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Machine based systems can't keep up with the task of organizing the data in an up-to-date manner unless and until the data acquired is being planned or scheduled and managed in an appropriate manner. Today's datasets start as small chunk of information and grow exponentially over a period of time. Once the size is extremely large it becomes difficult to make decisions and to predict consistently and...
In the automotive industry the issue of safety remains a major priority. This aspect is not focused just on the driver but also on the other participants of the traffic like the pedestrians. This paper describes a pedestrian detection system where three different classification methods are used for detecting pedestrians with a far infrared camera. The three methods are tested and compared on variable...
Credit scoring is becoming a competitive issue with rapid growth and significant advance. Building a satisfactory credit model has attracted lots of researchers in the past decades and it is still one of the hottest research topics in the field of credit industry. This paper emphasizes on surveying the development of the artificial intelligence technologies for solving credit scoring. It covers algorithms...
Artificial neural networks are used to classify the writing system of an unseen glyph. The complexity of the problem necessitates a large network, which hampers the training of the weights. Three hybrid algorithms — combining evolution and back-propagation learning — are compared to the standard back-propagation algorithm. The results indicate that pure back-propagation is preferable to any of the...
Signal classification is based on the extraction of several features that will be used as inputs of a classifier. The selection of these features is one of the most crucial parts, because they will design the search space, and, therefore, will determine the difficulty of the classification. Usually, these features are selected by using some prior knowledge about the signals, but there is no method...
Based on the advantages and disadvantages of the improved GA and LM algorithm, in this paper, the Hybrid Neural Network Algorithm (HNNA) is presented. Firstly, the algorithms use the advantage of the improved GA with strong whole searching capacity to search global optimal point in the whole question domain. Then, it adopts the strongpoint of the LM algorithm with fast local searching to fine search...
Artificial neural networks (ANN) and evolutionary algorithms are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of different evolutionary algorithms, imperialist competitive algorithm (ICA), genetic algorithm (GA), ICA-GA and recursive ICA-GA (R-ICA-GA) to train a classification problem on a multi layer perceptron...
Data mining an non-trivial extraction of novel, implicit, and actionable knowledge from large data sets is an evolving technology which is a direct result of the increasing use of computer databases in order to store and retrieve information effectively. It is also known as Knowledge Discovery in Databases (KDD) and enables data exploration, data analysis, and data visualization of huge databases...
A high rate of expression of Endothelin protein in the placental cell is very much regulated by inhalation of tobacco smoke and leads to placental abnormalities subjected to birth failure. Our application developed using Image Processing, Nearest Neighbor algorithm (NN) and Genetic Algorithms (GA), automates the study of these proteins to assist pathologists and lab technicians in achieving a more...
Classification is the most researched topic of neural networks. There are a great number of literature that analyze application of neural networks as a method of classification in different spheres of humans' life. This paper shows the general review of these spheres. Examples include many problems in business, science and medicine, that can be solved by the neural networks algorithms of classification.
Internet e-mails have become a common medium of communication for nearly every one. With the fast growing, spam interferes with valid email, and bothers users. This paper proposes a new fuzzy adaptive multi-population genetic algorithm (FAMGA), in order to automatically find the best feature subset to classify spam e-mails. FAMGA consists of multiple subpopulations, and each population runs independently...
Recent advances in microarray technology allow an unprecedented view of the biochemical mechanisms contained within a cell. Deriving useful information from the data is still proving to be a difficult task. In this paper a novel method based on a multi-objective genetic algorithm that discovers relevant sets of genes and uses a neural network to create rules using the evolved genes is described. This...
Using microfossil-based transfer functions, domain scientists from the field of pale oceanography seek to reconstruct environmental conditions at various times in the past. This is accomplished by first determining a quantitative relationship between a forcing function, such as temperature, and the modern for aminiferal response using a calibration data set based on environmental data from an oceanographic...
It is well-known that, the pattern recognition performances assigned to RBF neural networks depends a lot by their specific training algorithms, and by the methods used for RBF center selection (e.g., a clustering technique), particularly. Having as starting point the membership of genetic algorithms to the powerful class of global optimization methods, an optimal full-genetic training procedure of...
The credit card industry has been growing rapidly in recent years, and credit risk assessment becomes critically important for financial companies. In this paper, a novel support vector machine (SVM) based ensemble model is proposed for credit risk assessment. In the proposed method, principles component analysis (PCA) is firstly employed for credit feature selection. Secondly, SVMs with different...
One of the major issues concerning the Artificial Neural Networks (ANNs) design is a proper adjustment of the weights of the network. There have been a number of studies comparing the performance of evolutionary and gradient based ANNs learning. But the results of the studies, sometime conflicting to each other although the same and standard dataset development had been used. Motivated by this finding,...
This study proposes a novel classification technique of GA/k-prototypes in combination with a genetic algorithm to take the advantage of k-prototypes clustering mechanism for supporting the classification purpose. A genetic algorithm is used to adjust the weight applied to input attributes in order to enable a majority of the data records in each cluster to be with the same outcome class. We conduct...
In a previous set of analyses and researches we have proved the strong relationship that exists between each particular subject and its corresponding particular set of imagery cognitive tasks (determined out of several proposed mental tasks); these individual sets of tasks were the ones on which the obtained classification performances were significantly superior than on the other possible combinations...
Unsolicited commercial e-mail (spam) has shocked economies world over and is threatening the productivity. In this paper, an attempt has been made to classify email spam by combining Bayesian network and neural network classification approach. The header information like sender details and origin IP etc. was analyzed by centered Bayesian network, whereas the content and subject of the email were separately...
A novel classification method based on relevance vector machine with genetic algorithm is presented in the paper. In the model, genetic algorithm is applied to gain the suitable training parameters of relevance vector machine. State classification of roll bearing is applied to testify the classification ability of the proposed method, and state classification data of roll bearing are given. The experimental...
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