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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.
In the large for gestational age infant’s classification and prediction, noisy features are distilled to improve the classifier performance. It is accomplished with the creation of a suitable feature vector followed by GridSearch-based Recursive Feature Elimination with Cross-Validation (RFECV) scheme. It attempts to elect features that are influential and independent. We executed experiments on the data obtained from the National Pregnancy and Examination Program of China (2010–2013). The results are compared with the results already reported in the literature. The GridSearch-based RFECV scheme exhibited smaller features subset size with an increased classifier performance. The precision and area under the curve (AUC) scores are drastically improved from 0.7134 and 0.7074 to 0.96 to 0.86 respectively. Therefore, pediatricians are suggested to use fifty-three features subset, ranked by GridSearch-based RFECV scheme using Support Vector Machine (SVM) for the establishment of an efficient LGA prognosis process.
Faheem Akhtar; Jianqiang Li; Yan Pei; Yang Xu; Asif Rajput; Qing Wang. Optimal Features Subset Selection for Large for Gestational Age Classification Using GridSearch Based Recursive Feature Elimination with Cross-Validation Scheme. Lecture Notes in Electrical Engineering 2020, 63 -71.
AMA StyleFaheem Akhtar, Jianqiang Li, Yan Pei, Yang Xu, Asif Rajput, Qing Wang. Optimal Features Subset Selection for Large for Gestational Age Classification Using GridSearch Based Recursive Feature Elimination with Cross-Validation Scheme. Lecture Notes in Electrical Engineering. 2020; ():63-71.
Chicago/Turabian StyleFaheem Akhtar; Jianqiang Li; Yan Pei; Yang Xu; Asif Rajput; Qing Wang. 2020. "Optimal Features Subset Selection for Large for Gestational Age Classification Using GridSearch Based Recursive Feature Elimination with Cross-Validation Scheme." Lecture Notes in Electrical Engineering , no. : 63-71.
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.
For autonomous robots, 3D perception of environment is an essential tool, which can be used to achieve better navigation in an obstacle rich environment. This understanding requires a huge amount of computational resources; therefore, the real-time 3D reconstruction of surrounding environment has become a topic of interest for countless researchers in the recent past. Generally, for the outdoor 3D models, stereo cameras and laser depth measuring sensors are employed. The data collected through the laser ranging sensors is relatively accurate but sparse in nature. In this paper, we propose a novel mechanism for the incremental fusion of this sparse data to the dense but limited ranged data provided by the stereo cameras, to produce accurate dense depth maps in real-time over a resource limited mobile computing device. Evaluation of the proposed method shows that it outperforms the state-of-the-art reconstruction frameworks which only utilizes depth information from a single source.
Muhammad Kashif Ali; Asif Rajput; Muhammad Shahzad; Farhan Khan; Faheem Akhtar; Anko Borner. Multi-Sensor Depth Fusion Framework for Real-Time 3D Reconstruction. IEEE Access 2019, 7, 136471 -136480.
AMA StyleMuhammad Kashif Ali, Asif Rajput, Muhammad Shahzad, Farhan Khan, Faheem Akhtar, Anko Borner. Multi-Sensor Depth Fusion Framework for Real-Time 3D Reconstruction. IEEE Access. 2019; 7 (99):136471-136480.
Chicago/Turabian StyleMuhammad Kashif Ali; Asif Rajput; Muhammad Shahzad; Farhan Khan; Faheem Akhtar; Anko Borner. 2019. "Multi-Sensor Depth Fusion Framework for Real-Time 3D Reconstruction." IEEE Access 7, no. 99: 136471-136480.
Asif Rajput; Eugen Funk; Anko Börner; Olaf Hellwich. A Regularized Volumetric Fusion Framework for Large-Scale 3D Reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing 2018, 141, 124 -136.
AMA StyleAsif Rajput, Eugen Funk, Anko Börner, Olaf Hellwich. A Regularized Volumetric Fusion Framework for Large-Scale 3D Reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing. 2018; 141 ():124-136.
Chicago/Turabian StyleAsif Rajput; Eugen Funk; Anko Börner; Olaf Hellwich. 2018. "A Regularized Volumetric Fusion Framework for Large-Scale 3D Reconstruction." ISPRS Journal of Photogrammetry and Remote Sensing 141, no. : 124-136.
3D reconstruction from image based depth sensor is essential part of many offline or online robotic applications. Numerous techniques have been developed to integrate multiple depth maps to create 3D model of environment, however accuracy of the reconstructed 3D model exclusively depends upon the precision of depth sensing. Economical depth sensors such as Kinect and stereo camera sensors provide imprecise depth data which affect the integration process and produce unwanted noisy surfaces in 3D model. There exist several approaches which use image filtering based depth map denoising, however applying filtering directly on depth data can result in inconsistent and deformed 3D model. In this paper we investigate and extend a recursive variant of total variation based filtering to incorporate multi-view based depth images while applying implicit depth smoothing. Proposed framework uses sparse voxel representation to aid large scale 3D model reconstruction and is shown to reduce absolute surface error of final reconstructed 3D model by up to 77% in comparison with state of the art 3D fusion techniques.
M. A. A. Rajput; E. Funk; A. Börner; O. Hellwich. Boundless Reconstruction Using Regularized 3D Fusion. Programmieren für Ingenieure und Naturwissenschaftler 2017, 359 -378.
AMA StyleM. A. A. Rajput, E. Funk, A. Börner, O. Hellwich. Boundless Reconstruction Using Regularized 3D Fusion. Programmieren für Ingenieure und Naturwissenschaftler. 2017; ():359-378.
Chicago/Turabian StyleM. A. A. Rajput; E. Funk; A. Börner; O. Hellwich. 2017. "Boundless Reconstruction Using Regularized 3D Fusion." Programmieren für Ingenieure und Naturwissenschaftler , no. : 359-378.
M. A. A. Rajput; E. Funk; A. Börner; O. Hellwich. Recursive Total Variation Filtering Based 3D Fusion. Proceedings of the 10th International Conference on Security and Cryptography 2016, 72 -80.
AMA StyleM. A. A. Rajput, E. Funk, A. Börner, O. Hellwich. Recursive Total Variation Filtering Based 3D Fusion. Proceedings of the 10th International Conference on Security and Cryptography. 2016; ():72-80.
Chicago/Turabian StyleM. A. A. Rajput; E. Funk; A. Börner; O. Hellwich. 2016. "Recursive Total Variation Filtering Based 3D Fusion." Proceedings of the 10th International Conference on Security and Cryptography , no. : 72-80.