The European Journal on Artificial Intelligence

Artificial neural networks (ANNs) have been recognized as powerful tools able to automatically learn complex relationships in data. To the best of our knowledge, ANNs have not yet been applied to forest regeneration modeling. Such models are essential to evaluate the effects of reforestation techniques. To fill this gap, the capacity of ANNs to simulate the initial recruitment of pine species in Mediterranean forests has been evaluated in this study. A feed-forward multilayer neural network has been applied to a case study of pine forests of Castilla La Mancha (Central–Eastern Spain), where seed germination and seedling survival, with or without seed protection, of four pine species (Pinus nigra, Pinus pinaster, Pinus halepensis, and Pinus sylvestris) were observed throughout 10 years under three soil conditions (scalped, wildfire-affected, and unaltered soils). The results we obtained have witnessed the good capacity of ANNs to predict both stages of pine initial recruitment. This may be of help in predicting the success of natural regeneration in Mediterranean pine forests under different tree species, soil characteristics, and management strategies.

The current trend in recommendation services is prioritizing personalization to ensure accurate recommendations. This study aims to enhance the user-based collaborative filtering algorithm for cross-domain recommendations by exploiting the similarity in user cognition across multiple domains. The research suggests three steps: (i) gathering user feedback from various domains to represent their cognition, (ii) constructing a user cognition-based collaborative filtering model for multi-domain recommendations, and (iii) generating recommendations in the target domain. The experimental results demonstrate that the proposed model outperforms all baseline methods. In particular, the proposed method is better than the baselines, approximately 12% up to 16%, regarding mean average precision and normalized discounted cumulative gain metrics.

Anomalies in the work of data center users can be caused by both Structured Query Language (SQL) injection attacks and user attempts to make unauthorized access to data. The paper explores various machine learning models to detect such anomalies. The peculiarity of the problem being solved is its focus on the university data centers, whose databases have a non-normalized structure. In this case, the problem of reducing the feature space arises. The paper proposes an algorithm for generating a dataset based on typing the data table names. The experimental results obtained on supervised, unsupervised and semi-supervised machine learning models confirmed the high efficiency of the proposed approach. They showed that the support vector machine, random forest, Gaussian Naive Bayes, and neural network models are the most effective in detecting known SQL injections, and the local outlier factor semi-supervised learning model is the most effective in detecting unknown SQL injections and unauthorized access attempts.

The integration of artificial intelligence (AI) and art design has unlocked new potential for personalized, creative, and emotion-driven content generation. However, existing methods still face major challenges in style controllability, emotion consistency, and user satisfaction prediction. This study proposes a multimodal AI art generation strategy based on Stable Diffusion and BERT, using ControlNet, text-to-image (T2I)-Adapter, style–emotion mapping (SEM), sentiment prediction optimization, guided score distillation (GSD), and score-based generative model (SGM) to achieve high-quality, personalized art generation. First, ControlNet and T2I-Adapter are introduced into the Stable Diffusion method to enhance the fine control of text descriptions, visual references, and style labels, and improve the controllability and emotion consistency of generated content. In addition, a SEM model is constructed to establish a deep correspondence between user emotions and visual aesthetics using multimodal feature learning (including color, composition, and style attributes). Afterwards, GSD and SGM are used to optimize the diffusion model to minimize the interference of irrelevant information and ensure high-quality, emotion-consistent art output. The test results show that this method has significant advantages in style controllability, user emotion consistency, and satisfaction prediction accuracy, and provides new ideas for personalized art design, emotion-driven content generation, and optimization of human–computer interaction experience.

Word sense disambiguation (WSD) remains a critical challenge in natural language processing (NLP), requiring precise identification of context-dependent word meanings. This article proposes a novel global optimization framework for WSD by reformulating it as a combinatorial optimization problem. We introduce a discrete variant of the rat swarm optimizer (RSO), a metaheuristic inspired by rat foraging behavior, to efficiently select optimal sense combinations across sentences. Unlike local disambiguation methods, our approach leverages phrase-level embeddings and WordNet gloss definitions within a unified semantic fitness function, enabling robust global sense selection. Experiments on SensEval and SemEval benchmarks demonstrate state-of-the-art performance, achieving accuracy improvements of 76.32% (SensEval2), 71.80% (SensEval3), 58.30% (SemEval2007), 77.17% (SemEval2013), and 74.40% (SemEval2015) surpassing previous methods. The effectiveness of the model shows its benefits for downstream NLP tasks such as machine translation, information retrieval, and document indexing. Future work will focus on hyperparameter optimization and multilingual adaptation to enhance scalability for low-resource languages.

