Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder.

Diabetic retinopathy (DR) presents a significant concern among diabetic patients, often leading to vision impairment or blindness if left untreated. Traditional diagnosis methods are prone to human error, necessitating accurate alternatives. While various computer-aided systems have been developed to assist in DR detection, there remains a need for accurate and efficient methods to classify its stages.

Energy optimization and plant comfort management in smart greenhouses using the artificial bee colony algorithm.

Agriculture is an essential component of human sustenance in this world. These days, with a growing population, we must significantly increase agricultural productivity to meet demand. Agriculture moved toward technologies as a result of the demand for higher yields with less resources.

A deep neural network-based approach for seizure activity recognition of epilepsy sufferers.

Epilepsy is one of the most frequent neurological illnesses caused by epileptic seizures and the second most prevalent neurological ailment after stroke, affecting millions of people worldwide. People with epileptic disease are considered a category of people with disabilities. It significantly impairs a person's capacity to perform daily tasks, especially those requiring focusing or remembering.

A novel assessment of potentially toxic elements (PTEs) in water and sediment samples from the Indus River, Pakistan: An ecological risk assessment approach.

Pakistan, a country with limited water resources and highly vulnerable to the adverse effects of climate change, faces numerous challenges in managing its water supply. In this sense, this study assessed potentially toxic elements (PTEs) in the surface water and sediments of Pakistan's Indus River and its tributaries. Key water quality parameters such as pH, electrical conductivity (EC), and total dissolved solids (TDS) were determined, with respective average values of 7.