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1 – 3 of 3Theodosios Theodosiou, Stavros Valsamidis, Georgios Hatziliadis and Michael Nikolaidis
A huge amount of data are produced in the agriculture sector. Due to the huge number of these datasets it is necessary to use data analysis techniques in order to comprehend the…
Abstract
Purpose
A huge amount of data are produced in the agriculture sector. Due to the huge number of these datasets it is necessary to use data analysis techniques in order to comprehend the data and extract useful information. The purpose of this paper is to measure, archetype and mine olea europaea production data.
Design/methodology/approach
This work applies three different data mining techniques to data about Olea europaea var. media oblonga from the island of Thassos, at the northern part of Greece. The data were from 1,063 farmers from three different municipalities of Thassos, namely Kallirachi, Limenaria and Prinos and concerned the year 2010. They were analysed using the classification algorithm OneR, the clustering algorithm k‐means and the association rule mining algorithm, Apriori from the WEKA data mining package. Also, new measures which quantify the performance of the productions of olives and oil are applied. Finally, archetypal analysis is applied in order to distinguish the most typical/stereotype farms for each region and describe their specific characteristics.
Findings
The results indicate that organic cultivation could improve the production of olives and olive oil. Furthermore, the climate differences among the three municipalities seems to be a factor involved in production efficacy.
Originality/value
It is the first time that data from the island of Thassos have been analysed systematically using a variety of data mining methods. Also, the measures proposed in the paper in order to analyse the data are new. Furthermore, archetypal analysis is proposed as a method to extract sterotypes/representative farms from the dataset.
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Salsabeel F.M. AlFalah, David K. Harrison, Vassilis Charissis and Dorothy Evans
Current healthcare applications produce a complex and inaccessible set of data that often needs to be investigated simultaneously. As such the conflicting software applications…
Abstract
Purpose
Current healthcare applications produce a complex and inaccessible set of data that often needs to be investigated simultaneously. As such the conflicting software applications and mental effort being demanded from the user result in time‐consuming analysis and diagnosis. The purpose of this paper is to provide a prototype, interactive system for management of multiple data sets, currently used for gait analysis capturing, reconstruction and diagnosis. In summary, this work is concerned with the development of interactive information‐visualisation software that assists medical practitioners in simplifying and enhancing the retrieval, visualisation and analysis of medical data with the intention of improving the overall system leading to an improved service for the user and patient experience.
Design/methodology/approach
The design of the proposed system aims to combine all the related existing software currently used for gait analysis and diagnosis under one, user‐friendly package. The latter will have the capacity to offer also real‐time, three dimensional (3D) representations of all the derived data (CT, MRI, motion capture) in an interactive virtual reality (VR) environment.
Findings
It is intended that the proposed prototype solutions will enhance interactive systems for management of multiple data sets, currently used for gait analysis capturing, reconstruction and diagnosis. The derived data encapsulate a plethora of multimedia information aiming to enhance medical visualisation.
Originality/value
The proposed system offers simulation capacity and a VR visualisation experience, which enhances the gait analysis diagnostic process. The 3D data can be manipulated in real‐time through a novel human‐computer interface which uses multimodal interaction through the use of graphical user interfaces and gesture recognition. The system aims towards a cost‐effective, clearly presented and timely accessible system that follows a threefold approach; It entails managing the extensive amount of the daily produced medical data, combining the scattered information related to one patient in one interface with a filtering criteria to the required information, and visualising in 3D the data from different sources, in order to improve 3D mental mapping, increase productivity and consequently ameliorate quality of service and management.
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