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RISS 인기검색어
- 검색키워드
- 저자 : A. Abass (검색결과 5 건)
- 무료
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- 유료
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Feature Selection with Ensemble Learning for Prostate Cancer Prediction from Gene Expression
Abass, Yusuf Aleshinloye,Adeshina, Steve A. International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.spc12
Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.
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A Novel Online Controller for Tuning Shunt Active Power Filters Based Upon Switched-Capacitors
Ossama M. Elgendy,Ahmed M. A. Mahmoud,A. Abass,A. D. Alkoshairy 전력전자학회 2007 ICPE(ISPE)논문집 Vol.- No.-
This paper presents a novel online controller for tuning shunt active power filters (APF). The idea of the proposed controller is based upon using a low rating switched-capacitor circuit as online estimator to extract the fundamental reactive component of the nonlinear load current. This reactive component is shifted in appropriate phase using two phase-shifters to obtain a 3φ signal. The line current is sensed and compared instantaneously with this 3φ signal using three comparators. The outputs of the comparators represent each moment the profile of the harmonic current. This harmonic current is taken as a reference signal to drive a simple closed-loop P-I controller in order to produce the necessary modulating signals for the triggering module of the APF. In the case of balanced load, which is the case under investigation in this paper, one switched-capacitor circuit is needed. In spite of being simple and straightforward, the proposed controller will be able to solve the problems arising with the p-q algorithm based controllers.
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Feature Selection with Ensemble Learning for Prostate Cancer Prediction from Gene Expression
Abass, Yusuf Aleshinloye,Adeshina, Steve A. International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.12
Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.
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EXISTENCE OF SOLUTION OF DIFFERENTIAL EQUATION VIA FIXED POINT IN COMPLEX VALUED b-METRIC SPACES
A. A. Mebawondu,H.A. Abass,M.O. Aibinu,O.K. Narain 경남대학교 수학교육과 2021 Nonlinear Functional Analysis and Applications Vol.26 No.2
The concepts of new classes of mappings are introduced in thespaces which are more general space than the usual metric spaces. The existence and uniqueness of common fixedpoints and fixed point results are established in the setting of complete complex valued $b$-metric spaces. An illustration is given by establishing the existence of solution of periodic differential equations in theframework of a complete complex valued $b$-metric spaces.
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Hammed A. Abass,Ojen K. Narain,Olayinka M. Onifade 경남대학교 수학교육과 2023 Nonlinear Functional Analysis and Applications Vol.28 No.2
Numerous problems in science and engineering defined by nonlinear functional equations can be solved by reducing them to an equivalent fixed point problem. Fixed point theory provides essential tools for solving problems arising in various branches of mathematical analysis, such as split feasibility problems, variational inequality problems, nonlinear optimization problems, equilibrium problems, complementarity problems, selection and matching problems, and problems of proving the existence of solution of integral and differential equations.The theory of fixed is known to find its applications in many fields of science and technology. For instance, the whole world has been profoundly impacted by the novel Coronavirus since 2019 and it is imperative to depict the spread of the coronavirus. Panda et al. [24] applied fractional derivatives to improve the 2019-nCoV/SARS-CoV-2 models, and by means of fixed point theory, existence and uniqueness of solutions of the models were proved. For more information on applications of fixed point theory to real life problems, authors should (see [6, 13, 24] and the references contained in).
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