Human fall detection is a critical area of research in the field of healthcare and assistive technologies. Accurate and timely detection of falls is crucial for ensuring the well-being and safety of individuals, particularly the elderly and those with mobility impairments.
Abnormal detection, also known as anomaly detection, plays a crucial role in various domains, including cybersecurity, industrial monitoring, and healthcare. Detecting abnormal events or behaviors that deviate from expected patterns is essential for identifying potential threats, system failures, or critical health conditions.
The preprocessing of electrocardiogram (ECG) signals is a critical step in cardiac research and clinical practice. ECG signals are prone to various types of noise and artifacts that can obscure important cardiac information and affect diagnostic accuracy.
Content-Based Image Retrieval (CBIR) is a prominent research area within the field of visual information retrieval. It focuses on developing techniques and algorithms to retrieve images from large databases based on their visual content rather than relying on textual descriptions.
Glaucoma is a chronic eye condition characterized by increased pressure within the eye, leading to damage to the optic nerve and potential loss of vision if left untreated. It is one of the leading causes of irreversible blindness worldwide.