"Annals. Computer Science Series" Journal
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Annals. Computer Science Series
Tome 17, Fasc. 1


ISSN: 1583-7165 (printed journal)
ISSN: 2065-7471 (e-journal)
Owner: "Tibiscus" University of Timişoara, România
Editor: Mirton Publishing House of Timişoara, România
Distributor: "Tibiscus" University of Timişoara, România
Appearance: July, 2019


Next papers follows the review process for the Journal inclusion:

  • [ARR: 28.04.2017] Saheed Y.K., Arowolo M.O., Ibrahim S.A. - Artificial Neural Network For Breast Cancer Diagnosis
  • [ARR: 25.07.2017] G. Ojemeri, S. Abdulsalam, J. F. Yayock - Mathematical Modeling of Radiative Heat Source and Magnetic Field Effects on Free Convection Flow Embedded in a Moving Vertical Permeable Plate in the Presence of Porous Medium
  • [ARR: 26.07.2017] Y.K. Saheed, K. A. Gbolagade - Chinese Remainder Theorem Based Domain Name Security System
  • [ARR: 07.10.2017] Muhsin Hassanu Saleh, Mukhtar Bello - Online Hospital Management System
  • [ARR: 27.11.2017] Yakubu A. Ibrahim, Tunji S. Ibiyemi - Technical Algorithms For Efficient Optical Character Recognition System: An Overview
  • [ARR: 05.12.2017] Osuolale Peter Popoola, Matthew Taiwo Odusina - Survey of Strategies for Teaching Statistics at School
  • [ARR: 19.01.2018] Y. Zakari, A. Hassan - Application of First Order Differential Equation in Growth and Decay Problems Problems
  • [ARR: 19.01.2018] Adebayo O.Y., Adigun A.A., Isola E.O., Sijuade A.A. - Comparative Performance of Fingerprint and Face Recognition System
  • [ARR: 23.01.2018] Tochukwu Churchill Micheal AKUBUE - IoT; the Future of Industrial/Machine Automation
  • [ARR: 27.03.2018] Nguyen Thu Ha - The Trend of Education Reform in the World at the Present Context of Integration and Knowledge Economy
  • [ARR: 10.05.2018] Fagbola Temitayo Matthew, Oloyede Ayodele, Egbetola Funmilola Ikeolu, Akinpelu James Abiodun - CALPAS - An Employee Information and Payroll System for a Prototype University Enterprise in a Developing Economy Context
  • [ARR: 19.05.2018] Aliyu Rufai Yauri ,Salim Yusha’u - Hausa-English Cross Language Information Retrieval Disambiguation Approach
  • [ARR: 21.05.2018] S. O. Yusuff, I. A. Osinuga, O. J. Adeniran, S. A. Onashoga - Robust Three-step Broyden-like Algorithms for Functions of Several Variables
  • [ARR: 21.06.2018] Onatunji, Adewale.P. - Application of Seemingly Unrelated Regression and Ordinary Least Squares Estimators on Blood Pressure
  • [ARR: 28.06.2018] Osuolale Peter Popoola, Omotola Omotayo Dawodu, Olufemi Olusola Yusuff, Ayaniyi Wole Ayanrinde - Modelling the Effect of Climatic Change using Quadratic Regression and Factorial Analysis on Global Food Production
  • [ARR: 23.07.2018] Yakubu A. Ibrahim, Silas A. Faki, Tunji S. Ibiyemi - Automatic Speech Recognition using MFCC in Feature Extraction based HMM for Human Computer Interaction in Hausa
