With the continuous development of human society, people's over-exploitation of nature leads to frequent environmental problems. A large number of floating objects appear on lakes, rivers, reservoirs and other water surfaces. Water floats have seriously damaged the ecological environment and directly threatened the survival and development of human beings. Therefore, for the sustainable development of human beings, we must solve the problem of water pollution. The detection of floating pollutants on water surface is the primary goal of water resource management.
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the brain that ultimately results in the death of neurons and dementia. The prevalence of the disease in the world is increasing rapidly. In recent years, many studies have been done to automatically detect this disease from brain signals. Method: In this paper, the Hjorth parameters are used along with other common features to improve the AD detection accuracy from EEG signals in early stages.
The enormous social and economic cost of Alzheimer's disease (AD) has driven a number of neuroimaging investigations for early detection and diagnosis. Towards this end, various computational approaches have been applied to longitudinal imaging data in subjects with Mild Cognitive Impairment (MCI), as serial brain imaging could increase sensitivity for detecting changes from baseline, and potentially serve as a diagnostic biomarker for AD. However, current state-of-the-art brain imaging diagnostic methods have limited utility in clinical practice due to the lack of robust predictive power.
