The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
High performance biometrics helps in reliably identifying persons for access authorization and other purposes. Iris recognition is very effective in identifying persons due to the iris' unique features and the protection of the iris from the environment and aging. We focus on the design and training of a feed-forward artificial neural network for high-performance iris recognition and investigate the...
Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and...
The generalization ability of neural network is an important aspect affecting its application. Meanwhile, the selection of training samples has a great impact on this ability. In order to improve the completeness of training samples, a method of samples self-learning of BP neural network based on clustering is put forward in this paper. By using the method of clustering, new samples can be collected...
Under real and continuously improving manufacturing conditions, lithography hotspot detection faces several key challenges. First, real hotspots become less but harder to fix at post-layout stages; second, false alarm rate must be kept low to avoid excessive and expensive post-processing hotspot removal; third, full chip physical verification and optimization require fast turn-around time. To address...
An Intrusion detection system is designed to classify the system activities into normal and abnormal. We use a combination of machine learning approaches as to detect the system attacks. The experimental results of the study show that increasing the number of classifiers has a threshold limit and the system accuracy will remain constant if the number of classifiers goes beyond this limit. The determination...
Non-stationary data distributions are a challenge in activity recognition from body worn motion sensors. Classifier models have to be adapted online to maintain a high recognition performance. Typical approaches for online learning are either unsupervised and potentially unstable, or require ground truth information which may be expensive to obtain. As an alternative we propose a teacher signal that...
MicroRNAs are one type of noncoding RNA that regulate their target mRNAs before mRNAs are translated into proteins. Although it has been demonstrated that the regulation is through partial binding of the seed region of a miRNA and its targets, the mechanism of this process is not fully discovered. Some biological experiments have shown that even perfect base pairing in the seed region does not always...
Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight women. In this paper we develop a hybrid intelligent system for diagnosis, prognosis and prediction for breast cancer using SANE (Symbiotic, Adaptive Neuro-evolution) and compare with ensemble ANN, modular neural network, fixed architecture evolutionary neural network (F-ENN) and Variable...
In this paper, we investigate the machine learning based strategies for dynamic channel selection in Cognitive Access Points (CogAPs) of WLANs. We employ Multi-layer Feedforward Neural Network (MFNN) models that utilize historical traffic information from network environment for learning the influence of spatio-temporal-spectral factors on the network and then predicting future traffic loads on each...
Abstract-Prediction of protein-proteininteraction sites is very important to the function of a protein and drug design. In this paper, we adequately utilize the characters of ensemble learning, which can improve the accuracy of individual classifier and generalization ability of the system, and propose a new prediction method of protein-protein interaction sites: ensemble learning method based on...
The concept of ensemble feature selection has been raised by Optiz in his earlier work. And yet, for models like neural networks, new models should be trained and created for every change in its feature subspace, this problem may become tricky when evolutionary algorithms are used to select features, for the slow-training process of neural networks may dramatically extend the whole process of ensemble...
Reducing power consumption has become a priority in microprocessor design as more devices become mobile and as the density and speed of components lead to power dissipation issues. Power allocation strategies for individual components within a chip are being researched to determine optimal configurations to balance power and performance. Modelling and estimation tools are necessary in order to understand...
Recently the Runge-Kutta neural network (RKNN) in series-parallel configuration for identification of ordinary differential equation (ODE) was introduced. The neural network is constructed according to the Runge-Kutta approximation method whereby a precise estimate of the changing rates of the system states is possible. In this contribution we extend the approach of to a general state space representation...
The problem of spam detection is a crucial task in the web information retrieval systems. The dynamic nature of information resources as well as the continuous changes in the information demands of the users makes the task of web spam detection a challenging topic. So far many different methods from researchers with different backgrounds have been proposed to tackle with spam web pages problem. In...
Structural machine learning method-covering algorithm (CA) possesses faster speed, lower complexity and higher precision. But construction of the weight of the neurons for new center of sphere domain is usually given a manmade criteria, could not follow the distribution of samples to achieve the optimal solution. In this paper, a new constructive algorithm which combines the cross covering algorithm...
We present a new framework and method for solving Multiple Instance Learning (MIL) problems. As a variation on supervised learning, MIL addresses the problem of classifying a bag of instances. If at least one of the instances in a bag is positive the bag is labeled positive, otherwise it is negative. We use a divide and conquer strategy to identify true positive group of instances in the positive...
This paper presents an optimizing methodology for implementing a multi-layer perceptron (MLP) neural network in a Field Programmable Gate Array (FPGA) device. In order to obtain an efficient implementation, a compromise of time and area is needed. Starting from simulation in the learning phase with fixed point operators, we have developed a methodology which allows the automatic generation of a VHDL...
In order to reduce the relativity and improve the separability of prototype pattern vectors, a spectral-based synergetic network learning algorithm is proposed in this paper. The most attractive feature of the new method is that its complexity is linear with data dimension. To approximate the optimal cut and prevent instability due to information loss, all eigenvectors are used. The eigenvalues and...
Creating an applicable and precise failure prediction system is highly desirable for decision makers and regulators in the finance industry. This study develops a new Failure Prediction (FP) approach which effectively integrates a fuzzy logic-based adaptive inference system with the learning ability of a neural network to generate knowledge in the form of a fuzzy rule base. This FP approach uses a...
This paper presents a new approach for breast cancer diagnosis using a combination of an Adaptive Network based Fuzzy Inference System (ANFIS) and the Information Gain method. In this approach, the ANFIS is to build an input-output mapping using both human knowledge and machine learning ability and the information gain method is to reduce the number of input features to ANFIS. An experimental result...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.