This study has developed a theoretical model capable of evaluating the relationships among requirement clarity, sprint efficiency, and software quality in Agile software development. Although Agile adopts a highly flexible and continuous delivery approach, requirement ambiguity remains one of the major issues that negatively impacts both sprint performance and product quality. Grounded in information processing theory, cognitive load theory, and systems theory, this research reveals how unclear or insufficient requirements lead to increased defect rates, reduced sprint velocity, and diminished planning efficiency. Furthermore, the model establishes a set of metrics to support outcome measurement, including the Requirement Volatility Index (RVI), Sprint Velocity (SV), Defect Density (DD), and Quality Index (QI). From a practical perspective, this study affirms that requirement validation processes, stakeholder alignment, and continuous feedback are essential to Agile success. Overall, this research develops a theoretically and empirically grounded model that assists Agile practitioners, project managers, and quality engineers in anticipating and managing requirements-based risks, while also serving as a tool for real-time quality assurance during sprints.
Convolutional Neural Networks (CNNs) have exposed robust results for classifying images in data analytics. On the other hand, the effectiveness of these models is often limited by their substantial computational complexities, particularly when dealing with features represented in complex mathematical environments, multi class image classification, and especially when dealing with complex nonlinear relationships between features, which need to be addressed for transparent decision-making processes. This study developed a Quantum-Enhanced Convolutional Neural Network (Q-CNN) model, which incorporates traditional convolution-based feature extraction, Quantum Feature Encoding, and quantum processing. The developed model applies a quantum encoding mechanism to map classical features to quantum states for alternative feature representations and classification purposes. Experimental results show that the Q-CNN outperformed the standard CNN, reaching 97% training and validation accuracy compared to 94%, and lowering training and validation losses from 17% and 18% to 9% and 11%, respectively. Furthermore, precision, recall, and F1-score were increased from 90%, 88%, and 89% to 92%, 94%, and 93%, respectively. These findings demonstrate the potential effectiveness of quantum feature encoding for image classification tasks and provide a structured framework for enriched feature representation in convolution-based image processing models.
This study explores TVET students’ use of ChatGPT, its relationship with problem-solving abilities, and their ethical awareness. A quantitative cross-sectional survey was conducted with 381 students at Polytechnic Ungku Omar (PUO). Convenience sampling was used to select the participants. The data were analyzed using descriptive statistics and correlation analysis. The findings show that students often use ChatGPT to understand difficult concepts. Higher levels of use were positively related to self-reported problem-solving skills (r = 0.729, p < 0.01). The study contributes to the literature by using the Affective, Behavioral, Cognitive, and Ethical (ABCE) framework in the Malaysian TVET context. The framework demonstrates the connection between behavioral engagement and cognitive development. However, the use of convenience sampling and data from only one institution limits the findings. The results emphasize the need for AI ethics workshops, responsible AI use guidelines, proper citation practices, and digital literacy training in educational institutions.
The goal of the study is to make a hybrid prediction model that uses both machine learning and deep learning methods to make diabetes predictions more accurate, generalizable, and strong. It combines ML and DL models, fixes class imbalance in medical datasets, and tests performance on several datasets, such as the Pima Indians Diabetes Dataset and the LMCH dataset, to see how well it works in real-life healthcare. The ML Ensemble, which included RF, LR, and XGBoost, and the DL Ensemble, which included CNN, FNN, and ENN, were the two stacked ensembles used in the study. Soft voting was used to aggregate the results in order to improve the accuracy of the predictions. In order to prepare the structured medical data, we employed feature preprocessing techniques and the Synthetic Minority Over-sampling Technique (SMOTE). Cross-validation was used to ensure that the results were good and to prevent them from being overly specific. The performance was compared to independent models and standard methods. The ensemble hybrid AI model performed better than the traditional ML and DL models. Its best performance metrics were Accuracy of 98.89%, Precision of 98.99%, Recall of 87.07%, F1-score of 92.05%, ROC-AUC of 92.48%, and Cohen's Kappa of 84.96%. This shows that it was better at making generalizations and working with datasets that weren't balanced. The stacking of ensembles with soft voting combines machine learning and deep learning models to improve diabetes prediction performance and fix problems with class imbalance in medical datasets. The model's ability to be used in the real world and its ability to be generalized show that it could be used to find diabetes early and accurately, which could help with preventive healthcare strategies.
In outpatient clinics, where extended patient waits times frequently result in discontent and inefficient workflow, effective queue management is still a recurring problem. The effectiveness of current digital queuing systems in cutting down on overall waiting time is sometimes limited by their lack of interaction with pharmacy services or mobile-based real-time updates. In order to improve patient flow and service transparency in outpatient settings, this study introduces QKlinik, a web-based queue management system. Through an integrated interface, the system enables patients to easily make pharmacy purchases, track real-time wait status, and reserve queue numbers online using QR codes. The system was developed iteratively via planning, design, development, testing, and evaluation phases using the Agile methodology. PHP, HTML, and PhpMyAdmin were used to implement the prototype and maintain the backend data. Despite not having been verified in a real-world clinical setting yet, QKlinik was put through simulated user assessments and black box testing with patients, physicians, and clinic employees. According to the results, the system successfully lowers perceived waiting time, maintains precise queue sequencing, and offers a smooth and user-friendly experience, with an average usability rating of 4.78 out of 5. All things considered, QKlinik shows how web-based queue management may enhance clinical operations and raise patient happiness in outpatient environments.
