Browsing by Author "Ahmed, S."

Now showing 1 - 3 of 3
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    Directors Heterogeneity and Financial Performance of Listed Deposit Money Banks in Nigeria
    (Department of Management and Accounting, Obafemi Awolowo University, Ile-Ife, 2017) Olubunmi Florence, Osemene; Abogun, S.; Olaoti, Y.I.; Ahmed, S.
    The study investigated directors' heterogeneity and its impact on banks financial performance over a period of seven years (2008-2014), Secondary data were collected from the annual financial statements of banks and were analysed with the ordinary least square regression technique. The result showed that board nationals have a negative significance on return on equity, board gender composition have a positive significance on return on equity, board ethnicity have a positive impact on return on equity, and board size have no significance on return on equity while bank size have negative significance impact on return on equity. The study concluded that directors' heterogeniety has significant impact on banks performance.
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    Directors’ Heterogeneity and Financial Performance of listed Deposit Money Banks in Nigeria.
    (International Journal of Accounting and Management, 2017) Osemene, O. F.; Abogun, S.; Olaoti, Y.I.; Ahmed, S.
    The study investigated directors’ heterogeneity and its impact on banks financial performance over a period of seven years (2008-2014). Secondary data were collected from the annual financial statements of banks and were analyzed with ordinary least square regression technique. The result showed that board nationals have a negative significance on return on equity, board ethnicity have a positive impact on return on equity, and board size have no significance on return on equity while bank size have negative significant impact on return on equity. The study concluded that directors’ heterogeneity has significant impact on banks performance.
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    A Reinforcement Learning Approach for Interference Management in Heterogeneous Wireless Networks
    (Kassel University Press GmbH, 2021) Afolabi, A. S.; Ahmed, S.; Akinola, O. A.
    Due to the increased demand for scarce wireless bandwidth, it has become insufficient to serve the network user equipment using microcell base stations only. Network densification through the addition of low power nodes (picocell) to conventional high-power nodes addresses the bandwidth dearth issue, but unfortunately introduces unwanted interference into the network which causes a reduction in throughput. The purpose of this paper is to develop a model for controlling the interference between picocell and microcell users of a cellular network so as to increase the overall network throughput. In order to achieve this, a reinforcement learning model was developed which was used in coordinating interference in a heterogeneous network comprising microcell and picocell base stations. The learning mechanism was derived based on Q-learning, which consisted of agent, state, action, and reward. The base station was modeled as the agent, while the state represented the condition of the user equipment in terms of Signal to Interference Plus Noise Ratio. The action was represented by the transmission power level and the reward was given in terms of throughput. Simulation results showed that the trend of values of the learning rate (e.g., high to low, low to high, etc.) plays a major role in throughput performance. It was particularly shown that a multi-agent system with a normal learning rate could increase the throughput of associated user equipment by a whopping 212.5% compared to a macrocell-only scheme.