Bio-Inspired Models for Semantic Web

Sajja, Priti Srinivas; Akerkar, Rajendra

doi:10.1016/b978-0-12-405163-8.00012-0

Cited by 8 publications

(5 citation statements)

References 39 publications

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“…The SOM algorithm applied in the application example shown below can be summarized in the following sequence of six steps [ 34 ] (Fig. 1 b): Initialization—Initialize randomly all the weights of the connection reference weight vectors ( ).…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal evolution of COVID-19 in Portugal’s Mainland with self-organizing maps

et al. 2023

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Background Self-Organizing Maps (SOM) are an unsupervised learning clustering and dimensionality reduction algorithm capable of mapping an initial complex high-dimensional data set into a low-dimensional domain, such as a two-dimensional grid of neurons. In the reduced space, the original complex patterns and their interactions can be better visualized, interpreted and understood. Methods We use SOM to simultaneously couple the spatial and temporal domains of the COVID-19 evolution in the 278 municipalities of mainland Portugal during the first year of the pandemic. Temporal 14-days cumulative incidence time series along with socio-economic and demographic indicators per municipality were analyzed with SOM to identify regions of the country with similar behavior and infer the possible common origins of the incidence evolution. Results The results show how neighbor municipalities tend to share a similar behavior of the disease, revealing the strong spatiotemporal relationship of the COVID-19 spreading beyond the administrative borders of each municipality. Additionally, we demonstrate how local socio-economic and demographic characteristics evolved as determinants of COVID-19 transmission, during the 1st wave school density per municipality was more relevant, where during 2nd wave jobs in the secondary sector and the deprivation score were more relevant. Conclusions The results show that SOM can be an effective tool to analysing the spatiotemporal behavior of COVID-19 and synthetize the history of the disease in mainland Portugal during the period in analysis. While SOM have been applied to diverse scientific fields, the application of SOM to study the spatiotemporal evolution of COVID-19 is still limited. This work illustrates how SOM can be used to describe the spatiotemporal behavior of epidemic events. While the example shown herein uses 14-days cumulative incidence curves, the same analysis can be performed using other relevant data such as mortality data, vaccination rates or even infection rates of other disease of infectious nature.

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Section: Methodsmentioning

confidence: 99%

Spatiotemporal evolution of COVID-19 in Portugal’s Mainland with self-organizing maps

et al. 2023

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“…Neurons are connected to each other through synaptic weight [62]. Considering a neural network with three layers such as in Figure 4: an input layer, a hidden layer and an output layer the intermediate layer is considered self-organized Kohonen map, which consists of two layers of processing units (input and output), depending on the complexity of the network (there may be several hidden layers in each network) [79]. In FFNNs, information progresses, from the input nodes to the hidden nodes and from the hidden nodes to the output nodes.…”

Section: Feedforward Neural Networkmentioning

confidence: 99%

Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development

et al. 2020

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This article reviews the applications of artificial neural networks (ANNs) in greenhouse technology, and also presents how this type of model can be developed in the coming years by adapting to new technologies such as the internet of things (IoT) and machine learning (ML). Almost all the analyzed works use the feedforward architecture, while the recurrent and hybrid networks are little exploited in the various tasks of the greenhouses. Throughout the document, different network training techniques are presented, where the feasibility of using optimization models for the learning process is exposed. The advantages and disadvantages of neural networks (NNs) are observed in the different applications in greenhouses, from microclimate prediction, energy expenditure, to more specific tasks such as the control of carbon dioxide. The most important findings in this work can be used as guidelines for developers of smart protected agriculture technology, in which systems involve technologies 4.0.

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“…Moosa (2010) developed a firewall named ANNbWAF with the purpose to watch such attacks. In this approach, a trained ANN is embedded in the firewall applications where the normal and malicious data is used to give training to the neuron (Sajja and Akerkar, 2013). Many researchers have also designed ANN based algorithms for intrusion detection and proper authentication.…”

Section: Securitymentioning

confidence: 99%

“…Yohanes et al (2013) also implements GA for web crawling and finds the requested web pages. They also proved that GA is better than the traditional crawling methods (Sajja and Akerkar, 2013). Dounias et al (2006) have designed a hybrid technique for image processing and analysis by use of Genetic Algorithms.…”

Section: Genetic Algorithms and Semantic Webmentioning

confidence: 99%

“…Swarm Intelligence (SI) is a discipline based on the principle of social interaction between live entities. These entities are represented as agents/swarms (Sajja and Akerkar, 2013). Therefore, SI is defined as collective behavior of the groups of agents communicating locally with the environment resulting in global patterns.…”

Section: Swarm Intelligence and Semantic Webmentioning

confidence: 99%

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Applications of Nature-Inspired Algorithms in Different Aspects of Semantic Web

Chaudhary¹,

Singh²,

Kothari³

2018

Journal of Computer Science

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Nature has always inspired us all the waggle dance of Honey bee, the school of whales and the swarm of ants, each element when observed carefully has the abundance of teachings. If we carefully observe nature, we find that although Nature seems to be very simple and systematic, it hides many complexities underneath it. As technology also follows the same principle of 'simple-yet-complex', the researchers have always tried to apply the learning from Nature to complex technological Algorithms used to solve few real life human problems. Since the past decade, there has been a rapid increase of research in this field. Today Nature Inspired algorithms have permeated into almost all areas of sciences. Although it had been applied to various areas of sciences, the scope of this paper is limited to its application in the domain of The Semantic Web. The main objective of Semantic web applications is to obtain, manage and utilize the huge amount of information that is available in either structured semistructured or unstructured databases in distributed environment. This is an emerging domain and is advancing towards more and more intelligent and human oriented applications. This paper presents a survey of vital nature-Inspired techniques that can be used for optimizing various areas of Semantic web applications such as knowledge base, content filtering, Information Retrieval and Inference mechanism.

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Bio-Inspired Models for Semantic Web

Cited by 8 publications

References 39 publications

Spatiotemporal evolution of COVID-19 in Portugal’s Mainland with self-organizing maps

Spatiotemporal evolution of COVID-19 in Portugal’s Mainland with self-organizing maps

Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development

Applications of Nature-Inspired Algorithms in Different Aspects of Semantic Web

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