Bandwidth Allocation Mechanism based on Users' Web Usage Patterns for Campus Networks

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

Green coffee beans feature extractor using image processing

This study offers a novel solution to deal with the low signal-to-noise ratio and slow execution rate of the first derivative edge detection algorithms namely, Roberts, Prewitt and Sobel algorithms. Since the two problems are brought about by the complex mathematical operations being used by the algorithms, these were replaced by a discriminant. The developed discriminant, equivalent to the product of total difference and intensity divided by the normalization values, is based on the “pixel pair formation” that produces optimal peak signal to noise ratio. Results of the study applying the discriminant for the edge detection of green coffee beans shows improvement in terms of peak signal to noise ratio (PSNR), mean square error (MSE), and execution time. It was determined that accuracy level varied according to the total difference of pixel values, intensity, and normalization values. Using the developed edge detection technique led to improvements in the PSNR of 2.091%, 1.16 %, and 2.47% over Sobel, Prewitt, and Roberts respectively. Meanwhile, improvement in the MSE was measured to be 13.06%, 7.48 %, and 15.31% over the three algorithms. Likewise, improvement in execution time was also achieved at values of 69.02%, 67.40 %, and 65.46% over Sobel, Prewitt, and Roberts respectively

QR Code Integrity Verification Based on Modified SHA-1 Algorithm

The modified SHA-1 algorithm was applied in the data integrity verification process of certificates with QR code technology. This paper identified the requirements needed in the certificate verification that uses the modified SHA-1. The application was tested using legitimate and fraudulent certificates. Based on the results, the application successfully generated QR codes, printed certificates, and verified certificates with 100% accuracy. During the trial run of the app, four test cases were seen which involves correct names and QR codes, and three other possible test cases of faking certificates such as modification of the name, regeneration of QR codes using valid hash and a fake name, and modification of the QR code. Although these cases exist, the app successfully verified all thirty certificates correctly. Also, it is noticed that during the scanning, the smartphone camera should be in focus to capture the QR code clearly

An Improved Overlapping Clustering Algorithm to Detect Outlier

MCOKE algorithm in identifying data objects to multi cluster is known for its simplicity and effectiveness. Its drawback is the use of maxdist as a global threshold in assigning objects to one or more cluster while it is sensitive to outliers. Having outliers in the datasets can significantly affect the effectiveness of maxdist as regards to overlapping clustering. In this paper, the outlier detection is incorporated in MCOKE algorithm so that it can detect and remove outliers that can participate in the calculation of assigning objects to one or more clusters. The improved MCOKE algorithm provides better identification of overlapping clustering results. The performance was evaluated via F1 score performance criterion. Evaluation results revealed that the outlier detection demonstrated higher accuracy rate in identifying abnormal data (outliers) when applied to real datasets

Implementation of Modified AES as Image Encryption Scheme

Since images have bigger size than text, a faster encryption algorithm is needed to provide higher security in digital images. The paper presents a modified AES algorithm that address the requirement in image encryption. The modified algorithm used bit permutation in replacement of MixColumns to reduce the computational requirement of the algorithm in encrypting images. Results of the study show that the modified algorithm exhibited faster encryption and decryption time in images. The modified algorithm also achieved a good result in the key sensitivity analysis, histogram analysis, information entropy, the correlation coefficient of adjacent pixels, Number of Pixel Change Rate and Unified Average Change Intensity making the modified algorithm resistant to statistical and differential attack

A Distance-Based Data-Mule Scheduling Technique for Lesser Nodal Delay in Wireless Sensor Network

Nodal delay in wireless sensor network is an indisputable factor in the medium of communication. Factor such as changeability of communication devices, network topologies, packet-sizes, and transmission rate demands to develop data-mule queue scheduling technique. Our proposed data-mule scheduling technique accomplish this through simulations using standard software written in C# by controlling data-mule schedules that collects data from all the nodes connected to the hop. The scheme identifies the hierarchical positions of static source nodes and the distance of mobile source nodes from the hop with rescheduling based on the newly acquired distances. Source nodes applied with data-mule scheduling technique resulted to lower nodal delay. Transmission of packet-data is efficiently and effectively improved

Securing Electronic Medical Records Using Modified Blowfish Algorithm

EMR helped improve services to patients by delivering organization and accuracy of patient information, but issues regarding security breaches and medical identity theft are growing concerns. This paper enhance the current EMR system by integrating modified encryption. The simulation used modified Blowfish algorithm in an EMR system that focuses on four goals: 1) define the requirements, 2) design and identify features, 3) develop the EMR incorporating added security mechanism using modified Blowfish algorithm, and 4) test the application with sample data. Based on the results, the incorporation of the encryption was successful based on testing and checking done on the input terminal and the database server. Data inputted on the EMR system was successfully encrypted before transmission and decrypted only on the terminal for viewing. Performance results show that without encryption, saving took an average of 87.8ms while encrypted, it acquired 88.8ms, a difference of 1ms can be noted. The minimal difference is because of the size of the data. The average decryption time of all records using modified algorithm took 1342ms while using plaintext took 1322ms. The decryption time is higher by 20ms due to the application of the decryption algorithm

A modified genetic algorithm with a new crossover mating scheme

This study introduced the Inversed Bi-segmented Average Crossover (IBAX), a novel crossover operator that enhanced the offspring generation of the genetic algorithm (GA) for variable minimization and numerical optimization problems. An attempt to come up with a new mating scheme in generating new offspring under the crossover function through the novel IBAX operator has paved the way to a more efficient and optimized solution for variable minimization particularly on premature convergence problem using GA. A total of 597 records of student-respondents in the evaluation of the faculty instructional performance, represented by 30 variables, from the four State Universities and Colleges (SUC) in Caraga Region, Philippines were used as the dataset.  The simulation results showed that the proposed modification on the Average Crossover (AX) of the genetic algorithm outperformed the genetic algorithm with the original AX operator. The GA with IBAX operator combined with rank-based selection function has removed 20 or 66.66% of the variables while 13 or 43.33% of the variables were removed when GA with AX operator and roulette wheel selection function was used