Journal of Advances in Mathematics and Computer Science http://journaljamcs.com/index.php/JAMCS <p style="text-align: justify;"><strong>Journal of Advances in Mathematics and Computer Science (ISSN:&nbsp;2456-9968) </strong>aims to publish original research articles, review articles and short communications, in all areas of mathematics and computer science. Subject matters cover pure and applied mathematics, mathematical foundations, statistics and game theory, use of mathematics in natural science, engineering, medicine, and the social sciences, theoretical computer science, algorithms and data structures, computer elements and system architecture, programming languages and compilers, concurrent, parallel and distributed systems, telecommunication and networking, software engineering, computer graphics, scientific computing, database management, computational science, artificial Intelligence, human-computer interactions, etc. This is a quality controlled, OPEN peer reviewed, open access INTERNATIONAL journal.</p> en-US contact@journaljamcs.com (Journal of Advances in Mathematics and Computer Science) contact@journaljamcs.com (Journal of Advances in Mathematics and Computer Science) Fri, 06 Mar 2020 12:10:45 +0000 OJS 3.1.1.4 http://blogs.law.harvard.edu/tech/rss 60 Efficiency and Consistency Assessment of Value at Risk Methods for Selected Banks Data http://journaljamcs.com/index.php/JAMCS/article/view/30245 <p>The study assesses Value at Risk (VaR) methods with respect to their efficiency and consistency in selected banks of the Nigeria Stock Market. The daily data on share prices of each bank was used from 2006 to 2018. The Value at Risk of each bank was estimated and the predictive performance of each method was assessed using the Failure Ratio and the Confidence Interval. The quality of each method was assessed based on the efficiency and consistency of the estimates. The VaR of each bank was estimated using Historical Simulation, Kernel Estimator, Empirical Estimator and Weighted Mean methods. The weighted mean method had the least estimates while Kernel estimator method had the highest estimates. The Failure Ratio and Confidence Interval show that Historical and Empirical methods had the least number of rejections at both confidence levels. The efficiency and consistency of the various methods shows the Historical Simulation and Weighted mean method had the minimum mean square errors (MSE). The Banks A, D and E gives an efficient and consistent result with Historical Simulation while B and C, is more efficient and consistent with weighted mean method.</p> Yakubu Musa, Iniabasi Emmanuel Etuk, S. U. Gulumbe ##submission.copyrightStatement## http://journaljamcs.com/index.php/JAMCS/article/view/30245 Fri, 06 Mar 2020 00:00:00 +0000 Relative Extension of Continuous Mappings http://journaljamcs.com/index.php/JAMCS/article/view/30246 <p>In this paper, the notion of a relative extension of continuous mappings is defined. The relative extension of continuous mappings is the generalization of the notion of a relative retract in topological spaces. The relative extension of continuous mappings will be applied to fixed point theory.</p> Miroslaw Slosarski ##submission.copyrightStatement## http://journaljamcs.com/index.php/JAMCS/article/view/30246 Fri, 06 Mar 2020 00:00:00 +0000 Security Detection in Audio Events: A Comparison of Classification Methods http://journaljamcs.com/index.php/JAMCS/article/view/30247 <p>The security of public places is becoming important with the increased rate of violence and subversion. Recently, several types of research have been proposed to automatically detect abnormal behavior in public places like a car crash, violence or other hazardous events in an attempt to improve security and save lives. Furthermore, most of the researches are using supervised classifications techniques to classify the audio signals. This paper proposes the use of the kernel principal component analysis (KPCA) to reduce the number of MFCC features extracted from the audio signal and then apply an unsupervised classification algorithm. Moreover, this paper presents the results of several supervised and unsupervised classification methods for audio events detection and compares these results with the result of the proposed approach. Experiments are done using a real data set recorded at the mean of public transportation. The obtained results reveal that K-means on 2 KPCA components gave good results for triggering a true alarm as well as detecting a false alarm; where the percentages of false and missed alarms were 4.5% and 7.8% respectively; whereas these values were 0.8% and 9.3% respectively for kernel k-means. Notwithstanding the DNN network gave the best results with a false alarm rate of 0% and 1.4% missed alarm.</p> Alissar Nasser ##submission.copyrightStatement## http://journaljamcs.com/index.php/JAMCS/article/view/30247 Sat, 14 Mar 2020 00:00:00 +0000 On Generalized Grahaml Numbers http://journaljamcs.com/index.php/JAMCS/article/view/30248 <p>In this paper, we introduce the generalized Grahaml sequences and we deal with, in detail, three special cases which we call them Grahaml, Grahaml-Lucas and modified Grahaml sequences. We present Binet’s formulas, generating functions, Simson formulas, and the summation formulas for these sequences. Moreover, we give some identities and matrices related with these sequences.</p> Yuksel Soykan ##submission.copyrightStatement## http://journaljamcs.com/index.php/JAMCS/article/view/30248 Sat, 14 Mar 2020 00:00:00 +0000 Application of a Novel Fractional Order Grey Support Vector Regression Model to Forecast Wind Energy Consumption in China http://journaljamcs.com/index.php/JAMCS/article/view/30249 <p>In order to achieve accurate prediction of new energy related data, a fractional grey support vector regression model based on nested cross-validation is proposed. In order to verify the superiority of the new model, China’s wind energy consumption data from 2001 to 2014 were selected, and a fractional grey prediction model, a support vector regression model and a fractional support vector regression combination model were established, and wind energy consumption in China was predicted from 2015 to 2018. Numerical experimental results show that the newly proposed combined prediction model has higher prediction accuracy.</p> Jiahao Cao, Liang Liu, Lizhi Yang, Shuchuan Xie ##submission.copyrightStatement## http://journaljamcs.com/index.php/JAMCS/article/view/30249 Wed, 18 Mar 2020 00:00:00 +0000