Aspect-Based Sentiment Analysis (ABSA) of app reviews allows a better understanding of user preferences regarding specific product features and helps the development team elicit requirements effectively. The existing literature faces challenges such as limited focus on the automation of Requirement Elicitation (RE), insufficient task-specific fine-tuning of models such as Bidirectional Encoder Representations from Transformers (BERT), and lack of interpretability owing to the black-box nature of these models. Therefore, our work makes the following significant contributions to address these challenges: (1) development and evaluation of a robust method based on ABSA for the automation of the RE process; (2) optimization of ABSA using BERT fine-tuning for enhanced performance, which includes conducting a comprehensive ablation study to obtain the best hyperparameters that guarantee the best model performance and robustness; and (3) integration of Explainable Artificial Intelligence (XAI) techniques for enhanced BERT model interpretability. Our work was evaluated on the ABSA Warehouse of Apps REviews (AWARE) dataset, a specifically tailored dataset for the RE process. Our study outperformed baseline models such as the Support Vector Machine (SVM), Convolutional Neural Network (CNN), and BERT, and achieved an average F1-Score of 0.83 for the Aspect Category Detection (ACD) task and 0.94 for the Aspect Category Polarity (ACP) task. In addition, we employed XAI using Locally Interpretable Model-Agnostic Explanations (LIME) to explain the BERT model prediction results, which aids in the improved visualization and interpretability of the app review analysis for the automated RE process.

The gastrointestinal (GI) tract is critical in digestion and nutrient absorption, thus vital for human health. However, it is prone to diseases like cancer. Manual assessments introduce accuracy variations, consistency issues, and delays. Resources like the GastroVision dataset were introduced to advance AI in this field. Yet, it faces class imbalance issues, and baseline evaluation lacks novel methodologies, impacting accuracy. We propose a novel deep-learning model to enhance accuracy and robustness. Our approach involves averaging weights of multiple models fine-tuned with diverse hyper-parameters. In contrast to classical ensembles, our approach uses DenseNet-121 as a baseline and enables the averaging of numerous models without incurring extra inference or memory costs. Data augmentation techniques are incorporated to address class imbalance. We achieve promising results on standard performance metrics, substantially improving over baseline, notably 2.4% points in Macro Precision. Additionally, we integrate explainable AI (XAI) techniques to enhance reliability and interpretability, shedding light on the model's decision-making processes. Our study contributes to robust methodologies for imbalanced datasets, promoting model transparency and trust in predictive outcomes for clinical decision support systems.

Large Language Models (LLMs) have revolutionized many industrial applications and paved the way for fostering a new research direction in many fields. Conventional Natural Language Processing (NLP) techniques, for instance, are no longer necessary for many text-based tasks, including polarity estimation, sentiment and emotion classification, and hate speech detection. However, training a language model for domain-specific tasks is hugely costly and requires high computational power, thereby restricting its true potential for standard tasks. This study, therefore, provides a comprehensive analysis of the latest pre-trained LLMs for various NLP-related applications without fine-tuning them to evaluate their effectiveness. Five language models are thus employed in this study on six distinct NLP tasks (including emotion recognition, sentiment analysis, hate speech detection, irony detection, offensiveness detection, and stance detection) for 12 languages from low- to medium- and high-resource. Generative Pre-trained Transformer 4 (GPT-4) and Gemini Pro outperform state-of-the-art models, achieving average F1 scores of 70.6% and 68.8% on the Tweet Sentiment Multilingual dataset compared to the state-of-the-art average F1 score of 66.8%. The study further interprets the findings obtained by the LLMs using Explainable Artificial Intelligence (XAI). To the best of our knowledge, it is the first time any study has employed explainability on pre-trained language models.

Students' feedback is an essential part of the teaching-learning process and serves as an effective instrument for continuous improvement in educational environments. The insights gathered from students' experiences and perceptions expressed in reviews provide instructors with a valuable resource to enhance their teaching methods, instructional design, and overall classroom dynamics. However, students' reviews are often unclear, contradictory, and conflicting with each other, making their interpretation and use challenging. Therefore, this study proposes a novel deep learning-based approach that helps course designers and instructors effectively identify constructive and useful reviews. The approach leverages the integration of several attributes, including textual review, student satisfaction, meta-data of the course, and review-derived information such as sentiment, readability, and review depth. The approach is tested on a real-life dataset comprising 38,717 reviews gathered from the Coursera learning platform for the purpose of this study. The experimental results, with an F1-score of 0.91, suggest that the approach can be an effective tool for educators and instructional designers to identify helpful student reviews.

Medical image analysis has undergone significant advancements with the integration of machine learning techniques, particularly in the realm of bone anomaly detection. The availability of recent datasets and the lack of benchmarking and explainability components provide numerous opportunities in this domain. This study proposes a benchmarking approach to a recently published FracAtlas dataset utilizing state-of-the-art deep-learning models coupled with explainable artificial intelligence (XAI) having two distinct modules. The first module involves the binary classification of fractures in different body parts and explains the decision-making process of the best-performing model using an XAI technique known as EigenCAM. EigenCAM generates heatmaps on every layer of the YOLOv8m model to explain how the model reached a conclusion and localizes the fracture using a heatmap. To verify the heatmap, we also detected fractures using the YOLOv8m detection model, which achieved a mAP@O.5 of 59.5%, outperforming the baseline results on this dataset. The second module involves a multi-class classification task to categorize images into one of the five anatomical regions. The best-performing model for binary classification is the YOLOv8m model, with an accuracy of 83.1%, whereas the best-performing model for multi-class classification is the YOLOv8s, achieving an accuracy of 96.2%.

