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Given the ever changing needs of the job markets, education and training centers are increasingly held accountable for student success. Therefore, education and training centers have to focus on ways to streamline their offers and educational processes in order to achieve the highest level of quality in curriculum contents and managerial decisions. Educational process mining is an emerging field in...
As modern processors are becoming increasingly complex, fast and accurate performance prediction is crucial during the early phases of hardware and software co-development. To accurately and efficiently predict the performance of a given software workload is, however, a challenging problem. Traditional cycle-accurate simulation is often too slow, while analytical models are not sufficiently accurate...
Big-data applications are being increasingly used in today's large-scale data enters for a large variety of purposes, such as solving scientific problems, running enterprise services, and computing data-intensive tasks. Due to the growing scale of these systems and the complexity of running applications, jobs running in big-data systems experience unsuccessful terminations of different nature. While...
Modeling the behavior of elderly people to detect changes in their health status or mobility is challenging and thus requires to combine temporal and spatial knowledge. Spatial knowledge is obtained by a novel human centered scene understanding approach, being able to accurately model sitting and walking regions based on noisy long-term tracking data from a depth sensor, without exploiting geometric...
Lorenz time-series is characterized by non-linearity, noise, volatility and is chaotic in nature thus making the process of forecasting cumbersome. The main aim of forecasters is to apply an approach that focuses on improving accuracy in both one-step and multi-step-ahead forecasts. This paper presents an empirical analysis of Lorenz time-series using Scaled UKF-NARX hybrid model to perform one-step...
This paper displays a structure of the work exercised through the DM methods which helps in the prediction and treatment of various diseases. This examines two problems (elongated work based on prediction of RCT and Heart Disease) with classification technique based on cross validation, decision tree to discover RCT, split validation and model to detect heart disease before probing advice from the...
This article deals with the modeling issue of plants with unknown description. The modeling approach is based on the Local Model Network structure. Local Models perform local linearization and their structure is quite similar to the Takagi-Sugeno fuzzy models. Local Neural Models (LNM) function as linear filters, giving a satisfying estimation for the plant's output within parts of the operating regime...
Statistical shape models generally characterize shape variations linearly by principal component analysis (PCA), which assumes that the non-rigid shape parameters are drawn from a Gaussian distribution. This practical assumption is often not valid. Instead, we propose a constrained local model based on independent component analysis (ICA) and use kernel density estimation (KDE) for non-parametrically...
Sparse models in dictionary learning have been successfully applied in a wide variety of machine learning and computer vision problems, and have also recently been of increasing research interest. Another interesting related problem based on a linear equality constraint, namely the sparse null space problem (SNS), first appeared in 1986, and has since inspired results on sparse basis pursuit. In this...
In the development of drugs compounds suitable for human being, many experiments have to be conducted to ensure drugs safe consumption and generally takes almost 10 to 12 years for a particular drugs to enter the market from laboratory. Therefore, the pattern recognition in QSAR is significant for analyzing the data and developing several necessary models, so that only novel drugs candidate will be...
We have designed a Turkish dictation system for Broadcast news applications. Turkish is an agglutinative language with free word order. These characteristics of the language result in the vocabulary explosion, large number of out-of-vocabulary (OOV) words and the complexity of the N-gram language models in speech recognition when words are used as recognition units. Therefore, we proposed new recognition...
The Enterprise Resource Planning (ERP) system is an integrated software package applied by many enterprises as an operations platform over the past years. However, according to an industrial survey, the failure rate of ERP system implementation is relatively high because of high implementation costs and long implementation time, incapable implementation teams, process misfits, resistance to change...
Engine testing technology has made great development and gathering the engine data of failure becomes more and more easily. The engine fault recognition method based on data driving has made rapid development. The support vector machine (SVM) is currently a well-known machine learning technique. It has been applied to deal with range of fault recognition problems due to its unique advantages. There...
Model-based FDI systems are considered here. The problem of constructing the diagnosed system model as well as the automatic search for the best rule base of the residual analyzer is reduced to a set of global optimization tasks. Various optimization problems are considered depending on the chosen technology of the non-analytical model construction as well as that of the residual evaluation. Most...
Increasing energy demands has led to expansion of power infrastructure which also means that there is an increase in the number of lines subjected to faults due to short circuits or unintentional causes such as birds, falling of branches, etc,. Sometimes this causes an outage of power. Identification of the right fault type is necessary for quick power restoration. Hence accurate fault classification...
Over the last decades there has been a growing interest in modeling human performance and analyzing human activity and human operator behavior to improve system design. A variety of tools and approaches which are based on task analysis methods and tools have been proposed. As technology advances and tasks become more demanding, human work changes increasing the need to create new methodologies and...
Nowadays Learning Games are widely utilized in various domains to ease learning and capture the focus of learner using playful spring. Although many researchers show that using Learning Games increase motivation and interest. However Learning Games users still suffer from the high cost and difficulties of developing and designing an attractive learning game without being a designer or an informatics...
Information Technology (IT) is described as a science and an activity of using computers and other electronic equipment to store and send information. Now, IT becomes the most important part of human and organization life. In an organization, IT will act as a support or barrier in organization. In this matter, organization should define and realize the IT which they must use. To gain an advantage...
Topic modeling is a powerful technique for unsupervised analysis of large document collections. Topic models conceive latent topics in text using hidden random variables, and discover that structure with posterior inference. Topic models have a wide range of applications like tag recommendation, text categorization, keyword extraction and similarity search in the broad fields of text mining, information...
Identifying the tasks a given piece of malware was designed to perform (e.g. logging keystrokes, recording video, establishing remote access, etc.) is a difficult and time-consuming operation that is largely human-driven in practice. In this paper, we present an automated method to identify malware tasks. Using two different malware collections, we explore various circumstances for each — including...
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