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Temporal fusion transformer-based strategy for efficient multi-cloud content replication

PeerJ computer science 2025年 11卷 1-22页

作者： Naganandhini, S. Shanthi, D. Computer Science and Engineering PSNA College of Engineering and Technology Tamilnadu Dindigul India

In cloud computing, ensuring the high availability and reliability of data is dominant for efficient content delivery. Content replication across multiple clouds has emerged as a solution to achieve the above. However, managing optimal replication while considering dynamic changes in data popularity and cloud resource availability remains a formidable challenge. In order to address these challenges, this article employs TFT-based Dynamic Data Replication Strategy (TD2RS), leveraging the Temporal Fusion Transformer (TFT), a deep learning temporal forecasting model. This proposed system collects historical data on content popularity and resource availability from multiple cloud sources, which are then used as input to TFT. Then TFT is used to capture temporal patterns and forecasts future data demands. An intelligent replication is performed to optimize content replication across multiple cloud environments based on these forecasts. The framework’s performance was validated through extensive experiments using synthetic time-series data simulating with varied cloud resource characteristics. Some of the findings include that the proposed TFT approach improves the availability of data by 20% when compared to traditional replication techniques and also cuts down the latency level by 15%. These outcomes indicate that the TFT-based replication strategy targets to improve content delivery efficiency in the dynamic cloud computing environment, thus providing effective solution to dynamically address the availability, reliability, and performance challenges. © Copyright 2025 S. and D.

关键词： Spatio-temporal data

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Chess Puzzle: Evolution of Cyber Deception Strategies for IoT Security Enhancement

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IEEE Internet of Things Journal 2025年第11期12卷 16668-16680页

作者： Wang, Xiran Shi, Leyi Zhao, Zhihao Luo, Wenbin Ding, Bingtao School of Computer Science and Technology Qingdao266580 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

Nowadays, the proliferation of open Internet of Things (IoT) devices has made IoT systems increasingly vulnerable to cyber attacks. It is of great practical significance to solve the security issues of IoT systems. Drawing inspiration from the concept of "Pawn Promotion in chess, this paper proposes a cyber deception defense system based on "Chess Puzzle (CP-CDD), which enhances the security of IoT systems by continuously changing the real and fake attributes of services. Firstly, we design a distributed election control device scheme to address the security concerns arising from centralized control in the IoT system. Secondly, we introduce an adaptive differential evolution cyber deception strategy optimization method, aiming to derive effective cyber deception defense strategies for the CP-CDD system. Specifically, we design two key components of cyber deception strategy evolution: service strategy coding and fitness function, and describe the adaptive differential evolution cyber deception strategy algorithm. Finally, the security and effectiveness of the CP-CDD system is proven through attack and defense experiments conducted in a real-world environment. © 2014 IEEE.

关键词： Cyber attacks

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AGBUNet: an enhanced CNN-UNET architecture for the prediction of above ground biomass using deep learning

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Neural Computing and Applications 2025年第5期37卷 3809-3826页

作者： Arumai Shiney, S. Geetha, R. Department of Computer Science and Engineering S.A. Engineering College Chennai India

Accurate prediction of above ground biomass (AGB) is critical for monitoring forest health and carbon cycling. It is crucial for understanding and managing forest ecosystems. In this paper, we propose an enhanced framework combining convolutional neural network (CNN) and UNet, termed AGBUNet, specifically designed for predicting AGB using remote sensing data. The framework consists of separate CNNs for processing each type of image, whose outputs are subsequently fused in a UNet architecture to enhance prediction accuracy. These modifications include customized convolutional layers, advanced preprocessing techniques, and a novel integration of data prompts from separate Sentinel-1 and Sentinel-2 image processing streams. The AGBUNet integrates Sentinel-1 and Sentinel-2 images to leverage complementary information from synthetic aperture radar (SAR) and optical sensors. This study underscores the potential of the AGBUNet model for enhancing biomass estimation from remote sensing data, contributing to better forest management and ecological monitoring. The performance measures obtained are compared with the other models, and the following results are obtained as follows for MSE value of 298.25, RMSE value of 15.27 and MAE value of 12.21, and the values are satisfying compared with earlier benchmarks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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Apple Leaf Disease Detection Using Transfer Learning

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Annals of Data science 2025年第1期12卷 213-222页

作者： Wani, Ozair Ahmad Zahoor, Umer Shah, Syed Zubair Ahmad Khan, Rijwan Department of Computer Science & Engineering Islamic University of Science and Technology Jammu & Kashmir Awantipora India Department of Computer Science and Engineering ABES Institute of Technology Uttar Pradesh Ghaziabad India

