576 research outputs found
Construcción de un sistema de información y de ayuda a la decisión mediante lógica difusa para el cultivo del olivar en AndalucÃa
In Southern Spain, olive (Olea europaea L.) growing is an important part of the economy, especially in the provinces of Jaén, Córdoba and Granada. This work proposes the first stages of an Information and Decision-Support System (IDSS) for providing different types of users (farmers, agricultural engineers, public services, etc.) with information on olive growing and the environment, and also assisting in decision-making. The main purposes of the project reported in this paper are to process uncertain or imprecise data, such as those concerning the environment or crops, and combine user data with other scientific-experimental data. The possibility of storing agricultural and ecological information in fuzzy relational databases, vital to the development of an IDSS is described. The information will be processed using knowledge extraction tools (fuzzy data-mining) that will allow rules on expert knowledge for assessing suitability of land to be developed and making thematic maps with the aid of Geographic Information Systems. Flexible querying will allow the users to collect information interactively from databases, while user information is constantly added. Flexible querying of databases, land suitability and thematic maps may be used to help in decisionmaking.El cultivo del olivo (Olea europaea L.) tiene una enorme importancia económica en la zona sur de España y concretamente
en las provincias de Jaén, Córdoba y Granada. En este trabajo se propone la construcción de un sistema
de información y ayuda a la toma de decisión (IDSS) que permita en el futuro a distintos tipos de usuarios (agricultores,
agrónomos, administraciones públicas, etc.) obtener y manejar información sobre el cultivo de olivar y el soporte
ambiental del mismo, asà como ayudar en la toma de decisiones. Los principales objetivos desarrollados en este
trabajo son el tratamiento de datos inciertos e imprecisos, como es el caso de la información ambiental y sobre
cultivos, y la fusión de datos sobre cultivo y otros de carácter cientÃfico-experimental. Se describe la posibilidad de
almacenar la información de carácter agronómico y ecológico en bases de datos relacionales, que es vital para el desarrollo
de un IDSS. La información será procesada a través de herramientas de extracción de conocimiento (minerÃa
de datos difusa) y permitirá sobre la base del conocimiento experto el desarrollo de reglas para la clasificación de aptitud
del terreno y para la obtención de mapas temáticos con la ayuda de Sistemas de Información Geográfica. La consulta
flexible permitirá a los distintos usuarios la consulta interactiva de toda la información almacenada en las bases
de datos, asà como una implementación constante de las mismas. La consulta flexible de bases de datos, la idoneidad
de los terrenos y los mapas temáticos pueden ser de gran utilidad en la toma de decisiones.This work is part of the research projects 1FD97-0244-CO3-2 (financed with FEDER funds) and CGL2004-02282BTE (Spanish Ministry of Education and Science)
Spark solutions for discovering fuzzy association rules in Big Data
The research reported in this paper was partially supported the COPKIT project from the 8th Programme Framework (H2020) research and innovation programme (grant agreement No 786687) and from the BIGDATAMED projects with references B-TIC-145-UGR18 and P18-RT-2947.The high computational impact when mining fuzzy association rules grows significantly when managing very large data sets, triggering in many cases a memory overflow error and leading to the experiment failure without its conclusion. It is in these cases when the application of Big Data techniques can help to achieve the experiment completion. Therefore, in this paper several Spark algorithms are proposed to handle with massive fuzzy data and discover interesting association rules. For that, we based on a decomposition of interestingness measures in terms of α-cuts, and we experimentally demonstrate that it is sufficient to consider only 10equidistributed α-cuts in order to mine all significant fuzzy association rules. Additionally, all the proposals are compared and analysed in terms of efficiency and speed up, in several datasets, including a real dataset comprised of sensor measurements from an office building.COPKIT project from the 8th Programme Framework (H2020) research and innovation programme 786687BIGDATAMED projects B-TIC-145-UGR18
P18-RT-294
A fuzzy-based medical system for pattern mining in a distributed environment: Application to diagnostic and co-morbidity
In this paper we have addressed the extraction of hidden knowledge from medical records using
data mining techniques such as association rules in conjunction with fuzzy logic in a distributed
environment. A significant challenge in this domain is that although there are a lot of studies devoted
to analysing health data, very few focus on the understanding and interpretability of the data and
the hidden patterns present within the data. A major challenge in this area is that many health data
analysis studies have focussed on classification, prediction or knowledge extraction and end users find
little interpretability or understanding of the results. This is due to the use of black-box algorithms or
because the nature of the data is not represented correctly. This is why it is necessary to focus the
analysis not only on knowledge extraction but also on the transformation and processing of the data
to improve the modelling of the nature of the data. Techniques such as association rule mining and
fuzzy logic help to improve the interpretability of the data and treat it with the inherent uncertainty
of real-world data. To this end, we propose a system that automatically: a) pre-processes the database
by transforming and adapting the data for the data mining process and enriching the data to generate
more interesting patterns, b) performs the fuzzification of the medical database to represent and
analyse real-world medical data with its inherent uncertainty, c) discovers interrelations and patterns
amongst different features (diagnostic, hospital discharge, etc.), and d) visualizes the obtained results
efficiently to facilitate the analysis and improve the interpretability of the information extracted. Our
proposed system yields a significant increase in the compression and interpretability of medical data
