Browsing by Author "Anyachebelu, Tochukwu Kene"

Now showing 1 - 8 of 8
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    Basics of Computer Programming Java in Theory and Practice
    (Department of Computer Science, Nasarawa State University Keffi, 2020-05-05) Mustapha, Ahmed U.; Salihu, Abdullahi A.; Anyachebelu, Tochukwu Kene; Kefas, Yunana; Sulaiman, Alhaji Ismaila
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    HARNESSING VIRTUAL CLASSROOM SYSTEM AS AN ALTERNATIVE FOR PART-TIME STUDIES IN NIGERIA
    (Department Of Mathematics, Nasarawa State University Keffi., 2011-09-26) Chaku, Shammah Emmanuel; Anyachebelu, Tochukwu Kene; Offiong, N.M.
    The clamour for higher education is on the increase globally, precisely, there is great need for higher education in Nigeria. For this mere fact, there was great need for part-time studies in the Nigerian universities. This need was short lived when the governing body of the Nigerian Universities; Nigerian University Commission (NUC) put a stop to this viable means of acquiring knowledge. The hope of Nigerians was salvaged with the advancement in technology, especially in the area of Information and Communication Technology (ICT). With these inventions, we have what is known as the virtual classroom (VC), a medium that works like the traditional classroom system. The virtual class room is a teaching and learning environment located within a computer-mediated communication system which supports collaborative learning. The virtual classroom has three prominent components: A Virtual Instructor, a Server and Virtual Students. These three components interact together to form the Virtual Classroom System (VCS). This paper shows how virtual classroom can be harnessed as a replacement for part-time studies in Nigeria with the aid of synchronous and asynchronous tools. The virtual instructor sends lecture notes and materials through the server (internet) and the virtual student receives the note and sends feed back through the server. The server here serves as the point of contact between the virtual lecturer and the virtual student just like what is obtainable in a traditional classroom.
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    Introduction to Computer Science A beginner’s Guide to Computing
    (Department of Computer Science, Nasarawa State University Keffi, 2020-03-03) Salihu, Abdullahi A.; Mustapha, Ahmed U.; Anyachebelu, Tochukwu Kene; Kefas, Yunana; Sulaiman, Alhaji Ismaila
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    Introduction to Programming II, JavaScript for Web Note, Practical manual & record book
    (Department of Computer Science, Nasarawa State University Keffi, 2021-06-05) Mustapha, Ahmed U.; Ajayi, Binyamin A.; Anyachebelu, Tochukwu Kene; Abdullahi, S.A.; Suleiman, H.A.; Rabiu, Asmau A.; Ogah, M.U.
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    Modeling and Prediction of Surface Water Contamination using On-line Sensor Data
    (Department of Computer Science, Nasarawa State University Keffi, 2014-01-01) Anyachebelu, Tochukwu Kene; Conrad, Marc; Ajmal, Tahmina
    Water contamination is a great disadvantage to humans and aquatic life. Maintaining the aesthetics and quality of water bodies is a priority for environmental stake holders. The water quality sensor data can be analyzed over a period of time to give an indication of pollution incidents and could be a useful forecasting tool. Here we show our initial finding from statistical analysis on such sensor data from one of the lakes of the river Lea, south of Luton. Our initial work shows patterns which will form the basis for our forecasting model.
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    Neural Network Prediction of Self-Similarity Network Traffic
    (Department of Computer Science, Nasarawa State University Keffi, 2022-12-02) Ikharo, A.B.; Anyachebelu, Tochukwu Kene
    Several factors are found to influence either short or long-term burstiness in Transmission Control Protocol (TCP) flow across many networking facilities and services. Predicting such self-similar traffic has become necessary to achieve better performance. In this study, ANN model was deployed to simulate College Campus network traffic. A Feed Forward Backpropagation Artificial Neural Network (ANN) and Wireshark tools were implemented to study the network Scenario. The predicted series were then compared with the corresponding real traffic series (Mobile Telephone-Network (MTN)-Nigeria). Suitable performance measurements of the Means Square Error (MSE) and the Regression Coefficient were used. Our results showed that burstiness is present in the network across many time scales. With the increasing number of data packet distributions thereby providing a steady flow of burst over the entire period of system load as the traffic network performance improves.
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    Optimising Self-Similarity Network Traffic for Better Performance
    (Department of Computer Science, Nasarawa State University Keffi, 2020-08-08) Ikharo, A.B.; Anyachebelu, Tochukwu Kene; Blamah, N.V.; Abanihi, V.K.
    Given the ubiquity of the burstiness present across many networking facilities and services, predicting and managing self-similar traffic has become a key issue owing to new complexities associated with self-similarity which makes difficult the achievement of high network performance and quality of service (QoS). In this study ANN model was used to model and simulate FCE Okene computer network traffic. The ANN is a 2-39-1 Feed Forward Backpropagation network implemented to predict the bursty nature of network traffic. Wireshark tools that measure and capture packets of network traffic was deployed. Moreover, variance-time method is a log-log scale plot, representing variance versus a non-overlapping block of size m aggregate variance level engaged to established conformity of the ANN approach to self-similarity characteristic of the network traffic. The predicted series were then compared with the corresponding real traffic series. Suitable performance measurements used were the Means Square Error (MSE) and the Regression Coefficient. Our results showed that burstiness is present in the network across many time scales. The study also established the characteristic property of a long-range dependence (LRD). The work recommended that network traffic observation should be longer thereby enabling larger volume of traffic to be capture for better accuracy of traffic modelling and prediction.
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    Surface water quality prediction system for Luton Hoo lake: A statistical approach
    (Department of Computer Science, Nasarawa State University Keffi, 2014-03-05) Anyachebelu, Tochukwu Kene; Conrad, Marc; Ajmal, Tahmina
    Lake monitoring is a necessity for aquatic healthy living. Stakeholders are particularly interested not just in the aesthetics but also in the quality of water bodies. Our work tends to initially analyze historic data of the sensed water quality parameters at Luton Hoo lake to detect outliers. Dissolved oxygen has been predicted from available data since its one of the major surface water contaminants