Search Results (69)

RipenessGAN is a novel Generative Adversarial Network (GAN) designed to generate synthetic images across different ripeness stages of jujubes (green fruit, white ripe fruit, semi-red fruit, and fully red fruit), aiming to provide balanced training data for diverse applications beyond classification accuracy. This study addresses the problem of data imbalance by augmenting each ripeness stage using our proposed Growth Day Embedding mechanism, thereby enhancing the performance of downstream classification models. The core innovation of RipenessGAN lies in its ability to capture continuous temporal transitions among discrete ripeness classes by incorporating fine-grained growth day information (0–56 days) in addition to traditional class labels. The experimental results show that RipenessGAN produces synthetic data with higher visual quality and greater diversity compared to CycleGAN. Furthermore, the classification models trained on the enriched dataset exhibit more consistent and accurate performance. We also conducted comprehensive comparisons of RipenessGAN against CycleGAN and class-conditional diffusion models (DDPM) under strictly controlled and fair experimental settings, carefully matching model architectures, computational resources, training conditions, and evaluation metrics. The results indicate that although diffusion models yield highly realistic images and CycleGAN ensures stable cycle-consistent generation, RipenessGAN provides superior practical benefits in training efficiency, temporal controllability, and adaptability for agricultural applications. This research demonstrates the potential of RipenessGAN to mitigate data imbalance in agriculture and highlights its scalability to other crops. Full article

Predicting the quality of freshwater, both surface and groundwater, is essential for the sustainable management of water resources. This study collected 1822 articles from the Scopus database (2000–2024) and filtered them using Topic Modeling to create the study corpus. The B-SLR analysis identified exponential growth in scientific publications since 2020, indicating that this field has reached a stage of maturity. The results showed that the predominant techniques for predicting water quality, both for surface and groundwater, fall into three main categories: (i) ensemble models, with Bagging and Boosting representing 43.07% and 25.91%, respectively, particularly random forest (RF), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGB), along with their optimized variants; (ii) deep neural networks such as long short-term memory (LSTM) and convolutional neural network (CNN), which excel at modeling complex temporal dynamics; and (iii) traditional algorithms like artificial neural network (ANN), support vector machines (SVMs), and decision tree (DT), which remain widely used. Current trends point towards the use of hybrid and explainable architectures, with increased application of interpretability techniques. Emerging approaches such as Generative Adversarial Network (GAN) and Group Method of Data Handling (GMDH) for data-scarce contexts, Transfer Learning for knowledge reuse, and Transformer architectures that outperform LSTM in time series prediction tasks were also identified. Furthermore, the most studied water bodies (e.g., rivers, aquifers) and the most commonly used water quality indicators (e.g., WQI, EWQI, dissolved oxygen, nitrates) were identified. The B-SLR and Topic Modeling methodology provided a more robust, reproducible, and comprehensive overview of AI/ML/DL models for freshwater quality prediction, facilitating the identification of thematic patterns and research opportunities. Full article

With the rapid growth of FinTech, time-series data has become pervasive in financial markets. However, the nonstationarity, high noise levels, and complex temporal dependencies of financial data pose significant challenges to the efficacy and stability of standard generative models. To overcome these limitations, we propose MiT-WGAN, a gradient-penalized Wasserstein generative adversarial network that integrates a multi-convolutional dynamic fusion (MCDF) module in parallel with an enhanced Transformer (iTransformer) to jointly capture local patterns and long-range dependencies. We evaluate MiT-WGAN on S&P 500 stock trading data using a comprehensive set of baseline models and evaluation metrics. Experimental results demonstrate that MiT-WGAN achieves superior sample quality, better preservation of statistical properties, and improved training stability, confirming its effectiveness for financial time-series modeling. Full article

