Search

In recent years, descriptive evaluation has been introduced as a new model for educational evaluation of Iranian students. The current descriptive evaluation method is based on four-valued logic. Assessing all students with only four values is led to a lack of relative justice and creation of unrealistic equality. Also, the complexity of the evaluation process in the current method increases teacher error's likelihood. As a suitable solution, in this paper, a fuzzy descriptive evaluation system has been proposed. The proposed method is based on fuzzy logic, which is an infinite-valued logic, and it can perform approximate reasoning on natural language propositions. By the proposed fuzzy system, student assessment is performed over the school year with infinite values instead of four values. In order to eliminate the diversity of assigned values to students, at the end of the school year, the calculated values for each student will be rounded to the nearest value of the four standard values of the current descriptive evaluation method. It can be implemented in an appropriate smartphone application, which makes it much easier for teachers to assess the educational process of students. In this paper, the evaluation process of the elementary third-grade mathematics course in Iran during the period from the beginning of the MEHR (the seventh month of Iran) to the end of BAHMAN (the eleventh month of Iran) is examined by the proposed system. To evaluate the validity of this system, the proposed method has been simulated in MATLAB software. [ABSTRACT FROM AUTHOR]

Objective(s): This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. Materials and Methods: To evaluate effectiveness of proposed feature selection method, we employed three different classifiers artificial neural network (ANN) and PS-classifier and genetic algorithm based classifier (GA-classifier) on Wisconsin breast cancer datasets include Wisconsin breast cancer dataset (WBC), Wisconsin diagnosis breast cancer (WDBC), and Wisconsin prognosis breast cancer (WPBC). Results: For WBC dataset, it is observed that feature selection improved the accuracy of all classifiers expect of ANN and the best accuracy with feature selection achieved by PS-classifier. For WDBC and WPBC, results show feature selection improved accuracy of all three classifiers and the best accuracy with feature selection achieved by ANN. Also specificity and sensitivity improved after feature selection. Conclusion: The results show that feature selection can improve accuracy, specificity and sensitivity of classifiers. Result of this study is comparable with the other studies on Wisconsin breast cancer datasets. [ABSTRACT FROM AUTHOR]

Controlling of vital signs is crucial for patients in an intensive care unit (ICU) who need safe diagnostic and therapeutic interventions. The devices used in ICU should ensure accuracy, reliability and safety of alarms. The goal of personalized medicine in the ICU is to predict which diagnostic tests, monitoring interventions and treatments are necessary. In this study, we propose an intelligent approach based on artificial neural networks which is able to automatically learn the features of a patient and consequently send the required alarms in order to reduce the number of wrong alarms in ICU. Six of the most important risk factors are used and the importance of input variables is quantified by weighting according to expert's knowledge. The data chosen for this study have been provided in a real ICU environment in the University of Queensland in Australia. The results demonstrate that the proposed neural approach can be used as an efficient method for controlling vital signs in a real ICU environment. [ABSTRACT FROM AUTHOR]

Introduction: This paper proposes the concept of autonomous drug-encapsulated nanoparticle (ADENP) as a novel noninvasive approach to prevent atherosclerosis. ADENP consists of three simple units of sensor, controller (computing), and actuator. The hardware complexity of ADENP is much lower than most of the nanorobots, while the performance is maintained by the synergism in the swarm architecture. Materials and Methods: Since high accumulation of low density lipoprotein (LDL) macromolecules within the arterial wall plays a critical role in the initiation and development of atherosclerotic plaques, the task of the swarm of ADENPs is autonomous feedback control of LDL level in the interior of the arterial wall. In this study, we consider two specific types of ADENPs with distinguishing capabilities. The performance of each type is evaluated and compared on a well-known mathematical model of the arterial wall through computer simulation. Results: Simulation results demonstrate that the proposed approach can successfully reduce the LDL level to a desired value in the arterial wall of a patient with very high LDL level that is corresponding to the highest rates of cardiovascular disease events. Moreover, it is shown that ADENP is capable of distinguishing between healthy and unhealthy arterial walls to reduce the drug side effects. Conclusion: The proposed approach is a promising autonomous non-invasive method to prevent and treat complex diseases such as atherosclerosis. [ABSTRACT FROM AUTHOR]

Atherosclerosis, or hardening of the arteries, is one of the major causes of death in humans. High accumulation of Low-Density Lipoprotein (LDL) macromolecules within the arterial wall plays a critical role in initiation and development of atherosclerotic plaques. This paper proposes a proportional drug-encapsulated nanoparticle (PDENP) that utilizes a simple piecewise-proportional controller to realize swarm feedback control of LDL concentration in the interior of the arterial wall. In contrast to the competing strategies on nanorobotics, PDENPs carry simpler hardware architecture in order to be more reasonably realized technologically as well as to penetrate the interior arterial wall. Furthermore, in contrast to the existing targeted DENPs that usually target the surface proteins of atherosclerotic plaque, the proposed PDENPs directly sense the LDL level in the arterial walls. Hence, they can diagnose abnormal LDL accumulation before plaque formation, prevent critical growth of atherosclerotic plaques, while considerably reducing the unwanted drug side effects in healthy tissue. Simulation results on a well-known mathematical model of the arterial wall demonstrate that the proposed approach successfully reduces the LDL level to a desired value in the arterial wall of a patient with very high LDL level. Also, the mass of the released drug by PDENPs in a healthy wall is 11 times less than its corresponding value in an unhealthy wall. [ABSTRACT FROM PUBLISHER]

Omics data are multidimensional, heterogeneous, and high throughput. Robust computational methods and machine learning (ML)-based models offer new prospects to accelerate the data-to-knowledge trajectory. Deep learning (DL) is a powerful subset of ML inspired by brain structure and has created unprecedented momentum in bioinformatics and computational biology research. This article provides an overview of the current DL models applied to multi-omics data for both the beginner and the expert user. Additionally, COVID-19 will continue to impact planetary health as a pandemic and an endemic disease, with genomic and multi-omic pathophysiology. DL offers, therefore, new ways of harnessing systems biology research on COVID-19 diagnostics and therapeutics. Herein, we discuss, first, the statistical ML algorithms and essential deep architectures. Then, we review DL applications in multi-omics data analysis and their intersection with COVID-19. Finally, challenges and several promising directions are highlighted going forward in the current era of COVID-19.