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Information Retrieval Implementing And Evaluating Search Engines

Organizational learning (OL) enables organizations to transform individual knowledge into organizational knowledge. Organizations struggle to implement practical approaches due to the lack of concr...

https://journals.sagepub.com/doi/pdf/10.1177/2158244018794224

Approaches for organizational learning: a literature review

Community Question Answering is one of the valuable information resources which provide users with a platform to share their knowledge. Finding potential experts in CQA is beneficial to several problems like low participation rate of the users, long waiting time to receive answers and to the low quality of answers. Many research papers focused on retrieving the expert users of CQAs. Most of them are taking expertise into consideration at the query time and ignore the temporal aspects of the expert finding problem. However, considering the evolution of personal expertise over time can improve the quality of expert finding. In many applications, it is beneficial to find the potential experts in future. The proper identification of potential experts in CQA can improve their skills and the overall user participation and engagement. Considering dynamic aspects of the expert finding problem, we introduce the new problem of Future Expert Finding in this paper.Here, given the expertise evidence in current time, we aim to predict the best ranking of experts in future. We proposed a learning framework to predict such ranking on StackOverflow which is currently one of the most successful CQAs. We examine the impact of various features to predict the probability of becoming an expert user in future time. Specifically, we consider four feature groups; namely, topic similarity, emerging topics, user behavior and topic transition . The experimental results indicate the efficiency of the proposed models in comparison with several baseline models. Our experiments show that the performance of our proposed models can improve the MAP measure up to 39.7% in comparison with our best baseline method. Moreover, we found that among all of these feature groups, user behaviors have the most influence in the estimation of future expertise probability.

On dynamicity of expert finding in community question answering

The intention of this study is to carry out a numerical investigation of time-dependent magneto-hydro-dynamics (MHD) Eyring-Powell liquid by taking a moving/static wedge with Darcy-Forchheimer relation. Thermal radiation was taken into account for upcoming solar radiation, and the idea of bioconvection is also considered for regulating the unsystematic exertion of floating nanoparticles. The novel idea of this work was to stabilized nanoparticles through the bioconvection phenomena. Brownian motion and thermophoresis effects are combined in the most current revision of the nanofluid model. Fluid viscosity and thermal conductivity that depend on temperature are predominant. The extremely nonlinear system of equations comprising partial differential equations (PDEs) with the boundary conditions are converted into ordinary differential equations (ODEs) through an appropriate suitable approach. The reformed equations are then operated numerically with the use of the well-known Lobatto IIIa formula. The variations of different variables on velocity, concentration, temperature and motile microorganism graphs are discussed as well as force friction, the Nusselt, Sherwood, and the motile density organism numbers. It is observed that Forchheimer number Fr decline the velocity field in the case of static and moving wedge. Furthermore, the motile density profiles are deprecated by higher values of the bio convective Lewis number and Peclet number. Current results have been related to the literature indicated aforementioned and are found to be great achievement. 

/pdf/numerical-computation-for-gyrotactic-microorganisms-in-mhd-20qe5pe0.pdf

Numerical Computation for Gyrotactic Microorganisms in MHD Radiative Eyring–Powell Nanomaterial Flow by a Static/Moving Wedge with Darcy–Forchheimer Relation

The rapid development recently of Community Question Answering (CQA) satisfies users quest for professional and personal knowledge about anything. In CQA, one central issue is to find users with expertise and willingness to answer the given questions. Expert finding in CQA often exhibits very different challenges compared to traditional methods. Sparse data and new features violate fundamental assumptions of traditional recommendation systems. This paper focuses on reviewing and categorizing the current progress on expert finding in CQA. We classify all the existing solutions into four different categories: matrix factorization based models (MF-based models), gradient boosting tree based models (GBT-based models), deep learning based models (DL-based models) and ranking based models (R-based models). We find that MF-based models outperform other categories of models in the field of expert finding in CQA. Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boosting the performance. Further, we compare the performance of different models on different types of matching tasks, including text vs. text, graph vs. text, audio vs. text and video vs. text. The results can help the model selection of expert finding in practice. Finally, we explore some potential future issues in expert finding research in CQA.

Expert Finding in Community Question Answering: A Review

Effective knowledge management is a key factor in the development and success of any organisation. Many different methods have been devised to address this need. Applying these methods to identify the experts within an organisation has attracted a lot of attention. We look at one such problem that arises within universities on a daily basis but has attracted little attention in the literature, namely the problem of a searcher who is trying to identify a potential PhD supervisor, or, from the perspective of the university's research office, to allocate a PhD application to a suitable supervisor. We reduce this problem to identifying a ranked list of experts for a given query (representing a research area).

