Parallel tensor factorization for relational learning

Link prediction is a statistical relational learning problem that has a variety of applications in recommender systems, expert systems, and knowledge bases. Numerous approaches have already been devised to solve the problem. Tensor factorization is one of the ways to solve the link prediction problem. Many tensor factorization techniques have been devised in the last few decades, including Tucker, CANDECOMP/PARAFAC, and DEDICOM. RESCAL is one of the famous tensor factorization technique that can solve large scale problems with relatively less time and space complexity. The time complexity of RESCAL can further be reduced by making it parallel. This variant can also be applied to large scale datasets. This article focuses on devising a parallel version for RESCAL. A decent decrease in execution time has been observed in the execution of parallel RESCAL

COVID-19 Patient Count Prediction Using LSTM

IEEE In December 2019, a pandemic named COVID-19 broke out in Wuhan, China, and in a few weeks, it spread to more than 200 countries worldwide. Every country infected with the disease started taking necessary measures to stop the spread and provide the best possible medical facilities to infected patients and take precautionary measures to control the spread. As the infection spread was exponential, there arose a need to model infection spread patterns to estimate the patient volume computationally. Such patients\u27 estimation is the key to the necessary actions that local governments may take to counter the spread, control hospital load, and resource allocations. This article has used long short-term memory (LSTM) to predict the volume of COVID-19 patients in Pakistan. LSTM is a particular type of recurrent neural network (RNN) used for classification, prediction, and regression tasks. We have trained the RNN model on Covid-19 data (March 2020 to May 2020) of Pakistan and predict the Covid-19 Percentage of Positive Patients for June 2020. Finally, we have calculated the mean absolute percentage error (MAPE) to find the model\u27s prediction effectiveness on different LSTM units, batch size, and epochs. Predicted patients are also compared with a prediction model for the same duration, and results revealed that the predicted patients\u27 count of the proposed model is much closer to the actual patient count

Quran Wordnet: A Framework for Semantic and Sentiment Search

Quran is a source of guidance with diverse and complete knowledge for mankind. Performing a subjective search in a Quran knowledge base is a non-trivial task. This article focuses on developing Quran WordNet and searching techniques that can provide appropriate results from the Quran knowledge base. Existing search engine techniques focus on keyword-based searching along with English WordNet, which led to often irrelevant results. In this framework, a synonym-based search is introduced to perform sentiment and semantic-based searches for retrieving the most relevant translation of verses. Both sentiment and semantic-based approaches improved the relevant retrieval of results and calculated the sentiment and polarity of the user query. A Quran WordNet will also be a baseline for developing vocabulary and separate ontology. Promising results are reported using Quran WordNet and Part of Speech Tagger for sentiment analysis and concept-based retrieval