Hello everyone. My name is Asma Jana and I'm in early stages searcher under very cure reactions in a way to training Nekwa project, NAM area. And I'm currently doing my double PhD from University Polytechnica Obokers and University Mediterranean of Regional Calibria Italy. And what today's research topic is machine learning algorithms for post- identification of obstructive sleep apnea patients using IoT solutions. Let's first move towards the outline. I will first start with the introduction. Irregular breathing among individuals is the serious sleep disorder referred as sleep apnea. The symptoms of sleep apnea are snoring, fatigue and somnolence. The main types of sleep apnea are obstructive sleep apnea, central sleep apnea and complex sleep apnea. Among these obstructive sleep apnea is most common and can be treated by adjusting sleeping postures. Internet of things solutions play a very important role in addressing sleep apnea with proper monitoring of sleeping postures and adjusting in timely manner. Sleep apnea threats as sudden cause of that to people with heart disease. Researchers recommend that people with serious respiratory disorders should avoid sleeping in supine positions. It has also been reported that 75 to 80% of cases remains undagnosed because of expenses and people avoiding spending long hours in labs. So the main objective of this study is to monitor sleeping postures or parts spent using IoT best pressure makers. Pressure made is designed with total 2048 pressure sensor points with a scan date of 322 sensors per second. And the dimension of the matrix is 32 by 24 inch. Each sensor is 1 inch apart from other and all the sensors are equally distributed. The prime goal is to avoid the supine sleep position using objective measurements. Our study is divided into different sections. Section 3 addresses the data setup and collection from participants. Section 4 illustrates the basic methodology we adopted. And finally, Section 5 addresses the conclusion and later future directions. This is the data collection and the detail for this study. The data was collected under IRP approval in units few of textiles in the last from 13 parts spent in different postures. All the 13 parts spent in study were healthy with no history of sleeping problems. From the table, you can see the detail of the 13 parts in age, height and weight. Two types of matrix were used to conduct two different experiments on 13s participants. And the basic methodology that we adopted is first we collected data using the pressure made and we pre-processed it. And we did the data labeling and moved to our classification procedure and then the post-traumoniting. The main focus of this study is to find best or correct in weight posture for sleep hypnea patients. For classifying the standard posture from level data, we used classification learner app of medleps software. And the basic standard posture considered in experiment are these that is supine left right feet and left right knee. And in experiment number one, we considered these five standard postures for the finding out the postures of e-patient. And in methodology of second experiment, we asked eight parts spent to participate and this time the data was collected from ear macros and different postures with different roll angles were counted to see the difference in classification models. The results we achieved for the other experiment number one in which different supervised machine learning algorithms were used. And we find out that each algorithm gives a very good accuracy mostly in 90s. And this table shows the accuracy of all machine learning classifier for posture detection of each participant. And among these classifiers, machine learning algorithms weighted canine and lean at classifying gave promising results with accuracy of 98.7%. In experiment number two, the result of the posture detection using ear macros shows these accuracy. And from the table, we can realize that the accuracy of these algorithms of the ear macros is very lesser than the one in experiment number one. And this shows that the macros also macros in finding out the proper, in finding and detecting the posture for the subjects. And these are the results that we obtained from the experiment number one with a geared accuracy. And the first table, first floor shows the number observation of model. And the rows show the true class and columns show the predictive class. And the diagonal emphasized with the true class and predictive class match the blue boxes in diagonal depicts that the classifier has made classifications and the observation of this true class are classified correctly. And the plot B shows true positive rates and false negative rates of predictive model. The true positive rates proportion of correctly classified observations per a true class. And false negative rate is a proportion of incorrectly classified observation per true class. And the plot C shows positive, productive values and false discovery rates of predictive model. Positive productive values is the proportion of correctly classified observation per predicted class. And false discovery rates is the proportion of incorrectly predicted points in each class. The figure four shows the parallel coordinate plots. And the H, plot shows predictive model in range scaling and B shows predicting model in straight right scaling. And the crosses shows the incorrect predictions here. So this is the conclusion of a WebSkirt. This research deal with the different postures and highlighted the most common posture associated with multiple health issues. Such as sleeping on a supine position. In this research, the available data has been level and after pre-posing we ran it through multiple algorithm with the help of classification learner app in MATLAB in order to monitor sleep posture successfully with high-agrecedents. This research proves that with slide improvisation including pattern one can avoid several health risks. The type of matrix does matter in detecting postures. In future, we also am to build a system which not only can monitor sleeping posture but also is capable of tracking records of heart rate to avoid the risk of heart failures while sleeping. In the end, we also, we all authors are greatly acknowledge funding from European Union Horizon 2020 research innovative program and the Mary Kirichrand Agreement of AVIER project. And these are some references of our research study. Thank you so much.