This paper presents a semi-autonomous AI-based platform designed for the efficient management and quantitative analysis of human spermatozoa. Addressing the limitations of manual semen analysis, this system integrates advanced image processing and analytical techniques to offer a high-throughput diagnostic solution. During operation, the proposed system autonomously performs a precise quantitative assessment of sperm concentration, accurately tracks individual sperm motility patterns, and systematically classifies morphological abnormalities. The result is a comprehensive sperm analysis report, meticulously generated according to the latest established World Health Organization (WHO) guidelines for concentration, motility, and morphology. A distinguishing feature of this system is the ability to yield reliable preliminary results even with minimally pre-processed clinical samples, thereby enhancing diagnostic objectivity, efficiency, and reliability in male reproductive health assessments.
The rapid urbanization phenomenon has introduced multifaceted challenges across various domains, including housing, transportation, education, health, and the economy. This necessitates a significant transformation of seaport operations in order to optimize smart mobility and fac…
Accurate and robust power quality disturbance (PQD) classification is critical for modern electrical grids, particularly in noisy environments. This study presents a comprehensive comparative evaluation of machine learning (ML) and deep learning (DL) models for automatic PQD iden…
The significant number of road traffic accidents caused by fatigued drivers presents substantial risks to the public’s overall safety. In recent years, there has been a notable convergence of intelligent cameras and artificial intelligence (AI), leading to significant advancement…
Lung cancer remains a leading cause of global mortality, with early detection being critical for improving the patient survival rates. However, applying machine learning and deep learning effectively for lung cancer prediction using symptomatic and lifestyle data requires the car…
Atrial fibrillation is the most prevalent sustained cardiac arrhythmia and a major risk factor for stroke, heart failure, and premature mortality. Automatic detection remains challenging due to the variability of electrocardiogram (ECG) morphology, noise, and the paroxysmal natur…
The recognition of human movements is a crucial aspect of AI-related research fields. Although methods using vision and sensors provide more valuable data, they come at the expense of inconvenience to users and social limitations including privacy issues. WiFi-based sensing metho…