We report on experiments to find experts in a university domain using two different methods to extract a ranked list of candidates: a database-driven method and a data-driven method. The first one is based on a fixed list of experts (e.g. all members of academic staff) while the second method is based on automatic Named-Entity Recognition (NER). We use a graded weighting based on proximity between query and candidate name to rank the list of candidates. As a baseline, we use a system that ranks candidates simply based on frequency of occurrence within the top documents.

Finding the Right Supervisor: Expert-Finding in a University Domain

People can make use of credit card for online transactions as it provides efficient and easy-to-use facility. With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. In this research study, the main aim is to detect such frauds which include the accessibility of public data, high-class imbalance data and fraud nature can be changed and the false alarm is in high rates. The relevant literature presents a number of machines learning based approaches for credit card detection. Such as Extreme Learning Method, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and XG Boost. But due to low accuracy, there is still need to apply state of the art deep learning algorithms to reduce the fraud losses. The main focus has been to apply the recent development of deep learning algorithms for this purpose. Comparative analysis of both machine learning and deep learning algorithms was performed to find efficient outcomes. The detail empirical analysis is carried out using European card benchmark dataset for fraud detection. Machine learning algorithm is first applied on the data set which showed improvement in the accuracy of detection of the frauds to some extent. Later, three architectures based on convolutional neural network are applied to improve fraud detection performance. Further addition of layers further increased the accuracy of detection. A comprehensive empirical analysis has been carried out by applying variations in number of hidden layers, epochs and applying the latest models. The evaluation of research work shows the improved results achieved such as accuracy, f1-score, precision and AUC Curves having optimized values 99.9%,85.71%,93%,98% respectively. The purposed model outperforms over state of art machine learning and deep learning algorithms for credit card detection problems. In addition, we have performed experiments by balancing the data and applying deep learning algorithms to minimize the false negative rate. The proposed approaches can be implemented effectively for the real-world detection of credit card frauds.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09755930.pdf

Credit Card Fraud Detection Using State-of-the-art Machine Learning and Deep Learning Algorithms

cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding model

In this paper, we analyzed the mass transfer model with chemical reactions during the absorption of carbon dioxide (CO2) into phenyl glycidyl ether (PGE) solution. The mathematical model of the phenomenon is governed by a coupled nonlinear differential equation that corresponds to the reaction kinetics and diffusion. The system of differential equations is subjected to Dirichlet boundary conditions and a mixed set of Neumann and Dirichlet boundary conditions. Further, to calculate the concentration of CO2, PGE, and the flux in terms of reaction rate constants, we adopt the supervised learning strategy of a nonlinear autoregressive exogenous (NARX) neural network model with two activation functions (Log-sigmoid and Hyperbolic tangent). The reference data set for the possible outcomes of different scenarios based on variations in normalized parameters (α1, α2, β1, β2, k) are obtained using the MATLAB solver “pdex4”. The dataset is further interpreted by the Levenberg–Marquardt (LM) backpropagation algorithm for validation, testing, and training. The results obtained by the NARX-LM algorithm are compared with the Adomian decomposition method and residual method. The rapid convergence of solutions, smooth implementation, computational complexity, absolute errors, and statistics of the mean square error further validate the design scheme’s worth and efficiency.

Theoretical Analysis on Absorption of Carbon Dioxide (CO2) into Solutions of Phenyl Glycidyl Ether (PGE) Using Nonlinear Autoregressive Exogenous Neural Networks

Knowledge extraction from unstructured data is a challenging research problem in research domain of Natural Language Processing (NLP). It requires complex NLP tasks like entity extraction and Information Extraction (IE), but one of the most challenging tasks is to extract all the required entities of data in the form of structured format so that data analysis can be applied. Our focus is to explain how the data is extracted in the form of datasets or conventional database so that further text and data analysis can be carried out. This paper presents a framework for Hadith data extraction from the Hadith authentic sources. Hadith is the collection of sayings of Holy Prophet Muhammad, who is the last holy prophet according to Islamic teachings. This paper discusses the preparation of the dataset repository and highlights issues in the relevant research domain. The research problem and their solutions of data extraction, pre-processing and data analysis are elaborated. The results have been evaluated using the standard performance evaluation measures. The dataset is available in multiple languages, multiple formats and is available free of cost for research purposes.

/pdf/a-multilingual-datasets-repository-of-the-hadith-content-3jzfotx4jb.pdf

Fawaz Alarfaj

Papers

Finding the Right Supervisor: Expert-Finding in a University Domain

Credit Card Fraud Detection Using State-of-the-art Machine Learning and Deep Learning Algorithms

cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding model

Theoretical Analysis on Absorption of Carbon Dioxide (CO2) into Solutions of Phenyl Glycidyl Ether (PGE) Using Nonlinear Autoregressive Exogenous Neural Networks

A Multilingual Datasets Repository of the Hadith Content