Automated detection of plant diseases is crucial as it simplifies the task of monitoring large farms and identifies diseases at their early stages to mitigate further plant degradation. Besides the decline in plant health, reduced production severely impacts the country’s economy. Traditional disease identification methods, relying on human experts, are slow, time-consuming, and impractical for large farms. Our proposed model utilizes a combination of pre-trained Resnet18, Alexnet, GoogLeNet, and VGG16 networks to classify apple tree leaves into categories such as healthy, black rot, apple cedar rust, and apple scab based on images. Various image enhancement techniques were employed to enhance the model’s accuracy. Ultimately, our model achieved an accuracy of 97.25% on the validation dataset, demonstrating excellent performance across various metrics. This suggests its potential for efficient and accurate plant health monitoring in the agricultural sector. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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An active learning framework for adversarial training of deep neural networks

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Neural Computing and Applications 2025年第9期37卷 6849-6876页

作者： Ghosh, Susmita Chatterjee, Abhiroop Fiondella, Lance Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Electrical and Computer Engineering University of Massachusetts Dartmouth United States

This article introduces a novel approach to bolster the robustness of Deep Neural Network (DNN) models against adversarial attacks named "Targeted Adversarial Resilience Learning (TARL)". The initial evaluation of a baseline DNN model reveals a significant accuracy decline when subjected to adversarial examples generated through techniques like FGSM, PGD, Carlini Wagner, and DeepFool attacks. To address this vulnerability, the article proposes an active learning framework, wherein the model iteratively identifies and learns from the most uncertain and misclassified instances. The key components of this approach include uncertainty estimation score in predicting the class of the input sample, selecting challenging samples based on this uncertainty score, labeling these challenging examples and augmenting them into the training set, and thereafter retraining the model with the expanded training set. The iterative active learning process, governed by parameters such as the number of iterations and batch size, demonstrates the potential to systematically enhance the resilience of DNN against adversarial threats. The proposed methodology has been investigated on several popular datasets such as the SARS-CoV-2 CT scan, MNIST, CIFAR-10, and Caltech-101, and demonstrated to be effective. Experiments illustrate that the learning framework improves the adversarial accuracies from 17.4% to 98.71% for the SARS-CoV-2 dataset, from 8.4% to 99.89% for the MNIST dataset, 1.6% to 78.84% for the CIFAR-10, and 12% to 92.92% for Caltech-101. Further, comparative analysis with several state-of-the-art methods suggests that the proposed framework offers superior defense against various attack methods and offers promising defensive mechanisms to deep neural networks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Active learning

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Recent advances in text line segmentation and baseline detection in historical document images: a systematic review

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International Journal on Document Analysis and Recognition 2025年 1-37页

作者： Rabaev, Irina Litvak, Marina Software Engineering Shamoon College of Engineering Street Beersheba100190 Israel Computer Science KIT Karlsruhe10587 Germany Computer Science FCUP Porto610101 Portugal

The purpose of this survey is to provide a comprehensive overview of recent advancements in text line segmentation and baseline detection techniques within the analysis of historical document images. Text line extraction is an essential step in the historical documents image analysis pipeline, as its results significantly impact the accuracy of subsequent processes, such as handwritten text recognition (HTR). Through a multi-stage procedure, we carefully selected 49 peer-reviewed studies published since 2019. Based on careful analysis of these studies, we summarize the information of the existing datasets, describe and categorize different methods, and summarize evaluation protocols. In addition, we compare the results of various methods on benchmark datasets. Finally, we highlight the gaps and suggest directions for future research. We believe that this comprehensive survey will be of great assistance to researchers working in the field of historical document image analysis, as it offers critical insights into the latest advancements and developments, providing a foundation for future research. © The Author(s) 2025.

关键词： Image segmentation

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Analysis of computer networks courses in undergraduate computer science, electrical engineering and information science programs

Analysis of computer networks courses in undergraduate compu...

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ASEE 2004 Annual Conference and Exposition, "engineering Researchs New Heights"

作者： Aburdene, Maurice F. Meng, Xiannong Mokodean, Gregory L. Bucknell University Department of Electrical Engineering Bucknell University Department of Computer Science Bucknell University Lewisburg PA United States

This paper presents an analysis of computer networks courses offered by universities and colleges in the departments of computer science, electrical engineering, or information science. The results are based on the information collected from course web sites from twenty-seven universities and colleges in computer science, electrical engineering and information science departments, primarily within the United States. The data analyzed include the course titles, course structure, textbooks used, major topics and how they are covered, projects, and laboratory exercises, if any. We found that the courses can be divided into three categories: those that cover the general topics of computer networks using some practical examples, those that specifically discuss Internet protocols, and those that work through a set of programming projects after students have had a previous network course.