  • [ARR: 17.08.2018] Usman M. A., Sikiru A. B., Olayiwola M. O. - Analytical Solution of a Reactive Hydromagnetic Fluid through Porous Media between Permeable Pipes under Optically Thick Limit Radiation
  • [ARR: 22.09.2018] Tajudeen Niyi Madandola, Kazeem Alagbe Gbolagade - A Framework for Improving the Speed of Principal Component Analysis Algorithm Based on Chinese Remainder Theorem
  • [ARR: 11.11.2018] Ali Sadiqui, Ahmed Zinedine, Mohamed El Hari - Arabic digital resources in the service of learning
  • [ARR: 12.11.2018] Yaser Sadra - A New Image Compression by Gradient Haar Wavelet
  • [ARR: 13.11.2018] Lanre Adebara, Babatunde Lateef Adeleke - Balanced Incomplete Sequence Crossover Design For First Order Residual Effect
  • [ARR: 29.11.2018] Osuolale Peter Popoola, Abosede Titilope Popoola, Ayaniyi Wole Ayanrinde, Mattehew Taiwo Odusina - Modelling the Devastation Effect of Climate Change on Food Production in Asia and Europe
  • [ARR: 05.12.2018] Ziham Zawawi Mazlan - GeoGebra: Does it really motivates students to learn Mathematics?
  • [ARR: 05.12.2018] Osuolale Peter Popoola, Ayomide Oluwaghenga, Abosede Titilope Popoola - Survival Analysis of Breast Cancer in Nigeria
  • [ARR: 14.12.2018] J.O. Muili, A. Audu , R. V. K. Singh, A.B. Odeyale - Improved Variance Estimator using Linear Combination of Tri-mean and Quartile Average
  • [ARR: 14.12.2018] J.O. Muili, A. Audu, R. V. K. Singh, A. Adebiyi - Improved Estimators of Finite Population Variance using Unknown Weight of Auxiliary Variable
  • [ARR: 19.12.2018] P.O. Adebayo, G.M. Oyeyemi - An Alternative Solution to Hotelling T square under the Heteroscedasticity of the dispersion Matrix
  • [ARR: 20.12.2018] Onitilo, S.A, Usman, M.A. and Hammed, F.A. - Unsteady MHD Oscillatory Flow through a Porous Channel Saturated with Porous Medium
  • [ARR: 23.12.2018] K.Srikala - Flexible and Extensible Design Patterns of Software
  • [ARR: 06.01.2019] Dare Remi Julius, Adesina Sunday Olumide, Famurewa Kehine Dolapo, Adedotun Dayo, Owoseni Timothy - Detection of Upper Outliers in an Exponential Sample using Multiple Outlier Tests
  • [ARR: 09.01.2019] Timothy Moses, Edward O. Agu, Okwori Anthony Okpe, John A. Oladunjoye - An Enhanced Round Robin Virtual Machine Load Balancer for Cloud Infrastructure
  • [ARR: 11.01.2019] Osuolale Peter Popoola, Abosede Titilope Popoola, Ayaniyi Ayanwole Ayanride - Modelling the Devastation Effect of Climate Change on Food Production in Nigeria
  • [ARR: 12.01.2019] Osuolale Peter Popoola, Benjamin A. Oyejola - Construction of Congruent Classes of Pairwise Balanced Designs using Lotto Designs
  • [ARR: 14.01.2019] Osuolale Peter Popoola, Benjamin A. Oyejola - Construction of Steiner Triple System STS(2n +1) from a Class of Pairwise Balanced Designs
  • [ARR: 15.01.2019] Alaba T. Owoseni, Olatunde Iyaniwura, Lukman Adebayo Ogundele - Adaptive Particle Swarm Optimization Scheduling Model for Jobs on Cloud
  • [ARR: 18.01.2019] Ibraheem B. A., Adeleke B. L. - A Proposed Method of Identifying Significant Effects in Unreplicated Factorial Experiments

