Marshima Mohd Rosli’s research while affiliated with MARA University of Technology and other places

Most organizations have begun to adopt agile methods to pursue successful software development. However, the adoption and implementation of agile approaches are facing various challenges. The success of agile software development depends on Critical Success Factors (CSFs), which this study aims to identify and classify based on their relative importance. Through an extensive literature review, these factors are summarized, screened, and categorized into six dimensions. Their evolution is also outlined and analyzed. Then, the factors are illustrated through a bubble chart. Furthermore, this study determines the relevant CFSs that have a significant impact on how effectively can agile software development be implemented in China. The findings suggest certain recommendations to ensure that agile software projects are efficiently implemented in China, maximizing the chances of project success, providing valuable insights and practical guidance.

Objectives This study aims to automatically determine the cervical vertebral maturation staging (CVM) on lateral cephalometric radiograph images using a customized deep convolutional neural network (DCNN) model and to evaluate the classification performance using a custom DCNN model in detecting CVM stages. Methods A dataset of 922 digital lateral cephalometric radiographs from individuals aged 7–20 years was collected. Image quality was assessed for artifacts and clarity of C2-C4 vertebrae. CVM staging was independently performed by two orthodontists, with inter-observer reliability assessed using kappa coefficient. Image pre-processing involved random oversampling to address class imbalance and resizing to 128x128 pixels. A custom convolutional neural network was developed, with hyperparameters optimized using random search. The final architecture comprised convolutional layers, global average pooling, dense layers, and dropout. The model was trained for 50 epochs using Adam optimizer and categorical cross-entropy loss. Performance evaluation included accuracy, loss, and confusion matrix analysis on a validation set. Results A novel convolutional neural network was developed for the classification of CVM staging. This custom model initially exhibited overfitting, achieving perfect training accuracy but only 57% validation accuracy due to class imbalance. Implementing Random Oversampling (ROS) addressed this issue by balancing the dataset. Hyperparameter tuning optimized the model architecture, resulting in a final validation accuracy of 85.96%. The model demonstrated strong performance in classifying CVMS 1, 2, and 3, with precision and recall exceeding 95%. However, classification of CVMS 4 and 5 posed challenges, with lower precision and recall values. Overall accuracy reached 88.2%, indicating a generally robust model, though further improvements are necessary for CVMS 5. Conclusion This study successfully developed a custom deep convolutional neural network for automated cervical vertebral maturation (CVM) staging on lateral cephalometric radiographs. By addressing class imbalance and optimizing hyperparameters, the model achieved a validation accuracy of 88.2%. While demonstrating potential for clinical application, the model’s performance varied across CVM stages, indicating a need for further refinement to improve accuracy and robustness.

The recent healthcare transformations emphasize the importance of individuals maintaining a healthy lifestyle through proper nutrition and physical activity to reduce the risk of severe illnesses. Patients often search for information on their own, leading to uncertainty about appropriate diets or fitness activities. Consequently, many individuals cross-check information or health advice from various sources. However, some people hesitate to verify online health-related information with their clinicians, fearing that it may be perceived as a challenge to their expertise and authority. This study aimed to determine a useful way to monitor a patient’s lipid profile and provide recommendations for meal plans and fitness activities. A content-based approach that utilizes a vector space model is employed in the development of a recommender method. The vector space model uses meal plan keywords to suggest similar items, and selection rules are applied to identify relevant meal plan and fitness activity options. This approach has been integrated into a mobile application for healthcare, enabling patients to receive personalized recommendations based on their lipid levels. To assess the usability of the mobile application, an initial user study was conducted, which revealed that most respondents had a positive opinion of the application. In the future, the application could be enhanced with a wider variety of meal plans and additional features.

Multi-class data classification is distinguished as a significant and challenging research topic in contemporary machine learning, particularly when concerning imbalanced data sets. Hence, a thorough investigation of multi-class imbalanced data classification is becoming increasingly pertinent. In this paper, an overview of multi-class imbalanced data classification was generated via conducting a systematic mapping study, which endeavors to analyze the state of contemporary multi-class imbalanced data classification, with the primary goal of ascertaining the corpus of research undertaken in machine learning. To achieve this aim, 7,164 papers were assessed and the 147 prominent ones were selected from five digital libraries, which were further categorized according to techniques, issues, and types of datasets. After a thorough review of these papers, a taxonomy of multi-class imbalanced data classification techniques is proposed. Based on the results, researchers widely employ algorithmic-level, ensemble, and oversampling strategies to address the issue of multi-class imbalance in medical datasets, primarily to mitigate the impact of challenging data factors. This research highlights an urgent need for more studies on multi-class imbalanced data classification.

The current paper describes the development of an online Collaborative Intelligent Individual Education Platform (CIIP) that is specifically designed for children with ASD based on experts' assessments and progress reports. The online platform facilitates the progress of children with special needs as it is established on their individual needs and can be accessible anywhere. The CIIP system was developed following a prototyping model approach that comprised initial requirements, design, prototyping, customer evaluation, review and refinement, development, testing, and maintenance. Two cycles of prototyping evaluation were conducted to confirm the final requirements. The results of the prototype evaluation by the stakeholders indicated that 29 changes were required before progressing to the final development of CIIP. System testing was carried out with expert testers to ensure the CIIP functions and the satisfaction of the expected requirements. The results showed that 22% of the test cases failed due to difficulties with complicated interconnections in several modules. Despite these challenges, CIIP was able to meet the requirement specifications and perform as expected.