Artificial Intelligence
Amir Mahdi Sedghi; Shahla Nemati
Abstract
Background and Objectives: Brain tumor detection in MRI images is critical for early diagnosis and effective treatment, yet manual interpretation is time-consuming and prone to variability. Deep learning models such as YOLO have advanced real-time object detection, but their speed–accuracy tradeoff ...
Read More
Background and Objectives: Brain tumor detection in MRI images is critical for early diagnosis and effective treatment, yet manual interpretation is time-consuming and prone to variability. Deep learning models such as YOLO have advanced real-time object detection, but their speed–accuracy tradeoff remains a challenge for medical tasks involving small or low-contrast lesions. The potential of transformer-based detectors like RT-DETR to simultaneously improve accuracy and maintain real-time speed in clinical settings is not well established.Methods: This study performed a controlled head-to-head comparison between the proposed model (RT-DETR-L-based model) and the YOLOv8s models using a curated, single-class brain tumor MRI dataset of 300 images. Both models were trained and evaluated under identical conditions with comprehensive data augmentation strategies, and their performance was assessed using standard object detection metrics including precision, recall, specificity, and mean Average Precision (mAP) across multiple IoU thresholds.Results: The proposed model achieved higher localization fidelity and overall accuracy compared to YOLOv8s, with mAP@0.5:0.95 of 0.493 versus 0.421 and mAP@0.5 of 0.963 versus 0.941. Precision and specificity for the proposed model reached 1.000, eliminating false positives, while recall was slightly lower than YOLOv8s (0.925 vs. 0.932), indicating a marginal increase in missed detections. Qualitative analysis confirmed robust detection across various tumor sizes and intensities, though some small or low-contrast lesions were missed.Conclusion: Proposed model surpasses YOLOv8s in accuracy and specificity for real-time brain tumor detection in MRI images, offering a promising balance between speed and precision. However, its slightly lower recall underscores the need for further refinement to minimize false negatives. The findings suggest transformer-based detectors can narrow the speed–accuracy gap in medical imaging, but broader validation and optimization for resource-constrained environments are required for clinical deployment. Future work should focus on enhancing sensitivity and generalizability through advanced augmentation, larger datasets, and ensemble approaches.
Artificial Intelligence
S. Nemati
Abstract
Background and Objectives: Community question-answering (CQA) websites have become increasingly popular as platforms for individuals to seek and share knowledge. Identifying users with a special shape of expertise on CQA websites is a beneficial task for both companies and individuals. Specifically, ...
Read More
Background and Objectives: Community question-answering (CQA) websites have become increasingly popular as platforms for individuals to seek and share knowledge. Identifying users with a special shape of expertise on CQA websites is a beneficial task for both companies and individuals. Specifically, finding those who have a general understanding of certain areas but lack expertise in other fields is crucial for companies who are planning internship programs. These users, called dash-shaped users, are willing to work for low wages and have the potential to quickly develop into skilled professionals, thus minimizing the risk of unsuccessful recruitment. Due to the vast number of users on CQA websites, they provide valuable resources for finding individuals with various levels of expertise. This study is the first of its kind to directly classify CQA users based solely on the textual content of their posts. Methods: To achieve this objective, we propose an ensemble of advanced deep learning algorithms and traditional machine learning methods for the binary classification of CQA users into two categories: those with dash-shaped expertise and those without. In the proposed method, we used the stack generalization to fuse the results of the dep and machine learning methods. To evaluate the effectiveness of our approach, we conducted an extensive experiment on three large datasets focused on Android, C#, and Java topics extracted from the Stack Overflow website. Results: The results on four datasets of the Stack Overflow, demonstrate that our ensemble method not only outperforms baseline methods including seven traditional machine learning and six deep models, but it achieves higher performance than state-of-the-art deep models by an average of 10% accuracy and F1-measure. Conclusion: The proposed model showed promising results in confirming that by using only their textual content of questions, we can classify the users in CQA websites. Specifically, the results showed that using the contextual content of the questions, the proposed model can be used for detecting the dash-shaped users precisely. Moreover, the proposed model is not limited to detecting dash-shaped users. It can also classify other shapes of expertise, such as T- and C-shaped users, which are valuable for forming agile software teams. Additionally, our model can be used as a filter method for downstream applications, like intern recommendations.
Artificial Intelligence
S. Nemati
Abstract
Background and Objectives: Twitter is a microblogging platform for expressing assessments, opinions, and sentiments on different topics and events. While there have been several studies around sentiment analysis of tweets and their popularity in the form of the number of retweets, predicting the sentiment ...
Read More
Background and Objectives: Twitter is a microblogging platform for expressing assessments, opinions, and sentiments on different topics and events. While there have been several studies around sentiment analysis of tweets and their popularity in the form of the number of retweets, predicting the sentiment of first-order replies remained a neglected challenge. Predicting the sentiment of tweet replies is helpful for both users and enterprises. In this study, we define a novel problem; given just a tweet's text, the goal is to predict the overall sentiment polarity of its upcoming replies.Methods: To address this problem, we proposed a graph convolutional neural network model that exploits the text's dependencies. The proposed model contains two parallel branches. The first branch extracts the contextual representation of the input tweets. The second branch extracts the structural and semantic information from tweets. Specifically, a Bi-LSTM network and a self-attention layer are used in the first layer for extracting syntactical relations, and an affective knowledge-enhanced dependency tree is used in the second branch for extracting semantic relations. Moreover, a graph convolutional network is used on the top of these branches to learn the joint feature representation. Finally, a retrieval-based attention mechanism is used on the output of the graph convolutional network for learning essential features from the final affective picture of tweets.Results: In the experiments, we only used the original tweets of the RETWEET dataset for training the models and ignored the replies of the tweets in the training process. The results on three versions of the RETWEET dataset showed that the proposed model outperforms the LSTM-based models and similar state-of-the-art graph convolutional network models. Conclusion: The proposed model showed promising results in confirming that by using only the content of a tweet, we can predict the overall sentiment of its replies. Moreover, the results showed that the proposed model achieves similar or comparable results with simpler deep models when trained on a public tweet dataset such as ACL 2014 dataset while outperforming both simple deep models and state-of-the-art graph convolutional deep models when trained on the RETWEET dataset. This shows the proposed model's effectiveness in extracting structural and semantic relations in the tweets.
