1. Average Localization Error (ALE) in sensor node localization process in WSNs: This data set can be used to test any regression-based machine learning algorithm. You can predict the ALE variable using four features.
2. Computer Hardware: Relative CPU Performance Data, described in terms of its cycle time, memory size, etc.
3. Concrete Slump Test: Concrete is a highly complex material. The slump flow of concrete is not only determined by the water content, but that is also influenced by other concrete ingredients.
4. DrivFace: The DrivFace contains images sequences of subjects while driving in real scenarios. It is composed of 606 samples of 640×480, acquired over different days from 4 drivers with several facial features.
5. Energy efficiency: This study looked into assessing the heating load and cooling load requirements of buildings (that is, energy efficiency) as a function of building parameters.
6. Gas sensor array exposed to turbulent gas mixtures: A chemical detection platform composed of 8 chemoresistive gas sensors was exposed to turbulent gas mixtures generated naturally in a wind tunnel. The acquired time series of the sensors are provided.
7. Optical Interconnection Network : This dataset contains 640 performance measurements from a simulation of 2-Dimensional Multiprocessor Optical Interconnection Network.
8. Residential Building Data Set: Data set includes construction cost, sale prices, project variables, and economic variables corresponding to real estate single-family residential apartments in Tehran, Iran.
9. Servo: Data was from a simulation of a servo system
10. Synchronous Machine Data Set: Synchronous motors (SMs) are AC motors with constant speed.A SM dataset is obtained from a real experimental set. The task is to create the strong models to estimate the excitation current of SM.
11. Synchronous Machine Data Set: Synchronous motors (SMs) are AC motors with constant speed.A SM dataset is obtained from a real experimental set. The task is to create the strong models to estimate the excitation current of SM.
12. Twin gas sensor arrays: 5 replicates of an 8-MOX gas sensor array were exposed to different gas conditions (4 volatiles at 10 concentration levels each).
13. Water Quality Prediction: The goal is to predict the spatio-temporal water quality in terms of the “power of hydrogen (pH)” value for the next day based on the historical data of water measurement indices.