Performance Evaluation of ANOVA and RFE Algorithms for Classifying Microarray Dataset Using SVM

Abdulsalam, Sulaiman Olaniyi; Mohammed, Abubakar Adamu; Ajao, Jumoke Falilat; Babatunde, Ronke Seyi; Ogundokun, Roseline Oluwaseun; Nnodim, Chiebuka T.; Arowolo, Micheal Olaolu

doi:10.1007/978-3-030-63396-7_32

Cited by 18 publications

(11 citation statements)

References 22 publications

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“…The corresponding n% percent of eigenvectors would then form the transformation matrix. ANOVA is also a common method for feature selection ( Sheikhan et al, 2013 ; Bejani and Gharavian, 2014 ; Li et al, 2018 ; Abdulsalam et al, 2020 ). It first performs the F test on each separate feature together with data labels.…”

Section: Methodsmentioning

confidence: 99%

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

et al. 2021

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Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.

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Section: Methodsmentioning

confidence: 99%

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

et al. 2021

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“…The pot holder as shown in Figure 1 was constructed using a 5 mm rod mild steel to enable the pot (s) to maintain balance. With this holder, the cookware is established and the cooking pots were placed on the cookware which also form the focal point where uniform heat distribution is achieved [31][32][33][34][35][36][37][38]. Regardless of the dish tilt inclination angle, the pot is effortless placed on the pot-holder even as the device is been used to tracked the solar movement and elevation which contributes to asymmetry of the cooker.…”

Section: Pot Holdermentioning

confidence: 99%

Performance Evaluation of Locally Designed and Produced Parabolic Solar Cooker

Aliyu¹,

Nnodim²,

Adebayo³

et al. 2021

IJEES

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Despite being able to supply beyond energy required for global use, the sun is still being greatly under-utilized possibly due to the availability of alternative sources of energies which are finite, costly, and far more hazardous. The aim of this research work is to seek better ways of utilizing the luminous flux (luminous energy emitted per second) or power from solar radiation, which is renewable, pollution free and which is freely available to mankind to generate heat or power required for cooking and for related needs of a cooker. The solar cooker, which is a family size type was designed (with the design specification temperature of 180°C) which was constructed and tested between the periods of 9.00 am and 4.00 pm during sun shine days. The maximum temperature attained was 140°C. The parabolic solar cooker has four panel booster reflectors (with rectangular plane mirrors on each booster reflector) each of which is detachable and a mechanical device is provided to constantly tilt the reflectors in the azimuthally direction so as to track the sun. These reflectors reflect the sun rays which falls incident on it to a focal point which give solar energy of high intensity. A known size pot was positioned with aid of a potholder on the focal point. The use of the parabolic cooker will help tremendously in the conservation of fossil fuel energy. However, on the sunny and cloudless days, the cooker can work effectively for various cooking purposes at almost the same rate as the conventional stoves. The efficiency of the cooker was determined and recorded. Finally, the cooker could be more efficient if the entire reflector surfaces of the cooker can be able to radiate heat energy from all its surfaces to the focal point. The cooker could be used for cooking on sunny days and in areas where electricity availability is questionable in terms of supply.

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“…KNN identifies new locations based on the majority of noises from the sur-rounding k. According to the function of distance, the position given in the class is extremely mutually exclusive between the closest neighbors K. The following Eqs. ( 1)-(3) are the mathematical formulas for calculating the distance between two places [72].…”

Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

“…The equations of these evaluation metrics are shown in Eqs. ( 4)-( 8) [72,74]. The true positives (TPs) signified news that was forecasted as fake and was FN, the true negatives (TNs) signified news that was fore-casted as not fake and was non-fake, the false positives (FPs) signified news that was forecasted as fake but was not fake, and the false negatives (FNs) signified news that was forecast as fake but was not fake [72,75].…”

Section: Evaluation Measuresmentioning

confidence: 99%

Early Detection of Fake News from Social Media Networks Using Computational Intelligence Approaches

Ogundokun

Arowolo

Misra

et al. 2021

Studies in Computational Intelligence

Self Cite

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In recent years, misinformation which includes false news (FN) has become a worldwide issue owing to its exponential development, mostly on social media (SM). The broad dissemination of misinformation and FN can create harmful societal repercussions. Despite current improvements, spotting fake news remains difficult owing to its intricacy, multiplicity, multi-modality, and fact-scrutiny or annotation expenses. Therefore, there is a necessity for Computational Intelligence Approaches (CIA) that can identify this fake news automatically. This study projected using dimensionality reduction (DR) approach to decrease the dimensionality of the feature vectors before sending them to the classifiers. The study focuses on a computational intelligence-based fake news detection system and a novel approach of employing three CIA for the detection of FN was proposed. The CIA employed for this study are Genetic Algorithm (GA), K-Nearest Neighbor (KNN), and Bagged Ensembled Learning (BEL). The proposed system performance was evaluated utilizing confusion matrix measures like accuracy, sensitivity, specificity, precision, and f-measure. The system was compared with the existing system and it was deduced that the projected system outperformed that of existing systems with an accuracy of 99.28%, sensitivity of 99.28%, precision of 99.99%, and f-measure of 99.63%. In conclusion, it was discovered that GA + KNN performance in terms of accuracy, sensitivity, specificity, precision, and f-measure sur-passed that of the GA + BEL.

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Performance Evaluation of ANOVA and RFE Algorithms for Classifying Microarray Dataset Using SVM

Cited by 18 publications

References 22 publications

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

Performance Evaluation of Locally Designed and Produced Parabolic Solar Cooker

Early Detection of Fake News from Social Media Networks Using Computational Intelligence Approaches

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