This paper discusses the development of an expert system prototype for use in college institutions. Our aim is to enhance exam quality and student performance by obtaining metrics pertaining to assignments, study materials, textbooks, and lecture quality, then learning dynamically from this information to create a human-readable course evaluation. The goal is to obtain a model which can be applied to courses in which students struggle, so we can identify ways to enhance the most determining factor of their grade: the quality of the exam. This expert system will serve as a prototype for a larger, more comprehensive automated system which will be proposed to enhance curricula
Abstract. Analyzing trajectories is important and has many applications, such as surveillance, analyzing tra c patterns and hurricane path prediction. In this paper, we propose a unique, non-parametric trajectory density estimation approach to obtain trajectory density functions that are used for two purposes. First, a density-based clustering algorithm DENTRAC that operates on such density functions is introduced.Second, unique post-analysis techniques that use the trajectory density function are proposed. Our method is capable of ranking trajectory clusters based on di erent characteristics of density clusters, and thus has the ability to summarize clusters from di erent perspectives, such as the compactness of member trajectories or the probability of their occurrence. We evaluate the proposed methods on synthetic tra c and real-world Atlantic hurricane datasets. The results show that our simple, yet e ective approach extracts valuable knowledge from trajectories that is di cult to obtain with other approaches.
Most health care systems use various physiological signals to provide an accurate diagnosis performance. The main common signals functional in health care applications are the electrocardiogram (ECG) and photoplethysmogram (PPG). ECG signal represents the electrical cardiac activity of the heart, while the PPG signal measures the changes in the blood volume. There are several applications in which the ECG combined with PPG can be used in the field of medical health care. This survey illustrates the various applications that combine features from the ECG and PPG signals. The review manifests the techniques, methodologies used in the data acquisition, pre-processing of the signals. The feature extraction and classification phases for both ECG and PPG are explained. The limitations, challenges, and future directions for the combined application of ECG and PPG are clarified to solve the medical problems that existed, presented, and feasible. This study aims to increase the interest in applying the combination between ECG and PPG signals in more applications and to obtain optimal measurements related to cardiac activity.
One of the pandemics that have caused many deaths is the Coronavirus disease 2019 (COVID-19). It first appeared in late 2019, and many deaths are increasing day by day until now. Therefore, the early diagnosis of COVID-19 has become a salient issue. Additionally, the current diagnosis methods have several demerits, and a new investigation is required to enhance the diagnosis performance. In this paper, a set of phases are performed, such as collecting data, filtering and augmenting images, extracting features, and classifying ECG images. The data were obtained from two publicly available ECG image datasets, and one of them contained COVID ECG reports. A set of preprocessing methods are applied to the ECG images, and data augmentation is performed to balance the ECG images based on the classes. A deep learning approach based on a convolutional neural network (CNN) is performed for feature extraction. Four different pre-trained models are applied, such as Vgg16, Vgg19, ResNet-101, and Xception. Moreover, an ensemble of Xception and the temporary convolutional network (TCN), which is named ECGConvnet, is proposed. Finally, the results obtained from the former models are fed to four main classifiers. These classifiers are softmax, random forest (RF), multilayer perception (MLP), and support vector machine (SVM). The former classifiers are used to evaluate the diagnosis ability of the proposed methods. The classification scenario is based on fivefold cross-validation. Seven experiments are presented to evaluate the performance of the ECGConvnet. Three of them are multi-class, and the remaining are binary class diagnosing. Six out of seven experiments diagnose COVID-19 patients. The aforementioned experimental results indicated that ECGConvnet has the highest performance over other pre-trained models, and the SVM classifier showed higher accuracy in comparison with the other classifiers. The resulting accuracies from ECGConvnet based on SVM are (99.74%, 98.6%, 99.1% on the multi-class diagnosis tasks) and (99.8% on one of the binary-class diagnoses, while the remaining achieved 100%). It is possible to develop an automatic diagnosis system for COVID based on deep learning using ECG data.
SUMMARYA number of software packages are available for the construction of comprehensive human genetic maps. In this paper we parallelize the widely used package Genehunter. We restrict our attention to only one function of the package, namely the computations of Identity By Descent (IBD) genes of a family. We use a master-slave model with the Message Passing Interface parallel environment. Our tests are done on two different architectures: a network of workstations and a shared memory multiprocessor. A new and efficient strategy to classify the parallelization of genetic linkage analysis programs results from our experiments. The classification is based on values of parameters which affect the complexity of the computation.
Solving economic dispatching problems in which generators suffer from prohibited zones is a challenging problem. In this paper, a solution based on genetic algorithm (GA) is suggested. An advantage of GA solutions is that they do not impose any convexity restrictions on the generators cost functions. Therefore, they are well suited to deal with prohibited operating zones.
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