Papers by Sunday Olatunji
Journal of Superconductivity and Novel Magnetism, 2014
ABSTRACT Distortions in lattice parameters of MgB2 superconductor occur when dopants are introduc... more ABSTRACT Distortions in lattice parameters of MgB2 superconductor occur when dopants are introduced into the crystal lattice structure which finally affects its superconducting transition temperature (TC). We hereby developed a superconducting transition temperature estimator (STTE) that is capable of estimating the TC of superconductors of the doped MgB2 systems using crystal lattice parameters obtained when dopants are introduced into the crystal structure as descriptors. The model (STTE) was developed with the aid of support vector regression via test-set crossvalidation technique using twenty datasets. The developed model was used to estimate the TC of forty different superconductors of doped MgB2 system, and the obtained values agree well with the experimental data. The predictive ability of the developed model to directly link the lattice parameters of doped MgB2 superconductors to TC is advantageous for quick estimation of TC of ideal superconductors of the doped MgB2 system without any sophisticated equipment
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Effective methods of reducing excessive water from oil and gas wells are of immense significance ... more Effective methods of reducing excessive water from oil and gas wells are of immense significance in mature oil fields. Thermal instability of many polymer gel systems at elevated temperatures (above 80 °C) calls for the incorporation of suitable macro/nanoparticles into the gelling solutions to achieve enhanced thermal stability. The present work develops for the first time, a support vector regression (SVR)-based model for estimating the effect of coal fly ash (CFA) as reinforcing inorganic additive in polyacrylamide (PAM) gelling solutions crosslinked by polyethyleneimine (PEI). Effect of various CFA addition on the thermal stability of PAM/PEI gels at different temperatures shows a parabolic trend. This observation indicates that various temperatures have significant impact on the PAM/PEI-CFA gel resistance to thermal degradation. Besides, weight losses experienced by these polymer gels are attributed to the escape of water molecules attached to the PAM and PEI and also due to the thermal decomposition of amide and carboxylate side groups on PEI and PAM. The results of the developed model conform to the experimental outcomes with correlation coefficient of 0.9381 and root mean square error of 14.15% measured on the testing set of data. Impregnation of CFA in the matrix of PAM/PEI gel structural network led to an improved resistance to thermal decomposition. Additionally, effects of PAM and PEI concentrations on the thermal stability of the PAM/PEI-CFA composite gels containing various CFA quantities at elevated temperatures using the developed model agree well with the experimental results.
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International Journal of Pervasive Computing and Communications, Jun 28, 2011
... The Authors. Teddy Mantoro, Department of Computer Science, KICT, International Islamic Unive... more ... The Authors. Teddy Mantoro, Department of Computer Science, KICT, International Islamic University Malaysia, Kuala Lumpur, Malaysia. ... Efforts at using location fingerprinting to determine mobile user locations have been explored in several previous works such as in Tsai et al. ...
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Applied Soft Computing, 2015
ABSTRACT Optimal performances of thin film devices such as those of micro/nano-electromechanical ... more ABSTRACT Optimal performances of thin film devices such as those of micro/nano-electromechanical systems like sensors and actuators are possible with accurate and reliable characterization techniques. Such techniques can be enhanced if predictive models are constructed and deployed for production and monitoring. This paper presents functional networks as a novel modeling approach for rapid characterization of thin films such as thickness, deposition rate, resistivity and uniformity based on 8 deposition parameters. The functional network (FN) models were developed and tested using 154 experimental data sets obtained from ultrathin polycrystalline silicon germanium films deposited by Applied Materials Centura low pressure chemical vapour deposition system. The results showed that the proposed FN models perform excellently for all the outputs with minimum and maximum regression coefficients of 0.95 and 0.99, respectively. To further demonstrate the robustness of these models, several trend analyses were conducted. The performance statistics indicates that the mean percentage error for the model, based on the deposition rate, lies between 0.3% and 0.8% for silane, germane, diborane flow rates and pressure. For these deposition variables, the probability or p-value at a significance level of 0.01 implies that no significant difference exists between the means of the predicted and the measured values. The results are further discussed in light of physics of the CVD process.