关键词： engineering education

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ElHBiAt: Electra pre-training network Hybrid of BiLSTM, the Attention layer to Aspect-Based Sentiment Analysis

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IEEE Access 2025年 13卷 88342-88370页

作者： Ghanee, Amin Jahanbin, Kia Yamchi, Abolfazl Razzaghi Shahid Beheshti University Department of Computer Engineering Tehran Iran Yazd University Department of Computer Engineering Yazd Iran Shiraz University Department of Computer Science Engineering and Information Technology Shiraz Iran

Aspect-based sentiment analysis is one of the famous and practical subjects in natural language processing. Traditional sentiment analysis assigns a polarity to the whole text or document and does not consider the aspects of the text, so it cannot correctly show the author’s opinion. Extracting aspects and determining their polarity has many challenges, such as the lack of rich datasets, negative transfer in cross-domain datasets, and fine-tuning of deep models of the ratio of texts with different contexts. In this research, a model has been proposed for aspect-based sentiment analysis with different contexts. This model uses the Electra as the backbone of the architecture, which is trained with different cross-domain datasets to transfer knowledge to the target domain. Also, in the proposed model of ElHBiAt, the resampling method is used to deal with imbalanced datasets, which causes the neural model to find less orientation than the polarities in the majority. In this model, two layers of attention are used to extract the essential aspects of the text, and the deep neural network BiLSMT is used to process the text in a two-way. The results show that the ElHBiAt model has been able to improve the efficiency by an average of 5.78% in the SemEval dataset, by 2% in the SEntFiN dataset, and by an average of 4.3% in the Drug dataset in terms of the F1-score. © 2013 IEEE.

关键词： Emotion Recognition

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Enhancing Voice Phishing Detection Using Multilingual Back-Translation and SMOTE: An Empirical Study

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IEEE Access 2025年 13卷 37946-37965页

作者： Boussougou, Milandu Keith Moussavou Hamandawana, Prince Park, Dong-Joo Soongsil University Department of Computer Science and Engineering Seoul06978 Korea Republic of Ajou University Department of Software and Computer Engineering Suwon16499 Korea Republic of Soongsil University School of Computer Science and Engineering Seoul06978 Korea Republic of

With the widespread global trend of voice phishing or vishing attacks, the development of effective detection models using artificial intelligence (AI) has been hindered by the lack of high-quality and large volumes of data. This lack of data reflecting a real vishing scenario often leads to imbalanced datasets and biased detection models. Therefore, we present in this paper a data augmentation (DA) method for expanding the imbalanced Korean call content vishing (KorCCVi) dataset to address the existing data asymmetry problem and enhance the performance of Korean vishing detection. The proposed approach for DA involves using the back-translation (BT) method with three different intermediate languages: English, Chinese, and Japanese. The proposed method offers several advantages over the traditional synthetic minority oversampling technique (SMOTE), which is the main technique used to compare with our multilingual BT approach. Using these two DA techniques, several machine learning (ML) and deep learning (DL) models were trained on the original imbalanced dataset, the dataset balanced with SMOTE and its variants, and the dataset augmented with our method. We analyzed the impact of these DA methods on the performance of the models, demonstrated the benefits of each approach, and suggested the most suitable approach. The performance of the trained models was evaluated using the accuracy, precision, recall, and F1-score metrics. The experimental results demonstrated that the proposed multilingual BT method effectively expands the dataset while preserving its contextual and linguistic characteristics. The average performance of the models revealed that those trained on the augmented dataset outperformed the other models. They achieved F1-scores of 98.91% for the back-translated data, 98.14% for the original data, and 97.23% for SMOTE. © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecomm

关键词： Phishing

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A verifiable multi-secret image sharing scheme based on DNA encryption

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Multimedia Tools and Applications 2025年第4期84卷 1967-1983页

作者： Chattopadhyay, Arup Kumar Saha, Sanchita Nag, Amitava Singh, Jyoti Prakash Department of Computer Science and Engineering Central Institute of Technology Kokrajhar Kokrajhar India Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

A multi-secret image sharing (MSIS) scheme facilitates the secure distribution of multiple images among a group of participants. Several MSIS schemes have been proposed with a (n, n) structure that encodes secret input images into n share images. This encoding ensures that if a subset of participants collude, no information about the secret images can be revealed. The secret images can only be recovered if all participants cooperate. Most of these schemes are built using basic Boolean operations, primarily XOR operations. MSIS schemes that rely on Boolean logic offer various benefits compared to other methods, such as the ability to recover data without any loss, no increase in pixel size, and efficient computation. This article presents a verifiable MSIS scheme that relies on a secure two-variable one-way function and a secure hash function. The scheme allows for independent verification of both the dealer and the participants to detect any cheating attempts. The proposed scheme utilizes the Deoxyribonucleic acid (DNA) encryption technique to leverage the inherent benefits of DNA computing, such as enhanced security, fast computation, minimal storage needs, and low power usage. The share images produced by the proposed scheme exhibit an average entropy of 7.99, indicating that the generated 8-bit share images are completely random. Additionally, the correlation coefficient is approximately 0.005, suggesting that neighboring pixels in the images are not related to each other. The proposed scheme can detect any instance of cheating, whether it is done by the dealer or by a participant. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： DNA

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