GPH - International Journal of Mathematics

ON THE NORM OF ELEMENTARY OPERATOR OF LENGTH TWO IN TENSOR PRODUCT OF C*-ALGEBRAS

2024-09-25T11:46:32+00:00

Considerable research has been done on Norm property of different examples of Elementary operatorswith significant findings. From available literature not much have been done in determining the norm of elementary operator in tensor product of C*-algebras. The norms of Basic elementary operator, Jordan elementary operator and finite length elementary operator in tensor product of C*-algebras have been determined and results obtained. The main focus of this work is to investigate the norm of the elementary operator of length two in the tensor product of C*-algebras and to expand on our previous discussion on the elementary operator in tensor product of C*-algebras. To reach the goals, methods such as finite rank operator, tensor products of C*-algebras, and other well-known results were applied.

Logistic Regression Model for Predicting Patient Outcomes: A Fusion of Mathematical Modelling and Machine Learning in the Health Sector

2024-10-19T10:18:32+00:00

The integration of Mathematical Modeling and Machine Learning in the health sector has led to significant advancements in predicting patient outcomes and addressing healthcare challenges. This paper explores various methodologies, including logistic regression model, which serves as a robust statistical tool for predicting binary health outcomes. MATLAB is employed to obtain solutions for the logistic regression model by assuming patient data, and the graphical results demonstrate the model's effectiveness in predicting patient outcomes. Furthermore, machine learning techniques have emerged as vital for modeling disease progression in chronic conditions, enabling personalized treatment plans through analysis of historical patient data. Other areas explored include Clinical Decision Support Systems (CDSS) that leverage machine learning algorithms to enhance clinical decision-making by analyzing electronic health records and providing evidence-based recommendations, as well as personalized medicine, medical imaging analysis using deep learning, patient risk stratification, and healthcare resource optimization. The novelty of this work lies in its comprehensive examination of the interconnected roles of mathematical modeling and machine learning across various facets of healthcare, offering insights into how these technologies can be effectively integrated to improve patient care and outcomes. By addressing the challenges associated with data privacy and algorithm transparency, this paper highlights the transformative potential of machine learning in enhancing predictive analytics within the health sector.

Concepts for Elementary School Mathematics

2024-10-30T09:48:04+00:00

Continuing studies show that fifty percent, or less, of school age children are working at grade level in mathematics. This paper presents some ideas and concepts that may help school age children gain mastery of early mathematical principles.