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Explore the Archive
Breast cancer is one of the causes of cancer-related death in all parts of the world, and in this case, early and accurate cancer diagnosis is the key to enhancing patient survival and treatment results. Even though medical imaging techniques, e.g., mammography and ultrasound, are commonplace, the current computer-aided diagnostic strategies tend to be ineffective in terms of spatial interdependencies and complementary information among different imaging modalities. In order to overcome this drawback, this paper introduces a multi-modal breast cancer detection model, which aids in the representation of mammogram and ultrasound images as a graph, and uses the representational characteristics of Graph Neural Networks (GNNs) to learn complex spatial relationships among regions of interest. Two high-level architectures are explored, one is a Spatial-Temporal Graph Neural Network (ST-GNN) designed to learn the contextual appearance of the spatial dimension, and the other is an LSTM-enhanced GNN (LSTM-GNN) designed to learn the temporal appearance of the temporal dimension. The experiment is carried out on a clinically validated dataset of 205 cases, and 405 high-resolution mammogram and ultrasound images of participants gathered with the help of the standardized medical imaging device. Quantitative analysis proves that the ST-GNN is much better than the LSTM-GNN, which has an accuracy of 85, a precision of 74, a recall of 93, an F1-score of 82, and an AUC of 0.87, indicating the efficiency of spatial graph modeling in breast cancer detection. The findings validate the hypothesis that multi-modal fusion based on graphs will offer a strong and scalable solution to improve diagnostic accuracy, sensitivity, and clinical decision support in automated systems of breast cancer screening.
Automated assessment of cardiac health from magnetic resonance imaging (MRI) plays an important role in assisting the clinical diagnosis of cardiac problems with great accuracy and in a timely manner; however, it is still challenged by variations in image quality and complex structures of the heart. This paper presents a multi-faceted deep learning model of cardiac image segmentation and classification with a multifaceted preprocessing and modeling pipeline. First, a strong preprocessing method, such as resampling, intensity normalization, and histogram equalization, was used to increase image quality and consistency, and then the process of data augmentation was used to increase the diversity of the dataset and model robustness. For the segmentation phase, an Inverted U-Net-based architecture is created by incorporating Deep Neural Networks (DNN), Graph Neural Networks (GNN), and Long Short-Term Memory (LSTM) networks to properly capture spatial and contextual dependency in the cardiogram images. The segmentation performance was evaluated with accuracy, precision, and dice coefficient, as the Inverted U-Net++ with LSTM showed the best performance, with the maximum accuracy being 0.813 by successfully modeling the complicated structural patterns. In the classification phase, the segmented cardiac images were categorized into normal and abnormal segmentation using Support Vector Machine or SVM, Long Short Term Memory or LSTM, and Convolutional Neural Networks or CNN. Comprehensive evaluation with the help of accuracy, precision, recall, and F1-score proved that LSTM-based classifiers outperform the competing models with the highest classification accuracy of 0.813. The comparison between segmentation and classification stages reveals the effectiveness and strength of the offered framework. Overall, the results show the potential of the proposed approach to provide accurate and reliable automated cardiac health assessment, and is a promising decision support tool for clinical cardiac diagnosis.
Artificial intelligence (AI) has been a transformational force in higher education today, providing new opportunities to differentiate instruction to meet individual learning needs. Despite the creation of multiple online platforms, digital assessments and automated feedback systems, many universities still use traditional, uniform teaching practices that do not cater for different learner backgrounds, motivations and cognitive preferences. Although AI-powered personalization is widely acknowledged as an exciting solution, empirical evidence on how well it works, especially in real classroom settings and in developing regions is scarce. This study addresses this gap by assessing the impact of an AI-based adaptive learning system that dynamically aligns the instructional content according to the performance trend, pace of learning and engagement behaviors of the students. The research will be conducted using a mixed-methods methodology, which will combine performance metrics and analytics, tailored content recommendations, engagement metrics, and surveys of student perceptions to analyze both the quantitative increase in learning outcomes and qualitative information about the perceptions of the learner. Findings show that AI-powered personalization has an important impact on improving academic performance, engagement, and be well received by students as supportive and accessible. The study adds to the practical and theoretical implications for the institutions who are interested in incorporating adaptive learning technologies, providing evidence-based recommendations for effective adoption in higher education settings.
Journal of Innovations in Social and Applied Sciences (JISAS) is a leading international, peer-reviewed, open-access journal dedicated to advancing knowledge across the diverse fields of social and applied sciences. We provide a dynamic platform for researchers, practitioners, and thought leaders to share original research, comprehensive reviews, and innovative methodologies that address complex societal and technological challenges.
Our journal welcomes interdisciplinary contributions that bridge the gap between theory and practice, encouraging collaboration among scholars from various disciplines. By fostering a culture of academic excellence, transparency, and inclusivity, JISAS aims to accelerate the dissemination of impactful research that drives positive change in communities worldwide.
At JISAS, we are committed to upholding the highest standards of scholarly publishing, ensuring rapid peer review, and maintaining open access to all published content. Our mission is to inspire new perspectives, promote ethical research practices, and support the global exchange of ideas that lead to innovative solutions for real-world problems.
“JISAS is a beacon for interdisciplinary research, connecting scholars and practitioners worldwide.”
The aims and objectives of JISAS are to advance the frontiers of knowledge and practice in the social and applied sciences by publishing high-quality, original research, comprehensive reviews, and innovative methodologies. We are dedicated to fostering a vibrant academic community that values interdisciplinary collaboration, critical thinking, and the translation of research into real-world impact.
JISAS welcomes submissions in a wide range of topics at the intersection of social and applied sciences. We encourage innovative, interdisciplinary, and impactful research in, but not limited to, the following areas: