Extreme deep learning in biosecurity: the case of machine hearing for marine species identification

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

doi:10.1080/24751839.2018.1501542

“…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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“…Demertzis et al [23] proposed an automatic Machine Hearing Framework (MHF) for Marine Species classification and recognition in underwater environment. It identifies fish species based on their sounds.…”

Section: Literature Surveymentioning

confidence: 99%

FishResNet: Automatic Fish Classification Approach in Underwater Scenario

Mathur

¹

,

Goel

²

2021

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Fish species classification in underwater images is an emerging research area for scientists and researchers in the field of image processing. Fish species classification in underwater images is an important task for fish survey i.e. to audit ecological balance, monitoring fish population and preserving endangered species. But the phenomenon of light scattering and absorption in ocean water leads to hazy, dull and low contrast images making fish classification a tedious and tough task. Convolutional Neural Networks (CNNs) can be the solution for fish species classification problem but the scarcity of ample fish images leads to the serious issue of training a neural network from scratch. To overcome the issue of limited dataset the present paper proposes a transfer learning based fish species classification method for underwater images. ResNet-50 network has been used for transfer learning as it reduces the vanishing gradient problem to minimum by using residual blocks and thus improving the accuracies. Training only last few layers of ResNet-50 network with transfer learning increases the classification accuracy despite of scarce dataset. The proposed method has been tested on two datasets comprising of 27, 370 (i.e. large dataset) and 600 images (i.e. small dataset) without any data augmentation. Experimental results depict that the proposed network achieves a validation accuracy of 98.44% for large dataset and 84.92% for smaller dataset. With the performance analysis, it is observed that this transfer learning based approach led to better results by providing high precision, recall and F1score values of 0.94, 0.85 and 0.89, respectively.

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Extreme deep learning in biosecurity: the case of machine hearing for marine species identification

Cited by 16 publications

References 22 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

FishResNet: Automatic Fish Classification Approach in Underwater Scenario

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