Journal of Electronics, Electromedical Engineering, and Medical Informatics

Rule-Based Adaptive Chatbot on WhatsApp for Visual, Auditory, and Kinesthetic Learning Style Detection

2025-11-28T05:43:44+07:00

Adapting learning methods to individual learning styles remains a major challenge in digital education due to the static nature of traditional questionnaires and the absence of adaptive feedback mechanisms. This study aimed to develop a rule-based adaptive WhatsApp chatbot capable of automatically identifying users’ learning styles, visual, auditory, and kinesthetic, through a weighted questionnaire enhanced with probabilistic refinement. The proposed system introduces an adaptive decision framework that dynamically manages conversation flow using score dominance evaluation, early termination, and selective question expansion. Bayesian posterior probability estimation is employed to strengthen decision confidence in borderline cases, ensuring consistent and interpretable results even when user responses are ambiguous. The chatbot was implemented using WhatsApp-web.js and MongoDB, supported by session validation and activity log monitoring to ensure operational reliability and data integrity. System validation involved white-box testing using Cyclomatic Complexity to verify logical accuracy and 20-fold cross-validation using a Support Vector Machine (SVM) to evaluate classification performance. The adaptive model achieved an accuracy of 80.2% and an AUC of 0.902, supported by a balanced precision (0.738), recall (0.662), and F1-score (0.698). These results demonstrate stable discriminative capability and confirm that the adaptive scoring mechanism effectively reduces redundant questioning, lowers cognitive load, and improves interaction efficiency without compromising reliability. In conclusion, the study successfully achieved its objective of developing an adaptive, efficient, and mathematically transparent learning style detection system. The findings confirm that adaptive rule-based logic reinforced by probabilistic reasoning can significantly enhance the efficiency and reliability of digital learning assessments. Future research will extend this framework by incorporating multimodal behavioral indicators and personalized learning content to further strengthen adaptive learning support

BTISS-WNET: Deep Learning-based Brain Tissue Segmentation using Spatio Temporal WNET

2025-11-26T14:07:41+07:00

Brain tissue segmentation (BTISS) from magnetic resonance imaging (MRI) is a critical process in neuroimaging, aiding in the analysis of brain morphology and facilitating accurate diagnosis and treatment of neurological disorders. A major challenge in BTISS is intensity inhomogeneity, which arises from variations in the magnetic field during image acquisition. This results in non-uniform intensities within the same tissue class, particularly affecting white matter (WM) segmentation. To address this problem, we propose an efficient deep learning-based framework, BTISS-WNET, for accurate segmentation of brain tissues. The main contribution of this work is the integration of a spatio-temporal segmentation strategy with advanced pre-processing and feature extraction to overcome intensity inconsistency and improve tissue differentiation. The process begins with skull stripping to eliminate non-brain tissues, followed by Empirical Wavelet Transform (EWT) for noise reduction and edge enhancement. Data augmentation techniques, including random rotation and flipping, are applied to improve model generalization. The preprocessed images are fed into Res-GoogleNet (RGNet) to extract deep semantic features. Finally, a Spatio-Temporal WNet is used for precise WM segmentation, leveraging spatial and temporal dependencies for improved boundary delineation. The proposed BTISS-WNET model achieves a segmentation accuracy of 99.32% for white matter. It also demonstrates improved accuracy of 1.76%, 18.23%, and 16.02% over DDSeg, BISON, and HMRF-WOA, respectively. In conclusion, BTISS-WNET provides a robust and high-accuracy framework for WM segmentation in MRI images, with promising applications in clinical neuroimaging. Future work will focus on validating the model using real clinical datasets and extending it to multi-tissue and multi-modal MRI segmentation

Deep Learning Based Ovarian Cancer Classification Using EfficientNetB2 with Attention Mechanism

2025-11-28T05:50:52+07:00

Ovarian cancer is a gynecological malignancy comprising multiple histopathological subtypes. Traditional diagnostic tools like histopathology and CA-125 tests suffer from limitations, including inter-observer variability, low specificity, and time-consuming procedures, often leading to delayed or incorrect diagnoses, which are subjective and error-prone. Conventional machine learning models, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), have been applied but often struggle with high-dimensional image data and fail to extract deep morphological features. This study proposes a DL-based framework to classify ovarian cancer subtypes from histopathological images, aiming to enhance diagnostic accuracy and clinical decision-making. Initially, Deep learning was applied using pre-trained architectures such as VGG-16, Xception, and EfficientNetB2. However, the standout innovation in this study is the integration of EfficientNetB2 with Convolutional Block Attention Module (CBAM), an attention mechanism module. An attention mechanism allows the model to focus on the most informative regions of the image, thereby improving diagnostic precision. The proposed system was trained and validated on a diverse, well-structured dataset, achieving high accuracy and strong generalization capability. EfficientNetB2 with CBAM outperformed other models, achieving a 91% accuracy rate compared to 52% for VGG-16, 72% for Xception, and 82% for the baseline EfficientNetB2 model. This attention-enhanced, scalable AI model demonstrates strong potential for clinical application. It provides faster and more efficient classification of ovarian cancer subtypes compared to conventional approaches. The framework has the potential to improve survival outcomes for patients with ovarian cancer. The proposed system demonstrates a significant improvement in ovarian cancer subtype classification (High-Grade Serous Carcinoma, Low-Grade Serous Carcinoma, Clear-Cell, Endometrioid, and Mucinous Carcinoma). It provides a practical tool for aiding early diagnosis and treatment planning, with potential for integration into clinical workflows.