"Annals. Computer Science Series" Journal
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Ovidiu Crista
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Annals. Computer Science Series
Tome 16, Fasc. 2


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: December, 2018


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: 17.06.2018] Dharm Singh Jat, Chucknorris Garikayi Madamombe - Qualitative Study to Enhance the Security Features of Automated Teller Machines
  • [ARR: 21.06.2018] Onatunji, Adewale.P. - Application of Seemingly Unrelated Regression and Ordinary Least Squares Estimators on Blood Pressure
  • [ARR: 26.06.2018] Nikolaos Diamantopoulos, Ilias Spanos - STEM Education with Educational Robotics to Tackle Failure in Mathematics
  • [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: 01.07.2018] Nikolaos Diamantopoulos, Anastasia Brami, Ilias Spanos - Flipped Classroom STEM Teaching: An Innovative Practice of Technologically Supported Teaching of Physics
  • [ARR: 11.07.2018] Adriana Mariş, Florin Mariş - Global Math Project - seeing algebra in a different light
  • [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: 03.08.2018] S. O. Adejuwon, V. G. Jemilohun, A. O. Ilesanmi - Application of one Server Queuing Models to Customers Management in the Cafeteria: A Case Study of Afe Babalola University, Ado-Ekiti (Abuad)
  • [ARR: 08.08.2018] Odunayo Olanloye, Esther Oduntan, Olawumi Olasunkanmi - Using Predictive Machine Learning Regression Model To Predict The Population Of Nigeria
  • [ARR: 16.08.2018] Abdullahi Yola Musa, Rasheed Gbenga Jimoh - Usuability Evaluation Of Twitter Micro-Blog Using Heuristic Approach
  • [ARR: 16.08.2018] Usman M. A, Olayiwola M. O, Sikiru A. B. - Steady Flow of a Reactive MHD Fluid through a Permeable Pipe under Optically Thick Limit Radiation
  • [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: 19.08.2018] Usman M. A, Olayiwola M. O, Sikiru A. B. - Steady Flow of a Reactive MHD Fluid through a Permeable Pipe under Optically Thick Limit Radiation
  • [ARR: 19.08.2018] Jinmisayo Awokola, Justice Emuoyibofarhe, Funmi Ajala - Performance Evaluation of a Cloud-based Picture Archivin and Communication System (PACS)
  • [ARR: 28.08.2018] Obafemi O. S., Oyeyemi G. M. - Alternative Estimator for Multivariate Location and Scatter Matrix in the Presence of Outlier
  • [ARR: 28.08.2018] Olusayo D. Fenwa., Funmilola A. Ajala, Adeleye S. Falohun - Development of an Expert System for Selected Blood Diseases Diagnosis and Treatment
  • [ARR: 03.09.2018] Gizela – Agneta Fuioagă - Optional Course for High School Learners Discover Real Everywhere Applications of Maths
  • [ARR: 04.09.2018] Alexandra Fortiș, Minerva Patriciu - Integrating Geogebra into Interactive Electronic Books
  • [ARR: 04.09.2018] Mihai Ș. Neamțu, Mihaela Neamțu - Derivative Application in Navigation into Outerspace
  • [ARR: 07.09.2018] Virginia Popovic, Kristijan Cincar - Complexity of Mathematical Concepts in Poetical Work of Ion Barbu (Dan Barbilian)

    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: 15.05.2018] Adebowale Olusola Adejumo, Tobi Oladayo Oloyede, Oluyemisi Adedola Adejumo, Pelumi Emmanuel Oguntunde, Oluwole Akinwumi Odetunmibi, Nehemiah Arhoesere Ikoba, Obalowu Job - Modelling of Enugu State Monthly Rainfall Using Box and Jenkins Methodology
  • [ACK: 26.07.2018] Adebowale Olusola Adejumo, James Daniel - A Bootstrap Method for Box-Jenkins Models With Application on Brent Crude Oil Prices Per Barrel
  • [ACK: 04.09.2018] Shervan Fekri-Ershad - Rotation Invariant Skin Detection Approach based on Combination of Probabilistic Distribution Estimation and Single Scale Retinex
  • [ACK: 04.09.2018] Abass Taiwo, Timothy Olatayo - An Improved Procedure for Fourier Regression Analysis
  • [ACK: 04.09.2018] P. A. Ozoh, M. O. Olayiwola - Predicting Energy Consumption using Typical Machine Learning and Computational Techniques

    » Analytic Approach To Face Emotion Recognition With SVM Kernels

    » Omobolaji F. Oyedokun, Elijah O. Omidiora, Ibrahim A. Adeyanju and Fagbola M. Temitayo

    ABSTRACT:

