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1. 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.

2. 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).

3. 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.

4. 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.

5. MHEALTH Dataset: The MHEALTH (Mobile Health) dataset is devised to benchmark techniques dealing with human behavior analysis based on multimodal body sensing.

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. 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.

8. 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.

9. 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.

10. 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.


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