scholarly journals The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

2020 ◽  
Vol 10 (10) ◽  
pp. 270
Author(s):  
Dang-Nhac Lu ◽  
Hong-Quang Le ◽  
Tuan-Ha Vu
Keyword(s):  
Online Learning ◽  
Blended Learning ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Sufficient Conditions ◽  
Learning System ◽  
Support Vector ◽  
Learning Method ◽  
K Nearest Neighbor ◽  
E Learning

The Covid-19 epidemic is affecting all areas of life, including the training activities of universities around the world. Therefore, the online learning method is an effective method in the present time and is used by many universities. However, not all training institutions have sufficient conditions, resources, and experience to carry out online learning, especially in under-resourced developing countries. Therefore, the construction of traditional courses (face to face), e-learning, or blended learning in limited conditions that still meet the needs of students is a problem faced by many universities today. To solve this problem, we propose a method of evaluating the influence of these factors on the e-learning system. From there, it is a matter of clarifying the importance and prioritizing construction investment for each factor based on the K-means clustering algorithm, using the data of students who have been participating in the system. At the same time, we propose a model to support students to choose one of the learning methods, such as traditional, e-learning or blended learning, which is suitable for their skills and abilities. The data classification method with the algorithms multilayer perceptron (MP), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and naïve bayes (NB) is applied to find the model fit. The experiment was conducted on 679 data samples collected from 303 students studying at the Academy of Journalism and Communication (AJC), Vietnam. With our proposed method, the results are obtained from experimentation for the different effects of infrastructure, teachers, and courses, also as features of these factors. At the same time, the accuracy of the prediction results which help students to choose an appropriate learning method is up to 81.52%.

scholarly journals Creating a Blended Learning Model for Online Learning System in Indonesia

2018 ◽  
Vol 7 (3.36) ◽  
pp. 156
Author(s):  
Uwes Anis Chaeruman ◽  
Basuki Wibawa ◽  
Zulfiati Syahrial
Keyword(s):  
Online Learning ◽  
Blended Learning ◽  
Learning Process ◽  
Learning Experience ◽  
Lesson Plan ◽  
Learning System ◽  
Communication Tool ◽  
Design Component ◽  
Technological Advances ◽  
E Learning

Online system born out of technological advances benefits the world of education. This system utilizes the internet to disseminate information and act as a communication tool. The application of Blended Learning is generally able to improve the result of the learning process by changing learning habits and methods in many countries. By involving 7 e-learning instructors and 235 respondents, a formative research was conducted to investigate the increase in learning characteristics, i.e. innovation, compatibility and complexity to determine the appropriate model for the application of Blended Learning in an online learning system (SPADA) in Indonesia. The result of this research can be used as a guideline in the lesson plan design component, learning experience management and the balance interaction between lecturers and students during the learning process.  

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

scholarly journals نظام تعليم اللغة العربية عن بعد عبر الإنترنت في جائحة الوباء كوفيد-١٩ بالمدرسة المتوسطة الإسلامية الحكومة ١ بسوراكرتا

2021 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Yuliana Prativi ◽  
Muhammad Zaenuri
Keyword(s):  
Online Learning ◽  
Data Collection ◽  
Qualitative Approach ◽  
Learning System ◽  
The Other ◽  
Face To Face ◽  
Teachers And Students ◽  
E Learning ◽  
Evaluation Stage ◽  
The Subject

Online learning is a learning via internet without meeting face-to-face between teachers and students. This online learning system is relatively new, therefore teachers and students should adapt quickly. This study aims to determine the online Arabic learning system during the COVID-19 pandemic at Madrasah Tsanawiyah Negeri (MTsN) 1 Surakarta. Researcher used a qualitative approach and observation, interview, and documentation as data collection techniques. The results described that e-learning madrasah was used as the main media for online Arabic learning at MTsN 1 Surakarta during the covid-19 pandemic, then assisted by Whatsapp and Youtube channel. The subject matter was presented in video, powerpoint, and pdf. The learning stages were divided into three: preparation, implementation (pre-activities, whilst-activities and post-activities), and evaluation stage. This online learning helps teachers to coordinate with and supervise students easily, on the other hand, it is difficult for them to monitor the students’ understanding and bad internet network make some students could not follow the learning process in time. 

scholarly journals Comparative Study Management Of Training (MOT) Between Blended Learning And Fully Online Learning Methods

2021 ◽  
Vol 24 (2) ◽  
pp. 24
Author(s):  
Yunni Susanty
Keyword(s):  
Online Learning ◽  
Blended Learning ◽  
Learning Outcomes ◽  
Learning Process ◽  
Quantitative Methods ◽  
Sampling Technique ◽  
Statistical Test ◽  
Training Institution ◽  
Learning Method ◽  
Parametric Statistical Test

