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Lie detection is an assessment of a verbal statement with the goal to reveal a possible intentional deceit. Lie detection may refer to a cognitive process of detecting deception by evaluating message content as well as non-verbal cues. [ 1 ]
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Articles relating to lie detection, the assessment of a verbal statement with the goal to reveal a possible intentional deceit. Lie detection may refer to a cognitive process of detecting deception by evaluating message content as well as non-verbal cues.
An Introduction to Computational Learning Theory. MIT Press, 1994. A textbook. M. Mohri, A. Rostamizadeh, and A. Talwalkar. Foundations of Machine Learning. MIT Press, 2018. Chapter 2 contains a detailed treatment of PAC-learnability. Readable through open access from the publisher. D. Haussler.
As "Prospects of fMRI as a Lie Detector" [9] states, fMRIs use electromagnets to create pulse sequences in the cells of the brain. The fMRI scanner then detects the different pulses and fields that are used to distinguish tissue structures and the distinction between layers of the brain, matter type, and the ability to see growths.
C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. [1] C4.5 is an extension of Quinlan's earlier ID3 algorithm.The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier.
Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output.With this form of generative deep learning, the output layer can get information from past (backwards) and future (forward) states simultaneously.
Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate nonlinear functions. [3] Hence, ANFIS is considered to be a universal estimator. [4] For using the ANFIS in a more efficient and optimal way, one can use the best parameters obtained by genetic algorithm.