Biomedical named entity recognition (BioNER) is a preliminary task for many other tasks, e.g., relation extraction and semantic search. Extracting the text of interest from biomedical documents becomes more demanding as the availability of online data is increasing. Deep learning models have been adopted for biomedical named entity recognition (BioNER) as deep learning has been found very successful in many other tasks. Nevertheless, the complex structure of biomedical text data is still a challenging aspect for deep learning models. Limited annotated biomedical text data make it more difficult to train deep learning models with millions of trainable parameters. The single-task model, which focuses on learning a specific task, has issues in learning complex feature representations from a limited quantity of annotated data. Moreover, manually constructing annotated data is a time-consuming job. It is, therefore, vital to exploit other efficient ways to train deep learning models on the available annotated data. This work enhances the performance of the BioNER task by taking advantage of various knowledge transfer techniques: multitask learning and transfer learning. This work presents two multitask models (MTMs), which learn shared features and task-specific features by implementing the shared and task-specific layers. In addition, the presented trained MTM is also fine-tuned for each specific dataset to tailor it from a general features representation to a specialized features representation. The presented empirical results and statistical analysis from this work illustrate that the proposed techniques enhance significantly the performance of the corresponding single-task model (STM).

Single-task models (STMs) struggle to learn sophisticated representations from a finite set of annotated data. Multitask learning approaches overcome these constraints by simultaneously training various associated tasks, thereby learning generic representations among various tasks by sharing some layers of the neural network architecture. Because of this, multitask models (MTMs) have better generalization properties than those of single-task learning. Multitask model generalizations can be used to improve the results of other models. STMs can learn more sophisticated representations in the training phase by utilizing the extracted knowledge of an MTM through the knowledge distillation technique where one model supervises another model during training by using its learned generalizations. This paper proposes a knowledge distillation technique in which different MTMs are used as the teacher model to supervise different student models. Knowledge distillation is applied with different representations of the teacher model. We also investigated the effect of the conditional random field (CRF) and softmax function for the token-level knowledge distillation approach, and found that the softmax function leveraged the performance of the student model compared to CRF. The result analysis was also extended with statistical analysis by using the Friedman test.

Modern web applications change frequently in response to user and market needs, making their testing challenging. Manual testing and automation methods often struggle to keep up with these changes. We propose an automated testing framework, AutoQALLMs, that utilises various LLMs (Large Language Models), including GPT-4, Claude, and Grok, alongside Selenium WebDriver, BeautifulSoup, and regular expressions. This framework enables one-click testing, where users provide a URL as input and receive test results as output, thus eliminating the need for human intervention. It extracts HTML (Hypertext Markup Language) elements from the webpage and utilises the LLMs API to generate Selenium-based test scripts. Regular expressions enhance the clarity and maintainability of these scripts. The scripts are executed automatically, and the results, such as pass/fail status and error details, are displayed to the tester. This streamlined input–output process forms the core foundation of the AutoQALLMs framework. We evaluated the framework on 30 websites. The results show that the system drastically reduces the time needed to create test cases, achieves broad test coverage (96%) with Claude 4.5 LLM, which is competitive with manual scripts (98%), and allows for rapid regeneration of tests in response to changes in webpage structure. Software testing expert feedback confirmed that the proposed AutoQALLMs method for automated web application testing enables faster regression testing, reduces manual effort, and maintains reliable test execution. However, some limitations remain in handling complex page changes and validation. Although Claude 4.5 achieved slightly higher test coverage in the comparative evaluation of the proposed experiment, GPT-4 was selected as the default model for AutoQALLMs due to its cost-efficiency, reproducibility, and stable script generation across diverse websites. Future improvements may focus on increasing accuracy, adding self-healing techniques, and expanding to more complex testing scenarios.

HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.