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As per our health is concern main parameter we can consider like sleep for our health regeneration, memorization and main aspect like recovery of our immune system. An essential part of overall health. Before detecting sleep issues in our body suppose if we have some machine or any ideas that helps to patients who is goes in narrow for their sleep issues for that if we can go with some tools and help us to analyzed before happening to sleep disorder. Using the algorithm of DL, we can explore the research in healthcare system. DL models helps to work with wide range of unformatted dataset which helps to understand how sleep issues are coming into human being based on some parameters we can learn some procedure. By using survey, we are going to specifies the different DL models are used to detect various types of problems present into the human being with different sleep stages. It compares different approaches by several authors who are working in this area, consider some different channels like eeg, ecg and many more. Also, they have worked with classification of DL models and different Methodologies with some pros, cons and conclusion. The main and very important aspect is sleep for regenerating some cells of body during sleeping time of any human being. As we consider better sleep in any people, they need to take proper nutrition, sleep, enough amount of H2O, breathing rate to balance. It is as important as eating, drinking and breathing, necessary for the normal maintenance of fitness and psychological filling well. There are many health issues are related to sleep disorders like sugar level, heart problems, long time sleeping, not getting sleep and many others. [ABSTRACT FROM AUTHOR]

Emotion recognition plays an important role in human-computer intercommunication, in the medical field, in call centers, etc. Gradually, the chances of Emotion Computing are increasing. The main six primary emotions are anger, disgust, fear, joy, sadness, and surprise. Many types of emotion recognition methods are available to recognize these emotions. But they mainly focus on facial expressions, speech, and gestures. Using visible emotion cues cannot capture people's true emotions. To get real emotions, emotion recognition using a Convolutional neural network (CNN), Recurrent neural network (RNN), and support vector machine (SVM), LSTM becomes essential. This article discusses various methods used for emotion recognition using RNN, LSTM using machine learning and deep learning techniques, data mining methods, and their databases. It also includes the experimental results and accuracy of each method. With this focus on building an emotionless machine, brilliance can be achieved. [ABSTRACT FROM AUTHOR]

Anemia disease is a common health problem in emerging countries and constitutes a challenge to public health in India as well. It affects persons of all age groups, especially women and children. India has the maximum total prevalence of anemia at 39.86%. According to WHO, about 32.4 million pregnant women suffer from anemia disease. The NFHS-4 provides crucial date related to anemic status in India. The prevalence percentage of anemia in pregnant women in India using the NFHS-4 survey remained 50.2%. Machine learning algorithms and data mining techniques open new doors of opportunities for precise prediction of Anemia disease. Still, the resourceful processing of such huge data is exciting, so we need a system that infers from the data. ML methods make systems learn itself. In this paper, we have presented a survey of ML algorithms used for prediction of anemia in pregnant women from NFHS-4. [ABSTRACT FROM AUTHOR]