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Salas Rueda, Ricardo Adán - One of the best experts on this subject based on the ideXlab platform.
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Uso del ciclo de Deming para asegurar la calidad en el proceso educativo sobre las Matemáticas.
2018Co-Authors: Salas Rueda, Ricardo AdánAbstract:Universities are looking to meet the needs and demands of students in the 21st century, therefore, quality acquires a fundamental role to achieve success in organizations. This mixed research proposes the use of the Deming cycle to improve the teaching-learning process related to the Gauss-Jordan Method through technology. The sample consists of 31 students who took the course Intermediate Mathematics for Business during the school year 2017. The use of the MsSchool web application and the cloud service Desmos allow to improve the development of skills and the assimilation of knowledge on the Gauss-Jordan Method. Even these technological tools are innovative and useful for the learning process. In conclusion, the stages of the Deming cycle (planning, doing, reviewing and acting) allow the construction of creative educational experiences for the field of mathematics.Las universidades están buscando satisfacer las necesidades y demandas de los estudiantes en el Siglo XXI, por consiguiente, la calidad adquiere un papel fundamental para lograr el éxito en las organizaciones. Esta investigación mixta propone el uso del ciclo de Deming para mejorar el proceso de enseñanza-aprendizaje sobre el método de Gauss-Jordan por medio de la tecnología. La muestra está conformada por 31 estudiantes que cursaron la asignatura Matemáticas Intermedias para los negocios durante el ciclo escolar 2017. El uso de la aplicación web MsSchool y el servicio en la nube Desmos permiten mejorar el desarrollo de las habilidades y la asimilación del conocimiento sobre el método de Gauss-Jordan. Incluso, estas herramientas tecnológicas son innovadoras y útiles para el proceso de aprendizaje. En conclusión, las etapas del ciclo de Deming (planificar, hacer, revisar y actuar) permiten construir experiencias educativas creativas para el campo de las matemáticas
Salas-rueda Ricardo - One of the best experts on this subject based on the ideXlab platform.
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Uso del ciclo de Deming para asegurar la calidad en el proceso educativo sobre las Matemáticas. // Use of the Deming cycle to ensure quality in the educational process on mathematics.
'Universidad Estatal de Milagro (UNEMI)', 2018Co-Authors: Salas-rueda RicardoAbstract:Las universidades están buscando satisfacer las necesidades y demandas de los estudiantes en el Siglo XXI, por consiguiente, la calidad adquiere un papel fundamental para lograr el éxito en las organizaciones. Esta investigación mixta propone el uso del ciclo de Deming para mejorar el proceso de enseñanza-aprendizaje sobre el método de Gauss-Jordan por medio de la tecnología. La muestra está conformada por 31 estudiantes que cursaron la asignatura Matemáticas Intermedias para los negocios durante el ciclo escolar 2017. El uso de la aplicación web MsSchool y el servicio en la nube Desmos permiten mejorar el desarrollo de las habilidades y la asimilación del conocimiento sobre el método de Gauss-Jordan. Incluso, estas herramientas tecnológicas son innovadoras y útiles para el proceso de aprendizaje. En conclusión, las etapas del ciclo de Deming (planificar, hacer, revisar y actuar) permiten construir experiencias educativas creativas para el campo de las matemáticas.AbstractUniversities are looking to meet the needs and demands of students in the 21st century, therefore, quality acquires a fundamental role to achieve success in organizations. This mixed research proposes the use of the Deming cycle to improve the teaching-learning process related to the Gauss-Jordan Method through technology. The sample consists of 31 students who took the course Intermediate Mathematics for Business during the school year 2017. The use of the MsSchool web application and the cloud service Desmos allow to improve the development of skills and the assimilation of knowledge on the Gauss-Jordan Method. Even these technological tools are innovative and useful for the learning process. In conclusion, the stages of the Deming cycle (planning, doing, reviewing and acting) allow the construction of creative educational experiences for the field of mathematics
Duygu Çelik Ertuğrul - One of the best experts on this subject based on the ideXlab platform.
