Lecture 7 control

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cairo university

Lecture 7 control

Engineering

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In this study, using the fuzzy logic method, a stress detection tool was created with body temperature and blood pressure parameters as indicators to determine a person's stress level. This tool uses the LM35DZ sensor to detect body temperature, the MPX5100GP sensor to read blood pressure values, and Arduino Uno as a data processor from sensor readings which are then calculated using the fuzzy logic method as a stress level decisionmaker. The resulting output measures blood pressure, body temperature, and the stress level experienced by a person, which will be displayed on the liquid crystal display. Based on the results of testing the body temperature parameter, the highest error generated was 1.17%, and for the blood pressure parameter, the highest error was 2.5% for systole and 0.93% for diastole. Furthermore, testing the stress level displayed on the tool is compared to the depression, anxiety, and stress scales 42 (DASS 42), a psychological stress measuring instrument. From the results of testing the tool with the questionnaire, the average conformity level is 74%.

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It is a new concept in town planning, and it may have a highly positive impact on cities and communities. It not only promotes sustainability but also helps increase the health of people and boost the economy. Although it has a lot of advantages, critics argue that this concept is not for all cities and describe its effects as well. New cities should follow this pattern to build and develop their cities

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According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.

Seizure stage detection of epileptic seizure using convolutional neural networks

IJECEIAES

8th International Conference on Soft Computing, Mathematics and Control (SMC 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications impacts and challenges of Soft Computing, Mathematics and Control. The conference documents practical and theoretical results which make a fundamental contribution for the development of Soft Computing, Mathematics and Control. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

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This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.

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