E-Learning: Challenges and Research Opportunities Using Machine Learning &amp; Data Analytics

Moubayed, Abdallah; Injadat, MohammadNoor; Nassif, Ali Bou; Lutfiyya, Hanan; Shami, Abdallah

doi:10.1109/access.2018.2851790

Cited by 121 publications

(105 citation statements)

References 48 publications

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“…(i) Support Vector Machine: The first ML-based leak detection method is using support vector machines (SVM). In general, SVM is a supervised machine learning classification algorithm that focuses on find-ing the optimal hyperplane that separates the labeled training data with the maximum margin from the closest point [31], [32]. It is a more powerful and restrictive classifier than other classification algorithms such as logistic regression [31], [32].…”

Section: ) Sensor-specificmentioning

confidence: 99%

“…In general, SVM is a supervised machine learning classification algorithm that focuses on find-ing the optimal hyperplane that separates the labeled training data with the maximum margin from the closest point [31], [32]. It is a more powerful and restrictive classifier than other classification algorithms such as logistic regression [31], [32]. Due to its versatility and classification accurateness, it has been proposed as an effective leak detection method using data collected by different types of sensors ranging from optical signals to pressure sensors and flow sensors [33], [34].…”

Section: ) Sensor-specificmentioning

confidence: 99%

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Water Leak Detection Survey: Challenges & Research Opportunities Using Data Fusion & Federated Learning

et al. 2021

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With the increase in pipeline usage for fluid transportation, leak detection has become a major concern. More specifically, detecting water leaks has become a pressing challenge to both governmental and industrial stakeholders due to the financial losses it causes as well as the safety concerns associated with it. This issue is further highlighted in industrial and manufacturing environments such as the steelmaking process in which a water leak into a furnace can cause a significant explosion that would threaten both the facility and its operators. Therefore, many different water leak detection methods belonging to different types (hardware-in-the-loop-based, simulation-in-the-loop-based, or hybrid) have been proposed in the literature. However, many of these methods either are computationally complex or only suitable for particular applications. Hence, there is a need to develop innovative and novel frameworks that offer effective and efficient water leak detection mechanisms. To that end, this paper discusses two different paradigms, namely sensor data fusion and federated learning, that have the potential to further enhance water leak detection methods. Therefore, this paper first surveys the different water leak detection methods proposed in the literature along with their merits and limitations. It then describes the sensor data fusion and federated learning paradigms in more detail. Moreover, it presents different research opportunities in which these paradigms can be implemented to offer a more effective and computationally efficient water leak detection framework.

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Section: ) Sensor-specificmentioning

confidence: 99%

Section: ) Sensor-specificmentioning

confidence: 99%

Water Leak Detection Survey: Challenges & Research Opportunities Using Data Fusion & Federated Learning

et al. 2021

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“…LITERATURE REVIEW E-Learning: E-learning can be defined to be the use of electronic devices and technology for learning new information and skills (Moubayed et al, 2018).Creation, dissemination, managing data, and strong information are some of the activities performed in the academic institution through different information and communication tools called e-learning (Muhammad et al, 2016). Learning Domains: Benjamin Bloom had developed a taxonomy of Educational objectives.…”

Section: Research Questionsmentioning

confidence: 99%

Can Tailoring Skills Be Delivered Through E-Learning Platform; Perspective of the Three-Learning Domains (Cognitive, Affective and Psychomotor Domain)

2021

aijelr

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E-learning is a process of learning where we use the electronic-based platform through the internet. Nowadays, e-learning has become very essential for academia and vocational skills training through the use of the internet. During the ongoing lockdown, the Ministry of Education of Bangladesh is conducting online classes through various e-learning platforms. Education activities have been completed through e-learning but technical education is lagging in Bangladesh in the new-norm situation. With this backdrop, the study has developed a model for teaching tailoring curriculum to train the tailors' course to participants in selected areas of Bangladesh. The approach of this research is classroom action research in e-platform involving teachers to examine common issues. Teachers have followed the three learning domains (Cognitive, affective & psychomotor domains) to make the students skilled in tailoring via eplatform. The study will show how the new teaching method was applied using e-platform and the impact of the proposed teaching method in the gradual improvement of the psychomotor skill in the tailoring of the students. This study implies the NSD program of the Ministry of Education. It is expected that the study will help to improve psychomotor skills by a proposed teaching system using an e-platform in the new-norm situation.

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“…Knowledge development within the modern era is a technical support activity [10]. The value of e-learning lies in its ability to teach, operate and maintain e-learning programs anywhere, anytime, beyond just moving education and learning online [14]. Second, enormous interests in ICT foundation are expected to create, convey and oversee e-learning programs, and to change educators into proficient e-learning offices [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey

et al. 2021

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As of late, with the progression of AI and man-made brainpower, there has been a developing spotlight on versatile e-learning. As all ways to deal with e-learning lose their allure and the level of online courses builds, they move towards more customized versatile learning so as to collaborate with students and achieve better learning results. The schools focus on the examination, mindfulness, and arranging techniques that infuse innovation into the vision and educational program. E-learning issues are a standard examination issue for us all. The motivation behind this research analysis is to separate the potential outcomes of assessing e-learning models utilizing AI strategies such as Supervised, Semi Supervised, Reinforced Learning advances by investigating upsides and downsides of various methods organization. The literature review methodology is to review the cross sectional impacts of e-learning and Machine learning algorithms from existing literatures from the year 1993 to 2020 and to assess the essentialness of elearning features to optimize the e-learning models with available Machine learning techniques from peerinspected journals, capable destinations, and books. Second, it legitimizes the chances of e-learning structures introduction, and changes demonstrated through AI and Machine Learning algorithms. This examination assists in providing helpful new highlights to analysts, researchers and academicians. It gives an exhaustive structure of existing e-learning frameworks for the most recent innovations identified with learning framework capacities and learning tasks to envision ML research openings in appropriate spaces. The survey paper identifies and demonstrates the important role of different types of e-learning features such as Individual pertinent feature, Course pertinent feature, Context pertinent feature and Technology pertinent feature in framework performance tuning. The performance of Machine Learning algorithms to optimize the features of E-Learning models were reviewed in previous literatures and Support Vector Machine technique was found to be the one of the best to predict the input and output parameters of elearning models and it is found that Fuzzy C Means, Deep Learning algorithms are producing better results for Big Data sets.

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E-Learning: Challenges and Research Opportunities Using Machine Learning & Data Analytics

Cited by 121 publications

References 48 publications

Water Leak Detection Survey: Challenges & Research Opportunities Using Data Fusion & Federated Learning

Water Leak Detection Survey: Challenges & Research Opportunities Using Data Fusion & Federated Learning

Can Tailoring Skills Be Delivered Through E-Learning Platform; Perspective of the Three-Learning Domains (Cognitive, Affective and Psychomotor Domain)

Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey

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