An Extensive Investigation on Coronory Heart Disease using Various Neuro Computational Models

The diagnosis of heart disease at the early time is important to save the life of people as it is absolutely annoying process which requires extent knowledge and rich experience. By and large the expectation of heart infections in conventional method for inspecting reports, for example, Electrocardiogram-ECG, Magnetic Resonance Imaging- MRI, Blood Pressure-BP, Stress tests by medicinal professionals. Presently a-days a huge volume of therapeutic information is accessible in restorative industry in all maladies and these truths goes about as an incredible source in foreseeing the coronary illness by the professionals took after by appropriate ensuing treatment at an early stage can bring about noteworthy life sparing. There are numerous systems in ANN ideas which are likewise contributing themselves in yielding most elevated expectation precision over medical information. As of late, a few programming devices and different techniques have been proposed by analysts for creating powerful decision supportive systems. More over many new tools and algorithms are continued to develop and representing the old ones day by day. This paper aims the study of such different methods by researchers with high accuracy in predicting the heart diseases and more study should go on to improve the accuracy over predictions of heart diseases using Neuro Computing

Lipase Mediated Transesterification Of Waste Cooking Palm Oil For Biodiesel Production : Batch And Continuous Studies [TP359.B46 S623 2008 f rb].

Pembangunan strategi baru yang lebih cekap untuk menghasilkan biodiesel adalah perkara yang sangat penting. Ini kerana biodiesel telah diterima di seluruh dunia sebagai bahan bakar alternatif untuk enjin diesel. The development of new strategies to efficiently synthesize biodiesel is of extreme important. This is because biodiesel has been accepted worldwide as an alternative fuel for diesel engines

Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications

Hybrid Computational Intelligence Models With Symbolic Rule Extraction For Pattern Classification

Tesis ini adalah berkenaan dengan pembangunan model kecerdikan berkomputer hibrid bagi menangani masalah pengelasan corak. This thesis is concerned with the development of hybrid Computational Intelligence (CI) models for tackling pattern classification problems

Avances recientes en la predicción de la demanda de electricidad usando modelos no lineales

La predicción de la demanda es un problema de gran importancia para el sector eléctrico, ya que a partir de sus resultados, los agentes del mercado de energía toman las decisiones más adecuadas para su labor. En este artículo se presenta un análisis de las técnicas y modelos más usados en el pronóstico de la demanda de electricidad y la problemática o dificultades a las que se enfrentan los investigadores al momento de realizar un pronóstico. El análisis muestra que las técnicas más usadas son los modelos ARIMA y las redes neuronales artificiales. Sin embargo, se encontró poca claridad sobre cuál modelo es más adecuado y en qué casos, adicionalmente, los estudios no presentan una recomendación específica para desarrollar modelos de pronóstico de demanda, específicamente en el caso colombiano. Finalmente, se propone realizar un estudio sistemático con el fi n de determinar los modelos más adecuados para predicción de demanda para el caso colombiano

Advances in fuzzy rule-based system for pattern classification

Ph.DDOCTOR OF PHILOSOPH

Hierarchical Neuro-Fuzzy BSP Model- HNFB

This paper presents a new hybrid neuro-fuzzy model which is capable of learning structure and parameters by means of recursive binary space partitioning- BSP