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Information Retrieval Using the Reduced Row Echelon Form of a Term-Document Matrix
Journal of Internet Technology, 2019Co-Authors: Ufuk Parali, Metin Zontul, Duygu Çelik ErtuğrulAbstract:It is getting more difficult to retrieve relevant information regarding the user input query due to the large amount of information in the web. Unlike the conventional information retrieval (IR) algorithms, this study presents a new algorithm – reduced row echelon form IR Method (rrefIR) – with higher average similarity precision to get more relevant and noise-free documents. For dimension reduction in the proposed algorithm, singular value decomposition (SVD) is applied on the reduced row echelon form – obtained by utilizing Gauss-Jordan Method – of the covariance of term-document matrix (TDM). The rrefIR algorithm outperforms the LSI and COV algorithms with respect to Jaro-Winkler, Overlap, Tanimoto and Jaccard similarity measures in the means of average similarity precision. The physical reason for the better IR performance is the linear independent basis vectors set obtained by Gauss-Jordan operation. This basis set can be considered as the generating roots of the vector space spanned by TDM. Utilizing these vectors increases the latent semantic charateristics of the SVD phase of the proposed IR algorithm.
Ertuğrul, Duygu Çelik - One of the best experts on this subject based on the ideXlab platform.
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Information retrieval using the reduced row echelon form of a term-document matrix
Taiwan Academic Network Management Committee, 2019Co-Authors: Paralı Ufuk, Zontul Metin, Ertuğrul, Duygu ÇelikAbstract:Zontul, Metin (Arel Author)It is getting more difficult to retrieve relevant information regarding the user input query due to the large amount of information in the web. Unlike the conventional information retrieval (IR) algorithms, this study presents a new algorithm – reduced row echelon form IR Method (rrefIR) – with higher average similarity precision to get more relevant and noise-free documents. For dimension reduction in the proposed algorithm, singular value decomposition (SVD) is applied on the reduced row echelon form – obtained by utilizing Gauss-Jordan Method – of the covariance of term-document matrix (TDM). The rrefIR algorithm outperforms the LSI and COV algorithms with respect to Jaro-Winkler, Overlap, Tanimoto and Jaccard similarity measures in the means of average similarity precision. The physical reason for the better IR performance is the linear independent basis vectors set obtained by Gauss-Jordan operation. This basis set can be considered as the generating roots of the vector space spanned by TDM. Utilizing these vectors increases the latent semantic charateristics of the SVD phase of the proposed IR algorithm. © 2019 Taiwan Academic Network Management Committee. All rights reserved
Meirui Ren - One of the best experts on this subject based on the ideXlab platform.
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implementing the matrix inversion by gauss jordan Method with cuda
Wireless Algorithms Systems and Applications, 2014Co-Authors: Ning Tian, Longjiang Guo, Meirui RenAbstract:Solving the matrix inversion is an open problem which is often related to scientific computation. Moreover, matrix inverse also has wide applications in social networks. Individuals in social networks are described as nodes, and the similarity among nodes are significant for link prediction. Usually, the problem of calculating similarities among nodes is converted to the problem of matrix inversion. With the increasing of the orders of matrices, traditional sequential algorithms are unable to meet the needs for the short calculation time. Although cluster systems can solve the inversion of large-scale matrices efficiently, the equipment cost and power consumption are very high. This paper proposes a parallel algorithm PA-Gauss, which is based on the Gauss-Jordan Method of selecting the main element. CUDA (Computer Unified Device Architecture) of GPU (Graphic Process Unit) is used to implement the proposed algorithm to solve inversions of the real and complex matrices. The experimental results show that the Gauss-Jordan algorithm can save more running time than traditional sequential algorithms and the speedup ratio of PA-Gauss for Real Matrices is 633~100435, and the speedup ratio of PA-Gauss for Complex Matrices is 224~36508. Therefore,the computing time of solving the matrix inversions is reduced significantly.