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.
The impact of power efficient wireless sensor networks (WSN) is getting more and more important, as it is built of battery driven sensor nodes (SN). Beside common low power techniques like voltage scaling, variable-rate sampling (VRS) has been exposed as an adequate possibility to minimize the transceiver activity [1]. In this paper a high performance algorithm based on an artificial neural network...
The growing interest in predictive maintenance makes industrials and researchers turning themselves to artificial intelligence methods for fulfilling the tasks of condition monitoring and prognostics. Within this frame, the general purpose of this paper is to investigate the capabilities of an Evolving extended Takagi Sugeno (exTS) based neuro-fuzzy algorithm to predict the tool condition in high-speed...
The monitoring and management of the high density crowd in large scale public place is an important factor of city disaster reduction and mitigation. Automatic short term prediction of crowd density is a key problem. This paper introduces a prediction algorithm using v-support vector regression (v-SVR), which can control the accuracy of fitness and prediction error by adjusting the parameter v. An...
Prediction of the sports game results is an interesting topic that has gained attention lately. Mostly there are used stochastical methods of uncertainty description. In this work it is presented a preliminary approach to build a fuzzy model to basketball game results prediction. Ten fuzzy rule learning algorithms are selected, conducted and compared against standard linear regression with use of...
Lately, many notorious financial distress and bankruptcy events occurred in the world economic. As we known, bankruptcy of Lehman Brothers Holdings Inc. (LEH) is the largest bankruptcy filing in U.S. history in 2008. These events have serious impacted on the socio-economic and investment in public wealth. Due to solve this dilemma, this research collected 68 listed companies as the raw data from Taiwan...
Assigning functions to proteins that have not been annotated is an important problem in the post-genomic era. Meanwhile, the availability of data on protein-protein interactions provides a new way to predict protein functions. Previously, several computational methods have been developed to solve this problem. Among them, Deng et al. developed a method based on the Markov random field (MRF). Lee et...
A data stream prediction algorithm using Linear Regression based on Exponential Smoothing method was proposed in this paper, namely Exponential Smoothing based Linear Regression Analysis (ES_LRA) data stream prediction algorithm. The ES_LRA algorithm only processes the current sliding window, which can improve the operation efficiency; In the meantime, it applied a Smoothing Coefficient(α) through...
In recent years, collaborative filtering becomes one of the most successful recommender systems. Its key technique is to predict new ratings from the known ratings. Unfortunately, in the previous research, the temporal information was rarely applied. That is to say, the ratings at different time were considered the same. However, from our point of view, not only the mean values of ratings in different...
Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The kernel function and parameter selection is a key problem in the research of RVM. The real-world application and recent researches have emphasized the requirement to multiple kernel learning. This paper proposes a novel regression...
Combined the modified AdaBoost.RT with extreme learning machine (ELM), a new hybrid artificial intelligent technique called ensemble ELM is developed for regression problem in this study. First, a new ELM algorithm is selected as ensemble predictor due to its rapid speed and good performance. Second, a modified AdaBoost.RT is proposed to overcome the limitation of original AdaBoost.RT by self-adaptively...
Methods for learning decision rules are being successfully applied to many problem domains, especially where understanding and interpretation of the learned model is necessary. In many real life problems, we would like to predict multiple related (nominal or numeric) target attributes simultaneously. Methods for learning rules that predict multiple targets at once already exist, but are unfortunately...
A gradient-based iterative (GI) identification algorithm is developed for Box-Jenkins systems (or models) with finite measurement input-output data. Compared with the pseudo-linear regression stochastic gradient approach, the proposed algorithm updates the parameter estimation using all the available data at each iterative computation (at each iteration), and thus can produce highly accurate parameter...
Prediction of wireless network conditions enables the reconfiguration of mobile applications in a varying network environment, which in turn might gain more energy savings and better quality of service. In this paper, we focus on the prediction of network signal strength and its potential of improving energy saving in network-based power adaptations. We evaluate the performance of three prediction...
Linear regression model is widely used in data stream prediction processing. In order to eliminate the prediction deviation caused by small data set, curve tendency correction technique is used to increase the prediction accuracy. Firstly the weighted moving method is used to modify the prediction function parameters. This algorithm improves the predicting accuracy, but causes low efficiency of time...
Event prediction in event stream is an important problem in temporal data mining. However, existing event prediction algorithms are based on string prediction in which a character represents an event or an event type, do not take into account event sequence semantic and can not predict for infrequent event sequences. In this paper, an event prediction algorithm based on event sequence semantic called...
Generalization performance of support vector machines (SVM) is affected by parameter selection. How to select optimal parameters to achieve the best training model has been a hot research spot. In order to improve generalization performance of SVM, K-fold cross validation is used to select parameters for training. However, K-fold cross validation is time-consuming, especially for large number of samples,...
The purpose of this study is to produce algorithms that are able to predict the intramuscular fat (IMF) percentage of live cattle. Two algorithms based on linear regression analysis and neural network models are developed. These two algorithms extract feature information from live cattle ultrasound images and calculate the predicted IMF percentage values. The results show that these algorithms perform...
Bankruptcy prediction has been one of the most challenging tasks and a major research topic in accounting and finance. In this paper, bagging ensemble, a popular technique in the machine learning community, is proposed to improve the prediction performance of artificial neural networks in bankruptcy prediction analysis. The experiments conducted on the public dataset show that the proposed approach...
Prognostics has taken center stage in condition based maintenance (CBM) where it is desired to estimate remaining useful life (RUL) of a system so that remedial measures may be taken in advance to avoid catastrophic events or unwanted downtimes. Validation of such predictions is an important but difficult proposition and a lack of appropriate evaluation methods renders prognostics meaningless. Evaluation...
Microarray Classification compares thousands of genes of an unknown patient with the genes of known patients in order to predict and diagnose diseases. Since first introduced, many algorithms and techniques have been applied in search of the best solution. In response, the Seventh International Conference on Machine Learning and Applications (ICMLA) is holding a competition in search of the best classification...
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.