In this research, the photoplethysmogram (PPG) waveform analysis is utilized to develop a logistic regression-based predictive model for the classification of diabetes. The classifier has three predictors age, b/a, and SP indices in which they achieved an overall accuracy of 92.3% in the prediction of diabetes. In this study, a total of 587 subjects were enrolled. A total of 459 subjects were used for model training and development, while the rest of the 128 subjects were used for model testing and validation. The classifier was able to diagnose 63 patients correctly as diabetes while 27 subjects were wrongly classified as nondiabetes with an accuracy of 70%. Again, the model classified 479 subjects as nondiabetes correctly while it incorrectly classified 18 subjects as diabetes with an accuracy of 96.4%. Finally, the proposed model revealed an overall predictive accuracy of 92.3% which makes it a reliable surrogate measure for diabetes classification and prediction in clinical settings.
Background
This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively.
Methodology
SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized.
Results
It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm.
Conclusions
The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.
In the last few years, the use of educational technology, particularly the concept of Learning Management System (LMS), has increased rapidly. With this fast development, the question arises as how to manage the LMS to obtain success and efficiency in online courses. One of the important factors that have received many citations in literature studies (and has a special position in information system research) is the user satisfaction. It is a crucial factor that can predict the success or failure of any LMS. In relation, this research examined the success factors that affect the user satisfaction and outcomes of LMS. This paper discusses the conceptual User Satisfaction Evaluation Model (USEM) employed to measures LMS success. In particular, it seeks to examine "the relationship between: Service quality, system quality, ease of use, perceived usefulness, information quality and students satisfaction, as well as to measure the outcomes of the LMS." Results from the data analysis indicate that all proposed factors have a positive effect on student satisfaction. The result also concludes that a higher rate of user satisfaction will lead to greater benefits for the students.
Photoplethysmogram (PPG) and its second derivative of the photoplethysmogram (SDPTG) are simple and low cost optical techniques for detecting and tracking blood volume changes. The PPG waveform and its SDPTG have been used by many scholars to obtain valuable information about heart and cardiovascular system. Since PPG and SDPTG reflect blood volume changes, much work has been done on its application as a diagnostic tool for screening arterial structure and its related diseases and disorders. In this article, we first provide a short review of the effects of atherosclerosis in losing arterial elasticity. Secondly, we introduce the PPG waveform and discuss in details the analysis methods and applications of its SDPTG waveform. Finally, we demonstrate links between elastic properties of arteries, atherosclerosis, PPG and SDPTG. The main focus of the review is on the analysis methods and applications of SDPTG.
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