Subana Shanmuganathan

Self-organising map methods in integrated modelling of environmental and economic systems

Abstract The need for better techniques, tools and practices to analyse ecological and economic systems within an integrated framework has never been so great. Many institutions have made tremendous efforts in the implementation of sustainable environment management based on ‘integrated’ approaches, as opposed to that of late 20th centurys in-depth knowledge or ‘reductionism’ concepts. However, achieving sustainable environment management seems remote, as our understanding of ecosystem response to human influence is insufficient to predict the environmental outcome of proposed development activities. This has left environmentalists and land developers wrangling over the reliability of current environmental modelling techniques, assessment methodologies and their results. As a result, ecosystems continue to deteriorate with commensurate biodiversity loss. The paper elaborates on how self-organising map (SOM) methodologies within the connectionist paradigms (connectionist paradigms refer to the late 20th century neural network architectures) of artificial neural networks (ANNs) could be applied to disparate data analysis at two different scales: regional (using river water quality monitoring data to evaluate ecosystem response to human influence) and global (for modelling of environmental and economic system data and trade-off analysis) within an integrated framework to inform sustainable environment management.

Unsupervised Artificial Neural Nets for Modeling Movie Sentiment

Sentiment mining aims at extracting features on which users express their opinions in order to determine the user’s sentiment towards the query object. Movie sentiment in Twitter provides an excellent base upon which to evaluate sentiment mining methodologies both because of the pervasiveness of discussions devoted to movie topics and because of the brevity of expression induced by twitters 140 word limitation. In this paper we explore movie sentiment expressed in Twitter microblogs. A multi-knowledge based approach is proposed using, Self-Organizing Maps and movie knowledge in order to model opinion across a multi-dimensional sentiment space. We develop a visual model to express this taxonomy of sentiment vocabulary and then apply this model in test data. The results show the effectiveness of the proposed visualization in mining sentiment in the domain of Twitter tweets.

Text Mining in Radiological Data Records: An Unsupervised Neural Network Approach

The rapid growth in digitalized medical records presents new opportunities for coalescing terra bytes of data into information that could provide us with new knowledge. The knowledge discovered as such could assist medical practitioners in a myriad of ways, for example in selecting the optimal diagnostic tool from among many possible choices. We analyzed the radiology department records of children who had undergone a CT scanning procedure at Nagasaki University Hospital in the year 2004. We employed self organizing maps (SOM), an unsupervised neural network based text-mining technique for the analysis. This approach led to the identification of keywords within the narratives accompanying the medical records that could contribute to reduction of unnecessary CT requests by clinicians. This is important because overuse of medical radiation poses significant health risks to children in spite of the invaluable diagnostic capacity of such procedures

Data Mining Techniques for Modelling the Influence of Daily Extreme Weather Conditions on Grapevine, Wine Quality and Perennial Crop Yield

The influences of daily weather extremes, such as maximum/ minimum temperatures, humidity, and precipitation, are observable in perennial crop phenology that in turn determines the annual crop yield in quality and quantity. In viticulture, grapevine phenology determines the quality of vintage produced from the grapes apart from the best effects by winemaker. Following a brief review of current literature in this research domain, the paper describes a data mining approach being developed to data association modelling to depict dependency relationships between daily weather extremes, grapevine phenology and yield indicators using data from a vineyard in northern New Zealand and daily weather extremes logged at a nearby meteorology station. An artificial neural network algorithm was used to classify the data associations and the chi-square test was used to establish the degree of dependence between the related variable values. The initial results of the approach to daily maximum weather conditions show potential.

A Hybrid Approach to Modelling the Climate Change Effects on Malaysia’s Oil Palm Yield at the Regional Scale

Understanding the climate change effects on local crops is vital for adapting new cultivation practices and assuring world food security. Given the volume of palm oil produced in Malaysia, climate change effects on oil palm phenology and fruit production have greater implications at both local and international scenes. In this context, the paper looks at analysing the recent climate change effects on oil palm yield within a five year period (2007-2011) at the regional scale. The hybrid approach of data mining techniques (association rules) and statistical analyses (regression) used in this research reveal new insights on the effects of climate change on oil palm yield within this small data set insufficient for conventional analyses on their own.

Text Mining of Medical Records for Radiodiagnostic Decision-Making

The rapid growth of digitalized medical records presents new opportunities for mining terra bytes of data that may provide new information & knowledge. The knowledge discovered as such could assist medical practitioners in a myriad of ways, for example in selecting the optimal diagnostic tool from among numerous possible choices. We analyzed the radiology department records of children who had undergone a CT scan procedure at Nagasaki University Hospital in the year 2004. We employed Self Organizing Maps (SOM), an unsupervised neural network based text-mining technique for the analysis. This approach led to the identification of keywords with a significance value within the narratives of the medical records that could predict & thereby lower the number of unnecessary CT requests by clinicians. This is important because, in spite of the valuable diagnostic capacity of such procedures, the overuse of medical radiation does pose significant health risks and staggering cost especially with regard to children.

Climate Change and Grape Wine Quality: A GIS Approach to Analysing New Zealand Wine Regions

© 2012 Shanmuganathan et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Climate Change and Grape Wine Quality: A GIS Approach to Analysing New Zealand Wine Regions

Modelling climate change effects on wine quality based on expert opinions expressed in free-text format: the WEBSOM approach

The motivation for modelling the effects of climate change on viticulture and wine quality using both quantitative and qualitative data within an integrated analytical framework is described. The constraints and solutions evident when taking such an approach are outlined. WEBSOM is a novel self-organising map (SOM) method for extracting relevant domain-dependent characteristics from web based texts and in this case, investigated for modelling wine quality resulting from climate variation, by web text mining published descriptions made by sommeliers about this phenomenon. This paper describes experiments using the WEBSOM method with their results.

Artificial Neural Network Modelling: An Introduction

While scientists from different disciplines, such as neuroscience, medicine and high performance computing, eagerly attempt to understand how the human brain functioning happens, Knowledge Engineers in computing have been successful in making use of the brain models thus far discovered to introduce heuristics into computational algorithmic modelling. Gaining further understanding on human brain/nerve cell anatomy, structure, and how the human brain functions, is described to be significant especially, to devise treatments for presently described as incurable brain and nervous system related diseases, such as Alzheimer’s and epilepsy. Despite some major breakthroughs seen over the last few decades neuroanatomists and neurobiologists of the medical world are yet to understand how we humans think, learn and remember, and how our cognition and behaviour are linked. In this context, the chapter outlines the most recent human brain research initiatives following which early Artificial Neural Network (ANN) architectures, components, related terms and hybrids are elaborated.

Prediction Model for Chilli Productivity Based on Climate and Productivity Data

The global trade increases the competition in agricultural product export all around the world. The Indonesian Agricultural Industry needs to improve their competitiveness by fulfilling the requirements and restrictions imposed by some countries with regards to trace ability information features of the products, such as location of farming field cultivation method, chemical contaminants and supply chain information. Some European countries require the implementation of E-GAP (European Good Agricultural Practices) in order to secure food safety. Food safety provides the control and monitoring during, pre-, and post-harvest stages of agricultural products. This paper describes a prediction model based on the climate and productivity data on Indonesian agricultural products. The prediction model with an iteration of the climate and their possible increase or decrease in productivity. The model relies on historical data and an analytical algorithm. The decision support and early warning system provides the farmer some advice to reduce the crop failure risks due to climate change.