Human motion prediction is a classic problem in computer vision and graphics, and the prediction of human motion diversity has a wide range of practical applications. To tackle this problem, this study proposes predicting the future motion diversity of the human body based on conditional denoising diffusion probabilistic models combined with the kinematics of human joints. First, the observed and predicted sequences were integrated into the same sample space using the mask mechanism, and Gaussian noise was gradually injected into the predicted sequence leveraging the cosine noise scheduler to destroy the sequence structure. Subsequently, the spatial-temporal feature extractor and channel enhancement module were used to form a denoiser to learn the temporal dynamic evolution of the sample and the potential correlation between the nodes in the diffusion process to complete the noise prediction and restore the sample information. The proposed method was verified on the Human3.6M and HumanEva-I datasets, and the experimental results show that the proposed method is competitive with previous methods in diversity prediction.

To address the challenges of urban traffic complexity, such as occlusion and lighting variations that impact road target detection, this study introduces the KGKPD algorithm. This algorithm integrates knowledge graphs with keypoint detection based on the CenterNet concept. It enhances robustness by introducing salt-and-pepper noise and uses the RepVit network as the backbone. The weighted fusion adaptive feature pyramid network module fuses multi-scale features to optimize the extraction of small target features. The efficient linear deformable convolutional head improves detection of occluded targets. The Poly-1 loss function addresses class imbalance, thereby improving accuracy. The integration of prior knowledge enhances the model’s ability to understand relationships between targets. Compared to CenterNet, the KGKPD algorithm reduces parameters and computational load by 92.32% and 91.85%, respectively, increases the mean average precision by 4.1%, and achieves a frame rate of 40.5 frames per second, meeting the requirements for real-time detection. The code is available at https://github.com/yjx-cup/kgkpd.

With the rapid development of Deepfake technologies, the prevalence of forged facial images and videos on social media has surged. However, these technologies have also been exploited for malicious purposes, posing significant threats to social security. Although existing detection methods have shown effectiveness in identifying high-quality facial forgeries, their performance drops significantly for low-quality forgeries, as compression causes detail loss and feature blurring. To address this problem, a Frequency Assisted Multiscale Dual-Stream Network (FAMDnet) is proposed for low-quality Deepfake detection in this article. First, the multiscale spatial feature extraction module is designed to extract RGB features from image patches of different sizes, thereby capturing forgery traces within multiscale regions. Then, the dynamic global frequency feature extraction module is constructed by combining learnable dynamic filters with the Fast Fourier Transform to extract frequency domain features, supplementing the forgery artifacts that may vanish in the spatial domain. Finally, the multimodal attention fusion module is utilized to explore the correlation between RGB features and frequency domain features, effectively capturing local texture details and global context patterns in facial images, thereby achieving better fusion of RGB features and frequency domain features. Moreover, a parameter-free attention mechanism is introduced into the classifier to enhance the fused features, further improving the model's discriminative ability for facial forgery. Comparative experiments on the FaceForensics++, Celeb-DF, and WildDeepfake datasets demonstrate that FAMDnet has better performance in detecting low-quality fake images and videos. The code is available at https://github.com/daisy-12138/FAMDnet.

