Geology and alteration of building stones using conventional architecture in the kırklareli vize region

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Ahmet Konus

Engineering

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The objective is to design and develop an application that will help to imp rove the efficiency and effectiveness of the police security measures in keeping criminal records. ABSTRACT The victims can file the FIR through the website under various sections. The user can send photo evidence if any online. There will be an 'SOS 'capability in which, the user can press a button and his/her location will be sent to the nearest Police station. There will be a separate component for the accident victims so that FIR can be registered fast and treatment can be started as soon as possible. In this current system, user's information will be kept confidential and only users complain will be forwarded to its nearest police station. Users complain number will be forwarded from the server side automatically. For identifying location and authentic person, concept of cookies and IP addressing has been used. To eliminate the location conflicts between police station, server will play a vital role. It will search the address table using IP address and forward message to that police location from where the message has been received. We intend to create a project which will help bridge the gap between the police department and the common man. 'Online FIR Registration and SOS system' project will have an app from where the users can file FIR against the offender under the various sections. The main site will be maintained by the admin (from the police) who will then notify the user if the FIR has been registered and the necessary action has been taken.

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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.

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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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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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Geology and alteration of building stones using conventional architecture in the kırklareli vize region

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