Low Resource Summarization Using Pre-Trained Language Models

Our approach involves a fusion of deformable style transfer (DST), an optimization-centric technique that harmonizes the texture and geometry of a given content image to closely align with a chosen style image. This methodology is coupled with diffusion model inpainting, which is applied to content images originating from Calliar: an online dataset featuring handwritten Arabic calligraphy. Diverging from previous generative art methodologies, our approach presents aesthetically pleasing results, all the while preserving the integrity and structure of the Arabic script.

Detection and Analysis of Mental Health Illness using social media

Recently social media has been a widely used network that connects people around the world. Not only this but people sharing their life events, thoughts through posts, status updates all gather up as a big data resource. This resource is helpful in conducting various researches, analyses including big data and machine learning. In this study, we analyzed six mental health issues using Reddit’s data. The data obtained summarizes; Depression, Anxiety, Bipolar, Bipolar Disorder, Schizophrenia, Autism and Mental Health which is a general class which discusses mental health. Experimentation is done using various deep learning and NLP techniques applied for classification such as Convolutional Neural Network, Long-short term memory network, Gated Recurrent Unit, Bi- Long-short term memory network and Bi-Gated Recurrent Unit. In addition to these traditional techniques, pre-trained BERT model and …

Personality Detection Using Deep Learning Techniques

This research proposes a machine learning model and a deep learning model to predict the personality of an individual based on Myers–Briggs Type Indicator (MBTI) personality model. The proposed machine learning models (SVM, LR, MLP and XGBoost) were trained on MBTI and MBTI500 datasets with imbalanced and balanced instances (using SMOTE). The proposed deep learning model was trained using CNN with GloVe word embeddings. SVM model achieved the highest accuracy of 96.81% for machine learning model on MBTI500 dataset with SMOTE. However, CNN exhibited the highest accuracy of 99.54% on MBTI dataset which supersedes the existing models

Towards Creation of Linguistic Resources for Bilingual Sentiment Analysis of Twitter Data

This paper presents an approach towards bi-lingual sentiment analysis of tweets. Social networks being most advanced and popular communication medium can help in designing better government and business strategies. There are a number of studies reported that use data from social networks; however, most of them are based on English language. In this research, we […]

Semantic search of Holy Quran using English Wordnet

This paper presents a framework to perform concept- and keyword-based English search of the Holy Quran. The concept-based search is challenging as it should be able to handle the abstract queries which often do not occur verbatim in text. In order to implement it, a Qur’anic English WordNet is implemented, which is a WordNet-derived database […]

Predicting Trust Using Reputation Features

Recommender systems are increasingly being used in e-commerce websites for solving the problem of finding right kind of information. Collaborative filtering is considered as most promising method for recommendation because it recommends items based on common interests of users. Different elements like trust and emotion are being considered in research for improving recommendation accuracy.In trust […]

Design and implementation of a framework to aggregate feedbacks about consumer products from social media

With the growth in business’s competitive environment, organizations are obliged in tracking consumer’s data so that they can gain success in their business’s aspects. With the advancement in technology of 21st century, a new phase of business competition has started.  This phase is to use online data to get more information about the market trends […]