A knowledge-based decision support system for roofing materials selection and cost estimating: a conceptual framework and data modelling

A plethora of materials is available to the modern day house designer but selecting the appropriate material is a complex task. It requires synthesising a multitude of performance criteria such as initial cost, maintenance cost, thermal performance and sustainability among others. This research aims to develop a Knowledge-based Decision support System for Material Selection (KDSMS) that facilitates the selection of optimal material for different sub elements of a roof design. The proposed system also has a facility for estimating roof cost based on the identified criteria. This paper presents the data modelling conceptual framework for the proposed system. The roof sub elements are modelled on the Building Cost Information Service (BCIS) Standard Form of Cost Analysis. This model consists of a knowledge base and a database to store different types of roofing materials with their corresponding performance characteristics and rankings. The system s knowledge is elicited from an extensive review of literature and the use of a domain expert forum. The proposed system employs the multi criteria decision method of TOPSIS (Technique of ranking Preferences by Similarity to the Ideal Solution), to resolve the materials selection and optimisation problem. The KDSMS is currently being developed for the housing sector of Northern Ireland

A knowledge-based decision support system for roofing materials selection

Varieties of materials are available for roof housing construction but selecting the appropriate material is a complex and ponderous task. In order to choose the right material, a multitude of performance criteria would need to be considered. This research aims to develop a knowledge-based decision support system for material selection (KDSMS) to facilitate the selection of optimal material for different sub elements of roof design. This model consists of a knowledge base and databases to store different types of roofing materials with their corresponding performance characteristics. Knowledge is elicited from domain experts and extensive literature review. The proposed system employs the use of TOPSIS (Technique of ranking Preferences by Similarity to the Ideal Solution) multiple criteria decision making method, to solve the materials selection and optimisation problem where initial cost, maintenance cost, thermal performance and sustainability criteria are considered among others. The proposed system is currently being developed for the housing sector in Northern Ireland. This paper presents and explains the framework of the proposed system

A Cumulative Training Approach to Schistosomiasis Vector Density Prediction

Part 1: Medical Artificial Intelligence Modeling (MAIM)International audienceThe purpose of this paper is to propose a framework of building classification models to deal with the problem in predicting Schistosomiasis vector density. We aim to resolve this problem using remotely sensed satellite image extraction of environment feature values, in conjunction with data mining and machine learning approaches. In this paper we assert that there exists an intrinsic link between the density and distribution of the Schistosomiasis disease vector and the rate of infection of the disease in any given community; it is this link that the paper is focused to investigate. Using machine learning techniques, we want to accumulate the most significant amount of data possible to help with training the machine to classify snail density (SD) levels. We propose to use a novel cumulative training approach (CTA) as a way of increasing the accuracy when building our classification and prediction model

Improved lexicon-based sentiment analysis for social media analytics

Social media channels, such as Facebook or Twitter, allow for people to express their views and opinions about any public topics. Public sentiment related to future events, such as demonstrations or parades, indicate public attitude and therefore may be applied while trying to estimate the level of disruption and disorder during such events. Consequently, sentiment analysis of social media content may be of interest for different organisations, especially in security and law enforcement sectors. This paper presents a new lexicon-based sentiment analysis algorithm that has been designed with the main focus on real time Twitter content analysis. The algorithm consists of two key components, namely sentiment normalisation and evidence-based combination function, which have been used in order to estimate the intensity of the sentiment rather than positive/negative label and to support the mixed sentiment classification process. Finally, we illustrate a case study examining the relation between negative sentiment of twitter posts related to English Defence League and the level of disorder during the organisation’s related events

Building Earthquake Damage Recognition Performance of Frequency Domain Texture Features

Building collapse arising from destructive earthquakes is often the primary cause of casualties and economic loss. Building damage assessment is one of the top priorities in earthquake emergency work. Quad-polarimetric synthetic aperture radar (PolSAR) data not only has the advantages of radar imaging being neither exposed to sunlight nor blocked by clouds, but also contains the most abundant information of the four polarimetric channels. In many cases, the texture feature even outperforms other kinds of features. The texture features of buildings include not only spatial texture but also frequency texture. We proposed a parameter called the sector texture feature of the Fourier amplitude spectrum (STFFAS) to describe frequency-domain texture features based on the Fourier amplitude spectrum for building damage recognition. Our experimental results show that the recognition performance of the frequency texture feature performs satisfactorily for building damage information extraction