Machine Learning
This study examined the effectiveness of a detection-based lie detection method that determines lying conditions based on facial autonomic reactions. This technique combines with two other lie detection techniques using a multi sensor fusion technique that is used in the polygraph test to differentiate moments of participants lying and telling the truth about a picked-up card from a deck of cards. Experiments were conducted with 19 participants sitting in front of a camera connected to Galvanic Skin Response (GSR) probes and ECG probes for a polygraph test.
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1606 Views
This datasets consist of XRays datasets
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147 Views
CSP and GCP dataset for column generation, the format follows BPPLIB benchmark.
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30 Views
CSP and GCP dataset for column generation, the format follows BPPLIB benchmark.
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The dataset consists of NumPy arrays for each alphabet in Indian Sign Language, excluding 'R'. The NumPy arrays denote the (x,y,z) coordinates of the skeletal points of the left and right hand (21 skeletal points each) for each alphabet. Each alphabet has 120 sequences, split into 30 frames each, giving 3600 .np files per alphabet, using MediaPipe.
The dataset is created on the basis of skeletal-point action recognition and key-point collection.
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1381 Views
Features extracted from EEG when subjects imagined the musical pitch from C4 to B4. The feature extraction method is introduced in "Decoding Imagined Musical Pitch from Human Scalp Electroencephalograms".
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301 Views
Description
Prognostics and health management is an important topic in industry for predicting state of assets to avoid downtime and failures. This data set is the Kaggle version of the very well known public data set for asset degradation modeling from NASA. It includes Run-to-Failure simulated data from turbo fan jet engines.
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1984 Views
The India Weather Forecast built a state-level standard rainfall forecast system using a multi-model ensemble approach with model outputs from five prominent worldwide NWP centers. Pre-assigned grid point weights based on anomalous correlations (CC) between values observed and predicted are established for each element model using two seasonal datasets, and multi provision of appropriate predictions are created in real-time similar resolution. Then, forecasts are created for each state node lying within a given district by averaging the ensemble prediction fields' values.
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4005 Views
Human activity recognition, which involves recognizing human activities from sensor data, has drawn a lot of interest from researchers and practitioners as a result of the advent of smart homes, smart cities, and smart systems. Existing studies on activity recognition mostly concentrate on coarse-grained activities like walking and jumping, while fine-grained activities like eating and drinking are understudied because it is more difficult to recognize fine-grained activities than coarse-grained ones.
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644 Views