Department of Computer Science

Permanent URI for this collectionhttp://197.255.125.131:4000/handle/123456789/23129

Browse

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    El Niño-Southern Oscillation forecasting using complex networks analysis of LSTM neural networks
    (Artificial Life and Robotics, 2019-06-04) Broni-Bedaiko, C.; Katsriku, F.A.; Unemi, T.; Atsumi, M.; Abdulai, J-D.; Shinomiya, N.; Owusu, E.
    Arguably, El Niño-Southern Oscillation (ENSO) is the most influential climatological phenomenon that has been intensively researched during the past years. Currently, the scientific community knows much about the underlying processes of ENSO phenomenon, however, its predictability for longer horizons, which is very important for human society and the natural environment is still a challenge in the scientific community. Here we show an approach based on using various complex networks metrics extracted from climate networks with long short-term memory neural network to forecast ENSO phenomenon. The results suggest that the 12-network metrics extracted as predictors have predictive power and the potential for forecasting ENSO phenomenon longer multiple steps ahead.
  • Thumbnail Image
    Item
    A Hybrid Heuristic Model for Duty Cycle Framework Optimization
    (International Journal of Distributed Sensor Networks, 2024) Ansah, K.; Appati, J.K.; Owusu, E.; Abdulai, J-D.
    This paper proposes a hybrid metaheuristic approach to optimize a duty cycle framework based on Seagull and Mayfly Optimization (HSMO-DC) Algorithm. This approach becomes crucial as current clustering protocols are unable to efficiently tune the clustering parameters in accordance to the diversification of varying WSNs. The proposed HSMO-DC primarily has two parts, where the first part takes care of the online cluster head selection and network communication using the seagull algorithm while the second part performs parameter optimization using the mayfly algorithm. The seagull is aimed at improving the energy distribution in the network through an effective bandwidth allocation procedure while reducing the total energy dissipation. Comparatively, with other clustering protocols, our proposed methods reveal an enhanced network lifetime with an improved network throughput and adaptability based on selected standard metric of performance measurement.