    Face emotion recognition is one of the challenges known with emotion recognition and it has received much attention during the recent years due to its application in different fields. SVM kernels were adopted to increase the robustness of face emotion recognition systems and to identify the most suitable kernel for emotion recognition. This paper uses radial basis function, linear function, sigmoid and polynomial function to identify the six basic emotions and neutral inclusive. In an attempt to achieve this aim the following steps were taken; collection of face emotion images, image pre- processing, features extraction and classification. Face emotion database was created by taken emotional photographs of persons who willing volunteer to help in this paper. The database contains 714 images from 51 persons. However, the photographs were converted from colored images to grayscale images for uniform distribution of colors. Relevant features for classification were extracted from the processed images such as the eyelids, cheeks, nose, eyebrows and lips. Our face emotion database was splitted into two dataset: training set and testing set. SVM classifier used images in the training set to train while images in the testing set were used to test SVM models. The evaluation of the system was performed on MATLAB using classification accuracy and classification time to identify the most suitable kernel for the system. The results obtained shows that sigmoid outperformed other kernels in terms of classification accuracy with overall performance accuracy of 99.33% while polynomial achieved the shortest classification time. In the future, we intend to investigate other classifiers for face emotion recognition and to classify more emotions.

    KEYWORDS:

    Feature, extraction, image preprocessing, classifiers, SVM, recognition, Kernels, face emotion.

    » Received: 30 October 2017
    » Accepted: 18 January 2018

    9 - 13

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    » A perspective of metacognition in solving math problems in Vietnam secondary schools

    » Nguyen Thi Huong Lan

    ABSTRACT:

    In an effort to innovate teaching and learning process to prepare for a new generation for the demands of the new era, many educators have discovered the value of metacognition. Students will need both mathematical skills and problem-solving skills, therefore, teachers should focus on both math content and thinking processes in students’ math learning. This paper presents the importance of metacognition in solving mathematical problems. A project was conducted for students at the age of thirteen, and the findings suggest that students used four problem-solving steps highlighted by George Polya. However, students feel better when they adjust their thinking processes or use metacognitive skills in the process of solving math problems.

    KEYWORDS:

    Skill, metacognition, problem-solving, mathematical thinking.

    » Received: 05 March 2018
    » Accepted: 15 May 2018

    14 - 20

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    » Homogenous Ensembles of Data Mining Algorithms in Predicting Liver Disease

    » Samuel Omokanye and Taye Aro

    ABSTRACT:

    Application of data mining algorithms to medical fields have been of interest as it helps patients get access to a better and faster healthcare. In this study, the effect of homogenous ensemble methods of bagging and boosting has been investigated as related to the prediction of the presence or absence of liver diseases. Experimental results show that while bagging and boosting did not improve the accuracy and sensitivity of algorithms in predicting liver disease, Boosting increased the specificity of algorithms.

    KEYWORDS:

    Homogenous, Bagging, Boosting, algorithms, data mining, classification.

    » Received: 03 November 2017
    » Accepted: 15 February 2018

    21 - 24

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    » Topology Management for Wireless Mesh Network

    » Justice Emuoyibofarhe, T. Madandola and Elizabeth Amusan

    ABSTRACT:

    This research formulated and simulated a topology management scheme for wireless mesh network (WMN) in areas of scalability and reliability; considering its vast present limitations in commercialization in many application areas. WMNs framework was devised and OMNeT++network simulator was used to simulate the WMNs model. A research method structure was devised that showed clearly how the project is carried out. An improved model for WMNs that use better approach To Mobile Adhoc Network routing protocol over the existing model was designed and formulated. Pearson Product Moment Correlation (PPMC) was used to find out the correlation between the Scenarios. PPMC produced 0.923 coefficients which showed a high positive correlation between the scenarios indicating that WMNs is reliable and scalable even with additional gateways.

    KEYWORDS:

    Correlation, Mesh, Scalability, Simulator, Topology, Wireless router.

    » Received: 23 May 2018
    » Accepted: 27 july 2018

    25 - 30

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    » Analysis of the Electrocardiogram by Means of Characteristics of the Disproportionality of Numerical Functions

    » Munther Al Shehab, Safwan Al Salaimeh, Shaker Dwairy and Wa’ed Hatamleh

    ABSTRACT:

    Electrocardiography: this is a method of recording the potential difference between two points in the electric field of the heart during its excitation. The modern technology allow you to record ECG on a long time interval in condition of person normal life(portable cardio); when carrying out functional tasks with doses physical exertion (bicycle ergometer, treadmill) and tests with the introduction of medicament preparations. To solve the problem, the following non-proportionality can be used: by the first - order derivative, by the value of the first order, as well as the disproportionality for the function y(t) by x(t), given parametrically. In this paper was an analyzed ECG assistant with the characteristics of the proportionality of numerical functions.

    KEYWORDS:

    ECG, Process, Excitation, Function, Proportionality, Record.