Scribble-supervised semantic segmentation has emerged as a promising alternative to fully supervised methods in medical imaging, owing to its low annotation cost and the inherent inclusion of contour information. However, the sparse supervision provided by scribble annotations, coupled with the underutilization of contour cues, often results in incomplete boundary representations in pseudo labels generated by the model. This limitation hinders the model’s ability to accurately capture complex anatomical structures, posing a significant challenge to achieving precise segmentation. To address this issue, we propose a novel three-branch network architecture. Built upon a multi-task learning framework, the model introduces a contour-assisted label supervision (contour auxiliary labels supervision) mechanism and a contour attention module to enhance the auxiliary decoder’s capability in extracting contour features. In addition, we design a contour mixed pseudo labels supervision strategy, which incorporates contour-enhanced representations into the pseudo-label generation process, thereby providing more informative and higher-quality supervision for scribble-based learning. We evaluate our method on the ACDC, MSCMR, and SegPC-2021 datasets. Experimental results demonstrate that our approach consistently outperforms state-of-the-art methods in terms of accuracy, robustness, and generalization. The scribble annotations and experimental code for the SegPC-2021 dataset are available at Github.

Personalized news recommendation aims to address information overload problems and find interesting news for users, which is essential for online news portals and platforms nowadays. Existing deep neural networks and pretrained language models require some labeled data to train end-to-end models or fine-tune object functions. More recently, prompt-tuning methods have been proposed to minimize the gap between the knowledge acquired during pretraining and the fine-tuned model. Although these methods have achieved fairly good results in news recommendation, how to take the personalized characteristics of items into template construction and achieve competitive performance compared to hand-crafted prompts through soft prompt-tuning is still an important academic and practical challenge. In this article, we propose a soft prompt-tuning method for personalized news recommendation. The side information of news is introduced to learn the characteristics of items in the recommendation, and different strategies of verbalizer optimization are designed for performance improvement. Specifically, the summaries and subcategories of news are first introduced for template construction to consider the characteristics of news, which can provide a more comprehensive and accurate description of news. Secondly, three different strategies are designed to expand the label word space for modifying soft prompts, and the integration of these strategies is used for final verbalizer optimization, which can significantly reduce additional noise and improve recommendation accuracy. Extensive experiments conducted on the MINDsmall and MINDlarge datasets validated the effectiveness of the proposed method compared to other state-of-the-art prompt methods.

Fine-grained image retrieval (FGIR) is a challenging task that demands precise representation of subtle inter-class variations and intra-class similarities. Traditional methods, heavily reliant on labeled data, face scalability and cost-efficiency challenges, limiting their applicability in real-world scenarios. To address these limitations, this study introduces a novel self-supervised pre-training framework tailored for FGIR. Central to our approach is the adaptive sample selector module, which dynamically selects training samples of varying difficulties, enhancing the learning of discriminative features without significantly increasing training costs. Additionally, we propose an integrated learning strategy that synergistically combines contrastive and generative learning, enabling the model to capture both intra-image contextual information and inter-image similarities. This approach significantly improves feature extraction, setting a new benchmark for FGIR tasks. Extensive experiments on multiple FGIR benchmarks demonstrate that our model achieves state-of-the-art performance, consistently surpassing existing methods. By reducing reliance on labeled data and advancing self-supervised learning for fine-grained image analysis, this framework offers a scalable and efficient solution for FGIR and beyond.

In the field of unpaired image-to-image (I2I) translation, enforcing consistency through similarity constraints on features from corresponding stages of the encoder and decoder has led to state-of-the-art performance. However, this simple similarity constraint, typically based on mean-squared error, is limited in terms of feature alignment, spatial information utilization, and global context modeling. These limitations result in suboptimal detail preservation and global structure in the generated images. To address these issues, we propose RFA-GAN, an I2I translation framework that integrates noise injection and attention mechanisms. To enhance the generator’s ability to preserve fine details, we propose a channel shuffle dual attention module. Although generative adversarial networks have achieved remarkable progress in generating high-quality samples, the presence of noise and distortion still impairs translation performance. To mitigate this, we inject Gaussian noise into the model, which not only improves performance under noisy conditions but also reduces sensitivity to noise. Furthermore, to compensate for the insufficient capture of local details and the lack of global style information, we design a focal-frequency residual block. Extensive experiments on multiple benchmark datasets demonstrate that our method significantly improves the quality and detail preservation of generated images while enhancing model robustness. These results suggest that our approach provides an efficient and stable solution for unpaired I2I translation.