A deep spiking machine-hearing system for the case of invasive fish species

2020

Algorithms

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Deep learning architectures are the most effective methods for analyzing and classifying Ultra-Spectral Images (USI). However, effective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely costly and time-consuming. It requires huge datasets with hundreds or thousands of labeled specimens from expert scientists. This research exploits the MAML++ algorithm in order to introduce the Model-Agnostic Meta-Ensemble Zero-shot Learning (MAME-ZsL) approach. The MAME-ZsL overcomes the above difficulties, and it can be used as a powerful model to perform Hyperspectral Image Analysis (HIA). It is a novel optimization-based Meta-Ensemble Learning architecture, following a Zero-shot Learning (ZsL) prototype. To the best of our knowledge it is introduced to the literature for the first time. It facilitates learning of specialized techniques for the extraction of user-mediated representations, in complex Deep Learning architectures. Moreover, it leverages the use of first and second-order derivatives as pre-training methods. It enhances learning of features which do not cause issues of exploding or diminishing gradients; thus, it avoids potential overfitting. Moreover, it significantly reduces computational cost and training time, and it offers an improved training stability, high generalization performance and remarkable classification accuracy. Algorithms 2020, 13, 61 2 of 25 Algorithms 2020, 13, 61 3 of 25In contrast to the learning process of the traditional neural networks, it is not sufficient for the network to learn good representations of the training classes, as the testing classes are distinct and they are not presented in training. However, it is desirable to learn features which distinguish the existing classes.The evaluation process consists of two distinct stages of the following format [4]:Step 1: Given k examples (value of k-shot), if k = 1, then the process is called one-shot; if k = 5, five-shot, and so on. The parameter k represents the number of labeled samples given to the algorithm by each class. By considering these samples, which comprise the support set, the network is required to classify and eventually adapt to existing classes.Step 2: Unknown examples of the labeled classes are presented randomly, unlike the ones presented in the previous step, which the network is called to correctly classify. The set of examples in this stage is known as the query set.The above procedure (steps) is repeated many times using random classes and examples which are sampled from the testing-evaluation set.As it is immediately apparent from the description of the evaluation process, as the number of classes increases, the task becomes more difficult, because the network has to decide between several alternatives. This means that Zero-shot Learning [5] is clearly more difficult than the one-shot, which is more difficult than the five-shot, and so on. Although humans have the ability to cope with this process, traditional ANN require many more examples to genera...

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Section: Discussionmentioning

confidence: 99%

GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspectral Image Analysis and Classification

2020

Algorithms

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“…In this way, they are contributing substantially to slowing the uncontrolled expansion of IAS. The significance of these intelligent models towards the identification and classification of IAS has been supported by some recent researches (Demertzis & Iliadis, 2015;Demertzis & Iliadis, 2017b, 2017cDemertzis, Iliadis, & Anezakis, 2017).…”

Section: Our Research Approachmentioning

confidence: 98%

“…Checking whether the recognized item is native or not in its locality, is carried out by an innovative Geo Location Country Based Services (GCBS) (Demertzis et al, 2017).…”

Section: Our Research Approachmentioning

confidence: 99%

“…The proposed GCBS algorithm performs the precise determination of the country to which coordinates (obtained by a GPS device) are referenced each time. It has been introduced by (Demertzis et al, 2017). According to this method, the world borders were originally taken from the states, as they appear in the shapefile available at the following URL http://thematicmapping.org/downloads/world_borders.php.…”

Section: The Innovative Proposed Geolocation Country-based Services Amentioning

confidence: 99%

“…It deals with objective electrophysiological measurements performed on animals for the study of the hearing instrument, with emphasis on the bioelectric potentials. The Sea Audio Dataset used in this study was created for the extension of a previous research effort of our team (Demertzis et al, 2017). More details are presented in the next paragraphs.…”

Section: Data Set Used In This Researchmentioning

confidence: 99%

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

Journal of Information and Telecommunication

Anezakis

2018

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Biosafety is defined as a set of preventive measures aimed at reducing the risk of infectious diseases' spread to crops and animals, by providing quarantine pesticides. Prolonged and sustained overheating of the sea, creates significant habitat losses, resulting in the proliferation and spread of invasive species, which invade foreign areas typically seeking colder climate. This is one of the most important modern threats to marine biosafety. The research effort presented herein, proposes an innovative approach for Marine Species Identification, by employing an advanced intelligent Machine Hearing Framework (MHF). The final target is the identification of invasive alien species (IAS) based on the sounds they produce. This classification attempt, can provide significant aid towards the protection of biodiversity, and can achieve overall regional biosecurity. Hearing recognition is performed by using the Online Sequential Multilayer Graph Regularized Extreme Learning Machine Autoencoder (MIGRATE_ELM). The MIGRATE_ELM uses an innovative Deep Learning algorithm (DELE) that is applied for the first time for the above purpose. The assignment of the corresponding class 'native' or 'invasive' in its locality, is carried out by an equally innovative approach entitled 'Geo Location Country Based Service' that has been proposed by our research team.

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A Dynamic Ensemble Learning Framework for Data Stream Analysis and Real-Time Threat Detection

Artificial Neural Networks and Machine Learning – ICANN 2018

Anezakis

2018