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IEEE Open Journal of the Communications Society 2025年 6卷 4818-4839页

作者： Jeong, Eunjeong Kountouris, Marios Linköping University Department of Computer and Information Science Sweden Department of Computer Science and Artificial Intelligence Spain EURECOM Communication Systems Department Sophia Antipolis France

Emerging technologies and use cases, such as smart Internet of Things (IoT), Internet of Agents, and Edge AI, have generated significant interest in training neural networks over fully decentralized, serverless networks. A major obstacle in this context is ensuring stable convergence without imposing stringent assumptions, such as identical data distributions across devices or synchronized updates. In this paper, we introduce DRACO, a novel framework for decentralized asynchronous Stochastic Gradient Descent (SGD) over row-stochastic gossip wireless networks. Our approach leverages continuous communication, allowing edge devices to perform local training and exchange model updates along a continuous timeline, thereby eliminating the need for synchronized timing. Additionally, our algorithm decouples communication and computation schedules, enabling complete autonomy for all users while effectively addressing straggler issues. Through a thorough convergence analysis, we show that DRACO achieves high performance in decentralized optimization while maintaining low variance across users even without predefined scheduling policies. Numerical experiments further validate the effectiveness of our approach, demonstrating that controlling the maximum number of received messages per client significantly reduces redundant communication costs while maintaining robust learning performance. © 2020 IEEE.

关键词： Federated learning

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Dynamic Resource Utilization Prediction Model for Cloud Datacenter

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IAENG International Journal of Applied Mathematics 2025年第2期55卷 307-331页

作者： Bliedy, Doaa Khafagy, Mohamed H. Badry, Rasha M. Department of Information Systems Faculty of Computers and Artificial Intelligence Fayoum University Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Fayoum University Egypt

Much research has been done on predicting resource utilization in the cloud to avoid over- and under-provisioning resources. Most existing systems focus on estimating the utilization of one or two resources at most, including memory, CPU, storage, network, or servers dedicated to cloud applications;they do not consider the correlation between resources. A maximum of one or two machine learning algorithms is employed for prediction purposes. Additionally, traditional prediction methods in the cloud provide a one-dimensional output. Most current solutions predict resources, such as memory and CPU utilization, as a single output. Unfortunately, a one-dimensional output in resource provision and usage cannot capture the relationship between the application requirements of different resources, such as CPU, memory, CPU cores, disk, and network;this results in incomplete and inaccurate information and prediction results. Efficient resource allocation and management require predicting several parameters using multivariate state variables. This study presents a multi-resource utilization prediction model that uses several machine learning approaches, such as support vector regression, random forests, MLP regression, neural networks (NN) using Adam and SGD optimizers, and decision tree regression. The prediction model is based on univariate and multivariate time series. Google cluster trace data is used to evaluate the work. Four experiments are executed on the dataset, seeking to predict the resources for different time series interval periods. The various algorithms’ mean absolute errors (MAE), root mean square error, mean absolute percentage error and R-squared are compared to determine which technique achieves the lowest error rate. The experimental results have shown that the prediction model yields better accuracy than previous research. © (2024), (International Association of Engineers). All rights reserved.

关键词： Prediction models

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Exploring Artificial intelligence’s Potential in Developing Advanced Distributed Denial of Service Defense Strategies

Exploring Artificial Intelligence’s Potential in Developing...

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International Conference on Computing and Machine Learning, CML 2024

作者： Alhindi, Ahmad Computer Science and Artificial Intelligence Department College of Computer and Information Systems Makkah Saudi Arabia

ISBN: (纸本)9789819778386

Facing the growing menace of distributed denial of service (DDoS) attacks, there’s a critical demand for enhanced detection mechanisms. This necessitates a shift from traditional methodologies to more advanced, dynamic detection techniques. The significance of advanced computational methods in pinpointing DDoS threats is increasingly recognized. This paper delves into the crucial role these techniques play in detecting DDoS activities, with a focus on the adaptability of network-based algorithms and the proficiency of advanced models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and autoencoders in identifying complex attack patterns that traditional methods may overlook. Our findings reveal that while advanced computational strategies offer considerable benefits over conventional methods in terms of detection accuracy and adaptability to evolving threats, they also present unique challenges, including higher computational demands and the need for continuous model training to keep pace with new attack vectors. The comparative analysis underscores the operational advantages and potential limitations of these approaches, providing a nuanced understanding of their application in real-world scenarios. This investigation aims to present a current view of advancements in digital security techniques and to serve as a basis for future research and the development of innovative, practical solutions. It suggests that enhancing the resilience of digital infrastructures against DDoS attacks will require not only technological innovation but also a comprehensive strategy that includes ongoing research, collaboration among stakeholders, and the adoption of adaptive, scalable detection systems capable of responding to the dynamic nature of cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Deepfake detection using convolutional vision transformers and convolutional neural networks

