scholarly journals ENHANCING EDUCATORS’ CREDIBILITY IN INTELLIGENT LEARNING ENVIRONMENTS

2021 ◽  
Author(s):  
Mario Mallia Milanes ◽  
◽  
Matthew Montebello
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Learning Experience ◽  
Research Work ◽  
Educational Process ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Intelligent Tools ◽  
Tools And Techniques ◽  
Intelligent Learning Environments

The deployment and use of Artificially Intelligent tools and techniques has proved to be effective and convenient, rendering them highly popular and desirable within any application. The benefits that such smart services provide are increasingly becoming popular within virtual learning environments as educators and learners continue to revert to such online portals during the academic process of higher education programmes. However, numerous educators have been sceptical of the inferences made by the machine learning techniques that implicitly at the background of the learning environment are monitoring the learner and collecting data to optimize the educational process as well as the learning experience. In an effort to enhance such a credibility we present an intelligent learning environment that justifies its decisions and propositions through an explainable interface that tracks back its conclusions to reasonable and plausible justifications. In this paper we present our research work together with our experiences in developing and deploying such a ground-breaking concept.

scholarly journals Decision Tree: A Machine Learning for Intrusion Detection

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1126-1130
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Legitimate User ◽  
Learning Techniques ◽  
Three Stages

The Intrusion is a major threat to unauthorized data or legal network using the legitimate user identity or any of the back doors and vulnerabilities in the network. IDS mechanisms are developed to detect the intrusions at various levels. The objective of the research work is to improve the Intrusion Detection System performance by applying machine learning techniques based on decision trees for detection and classification of attacks. The methodology adapted will process the datasets in three stages. The experimentation is conducted on KDDCUP99 data sets based on number of features. The Bayesian three modes are analyzed for different sized data sets based upon total number of attacks. The time consumed by the classifier to build the model is analyzed and the accuracy is done.

scholarly journals A framework to study learning in a complex learning environment

2013 ◽  
Vol 12 (1) ◽  
Author(s):  
Anders Berglund
Keyword(s):  
Learning Environment ◽  
Activity Theory ◽  
Learning Outcomes ◽  
Learning Environments ◽  
Learning Experience ◽  
Complex Learning ◽  
Complex Computer

This paper presents a framework for analysing variations in the ways in which students experience learning in complex, computer-supported environments. It involves an application of phenomenography extended to encompass the content of the learning as well as its context. Concepts from activity theory are drawn upon to enlarge the scope of phenomenography. By applying this framework, researchers, as well as course organisers, may gain insights into how courses are experienced by the participants and can thus develop these in a way that will be perceived as improvement by the course participants. Within this predominantly phenomenographic approach, the focus is upon the content of the learning experience, giving the ability to relate learning outcomes to the experience of the learning environment. Hence, insights can be gained that can help to improve learning environments and thereby outcomes. A case study illustrating the kinds of results than can be obtained when using the framework is provided.DOI: 10.1080/0968776042000211539

scholarly journals An Advance Resource Reservation Approach in a Cloud Database Environment

2017 ◽  
Author(s):  
Vinicius Da S. Segalin ◽  
Carina F. Dorneles ◽  
Mario A. R. Dantas
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Resource Reservation ◽  
Machine Learning Techniques ◽  
Advance Reservation ◽  
Running Time ◽  
Knowing That ◽  
Learning Techniques ◽  
Proposed Model ◽  
Cloud Database

AA well-known challenge with long running time queries in database environments is how much time a query will take to execute. This prediction is relevant for several reasons. For instance, by knowing that a query will take longer to execute than desired, one resource reservation mechanism can be performed, which means reserving more resources in order to execute this query in a shorter time in a future request. In this research work, it is presented a proposal in which the use of an advance reservation mechanism in a cloud database environment, considering machine learning techniques, provides resource recommendation. The proposed model is presented, in addition to some experiments that evaluate benefits and the efficiency of this enhanced proposal.

Survey of Breast Cancer Detection Using Machine Learning Techniques in Big Data

2019 ◽  
Vol 21 (3) ◽  
pp. 80-92
Author(s):  
Madhuri Gupta ◽  
Bharat Gupta
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Research Work ◽  
Cancer Recurrence ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Common Disease ◽  
Learning Techniques ◽  
Learning Frameworks ◽  
Processing Engine

Cancer is a disease in which cells in body grow and divide beyond the control. Breast cancer is the second most common disease after lung cancer in women. Incredible advances in health sciences and biotechnology have prompted a huge amount of gene expression and clinical data. Machine learning techniques are improving the prior detection of breast cancer from this data. The research work carried out focuses on the application of machine learning methods, data analytic techniques, tools, and frameworks in the field of breast cancer research with respect to cancer survivability, cancer recurrence, cancer prediction and detection. Some of the widely used machine learning techniques used for detection of breast cancer are support vector machine and artificial neural network. Apache Spark data processing engine is found to be compatible with most of the machine learning frameworks.

