1. User Knowledge Modeling: It is the real dataset about the students' knowledge status about the subject of Electrical DC Machines. The dataset had been obtained from Ph.D. Thesis.
2. Ultrasonic flowmeter diagnostics: Fault diagnosis of four liquid ultrasonic flowmeters
3. 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).
4. PEMS-SF: 15 months worth of daily data (440 daily records) that describes the occupancy rate, between 0 and 1, of different car lanes of the San Francisco bay area freeways across time.
5. Parkinson's Disease Classification: The data used in this study were gathered from 188 patients with PD (107 men and 81 women) with ages ranging from 33 to 87 (65.1±10.9).
6. Optical Interconnection Network : This dataset contains 640 performance measurements from a simulation of 2-Dimensional Multiprocessor Optical Interconnection Network.
7. MHEALTH Dataset: The MHEALTH (Mobile Health) dataset is devised to benchmark techniques dealing with human behavior analysis based on multimodal body sensing.
8. Mesothelioma’s disease data set : Mesothelioma’s disease data set were prepared at Dicle University Faculty of Medicine in Turkey.
Three hundred and twenty-four Mesothelioma patient data. In the dataset, all samples have 34 features.
9. Leaf: This dataset consists in a collection of shape and texture features extracted from digital images of leaf specimens originating from a total of 40 different plant species.
10. 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.
11. 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.
12. 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.
13. Detect Malacious Executable(AntiVirus): I extract features from malacious and non-malacious and create and training dataset to teach svm classifier.Dataset made of unknown executable to detect if it is virus or normal safe executable.