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Neural Computing and Applications 2024年第31期36卷 19759-19775页

作者： Soudy, Ahmed Hatem Sayed, Omnia Tag-Elser, Hala Ragab, Rewaa Mohsen, Sohaila Mostafa, Tarek Abohany, Amr A. Slim, Salwa O. Department of Computer Science Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Department of Information Systems Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh Egypt

Deepfake technology has rapidly advanced in recent years, creating highly realistic fake videos that can be difficult to distinguish from real ones. The rise of social media platforms and online forums has exacerbated the challenges of detecting misinformation and malicious content. This study leverages many papers on artificial intelligence techniques to address deepfake detection. This research proposes a deep learning (DL)-based method for detecting deepfakes. The system comprises three components: preprocessing, detection, and prediction. Preprocessing includes frame extraction, face detection, alignment, and feature cropping. Convolutional neural networks (CNNs) are employed in the eye and nose feature detection phase. A CNN combined with a vision transformer is also used for face detection. The prediction component employs a majority voting approach, merging results from the three models applied to different features, leading to three individual predictions. The model is trained on various face images using FaceForensics++ and DFDC datasets. Multiple performance metrics, including accuracy, precision, F1, and recall, are used to assess the proposed model’s performance. The experimental results indicate the potential and strengths of the proposed CNN that achieved enhanced performance with an accuracy of 97%, while the CViT-based model achieved 85% using the FaceForences++ dataset and demonstrated significant improvements in deepfake detection compared to recent studies, affirming the potential of the suggested framework for detecting deepfakes on social media. This study contributes to a broader understanding of CNN-based DL methods for deepfake detection. © The Author(s) 2024.

关键词： Face recognition

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Fusion-based anomaly detection system using modified isolation forest for internet of things

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Journal of Ambient intelligence and Humanized Computing 2023年第1期14卷 131-145页

作者： AbuAlghanam, Orieb Alazzam, Hadeel Alhenawi, Esra’a Qatawneh, Mohammad Adwan, Omar Computer Science Department University of Jordan Amman Jordan Intelligence Systems Department Al-Balqa Applied University Al-Salt Jordan Computer Science Department Al-Ahliyya Amman University Amman Jordan

In recent years, advanced threat and zero day attacks are increasing significantly, but the traditional network intrusion detection system based on feature filtering or based on a well known signature has some drawbacks. Accordingly, there is a need for security solutions that are suitable for IoT environment. A network intrusion detection system (NIDS) is a solution that examines network traffic and alerts system administrators if there are security breaches. In this paper, a fusion-based anomaly detection using modified isolation forest for Internet of Things (IoT) is proposed. The proposed NIDS has been evaluated using three benchmark datasets(UNSW-NB15, NLS-KDD and KDDCUP99) in terms of F-score, accuracy and detection rate. Results show that the suggested approach reduces the run time by 28.80% for UNSW-NB15 in the training model and achieves 97.2%, 97.4% accuracy and detection rate respectively. Moreover, M-iForest outperforms other NIDS techniques that are selected from state-of-the-art relevant research found in the literature. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Internet of things

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Revolutionizing online shopping with FITMI: a realistic virtual try-on solution

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Neural Computing and Applications 2025年第8期37卷 6125-6144页

作者： Samy, Tassneam M. Asham, Beshoy I. Slim, Salwa O. Abohany, Amr A. Department of Computer Science Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Department of Information Systems Faculty of Computers and Information Damanhour University Damanhour Egypt Department of Information Systems Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh Egypt

In today’s digital age, consumers increasingly rely on online shopping for convenience and accessibility. However, a significant drawback of online shopping is the inability to physically try on clothing before purchasing. This limitation often leads to uncertainty regarding fit and style, resulting in customer post-purchase dissatisfaction and higher return rates. Research indicates that online items are three times more likely to be returned than in-store ones, especially during the pandemic. To address this challenge, we propose a virtual try-on method called FITMI, an enhanced Latent Diffusion Textual Inversion model for virtual try-on purposes. The proposed architecture aims to bridge the gap between traditional in-store try-ons and online shopping by offering users a realistic and interactive virtual try-on experience. Although virtual try-on solutions already exist, recent advancements in artificial intelligence have significantly enhanced their capabilities, enabling more sophisticated and realistic virtual try-on experiences than ever before. Building on these advancements, FITMI surpasses ordinary virtual try-ons relying on generative adversarial networks, often producing unrealistic outputs. Instead, FITMI utilizes latent diffusion models to generate high-quality images with detailed textures. As a web application, FITMI facilitates virtual try-ons by seamlessly integrating images of users with garments from catalogs, providing a true-to-life representation of how the items would look. This approach differentiates us from competitors. FITMI is validated using two widely recognized benchmarks: the Dress-Code and Viton-HD datasets. Additionally, FITMI acts as a trusted style advisor, enhancing the shopping experience by recommending complementary items to elevate the chosen garment and suggesting similar options based on user preferences. © The Author(s) 2025.

