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This study investigates the evaluation of machine learning models based on multiple criteria. The criteria included are: predictive model accuracy, model complexity, and algorithmic complexity (related to the learning/adaptation algorithm and prediction delivery) captured by monitoring the execution time. Furthermore, it compares the models generated from optimising the criteria using two approaches...
Recently strong AI emerged from artificial intelligence due to need for a thinking machine. In this domain, it is necessary to deal with dynamic incomplete data and understanding of how machines make their decision is also important, especially in information system domain. One type of learning called Covering Algorithms (CA) can be used instead of the difficult statistical machine learning methods...
Today, good performance of handwriting recognition system has high complexity or complex computation especially in training and classification. We have developed an offline handwriting recognition system with structural approach. Each character through the stages of pre-processing, structural feature extraction and classification process using a combination of similarity endpoint, branch, line and...
This paper presents new methods for the recognition and categorization of object properties such as surface texture, weight, and compliance using a multi-modal artificial skin mounted on both arms of a humanoid. In addition, it introduces two novel feature descriptors, which are useful for providing high-level information to learning algorithms. The artificial skin has built-in 3-axis accelerometer,...
This work focuses on non-linear characterization of 61-channel electroencephalogram (EEG) signal for detecting alcoholics using ranked Approximate Entropy (ApEn) parameters. Significant channels that contribute to the detection of alcoholism are selected by ranking the ApEn features based on ANOVA test. In order to classify alcoholics from control, the ranked feature set is applied to two non-linear...
Energy efficiency in cognitive radio networks (CRN) is of paramount importance since secondary users (SU) are often likely to be energy constrained. While spectrum sensing (SS) is a critical CRN function, repetitive SS events can significantly reduce the battery life of sensing devices. However, energy efficiency can be improved by employing spectrum opportunity forecasting (SOF) and optimal scheduling...
Effective in-vehicle icons are used to convey quick and complete understanding of information to drivers for safe driving; however, misunderstood icons may result to worse. Former studies were able to point out the ineffectiveness (in terms of matching accuracy) of non-ISO vehicle icons. This study was conducted to fill in the gap in literature that addresses the need for redesigning ineffective non-ISO...
Regulating the power consumption to avoid peaks in demand is a common practice. Demand Response(DR) is being used by utility providers to minimize costs or ensure system reliability. Although it has been used extensively there is a shortage of solutions dealing with dynamic DR. Past attempts focus on minimizing the load demand without considering the sustainability of the reduced energy. In this paper...
Vector flow imaging is a critical component in the clinical diagnosis of cardiovascular diseases; however, most current methods are too computationally expensive to scale well to 3D. Less complex techniques, such as Doppler-based imaging (which cannot provide lateral flow measurements) and basic speckle tracking algorithms (which have poor lateral accuracy), are incapable of producing high quality...
Modelling and simulation (Mod-Sim) tools are becoming a crucial component in the design and evaluation of cyber and physical security systems. Many Mod-Sim tools exist for the elucidation of attack scenarios, characterization of facility vulnerabilities, and the construction and maintenance of optimal protection systems. The growing use of Mod-Sim tools for assessment of physical and cyber security...
In this paper, some results on the detection of variation in annotation in parsed corpora or tree banks are presented. Tree banks are generally built by means of using both automatic tools (i.e., taggers and parsers) and human intervention. In this process, inconsistencies (and, thus, variation) in the annotation arise, caused by a number of factors, for instance, disagreement in interpretation, incomplete...
The existing generative classifiers (eg. Naïve Bayes) estimate joint probability distribution p(x,y) or likelihood p(x|y) with the help of different density estimators, which are not suitable for large data sets due to their high time and space complexities. These classifiers also make different assumptions; allow limited dependencies among attributes and estimate one-dimensional likelihood. A new...
In this paper, we present a method to detect changes in high resolution remote sensing images based on superparsing proposed by Tighe et al. By comparing with several superpixel segmentation methods, we choose the SLIC (Simple Linear Iterative Clustering) method which can keep image boundary, produce consistent superpixels with similar size and shape, and also calculates fast. After superpixel segmentation,...
In this paper the authors evaluate in context of numerical calculations accuracy classical integer order and direct non-integer based order numerical algorithms of non-integer orders derivatives and integrals computations. Classical integer order based algorithm involves integer and fractional order differentiation and integration operators concatenation to obtain non-integer order. Riemann-Liouville...
This paper proposes an improved tone model for low complexity tone recognition. This model recognizes tone by using only estimated vowel signals, called vowel magnitude difference function, vowel-MDF (VMDF) so that we can reduce the number of input frames and the fundamental frequency (F0) negative influence from neighboring syllables in continuous speech. Vowel segmentation is therefore an important...
An enhanced version of the popular split-step Fourier method (SSFM) is presented. When used for digital backpropagation, the enhanced method allows a complexity reduction of up to one order of magnitude with respect to standard SSFM without sacrificing performance.
In large-scale software projects, build code has a high level of complexity, churn rate, and defect proneness. While it is desirable to have automated tools to help developers in localizing faults in build code, it is challenging to build such tools due to the dynamic nature of build code. Existing automatic fault localization methods focus on traditional code and none of them has such support for...
Refactorings are behavior-preserving source code transformations. While tool support exists for (semi) automatically identifying refactoring solutions, applying or not a recommended refactoring is usually up to the software developers, who have to assess the impact that the transformation will have on their system. Evaluating the pros (e.g., the bad smell removal) and cons (e.g., side effects of the...
Recent advances in computing technologies are increasing the expectations of high accuracy and reliability from sophisticated arithmetic programs. Multi Precision Arithmetic (MPA) plays a vital role in majority of scientific applications, where the accuracy levels are more considerable and even a small mistake may misguide the downstream experimental results. Normal testing strategies rely on test...
Multiple Sequence Alignment is an NP-hard problem. The complexity of finding the optimal alignment is O(LN) where L is the length of the longest sequence and N is the number of sequences. Hence the optimal solution is nearly impossible for most of the datasets. Progressive alignment solves MSA in very economic complexity but does not provide accurate solutions because there is a tradeoff between accuracy...
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