Browsing by Author "Kayode, Adewole"

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    Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis
    (Mobile Information Systems, 2021-01-21) Quadri, Ramon; Nasir, Faruk; Kayode, Adewole; Abubakar, Abdulkarim; Lukman, Olawoyin; Abdulkarim, Oloyede; Haruna, Chiroma; Aliyu, Usman; Carlos, Calafate
    The importance of wireless path loss prediction and interference minimization studies in various environments cannot be over- emphasized. In fact, numerous researchers have done massive work on scrutinizing the effectiveness of existing path loss models for channel modeling. The difficulties experienced by the researchers determining or having the detailed information about the propagating environment prompted for the use of computational intelligence (CI) methods in the prediction of path loss. This paper presents a comprehensive and systematic literature review on the application of nature-inspired computational approaches in radio propagation analysis. In particular, we cover artificial neural networks (ANNs), fuzzy inference systems (FISs), swarm intelligence (SI), and other computational techniques. The main research trends and a general overview of the different research areas, open research issues, and future research directions are also presented in this paper. This review paper will serve as reference material for researchers in the field of channel modeling or radio propagation and in particular for research in path loss prediction.
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    A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram.
    (Journal of Ambient Intelligence and Humanized Computing., 2022-01-15) Musa, Nehemiah; Gital, Abdulsalam Yau; Aljojo, Nahla; Haruna, Chiroma; Kayode, Adewole; Majeed, Hammed; Faruk, Nasir; Abdulkarim, Abubakar; Ifada, Emmanuel; Yusuf, Folawiyo; Abdulkarim, Oloyede; Lukman, Olawoyin; Sikiru, Ismaeel; Ibrahim, Katibi
    The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applica- tions of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG. The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on bio- metric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm.