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Purpose-This study proposes an adaptive bandwidth management system which can be explicitly used by educational institutions. The primary goal of the system is to increase the bandwidth of the users who access more on educational websites. Through this proposed bandwidth management, the users of the campus networks is encouraged to utilize the internet for educational purposes. Method-The weblog from a university's pfSense proxy server was utilized and undergo Web Usage Mining (WUM) to determine the number of educational and non-educational websites accessed by the users. Certain formulas were used in the computation of the bandwidth which was dynamically assigned to the users. A prototyping technique was applied in developing adaptive bandwidth management system. The prototype was simulated and evaluated by experts in compliance with ISO/IEC 14598-6 and ISO/IEC 9126-1 standards. 18 Results-This study found that the prototype is capable of adjusting the bandwidth of the network users dynamically. The users who browsed more on educational websites or contents were assigned with higher bandwidth compared to those who are not. Further, the evaluated prototype met the software standards of ISO. Conclusion-The proposed adaptive bandwidth management can contribute to the continuous development in the area of computer networking, especially in designing and managing campus networks. It also helps the network administrators or IT managers in allocating bandwidth with minimal effort. Recommendations-Further work on refining the process in the computation and allocation of the bandwidth is recommended. Other techniques should be tested as well to lessen the delay in assigning the bandwidth of each user. Research Implications-The proposed method can also be implemented in other opensource networking software and applied to other organizations by changing the rules instead of considering educational websites.

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Artificial Neural Networks (ANN) form a dynamic architecture for machine learning and have attained significant capabilities in various fields. It is a combination of interrelated calculation elements and derives outputs for new inputs after being trained. This study introduced a new mechanism utilizing ANN which was trained using Bayesian Regularization Back Propagation (BRBP) to improve the computational cost problem of the existing algorithm of the Generalized Singular Value Decomposition-based Linear Discriminant Analysis (LDA/GSVD). The proposed approach can minimize the number of iterations and mathematical processes of the existing LDA/GSVD algorithm which suffers time complexity. Through simulation using BLE RSSI Dataset from UCI which has 105 classes and 13 dimensions with 1420 instances, it was found out that ANN improved the computational cost during the classification of the data up to 57.14% while maintaining its accuracy. This new technique is recommended when classifying big data, and for pattern analysis as well.

Managing the bandwidth in campus networks becomes a challenge in recent years. The limited bandwidth resource and continuous growth of users make the IT managers think on the strategies concerning bandwidth allocation. This paper introduces a mechanism for allocating bandwidth based on the users’ web usage patterns. The main purpose is to set a higher bandwidth to the users who are inclined to browsing educational websites compared to those who are not. In attaining this proposed technique, some stages need to be done. These are the preprocessing of the weblogs, class labeling of the dataset, computation of the feature subspaces, training for the development of the ANN for LDA/GSVD algorithm, visualization, and bandwidth allocation. The proposed method was applied to real weblogs from university’s proxy servers. The results indicate that the proposed method is useful in classifying those users who used the internet in an educational way and those who are not. Thus, the developed ANN for LDA/GSVD algorithm outperformed the existing algorithm up to 50% which indicates that this approach is efficient. Further, based on the results, few users browsed educational contents. Through this mechanism, users will be encouraged to use the internet for educational purposes. Moreover, IT managers can make better plans to optimize the distribution of bandwidth.