The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.

Pneumonia remains a leading cause of morbidity and mortality worldwide, particularly in resource-limited settings and among elderly populations...

This study presents a comparative analysis of machine learning algorithms for Android malware detection using the TUANDROMD dataset. SMOTE was applied to address class imbalance and ensure robust model evaluation. Experimental results show that Random Forest achieved the best performance with near-perfect accuracy and ROC AUC, confirming its robustness for malware detection tasks.

Macrofungi signify an extensive reservoir of bioactive metabolites. Still, their chemical description is stalled by fragmented analytical pipelines and low reproducibility. This study introduces SysFungiNet, a unified, FAIR-compliant systems-bioinformatics framework designed to accelerate bioactives discovery in non-model fungi. Unlike existing pipelines, SysFungiNet integrates LC-MS/MS metabolomics, transcriptomics, and genomic data with explainable artificial intelligence and molecular docking validation. By reconstructing biosynthetic pathways for Ganoderma lucidum and Craterellus cornucopioides, the framework achieved a Pathway Completeness Index of 0.86. An ensemble machine-learning model predicted bioactivity with an F1-score of 0.91, identifying 312 annotated metabolites. Crucially, decision-making was transparent, with SHAP analysis identifying specific chemical substructures driving predicted immunomodulation. The framework prioritized five high-confidence candidates, including a putative novel terpenoid, Cornucopiolide, which showed high binding affinity (−9.4 kcal/mol) to human immune receptors in silico. Benchmarking against MetaFungi and PhytoOmics demonstrated superior annotation accuracy and reproducibility. SysFungiNet offers a scalable, open-source ecosystem for transforming fungal bioprospecting into an evidence-driven process.

Fatigue-related impairment is a major contributing factor to road accidents; however, detecting early visual indicators of driver tiredness remains challenging under realistic driving conditions. This study introduces an artificial intelligence–based system for distinguishing between alert and fatigued drivers using facial images captured in natural driving environments. A total of 41,793 annotated facial images from the Driver Drowsiness Dataset (DDD) were used in the experiments. Although the dataset reflects realistic driving scenarios captured by dashboard-mounted cameras, the proposed system was evaluated offline and not tested in live traffic environments. Deep visual features were extracted using the SqueezeNet architecture and subsequently classified using three supervised learning models: Artificial Neural Networks (ANN), Random Forests (RF), and Support Vector Machines (SVM). Among the evaluated classifiers, ANN achieved the highest performance with an accuracy of 99.97%, followed by RF with 99.78% and SVM with 96.33%. The results indicate that combining lightweight deep feature extraction with classical machine learning classifiers can yield highly accurate fatigue detection while maintaining computational efficiency. The proposed framework provides valuable insights into the development of efficient, real-time driver fatigue monitoring systems with potential applications in accident prevention and road safety enhancement.

Prompt injection has emerged as a critical security threat for Large Language Models (LLMs)...

Generative AI models can now produce artwork that is virtually indistinguishable from human-created art, posing critical verification challenges for art galleries, educational institutions, and digital content platforms. Existing detection approaches face notable limitations: handcrafted feature–based methods offer interpretability but achieve limited accuracy, while deep learning approaches typically require substantial computational resources and provide minimal explanation for their predictions. We propose an attention-guided fusion framework that integrates Discrete Cosine Transform (DCT)–based frequency-domain features with deep learned representations through a learned attention mechanism, enabling both improved detection performance and interpretable decision-making grounded in signal processing theory. The attention module dynamically weights each feature modality based on input-specific reliability patterns, allowing adaptive fusion across diverse artistic styles and generation methods. We evaluate the proposed framework using two convolutional backbones: the lightweight MobileNetV2 for efficiency-critical deployment scenarios and the higher-capacity ResNet50 for settings where computational resources permit stronger feature extraction. Experiments are conducted on 18,288 artwork images spanning traditional paintings, digital illustrations, and outputs from multiple generative models, using stratified train–validation–test splits and five random seeds for statistical robustness. Under frozen-backbone settings, MobileNetV2-based attention fusion achieves an F1-score of 90.1%, while ResNet50-based attention fusion reaches 90.5%. When backbones are fine-tuned end-to-end, incorporating handcrafted features via attention fusion yields consistent performance gains: MobileNetV2 improves from 93.9% to 94.5% F1-score (+0.6%), and ResNet50 improves from 94.2% to 95.1% F1-score (+0.9%). Feature importance analysis further reveals that low-frequency DCT energy is the most discriminative handcrafted feature, confirming that frequency-domain characteristics effectively distinguish AI-generated from human-created artwork across diverse artistic styles. These results demonstrate that attention-guided fusion of signal processing features and deep learned representations provides consistent accuracy–efficiency benefits across both lightweight and heavyweight architectures, offering a practical and interpretable solution to the emerging challenge of AI-generated artwork detection.