The COVID 19 pandemic also has an impact on the education and training aspects of the State Civil Apparatus. MOT training in Puslatbang PKASN LAN, which was originally carried out by blended learning in 2019, has been changed to fully online learning in 2020, as an effort to reduce the spread of the COVID 19 virus. The purpose of this study is to find out whether there are differences on the learning outcomes between MOT participants in 2019, which attended by 30 people, and MOT participants in 2020, which attended by 25 people. Data processing and analysis techniques in this study using quantitative methods. The statistical test used is the non-parametric statistical test using the Mann Whitney U test. The sampling technique used was total sampling, where all members of the population were used as samples. The results revealed that there was no difference in the learning outcomes of MOT participants between those using the blended learning method and those using the fully online learning method. Based on this information, fully online learning is very possible to be applied. Nevertheless, the Training Institution must pay attention to the availability of facilities and infrastructure that support the learning process electronically. Also, the limited interaction between lecturers and participants when doing online learning should be balanced with the ability of lecturers to convey material with technology-based learning techniques. In this case, the roles of all parties will determine the optimal achievement of the fully online learning process.

scholarly journals IntelliFin: Advanced Stock Prediction using Hybrid ML and LSTM Model with Financial Indicators powered by Sentiment Determination using NLP

2020 ◽  
Vol 9 (5) ◽  
pp. 428-433
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Majority Voting ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Financial History ◽  
The Right

Stock Trading has been one of the most important parts of the financial world for decades. People investing in the share market analyze the financial history of a corporation, the news related to it and study huge amounts of data so as to predict its stock price trend. The right investment i.e. buying and selling a company stock at the right time leads to monetary benefits and can make one a millionaire overnight. The stock market is an extremely fluctuating platform wherein data is produced in humongous quantities and is influenced by numerous disparate factors such as socio-political issues, financial activities like splits and dividends, news as well as rumors. This work proposes a novel system “IntelliFin” to predict the share market trend. The system uses the various stock market technical indicators along with the company's historical market data trends to predict the share prices. The system employs the sentiment determination of a company's financial and socio-political news for a more accurate prediction. This system is implemented using two models. The first is a hybrid LSTM model optimized by an ADAM optimizer. The other is a hybrid ML model which integrates a Support Vector Regressor, K-Nearest Neighbor classifier, an RF classifier and a Linear Regressor using a Majority Voting algorithm. Both models employ a sentiment analyzer to account for the news impacting the stock prices which is powered by NLP. The models are trained continuously using Reinforcement Learning implemented by the Q-Learning Algorithm to increase the consistency and accuracy. The project aims to support the inexperienced investors, who don't have enough experience in investing in the stock market and help them maximize their profit and minimize or eliminate the losses. The developed system will also serve as a tool for professional investors to help and aid their decision making.

An adaptive Dendrite-HDMR metamodeling technique for high dimensional problems

2022 ◽  
pp. 1-38
Author(s):  
Qi Zhang ◽  
Yizhong Wu ◽  
Li Lu ◽  
Ping Qiao
Keyword(s):  
Adaptive Sampling ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Weight Ratio ◽  
Propulsion System ◽  
High Dimensional ◽  
High Dimensional Model Representation ◽  
K Nearest Neighbor ◽  
Metamodeling Technique ◽  
Explicit Expressions

Abstract High dimensional model representation (HDMR), decomposing the high-dimensional problem into summands of different order component terms, has been widely researched to work out the dilemma of “curse-of-dimensionality” when using surrogate techniques to approximate high-dimensional problems in engineering design. However, the available one-metamodel-based HDMRs usually encounter the predicament of prediction uncertainty, while current multi-metamodels-based HDMRs cannot provide simple explicit expressions for black-box problems, and have high computational complexity in terms of constructing the model by the explored points and predicting the responses of unobserved locations. Therefore, aimed at such problems, a new stand-alone HDMR metamodeling technique, termed as Dendrite-HDMR, is proposed in this study based on the hierarchical Cut-HDMR and the white-box machine learning algorithm, Dendrite Net. The proposed Dendrite-HDMR not only provides succinct and explicit expressions in the form of Taylor expansion, but also has relatively higher accuracy and stronger stability for most mathematical functions than other classical HDMRs with the assistance of the proposed adaptive sampling strategy, named KKMC, in which k-means clustering algorithm, k-Nearest Neighbor classification algorithm and the maximum curvature information of the provided expression are utilized to sample new points to refine the model. Finally, the Dendrite-HDMR technique is applied to solve the design optimization problem of the solid launch vehicle propulsion system with the purpose of improving the impulse-weight ratio, which represents the design level of the propulsion system.