for end-users, allowing them to analyse the data correctly and make the right decisions. We present
one practical case using two health-related datasets to demonstrate the feasibility of our proposal for
real data.Junta de Andalucia P18-RT-1765Ministry of Universities through the E
Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks
Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin
Computational Tool for Post-Earthquake Evaluation of Damage in Buildings
A method and a computational tool oriented to assist the damage and safety evaluation of buildings after strong earthquakes is described in this article. The input of the model is the subjective and incomplete information on the building state, obtained by inspectors which are possibly not expert professionals of the field of building safety. The damage levels of the structural components are usually described by linguistic qualifications which can be adequately processed by computational intelligence techniques based on neuro-fuzzy systems what facilitate the complex and urgent tasks of engineering decision-making on the building occupancy after a seismic disaster. The hybrid neuro-fuzzy system used is based on a special three-layer feedforward artificial neural network and fuzzy rule bases and is an effective
tool during the emergency response phase providing decisions about safety, habitability, and reparability of the buildings. Examples of application of the computer program are given for two different building classes
SLEMS : a knowledge based approach to soil loss estimation and modelling
ThesisThesis (M.Sc.E.), University of New Brunswick, 199
CBR and MBR techniques: review for an application in the emergencies domain
The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system.
RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to:
a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions
b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location.
In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations.
This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
Ontology-based knowledge representation and semantic search information retrieval: case study of the underutilized crops domain
The aim of using semantic technologies in domain knowledge modeling is to introduce the semantic meaning of concepts in knowledge bases, such that they are both human-readable as well as machine-understandable. Due to their powerful knowledge representation formalism and associated inference mechanisms, ontology-based approaches have been increasingly adopted to formally represent domain knowledge. The primary objective of this thesis work has been to use semantic technologies in advancing knowledge-sharing of Underutilized crops as a domain and investigate the integration of underlying ontologies developed in OWL (Web Ontology Language) with augmented SWRL (Semantic Web Rule Language) rules for added expressiveness.
The work further investigated generating ontologies from existing data sources and proposed the reverse-engineering approach of generating domain specific conceptualization through competency questions posed from possible ontology users and domain experts. For utilization, a semantic search engine (the Onto-CropBase) has been developed to serve as a Web-based access point for the Underutilized crops ontology model. Relevant linked-data in Resource Description Framework Schema (RDFS) were added for comprehensiveness in generating federated queries.
While the OWL/SWRL combination offers a highly expressive ontology language for modeling knowledge domains, the combination is found to be lacking supplementary descriptive constructs to model complex real-life scenarios, a necessary requirement for a successful Semantic Web application. To this end, the common logic programming formalisms for extending Description Logic (DL)-based ontologies were explored and the state of the art in SWRL expressiveness extensions determined with a view to extending the SWRL formalism. Subsequently, a novel fuzzy temporal extension to the Semantic Web Rule Language (FT-SWRL), which combines SWRL with fuzzy logic theories based on the valid-time temporal model, has been proposed to allow modeling imprecise temporal expressions in domain ontologies
Seismic Risk Management
Seismic risk management is a problem of many dimensions, involving multiple
inputs, interactions within risk factors, criteria, alternatives and stakeholders.
The deployment of this process is inherently fraught with the issues of
complexity, ambiguity and uncertainty, posing extra challenges in the
assessment, modelling and management stages. The complexity of earthquake
impacts and the uncertain nature of information necessitate the establishment
of a systematic approach to address the risk of many effects of seismic events in
a reliable and realistic way.
To fulfill this need, the study applies a systematic approach to the assessment
and management of seismic risk and uses an integrated risk structure. The
fuzzy set theory was used as a formal mathematical basis to handle
uncertainties involved within risk parameters. Throughout the process, the
potential impacts of an earthquake as the basic criteria for risk assessment
were identified and relations between them were accommodated through a
hierarchical structure. The various impacts of an earthquake are then
aggregated through a composite fuzzy seismic risk index (FSRi) to screen and
prioritize the retrofitting of a group of school buildings in Iran.
Given the imprecise data which is the prime challenge for development of any
risk model, the proposed model demonstrates a more reliable and robust
methodology to handle vague and imprecise information. The significant
feature of the model is its transparency and flexibility in aggregating, tracing
and monitoring the risk impacts. The novelty of this study is that it serves as
the first attempt of the process of a knowledge base risk-informed system for
ranking and screening the retrofitting group of school buildings. The model is
capable of integrating various forms of knowledge (quantitative and qualitative
information) extracted from different sources (facts, algorithms, standards and
experience). The outcomes of the research collectively demonstrate that the
proposed system supports seismic risk management processes effectively and
efficiently
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
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