Leveraging a hybrid convolutional gated recursive diabetes prediction and severity grading model through a mobile app.

Diabetes mellitus is a common illness associated with high morbidity and mortality rates. Early detection of diabetes is essential to prevent long-term health complications. The existing machine learning model struggles with accuracy and reliability issues, as well as data imbalance, hindering the creation of a dependable diabetes prediction model.

Super learner model for classifying leukemia through gene expression monitoring.

Leukemia is a form of cancer that affects the bone marrow and lymphatic system, and it requires complex treatment strategies that vary with each subtype. Due to the subtle morphological differences among these types, monitoring gene expressions is crucial for accurate classification. Manual or pathological testing can be time-consuming and expensive.

Drug repurposing against galectin-3 using simulation-based studies.

The protein galectin, which binds to carbohydrates and is involved in a number of therapeutic processes including cell proliferation, inflammatory responses, apoptosis, etc., has been discovered as a potential therapeutic target. Galectin-3 is a stable biomarker that exhibits both increased and decreased expression in a variety of illnesses and infections, regardless of sex, age, or body mass index.

Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making.

Learning using the Internet or training through E-Learning is growing rapidly and is increasingly favored over the traditional methods of learning and teaching. This radical shift is directly linked to the revolution in digital computer technology. The revolution propelled by innovation in computer technology has widened the scope of E-Learning and teaching, whereby the process of exchanging information has been made simple, transparent, and effective.

Efficient Channel Allocation using Matching Theory for QoS Provisioning in Cognitive Radio Networks.

The focus of research efforts in cognitive radio networks (CRNs) has primarily remained confined to maximizing the utilization of the discovered resources. However, it is also important to enhance the user satisfaction in CRNs by finding a suitable match between the secondary users and the idle channels available from the primary network while taking into consideration not only the quality of service (QoS) requirements of the secondary users but the quality of the channels as well. In this work, the Gale Shapley matching theory was applied to find the best match, so that the most suitable channels from the available pool were allocated that satisfy the QoS requirements of the secondary users.