A Blended Course to Teach Graphical Programming Using LabVIEW

2014 ◽  
pp. 330-340
Author(s):  
Yücel Uğurlu
Keyword(s):  
Blended Learning ◽  
Engineering Students ◽  
Experimental Studies ◽  
Learning System ◽  
Self Assessment ◽  
Graphical Programming ◽  
Certification Test ◽  
E Learning ◽  
The Impact ◽  
Blended Course

In this chapter, the authors introduce a blended learning approach where LabVIEW, an e-learning environment, was integrated into a traditional graphical programming course for engineering students to teach advanced topics and to increase the programming skills of the students. In this course, the students were required to design projects using technology. The students designed small projects and frequently accessed the e-learning system to build real-world applications. The projects that students designed stimulated them to use the e-learning system. The impact of blended learning was evaluated on the basis of student surveys and certification test results. Experimental studies show that blended learning produced higher results in the students’ self-assessment and certification test.

A Blended Course to Teach Graphical Programming Using LabVIEW

2015 ◽  
pp. 299-310
Author(s):  
Yücel Uğurlu
Keyword(s):  
Blended Learning ◽  
Engineering Students ◽  
Experimental Studies ◽  
Learning System ◽  
Self Assessment ◽  
Graphical Programming ◽  
Certification Test ◽  
E Learning ◽  
The Impact ◽  
Blended Course

In this chapter, the authors introduce a blended learning approach where LabVIEW, an e-learning environment, was integrated into a traditional graphical programming course for engineering students to teach advanced topics and to increase the programming skills of the students. In this course, the students were required to design projects using technology. The students designed small projects and frequently accessed the e-learning system to build real-world applications. The projects that students designed stimulated them to use the e-learning system. The impact of blended learning was evaluated on the basis of student surveys and certification test results. Experimental studies show that blended learning produced higher results in the students' self-assessment and certification test.

Teaching Social Skills

2011 ◽  
pp. 1689-1713
Author(s):  
Graham Bodie ◽  
Margaret Fitch-Hauser ◽  
William Powers
Keyword(s):  
Online Learning ◽  
Social Skills ◽  
Blended Learning ◽  
Learning System ◽  
Future Research ◽  
Abstract Concepts ◽  
Traditional Lecture ◽  
Strict Adherence ◽  
Based Instruction ◽  
Practical Implications

The ubiquity of instructional technology necessitates a more critical look at the theories that drive adoption and the practical implications of its usage. Blended learning has been offered as one compromise to fully online learning or strict adherence to traditional lecture-based instruction that seems outdated. A particular approach to blended learning is examined in the present chapter through the use of an online learning system. Concept Keys was developed to assist instructors of social skills in breaking down these abstract concepts into manageable units of information appropriate for daily delivery via email. This program is shown to be easily integrated into existing curriculum through two studies. A concluding section attempts to tie these studies together and suggests potential limitations and avenues for future research.

scholarly journals A Deep Learning Based Method for the Non-Destructive Measuring of Rock Strength through Hammering Sound

2019 ◽  
Vol 9 (17) ◽  
pp. 3484
Author(s):  
Shuai Han ◽  
Heng Li ◽  
Mingchao Li ◽  
Timothy Rose
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Rock Strength ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Strength Measurement ◽  
Regression Algorithms ◽  
Almost All ◽  
The Relationship ◽  
Non Destructive

Hammering rocks of different strengths can make different sounds. Geological engineers often use this method to approximate the strengths of rocks in geology surveys. This method is quick and convenient but subjective. Inspired by this problem, we present a new, non-destructive method for measuring the surface strengths of rocks based on deep neural network (DNN) and spectrogram analysis. All the hammering sounds are transformed into spectrograms firstly, and a clustering algorithm is presented to filter out the outliers of the spectrograms automatically. One of the most advanced image classification DNN, the Inception-ResNet-v2, is then re-trained with the spectrograms. The results show that the training accurate is up to 94.5%. Following this, three regression algorithms, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) are adopted to fit the relationship between the outputs of the DNN and the strength values. The tests show that KNN has the highest fitting accuracy, and SVM has the strongest generalization ability. The strengths (represented by rebound values) of almost all the samples can be predicted within an error of [−5, 5]. Overall, the proposed method has great potential in supporting the implementation of efficient rock strength measurement methods in the field.

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