关键词： Generative adversarial networks

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DCFNet:Discriminant Correlation Filters Network for Visual Tracking

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Journal of computer science & Technology 2024年第3期39卷 691-714页

作者：胡卫明王强高晋李兵 Stephen Maybank State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences Beijing 100190China Department of Computer Science and Information Systems Birkbeck CollegeLondon WC1E 7HXU.K.

CNN(convolutional neural network)based real time trackers usually do not carry out online network update in order to maintain rapid tracking *** inevitably influences the adaptability to changes in object *** filter based trackers can update the model parameters online in real *** this paper,we present an end-to-end lightweight network architecture,namely Discriminant Correlation Filter Network(DCFNet).A differentiable DCF(discriminant correlation filter)layer is incorporated into a Siamese network architecture in order to learn the convolutional features and the correlation filter *** correlation filter can be efficiently updated *** previous work,we introduced a joint scale-position space to the DCFNet,forming a scale DCFNet which carries out the predictions of object scale and position *** combine the scale DCFNet with the convolutional-deconvolutional network,learning both the high-level embedding space representations and the low-level fine-grained representations for *** adaptability of the fine-grained correlation analysis and the generalization capability of the semantic embedding are complementary for visual *** back-propagation is derived in the Fourier frequency domain throughout the entire work,preserving the efficiency of the *** evaluations on the OTB(Object Tracking Benchmark)and VOT(Visual Object Tracking Challenge)datasets demonstrate that the proposed trackers have fast speeds,while maintaining tracking accuracy.

关键词： correlation filter convolutional neural network(CNN) visual tracking

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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

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Neural Computing and Applications 2025年第17期37卷 11583-11605页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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Online Information-Based Product Evolution Course Mining and Prediction

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International Journal of Information Technology and Decision Making 2024年第2期23卷 599-627页

作者： Chen, Yuh-Jen Chen, Yuh-Min Department of Accounting and Information Systems College of Business Intelligence National Kaohsiung University of Science and Technology Kaohsiung City Taiwan Institute of Manufacturing Information and Systems College of Electrical Engineering and Computer Science National Cheng Kung University Tainan City Taiwan

In today’s fiercely competitive business environment, product design and development play critical roles in an enterprise’s success;therefore, consumer demand must be understood. Enterprises used to understand their consumers’ demands through time-consuming questionnaire surveys and statistical analyses. As the Internet and the popularity of virtual communities have grown, more consumers are leaving comments about their perceptions of the appeal of products on online social media platforms, thus enabling enterprises to more objectively understand consumers’ product preferences and demands. Therefore, determining how to effectively assist enterprises in analyzing valuable information beneficial to product design and development that can be gleaned from the large amount of social media data available is critical to promoting an enterprise’s competitive advantage in the product market. However, previous studies have primarily focused on understanding consumer viewpoints of products through review articles or electronic word-of-mouth (eWOM;it is a common channel for spreading product appraisals) from online social media. For these review articles and eWOM that can imply consumer demand for product features, there were no relevant studies focused on analyzing consumer demand by using both online review articles and eWOM for product feature evolution. Therefore, for new product features, this study developed a mechanism for product evolution course mining from product-related review articles (such as product functions or specifications) and eWOM on online social media to realize the prediction of future product features or specifications, and then assist enterprises in rapidly and accurately grasping product development trends to effectively discern key reference information for product design and development. The study was achieved by (1) designing a process for online information-based product evolution course mining and prediction, (ii) developing techniques related to

关键词： Forecasting

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Posture Prediction in Response to Seat Angle Change with Smart Chair

Posture Prediction in Response to Seat Angle Change with Sma...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Esumi, Tsubasa Takemura, Noriko Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Department of Artificial Intelligence Fukuoka Japan

ISBN: (纸本)9781665410205

Long-term desk work with lousy posture causes physical disabilities. To solve this problem, we have developed a smart chair with a controllable seat angle in our previous research. In order to control the seat angle and guide the user to the correct posture using this smart chair, it is necessary to investigate how the posture changes in response to the seat angle change. In this study, we first constructed a dataset by capturing posture data from 30 subjects while changing the seat angle of the smart chair. This dataset trained a model based on a graph neural network to predict posture in response to seat angle changes. We compared the model's prediction performance in the evaluation experiment depending on the training data and the body part. © 2024 IEEE.

关键词： Graph neural networks

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