Feature Transformation for Efficient Blood Glucose Prediction in Type 1 Diabetes Mellitus Patients

Diabetes Mellitus, a metabolic disease, causes the body to lose control over blood glucose regulation. With recent advances in self-monitoring systems, a patient can access their personalized glycemic profile and may utilize it for efficient prediction of future blood glucose levels. An efficient diabetes management system demands the accurate estimation of blood glucose levels, which, apart from using an appropriate prediction algorithm, depends on discriminative data representation. In this research work, a transformation of event-based data into discriminative continuous features is proposed. Moreover, a multi-layered long short-term memory (LSTM)-based recurrent neural network is developed for the prediction of blood glucose levels in patients with type 1 diabetes. The proposed method is used to forecast the blood glucose level on a prediction horizon of 30 and 60 min. The results are evaluated for three patients using the Ohio T1DM dataset. The proposed scheme achieves the lowest RMSE score of 14.76 mg/dL and 25.48 mg/dL for prediction horizons of 30 min and 60 min, respectively. The suggested methodology can be utilized in closed-loop systems for precise insulin delivery to type 1 patients for better glycemic control

Feature Transformation for Efficient Blood Glucose Prediction in Type 1 Diabetes Mellitus Patients

Diabetes Mellitus, a metabolic disease, causes the body to lose control over blood glucose regulation. With recent advances in self-monitoring systems, a patient can access their personalized glycemic profile and may utilize it for efficient prediction of future blood glucose levels. An efficient diabetes management system demands the accurate estimation of blood glucose levels, which, apart from using an appropriate prediction algorithm, depends on discriminative data representation. In this research work, a transformation of event-based data into discriminative continuous features is proposed. Moreover, a multi-layered long short-term memory (LSTM)-based recurrent neural network is developed for the prediction of blood glucose levels in patients with type 1 diabetes. The proposed method is used to forecast the blood glucose level on a prediction horizon of 30 and 60 min. The results are evaluated for three patients using the Ohio T1DM dataset. The proposed scheme achieves the lowest RMSE score of 14.76 mg/dL and 25.48 mg/dL for prediction horizons of 30 min and 60 min, respectively. The suggested methodology can be utilized in closed-loop systems for precise insulin delivery to type 1 patients for better glycemic control

Capture Largest Included Circles: An Approach for Counting Red Blood Cells

Abstract. Complete Blood Count (CBC) is a standard medical test that can help diagnose various conditions and diseases. Manual counting of blood cells is highly tedious and time consuming. However, new methods for counting blood cells are customary employing both electronic and computer-assisted techniques. Image segmentation is a classical task in most image processing applications which can be used to count blood cells in a microscopic image. In this research work, we have employed a few existing segmentation techniques, and also proposed a new scheme to count total blood cells in a smear microscopic image. The proposed technique, called Capture Largest Included Circles (CLIC), is a parameterized segmentation algorithm that captures largest possible circles in an object boundary. The algorithm is perfectly suited for appliance in counting blood cells because of high circularity ratio of cells. Comparative study of segmentation by CLIC and a few other state-of-the-art segmentation algorithms such as Distance Regularized Level Set Evolution (DRLSE), Watershed segmentation and Pixcavator (a topology-based segmentation) is also part of this research work. Results have proven the superiority of CLIC over other schemes, especially in case of diseased red blood cells

Energy Efficient Proactive Routing Scheme for Enabling Reliable Communication in Underwater Internet of Things

The Underwater Internet of Things (UIoT) is a network of smart interconnected devices operating under various aqueous environments. In UIoT, due to aqueous feature of signal absorption, data signals are transmitted at low frequency. Similarly, significant interference and collisions degrade transmission quality, resulting in a low Packet Delivery Ratio (PDR) and a long End-to-End (E2E) delay. Moreover, a significant amount of energy is being wasted due to the void hole problem, which arises when the source node does not identify an instant forwarder node. Thus, a reliable communication with enhanced network lifetime is highly desired in UIoT. Therefore, this work proposes a proactive energy Efficient Layer-by-Layer Watchman-based Collision Free Routing (ELW-CFR) scheme to address the above mentioned issues. The proposed scheme provides the low E2E delay and high PDR while avoiding the void hole problem. Extensive simulations have been performed to show the performance superiority of the proposed scheme against the state-of-the-art schemes