Browsing by Author "Sadiku, J. S."
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Item Coactive Neuro-Fuzzy Expert System: A Framework for Diagnosis of Malaria(African Journal of Computing & ICTs (AJOCICT), 2014) Oladele, Tinuke Omolewa; Sadiku, J. S.; Oladele, R. O.;Item Drug Target Selection for Malaria: Molecular Basis for the Drug Discovery Process(Centrepoint Journal (Science Edition), 2012) Oladele, Tinuke Omolewa; Bewaji, C. O.; Sadiku, J. S.Item In Silico Characterization of some Hypothetical Proteins in the Proteome of Plasmodium Falciparum(Centrepoint Journal (Science Edition), 2011) Oladele, Tinuke Omolewa; Sadiku, J. S.; Bewaji, C. O.Item Iris Feature Extraction for Personal Identification using Fast Wavelet Transform (FWT)(International Journal of Applied Information Systems, 2014) Abikoye, O. C.; Sadiku, J. S.; Adewole, K. S.; Jimoh, R. G.Iris is the annular region of the eye bounded by the pupil and the sclera(white of the eye) on either side. The iris has many interlacing features such as stripes, freckles, coronas, radial furrow, crypts, zigzag collarette, rings etc collectively referred to as texture of the iris. This texture is well known to provide a signature that is unique to each subject. All these features are extracted using different algorithms i.e features extraction is the process of extracting information from the iris image. Iris feature extraction is the crucial stage of the whole iris recognition process for personal identification. This is a key process where the two dimensional image is converted to a set of mathematical parameters. The significant features of the iris must be encoded so that comparisons between templates can be made. In this study the feature of the iris is extracted using Fast Wavelet Transform (FWT). The algorithm is fast and has a low complexity rate. The system encodes the features to generate its iris feature codes.Item A Neuro-Fuzzy Framework for Finding Clinical Trials in the Drug Discovery Process(Computing, Information Systems, Development Informatics & Allied Research Journal (CISDIAR), 2015) Oladele, Tinuke Omolewa; Williams, F. E.; Sadiku, J. S.The drug discovery process is complex, time consuming and very expensive. Typically, the time to develop a candidate drug is about 5 years, while the clinical phases leading, possibly, to the commercial availability of the drug are even longer (>7 years) for a total cost of more than 700 Million dollars. Declining pharmaceutical industry productivity is well recognized by drug developers, regulatory authorities and patient groups. A key part of the problem is that clinical studies are increasingly expensive, driven by the rising costs of conducting Phase II and III trials. It is therefore crucial to ensure that these phases of drug development are conducted more efficiently and cost-effectively, and that attrition rates are reduced.In this paper, a neuro-fuzzy framework is presented for finding clinical trials in the drug discovery process. The proposed framework was developed based on the combination of two artificial intelligence techniques known as artificial neural networks and fuzzy logic.