A New Objective Diagnostic Tool for Attention-Deficit Hyper-Activity Disorder (ADHD): Development of the Distractor-Embedded Auditory Continuous Performance Test.

: Attention-Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity. Traditional diagnostic methods, which depend on subjective assessments, often lack precision. This study evaluates the validity and reliability of a newly developed diagnostic tool, the Distractor-Embedded Auditory Continuous Performance Test (da-CPT), which integrates auditory stimuli with distractors to enhance the clinical utility of ADHD diagnosis.

YOLO-IHD: Improved Real-Time Human Detection System for Indoor Drones.

In the field of unmanned systems, the combination of artificial intelligence with self-operating functionalities is becoming increasingly important. This study introduces a new method for autonomously detecting humans in indoor environments using unmanned aerial vehicles, utilizing the advanced techniques of a deep learning framework commonly known as "You Only Look Once" (YOLO). The key contribution of this research is the development of a new model (YOLO-IHD), specifically designed for human detection in indoor using drones.

Exploiting implicit social relationships via dimension reduction to improve recommendation system performance.

The development of Web 2.0 and the rapid growth of available data have led to the development of systems, such as recommendation systems (RSs), that can handle the information overload. However, RS performance is severely limited by sparsity and cold-start problems.