Diagnosing and treating abnormalities in the wrist, specifically distal radius, and ulna fractures, is a crucial concern among children, adolescents, and young adults, with a higher incidence rate during puberty. However, the scarcity of radiologists and the lack of specialized training among medical professionals pose a significant risk to patient care. This problem is further exacerbated by the rising number of imaging studies and limited access to specialist reporting in certain regions. This highlights the need for innovative solutions to improve the diagnosis and treatment of wrist abnormalities. Automated wrist fracture detection using object detection has shown potential, but current studies mainly use two-stage detection methods with limited evidence for single-stage effectiveness. This study employs state-of-the-art single-stage deep neural network-based detection models YOLOv5, YOLOv6, YOLOv7, and YOLOv8 to detect wrist abnormalities. Through extensive experimentation, we found that these YOLO models outperform the commonly used two-stage detection algorithm, Faster R-CNN, in fracture detection. Additionally, compound-scaled variants of each YOLO model were compared, with YOLOv8 m demonstrating a highest fracture detection sensitivity of 0.92 and mean average precision (mAP) of 0.95. On the other hand, YOLOv6 m achieved the highest sensitivity across all classes at 0.83. Meanwhile, YOLOv8x recorded the highest mAP of 0.77 for all classes on the GRAZPEDWRI-DX pediatric wrist dataset, highlighting the potential of single-stage models for enhancing pediatric wrist imaging.

According to WHO, approximately 1.71 billion people worldwide have musculoskeletal conditions, which include various issues such as intact muscles, bones, joints, and fractures. Among those, fractures are the most common. Due to the emergency nature of diagnosing fractures, there is a high chance of misdiagnosis for several reasons, such as the unavailability of radiologists, physicians' lack of experience, and other factors. Fracture diagnostic or X-ray interpretation errors can be reduced if radiographs are always read instantly by radiologists or automatically. In our study, we are focusing on automating wrist fracture diagnosis, where we are utilizing the publicly available GRAZPEDWRI-DX dataset, which consists of 20,327 wrist radiographs. We employed the YOLOv9 model for fracture detection, achieving an mAP@50 of 0.677, which surpasses previous benchmarks. For fracture classification, we trained several state-of-the-art deep learning models, including VGG16, VGG19, ResNet50, EfficientNetB7, DenseNet121, MobileNet, and ConvNeXtXLarge. In particular, the YOLOv8-cls model surpassed all others in accuracy (0.93), precision (0.9352), and recall (0.8855), reaching its peak performance at epoch 70. To elucidate the decision-making process of both the detection and classification models, we generated explainable saliency maps through EigenCAM, making the models more explainable and interpretable.

Wrist pathologies, particularly fractures common among children and adolescents, present a critical diagnostic challenge. While X-ray imaging remains a prevalent diagnostic tool, the increasing misinterpretation rates highlight the need for more accurate analysis, especially considering the lack of specialized training among many surgeons and physicians. Recent advancements in deep convolutional neural networks offer promise in automating pathology detection in trauma X-rays. However, distinguishing subtle variations between pediatric wrist pathologies in X-rays remains challenging. Traditional manual annotation, though effective, is laborious, costly, and requires specialized expertise. In this paper, we address the challenge of pediatric wrist pathology recognition with a fine-grained approach, aimed at automatically identifying discriminative regions in X-rays without manual intervention. We refine our fine-grained architecture through ablation analysis and the integration of LION. Leveraging Grad-CAM, an explainable AI technique, we highlight these regions. Despite using limited data, reflective of real-world medical study constraints, our method consistently outperforms state-of-the-art image recognition models on both augmented and original (challenging) test sets. Our proposed refined architecture achieves an increase in accuracy of 1.06% and 1.25% compared to the baseline method, resulting in accuracies of 86% and 84%, respectively. Moreover, our approach demonstrates the highest fracture sensitivity of 97%, highlighting its potential to enhance wrist pathology recognition.

Nowadays, various applications across industries, healthcare, and security have begun adopting automatic sentiment analysis and emotion detection in short texts, such as posts from social media. Twitter stands out as one of the most popular online social media platforms due to its easy, unique, and advanced accessibility using the API. On the other hand, supervised learning is the most widely used paradigm for tasks involving sentiment polarity and fine-grained emotion detection in short and informal texts, such as Twitter posts. However, supervised learning models are data-hungry and heavily reliant on abundant labeled data, which remains a challenge. This study aims to address this challenge by creating a large-scale real-world dataset of 17.5 million tweets. A distant supervision approach relying on emojis available in tweets is applied to label tweets corresponding to Ekman’s six basic emotions. Additionally, we conducted a series of experiments using various conventional machine learning models and deep learning, including transformer-based models, on our dataset to establish baseline results. The experimental results and an extensive ablation analysis on the dataset showed that BiLSTM with FastText and an attention mechanism outperforms other models in both classification tasks, achieving an F1-score of 70.92% for sentiment classification and 54.85% for emotion detection.

The exploration of automated wrist fracture recognition has gained considerable research attention in recent years. In practical medical scenarios, physicians and surgeons may lack the specialized expertise required for accurate X-ray interpretation, highlighting the need for machine vision to enhance diagnostic accuracy. However, conventional recognition techniques face challenges in discerning subtle differences in X-rays when classifying wrist pathologies, as many of these pathologies, such as fractures, can be small and hard to distinguish. This study tackles wrist pathology recognition as a fine-grained visual recognition (FGVR) problem, utilizing a limited, custom-curated dataset that mirrors real-world medical constraints, relying solely on image-level annotations. We introduce a specialized FGVR-based ensemble approach to identify discriminative regions within X-rays. We employ an Explainable AI (XAI) technique called Grad-CAM to pinpoint these regions. Our ensemble approach outperformed many conventional SOTA and FGVR techniques, underscoring the effectiveness of our strategy in enhancing accuracy in wrist pathology recognition.