Retrieval-Augmented Generation (RAG) enhances the capabilities of Large Language Models (LLMs) by integrating external knowledge retrieval into the generation pipeline, enabling responses that are grounded, adaptive, and up to date. While RAG can improve factual accuracy compared to models relying solely on pre-trained data, its effectiveness in practice depends strongly on the quality, relevance, and interpretability of retrieved context, and does not eliminate hallucinations entirely. Recent architectures such as Fusion-in-Decoder, Atlas, and ColBERT-RAG demonstrate measurable gains in retrieval precision, scalability, and cross-domain generalization. However, persistent challenges remain, including retrieval noise that can override model reasoning, hallucinations that persist even with high-quality evidence, latency constraints that hinder real-time deployment, and fragile domain adaptation. Moreover, although partial metrics and task-specific benchmarks exist, the absence of a unified evaluation framework for retrieval–generation grounding and robustness complicates fair comparison and reproducible progress. Rather than offering an exhaustive survey, this review provides a focused analytical perspective on retrieval design and architectural evolution in RAG systems. It consolidates representative architectures while critically examining structural limitations related to retrieval–generation coupling as a design choice, context over-reliance, and privacy-preserving computation. Building on these insights, the paper outlines future research directions, including structured knowledge integration via GraphRAG, modular agent-based orchestration in Agentic RAG, improved retrieval filtering, and unified evaluation methodologies. As RAG architectures continue to evolve rapidly in a pre-standardization phase, a more analytically grounded understanding of their design trade-offs is essential for advancing trustworthy and domain-adaptive language systems

This review examines the rapidly expanding landscape of Generative Artificial Intelligence (GenAI) in healthcare, focusing on how models such as GANs, VAEs, diffusion models, and large language models are being explored across medical imaging, clinical documentation, synthetic data generation, drug discovery, and decision-support workflows. Despite GenAI's growing influence, persistent challenges, including limited annotated datasets, concerns over model generalizability, privacy risks, and the opacity of generative architectures, underscore the need for careful evaluation and governance. Accordingly, this study aims to map current applications, assess methodological and ethical constraints, and identify future research opportunities. Using a structured search across ScienceDirect, Scopus, and other sources, the study follows a structured narrative review complemented by quantitative descriptive analysis of the literature. The review also adopts PRISMA-guided screening and standardized data extraction, the review synthesizes evidence from 110 studies published up to October 2025. The findings indicate that the literature frequently reports improvements in imaging quality, data augmentation, molecular modeling workflows, and clinical documentation through generative approaches, particularly in technically constrained settings; however, evidence of clinically validated impact remains uneven across domains. While issues of bias, hallucination, and limited interpretability persist as significant obstacles, imaging-focused applications appear comparatively more mature than decision-support and patient-level modeling tasks. Across domains, diffusion models are commonly associated with higher visual fidelity in biomedical image generation, whereas LLMs demonstrate promise in narrative-oriented tasks but require stronger factual grounding and external verification mechanisms. Overall, the evidence suggests that GenAI’s potential in healthcare is highly context-dependent and contingent upon robust validation frameworks, transparent governance, and human-in-the-loop oversight. The review concludes that responsible integration of GenAI—guided by ethical, legal, and clinical safeguards, will be essential for ensuring safe, equitable, and sustainable adoption in healthcare research, delivery, and policy.

This study examines the effect of personalization-based digital marketing strategies on brand loyalty within cross-cultural contexts, employing A/B testing and multivariate analysis methods. The research is motivated by the critical role of digital marketing in fostering consumer relationships and the challenges of understanding cultural differences that influence consumer responses to marketing strategies. The primary objective of the study is to analyze how cultural dimensions moderate the effectiveness of personalized marketing content in enhancing brand loyalty. The study was conducted through an experiment involving 200 participants divided into two groups: one group received personalized digital content, while the other received standard content. Multivariate Analysis of Variance (MANOVA) was used to evaluate the effects of culture and marketing strategies on brand loyalty, consumer satisfaction, and product recommendations. The results indicate that personalized content significantly increases brand loyalty (M = 4.5, SD = 0.8) compared to standard content (M = 3.8, SD = 1.0; p < 0.05). Additionally, consumers from collectivist cultures demonstrated a more positive response to personalized content, with higher brand loyalty scores (M = 4.6) compared to those from individualist cultures (M = 4.0). This study underscores the importance of cultural sensitivity in digital marketing strategy planning. Integrating personalization elements with an understanding of cultural contexts has been shown to enhance consumer engagement and foster stronger brand loyalty. These results provide both empirical contributions and practical guidance for companies in designing effective marketing campaigns for diverse global markets.