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The severity of global magnetic disturbances in Near-Earth space can crucially affect human life. These geomagnetic disturbances are often indicated by a Kp index, which is derived from magnetic field data from ground stations, and is known to be correlated with solar wind observations. Forecasting of Kp index is important for understanding the dynamic relationship between the magnetosphere and solar...
The City of Cape Town is declared the most fire-prone city in South Africa. This is attributed to its unique topographical, vegetative and climatic features. A novel data-driven intelligent system utilising artificial neural networks is proposed and developed for wildfire risk assessment for the City of Cape Town. The model uses vegetation, climate and location features to predict a rating corresponding...
Modern technologies such as DNA microarray have been developed to study the transcriptome of cancer cells. It has been used in many studies for tumor classification and of identification of marker genes associated with cancer. However, this technique often suffers the ‘curse of dimensionality’. A general approach to overcome this setback is to perform feature selection technique prior to classification...
The complexity of LUCC (Land Use / Cover Change, LUCC) determines that it is important to conduct the study of LUCC using complex system theories, in particular by the establishment of a mathematical model for complex systems. In this paper, the LUCC model was built based on GIS technology, CA (cellular automata) and Agent technology. Firstly, Landsat TM data of 2000 and 2010 are used to obtain the...
The Artificial Neural Networks are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. They are an artificial intelligence method for modeling complex target functions. For certain types of problems, such as learning to interpret complex real-world sensor data, Artificial Neural Networks are...
Smart cities combine technology and human resources to improve the quality of life and reduce expenditures. Ensuring the safety of city residents remains one of the open problems, as standard budgetary investments fail to decrease crime levels. This work takes steps toward implementing smart, safe cities, by combining the use of personal mobile devices and social networks to make users aware of the...
Malaysia is moving forward towards a developed country by the year 2020. Therefore, implementation of Private Financial Initiative (PFI) in Malaysia is really needed in order to improve the delivery and quality of infrastructure facilities and public services in this nation. The success of this program can only be made possible by healthy participation from both public and private sectors in Malaysia...
The paper presents a system of systems approach to tackle the air quality planning problem through a multi-objective optimization methodology. In this context the different axes of the air quality planning issue (economic, environmental, social) are described and modeled in a unique framework and in an integrated/systemic way allowing for the selection of policies that optimally consider the three...
This paper presents an intelligent model for stock market signal prediction using Multi Layer Perceptron (MLP) Artificial Neural Networks (ANN). Blind source separation technique, from signal processing, is integrated with the learning phase of the constructed baseline MLP ANN to overcome the problems of prediction accuracy and lack of generalization. Kullback Leibler Divergence (KLD) is used, as...
This paper formulates an employer's hiring and retention decisions as an infinite-armed bandit problem and characterizes the structure of optimal hiring and retention policies. We develop approximations that allow us to explicitly calculate these policies and to evaluate their benefit. The solution involves a balance of two types of learning: the learning that reflects the improvement in performance...
Nonparametric Linear Regression and Artificial Neural Network models have been developed based on different perspectives and assumptions. In this paper a survey is made to compare the predictive performances of the nonparametric models of closing prices of Stock Index data, where the data is non normal. Comparative studies with the existing statistical prediction models indicate that the proposed...
The paper studies the application of principal component analysis and ANN (Artificial Neural Networks) for pre-warning of enterprise financial crisis, analyzes the factors of financial crisis, and constructs the model of the enterprise financial crisis with principal component analysis and ANN. It integrates simplifying of enterprise financial crisis index, dynamic learning of financial crisis knowledge...
Water resources sustainable use evaluation plays an important role in water resources planning and management, and it is a typical multi-goals, multi-layers and multi-attributes decision-making process, it needs to consider comprehensively social, economic, resources and environmental factors. A new evaluation index system of water resources sustainable utilization has been set up based on Yunnan...
This paper innovatively proposes a hybrid intelligent system combining fuzzy comprehensive assessment approach and artificial neural network (ANN) that predicts the safety performance of construction site for breaking through the limitations of conventional method. And also inducts the sensibility analysis to discriminate the importance of each index in the assessment index system. The effectiveness...
In wireless sensor networks (WSNs), hierarchical routing protocol is commonly used for energy efficiency. However, routing protocols without proper security suffer from many security vulnerabilities. Hence, in this paper, we propose a sensor authentication scheme during key establishment procedure based on clustering routing protocol. It authenticates lower level nodes by using temporal keys which...
Flood disaster management is an important part of flood risk assessment. A regional flood disaster risk assessment index system is established in this paper. Then principal component analysis (PCA) method and BP neural network are combined, and a regional flood disaster risk assessment of PCA-BP neural network model is established. PCA-BP neural network model analyze the loss of flood disaster about...
Product innovation is the main way for the economic development of enterprise, but product innovation project is high ricky and uncertainty, so the risk assessment and management of product innovation project has become concerned about academic and business community.This paper intends to do a systemic analysis of product innovation project risk evaluation, researching the product innovation project...
Based on the experience of operations and support, the criticality class of spare parts (SPs) is usually uncertain and may result in excess or insufficient inventory. So it's an urgent issue to devise a way to evaluate the criticality class of SPs accurately. The investigation applied back-propagation network (BPN) to evaluate the criticality class (i, II, III, IV) of spare parts. By using group-discussing...
Combined with the characteristics of enterprise logistics system, the evaluation index system of enterprise supply logistics system was established using systematic evaluation method. The BP neural network for appraising the performance of enterprise supply logistics system was built. Through the training and simulation of neural network, it is indicated that this evaluation method proves feasible...
Mechanism type selection is a critical problem often encountered in conceptual design stage of mechanical system. A BP neural network based approach to mechanism type selection is proposed, which capitalizes on the features of nonlinearity, self-organization, and fault tolerance of a neural network to implement classification and selection. By using appropriate data sets to train the neural network...
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