Frontiers in Artificial Intelligence | New and Recent Articles

Background and objectivesAccurate medical image segmentation remains a challenging task in computer-aided diagnosis because of the intricacies and the variability in the biomedical data in terms of the anatomical complexity, inter-patient diversity, class imbalance, and irregular morphological patterns.MethodsIn the present work, a Context Aware Adaptive Progressive Network (CA2PNet) is proposed.…

Workplace stress is a significant concern, as it negatively impacts employee wellbeing and organizational productivity and is a major contributor to burnout and job turnover. Detecting stress in real-world work environments remains challenging; however, recent advances in machine learning and deep learning techniques offer promising solutions. Furthermore, the growing availability of multimodal d…

Artificial intelligence plays a central role in modern cybersecurity, yet systems optimized for detection accuracy often lack mechanisms for accountability, transparency, and policy compliance. This study proposes the Agentic AI Framework (AAIF), a policy-aware intrusion detection architecture that integrates predictive modeling with executable governance. Guided by Design Science Research, the f…

For accurate pesticide application in precision farming, weeds and tobacco plants must be detected to efficiently apply pesticides to weedy areas. There is potential for automated, precise weed and tobacco detection using unmanned aerial vehicle (UAV)-based imaging. Semantic segmentation is a challenge that can be applied to accurately detect weeds in crop field images. Deep learning-based semant…

IntroductionMental health issues among university students are becoming increasingly prominent, making an accurate and efficient mental state monitoring system a critical challenge in higher education management. Existing intelligent screening approaches mostly rely on single-modality data and are not tailored to counselor-student dialogue records or structured background information. Consequentl…

Artificial intelligence (AI) has rapidly become the focal point of global governmental attention and investment. Nations are launching AI for science strategies on a scale comparable to historic endeavors such as Apollo and the Manhattan Project. These coordinated programs carry profound promise for people living with cancer, for those at risk of disease and for transformative public benefit. Cen…

Pre-trained language models (PLMs) are widely used in sentiment analysis, but they may inherit gender-stereotypical bias from large-scale text corpora and transfer such bias to downstream sentiment predictions. Despite growing attention to gender-stereotypical bias in PLMs, existing studies predominantly focus on English corpora and static word embeddings, limiting understanding of how such bias …

Colorectal polyps are key determinants of colorectal cancer. Their accurate detection during colonoscopy has been a technically challenging work due to differences in shape size imaging conditions and texture. Emerging advances in Artificial Intelligence predominantly in deep learning have been making significant changes in the automatic detection and classification of polyps. This review present…

Artificial intelligence (AI) is increasingly shaping development trajectories across the Global South, yet limited attention has been paid to how AI is appropriated within highly informal and institutionally fragile economies. This article advances a conceptually driven, theory-building analysis supported by qualitative field insights used as illustrative grounding rather than for statistical gen…

The rapid growth of the Internet of Things (IoT) has increased the size, complexity, and vulnerability of network traffic, making intrusion detection a critical factor of modern cybersecurity. Traditional intrusion-detection systems (IDSs) analyze handcrafted features and rules to detect emerging attack patterns. To address these limitations, deep learning frameworks, such as attention-enhanced o…

IntroductionElectronic health records (EHRs) increasingly anchor clinical decision support and population-scale analytics, yet their concentration of sensitive information amplifies disclosure risk, widens the attack surface, and faces emerging threats from quantum computing. Existing frameworks fail to simultaneously address privacy preservation, quantum-resistant security, and cross-institution…

Agentic AI systems can plan, call tools, and coordinate specialized sub-agents, enabling multi-step scientific workflows that exceed what single-model text generation can reliably deliver. Yet in high-stakes domains such as regulatory science and toxicology, fluent outputs are not sufficient: adoption hinges on traceability, reproducibility, context-of-use validity, and explicit uncertainty commu…

BackgroundMotivated by the rising global trend of veterinary Complementary and Alternative Medicine (CAM) usage and a specific data gap in Taiwan, this study investigates the consumption behavior of future pet owners.MethodsA cross-sectional survey was conducted among Taiwanese medical university students using a validated online questionnaire. Beyond traditional descriptive statistics, this stud…

This paper focuses on ad hoc teamwork, the problem of enabling an AI agent to collaborate with other agents without prior coordination. Methods considered state of the art for ad hoc teamwork formulate it primarily as a learning problem, using a large labeled dataset of different situations to model the action choices of other agents (or agent types) and determine the actions of the ad hoc agent.…

IntroductionArtificial intelligence (AI) tools are increasingly applied to support second language writing. However, empirical research examining their potential to enhance the argumentative writing skills of STEM engineering students, particularly in the English as a Second Language (ESL) context in India, remains limited and requires more exploration.MethodThis study employed a quasi-experiment…

IntroductionMost modern threat detection frameworks rely on fixed class definitions and retrospective signatures derived from historical data, limiting their ability to adapt to evolving attack behaviors. However, contemporary threats are increasingly polymorphic, adaptive, and include a growing number of zero-day attacks, making traditional pattern-matching approaches insufficient. With expandin…

Customer-service question answering (QA) systems increasingly rely on conversational language understanding. While Large Language Models (LLMs) achieve strong performance, their high computational cost and deployment constraints limit practical use in resource-constrained environments. Small Language Models (SLMs) provide a more efficient alternative, yet their effectiveness for multi-turn custom…

IntroductionHazardous acoustic event detection is critically important for intelligent surveillance, emergency response systems, and public safety monitoring applications. Accurate and real-time identification of dangerous sound events such as explosions, alarms, screaming, and weapon-related sounds can significantly improve situational awareness and accelerate emergency response in safety-critic…

Precision agriculture demands accurate, real-time environmental monitoring, conventional soil moisture sensors face critical issues such as long-term drift, high energy consumption, and limited adaptability to dynamic environmental changes. These limitations often lead to suboptimal irrigation decisions, wasted resources, and unreliable data, especially in remote or resource-constrained farming r…

IntroductionRetrieval-Augmented Generation (RAG) in Domain-specific Question Answering (DSQA) often faces significant performance degradation due to semantic drift. Our analysis reveals that the main cause is the absence of a dedicated mechanism for handling low-frequency terms.MethodsMotivated by this observation, we propose a hierarchical context enhancement retrieval augmented generation (HCE-…