SLA-MLP: Enhancing Sleep Stage Analysis from EEG Signals Using Multilayer Perceptron Networks.

: Sleep stage analysis is considered to be the key factor for understanding and diagnosing various sleep disorders, as it provides insights into sleep quality and overall health. : Traditional methods of sleep stage classification, such as manual scoring and basic machine learning approaches, often suffer from limitations including subjective biases, limited scalability, and inadequate accuracy. Existing deep learning models have improved the accuracy of sleep stage classification but still face challenges such as overfitting, computational inefficiencies, and difficulties in handling imbalanced datasets.

A deep learning approach for ovarian cancer detection and classification based on fuzzy deep learning.

Different oncologists make their own decisions about the detection and classification of the type of ovarian cancer from histopathological whole slide images. However, it is necessary to have an automated system that is more accurate and standardized for decision-making, which is essential for early detection of ovarian cancer. To help doctors, an automated detection and classification of ovarian cancer system is proposed.

Research anomalies in criminology: How serious? How extensive over time? And who was responsible?

A variety of ways to detect questionable research practices in small sample social science surveys have been discussed by a variety of authors. However, some of those approaches (e.g.

Knowledge and awareness of parents in the Kingdom of Saudi Arabia regarding unintentional home injuries in children 2019 a descriptive cross-sectional study.

Aim: To evaluate the adherence of parent's preventive measures of unintentional home injury in their children by measuring the parents' knowledge and awareness regarding the risk factors of unintentional home injury.

Settings And Design: An online questionnaire was used. The study followed a descriptive cross-sectional design.