Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning

Sensors, Год журнала: 2021, Номер 21(22), С. 7665 - 7665

Опубликована: Ноя. 18, 2021

Emotion Recognition is attracting the attention of research community due to multiple areas where it can be applied, such as in healthcare or road safety systems. In this paper, we propose a multimodal emotion recognition system that relies on speech and facial information. For speech-based modality, evaluated several transfer-learning techniques, more specifically, embedding extraction Fine-Tuning. The best accuracy results were achieved when fine-tuned CNN-14 PANNs framework, confirming training was robust did not start from scratch tasks similar. Regarding recognizers, framework consists pre-trained Spatial Transformer Network saliency maps images followed by bi-LSTM with an mechanism. error analysis reported frame-based systems could present some problems they used directly solve video-based task despite domain adaptation, which opens new line discover ways correct mismatch take advantage embedded knowledge these models. Finally, combination two modalities late fusion strategy, 80.08% RAVDESS dataset subject-wise 5-CV evaluation, classifying eight emotions. revealed carry relevant information detect users' emotional state their enables improvement performance.

Язык: Английский

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A Proposal for Multimodal Emotion Recognition Using Aural Transformers and Action Units on RAVDESS Dataset

Applied Sciences, Год журнала: 2021, Номер 12(1), С. 327 - 327

Опубликована: Дек. 30, 2021

Emotion recognition is attracting the attention of research community due to its multiple applications in different fields, such as medicine or autonomous driving. In this paper, we proposed an automatic emotion recognizer system that consisted a speech (SER) and facial (FER). For SER, evaluated pre-trained xlsr-Wav2Vec2.0 transformer using two transfer-learning techniques: embedding extraction fine-tuning. The best accuracy results were achieved when fine-tuned whole model by appending multilayer perceptron on top it, confirming training was more robust it did not start from scratch previous knowledge network similar task adapt. Regarding recognizer, extracted Action Units videos compared performance between employing static models against sequential models. Results showed beat narrow difference. Error analysis reported visual systems could improve with detector high-emotional load frames, which opened new line discover ways learn videos. Finally, combining these modalities late fusion strategy, 86.70% RAVDESS dataset subject-wise 5-CV evaluation, classifying eight emotions. demonstrated carried relevant information detect users’ emotional state their combination allowed final performance.

Язык: Английский

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AHANet: Adaptive Hybrid Attention Network for Alzheimer’s Disease Classification Using Brain Magnetic Resonance Imaging

Bioengineering, Год журнала: 2023, Номер 10(6), С. 714 - 714

Опубликована: Июнь 12, 2023

Alzheimer’s disease (AD) is a progressive neurological problem that causes brain atrophy and affects the memory thinking skills of an individual. Accurate detection AD has been challenging research topic for long time in area medical image processing. Detecting at its earliest stage crucial successful treatment disease. The proposed Adaptive Hybrid Attention Network (AHANet) two attention modules, namely Enhanced Non-Local (ENLA) Coordinate Attention. These modules extract global-level features local-level separately from Magnetic Resonance Imaging (MRI), thereby boosting feature extraction power network. ENLA module extracts spatial contextual information on global scale while also capturing important long-range dependencies. captures local input images. It embeds positional into channel mechanism enhanced extraction. Moreover, Feature Aggregation (AFA) to fuse levels effective way. As result incorporating above architectural enhancements DenseNet architecture, network exhibited better performance compared existing works. was trained tested ADNI dataset, yielding classification accuracy 98.53%.

Язык: Английский

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Convolutional Neural Network-Based Parkinson Disease Classification Using SPECT Imaging Data

Mathematics, Год журнала: 2022, Номер 10(15), С. 2566 - 2566

Опубликована: Июль 23, 2022

In this paper, we used the single-photon emission computerized tomography (SPECT) imaging technique to visualize deficiency of dopamine-generated patterns inside brain. These are establish a patient’s disease progression, which helps distinguish patients into different categories. Furthermore, convolutional neural network (CNN) model classify based on dopamine level The dataset throughout paper is Parkinson’s progressive markers initiative (PPMI) dataset. collected was pre-processed and data amplification performed balance imbalanced A CNN-based defined input SPECT images four motivation behind proposed reduce number resources consumed while maintaining performance classification model. This will help healthcare ecosystem run mobile devices. contains 14 layers with layers, max-pool flatten dense dimensions. layer classifies categories, including PSD, healthy control, scans without evidence dopaminergic deficit (SWEDD), GenReg PSD from entire dataset, progression using images. trained large 58,692 for training 11,738 validation, 7826 testing. outperforms models surveyed papers. model’s accuracy 0.889, recall 0.9012, precision 0.9104, F1-score 0.9057.

