A deep learning approach to analyse stress by using voice and body posture

Soft Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

Language: Английский

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Integration of Explainable Artificial Intelligence into Hybrid Long Short-Term Memory and Adaptive Kalman Filter for Sulfur Dioxide (SO2) Prediction in Kimberley, South Africa

Atmosphere, Journal Year: 2025, Volume and Issue: 16(5), P. 523 - 523

Published: April 29, 2025

Air pollution remains one of the environmental issues affecting some countries, which leads to health globally. Though several machine learning and deep models are used analyze air pollutants, model interpretability is a challenge. Also, dynamic time-varying nature pollutants often creates noise in measurements, making pollutant prediction (e.g., Sulfur Dioxide (SO2) concentration) inaccurate, influences model’s performance. Recent advancements artificial intelligence (AI), particularly explainable AI, offer transparency trust models. In this regard, organizations using traditional confronted with how integrate AI into systems. paper, we propose novel approach that integrates (xAI) long short-term memory (LSTM) attempts address by Adaptive Kalman Filters (AKFs) also includes causal inference analysis. By utilizing LSTM, long-term dependencies daily concentration meteorological datasets (between 2008 2024) for City Kimberley, South Africa, captured analyzed multi-time steps. The proposed (AKF_LSTM_xAI) was compared Gate Recurrent Unit (GRU), LSTM-multilayer perceptron (LSTM-MLP) at different time performance evaluation results based on root mean square error (RMSE) one-day step suggest AKF_LSTM_xAI guaranteed 0.382, LSTM (2.122), LSTM_MLP (3.602), GRU (2.309). SHapley Additive exPlanations (SHAP) value reveals “Relative_humidity_t0” as most influential variable predicting SO2 concentration, whereas LIME values high “wind_speed_t0” reduces predicted concentration.

Language: Английский

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Advancements and Challenges in Video-Based Deception Detection: A Systematic Literature Review of Datasets, Modalities, and Methods

IEEE Access, Journal Year: 2025, Volume and Issue: 13, P. 28098 - 28122

Published: Jan. 1, 2025

Language: Английский

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Lie Detection Screening of the C-Suite with AI and ML

Published: Jan. 1, 2025

Language: Английский

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Multimodal Deception Detection Using Linguistic and Acoustic Features

Published: Jan. 1, 2025

Language: Английский

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Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Journal Year: 2024, Volume and Issue: unknown, P. 12011 - 12015

Published: March 18, 2024

Developing tools to automatically detect check-worthy claims in political debates and speeches can greatly help moderators of debates, journalists, fact-checkers. While previous work on this problem has focused exclusively the text modality, here we explore utility audio modality as an additional input. We create a new multimodal dataset (text English) containing 48 hours speech from past USA. then experimentally demonstrate that, case multiple speakers, adding yields sizable improvements over using alone; moreover, audio-only model could outperform text-only one for single speaker. With aim enable future research, make all our data code publicly available at https://github.com/petar-iv/audio-checkworthiness-detection.

Language: Английский

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Identifying the Risk in Lie Detection for Assessing Guilty and Innocent Subjects for Healthcare Applications

Published: Jan. 1, 2024

Language: Английский

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Deception and Lie Detection Using Reduced Linguistic Features, Deep Models and Large Language Models for Transcribed Data

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), Journal Year: 2024, Volume and Issue: unknown, P. 376 - 381

Published: July 2, 2024

Language: Английский

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