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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]

IoT has been developing, because of which the variety of customer demand for the layer of fog computing has similarly expanded. Fog Computing takes care of the actual time work environment and outputs are processed instantly by associated gadgets. The virtual machines (VMs) on the Fog Layer are becoming increasingly overloaded as customer demand for the Fog Layer has skyrocketed. A load balancing mechanism allows the load to be distributed equally across all VMs. Within the fog layer, the entire workload should be evenly and fairly distributed across multiple current VMs within the segment. So far, several load-balancing strategies have been initiated for Fog Computing Analysis. An empirical survey of current load-balancing techniques has been taken into consideration and a taxonomy is provided structurally. The paper also includes annual summaries and in-depth survey papers published in this field of load balancing from 2017 - 2022. In addition, this article also consists of fog computing constructs proposed to clarify the load balancing issue. In addition, we will discuss current problems and difficult situations that can be solved with destiny studies. The paper concludes by pointing out future directions. [ABSTRACT FROM AUTHOR]

Steganography is an art of writing secret information on the cover. Image Steganography refers to hiding secret text or image over the cover image. Information sharing over the channel or media is important nowadays. Shared information should be secured and confidential. The embedding of secret information on the cover image results in the Stego-Image. The Quality of the Stego-Image measured in terms of PSNR and the embedding Payload of the resultant image plays a vital role in imperceptibility of the Stego-Image. The imperception of Stego-Image depends on the number of bits altered during the embedding process. This is measured using a parameter called "psnr" and the number of bits embedded is the "payload". A Literature Review is done on various methods for the embedding capacity and PSNR. [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]