Hybrid Soft Computing for Atmospheric Pollution-Climate Change Data Mining

Iliadis, Lazaros; Anezakis, Vardis-Dimitris; Demertzis, Konstantinos; Spartalis, Stefanos

doi:10.1007/978-3-319-99810-7_8

“…This work presented a hybrid [53][54][55][56], innovative [57], reliable [58][59] and highly effective eLearning system that has the capacity to gather and analyze data from learning repositories and to adapt these to the educational curriculum according to the student skills and experience, based on sophisticated computational intelligence methods [60]. The AEeLS is a clearly innovative effort to effectively analyze and recommend relevant educational content based on semantic ontologies techniques.…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System

Demertzi¹,

Demertzis²

2020

Preprint

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The implementation of teaching interventions in learning needs has received considerable attention, as the provision of the same educational conditions to all students, is pedagogically ineffective. In contrast, more effectively considered the pedagogical strategies that adapt to the real individual skills of the students. An important innovation in this direction is the Adaptive Educational Systems (AES) that support automatic modeling study and adjust the teaching content on educational needs and students' skills. Effective utilization of these educational approaches can be enhanced with Artificial Intelligence (AI) technologies in order to the substantive content of the web acquires structure and the published information is perceived by the search engines. This study proposes a novel Adaptive Educational eLearning System (AEeLS) that has the capacity to gather and analyze data from learning repositories and to adapt these to the educational curriculum according to the student skills and experience. It is a novel hybrid machine learning system that combines a Semi-Supervised Classification method for ontology matching and a Recommendation Mechanism that uses a hybrid method from neighborhood-based collaborative and content-based filtering techniques, in order to provide a personalized educational environment for each student.

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“…In this paper proposed a hybrid [53][54][55][56], sophisticated [57], dependable [58][59] and vastly…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System

Demertzi¹,

Demertzis²

2020

Preprint

Self Cite

View full text Add to dashboard Cite

The implementation of teaching interventions in learning needs has received considerable attention, as the provision of the same educational conditions to all students, is pedagogically ineffective. In contrast, more effectively considered the pedagogical strategies that adapt to the real individual skills of the students. An important innovation in this direction is the Adaptive Educational Systems (AES) that support automatic modeling study and adjust the teaching content on educational needs and students' skills. Effective utilization of these educational approaches can be enhanced with Artificial Intelligence (AI) technologies in order to the substantive content of the web acquires structure and the published information is perceived by the search engines. This study proposes a novel Adaptive Educational eLearning System (AEeLS) that has the capacity to gather and analyze data from learning repositories and to adapt these to the educational curriculum according to the student skills and experience. It is a novel hybrid machine learning system that combines a Semi-Supervised Classification method for ontology matching and a Recommendation Mechanism that uses a hybrid method from neighborhood-based collaborative and content-based filtering techniques, in order to provide a personalized educational environment for each student.

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Data Analytics for Climate and Atmospheric Science

Demertzis

¹

,

Demertzi

²

,

Demertzis

³

2021

Int. J. Big Data Mini. Glob. Warm.

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Global climate change has already had observable effects on the environment. Glaciers have shrunk, ice on rivers and lakes is breaking up earlier, plant and animal ranges have shifted and trees are flowering sooner. Under these conditions, air pollution is likely to reach levels that create undesirable living conditions. Anthropogenic activities, such as industry, release large amounts of greenhouse gases into the atmosphere, increasing the atmospheric concentrations of these gases, thus significantly enhancing the greenhouse effect, which has the effect of increasing air heat and thus the speedup of climate change. The use of sophisticated data analysis methods to identify the causes of extreme pollutant values, the correlation of these values with the general climatic conditions and the general malfunctions that can be caused by prolonged air pollution can give a clear picture of current and future climate change. This paper presents a thorough study of preprocessing steps of data analytics and the appropriate big data architectures that are appropriate for the research study of Climate Change and Atmospheric Science.

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Hybrid Soft Computing for Atmospheric Pollution-Climate Change Data Mining

Cited by 4 publications

References 26 publications

A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System

A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System

A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System

Data Analytics for Climate and Atmospheric Science

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