Fitness applications are commonly used to monitor activities within the gym, but
they often fail to automatically track indoor activities inside the gym. This study
proposes a model that utilizes pose estimation combined with a novel data augmentation method, i.e., rotation matrix. We aim to enhance the classification accuracy of
activity recognition based on pose estimation data. Through our experiments, we
experiment with different classification algorithms along with image augmentation
approaches. Our findings demonstrate that the SVM with SGD optimization, using
data augmentation with the Rotation Matrix, yields the most accurate results, achieving a 96% accuracy rate in classifying five physical activities. Conversely, without
implementing the data augmentation techniques, the baseline accuracy remains at a
modest 64%.
@article{Pose2023MeVazan,title={AUGMENTING VISION-BASED HUMAN POSE ESTIMATION WITH ROTATION MATRIX},author={Vazan, Milad and Masoumi, Fatemeh Sadat and Rawassizadeh, Reza and Ou, Ruizhi},journal={arXiv preprint arXiv:2310.06068, 2023},year={2023},}
MDPI
Regularized Contrastive Masked Autoencoder Model for Machinery Anomaly Detection Using Diffusion-Based Data Augmentation
Esmaeil Zahedi, Mohammad Saraee, Fatemeh Sadat Masoumi, and Mohsen Yazdinejad
Unsupervised anomalous sound detection, especially self-supervised methods, plays a crucial role in differentiating unknown abnormal sounds of machines from normal sounds. Self-supervised learning can be divided into two main categories: Generative and Contrastive methods. While Generative methods mainly focus on reconstructing data, Contrastive learning methods refine data representations by leveraging the contrast between each sample and its augmented version. However, existing Contrastive learning methods for anomalous sound detection often have two main problems. The first one is that they mostly rely on simple augmentation techniques, such as time or frequency masking, which may introduce biases due to the limited diversity of real-world sounds and noises encountered in practical scenarios (e.g., factory noises combined with machine sounds). The second issue is dimension collapsing, which leads to learning a feature space with limited representation. To address the first shortcoming, we suggest a diffusion-based data augmentation method that employs ChatGPT and AudioLDM. Also, to address the second concern, we put forward a two-stage self-supervised model. In the first stage, we introduce a novel approach that combines Contrastive learning and masked autoencoders to pre-train on the MIMII and ToyADMOS2 datasets. This combination allows our model to capture both global and local features, leading to a more comprehensive representation of the data. In the second stage, we refine the audio representations for each machine ID by employing supervised Contrastive learning to fine-tune the pre-trained model. This process enhances the relationship between audio features originating from the same machine ID. Experiments show that our method outperforms most of the state-of-the-art self-supervised learning methods. Our suggested model achieves an average AUC and pAUC of (94.39%) and (87.93%) on the DCASE 2020 Challenge Task2 dataset, respectively. The paper is an open-access one and you can read the whole paper through this link:https://scholar.google.com/citations?view_op=view_citation&hl=en&user=2uS4LM0AAAAJ&citation_for_view=2uS4LM0AAAAJ:zYLM7Y9cAGgC.
@article{ASD2023meYazdiZahedi,author={Zahedi, Esmaeil and Saraee, Mohammad and Masoumi, Fatemeh Sadat and Yazdinejad, Mohsen},journal={MDPI Algorithms 2023},year={2023},}
2022
ArXiv
Utilizing DistilBert Transformer Model for Sentiment Classification of COVID-19’s Persian Open-text Responses
The COVID-19 pandemic has caused drastic alternations in human life in all aspects. The government’s laws in this regard affected the lifestyle of all people. Due to this fact studying the sentiment of individuals is essential to be aware of the future impacts of the coming pandemics. To contribute to this aim, we proposed an NLP (Natural Language Processing) model to analyze open-text answers in a survey in Persian and detect positive and negative feelings of the people in Iran. In this study, a distilBert transformer model was applied to take on this task. We deployed three approaches to perform the comparison, and our best model could gain accuracy: 0.824, Precision: 0.824, Recall: 0.798, and F1 score: 0.804.
@article{COVID2022MeBahrani,title={Utilizing DistilBert Transformer Model for Sentiment Classification of COVID-19's Persian Open-text Responses},author={Masoumi, Fatemeh Sadat and Bahrani, Mohammad},journal={arXiv preprint arXiv:2212.08407, 2022},year={2022},}
ArXiv
A Deep Convolutional Neural Networks Based Multi-task Ensemble Model for Aspect and Polarity Classification in Persian
Milad Vazan, Fatemeh Sadat Masoumi, and Sepideh Saeedi Majd
Aspect-based sentiment analysis is of great importance and application because of its ability to identify all aspects discussed in the text. However, aspect-based sentiment analysis will be most effective when, in addition to identifying all the aspects discussed in the text, it can also identify their polarity. Most previous methods use the pipeline approach, that is, they first identify the aspects and then identify the polarities. Such methods are unsuitable for practical applications since they can lead to model errors. Therefore, in this study, we propose a multi-task learning model based on Convolutional Neural Networks (CNNs), which can simultaneously detect aspect category and detect aspect category polarity. creating a model alone may not provide the best predictions and lead to errors such as bias and high variance. To reduce these errors and improve the efficiency of model predictions, combining several models known as ensemble learning may provide better results. Therefore, the main purpose of this article is to create a model based on an ensemble of multi-task deep convolutional neural networks to enhance sentiment analysis in Persian reviews. We evaluated the proposed method using a Persian language dataset in the movie domain. Jacquard index and Hamming loss measures were used to evaluate the performance of the developed models. The results indicate that this new approach increases the efficiency of the sentiment analysis model in the Persian language.
@article{NLP2022meVazan,title={A Deep Convolutional Neural Networks Based Multi-task Ensemble Model for Aspect and Polarity Classification in Persian},author={Vazan, Milad and Masoumi, Fatemeh Sadat and Saeedi Majd, Sepideh},journal={arXiv preprint arXiv:2201.06313},year={2022},}
Thesis
Thesis
Utilizng a Hybrid Ensemble Model based on Desicion Tree and Neural Network Algorithms for Breast Cancer Survival Status Classification
@phdthesis{masoumi2023maThesis,title={Utilizng a Hybrid Ensemble Model based on Desicion Tree and Neural Network Algorithms for Breast Cancer Survival Status Classification},author={Masoumi, Fatemeh Sadat},year={Thesis},school={ATU Iran,Tehran , 2023}}