scholarly journals Machine Learning for Predictions in Academics

2020 ◽  
Vol 8 (5) ◽  
pp. 4624-4627
Keyword(s):  
Machine Learning ◽  
Career Path ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
Bayes Classifier ◽  
Logistics Regression ◽  
Learning Techniques

In recent years, a lot of data has been generated about students, which can be utilized for deciding the career path of the student. This paper discusses some of the machine learning techniques which can be used to predict the performance of a student and help to decide his/her career path. Some of the key Machine Learning (ML) algorithms applied in our research work are Linear Regression, Logistics Regression, Support Vector machine, Naïve Bayes Classifier and K- means Clustering. The aim of this paper is to predict the student career path using Machine Learning algorithms. We compare the efficiencies of different ML classification algorithms on a real dataset obtained from University students.

scholarly journals Highly accurate and efficient two phase-intrusion detection system (TP-IDS) using distributed processing of HADOOP and machine learning techniques

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Abhijit Dnyaneshwar Jadhav ◽  
Vidyullatha Pellakuri
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Phase Ii ◽  
Intrusion Detection System ◽  
Detection System ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Network Connections ◽  
Learning Techniques

AbstractNetwork security and data security are the biggest concerns now a days. Every organization decides their future business process based on the past and day to day transactional data. This data may consist of consumer’s confidential data, which needs to be kept secure. Also, the network connections when established with the external communication devices or entities, a care should be taken to authenticate these and block the unwanted access. This consists of identification of the malicious connection nodes and identification of normal connection nodes. For that, we use a continuous monitoring of the network input traffic to recognize the malicious connection request called as intrusion and this type of monitoring system is called as an Intrusion detection system (IDS). IDS helps us to protect our network and data from insecure and malicious network connections. Many such systems exists in the real time scenario, but they have critical issues of performance like accuracy and efficiency. These issues are addressed as a part of this research work of IDS using machine learning techniques and HDFS. The TP-IDS is designed in two phases for increasing accuracy. In phase I of TP-IDS, Support Vector Machine (SVM) and k Nearest Neighbor (kNN) are used. In phase II of TP-IDS, Decision Tree (DT) and Naïve Bayes (NB) are used, where phase II is the validation phase of the system for increasing accuracy. Also, both the phases are having Hadoop distributed file system underlying data storage and processing architecture, which allows parallel processing to increase the speed of the system and hence achieve the efficiency in TP-IDS.

scholarly journals Student Engagement Level in e-Learning Environment: Clustering Using K-means

2021 ◽  
Author(s):  
Abdallah Moubayed ◽  
Mohammadnoor Injadat ◽  
Abdallah Shami ◽  
Hanan Lutfiyya
Keyword(s):  
Learning Experience ◽  
Machine Learning Techniques ◽  
American University ◽  
Performance Metric ◽  
Learning Techniques ◽  
Event Log ◽  
E Learning ◽  
Level Model ◽  
Learning Platforms ◽  
Undergraduate Science

E-learning platforms and processes face several challenges, among which is the idea of personalizing the e-learning experience and to keep students motivated and engaged. This work is part of a larger study that aims to tackle these two challenges using a variety of machine learning techniques. To that end, this paper proposes the use of k-means algorithm to cluster students based on 12 engagement metrics divided into two categories: interaction-related and effort-related. Quantitative analysis is performed to identify the students that are not engaged who may need help. Three different clustering models are considered: two-level, three-level, and five-level. The considered dataset is the students’ event log of a second-year undergraduate Science course from a North American university that was given in a blended format. The event log is transformed using MATLAB to generate a new dataset representing the considered metrics. Experimental results’ analysis shows that among the considered interaction-related and effort-related metrics, the number of logins and the average duration to submit assignments are the most representative of the students’ engagement level. Furthermore, using the silhouette coefficient as a performance metric, it is shown that the two-level model offers the best performance in terms of cluster separation. However, the three-level model has a similar performance while better identifying students with low engagement levels.

Code Clone Detection Using Machine Learning Techniques

2020 ◽  
Vol 11 (2) ◽  
pp. 49-75
Author(s):  
Amandeep Kaur ◽  
Sandeep Sharma ◽  
Munish Saini
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Machine Learning Techniques ◽  
Clone Detection ◽  
Learning Approaches ◽  
Code Clone ◽  
Online Databases ◽  
Learning Techniques ◽  
Tools And Techniques

Code clone refers to code snippets that are copied and pasted with or without modifications. In recent years, traditional approaches for clone detection combine with other domains for better detection of a clone. This paper discusses the systematic literature review of machine learning techniques used in code clone detection. This study provides insights into various tools and techniques developed for clone detection by implementing machine learning approaches and how effectively those tools and techniques to identify clones. The authors perform a systematic literature review on studies selected from popular computer science-related digital online databases from January 2004 to January 2020. The software system and datasets used for analyzing tools and techniques are mentioned. A neural network machine learning technique is primarily used for the identification of the clone. Clone detection based on a program dependency graph must be explored in the future because it carries semantic information of code fragments.

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