Fever status detection using artificial neuron network

This research paper proposes a monitoring system and a prototype that has been developed for detecting if a when fever is present in senior citizens or any other specific groups of people requiring continuous care. With various issues affecting the health of senior citizens, it is imperative to continuously monitor their health status. The monitoring system is beneficial as it will make it feasible to enable the real time detection of fever and thus allowing for the early treatment. Delaying treatment can lead to the underlining health issue going beyond the remediable condition. Thus, quick detection is vital. There are various issues that might causes illness in people. Some of the issues include virus outbreak, seasonal infections, disease, and old age. In this paper our focus is mainly on old age. This group of people is much more at risk of getting ill or frequently need more attention. In this project, the presence of fever or illness has been detected by using artificial intelligence (AI). The AI technique that is utilized in this project is artificial neural networks. The computation is done by first training the system and then secondly validating the trained system. After the training, the system is supplied with a new set of data, with a known state, to validate that the training was successful. To validate the system, it is provided with sample data to test its efficiency. If the system is well trained the validation data would label that data correctly. That label is known before the validation test, as the sample data had known labels. These known labels were not given to training but not validation system. The system is function properly if its label matched the sample data label. The conducted experiment demonstrated a successful detection with an efficiency rate of 82 percent.Faculty of Applied Informatics, Tomas Bata University in Zlin [IGA/CebiaTech/2021/001

Fuzzy logic application in automation control

This paper aims to investigate and apply Artificial Intelligence in solving a real-life problem. A fuzzy logic control (FLC) system is proposed for controlling and maintaining speed of a motor vehicle. The vehicle controller produces either a throttle force or brake force. This force is then feed into the vehicle's engine to generate an appropriate acceleration for the vehicle. Moreover, the vehicles' speed is further affected by the type of road the vehicles is moving on. Moving on an uphill road would require more throttle while moving downhill roads require less throttle and more brakes. A flat smooth road requires average throttle or brakes. To evaluate and validate the proposed solution, two models are used, and results are later analyzed. First model uses FLC while second model uses Proportional Integrator (PI) controller. The PI controller has proved to be less smooth in transitioning the actual to desired speed than the FLC. The FLC was further tested on its independence from the model used. It has been assigned to two different models to show that results obtained are based on fuzzy-rules and not the specific model. This experiment successfully demonstrated how motor vehicle speed can be better controlled by using a FLC than a PI controller syste

Smart house assistive technologies for senior citizens

A Smart house is a special type of house that has automated services it offers to its residents to enhance their comfortability. Smart houses can implement for various purposes including luxury automation capability, entertainment, security, mitigation of disability, health, or medical reasons. In this paper, a special smart house design prototype for senior citizens is proposed. The design is proposed to have assistive technologies services easily added to or removed from the smart house. Depending on the need, a service could be added, modified, or removed with little effort. The complexity of service can be extended or limited based on the requirements of the subject senior citizen. Some of the service offerings in this house include; (a) security services, for instance in case of a fire, a fall detection, or a sharp rise in fever symptoms, an alert distress signal would be sent to the caregivers center; (b) action execution services, for example, automated preparation of bathwater, preparation of breakfast, preparation of dinner, switching lights on or off, etc.; (c) interactive services, for instance, remainders for medicine taking schedule, prompt of beverage choice, prompt to send distress signal when possible danger is detected by the system, etc. The comprehensive design would have four main modules: smart house automated services, assistive technologies services, privacy policy protocol, and security services. The design has been developed and would be implemented in the future. © 2022 IEEE

Fuzzy logic application in automation control

This paper aims to investigate and apply Artificial Intelligence in solving a real-life problem. A fuzzy logic control (FLC) system is proposed for controlling and maintaining speed of a motor vehicle. The vehicle controller produces either a throttle force or brake force. This force is then feed into the vehicle's engine to generate an appropriate acceleration for the vehicle. Moreover, the vehicles' speed is further affected by the type of road the vehicles is moving on. Moving on an uphill road would require more throttle while moving downhill roads require less throttle and more brakes. A flat smooth road requires average throttle or brakes. To evaluate and validate the proposed solution, two models are used, and results are later analyzed. First model uses FLC while second model uses Proportional Integrator (PI) controller. The PI controller has proved to be less smooth in transitioning the actual to desired speed than the FLC. The FLC was further tested on its independence from the model used. It has been assigned to two different models to show that results obtained are based on fuzzy-rules and not the specific model. This experiment successfully demonstrated how motor vehicle speed can be better controlled by using a FLC than a PI controller system © 2020 IEEE