Robust adaptive learning approach to self-organizing maps

Hameed, Alaa Ali; Karlık, Bekir; Salman, Mohammad Shukri; Eleyan, Gülden

doi:10.1016/j.knosys.2019.01.011

Cited by 27 publications

(10 citation statements)

References 29 publications

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“…An instrument realizing these goals is the software package «Deductor», based on algorithms of neural network modeling. One of the ways of data visualization and generalization used in this package is SOM (Kohonen self-organizing maps) [11,[14][15].…”

Section: Methodsmentioning

confidence: 99%

Parametric clustering of cities

2020

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The article analyzes the master plans and functional areas of 42 cities from different federal districts of the Russian Federation. To identify the dependencies between the characteristics of localities and the allocation of functional zones in them, the data clustering method was applied. Also criteria for clustering were identified – these are quantitative characteristics that are universal for various localities: total area of a locality, population, population density and gross regional product. With these criteria clustering was carried out using the software package «Deductor», based on algorithms of neural network modeling. Self-organizing Kohonen maps (SOM) were used to visualize the obtained data. As a result of the clustering the connection between the characteristics of localities and the ratio of functional zones in them is revealed.

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Section: Methodsmentioning

confidence: 99%

Parametric clustering of cities

2020

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“…All the instructors and chairman appreciate your consistent performance throughout the semester. 18 Complexity where all learners are treated equally not considering individual weaknesses, strengths, preferences, and personalized learning behavior. By integrating the M-learning model with LMS, we might be successful in making the learning process self-paced and adaptive.…”

Section: Appreciationmentioning

confidence: 99%

“…ML and DL algorithms have the single most important goal of AI research: allowing computers to understand and model our real world well enough to exhibit something identical to what we human beings call intelligence. Some of the popular DL algorithms are artificial neural network (ANN) [15], convolutional neural network (CNN) [16], recurrent neural network (RNN) [17], selforganizing maps (SOMs) [18], deep Boltzmann machine (DBM) [19], and deep autoencoder [20].…”

Section: Introductionmentioning

confidence: 99%

Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model

et al. 2021

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Machine learning (ML) and deep learning (DL) algorithms work well where future estimations and predictions are required. Particularly, in educational institutions, ML and DL algorithms can help instructors in predicting the learning performance of learners. Furthermore, the prediction of the learning performance of learners can assist instructors and intelligent learning systems (ILSs) in taking preemptive measures (i.e., early engagement or early intervention measures) so that the learning performance of weak learners could be increased thus reducing learners’ failures and dropout rates. In this study, we propose an intelligent learning system (ILS) powered by the mobile learning (M-learning) model that predicts learners’ performance and classify them into various performance groups. Subsequently, adaptive feedback and support are provided to those learners who struggle in their studies. Four M-learning models were created for different learners considering their learning features (study behavior) and their weight values. The M-learning model was based on the artificial neural network (ANN) algorithm with the aim to predict learners’ performance and classify them into five performance groups, whereas the random forest (RF) algorithm was used to determine each feature’s importance in the creation of the M-learning model. In the last stage of this study, we performed an early intervention/engagement experiment on those learners who showed weak performance in their study. End-user computing satisfaction (EUCS) model questionnaire was adopted to measure the attitude of learners towards using an ILS. As compared to traditional machine learning algorithms, ANN achieved the highest prediction accuracy for all four learning models, i.e., model 1 = 90.77%, model 2 = 87.69%, model 3 = 83.85%, and model 4 = 80.00%. Moreover, the five most important features that significantly affect the students’ final performance were MP3 = 0.34, MP1 = 0.26, MP2 = 0.24, NTAQ = 0.05, and AST = 0.018.

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“…Organizational adaptation is important for firm survival, for competitive strength ( Ali Hameed et al, 2019 ; Ali Hameed et al, 2019 ). Organizational adaptation represents the critical ability to fit the environment or the ecosystem to survive and grow ( Bode et al, 2011 ).…”

Section: Theoretical Developmentmentioning

confidence: 99%

THE Impact of Disruption on the Relationship Between Exploitation, Exploration, and Organizational Adaptation

2021

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Firms should deploy exploration and exploitation to foster organizational adaptation. Previous research on exploration and exploitation lacked a focus on disruption implications in different contexts. This study aims to empirically test a moderation model including disruption events, exploration, exploitation, and organizational adaptation and enable a deeper understanding of organizational learning and innovation theory to yield competitive advantage and sustainability of innovative firms. Our results reveal that exploration is more effective during outside disruption events. The results do not support the concept that exploitation is more effective during inside disruptions. Disruptions also moderate the combined effect of exploration and exploitation. Although they are generally complementary in facilitating organizational adaptation, a singular focus on either exploration or exploitation is as effective as is combining exploration and exploitation during inside and outside disruption events. The results of an event study using seven Chinese international firms, including Alibaba, Meituan, Dianping, Baidu, Beibei, TP-link, and Maxio, provided 132 completed and usable questionnaires that supported our hypotheses. Our study contributes to a better understanding of disruption, exploration, exploitation, and related performance implications.

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Robust adaptive learning approach to self-organizing maps

Cited by 27 publications

References 29 publications

Parametric clustering of cities

Parametric clustering of cities

Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model

THE Impact of Disruption on the Relationship Between Exploitation, Exploration, and Organizational Adaptation

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