We have studied the significance of the N-terminal presequence of watermelon (Citrullus vulgaris) glyoxysomal malate dehydrogenase [gMDH; (S)-malate:NAD+ oxidoreductase; EC 1.1.1.37] in microbody targeting. The yeast Hansenula polymorpha was used as heterologous host for the in vivo expression of various genetically altered watermelon MDH genes, whose protein products were localized by immunocy...

background: municipal solid waste (msw) is the natural result of human activities. msw generation modeling is of prime im­portance in designing and programming municipal solid waste management system. this study tests the short-term pre­diction of waste generation by artificial neural network (ann) and principal component-regression analysis. methods: two forecasting techniques are presented in...

Objective: In general, financial time series such as stock indexes have nonlinear, mutable and noisy behavior. Structural and statistical models and machine learning-based models are often unable to accurately predict series with such a behavior. Accordingly, the aim of the present study is to present a new hybrid model using the advantages of the GMDH method and Non-dominated Sorting Genetic A...

A new methodology for Emergent System Identification is proposed in this paper. The new method applies the self-organizing Group Method of Data Handling (GMDH) functional networks, Particle Swarm Optimization (PSO), and Genetic Programming (GP) that is effective in identifying complex dynamic systems. The focus of the paper will be on how Particle Swarm Optimization (PSO) is applied within Grou...

An Artificial Neural Network is a flexible mathematical structure which is capable of identifying complex nonlinear relationships between input and output data sets. Such Neural Networks have been characterized by passive neurons that are not able to select and estimate their own inputs. In a new approach, which corresponds in a better way to the actions of human nervous system, the connections...

Network intrusion detection has been an area of rapid advancement in recent times. Similar advances in the field of intelligent computing have led to the introduction of several classification techniques for accurately identifying and differentiating network traffic into normal and anomalous. Group Method for Data Handling (GMDH) is one such supervised inductive learning approach for the synthe...

Accurate forecasting of solar energy is essential for photovoltaic (PV) plants, to facilitate their participation in the market and efficient resource planning. This article dedicated two models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach time series forecasting, using measured historical data, (2) ANN (Artificial Neural Network) machine learning techniques. The m...

Harmonic generation in power system networks presents significant issues that arise utilities. This paper describes a machine learning technique was used to conduct research study on the harmonic analysis of railway stations. The an investigation time series whose values represented total distortion (THD) for electric current. based information collected at station. In electrified substation, m...