Optical coherence tomography (OCT) is a leading imaging technique for diagnosing retinal disorders such as age-related macular degeneration and diabetic retinopathy. Its ability to detect structural changes, especially in the optic nerve head, has made it vital for early diagnosis and monitoring. This paper surveys techniques for ocular disease prediction using OCT, focusing on both hand-crafted and deep learning-based feature extractors. While the field has seen rapid growth, a detailed comparative analysis of these methods has been lacking. We address this by reviewing research from the past 20 years, evaluating methods based on accuracy, sensitivity, specificity, and computational cost. Key diseases examined include glaucoma, diabetic retinopathy, cataracts, amblyopia, and macular degeneration. We also assess public OCT datasets widely used in model development. A unique contribution of this paper is the exploration of adversarial attacks targeting OCT-based diagnostic systems and the vulnerabilities of different feature extraction techniques. We propose a practical, robust defense strategy that integrates with existing models and outperforms current solutions. Our findings emphasize the value of combining classical and deep learning methods with strong defenses to enhance the security and reliability of OCT-based diagnostics, and we offer guidance for future research and clinical integration. Full article

Rapid data growth in large systems has introduced significant challenges in real-time monitoring and analysis. One of these challenges is detecting anomalies in time series data with high-dimensional inputs that contain complex inter-correlations between them. In addition, the lack of labeled data leads to the use of unsupervised learning that relies on daily system data to train models, which can contain noise that affects feature extraction. To address these challenges, we propose PCA-AAE, a novel anomaly detection model for time series data using an Adversarial Autoencoder integrated with Principal Component Analysis (PCA). PCA contributes to analyzing the latent space by transforming it into uncorrelated components to extract important features and reduce noise within the latent space. We tested the integration of PCA into the model’s phases and studied its efficiency in each phase. The tests show that the best practice is to apply PCA to the latent code during the adversarial training phase of the AAE model. We used two public datasets, the SWaT and SMAP datasets, to compare our model with state-of-the-art models. The results indicate that our model achieves an average F1 score of 0.90, which is competitive with state-of-the-art models, and an average of 58.5% faster detection speed compared to similar state-of-the-art models. This makes PCA-AAE a candidate solution to enhance real-time anomaly detection in high-dimensional datasets. Full article

Precise recognition of individual ovine specimens plays a pivotal role in implementing smart agricultural platforms and optimizing herd management systems. With the development of deep learning technology, sheep face recognition provides an efficient and contactless solution for individual sheep identification. However, with the growth of sheep, their facial features keep changing, which poses challenges for existing sheep face recognition models to maintain accuracy across the dynamic changes in facial features over time, making it difficult to meet practical needs. To address this limitation, we propose the lifelong biometric learning of the sheep face network (LBL-SheepNet), a feature decoupling network designed for continuous adaptation to ovine facial changes, and constructed a dataset of 31,200 images from 55 sheep tracked monthly from 1 to 12 months of age. The LBL-SheepNet model addresses dynamic variations in facial features during sheep growth through a multi-module architectural framework. Firstly, a Squeeze-and-Excitation (SE) module enhances discriminative feature representation through adaptive channel-wise recalibration. Then, a nonlinear feature decoupling module employs a hybrid channel-batch attention mechanism to separate age-related features from identity-specific characteristics. Finally, a correlation analysis module utilizes adversarial learning to suppress age-biased feature interference, ensuring focus on age-invariant identifiers. Experimental results demonstrate that LBL-SheepNet achieves 95.5% identification accuracy and 95.3% average precision on the sheep face dataset. This study introduces a lifelong biometric learning (LBL) mechanism to mitigate recognition accuracy degradation caused by dynamic facial feature variations in growing sheep. By designing a feature decoupling network integrated with adversarial age-invariant learning, the proposed method addresses the performance limitations of existing models in long-term individual identification. Full article