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... The Authors. Teddy Mantoro, Department of Computer Science, KICT, International Islamic Unive... more ... The Authors. Teddy Mantoro, Department of Computer Science, KICT, International Islamic University Malaysia, Kuala Lumpur, Malaysia. ... Efforts at using location fingerprinting to determine mobile user locations have been explored in several previous works such as in Tsai et al. ...
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Proceeding of the 3rd International Conference on Information and Communication Technology for the Moslem World (ICT4M) 2010, 2010
Abstract:- There has been a rapid convergence to location based services for better resources man... more Abstract:- There has been a rapid convergence to location based services for better resources management. This is made possible by rapid development and lower cost of mobile and handheld devices. Due to this widespread usage however, localization and positioning systems, ...
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In this work, a technique for handwritten Arabic (Indian) numerals recognition using multi-span f... more In this work, a technique for handwritten Arabic (Indian) numerals recognition using multi-span features is presented. Angle, ring, horizontal, and vertical span features are used. All combinations of these features are tested and the combinations that result in the best recognition rates using Support Vector Machine (SVM) are identified. The SVM classifier is trained with 15840 digits and tested with
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ICTACT Journal on Soft Computing, 2013
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Sensors (Basel, Switzerland), Jan 19, 2016
The difficulty of real-time muscle force or joint torque estimation during neuromuscular electric... more The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG) of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR) due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and te...
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Transactions on Machine Learning and Artificial Intelligence, 2015
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ABSTRACT Extreme learning machines (ELM), as a learning tool, have gained popularity due to its u... more ABSTRACT Extreme learning machines (ELM), as a learning tool, have gained popularity due to its unique characteristics and performance. However, the generalisation capability of ELM often depends on the nature of the dataset, particularly on whether uncertainty is present in the dataset or not. In order to reduce the effects of uncertainties in ELM prediction and improve its generalisation ability, this paper proposes a hybrid system through a combination of type-2 fuzzy logic systems (type-2 FLS) and ELM; thereafter the hybrid system was applied to model permeability of carbonate reservoir. Type-2 FLS has been chosen to be a precursor to ELM in order to better handle uncertainties existing in datasets beyond the capability of type-1 fuzzy logic systems. The type-2 FLS is used to first handle uncertainties in reservoir data so that its final output is then passed to the ELM for training and then final prediction is done using the unseen testing dataset. Comparative studies have been carried out to compare the performance of the proposed T2-ELM hybrid system with each of the constituent type-2 FLS and ELM, and also artificial neural network (ANN) and support Vector machines (SVM) using five different industrial reservoir data. Empirical results show that the proposed T2-ELM hybrid system outperformed each of type-2 FLS and ELM, as the two constituent models, in all cases, with the improvement made to the ELM performance far higher against that of type-2 FLS that had a closer performance to the hybrid since it is already noted for being able to model uncertainties. The proposed hybrid also outperformed ANN and SVM models considered.
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Expert Systems With Applications an International Journal, Sep 1, 2011
... of Technology Malaysia, 81310 UTM Skudai, Johor, Malaysia (Email: [email protected] Tel.: +6019... more ... of Technology Malaysia, 81310 UTM Skudai, Johor, Malaysia (Email: [email protected] Tel.: +60197701757) Abdul Azeez Abdul Raheem Centre for ... El-Batanoney 1992, Mahmood and Al-Marhoun 1996, McCain 1991, Omar and Todd 1993, Petrosky and Farshad 1993, Saleh ...
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Fourth International Conference on the Applications of Digital Information and Web Technologies, 2011
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International Journal on Information Sciences and Computing, 2010
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Applied Computational Intelligence and Soft Computing, 2016
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Papers by Sunday Olatunji