    Next papers follows the Journal publication process:

  • [ACK: 06.04.2018] Adebowale Olusola Adejumo, James Daniel - Time Series Analysis of Brent Crude Oil Prices Per Barrel: A Box-Jenkins Approach
  • [ACK: 07.11.2018] Jinmisayo Awokola, Justice Emuoyibofarhe, Funmi Ajala - Performance Evaluation of a Cloud-based Picture Archivin and Communication System (PACS)
  • [ACK: 26.11.2018] A.Jerome Robinson, K.Satyanarayana, S.Ranganathan - Implementation of Cloud Computing in Archaeology to Track the Visitor for Prediction by Using the Hormonal Changes
  • [ACK: 26.11.2018] Oluyinka T. Adedeji, Adeleye S. Falohun, Oluwaseun M. Alade, Elijah O. Omidiora, Stephen O. Olabiyisi - Clonal Selection Algorithm for Feature Level Fusion of Multibiometric Systems
  • [ACK: 14.01.2019] Abimbola Akintola, Tunji Ibiyemi, Amos Bajeh - Evaluation of an Optical Character Recognition Model for Yoruba Text
  • [ACK: 14.01.2019] Rafiu Ganiyu, Oladotun Okediran, Omotola Busirat, Taofeeq Badmus - Development of a Modularized Model for a Multi-Process Food Manufacturing System using Hierarchical Timed Coloured Petri Nets
  • [ACK: 14.01.2019] Abba Almu, Abubakar Roko, Aminu Mohammed and Ibrahim Sa’idu - Towards Refining Unrated and Uninterested Items for Effective Collaborative Filtering Recommendations

    » Parameter Estimation of Cobb Douglas Production Function with Multiplicative and Additive Errors using the Frequentist and Bayesian Approaches

    » J.O. Iyaniwura, A. Adedayo Adepoju and Oluwaseun A. Adesina

    ABSTRACT:

    Nonlinear Models are generally classified as intrinsically nonlinear and intrinsically linear based on the specification of the errors. This study was aimed at estimating the parameters of Cobb-Douglas production function with additive and multiplicative errors using the classical and Bayesian approaches. The classical nonlinear method considered is the Gauss-Newton iterative Method while the Bayesian estimation was carried out using the Metropolis-within-Gibbs with independent normal-Gamma prior. For the classical, the results showed that the estimates of the parameters of the Cobb-Douglas function with additive errors performed better than those for the multiplicative errors. However, similar estimates were obtained for both multiplicative and additive errors for the Bayesian approach. Overall, the Bayesian method performed better than the classical approach.

    KEYWORDS:

    Cobb-Douglas Production function, Gauss-Newton Method, Normal-Gamma Prior, MCMC.

    » Received: 10 March 2018
    » Accepted: 30 September 2018

    9 - 15

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    » A Modified Ratio Techniques in Successive Sampling on Two Occasions


    » K. O. Adetunji and A. O. Adejumo

    ABSTRACT:

    Successive sampling is a known technique that can be used in longitudinal surveys to estimate population parameter and measurement of change or sum of study variables and auxiliary variables. However, the problem of ratio estimation is incorporated to successive sampling on the samples selected over two occasions has been considered. This study aimed at proposed Ratio Estimator technique in the context of devising efficient sampling strategies for estimators. The three estimators used as the instrument for parameter estimation in the successive sampling are Simple Estimator (SEst), Linear Estimator (LEst) and proposed Ratio Estimator (PRATEst). The proposed Ratio Estimator procedure was obtained through the properties of biasness, mean square Error (MSE), minimum mean square Errors (MMSE) and Efficiency comparison (EC). Two sets of Real life data were used in this study. The first data was collected from National Population Commission (NPC) of the census conducted in Nigeria for year 1991 and 2006, also the second data set was collected from ministry of education and human capital development on the teachers and students enrolment for 2013/2014 and 2016/2017 academic sessions. In, conclusion, it is clear and also visible that proposed estimator (PRATEst) μ_PRATE=(˥x)/(˥y)*(˥z) is highly rewarding than conventional ones μ_SE=(˥x),(˥y) and (˥z) ̅and μ_LE=(˥x)(˥y),(˥x)(˥z) and (˥y)(˥z) therefore, the proposed estimator is recommended for use in successive sampling scheme.

    KEYWORDS:

    Successive sampling, Ratio Estimator, Auxiliary Variable, Census, Students Enrolment.

    » Received: 22 October 2018
    » Accepted: 14 January 2019

    16 - 25

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