With the popularization of social media, information related to public health events has seen explosive growth online, making it essential to accurately identify informative tweets with decision-making and management value for public health emergency response and risk monitoring. However, existing methods often suffer performance degradation during cross-event transfer due to differences in data distribution, and research specifically targeting public health events remains limited. To address this, we propose the Label Confusion Domain Adversarial Network (LCDAN), which innovatively integrates label confusion with domain adaptation to enhance the detection of informative tweets across different public health events. First, LCDAN employs an adversarial domain adaptation model to learn cross-domain feature representation. Second, it dynamically evaluates the importance of different source domain samples to the target domain through label confusion to optimize the migration effect. Experiments were conducted on datasets related to COVID-19, Ebola disease, and Middle East Respiratory Syndrome public health events. The results demonstrate that LCDAN significantly outperforms existing methods across all tasks. This research provides an effective tool for information detection during public health emergencies, with substantial theoretical and practical implications. Full article

The rapid growth of Internet of Things (IoT) environments has increased security challenges due to heightened exposure to cyber threats and attacks. A key problem is the class imbalance in attack traffic, where critical yet underrepresented attacks are often overlooked by intrusion-detection systems (IDS), thereby compromising reliability. We propose Feature-Importance GAN SMOTE (FIGS), an innovative, realistic intrusion-detection framework designed for IoT environments to address this challenge. Unlike other works that rely only on traditional oversampling methods, FIGS integrates sensitivity-based feature-importance analysis, Generative Adversarial Network (GAN)-based augmentation, a novel imbalance ratio (GIR), and Synthetic Minority Oversampling Technique (SMOTE) for generating high-quality synthetic data for minority classes. FIGS enhanced minority class detection by focusing on the most important features identified by the sensitivity analysis, while minimizing computational overhead and reducing noise during data generation. Evaluations on the CICIoMT2024 and CICIDS2017 datasets demonstrate that FIGS improves detection accuracy and significantly lowers the false negative rate. FIGS achieved a 17% improvement over the baseline model on the CICIoMT2024 dataset while maintaining performance for the majority groups. The results show that FIGS represents a highly effective solution for real-world IoT networks with high detection accuracy across all classes without introducing unnecessary computational overhead. Full article

This review explores the intersection of Artificial Intelligence (AI) and Generative Design (GD) in engineering within the mechanical, industrial, civil, and architectural domains. Driven by advances in AI and computational resources, this intersection has grown rapidly, yielding over 14,000 publications since 2016. To map the research landscape, this review employed semantic search and Natural Language Processing, parsing 14,355 publications to ultimately select the 88 most relevant studies through clustering and topic modelling. These studies were categorised according to AI and GD techniques, application domains, benefits, and limitations, providing insights into research trends and practical implications. The results reveal a significant growth in the integration of advanced generative AI methods, notably Generative Adversarial Networks for direct design generation, alongside the continued use of genetic algorithms and surrogate models (e.g., Convolutional Neural Networks and Multilayer Perceptrons) to manage computational complexity. Structural and aerodynamic applications were the most common, with benefits including improvements in computational efficiency and design diversity. However, barriers remain, including data generation costs, model accuracy, and interpretability. Research opportunities include the development of generalisable foundation surrogate models, the integration of emerging generative methods such as diffusion models and large language models, and the explicit consideration of manufacturability constraints within generative processes. Full article

The evolution of social bots, i.e., accounts on social media platforms controlled by malicious software, is making them increasingly more challenging to discover. A practical solution is to explore the adversarial nature of novel bots and find the vulnerability of bot detectors in simulations in advance. However, current studies fail to realistically simulate the environment and bots’ actions, thus not effectively representing the competition between novel bots and bot detectors. Hence, we propose a new method for modeling the impact of bot actions and develop a new bot strategy to simulate various evolved bots within a large social network. Specifically, a bot influence model and a user engagement model are introduced to simulate the growth of followers, retweets, and mentions. Additionally, a profile editor and a target preselection mechanism are proposed to more accurately simulate the behavior of evolved bots. The effectiveness of the bots and two representative bot detectors are verified using adversarial simulations and the real-world dataset. In simulated adversarial scenarios against both RF-based and GNN-based detection models, the proposed approach achieves survival rates of 99.7% and 85.9%, respectively. The simulation results indicate that, despite utilizing the bots’ profile data, user-generated content, and graph information, the detectors failed to identify all variations of the bots and mitigate their impact. More importantly, for the first time, it is found that certain types of bots outperform those usually deemed more advanced in ablation experiments, demonstrating that such “penetration testing” can indeed reveal vulnerabilities in the detectors. Full article

