Academic Articles & Research Papers

Artifact segmentation using the U-Net architecture for powder X-ray diffraction images

Modern synchrotron X-ray facilities generate X-ray diffraction (XRD) image data at rates that far exceed the capacity of manual analysis, yet critical processing steps such as artifact removal rely on human intervention. Rule-based algorith...

Automated Cell Foreground–Background Segmentation with Phase-Contrast Microscopy Images: An Alternative to Machine Learning Segmentation Methods with Small-Scale Data

Cell segmentation is a critical step for image-based experimental analysis. Existing cell segmentation methods are neither entirely automated nor perform well under basic laboratory microscopy. This study proposes an efficient and automated...

Video-Based Deep Learning Approach for 3D Human Movement Analysis in Institutional Hallways: A Smart Hallway

New artificial intelligence- (AI) based marker-less motion capture models provide a basis for quantitative movement analysis within healthcare and eldercare institutions, increasing clinician access to quantitative movement data and improvi...

A Deep Learning-Driven Computer Vision and Web-Based Monitoring System for Automated Nutrient Management in Soilless Agriculture

Sustainable food production increasingly relies on intelligent automation technologies that optimize resource usage while maintaining crop health. This study presents a deep learning-driven computer vision and web based monitoring system de...

Monitoring the spread of a pathogenic insect on vineyards using UAS

Globalisation has contributed to rapid economic growth but has also exposed vulnerabilities such as the spread of pests in agriculture. An example is the Popillia Japonica Newman beetle, introduced to Italy in 2014, which has caused signifi...

Monitoring pedestrian walking dynamics under environmental and policy influences in urban spaces

Abstract Understanding how environmental conditions and policy interventions jointly shape pedestrian mobility is essential for designing resilient, adaptive, and human-centered urban spaces. This study presents a computer-vision framework ...

Deep learning based identification and tracking of railway bogie parts

The Train Rolling-Stock Examination (TRSE) is a safety examination process that physically examines the bogie parts of a moving train, typically at speeds over 30 km/h. Currently, this inspection process is done manually by railway personne...

Artificial Intelligence in Healthcare: Advancing Innovation and Ethics to Foster Well-Being

Artificial intelligence (AI) is reshaping healthcare by enhancing diagnostic precision, treatment personalization, and overall patient care. By leveraging technologies such as machine learning, deep learning, natural language processing, an...

From Data to Decisions: AI in Varicose Veins—Predicting, Diagnosing, and Guiding Effective Management

Background: Varicose veins (VVs) of the lower limbs, characterized by palpable, dilated, and tortuous veins, affect 2–73% of the global population. Artificial intelligence (AI) offers significant potential to enhance healthcare efficiency a...

SFCN: Symmetric feature comparison network for detecting ischemic stroke lesions on CT images

Abstract Ischemic stroke is the most common stroke and the leading cause of disability and death in the world. Computed tomography (CT) is a popular and economical diagnostic device for the stroke, However the ischemic stroke lesions are no...

AI and Chatbot-Supported Interventions for Physical Activity and Obesity-Related Lifestyle Behaviors: Scoping Review With Attention to Family Involvement

Abstract BackgroundAI-enabled chatbots and related conversational systems can facilitate human–computer interaction through natural language, personalization, and automated support. In pediatric health promotion, these tools hav...

LeafNST: an improved data augmentation method for classification of plant disease using object-based neural style transfer

Abstract Plant diseases significantly threaten global agriculture, impacting crop yield and food security. Nearly 30% of the crop yield is lost due to plant diseases. Efficient identification and classification of plant diseases through com...

RELAX Perinatal Digital Intervention to Prevent Increasing Anxiety: A Randomized Clinical Trial.

A coordinated and enhanced brain network supports the persistence of long-term cocaine memory

Abstract The persistence of drug memories accounts for the high risk of drug relapse, which is a major challenge in the treatment of substance use disorders. However, the neurobiological underpinnings, especially the dynamic changes of brai...

Noncanonical Signaling Network of Nogo-A: A New Perspective on Multi-target Regulation, Neuroplasticity, and Disease Intervention Beyond NgR.

Evaluating artificial intelligence for a focal nodular hyperplasia diagnosis using magnetic resonance imaging: preliminary findings

PURPOSE: This study aimed to evaluate the effectiveness of artificial intelligence (AI) in diagnosing focal nodular hyperplasia (FNH) of the liver using magnetic resonance imaging (MRI) and compare its performance with that of radiologists....

Co-designing physical activity intervention in Danish kindergartens: A design-based research approach.

High mechanical thrombectomy procedural volume is not a reliable predictor of improved thrombectomy outcomes in patients with acute ischemic stroke in the United States.

Multimodal artificial intelligence for subepithelial lesion classification and characterization: a multicenter comparative study (with video)

Abstract Background Subepithelial lesions (SELs) present significant diagnostic challenges in gastrointestinal endoscopy, particularly in differentiating malignant types, such as gastrointestinal stromal tumors (GISTs) and neuroendocrine tu...

Image-based cotton leaf disease diagnosis using YOLO and faster R-CNN techniques

Abstract Cotton has, in recent years, become one of the most important cash crops worldwide while being impacted in yield from leaf disease which generally goes unnoticed in the early stage. Detection methods depend on manual efforts produc...