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.
Automatic detection/prediction of pitch accent, which determines the existence of prominent syllable of a word and its corresponding pitch accent pattern, is crucial in making expressive Text-To-Speech (TTS) synthesis. To train a model to detect and predict pitch accent usually requires a large amount of annotated training data to be manually labeled by phonetically trained language experts, which...
Business practices over the years show that three types of rainfall patterns basically reflect the characteristics of summer rainfall in eastern China, and have great practical value for business forecasting. Therefore, in this paper, on the basis of one-versus-one support vector machine multi-classification algorithm, combining with the fuzzy support vector machine, a new model is put forward, and...
This paper presents an alternate test implementation based on model redundancy that permits to achieve lower prediction errors than a classical implementation, even if training is performed over a small set of devices. The idea is to build different regression models for each specification during the training phase, and then to verify prediction consistency between the different models during the...
Corporation financial distress has been an important issue for study in the financial fields. This paper uses traditional BP neural network model and proposes PNN model to predicate financial distress. The sample consists of 276 companies listed on the Shanghai Stock Exchange and Shenzhen Stock Exchange over the period 2001–2010. Factor analysis is used to lower correlation and reduce dimensionality...
When a set of categories with related frequencies of the observed variable is available for each time point we have a bar diagram-valued time series. This paper introduces exponential smoothing methods to forecast bar diagram-valued time series data. The proposed method is inspired in the approach introduced by Maia and De Carvalho (2011) to deal with intevalvalued time series. The smoothing parameters...
In the era of E-science, most scientific endeavors depend on intense data analysis to understand the underlying physical phenomenon. Predictive modeling is one of the popular machine learning tasks undertaken in such endeavors. Labeled data used for training the predictive model reflects understanding of the domain. In this paper we introduce data understanding as a computational problem and propose...
Churn prediction model guides the customer relationship management to retain the customers who are expected to quit. In recent times, a number of tree based ensemble classifiers are used to model the churn prediction in telecom. These models predict the churners quite satisfactorily; however, there is a considerable margin of improvement. In telecom, the enormous size, imbalanced nature, and high...
Research has shown that environment lighting influences the behavior of the employees in an office setting highly, making lighting configuration in an office space crucial. A breakout area may be used by the employees for various activities that need to be supported by different lighting conditions, e.g. informal meetings or personal retreat. The desired lighting conditions depend on user preferences...
Two rating patterns exist in the user-item rating matrix and influence each other: the personal rating patterns are hidden in each user's entire rating history, while the global rating patterns are hidden in the entire user-item rating matrix. In this paper, a Rating Pattern Subspace is proposed to model both of the rating patterns simultaneously by iteratively refining each other with an EM-like...
As the pace of data availability and access to cyberinfrastructure increases, weather data inputs to practical application models have gone from point data to raster grids of varying spatial and temporal resolution. Certainly there is a benefit to widespread access of data, but transforming models developed at point locations to raster datasets is not trivial. In addition, dramatic improvements can...
Fusarium head blight, caused by Gibberella zeae, produces trichothecene mycotoxins, primarily deoxynivalenol (DON) in the grain of barley. Severe price discounting can occur at the sale of the crop if the grain contains a DON concentration of greater than or equal to 0.5 mg/kg. An artificial neural network model was developed to predict the risk of DON accumulation greater than or equal to 0.5 mg/kg...
Consensus method is a means of communication between experts who assist the formation of a group judgment. This technique has great potential to be adopted to provide prediction based on outcomes obtained from several classification algorithms. In this study, data on water consumption was used to induce the classification model that will be used to predict the possibility of the occurrence of water...
Banking industry suffers lost in millions of dollars each year caused by credit card fraud. Tremendous effort, time and money have been spent to detect fraud where there are studies done on creating personalized model for each credit card holder to identify fraud. These studies claimed that each card holder carries different spending behavior which necessitates personalized model. However, to the...
In this paper, a methodology which aims to solve the accuracy susceptibility problem in PMC-driven energy estimation models is developed. The application characteristics representativeness of PMCs and training benchmarks to generate an accurate and stable energy estimation model are considered. The results show a good accuracy and stability with average relative error of 4.8%.
Pedestrian recognition is one of the main advantages of the currently introduced autonomous cars. It is expected that millions of lives will be saved just by implementing this technology in real roads. We study this problem from two points of view, i.e., the recognition algorithm and the data. A trained binary classifier based on a tuned RFB-kernel SVM is used for predicting pedestrians on new scenarios...
The efficient management of networks and the provisioning of services with desired QoS guarantees is a challenge which needs to be addressed through autonomous mechanisms which are intelligent, lightweight and scalable. Recent focus on applying Machine Learning approaches to model the network and service behavioural patterns have proved to be quite effective in fulfilling the objectives of autonomous...
Defect prediction can be useful to streamline testing efforts and reduce the development cost of software. Predicting defects is usually done by using certain data mining and machine learning techniques. A prediction model is said to be effective if it is able to classify defective and non defective modules accurately. In this paper we investigate the result of data pre-processing on the performance...
Accurate query performance prediction (QPP) is central to effective resource management, query optimization and query scheduling. Analytical cost models, used in current generation of query optimizers, have been successful in comparing the costs of alternative query plans, but they are poor predictors of execution latency. As a more promising approach to QPP, this paper studies the practicality and...
Text-to-speech synthesis (TTS) is the final stage in the speech-tospeech (S2S) translation pipeline, producing an audible rendition of translated text in the target language. TTS systems typically rely on a lexicon to look up pronunciations for each word in the input text. This is problematic when the target language is dialectal Arabic, because the statistical machine translation (SMT) system usually...
This study analyzes two implications of the Adaptive Market Hypothesis: variable efficiency and cyclical profitability. These implications are, inter alia, in conflict with the Efficient Market Hypothesis. Variable efficiency has been a popular topic amongst econometric researchers, where a variety of studies have shown that variable efficiency does exist in financial markets based on the metrics...
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.