Prediction and Analysis Based on Sensor Network Data Using Machine Learning Techniques
$ 60
Description
The remote sensing of sensor data is becoming more detailed and less costly. This allows for offline or real-time event detection in applications, including planning, policymaking, environmental monitoring, and emissions monitoring and warning systems. Users may now monitor their surroundings in greater detail because of advances in wireless sensor networks and the Internet of Things. A distributed sensor network controls air quality and weather comfort. Science and development projects involving dynamic structures are often carried out in collaboration with various institutions, engineers, and scientists. Certain parts of the framework are developed by several organizations located in different geographic areas in such a collaboration. Recently, there has been a surge in interest in Machine Learning (ML)-based scientific and engineering techniques. This growing excitement comes from the collaborative development and use of effective algorithms for analysis, the enormous amounts of data available from experimental equipment and other sources, and the achievements of researchers and the academic community.
Analyzing the environmental and interplanetary trajectory is an important element of the study duties as quickly connected instruments and sensory gadgets grow more prevalent in our daily lives. The sea of high-velocity information flow is increasing. This massive quantity of high-rate data produced necessitates quick insight into a variety of applications such as IoT, energy storage, and so on.