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Optical character recognition is gaining immense importance in the domain of deep learning. With each passing day, handwritten digits (0–9) data are increasing rapidly, and plenty of research has been conducted thus far. However, there is still a need to develop a robust model that can fetch useful information and investigate self-build handwritten digit data efficiently and effectively. The convolutional neural network (CNN) models incorporating a sigmoid activation function with a large number of derivatives have low efficiency in terms of feature extraction. Here, we designed a novel CNN model integrated with the extreme learning machine (ELM) algorithm. In this model, the sigmoid activation function is upgraded as the rectified linear unit (ReLU) activation function, and the CNN unit along with the ReLU activation function are used as a feature extractor. The ELM unit works as the image classifier, which makes the perfect symmetry for handwritten digit recognition. A deeplearning4j (DL4J) framework-based CNN-ELM model was developed and trained using the Modified National Institute of Standards and Technology (MNIST) database. Validation of the model was performed through self-build handwritten digits and USPS test datasets. Furthermore, we observed the variation of accuracies by adding various hidden layers in the architecture. Results reveal that the CNN-ELM-DL4J approach outperforms the conventional CNN models in terms of accuracy and computational time.
Saqib Ali; Jianqiang Li; Yan Pei; Muhammad Saqlain Aslam; Zeeshan Shaukat; Muhammad Azeem. An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification. Symmetry 2020, 12, 1742 .
AMA StyleSaqib Ali, Jianqiang Li, Yan Pei, Muhammad Saqlain Aslam, Zeeshan Shaukat, Muhammad Azeem. An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification. Symmetry. 2020; 12 (10):1742.
Chicago/Turabian StyleSaqib Ali; Jianqiang Li; Yan Pei; Muhammad Saqlain Aslam; Zeeshan Shaukat; Muhammad Azeem. 2020. "An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification." Symmetry 12, no. 10: 1742.
In recent years, a rapid rise in the incidence of Large for gestational age (LGA) neonate is reported, and health care professionals employed themselves to discover the cause. Utmost of the previous studies were cohort or observational studies that employed simple linear or multivariate regression models, and very few of them employed machine learning (ML) schemes. Therefore, this research proposes to use 1 expert-driven and 7 automated feature selection schemes with well-known ML classifiers using 10 and 30 folds cross-validation. The induced results were compared with existing baselines, and Wilcoxon signed-rank test and the Friedman test were also introduced to verify the results. The ranked 20 features of the proposed expert-driven feature selection scheme outperformed amongst 7 automated feature selection schemes with a prediction precision, accuracy, and AUC scores of 0.94606, 0.84529, and 0.86492, respectively. Out of twenty features, eleven features were found similar to twenty ranked features of the automated feature selection schemes subsets. The classification results of the extracted features were utmost identical to the results of twenty features subset proposed by the expert-driven feature selection scheme. Therefore, we suggest pediatricians to refresh LGA diagnosis process with the proposed scheme because of its practical usage and maximum expert involvement.
Faheem Akhtar; Jianqiang Li; Yan Pei; Azhar Imran; Asif Rajput; Muhammad Azeem; Bo Liu. Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data. Multimedia Tools and Applications 2020, 79, 34047 -34077.
AMA StyleFaheem Akhtar, Jianqiang Li, Yan Pei, Azhar Imran, Asif Rajput, Muhammad Azeem, Bo Liu. Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data. Multimedia Tools and Applications. 2020; 79 (45-46):34047-34077.
Chicago/Turabian StyleFaheem Akhtar; Jianqiang Li; Yan Pei; Azhar Imran; Asif Rajput; Muhammad Azeem; Bo Liu. 2020. "Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data." Multimedia Tools and Applications 79, no. 45-46: 34047-34077.
The demand for recommender systems in E-commerce industry has increased tremendously. Efficient recommender systems are being proposed by different E-business companies with the intention to give users accurate and most relevant recommendation of products from huge amount of information. To improve the performance of recommender systems, various stochastic variants of gradient descent based algorithms have been reported. The scalability requirement of recommender systems needs algorithms with fast convergence to generate recommendations of specific items. Using the concepts of fractional calculus, an efficient variant of the stochastic gradient descent (SGD) was developed for fast convergence. Such fractional SGD (F-SGD) is further accelerated by adding a momentum term, thus termed as momentum fractional stochastic gradient descent (mF-SGD). The proposed mF-SGD method is shown to offer improved estimation accuracy and convergence rate, as compared to F-SGD and standard momentum SGD for different proportions of previous gradients, fractional orders, learning rates and number of features.
Zeshan Aslam Khan; Syed Zubair; Hani Alquhayz; Muhammad Azeem; Allah Ditta. Design of Momentum Fractional Stochastic Gradient Descent for Recommender Systems. IEEE Access 2019, 7, 179575 -179590.
AMA StyleZeshan Aslam Khan, Syed Zubair, Hani Alquhayz, Muhammad Azeem, Allah Ditta. Design of Momentum Fractional Stochastic Gradient Descent for Recommender Systems. IEEE Access. 2019; 7 (99):179575-179590.
Chicago/Turabian StyleZeshan Aslam Khan; Syed Zubair; Hani Alquhayz; Muhammad Azeem; Allah Ditta. 2019. "Design of Momentum Fractional Stochastic Gradient Descent for Recommender Systems." IEEE Access 7, no. 99: 179575-179590.
An accurate and efficient Large-for-Gestational-Age (LGA) classification system isdeveloped to classify a fetus as LGA or non-LGA, which has the potential to assist paediatricians andexperts in establishing a state-of-the-art LGA prognosis process. The performance of the proposedscheme is validated by using LGA dataset collected from the National Pre-Pregnancy and ExaminationProgram of China (2010–2013). A master feature vector is created to establish primarily datapre-processing, which includes a features’ discretization process and the entertainment of missingvalues and data imbalance issues. A principal feature vector is formed using GridSearch-basedRecursive Feature Elimination with Cross-Validation (RFECV) + Information Gain (IG) featureselection scheme followed by stacking to select, rank, and extract significant features from the LGAdataset. Based on the proposed scheme, different features subset are identified and provided tofour different machine learning (ML) classifiers. The proposed GridSearch-based RFECV+IG featureselection scheme with stacking using SVM (linear kernel) best suits the said classification processfollowed by SVM (RBF kernel) and LR classifiers. The Decision Tree (DT) classifier is not suggestedbecause of its low performance. The highest prediction precision, recall, accuracy, Area Underthe Curve (AUC), specificity, and F1 scores of 0.92, 0.87, 0.92, 0.95, 0.95, and 0.89 are achievedwith SVM (linear kernel) classifier using top ten principal features subset, which is, in fact higherthan the baselines methods. Moreover, almost every classification scheme best performed with tenprincipal feature subsets. Therefore, the proposed scheme has the potential to establish an efficientLGA prognosis process using gestational parameters, which can assist paediatricians and experts toimprove the health of a newborn using computer aided-diagnostic system.
Faheem Akhtar; Jianqiang Li; Yan Pei; Azhar Imran; Asif Rajput; Muhammad Azeem; Qing Wang; Li; Pei; Wang. Diagnosis and Prediction of Large-For-Gestational-Age Fetus Using the Stacked GeneralizationMethod. Applied Sciences 2019, 9, 4317 .
AMA StyleFaheem Akhtar, Jianqiang Li, Yan Pei, Azhar Imran, Asif Rajput, Muhammad Azeem, Qing Wang, Li, Pei, Wang. Diagnosis and Prediction of Large-For-Gestational-Age Fetus Using the Stacked GeneralizationMethod. Applied Sciences. 2019; 9 (20):4317.
Chicago/Turabian StyleFaheem Akhtar; Jianqiang Li; Yan Pei; Azhar Imran; Asif Rajput; Muhammad Azeem; Qing Wang; Li; Pei; Wang. 2019. "Diagnosis and Prediction of Large-For-Gestational-Age Fetus Using the Stacked GeneralizationMethod." Applied Sciences 9, no. 20: 4317.