In recent years, the use of video content has experienced exponential growth. The rapid growth of video content has led to an increased reliance on various video codecs for efficient compression and transmission. However, several challenges are associated with codecs such as H.265/High Efficiency Video Coding and H.266/Versatile Video Coding (VVC) that can impact video quality and performance. One significant challenge is the trade-off between compression efficiency and visual quality. While advanced codecs can significantly reduce file sizes, they introduce artifacts such as blocking, blurring, and color distortion, particularly in high-motion scenes. Different compression tools in modern video codecs are vital for minimizing artifacts that arise during the encoding and decoding processes. While the advanced algorithms used by these modern codecs can effectively decrease file sizes and enhance compression efficiency, they frequently find it challenging to eliminate artifacts entirely. By utilizing advanced techniques such as post-processing after the initial decoding, this method can significantly improve visual clarity and restore details that may have been compromised during compression. In this paper, we introduce a Deep Residual Enhanced Feature Generative Adversarial Network as a post-processing method aimed at further improving the quality of reconstructed frames from the advanced codec VVC. By utilizing the benefits of Deep Residual Blocks and Enhanced Feature Blocks, the generator network aims to make the reconstructed frame as similar as possible to the original frame. The discriminator network, a crucial element of our proposed method, plays a vital role in guiding the generator by evaluating the authenticity of generated frames. By distinguishing between fake and original frames, the discriminator enables the generator to improve the quality of its output. This feedback mechanism ensures that the generator learns to create more realistic frames, ultimately enhancing the overall performance of the model. The proposed method shows significant gain for Random Access (RA) and All Intra (AI) configurations while improving Video Multimethod Assessment Fusion (VMAF) and Multi-Scale Structural Similarity Index Measure (MS-SSIM). Considering VMAF, our proposed method can obtain 13.05% and 11.09% Bjøntegaard Delta Rate (BD-Rate) gain for RA and AI configuration, respectively. In the case of the luma component MS-SSIM, RA and AI configurations get, respectively, 5.00% and 5.87% BD-Rate gain after employing our suggested proposed network. Full article

The COVID-19 pandemic presented many potentially traumatic circumstances. Research continues to investigate pandemic-related Post-traumatic Growth (PTG). However, most studies fail to fulfil the parameters of PTG whereby a triggering event must be of seismic intensity and have ceased before PTG can manifest, producing significant validity and reliability issues. The relationships between PTG, trait resilience and fear are also under-researched, particularly in circumstances where the parameters of PTG are met. This study examined the relationship between PTG, COVID-19-related fear and trait resilience. Participants (n = 229) completed an online questionnaire incorporating the Post-Traumatic Growth Inventory and the Connor–Davidson Resilience Scale. The sample participants were moderately traumatised with moderate–low PTG (M = 50.85). Participants reported greater levels of PTG compared to participants from pre-COVID studies, notably in relation to the constructs of Relating to Other (d = 0.29), New Possibilities (d = 0.47), Personal Strength (d = 0.39), and Spiritual Change (d = 0.29). Higher levels of resilience (B = 0.48) and COVID-19-related fear (B = 0.16) were associated with greater overall PTG. Younger participants also reported greater levels of PTG (B = −0.29). The findings advance current knowledge regarding the potential relationship between fear and PTG and demonstrate that trait resilience is a promotional factor, presenting opportunity for future intervention formulation. However, reform is required within the PTG literature pool. Future research investigating PTG must reach both parameters. In circumstances where this is impossible, research concerning newfound positive cognition during adverse circumstances should be re-explored as Post-Adversarial Appreciation (PAA) to maintain validity. Full article

