Application of Artificial Neural Network for Height Modelling

Levelling in surveying is the term stand for height difference determination between ground points. If a datum surface is defined for the observed points, the heights of these points (reduced levels) can be computed. In ordinary orthometric levelling Geoid surface is usually used as a datum. Levelling process can be carried out using different survey equipments and techniques. It may be precise; when precise optical or digital levels are used, or it may be ordinary; when using ordinary optical, automatic or digital levels. Moreover it can be carried out trigonometrically or barometrically. Now a day Global Positioning System (GPS) provides a quick modern technology to determine heights of points [4]. Leveling procedure can be considered as one of the most costly procedures. However; some mathematical models are used for condensing spot heights with a relatively low cost. Artificial neural networks appeared as one of the prediction methods used in many disciplines. Although it is widely applied in different fields, but it is not widely used in surveying applications. The objective of this research is to test the possibility of using artificial neural networks method for height modelling in different topographic areas, and assessing its resultant precision in comparison with currently used algorithms, taking into account two factors; number of iterations and random seed number (a value that used to stabilize the weight selection). Results showed that artificial neural networks can successfully be used in height modelling. It produced height precisions of 97%, 97.39, and 93.63% for flat, gently rolling and mountainous areas respectively. These precisions are sufficiently enough for many survey applications. Moreover, an artificial neural networks technique can produce better results compared with kriging method. DOI: 10.17762/ijritcc2321-8169.150310

Raman spectroscopy and advanced mathematical modelling in the discrimination of human thyroid cell lines

Raman spectroscopy could offer non-invasive, rapid and an objective nature to cancer diagnostics. However, much work in this field has focused on resolving differences between cancerous and non-cancerous tissues, and lacks the reproducibility and interpretation to be put into clinical practice. Much work is needed on basic cellular differences between malignancy and normal. This would allow the establishment of a clinically relevant cellular based model to translate to tissue classification. Raman spectroscopy provides a very detailed biochemical analysis of the target material and to 'unlock' this potential requires sophisticated mathematical modelling such as neural networks as an adjunct to data interpretation. Commercially obtained cancerous and non-cancerous cells, cultured in the laboratory were used in Raman spectral measurements. Data trends were visualised through PCA and then subjected to neural network analysis based on self-organising maps; consisting of m maps, where m is the number of classes to be recognised. Each map approximates the statistical distribution of a given class. The neural network analysis provided a 95% accuracy for identification of the cancerous cell line and 92% accuracy for normal cell line. In this preliminay study we have demonstrated th ability to distinguish between "normal" and cancerous commercial cell lines. This encourages future work to establish the reasons underpinning these spectral differences and to move forward to more complex systems involving tissues. We have also shown that the use of sophisticated mathematical modelling allows a high degree of discrimination of 'raw' spectral data

Short-term electric load forecasting using computational intelligence methods

Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce several methods for short-term electric load forecasting. All the presented methods stem from computational intelligence techniques: Random Forest, Nonlinear Autoregressive Neural Networks, Evolutionary Support Vector Machines and Fuzzy Inductive Reasoning. The performance of the suggested methods is experimentally justified with several experiments carried out, using a set of three time series from electricity consumption in the real-world domain, on different forecasting horizons

The Grand Challenges and Myths of Neural-Symbolic Computation

The construction of computational cognitive models integrating the connectionist and symbolic paradigms of artificial intelligence is a standing research issue in the field. The combination of logic-based inference and connectionist learning systems may lead to the construction of semantically sound computational cognitive models in artificial intelligence, computer and cognitive sciences. Over the last decades, results regarding the computation and learning of classical reasoning within neural networks have been promising. Nonetheless, there still remains much do be done. Artificial intelligence, cognitive and computer science are strongly based on several non-classical reasoning formalisms, methodologies and logics. In knowledge representation, distributed systems, hardware design, theorem proving, systems specification and verification classical and non-classical logics have had a great impact on theory and real-world applications. Several challenges for neural-symbolic computation are pointed out, in particular for classical and non-classical computation in connectionist systems. We also analyse myths about neural-symbolic computation and shed new light on them considering recent research advances