94 research outputs found
Exploring the impact of SEO-based ranking factors for voice queries through machine learning
No full textThe use of voice search is proliferating and expected to grow into the foreseeable future;
this is why websites increasingly optimize their content associated with voice-based search
to improve their ranking. In this era of rapid growth in voice search technology, it is a topical matter that needs research. Moreover, many predictions about its future excite the subject and require systematic investigation. This research aims to analyze important features
that contribute to the SEO of webpages. Therefore, there is a need to examine various ranking factors that improve the ranking of the webpages for voice search queries on the Search
Engine Results Page (SERP). This study consists of two phases. The frst phase comprises
systematic data acquisition and identifying important SEO-based ranking factors. The second phase includes a longitudinal case study to evaluate the impact and signifcance of
identifed factors. To achieve this goal, we conduct experiments on methodical combinations of features through machine learning algorithms such as Support Vector Machine,
Logistic Regression, Naive Bayes Classifer, K-Nearest Neighbors, Decision Trees and
Random Forest. Comparing results for multiple feature designs evaluates the contributing nature of specifc features in SEO-based optimization for ranking. Results suggest the
importance of the newly identifed feature set (FF) outperforms baselines (EF and EFN)
by a signifcant margin. A longitudinal case study on a blog over four months confrms
that optimizing these features improves page ranking; therefore, webmasters must optimize
these features while preparing the webpage
Retraction Note: Refining Parkinson’s neurological disorder identification through deep transfer learning (Neural Computing and Applications, (2020), 32, 3, (839-854), 10.1007/s00521-019-04069-0)
No full textThe Editor-in-Chief and the publisher have retracted this article. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. The authors Imran Razzak and Saeeda Naz disagree with this retraction. The author Muhammad Imran has not responded to correspondence regarding this retraction. The Publisher has not been able to obtain a current email address for the authors Amina Naseer, Monail Rani, and Guandong Xu
An Ontology-based Framework Aiming to Support Cardiac Rehabilitation Program
No full textAbstractHealth Information technology (HIT) played important role in wide range of healthcare settings. The strategy of HIT is to reduce cost and increase efficiency. The aim of cardiac implantable devices is to reduce the effect and risk of cardiovascular diseases. The number of patients that benefits from these devices are growing rapidly. Cardiac rehabilitation program improves the ability of cardiac patient to restore his health to an optimal level and reduce his stress and depression, especially after the cardiovascular surgeries. The need for the use of ontologies in cardiac rehabilitation domain has emerged over the last years, in order to improve the standardization of cardiac rehabilitation's practice worldwide. In this study, ontology-based system for managing the cardiac rehabilitation program has been developed. The ontology presents the required functionalities to implement the program management that helps to enhance the quality of cardiac rehabilitation program. Moreover, proposed ontology contributes to enhance the understanding, reusability, sharing and transferring knowledge in the domain of cardiac rehabilitation
Sparse Support Matrix Machines for the Classification of Corrupted Data
No full textUniversity of Technology Sydney. Faculty of Engineering and Information Technology.Support matrix machine is fragile to the presence of outliers: even few corrupted data points can arbitrarily alter the quality of the approximation, What if a fraction of columns are corrupted? In real world, the data is noisy and most of the features may be redundant as well as may be useless, which in turn affect the classification performance. Thus, it is important to perform robust feature selection under robust metric learning to filter out redundant features and ignore the noisy data points for more interpretable modelling. To overcome this challenge, in this work, we propose a new model to address the classification problem of high dimensionality data by jointly optimizing the both regularizer and hinge loss. We combine the hinge loss and regularization terms as spectral elastic net penalty. The regularization term which promotes the structural sparsity and shares similar sparsity patterns across multiple predictors. It is a spectral extension of the conventional elastic net that combines the property of low-rank and joint sparsity together, to deal with complex high dimensional noisy data. We further extends this approach by combining the recovery along with feature selection and classification could significantly improve the performance based on the assumption that the data consists of a low rank clean matrix plus a sparse noise matrix. We perform matrix recovery, feature selection and classification through joint minimization of p,q-norm and nuclear norm under the incoherence and ambiguity conditions and able to recover intrinsic matrix of higher rank and recover data with much denser corruption. Although, above both methods takes full advantage of low rank assumption to exploit the strong correlation between columns and rows of each matrix and able to extract useful features, however, are originally built for binary classification problems. To improve the robustness against data that is rich in outliers, we further extend this problem and present a novel multiclass support matrix machine by utilizing the maximization of the inter-class margins (i.e. margins between pairs of classes). We demonstrate the significance and advantage of our methods on different available benchmark datasets such as person identification, face recognition and EEG classification. Results showed that our methods achieved significantly better performance both in terms of time and accuracy for solving the classification problem of highly correlated matrix data as compared to state-of-the-art methods
Microscopic Blood Smear Segmentation and Classification Using Deep Contour Aware CNN and Extreme Machine Learning
No full textZoning Features and 2DLSTM for Urdu Text-line Recognition
No full textAbstractRecognition of Urdu cursive script is a challenging task due to the implicit complexities associated with it. The performance of a recognition system is immensely dependent on extracted features. There are various features extraction approaches proposed in recent years. Among many, an approach based on zoning features proved to be efficient and popular. Such zoning features represent significant information with low complexity and high speed. In this paper, we used zoning features for the classification of Urdu Nasta’liq text lines, with a combination of 2-Dimensional Long Short Term Memory networks (2DLSTM) as learning classifier. The proposed model is evaluated on publicly available UPTI dataset and character recognition rate of 93.39% is obtained
DAAB: Deep Authorship Attribution in Bengali
No full textAuthorship attribution identifies the true author of an unknown document. Authorship attribution plays a crucial role in plagiarism detection and blackmailer identification, however, the existing studies on authorship attribution in Bengali are limited. In this paper, we propose an instance-based deep authorship attribution model, called DAAB, to identify authors in Bengali. Our DAAB model fuses features from convolutional neural networks and another set of features from an artificial neural network to learn the stylometry of an author for authorship attribution. Extensive experiments with three real benchmark datasets such as Bengali-Quora and two online Bengali Corpus demonstrate the superiority of our authorship attribution model.Full Tex
Arabic Cursive Text Recognition from Natural Scene Images
No full textThis paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years’ publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers
Efficient Brain Tumor Segmentation with Multiscale Two-Pathway-Group Conventional Neural Networks
No full text© 2013 IEEE. Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features. Similarly, traditional methods of deep learning such as convolutional neural networks require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. Here, we describe a new model two-pathway-group CNN architecture for brain tumor segmentation, which exploits local features and global contextual features simultaneously. This model enforces equivariance in the two-pathway CNN model to reduce instabilities and overfitting parameter sharing. Finally, we embed the cascade architecture into two-pathway-group CNN in which the output of a basic CNN is treated as an additional source and concatenated at the last layer. Validation of the model on BRATS2013 and BRATS2015 data sets revealed that embedding of a group CNN into a two pathway architecture improved the overall performance over the currently published state-of-the-art while computational complexity remains attractive
Big data analytics for preventive medicine
No full text© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations
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