12,735 research outputs found

    Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses

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    Image analysis for ornamental crops is discussed with examples from the bedding plant industry. Feed-forward artificial neural networks are used to segment top and side view images of three contrasting species of bedding plants. The segmented images provide objective measurements of leaf and flower cover, colour, uniformity and leaf canopy height. On each imaging occasion, each pack was scored for quality by an assessor panel and it is shown that image analysis can explain 88.5%, 81.7% and 70.4% of the panel quality scores for the three species, respectively. Stereoscopy for crop height and uniformity is outlined briefly. The methods discussed here could be used for crop grading at marketing or for monitoring and assessment of growing crops within a glasshouse during all stages of production

    Role of Artificial Intelligence (AI) art in care of ageing society: focus on dementia

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    open access articleBackground: Art enhances both physical and mental health wellbeing. The health benefits include reduction in blood pressure, heart rate, pain perception and briefer inpatient stays, as well as improvement of communication skills and self-esteem. In addition to these, people living with dementia benefit from reduction of their noncognitive, behavioural changes, enhancement of their cognitive capacities and being socially active. Methods: The current study represents a narrative general literature review on available studies and knowledge about contribution of Artificial Intelligence (AI) in creative arts. Results: We review AI visual arts technologies, and their potential for use among people with dementia and care, drawing on similar experiences to date from traditional art in dementia care. Conclusion: The virtual reality, installations and the psychedelic properties of the AI created art provide a new venue for more detailed research about its therapeutic use in dementia

    Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

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    Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

    Hikester - the event management application

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    Today social networks and services are one of the most important part of our everyday life. Most of the daily activities, such as communicating with friends, reading news or dating is usually done using social networks. However, there are activities for which social networks do not yet provide adequate support. This paper focuses on event management and introduces "Hikester". The main objective of this service is to provide users with the possibility to create any event they desire and to invite other users. "Hikester" supports the creation and management of events like attendance of football matches, quest rooms, shared train rides or visit of museums in foreign countries. Here we discuss the project architecture as well as the detailed implementation of the system components: the recommender system, the spam recognition service and the parameters optimizer

    Neural Networks for Target Selection in Direct Marketing

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    Partly due to a growing interest in direct marketing, it has become an important application field for data mining. Many techniques have been applied to select the targets in commercial applications, such as statistical regression, regression trees, neural computing, fuzzy clustering and association rules. Modeling of charity donations has also recently been considered. The availability of a large number of techniques for analyzing the data may look overwhelming and ultimately unnecessary at first. However, the amount of data used in direct marketing is tremendous. Further, there are different types of data and likely strong nonlinear relations amongst different groups within the data. Therefore, it is unlikely that there will be a single method that can be used under all circumstances. For that reason, it is important to have access to a range of different target selection methods that can be used in a complementary fashion. In this respect, learning systems such as neural networks have the advantage that they can adapt to the nonlinearity in the data to capture the complex relations. This is an important motivation for applying neural networks for target selection. In this report, neural networks are applied to target selection in modeling of charity donations. Various stages of model building are described by using data from a large Dutch charity organization as a case. The results are compared with the results of more traditional methods for target selection such as logistic regression and CHAID.neural networks;data mining;direct mail;direct marketing;target selection

    Data analytics 2016: proceedings of the fifth international conference on data analytics

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