    » Received: 28 June 2018
    » Accepted: 27 July 2018

    31 - 34

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    » A Predictive Model for Tweet Sentiment Analysis and Classification

    » Abdullah K.-K. A., Folorunso S. O., Solanke O. O. and Sodimu S. M.

    ABSTRACT:

    Sentiment analysis over Twitter offers organisations and users a fast and effective way to monitor publics’ feelings towards events especially during crises, hence, motivated much work on twitter data. In this study, predictions on positive, negative and neutral sentiment based on security are analysed. A polarity classification of tweet messages was done with VADER algorithm considering contextual analysis. The analysis was performed by removing stop words in the tweet along with Wordnet lemmatiser for the morphological analysis of words in the features sets. As well as subjected to word sense disambiguation to consider contextual usages of words using a path length corpus based lexicon. Term Frequency (TF) and Term Frequency Inverse Document Frequency (TFIDF) are used as feature extraction from tweets and evaluation of the features reduction was carried out by calculating the accuracy of the predictions on sentiment and tweet messages with Chi-Square to explore the possibly useful features. Finally, validations are done with machine learning models at different sequence to compare the performance between each model.

    KEYWORDS:

    Sentiment analysis, Word sense disambiguation, Natural Language Processing, Chi-Square, VADER.

    » Received: 12 June 2018
    » Accepted: 25 July 2018

    35 - 44

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    » Neuro-Fuzzy Expert System For Diagnosis Of Thyroid Diseases

    » Tinuke Omolewa Oladele, Chuwunonso David Okonji, Adegun Adekanmi and Funke Florence Abiola

    ABSTRACT:

    The computerization of medical procedures has been identified to be one of the major challenges in the medical sector. Several techniques have been used in order to automate the processes in diagnosis of diseases; such processes include incorporation of artificial neural network and fuzzy logic techniques as expert systems with the knowledge about the domain. Such expert systems help to hasten the speed of diseases diagnosis, its accuracy and efficiency. In this paper, two artificial intelligence techniques; neural network and fuzzy logic system are considered. The two techniques are used to develop a hybrid intelligent computational system for the diagnosis of thyroid diseases. The experimental results obtain depend on the inputs to the neural network and also are set using a decision making system for making decisions on the thyroid disease according to the output of the neuro-fuzzy system.

    KEYWORDS:

    Expert System, Fuzzy Logic, Neural Network, Diagnosis, Thyroid Diseases.

    » Received: 29 May 2018
    » Accepted: 26 July 2018

    45 - 54

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    » Modelling Queuing System with Inverse Gamma Distribution: A Spreedsheet Simulation Approach

    » Olumide S. Adesina, Gbadebo Odularu and Adedodun Adedayo Funmi

    ABSTRACT:

    There is a need to provide user friendly approach to modeling and simulation for learners and business modeler. This study offers process-driven queuing simulation via spreadsheet which provides a user friendly, yet a readily available excel platform. Spreadsheet queuing simulation suggest a better way of understanding queue behavior than dedicated simulation software as it offers many benefits to students, practitioners and managers; helping them have to experience with modeling. In this study, single server queue is being simulated using Inverse Gama distribution. Creating single server queue (G/G/1) has been considered difficult to create, but this study presents easy to comprehend and applicable formulation. The spreadsheet simulation technique was applied to students queue at eatery in University of Lagos, Nigeria and results obtained were displayed and interpreted accordingly. This study recommends modelling and simulation of queuing systems with spreadsheet for students and business managers.

    KEYWORDS:

    Queuing theory, Simulation, Spreadsheets, Inverse Gamma distribution, G/G/1.

    » Received: 10 June 2018
    » Accepted: 27 July 2018

    55 - 60

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    » A Study On Efficient Automatic Speech Recognition System Techniques And Algorithms

    » Yakubu A. Ibrahim and Tunji S. Ibiyemi

    ABSTRACT:

    Automatic speech recognition is a system by which computer recognizes and responds accordingly to a spoken words of a person on the basis of his or her voice signal waveform. ASR system is being adopted in different aspects of life, such as telephones and home computer control system. Despite their growing presence, a proper technique for efficient ASR system remains a major issue for researchers. This study explains an analysis of different techniques and algorithms that can be used in ASR system such as LPC, LPCC, PLP, MFCC for feature extraction and DTW, SVM, HMM, VQ, GMM, MLP, ANN, KNN for feature classification and pattern recognition.

    KEYWORDS:

    ASR, DTW, HMM, Feature extraction, LPC, MFCC, SVM.

