Electrical & Electronics Engineering

Permanent URI for this collectionhttp://197.211.34.35:4000/handle/123456789/130

Electrical & Electronics Engineering

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    Performance Analysis of Data Normalization Methods
    (International Engineering Conference 2017, 2017-10-17) Ajiboye, Johnson Adegbenga; Aibinu M.A
    Statistical Data Normalization is a very important input preprocessing operation that should be done before data is fed into the training network. However, there is need for a suitable selection of normalization technique since normalization on the input has potential of varying the structure of the data and may impact on the outcome of the analysis. This paper investigates and evaluates some important statistical normalization techniques by studying thirty published papers that used wine dataset available in the UCI repository and their impact on performance accuracy. Results reveal that Min-Max normalization technique had the best performance accuracy of 95.91% on the average among all the other normalization types.
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    ANALYSIS OF SPECTRUM OCCUPANCY PREDICTION RESULTS FOR MAITAMA ABUJA
    (International Conference on Communication and Information Science (ICCIS), 2024) Ajiboye, Johnson Adegbenga; Mary Adebola Ajiboye; Babatunde Araoye Adegboye; Daniel Jesupamilerin Ajiboye; Jonathan Gana Kolo; Abiodun Musa Aibinu
    This research investigates the efficacy of Artificial Neural Networks (ANN) in predicting spectrum occupancy in Maitama, Abuja, Nigeria, focusing on frequency bands ranging from 30 MHz to 300 MHz. The primary objective was to evaluate the accuracy of ANN-based predictions of spectrum usage and compare these predictions with actual measurements. The study employed ANN to forecast spectrum occupancy across various frequency bands, and the predicted data were then compared with empirical measurements to assess the performance of the model. The analysis revealed that prediction errors were generally low across all frequency bands, with most errors falling below 1.5%. Specifically, the 30-47 MHz sub-band demonstrated an average percentage difference between the actual and predicted value of 0.087%, with a maximum error of 1.12% occurring at frequency of 44.65 MHz. For the 47.05-68 MHz band, the average percentage difference was slightly higher at 0.106%, and the maximum error was 2.18% occurring at frequency of 50.2 MHz. In the 68.05-74.8 MHz band, the average percentage error was 0.040%, but with highest error of 0.232% at frequency of 73.95 MHz. The 74.85-87.45 MHz band showed the most accurate predictions with an average error of just 0.010%, and a maximum error of 0.174% at 75.1 MHz. Overall, the highest prediction error was 0.106% in the 47.05-68 MHz band, whereas the lowest was 0.010% in the 74.85-87.45 MHz band. These results highlight the high accuracy of ANN in predicting spectrum usage, demonstrating its potential for effective spectrum management and planning in Maitama, Abuja.