Technology China

The development of the Internet of Things (IoT) has made the traditional Consumer Electronics (CE) evolve into a next-generation CE with many intelligent application which generates a large number of computing-intensive tasks and is sensitive to latency. The combination of blockchain technology and multi-UAV-assisted edge computing is considered a promising technology that can process tasks near CE devices instead of sending data to remote data centers, and it has advantages in flexible deployment, differentiated services, and security assurance. This paper considers a Blockchain-enabled MEC scenario where CE devices can offload tasks to multiple-UAV-edge layers for decentralized cooperative computing, and utilize the reputation mechanism of blockchain to identify abnormal nodes in edge offloading. To solve the problem of computation offloading and resource allocation in this scenario, we jointly optimize the CPU cycle frequencies and offloading resources of UAVs and CE devices, and the decision of UAVs. Our goal is to minimize system energy consumption while ensuring the quality of service. The stochastic optimization technique is employed to transform the original stochastic optimization problem into a deterministic optimization problem, decomposing it into multiple sub-problems which can be solved in parallel. Additionally, we design a dynamic priority scoring strategy to facilitate cooperative processing among multiple UAVs and propose a Blockchain-Based Multi-UAV Cooperative Task Offloading (BMCTO) algorithm. Through theoretical analysis and simulation experiments, we demonstrate that the BMCTO algorithm can effectively reduce system energy consumption while maintaining system performance. © 1975-2011 IEEE.

关键词： Computation offloading

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Android Application for Children to Learn Arabic Alphabets and Numbers

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第5期19卷 115-127页

作者： Al Khaldy, Mohammad Ali Shaheen, Ameen Al-Shaikh, Ala’A Alzyadat, Wael Alhroob, Aysh Department of Business Intelligence and Data Analytics University of Petra Amman Jordan Faculty of Science and Information Technology Al-Zaytoonah University Amman Jordan Faculty of Information Technology Zarqa University Zarqa Jordan

Mobile learning has become a new trend in e-learning that uses portable media such as smartphones. This offers a new space for creating kid-friendly educational game applications. Therefore, this helps parents fulfill their responsibility to teach kids. This study aims to develop and integrate an educational application that contains multimedia elements that inspire children and evaluate their trustworthiness. The waterfall model was used to develop the application of this study. The application includes four sections: numbers, alphabets, animals, and fruits. A survey was conducted and distributed among four experts and 20 children to validate our application. A selective sampling technique was used to choose these responders. Thematic analysis was used to analyze the data collected. According to the results, the application is appropriate for beginners who want to learn the Arabic alphabet and numbers can easily inspire them with the multimedia elements used and can be implemented in the classroom to enhance the effectiveness of the educational process. Future research could examine the effectiveness of the application across different learning styles or age groups. © 2025 by the authors of this article.

关键词： Contrastive Learning

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Toward Sustainable Development Goals (SDGs) with Statistical Modeling: Recursive Bivariate Binary Probit

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IAENG International Journal of Applied Mathematics 2024年第8期54卷 1515-1521页

作者： Ratnasari, Vita Utama, Syirrul Hadi Dani, Andrea Tri Rian Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia the Department of Statistics Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Surabaya Indonesia Statistics Study Program Department of Mathematics Faculty of Mathematics and Natural Sciences Mulawarman University Samarinda Indonesia

Poverty is still a global problem that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. In 2021, West Papua province will have the 2nd most significant percentage of poor people after Papua province, with 21.84% of the poor population. Poor households in West Papua province are dominated by families, with the Head of Household (KRT) working in the agricultural sector at 65.10 %. In this research, joint modelling was carried out between the level of household welfare and the employment sector of the head of the household in the West Papua province. It is suspected that these two variables have endogeneity problems, where one of the response variables becomes a predictor variable in the other equation, so a recursive bivariate binary probit regression model is used. Recursive bivariate binary probit regression parameter estimation uses Maximum Likelihood Estimation (MLE), but the results are not closed form, so it is continued using the Newton-Raphson iteration method. The results of hypothesis testing show that partially, variables that significantly influence the level of household welfare include the variable marital status, KRT formal/informal workers, health complaints, asset ownership status, migration status, number of household members, classification of area of residence (Village/City), age of head of household, and employment sector of head of household. Meanwhile, variables that significantly influence the choice of working in the agricultural sector include the director of household education, classification of area of residence (rural/city), and the age of the head of household. © (2024), (International Association of Engineers). All rights reserved.

