Hybrid structures in time series modeling and forecasting: A review

The rapid evolution of e-learning platforms, propelled by advancements in artificial intelligence (AI) and machine learning (ML), presents a transformative potential in education. This dynamic landscape necessitates an exploration of AI/ML integration in adaptive learning systems to enhance educational outcomes. This study aims to map the current utilization of AI/ML in e-learning for adaptive learning, elucidating the benefits and challenges of such integration and assessing its impact on student engagement, retention, and performance. A comprehensive literature review was conducted, focusing on articles published from 2010 onwards, to document the integration of AI/ML in e-learning. The review analyzed 63 articles, employing a systematic approach to evaluate the deployment of adaptive learning algorithms and their educational implications. Findings reveal that AI/ML algorithms are instrumental in personalizing learning experiences. These technologies have been shown to optimize learning paths, enhance engagement, and improve academic performance, with some studies reporting increased test scores. The integration of AI/ML in e-learning platforms significantly contributes to the personalization and effectiveness of the educational process. Despite challenges like data privacy and the complexity of AI/ML systems, the results underscore the potential of adaptive learning to revolutionize education by catering to individual learner needs.

Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review

Learning using the Internet or training through E-Learning is growing rapidly and is increasingly favored over the traditional methods of learning and teaching. This radical shift is directly linked to the revolution in digital computer technology. The revolution propelled by innovation in computer technology has widened the scope of E-Learning and teaching, whereby the process of exchanging information has been made simple, transparent, and effective. The E-Learning system depends on different success factors from diverse points of view such as system, support from the institution, instructor, and student. Thus, the effect of critical success factors (CSFs) on the E-Learning system must be critically analyzed to make it more effective and successful. This current paper employed the analytic hierarchy process (AHP) with group decision-making (GDM) and Fuzzy AHP (FAHP) to study the diversified factors from different dimensions of the web-based E-Learning system. The present paper quantified the CSFs along with its dimensions. Five different dimensions and 25 factors associated with the web-based E-Learning system were revealed through the literature review and were analyzed further. Furthermore, the influence of each factor was derived successfully. Knowing the impact of each E-Learning factor will help stakeholders to construct education policies, manage the E-Learning system, perform asset management, and keep pace with global changes in knowledge acquisition and management.

/pdf/evaluating-critical-success-factors-in-implementing-e-yc8awf620j.pdf

Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making

ContextSoftware requirements are often not set in concrete at the start of a software development project; and requirements changes become necessary and sometimes inevitable due to changes in customer requirements and changes in business rules and operating environments; hence, requirements development, which includes requirements changes, is a part of a software process. Previous work has shown that failing to manage software requirements changes well is a main contributor to project failure. Given the importance of the subject, there's a plethora of research work that discuss the management of requirements change in various directions, ways and means. An examination of these works suggests that there's a room for improvement. ObjectiveIn this paper, we present a systematic review of research in Requirements Change Management (RCM) as reported in the literature. MethodWe use a systematic review method to answer four key research questions related to requirements change management. The questions are: (1) What are the causes of requirements changes? (2) What processes are used for requirements change management? (3) What techniques are used for requirements change management? and (4) How do organizations make decisions regarding requirements changes? These questions are aimed at studying the various directions in the field of requirements change management and at providing suggestions for future research work. ResultsThe four questions were answered; and the strengths and weaknesses of existing techniques for RCM were identified. ConclusionsThis paper has provided information about the current state-of-the-art techniques and practices for RCM and the research gaps in existing work. Benefits, risks and difficulties associated with RCM are also made available to software practitioners who will be in a position of making better decisions on activities related to RCM. Better decisions will lead to better planning which will increase the chance of project success.

A systematic review of requirements change management

In personalized learning, each student gets a customized learning plan according to their pace of learning, instructional preferences, learning objects, etc. Hence the content recommender system in Personalized Learning Environment (PLE) should adapt to learner attributes and suggest appropriate learning resources to aid the learning process and improve the learning outcomes. This systematic literature review aims to analyze and summarize the studies on learning content recommenders in adaptive and personalized learning environments from 2015 to 2020. The publications were searched using proper keywords and filtered using the inclusion and exclusion criteria, which resulted in 52 publications. This paper summarizes the recent trends in research on different aspects of the recommender systems, such as learner attributes, recommendation methods, evaluation metrics, and the usability tests used by the researchers. It is observed that cognitive aspects of learners like learning style, preferences, knowledge level, etc., are used by most studies than non-cognitive aspects as social tags or trust. In most cases, recommendation engines are a hybrid of collaborative filtering, content-based filtering, ontological approaches, etc. All models were evaluated for the correctness of the prediction done, and a few studies have also done evaluations based on learner satisfaction or usability.

/pdf/a-systematic-literature-review-on-adaptive-content-545swk6vi3.pdf

A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020

Wind energy is one of the sustainable and renewable energies which have been the center of attention in industrial communities. The wind energy that is obtainable is arbitrary. So available wind power can't be known ahead in time. For the system organization as well as energy dispatching, a wind farm operator should know the power from wind in advance. Wind speed forecasting helps to minimize the unreliability in wind power forecasting. It enables for more desirable grid organization and incorporation of wind with power systems. It also helps to reduce the imbalance charges and penalties. For scheduling the power, the wind farm operator must at least know the wind speed data of one day ahead and for confirmation, he wants to know the wind speed data of one hour ahead. This paper is about the wind speed forecasting for different time horizons in three different sites in Tamil Nadu, India (Dharapuram, Kayathar, Nollur) using three different models such as Artificial Neural Network (ANN) method, Auto Regressive Integrated Moving Average Method (ARIMA) and Hybrid method, which is the combination of ARIMA and ANN and the comparison of their results. The wind speed data for three years with 1 hour time block is taken as inputs for the considered model. For predicting the accuracy of the model, numerical error evaluation methods such as Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Mean Absolute Error (MAE) were used. The outcome showed that the forecasted error for hybrid model is lesser than artificial neural network and autoregressive moving average model separately.

Forecasting of wind speed using ANN, ARIMA and Hybrid models

Collaborative optimization algorithm for learning path construction in E-learning

In the recent time, Permanent-Magnet Synchronous-Generator (PMSG) based variable-speed Wind-Energy Conversion-Systems (WECS) has become very attractive to many researchers. The research aim is to analyse different synchronous machine and compare them based on their maximum power generation. This paper reviews various aspects of PMSG such as topologies with controlled and uncontrolled rectifier, grid-connected and standalone mode of operation with various control methods of PMSG based WECS and recent optimization approaches. The performance analysis of PMSG can be enhanced by adopting a number of control mechanisms with the benefit of advanced optimization techniques.Acomparative analysis is carried out based on the techniques used and their corresponding advantages and drawbacks are discussed.

/pdf/power-generation-using-permanent-magnetsynchronous-generator-3ymtzxorfv.pdf

Power Generation Using Permanent MagnetSynchronous Generator (PMSG) BasedVariable Speed Wind Energy ConversionSystem (WECS): An Overview

2009 International Conference on Advances in Recent Technologies in Communication and Computing

Smart grid test bed based on gsm

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