Predictive Forensic Based—Characterization of Hidden Elements in Criminal Networks Using Baum-Welch Optimization Technique

Nwanga, Mathew Emeka; Okafor, K. C.; Achumba, Ifeyinwa E.; Chukwudebe, G. A.

doi:10.1007/978-3-030-93453-8_10

Cited by 2 publications

(1 citation statement)

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“…The Baum-Welch algorithm, also known as the forward-backward algorithm, is an expectation-maximization (EM) algorithm used for training hidden Markov models HMMs (tuning the parameters of the HMM). Adapting the Baum-Welch algorithm for an NHMM involves handling neutrosophic sets and updating the parameters accordingly [63,64]. Here is a high-level overview of the adaptation of the Baum-Welch algorithm for neutrosophic HMMs:…”

Section: A Training Phasementioning

confidence: 99%

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Al-Saidi,

Ballagi,

Hassen

et al. 2024

Computers

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In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human–computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM’s uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.

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Section: A Training Phasementioning

confidence: 99%