Health Information Technology (HIT) provides many opportunities for transforming and improving health care systems. HIT enhances the quality of health care delivery, reduces medical errors, increases patient safety, facilitates care coordination, monitors the updated data over time, improves clinical outcomes, and strengthens the interaction between patients and health care providers. Living in modern large cities has a significant negative impact on people’s health, for instance, the increased risk of chronic diseases such as diabetes. According to the rising morbidity in the last decade, the number of patients with diabetes worldwide will exceed 642 million in 2040, meaning that one in every ten adults will be affected. All the previous research on diabetes mellitus indicates that early diagnoses can reduce death rates and overcome many problems. In this regard, machine learning (ML) techniques show promising results in using medical data to predict diabetes at an early stage to save people’s lives. In this paper, we propose an intelligent health care system based on ML methods as a real-time monitoring system to detect diabetes mellitus and examine other health issues such as food and drug allergies of patients. The proposed system uses five machine learning methods: K-Nearest Neighbors, Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM). The system selects the best classification method with high accuracy to optimize the diagnosis of patients with diabetes. The experimental results show that in the proposed system, the SVM classifier has the highest accuracy of 83%.

Designing Raman amplifier with high On-Off again and low noise figure is required in in optical communication networks, due to wide and tunable amplification and low nonlinearity. This paper proposes a new configuration design to the single mode fiber Raman amplifier using a multi-objective bat algorithm. The main aim of the proposed method is to preserve the values of noise figure and ripple of the amplifier as low as possible while keeping the values of laser wavelength and the amplifier powers are high. The simulation results show that increasing the number of iterations is required, which would result in a flat gain spectrum with a considerable enhancement in the noise figure and minimal gain ripple that reaches to less than 0.18 DB.