Язык: Английский

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Explainable Machine Learning with Pairwise Interactions for Predicting Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Utilizing Multi-Modalities Data

Brain Sciences, Год журнала: 2023, Номер 13(11), С. 1535 - 1535

Опубликована: Окт. 31, 2023

Background: Predicting cognition decline in patients with mild cognitive impairment (MCI) is crucial for identifying high-risk individuals and implementing effective management. To improve predicting MCI-to-AD conversion, it necessary to consider various factors using explainable machine learning (XAI) models which provide interpretability while maintaining predictive accuracy. This study used the Explainable Boosting Machine (EBM) model multimodal features predict conversion of MCI AD during different follow-up periods providing interpretability. Methods: retrospective case-control conducted data obtained from ADNI database, records 1042 2006 2022 included. The exposures included this were MRI biomarkers, scores, demographics, clinical features. main outcome was aMCI follow-up. EBM utilized converting based on three feature combinations, obtaining ensuring Meanwhile, interaction effect considered model. combinations compared accuracy, sensitivity, specificity, AUC-ROC. global local explanations are displayed by importance ranking plots. Results: five-years prediction accuracy reached 85% (AUC = 0.92) both scores markers. Apart accuracies, we features’ periods. In early stage AD, markers play a major role, middle-term, more important. Feature risk scoring plots demonstrated insightful nonlinear interactive associations between selected outcome. one-year prediction, lower right inferior temporal volume (<9000) significantly associated conversion. For two-year low left thickness (<2) most critical. three-year higher FAQ (>4) During four-year APOE4 five-year entorhinal (<1000) critical feature. Conclusions: established glass-box EBMs superior ability detailed MCI. Multi significant identified. Further may be significance determine whether tool would management patients.

Язык: Английский

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Parkinson’s Disease Detection by Processing Different ANN Architecture Using Vocal Dataset

Eurasian Journal of Science and Engineering, Год журнала: 2023, Номер 9(2)

Опубликована: Янв. 1, 2023

Parkinson's Disease (PD) is a long-standing neurodegenerative condition of the central nervous system that mainly affects motor and origins full or partial damage in behavior, speech, reflexes, mental processing, other energetic functions.Doctors use different types datasets such as movement images from people to diagnose disease.In this paper, speech dataset collected with without PD detect disease.The voice recording samples are analyzed feature vectors extracted samples.A supervised ANN Multi-Layer Perceptron backpropagation algorithm presented accurately distinguish between healthy individuals.Different Architecture diverse neuron numbers hidden layers tested utilize model result each architecture compared select best for recognition.So far, our score highest which 93% testing dataset.

Язык: Английский

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Human Activity Recognition on Smartphones using Innovative Logistic Regression and Comparing Accuracy of Naive Bayes Algorithm

E3S Web of Conferences, Год журнала: 2024, Номер 491, С. 03023 - 03023

Опубликована: Янв. 1, 2024

The objective of this study is to compare the Naive Bayes algorithm with Innovative Logistic Regression in order enhance human activity identification for sitting and walking. To predict activity, are used different training testing splits. From each group, ten sets samples selected, yielding a total twenty samples. About 80% data from an independent sample T test were utilized Gpower (g power setup parameters: α = 0.05 0.80, β 0.2). Compared (90.7210%), (95.5680%) has higher accuracy, statistical significance value P 0.003 (p < 0.05). When compared Bayes, accuracy.

Язык: Английский

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AI for Automated Thoracic Disease Assessment from X-Ray Imaging: a Review

Опубликована: Окт. 21, 2023

With the increasing availability of digital X-ray imaging, artificial intelligence (AI) has emerged as a promising tool for automating assessment thoracic diseases. The objective this study is to systematically review and deep learning methods proposed automated diseases from chest images. A thorough search relevant literature was conducted, studies that met inclusion criteria were critically reviewed. Information on datasets, model architectures, evaluation metrics, results extracted. Convolutional neural networks are prevalent, achieving state-of-the-art classification performance. Recent have explored more complex tasks such disease localization, segmentation, report generation. multitask multimodal approaches promising. Challenges related data, evaluations, clinical adoption identified. This prevails there significant progress in using analysis. Further research needed validate these models real-world settings facilitate their integration into workflows.

Язык: Английский

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