No abstract

The tremendous growth of health-related digital information has transformed machine learning algorithms, allowing them to deliver more relevant information while remotely monitoring patients in modern telemedicine. However, patients with epilepsy are likely to die or have post-traumatic difficulties. As a result, early disease detection could be essential for a person’s survival. Hence, early diagnosis of epilepsy based on health parameters is needed. This paper presents a classification of epilepsy disease based on wearable-sensor health parameters that use a hybrid approach with ensemble machine learning and a fuzzy logic inference system. The ensemble machine learning classifiers are used to predict epilepsy events using ensemble bagging and ensemble boosting regression. The experimental results show that compared to the ensemble bagging classifiers and other state-of-the-art methods, the ensemble boosting classifier with the fuzzy inference system outperformed with a 97% accuracy rate.

For improved agricultural growth control, smart farming with precise greenhouses is essential, as is precision agriculture monitoring in a variety of situations. The Internet of Things (IoT) is a new era in computer communication that is gaining pace as a result of its wide variety of project development applications. Individuals may benefit from the IoT through smart and remote ways such as smart agriculture, smart environment, smart security, and smart cities. These are the latest technologies that are making life simpler in today’s world. The IoT has significantly increased remote control and the variety of networked things or devices, which is a fascinating aspect. The hardware and internet connectivity to the real-time application make up the Internet of Things (IoT). The Internet of Things is made up of sensors, actuators, embedded systems, and a network connection. As a result, we’d want to develop an IoT application for smart farms. This paper demonstrated a remote parameter sensing system in smart greenhouse agriculture. The goal is to monitor greenhouse parameters like CO2, soil moisture, temperature, humidity, and light, with adjusting actions for greenhouse windows/doors based on crops. In this experimentation, Gerbera and Broccoli is considered. The primary purpose is to adjust greenhouse conditions in line with plant needs in order to increase production and provide organic farming. As a result of the findings, it appears that the greenhouse might be operated remotely for CO2, soil moisture, temperature, humidity and light, resulting in improved management. Overall implementation is remotely monitored via IoT using MQTT on Adafruit IO Cloud Platform and sensor data is analyzed for its normal and anomaly behavior.

No abstract