Frontiers in Artificial Intelligence | New and Recent Articles

Skin lesions are one of the most prevalent form of diseases existing among us. Early detection and classification of potentially malignant skin lesions can give us a lead in the fight against skin cancer. There are many lesion classification divisions on medical grounds; however, an automated system that detects and classifies a majority of these classes is not prevalent. In view of this scenario…

IntroductionThe rapid emergence of the digital workplace has accelerated the integration of artificial intelligence (AI) into human resource management (HRM). However, empirical research on the determinants of AI adoption in organisational contexts remains limited, particularly in emerging economies. This study examines the factors influencing AI adoption in HRM within the South African telecommu…

In the busy and stressful modern world, people tend to disregard mental health, still it is an important factor of overall health. The constant pressure to achieve success, the invasive nature of technology, and the constantly growing needs of the contemporary world may all be the causes of stress, anxiety, and other mental health difficulties. Despite growing awareness, mental health remains a s…

IntroductionArtificial intelligence (AI) is increasingly embedded in journalism, yet audience responses may depend on both AI provenance, meaning who or what is presented as having written the story, and transparency cues that disclose AI use. This systematic literature review synthesises empirical studies examining how AI provenance cues and AI disclosure cues in journalism affect perceived cred…

IntroductionThis study examines the impact of Customer Big Data Analytics (CBDA) on customer satisfaction and firm performance in business-to-business (B2B) firms operating in emerging markets, specifically Pakistan. Despite the growing adoption of big data technologies, empirical evidence on their strategic value in B2B contexts remains limited. This study also investigates the moderating role o…

Accurate detection of pediatric fractures in radiographs remains challenging due to subtle visual cues and the high prevalence of false-positive detections produced by automated systems. To address this limitation, we propose a lightweight region-of-interest (Region of Interest) adjudication framework that operates as a second-stage verification module to refine detector-generated candidates. The…

This work presents a hybrid deep learning approach for identifying students who are likely to experience academic difficulties in virtual learning environments. The proposed framework is evaluated on the Open University Learning Analytics Dataset (OULAD) and combines two complementary types of information: temporal patterns of learner activity captured using Bidirectional Long Short-Term Memory (…

Chest X-ray (CXR) interpretation is essential for diagnosing pulmonary diseases, yet manual reading remains slow and prone to human error, especially in high-volume or resource-limited settings. To address delayed diagnoses and improve clinical efficiency, this study introduces (HyRA-CXR), a hybrid residual–attention convolutional neural network for automated CXR classification. The proposed mode…

IntroductionWe address moral uncertainty in reinforcement learning (RL) by proposing a framework that integrates multiple ethical theories into decision-making. Existing approaches rely on single moral frameworks or handcrafted rewards, limiting scalability and failing to capture moral pluralism. We introduce AMULED, a task-agnostic ethical layer that refines a pre-trained RL agent using large la…

Artificial intelligence (AI) presents a transformative opportunity for pediatric healthcare, promising advancements in early diagnosis, personalized treatment, and operational efficiency. However, the unique developmental, physiological, and ethical considerations inherent to children necessitate a specialized approach to AI integration. This study introduces the Pediatric AI Readiness (PAIR) Fra…

IntroductionFlood risk assessment has become increasingly important in regions vulnerable to climate-induced disasters. This study addresses the need for a robust decision-support framework by proposing a hybrid multi-criteria decision-making (MCDM) model to prioritize flood-prone zones in the Ernakulam district of Kerala, aligning with sustainable development goals focused on climate resilience.…

IntroductionTo optimize radiotherapy treatment and minimize toxicities, effective segmentation of organs-at-risk (OARs) and clinical target volume (CTV) is essential. Deep learning (DL) models can achieve high segmentation accuracy through careful tuning. However, their reliability also hinges on addressing uncertainties stemming from variability in clinical contouring practices. This study syste…

IntroductionReliable classification of lung nodules from computed tomography (CT) images remains a challenging problem due to variations in image intensity, noise, and unstable feature representations during deep network training. Although convolutional neural networks (CNNs) have achieved promising results in medical image analysis, their internal feature dynamics are often difficult to control,…

Cloud computing environments face persistent structural challenges in cost control, dynamic resource allocation, and security risk management, which traditional infrastructure approaches fail to address adequately. This systematic literature review aimed to synthesize empirical evidence on the application of artificial intelligence (AI) and machine learning (ML) models for cost optimisation, reso…

BackgroundThe integration of artificial intelligence (AI) into emergency medicine holds promise for enhancing early diagnosis and clinical decision-making. Traditional diagnostic approaches often rely on physician judgment or early warning scores that may delay identification of infections, especially when these tools prioritize outcomes such as mortality or ICU admission over early infection det…

IntroductionDietary planning is essential for managing non-communicable diseases, yet many AI-based nutrition systems lack structured knowledge grounding, demographic sensitivity, and explainability. These limitations are particularly evident in culturally diverse contexts such as India, where standard approaches often fail to align clinical dietary requirements with traditional meal patterns.Met…

The Novel Dhouib Matrix Minimum Spanning Tree Problem (DM-MSTP) method is used to illustrate a topological relationship between all Tunisian financial market for 2024. The results reveal a clear hierarchical organization with the financial sector playing a central bridging role, particularly through banks and financial services, which connect consumer-oriented and real-economy sectors. A two-clus…

IntroductionMalignant occurrences are increasing in frequency, and skin cancer is emerging as a significant public health concern. Conventional approaches, encompassing comprehensive procedures such as consulting expert opinions, tend to decelerate the therapeutic process.MethodsA method for classifying skin cancer that incorporates both manually extracted and automatically derived features. This…