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Found 9 papers, 1 papers with code
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering
First, the development of AI in the critical areas of ME, such as in material processing, the study of structure and material property, and measuring the performance of materials in various aspects, is discussed.
A Hybrid CNN-LSTM model for Video Deepfake Detection by Leveraging Optical Flow Features
In most cases, Convolutional Neural Network(CNN) based discriminators are being used for detecting such synthesized media.
Modelling Social Context for Fake News Detection: A Graph Neural Network Based Approach
This paper has analyzed the social context of fake news detection with a hybrid graph neural network based approach.
Machine learning based lens-free imaging technique for field-portable cytometry
The developed auto characterization algorithm so far for this custom-developed LSIT cytometer was based on the hand-crafted features of the cell diffraction patterns from the LSIT cytometer, that were determined from our empirical findings on thousands of samples of individual cell types, which limit the system in terms of induction of a new cell type for auto classification or characterization.
Artificial Intelligence Powered Material Search Engine
The use of AI in material engineering is becoming more viable as the number of material data such as X-Ray diffraction, various spectroscopy, and microscope data grows.
Glioblastoma Multiforme Patient Survival Prediction
We hypothesize that the radiomics shape features have the highest correlation with survival prediction.
ADIC: Anomaly Detection Integrated Circuit in 65nm CMOS utilizing Approximate Computing
An additional 42% energy saving can be achieved when a lighter version of OPIUM method is used for training with the same number of data samples lead to no significant compromise on the quality of inference.
ADEPOS: A Novel Approximate Computing Framework for Anomaly Detection Systems and its Implementation in 65nm CMOS
However, on the detection of anomalies, the complexity of the network and computing precision are adaptively increased for accurate predictions.
A Stacked Autoencoder Neural Network based Automated Feature Extraction Method for Anomaly detection in On-line Condition Monitoring
Most of these are handcrafted features, where these are manually obtained based on the nature of the raw data.
