Machine Learning Approaches to Digital Learning Performance Analysis

In the area of machine learning performance analysis is the major task in order to get a better performance both in training and testing model. In addition, performance analysis of machine learning techniques helps to identify how the machine is performing on the given input and also to find any improvements needed to make on the learning model. Feed-forward neural network (FFNN) has different area of applications, but the epoch convergences of the network differs from the usage of transfer function. In this study, to build the model for classification and moisture prediction of soil, rectified linear units (ReLU), Sigmoid, hyperbolic tangent (Tanh) and Gaussian transfer function of feed-forward neural network had been analyzed to identify an appropriate transfer function. Color, texture, shape and brisk local feature descriptor are used as a feature vector of FFNN in the input layer and 4 hidden layers were considered in this study. In each hidden layer 26 neurons are used. From the experiment, Gaussian transfer function outperforms than ReLU, sigmoid and tanh transfer function. But the convergence rate of Gaussian transfer function took more epoch than ReLU, Sigmoid and tanh.

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Evaluation of Machine Learning Approaches for Automated Diagnosis of COVID-19 using X-Ray images (Preprint)

10.2196/preprints.18947 ◽

2020 ◽

Author(s):

Mazin Mohammed ◽

Karrar Hameed Abdulkareem ◽

Mashael S. Maashi ◽

Salama A. Mostafa A. Mostafa ◽

Abdullah Baz ◽

...

Keyword(s):

Machine Learning ◽

Computational Method ◽

Learning Performance ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Data Set ◽

X Ray ◽

Wide Range ◽

Artificial Neural Network Ann

BACKGROUND In most recent times, global concern has been caused by a coronavirus (COVID19), which is considered a global health threat due to its rapid spread across the globe. Machine learning (ML) is a computational method that can be used to automatically learn from experience and improve the accuracy of predictions. OBJECTIVE In this study, the use of machine learning has been applied to Coronavirus dataset of 50 X-ray images to enable the development of directions and detection modalities with risk causes.The dataset contains a wide range of samples of COVID-19 cases alongside SARS, MERS, and ARDS. The experiment was carried out using a total of 50 X-ray images, out of which 25 images were that of positive COVIDE-19 cases, while the other 25 were normal cases. METHODS An orange tool has been used for data manipulation. To be able to classify patients as carriers of Coronavirus and non-Coronavirus carriers, this tool has been employed in developing and analysing seven types of predictive models. Models such as , artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbour (k-NN), Decision Tree (DT), and CN2 rule inducer were used in this study.Furthermore, the standard InceptionV3 model has been used for feature extraction target. RESULTS The various machine learning techniques that have been trained on coronavirus disease 2019 (COVID-19) dataset with improved ML techniques parameters. The data set was divided into two parts, which are training and testing. The model was trained using 70% of the dataset, while the remaining 30% was used to test the model. The results show that the improved SVM achieved a F1 of 97% and an accuracy of 98%. CONCLUSIONS :. In this study, seven models have been developed to aid the detection of coronavirus. In such cases, the learning performance can be improved through knowledge transfer, whereby time-consuming data labelling efforts are not required.the evaluations of all the models are done in terms of different parameters. it can be concluded that all the models performed well, but the SVM demonstrated the best result for accuracy metric. Future work will compare classical approaches with deep learning ones and try to obtain better results. CLINICALTRIAL None

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Performance Analysis of Various Machine Learning Approaches in Stroke Prediction

International Journal of Computer Applications ◽

10.5120/ijca2020920740 ◽

2020 ◽

Vol 175 (21) ◽

pp. 11-15

Author(s):

Md. Shafiul Azam ◽

Md. Habibullah ◽

Humayan Kabir Rana

Keyword(s):

Machine Learning ◽

Performance Analysis ◽

Learning Approaches

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Utilizing Educational Technologies to Optimize Student and Teacher Learning at Dharma Laksana Mataram Orphanage

Journal of Innovation and Community Engagement ◽

10.28932/jice.v2i1.3601 ◽

2021 ◽

Vol 2 (1) ◽

pp. 11-20

Author(s):

Paula Dewanti ◽

Ni Nyoman Supuwiningsih ◽

Desak Putu Saridewi

Keyword(s):

Information Technology ◽

Service Learning ◽

Teacher Learning ◽

Learning Performance ◽

Educational Technologies ◽

Teacher Competence ◽

Digital Learning ◽

Learning Approaches ◽

Governmental Organization ◽

Learning Platform

Educational technology is the study and practice of using technology to facilitate and enhance learning performance. During the Covid-19 pandemic, digital learning and the use of information technology were one of the solutions to minimize the risk of Covid-19, according to the government's health protocol. As a Non-Governmental Organization (NGO), the Dharma Laksana Orphanage receives computer set donations from their funders. Based on preliminary reviews, the use of these computer devices is not optimal, both for students and teachers. The inability to optimize the use of computer devices seems to have an impact on the effectiveness of learning approaches by utilizing information technology. This Community Service was performed using the Service Learning methodology and was designed to assist in the development of human resources related to learning methods and media used during the Covid-19 pandemic. During this activity, Edmodo, the leading social learning platform, was successfully introduced. Blended Learning with Google Apps is also covered. It increased both student and teacher awareness of digital learning, as well as teacher competence in creating interactive learning media, by introducing a variety of educational platforms.

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