A Topic Modeling Guided Approach for Semantic Knowledge Discovery in e-Commerce

The task of mining large unstructured text archives, extracting useful patterns and then organizing them into a knowledgebase has attained a great attention due to its vast array of immediate applications in business. Businesses thus demand new and efficient algorithms for leveraging potentially useful patterns from heterogeneous data sources that produce huge volumes of unstructured data. Due to the ability to bring out hidden themes from large text repositories, topic modeling algorithms attained significant attention in the recent past. This paper proposes an efficient and scalable method which is guided by topic modeling for extracting concepts and relationships from e-commerce product descriptions and organizing them into knowledgebase. Semantic graphs can be generated from such a knowledgebase on which meaning aware product discovery experience can be built for potential buyers. Extensive experiments using proposed unsupervised algorithms with e-commerce product descriptions collected from open web shows that our proposed method outperforms some of the existing methods of leveraging concepts and relationships so that efficient knowledgebase construction is possible

Blockchain for Healthcare: Securing Patient Data and Enabling Trusted Artificial Intelligence

Advances in information technology are digitizing the healthcare domain with the aim of improved medical services, diagnostics, continuous monitoring using wearables, etc., at reduced costs. This digitization improves the ease of computation, storage and access of medical records which enables better treatment experiences for patients. However, it comes with a risk of cyber attacks and security and privacy concerns on this digital data. In this work, we propose a Blockchain based solution for healthcare records to address the security and privacy concerns which are currently not present in existing e-Health systems. This work also explores the potential of building trusted Artificial Intelligence models over Blockchain in e-Health, where a transparent platform for consent-based data sharing is designed. Provenance of the consent of individuals and traceability of data sources used for building and training the AI model is captured in an immutable distributed data store. The audit trail of the data access captured using Blockchain provides the data owner to understand the exposure of the data. It also helps the user to understand the revenue models that could be built on top of this framework for commercial data sharing to build trusted AI models

Immediate and long-term results of bronchial artery embolisation for life-threatening hemoptysis in bronchiectasis

Background: Bronchial artery embolization (BAE) has been established as an effective technique in the emergency treatment of life-threatening hemoptysis, but few data concerning long-term results of the procedure are available The aim of this study was to analyze the immediate and long-term results of bronchial artery embolization (BAE) for hemoptysis due to bronchiectasis.Methods: Twenty five patients (aged 28–76 years) who underwent bronchial artery embolization with polyvinyl alcohol particles, gelatin sponge and coil for massive or continuing moderate hemoptysis caused by bronchiectasis were included in the study. These patients were followed up for 3 years. Initially patients were followed up monthly for 6months by clinical examination. Then yearly follow up by clinical and radiological examination. Results analyzed using SPSS 16 version.Results: In our study16 patients (64%) were males. 11 patients (44%) had bilateral bronchiectasis.14 patients (56%) had no rebleeding in the three year follow-up period. Only 8% developed rebleeding within 24hrs.Only one patient (4%) developed massive hemoptysis within one month and died. Other rebleed were very minimal. In our study there was no significant relation with side of bronchiectasis and rebleed.Conclusions: Bronchial artery embolisation can yield immediate and long-term benefit in patients with hemoptysis due to bronchiectasis.