    » Received: 23 December 2017
    » Accepted: 26 July 2018

    61 - 68

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    » New Trends in Modelling Climate Change in the Era of Big Data

    » Osuolale Peter Popoola and Nicholas Nsowah Nuamah

    ABSTRACT:

    Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with. It is typically characterized by the so called, seven “V’s” namely; volume, velocity, variety, Value, Veracity, variability and validity. Big Data can be thus defined as very high volume, velocity and variety of data that require a new high-performance processing. Thus, this research work survey different technique for handling big data for modelling climate change. Various statistical models were examined and X-ray. The survey shows that ANCOVA (analysis of covariance) is the appropriate technique for handling big data. ANCOVA contains a mixture of qualitative variables associated with analysis of variance (ANOVA) and the quantitative variables associated with regression analysis. ANCOVA is the meeting point under the umbrella of analysis of variance and regression techniques.

    KEYWORDS:

    Big Data, Pooled Data, Analysis of Variance, Analysis of Covariance and Multiple Regression.

    » Received: 28 July 2018
    » Accepted: 04 September 2018

    69 - 76

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    » An In-depth Study of Typical Machine Learning Methods via Computational Techniques

    » P. A. Ozoh, M. O. Olayiwola and A. A. Adigun

    ABSTRACT:

    The ability to model and perform decision modeling and analysis is an essential feature of many real-world applications ranging from emergency medical treatment in intensive care units to military command and control systems. Models are essential in providing support for businesses processes, systems and dealing with complex problems. The development of appropriate models for planning and management is a tool for improving efficiency in real world problems. Consequently, a thorough study of common machine learning techniques is undertaken with the aim of comparing the techniques and identifying a suitable technique that is applicable to modeling and forecasting real world data. Modeling helps make informed decisions, using techniques for analysis, estimation and, forecasting. There is a lot of research published about machine learning techniques, with the intention of developing models for estimations. In order to identify an appropriate machine learning technique, it is necessary to carry out a comparative study of commonly used machine learning techniques. For this purpose, a review of Box-Jenkins technique, regression method and artificial neural network (ANN) is undertaken with the aim of identifying a reliable and accurate technique for modeling data.

    KEYWORDS:

    Machine learning, models, planning, estimation, efficiency.

    » Received: 30 May 2018
    » Accepted: 04 September 2018

    77 - 81

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    » Developing Predictive Models using Typical Machine Learning and Computational Techniques

    » P. A. Ozoh, Shapiee Abd-Rahman and Moruf Olayiwola

    ABSTRACT:

    This study investigates the accuracy of developing predictive models using machine learning techniques. The machine learning techniques considered in this study include artificial neural network (ANN) and Kalman filter adaptation algorithm. Predictive values are computed based on these techniques. These techniques are tested on daily electricity consumption data and are computed using ANN technique and Kalman filter adaptation algorithm. The accuracy of the predicted values of these techniques are investigated using statistical parameters. This research identified Kalman technique as more accurate in making predictions than ANN technique.

    KEYWORDS:

    Accuracy, modelling, prediction, machine learning, perfornance measures.

    » Received: 31 July 2018
    » Accepted: 04 September 2018

    82 - 86

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    » Genetic Algorithm Approach for Fabric Pattern Generation in Textile Industries

    » Obe O. and Egwuche O. S.

    ABSTRACT:

    It is a known fact that there are more possibilities in nature than human brain can conceive. This phenomenon is more pronounced in fabric industry where experts struggle daily for creation of new fabric patterns when in reality the number of patterns seems infinite. In this research, we developed a system that will complements human reasoning in creation of more possible fabric patterns in locally made fabric in Nigeria. The possibility of Genetic Algorithm for pattern generation in textile production processes is investigated. The system has good credibility and ability to generate fabric patterns faster, easier and in more quantities than team of fabric designers. The system developed is able to save this cultural heritage from extinction as there are more patterns to produce.

    KEYWORDS:

    Genetic Algorithm, Textiles, Pattern Generation.

    » Received: 21 April 2018
    » Accepted: 04 September 2018

    86 - 91

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    » Technical Methods and Algorithms for Developing Efficient Optical Character Recognition System: An Overview

    » Yakubu A. Ibrahim and Tunji S. Ibiyemi

    ABSTRACT:

    Machine recognition problem of printed documents in Optical Character Recognition has been the target of research in the area of pattern recognition. Study in this aspect has been controlled by a need to join the natural process of image input with the data processing abilities of computer system. In this regard, engineers and scientists are not able to make judicious use of OCR systems for their technical work because they lack effective algorithms for interpretation of complex expressions even though it is important part of a Human Computer Interaction system. However, OCR in a nut shell is the electronic change of printed or handwritten text into images using machine encoding scheme representation like ASCII or Unicode. Hence, various methods and algorithms have been demonstrated in the study to increase the efficiency of system to effectively solve OCR problems. The study shows the concept of various OCR algorithms such as support vector machine, decision tree classifier, statistical, structural, artificial neural networks and template matching algorithms.

    KEYWORDS:

    ASCII, Unicode, Computer, HCI, OCR, Pattern recognition.

    » Received: 19 March 2018
    » Accepted: 04 September 2018

    XX - XX

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