Search

The optimization of solar photovoltaic (PV) cells and modules is crucial for enhancing solar energy conversion efficiency, a significant barrier to the widespread adoption of solar energy. Accurate modeling and estimation of PV parameters are essential for the optimal design, control, and simulation of PV systems. Traditional optimization methods often suffer from limitations such as entrapment in local optima when addressing this complex problem. This study introduces the Material Generation Algorithm (MGA), inspired by the principles of material chemistry, to estimate PV parameters effectively. The MGA simulates the creation and stabilization of chemical compounds to explore and optimize the parameter space. The algorithm mimics the formation of ionic and covalent bonds to generate new candidate solutions and assesses their stability to ensure convergence to optimal parameters. The MGA is applied to estimate parameters for two different PV modules, RTC France and Kyocera KC200GT, considering their manufacturing technologies and solar cell models. The significant nature of the MGA in comparison to other algorithms is further demonstrated by experimental and statistical findings. A comparative analysis of the results indicates that the MGA outperforms the other optimization strategies that previous researchers have examined for parameter estimation of solar PV systems in terms of both effectiveness and robustness. Moreover, simulation results demonstrate that MGA enhances the electrical properties of PV systems by accurately identifying PV parameters under varying operating conditions of temperature and irradiance. In comparison to other reported methods, considering the Kyocera KC200GT module, the MGA consistently performs better in decreasing RMSE across a variety of weather situations; for SD and DD models, the percentage improvements vary from 8.07% to 90.29%. [ABSTRACT FROM AUTHOR]

Healthcare is one of the industries that seeks to deliver medical services to patients on time. One of the issues it currently grapples with is real-time patient data exchange between various healthcare organizations. This challenge was solved by both centralized and decentralized cloud computing architecture solutions. In this paper, we review the current state of these two cloud computing architectures in the health sector with regard to the effect on the efficiency of Health Information Exchange (HIE) systems. Our study seeks to determine the relevance of these cloud computing approaches in assisting healthcare facilities in the decision-making process to adopt HIE systems. This paper considers the system performance, patient data privacy, and cost and identifies research directions in each of the architectures. This study shows that there are some benefits in both cloud architectures, but there are also some drawbacks. The prominent characteristic of centralized cloud computing is that all data and information are stored together at one location, known as a single data center. This offers many services, such as integration, effectiveness, simplicity, and rapid information access. However, it entails providing data privacy and confidentiality aspects because it will face the hazard of a single point of failure. On the other hand, decentralized cloud computing is built to safeguard data privacy and security whereby data are distributed to several nodes as a way of forming mini-data centers. This increases the system's ability to cope with a node failure. Thus, continuity and less latency are achieved. Nevertheless, it poses integration issues because managing data from several sites could be a problem, and the costs of operating several data centers are higher and complex. This paper also pays attention to the differences in aspects like efficiency, capacity, and cost. This paper assists healthcare organizations in determining the most suitable cloud architecture strategy for deploying secure and effective HIE systems. [ABSTRACT FROM AUTHOR]

Worldwide, the healthcare system is greatly impacted by the changing requirements of the people. Diabetes is a long-lasting condition that can lead to serious complications if not controlled correctly. It is divided into Type 1 (TID) and Type 2 (T2D) diabetes. Research shows that almost 90% of Diabetes cases are T2D, with TID making up around 10% of all Diabetes cases. This paper suggests a Rough-Neuro classification model for identifying Type 2 Diabetes, which includes a two-stage process. The approach includes utilising Rough sets JohnsonReducer to eliminate unnecessary features or characteristics and multi-layer perceptron for illness categorization. The suggested technique seeks to reduce the amount of input characteristics, which results in a reduction in the time needed to train the neural network and the storage space required. The findings show that decreasing the amount of input characteristics results in a lower neural network training time, enhances model performance, and reduces storage needs by 63%. It is worth mentioning that a smaller neural network with only seven hidden layers, trained for 1000 epochs with a learning rate of 0.01, attained the best performance, but time and storage were much decreased. [ABSTRACT FROM AUTHOR]

One of the predominant health issues affecting Saudi Arabia and leading to many complications is Type 2 diabetes (T2D). Early detection and significant preventative measures lead to curbing and controlling the health issue. There are fewer datasets in the literature for the detection of T2D in the Saudi population. Past studies using Saudi data have favoured machine learning algorithms to classify T2D. Although the application of this data in machine learning is evident, no studies exist in the literature that compare this data, especially those related to deep learning algorithms. This study's objective is to use specific Saudi data to develop multiple deep learning models that could be used to detect T2D. The research uses a Deep Neural Network (DNN), an Autoencoder (AE), and a Convolutional Neural Network (CNN) to create predictive models and compare their performance with a traditional machine learning classifier used on the same dataset that outperformed other machine learning algorithms such as a Decision Forest (DF). Various metrics were used to evaluate the effectiveness of the models, such as accuracy, precision, recall, F1 score and area under the ROC curve (AUC) where the ROC acts as a receiver operating characteristic curve. There are two cases in this paper: (i) uses all features of the dataset and (ii) uses six of the ten features, such as DF. In case (i), the results were shown that AE outperformed other models with the highest accuracy for imbalanced and balanced data 81.12% and 79.16%, respectively. The results for case (ii) showed that AE scored the highest 81.01% accuracy with imbalanced data compared to DF and DF achieved the highest accuracy of 82.1% with balanced data. As a result, both cases explored in this study revealed that AE has a constant superior performance if imbalanced data is used. In contrast, DF demonstrated the highest accuracy when a balanced dataset was used with a feature set reduction. They help to identify the undiagnosed T2D, and they are essential for professionals in Saudi Arabia in the health sector to promote health connections, identify risks and contain or improve their diabetes management. [ABSTRACT FROM AUTHOR]