For partitioning the dataset of financial ratios into abnormal and normal groups, this paper proposes a hybrid clustering technique by combining similarity matching (SM) algorithm with self-organizing maps (SOM), called SM-SOM technique. The hybrid system provides three stages: preprocessing stage, similarity matching with cosine algorithm and SOM cluster. For evaluating the performance of this...

An accurate estimation of soil organic matter (SOM) content for spatial non-point prediction is an important driving force for the agricultural carbon cycle and sustainable productivity. This study proposed a hybrid geostatistical method of extreme learning machine-ordinary kriging (ELMOK), to predict the spatial variability of the SOM content. To assess the feasibility of ELMOK, a case study w...

The hybrid SOM-NG algorithm was formulated to improve the quantization precision in Self Organizing Maps by the means of combine both SOM and Neural Gas properties using a parameter γ to tune the topology preservation. A supervised learning algorithm is proposed to take advantage of the balanced hybrid algorithm. The proposed algorithm makes a linear approximation of the goal function for every...

The dissertation presents Hybrid Intelligent Decision Support Systems (DSS) for the selection of alternatives (companies, stocks, and company groups) in stock trading under uncertainty. This study proposes a framework including three models using Hybrid Intelligent DSS. The framework aims to optimize trading decisions in the selection of appropriate alternatives and reduce risky decisions. This...

In this paper, a hybrid intelligent system that integrates the SOM (Self-Organizing Map) neural network, kMER (kernel-based Maximum Entropy learning Rule), and Probabilistic Neural Network (PNN) for data visualization and classification is proposed. The rationales of this Probabilistic SOM-kMER model are explained, and its applicability is demonstrated using two benchmark data sets. The results...

this paper investigates the forecasting gold coin futures contract price in iran mercantile exchange. this research has presented a hybrid model based on genetic fuzzy systems (gfs) and artificial neural network (ann) to forecast the gold futures contract, at first, we use stepwise regression analysis (sra) to determine factors which have most influence on stock prices. at the next stage we div...

This paper presents OntoShare, an automated ontology mapping and merging architecture for learning object retrieval and reuse. The architecture aims to offer contextual and robust ontology mapping and merging through hybrid unsupervised clustering techniques comprising of Formal Concept Analysis (FCA), Self-Organizing Map (SOM) and K-Means clustering incorporated with linguistic processing usin...

The phoneme recognition aims to process a speech signal, characterized by a non-linearity with very high dynamics, allowing to perform various tasks on an information processing machine by an operator using orally address. This paper focuses on a proposed strategy, which implements an evolutionary recurrent self organizing map (SOM) model in phonemes recognition to improve their rates. It is a ...

Successful river flow forecasting is a major goal and an essential procedure that is necessary in water resource planning and management. There are many forecasting techniques used for river flow forecasting. This study proposed a hybrid model based on a combination of two methods: Self Organizing Map (SOM) and Least Squares Support Vector Machine (LSSVM) model, referred to as the SOM-LSSVM mod...