Browsing by Author "KOMOLAFE Temitope Amos"

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    A SIMULATION-BASED COMPARATIVE STUDY OF CONTROLLED DELAY (CODEL) WITH RANDOM EARLY DETECTION (RED) FOR NETWORK PERFORMANCE EVALUATION
    (British Journal of Computer, Networking and Information Technolog, 2023) KOMOLAFE Temitope Amos
    The rising need to use the internet for time/delaysensitive applications with different Quality of Service (QOS) requirements has made network management and control even more challenging. The current congestion avoidance and control mechanisms for Transport Control Protocol (TCP) are insufficient to offer good service in all circumstances. A few decades ago, the TCP successfully regulated Internet congestion control. However, it is already widely acknowledged that TCP has reached its limits and that new congestion control protocols will be required in the near future. This has prompted a significant amount of research on novel congestion control designs that will meet the demands of the future Internet. With widespread public attention and study, the full buffer problem has not gone away, but rather worsened. As a result, there has been a surge in interest in using Active Queue Management (AQM) in Internet routers to minimize queue latency. The effectiveness of a recently developed AQM, Controlled Delay (CoDel) algorithm, designed to work in today’s network setups and can be deployed as a main part of the bufferbloat solution, is evaluated in this research study. CoDel's effectiveness is evaluated by running simulations in ns-3 and comparing its results to that of Random Early Detection (RED), another promising network queue management technique
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    PERFORMANCE EVALUATION OF SELECTED CLASSIFICATION ALGORITHMS FOR IRIS RECOGNITION SYSTEM
    (2023) KOMOLAFE Temitope Amos
    Quite a lot of techniques proven to be resourceful have been espoused to develop iris recognition system. Nearly hybridized, supervised and unsupervised artificial neural network techniques have been used individually in iris recognition system and other pattern recognitions but have not been compared based on some performance metrics. Counter Propagation Neural Network (CPNN) is a hybridized technique, Self-Organizing Feature Map (SOFM) is an unsupervised learning technique and Back Propagation Neural Network (BPNN) is a supervised learning technique. This research conducted a performance comparison of CPNN, SOFM and BPNN techniques to recognize iris dataset and establish the more efficient among the three techniques. A database of Three hundred (300) iris images was acquired from LAUIRIS dataset from LAUTECH Biometric Research Group database. The original images of 640*360 dimensions were resized to 200*200 without any alteration in the image using 80% for training and 20% for testing. Hough transform was applied to segment locate the iris region of eye image. Daugman’s Rubber Sheet Model was used to create a dimensionally consistent representation. Principle Component Analysis was applied for feature extraction and dimensionally reduction. Finally, classification and matching were done by using CPNN, SOFM and BPNN techniques. This was implemented using MATLAB (Matrix Laboratory) R2016b. The performance metrics used for classification were False Positive Rate (FPR), Sensitivity, Specificity, Recognition Accuracy and Recognition Time at 0.70 threshold value. The Recognition Accuracy (RA), Recognition Time (RT), False Acceptance Rate (FAR), Sensitivity and Specificity of the three selected techniques (CPNN, SOFM and BPNN) resulted in values of 95.17%, 177.48s, 6.33% and 93.67% for CPNN 92.50%, 179.69s, 9.00%, 94.00% and 90.99% for SOFM while BPNN had 91.17%, 187.88s, 10.33%, 92.67% and 89.67% respectively. This paper showed that CPNN classification technique performed best for iris recognition system in terms of RA and recognition time. This research output will serve as a basis to pre-inform and guide researchers in choosing an efficient kernel based feature extraction technique.