Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management.

Objective: The honeymoon phase in Type 1 Diabetes (T1D) presents a temporary improvement in glycemic control, complicating insulin management. This study aims to develop and validate a machine learning-driven method for accurately detecting this phase to optimize insulin therapy and prevent adverse outcomes.

Methods: Data from pediatric T1D patients aged 6-17 years, including continuous glucose monitoring (CGM) data, Glucose Management Indicator (GMI) reports, HbA1c values, and patient medical history, were used to train machine learning models.

ANFIS-PSO analysis on axisymmetric tetra hybrid nanofluid flow of Cu-CNT-Graphene-Tio with WEG-Blood under linear thermal radiation and inclined magnetic field: A bio-medicine application.

Background: The development of heat transfer devices used for heat conversion and recovery in several industrial and residential applications has long focused on improving heat transfer between two parallel plates. Numerous articles have examined the relevance of enhancing thermal performance for the system's performance and economics. Heat transport is improved by increasing the Reynolds number as the turbulent effects grow.

ODQN-Net: Optimized Deep Q Neural Networks for Disease Prediction Through Tongue Image Analysis Using Remora Optimization Algorithm.

Tongue analysis plays the major role in disease type prediction and classification according to Indian ayurvedic medicine. Traditionally, there is a manual inspection of tongue image by the expert ayurvedic doctor to identify or predict the disease. However, this is time-consuming and even imprecise.

Dense Convolutional Neural Network for Detection of Cancer from CT Images.

In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages.

Evolving Long Short-Term Memory Network-Based Text Classification.

Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM networks suffer from the parameter tuning problem.

Novel Face Mask Detection Technique using Machine Learning to control COVID'19 pandemic.

The COVID-19 pandemic has been scattering speedily around the world since 2019. Due to this pandemic, human life is becoming increasingly involutes and complex. Many people have died because of this virus.

Grey wolf assisted dragonfly-based weighted rule generation for predicting heart disease and breast cancer.

Disease prediction plays a significant role in the life of people, as predicting the threat of diseases is necessary for citizens to live life in a healthy manner. The current development of data mining schemes has offered several systems that concern on disease prediction. Even though the disease prediction system includes more advantages, there are still many challenges that might limit its realistic use, such as the efficiency of prediction and information protection.