Anomaly detection is a challenging task. Anomalies are deviant behavior from the normal pattern. However, unsupervised anomalies are mostly validated by domain experts. All-time experts are not available, and manually ...

Drug–Drug Interaction (DDI) prediction plays a critical role in computational pharmacology, where the accurate identification of potential adverse interactions can prevent harmful clinical outcomes and improve patient ...

Sickle Cell Anemia (SCA) is an inherited genetic blood disorder caused by mutations in hemoglobin genes. In this disorder, red blood cells become sickled and less flexible, leading to blocked blood flow in small vesicles, ...

The use of immersive technologies in education has transformed the way complicated technical concepts are taught and understood. Immersive Virtual Reality (IVR), in particular, promotes interactive and experiential learning, ...

In recent years, the rapid expansion of Virtual Learning Environments (VLEs) and online education platforms has significant ly transformed higher education, simultaneously introducing new challenges such as increased student ...

This thesis presents a context-aware image captioning framework that bridges the gap between visual perception and natural language generation by combining deep learning with structured semantic reasoning. While conventional ...

Skin diseases pose a worthy of attention in health concern thus millions of people across various demographics. Timely and error-free diagnosis is critical for effective medical care and control but unfortunately there is ...

Predicting a person’s personality can significantly enhance personalized recommendations across various domains. This study introduces PersonalityBERT, a deep learning model designed to determine personality traits from ...

Thalassemia, particularly beta-thalassemia major, is a severe genetic blood disorder that presents significant challenges in diagnosis due to its similarity to other anemias. The lack of advanced diagnostic tools, especially ...

The purpose of this paper is to develop a clear understanding of how to apply machine learning for the analysis of clinical data that can advance CIH, besides formal medicines. CIH methods are evolving into more common ...