关键词： Maximum likelihood estimation

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Progressive Multiscale Attention Network for Diabetic Retinopathy 24th

Progressive Multiscale Attention Network for Diabetic Retin...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Zhou, Qiuji Guo, Yongde Liu, Yifeng Lin, YanZhen Faculty of Data Science City university of Macau China Faculty of Innovation Engineering Macau University of Science and Technology China

ISBN: (纸本)9789819615476

Given the time-intensive nature of manual fundus examination and its significant dependence on expert knowledge, the task of computer-aided multi-classification of diabetic retinopathy (DR) severity is of crucial importance. However, the application of multi-classification for DR severity remains limited by a scarcity of high-quality training data, despite its potential. Moreover, challenges such as suboptimal data quality, ambiguous pathological characteristics, and minimal dissimilarity among categories are prevalent. Further research on image preprocessing methods is necessary. This study proposes a new framework for diabetic retinopathy classification, PMADR-Net (Progressive Multiscale Attention Network for Diabetic Retinopathy). By applying progressive multiscale training and attention mechanisms, this framework is suitable for few-shot medical image classification. It could automatically shift its focus towards learning samples of pathological features that are more challenging to discriminate. Moreover, in order to mitigate the class imbalance in the dataset, this research employs a balanced sampling approach and a patch reconstruction technique to produce multiscale features, thereby facilitating the model’s accurate differentiation of each category. Our proposed model achieved a classification accuracy of 93.97% and an AUC evaluation metric of 0.9850, demonstrating its superior effectiveness over state-of-the-art techniques and showing potential for widespread application in similar medical classification tasks involving small datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Novel MITM attack scheme based on built-in negotiation for blockchain-based digital twins

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Digital Communications and Networks 2025年第1期11卷 256-267页

作者： Xin Liu Rui Zhou Shohei Shimizu Rui Chong Qingguo Zhou Xiaokang Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China Faculty of Data Science Shiga UniversityHikone 522-8522Japan Beijing Zhongxin Xingkong Network Technology Co Beijing 100005China

As smart contracts,represented by Solidity,become deeply integrated into the manufacturing industry,blockchain-based Digital Twins(DT)has gained momentum in recent *** of the blockchain infrastructures in widespread use today are based on the Proof-of-Work(PoW)mechanism,and the process of creating blocks is known as“mining”.Mining becomes increasingly difficult as the blockchain grows in size and the number of on-chain business systems *** lower the threshold of participation in the mining process,“mining pools”have been *** can cooperate and share the mining rewards according to the hashrate they contributed to the *** is the most widely used communication protocol between miners and mining *** security is essential for the *** this paper,we propose two novel Man-In-The-Middle(MITM)attack schemes against Stratum,which allow attackers to steal miners'hashrate to any mining pool using hijacked TCP *** with existing attacks,our work is more secretive,more suitable for the real-world environment,and more *** Proof-of-Concept(PoC)shows that our schemes work perfectly on most mining softwares and ***,we present a lightweight AI-driven approach based on protocol-level feature analysis to detect Stratum MITM for blockchain-based *** detection model consists of three layers:feature extraction layer,vectorization layer,and detection *** prove that our detection approach can effectively detect Stratum MITM traffic with 98%*** work alerts the communities and provides possible mitigation against these more hidden and profitable attack schemes.

关键词： Digital twins MITM Blockchain Stratum Anomaly detection

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Heart Disease Prediction Model Using Feature Selection and Ensemble Deep Learning with Optimized Weight

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Computer Modeling in Engineering & sciences 2025年第4期143卷 875-909页

作者： Iman S.Al-Mahdi Saad M.Darwish Magda M.Madbouly Department of Information Technology Institute ofGraduate Studies and ResearchAlexandriaUniversityAlexandria21526Egypt Faculty of Computers and Data Science Alexandria UniversityAlexandria5432042Egypt

Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare *** problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical *** challenges often lead to inefficient and less *** predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational *** work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel *** features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction *** hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the *** enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional *** proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over ***,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medi

关键词： Heart disease prediction feature selection ensemble deep learning optimization genetic algorithm(GA) ensemble deep learning tunicate swarm algorithm(TSA) feature selection

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