The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
IJACSA research on disease prediction and diagnosis using machine learning and AI — covering heart, liver, kidney, respiratory, skin, and infectious disease prediction models.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed August 2026
This study proposes a hybrid machine learning framework for rice leaf disease detection by combining handcrafted feature extraction with metaheuristic optimization and classical classifiers. Using a dataset of 6,000 rice…
Multi-sensor data in medical monitoring includes waveform changes in physiological signals and time-series characteristics of disease progression. These features typically exhibit high-dimensionality, large-scale, and ti…
The integration of IoT in healthcare has remained very dynamic, with a lot of improvement in the health of patients and the running of operations. Integration also comes with new risks and threats, raising IoT healthcare…
Dengue fever continues to be a significant public health issue across the globe because it can lead to life-threatening complications. Severity prediction in a timely and precise manner is imperative for proper clinical…
According to international organizations’ statistics, the percentage of disabled people is considered not just a small percentage of the world's population. Improving the quality of life for people by using new technolog…
In resource-limited settings, assisting physicians with disease identification can significantly improve patient outcomes. Early diagnosis is crucial, as many patients could remain healthy with timely intervention. Recen…
In recent years, the healthcare sector has faced growing challenges in managing patient data securely and efficiently, especially when it comes to data privacy and the way information is shared across healthcare provider…
Radiological bone age assessment is essential for diagnosing pediatric growth and developmental disorders. The conventional Greulich-Pyle Atlas, though widely used, is manual, time-intensive, and prone to inter-observer…
Federated learning (FL) is a cutting-edge method of collaborative machine learning that lets organizations or companies train models without exchanging personal information. Adversarial attacks such as data poisoning, mo…
To that end, this study presents the Hierarchical Context-Aware Transformer (HCAT), a new model to perform analysis on unstructured healthcare data that resolves significant problems related to medical text. In the propo…