Factors governing the deep ventilation of the Red Sea

Author Posting. © American Geophysical Union, 2015. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 120 (2015): 7493–7505, doi:10.1002/2015JC010996.A variety of data based on hydrographic measurements, satellite observations, reanalysis databases, and meteorological observations are used to explore the interannual variability and factors governing the deep water formation in the northern Red Sea. Historical and recent hydrographic data consistently indicate that the ventilation of the near-bottom layer in the Red Sea is a robust feature of the thermohaline circulation. Dense water capable to reach the bottom layers of the Red Sea can be regularly produced mostly inside the Gulfs of Aqaba and Suez. Occasionally, during colder than usual winters, deep water formation may also take place over coastal areas in the northernmost end of the open Red Sea just outside the Gulfs of Aqaba and Suez. However, the origin as well as the amount of deep waters exhibit considerable interannual variability depending not only on atmospheric forcing but also on the water circulation over the northern Red Sea. Analysis of several recent winters shows that the strength of the cyclonic gyre prevailing in the northernmost part of the basin can effectively influence the sea surface temperature (SST) and intensify or moderate the winter surface cooling. Upwelling associated with periods of persistent gyre circulation lowers the SST over the northernmost part of the Red Sea and can produce colder than normal winter SST even without extreme heat loss by the sea surface. In addition, the occasional persistence of the cyclonic gyre feeds the surface layers of the northern Red Sea with nutrients, considerably increasing the phytoplankton biomass.Saudi ARAMCO Marine Environmental Centre of King Abdullah University of Science and Technology (KAUST); Saudi Aramco Oil Co.2016-05-1

A Topic Modeling Guided Approach for Semantic Knowledge Discovery in e-Commerce

The task of mining large unstructured text archives, extracting useful patterns and then organizing them into a knowledgebase has attained a great attention due to its vast array of immediate applications in business. Businesses thus demand new and efficient algorithms for leveraging potentially useful patterns from heterogeneous data sources that produce huge volumes of unstructured data. Due to the ability to bring out hidden themes from large text repositories, topic modeling algorithms attained significant attention in the recent past. This paper proposes an efficient and scalable method which is guided by topic modeling for extracting concepts and relationships from e-commerce product descriptions and organizing them into knowledgebase. Semantic graphs can be generated from such a knowledgebase on which meaning aware product discovery experience can be built for potential buyers. Extensive experiments using proposed unsupervised algorithms with e-commerce product descriptions collected from open web shows that our proposed method outperforms some of the existing methods of leveraging concepts and relationships so that efficient knowledgebase construction is possible

An adaptive rough fuzzy single pass algorithm for clustering large data sets

Cluster analysis has been widely applied in many areas such as data mining, geographical data processing, medicine, classification of statistical findings in social studies and so on. Most ofthese domains deal with massive collections of data. Hence the methods to handle them must be efficient both in terms of the number of data set scans and memory usage

A rough fuzzy approach to web usage categorization

This paper introduces a novel clustering scheme employing a combination of rough set theory and fuzzy set theory to generate meaningful abstractions from web access logs. Our experimental results show that the proposed scheme is capable of capturing the semantics involved in web access logs at an acceptable computational expense

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense

Multiclass Core Vector Machine

Even though several techniques have been proposed in the literature for achieving multiclass classification using Support Vector Machine(SVM), the scalability aspect of these approaches to handle large data sets still needs much of exploration. Core Vector Machine(CVM) is a technique for scaling up a two class SVM to handle large data sets. In this paper we propose a Multiclass Core Vector Machine(MCVM). Here we formulate the multiclass SVM problem as a Quadratic Programming(QP) problem defining an SVM with vector valued output. This QP problem is then solved using the CVM technique to achieve scalability to handle large data sets. Experiments done with several large synthetic and real world data sets show that the proposed MCVM technique gives good generalization performance as that of SVM at a much lesser computational expense. Further, it is observed that MCVM scales well with the size of the data set. 1

Learning from Bees: An Approach for Influence Maximization on Viral Campaigns.

Maximisation of influence propagation is a key ingredient to any viral marketing or socio-political campaigns. However, it is an NP-hard problem, and various approximate algorithms have been suggested to address the issue, though not largely successful. In this paper, we propose a bio-inspired approach to select the initial set of nodes which is significant in rapid convergence towards a sub-optimal solution in minimal runtime. The performance of the algorithm is evaluated using the re-tweet network of the hashtag #KissofLove on Twitter associated with the non-violent protest against the moral policing spread to many parts of India. Comparison with existing centrality based node ranking process the proposed method significant improvement on influence propagation. The proposed algorithm is one of the hardly few bio-inspired algorithms in network theory. We also report the results of the exploratory analysis of the network kiss of love campaign