The Dual Process Model of Coping with Bereavement (DPM, by Stroebe and Schut) is a well-known framework in contemporary grief research and counselling. It depicts how mourners oscillate between various tasks and reactions. There is a need to engage more with the intense feelings of loss (Loss-Oriented tasks), but also with other things in life and other parts of the adjustment process after a loss (Restoration-Oriented tasks). This interdisciplinary article applies the framework to ecological grief and extends it to collective levels. While the DPM has been broadened to family dynamics, many subjects of grief are even more collective and require mourning from whole communities or societies. Religious communities can play an important role in this. This article provides a new application called the DPM-EcoSocial and discusses the various tasks named in it, which are ultimately based on the grief researcher Worden’s work. The particularities of ecological grief are discussed, such as the complications caused by guilt dynamics, climate change denial, attribution differences about climate disasters, and nonfinite losses. Grief and grievance are intimately connected in ecological grief, and (religious) communities have important tasks for remembrance, mourning, and witness. The collective processes can lead to meaning reconstruction, transilience, and adversarial growth. Full article

Internet of Things (IoT) user authentication protocols enable secure authentication and session key negotiation between users and IoT devices via an intermediate server, allowing users to access sensor data or control devices remotely. However, the existing IoT user authentication schemes often assume that the servers (registration center and intermediate servers) are fully trusted, overlooking the potential risk of insider attackers. Moreover, most of the existing schemes lack critical security properties, such as resistance to ephemeral secret leakage attacks and offline password guessing attacks, and they are unable to provide perfect forward security. Furthermore, with the rapid growth regarding IoT devices, the servers must manage a large number of users and device connections, making the performance of the authentication scheme heavily reliant on the server’s computational capacity, thereby impacting the system’s scalability and efficiency. The design of security protocols is based on the underlying security model, and the current IoT user authentication models fail to cover crucial threats like insider attacks and ephemeral secret leakage. To overcome these limitations, we propose a new security model, IoT-3eCK, which assumes semi-trusted servers and strengthens the adversary model to better meet the IoT authentication requirements. Based on this model, we design an efficient protocol that ensures user passwords, biometric data, and long-term keys are protected from insider users during registration, mitigating insider attacks. The protocol also integrates dynamic pseudo-identity anonymous authentication and ECC key exchange to satisfy the security properties. The performance analysis shows that, compared to the existing schemes, the new protocol reduces the communication costs by over 23% and the computational overhead by more than 22%, with a particularly significant reduction of over 95% in the computational overhead at the intermediate server. Furthermore, the security of the protocol is rigorously demonstrated using the random oracle model and verified with automated tools, further confirming its security and reliability. Full article

There has been a growth of collaborative robots in Industry 5.0 due to the research in automation involving human-centric workplace design. It has had a substantial impact on industrial processes; however, physical exertion in human workers is still an issue, requiring solutions that combine technological innovation with human-centric development. By analysing real-world data, machine learning (ML) models can detect physical fatigue. However, sensor-based data collection is frequently used, which is often expensive and constrained. To overcome this gap, synthetic data generation (SDG) uses methods such as tabular generative adversarial networks (GANs) to produce statistically realistic datasets that improve machine learning model training while providing scalability and cost-effectiveness. This study presents an innovative approach utilising conditional GAN with auxiliary conditioning to generate synthetic datasets with essential features for detecting human physical fatigue in industrial scenarios. This approach allows us to enhance the SDG process by effectively handling the heterogeneous and imbalanced nature of human fatigue data, which includes tabular, categorical, and time-series data points. These generated datasets will be used to train specialised ML models, such as ensemble models, to learn from the original dataset from the extracted feature and then identify signs of physical fatigue. The trained ML model will undergo rigorous testing using authentic, real-world data to evaluate its sensitivity and specificity in recognising how closely generated data match with actual human physical fatigue within industrial settings. This research aims to provide researchers with an innovative method to tackle data-driven ML challenges of data scarcity and further enhance ML technology’s efficiency through training on SD. This study not only provides an approach to create complex realistic datasets but also helps in bridging the gap of Industry 5.0 data challenges for the purpose of innovations and worker well-being by improving detection capabilities. Full article