APPLYING HUNGER GAME SEARCH (HGS) FOR SELECTING SIGNIFICANT BLOOD INDICATORS FOR EARLY PREDICTION OF ICU COVID-19 SEVERITY

Millions of people around the world have been affected and some have died during the global pandemic Corona (COVID-19). This pandemic has created a global threat to people's lives and medical systems. The constraints of hospital resources and the pressures on healthcare workers during this period are among the reasons for wrong decisions and medical deterioration. Anticipating severe patients is an urgent matter of resource consumption by prioritizing patients at high risk to save their lives. This paper introduces an early prognostic model to predict the severity of patients and detect the most significant features based on clinical blood data. The proposed model predicts ICU severity within the first 2 hours of hospital admission, seeks to assist clinicians in decision-making and facilitates efficient use of hospital resources. The Hunger Game Search (HGS) meta-heuristic algorithm and the SVM are hybridized for building the proposed prediction model. Furthermore, they have been used for selecting the most informative features from the blood test data. Experiments have shown that using HGS for selecting the features with the SVM classifier achieved excellent results compared with the other four meta-heuristic algorithms. The model using the features selected by the HGS algorithm accomplished the topmost results, 98.6% and 96.5% for the best and mean accuracy, respectively, compared with using all features and features selected by other popular optimization algorithms

Enhancing prediction of user stance for social networks rumors

The spread of social media has led to a massive change in the way information is dispersed. It provides organizations and individuals wider opportunities of collaboration. But it also causes an emergence of malicious users and attention seekers to spread rumors and fake news. Understanding user stances in rumor posts is very important to identify the veracity of the underlying content as news becomes viral in a few seconds which can lead to mass panic and confusion. In this paper, different machine learning techniques were utilized to enhance the user stance prediction through a conversation thread towards a given rumor on Twitter platform. We utilized both conversation thread features as well as features related to users who participated in this conversation, in order to predict the users’ stances, in terms of supporting, denying, querying, or commenting (SDQC), towards the source tweet. Furthermore, different datasets for the stance-prediction task were explored to handle the data imbalance problem and data augmentation for minority classes was applied to enhance the results. The proposed framework outperforms the state-of-the-art results with macro F1-score of 0.7233

Review Aspects of Using Social Annotation for Enhancing Search Engine Performance

Recently, search engines have improved to be more efficient in supporting user’s search process. Although they enhanced their capabilities to support user, still searcher spend long times in navigation. This is due to the different nature of users, where users have changeable interest and different culture, domain, and expressions. So, for improving search and make it closed to user’s expectation; user’s preferences have to be discovered. Nowadays, Information Retrieval researchers concern with Personalized Search which provides user’s preferences discovering. In this contribution, many efforts put path extracting user’s preferences through follow their behaviors, and action. Recently, researches focus on social annotations as additional metadata that may be used for extracting user’s preferences and interests.This paper reviews different aspects of using social annotation (as additional metadata) for enhancing search engines capabilities. Moreover, it especially focuses on personalized search which became today part of web 3.0 improvements. So, it proposes to categorize efforts in this field into two parts. The first concerns with improving personalized search by extracting user’s interests, and the second is for supporting personalized search by linking search phases to standard model

Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning

In recent years, people have tended to use online social platforms, such as Twitter and Facebook, to communicate with families and friends, read the latest news, and discuss social issues. As a result, spam content can easily spread across them. Spam detection is considered one of the important tasks in text analysis. Previous spam detection research focused on English content, with less attention to other languages, such as Arabic, where labeled data are often hard to obtain. In this paper, an integrated framework for Twitter spam detection is proposed to overcome this problem. This framework integrates data augmentation, natural language processing, and supervised machine learning algorithms to overcome the problems of detection of Arabic spam on the Twitter platform. The word embedding technique is employed to augment the data using pre-trained word embedding vectors. Different machine learning techniques were applied, such as SVM, Naive Bayes, and Logistic Regression for spam detection. To prove the effectiveness of this model, a real-life data set for Arabic tweets have been collected and labeled. The results show that an overall improvement in the use of data augmentation increased the macro F1 score from 58% to 89%, with an overall accuracy of 92%, which outperform the current state of the art

Semantic Extraction of Arabic Multiword Expressions

A considerable interest has been given to Multiword Expression (MWEs) identification and treatment. The identification of MWEs affects the quality of results of different tasks heavily used in natural language processing (NLP) such as parsing and generation. Different approaches for MWEs identification have been applied such as statistical methods which employed as an inexpensive and language independent way of finding co-occurrence patterns. Another approach relays on linguistic methods for identification, which employ information such as part of speech (POS) filters and lexical alignment between languages is also used and produced more targeted candidate lists. This paper presents a framework for extracting Arabic MWEs (nominal or verbal MWEs) for bi-gram using hybrid approach. The proposed approach starts with applying statistical method and then utilizes linguistic rules in order to enhance the results by extracting only patterns that match relevant language rule. The proposed hybrid approach outperforms other traditional approaches

Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning

In recent years, people have tended to use online social platforms, such as Twitter and Facebook, to communicate with families and friends, read the latest news, and discuss social issues. As a result, spam content can easily spread across them. Spam detection is considered one of the important tasks in text analysis. Previous spam detection research focused on English content, with less attention to other languages, such as Arabic, where labeled data are often hard to obtain. In this paper, an integrated framework for Twitter spam detection is proposed to overcome this problem. This framework integrates data augmentation, natural language processing, and supervised machine learning algorithms to overcome the problems of detection of Arabic spam on the Twitter platform. The word embedding technique is employed to augment the data using pre-trained word embedding vectors. Different machine learning techniques were applied, such as SVM, Naive Bayes, and Logistic Regression for spam detection. To prove the effectiveness of this model, a real-life data set for Arabic tweets have been collected and labeled. The results show that an overall improvement in the use of data augmentation increased the macro F1 score from 58% to 89%, with an overall accuracy of 92%, which outperform the current state of the art

Automated radiology report generation using conditioned transformers

Radiology report writing in hospitals is a time-consuming task that also requires experience from the involved radiologists. This paper proposes a deep learning model to automatically generate radiology reports given a chest x-ray image from the public IU-Xray dataset. Our work consists of three stages: (1) Fine-tune a pre-trained Chexnet to predict specific tags from the image. (2) Calculate weighted semantic features from the predicted tag's pre-trained embeddings. (3) Condition a pre-trained GPT2 model on the visual and semantic features to generate the full medical reports. We analyze the generated reports using word-overlap metrics while also adding new meaningful semantic-based similarity metrics. The proposed model, which we call CDGPT2, surpassed most non-hierarchical recurrent models and transformer-based models in quantitative metrics while being considerably faster to train. Moreover, the model does not require a specific vocabulary and can be trained on different datasets without changing the architecture. Furthermore, we include a qualitative analysis from a radiologist from Egypt's national institute of cancer which showed that 61.6% of the generated reports on the test set were expertly written, and only 10% contained false information. We represent the first work to condition a pre-trained transformer on visual and semantic features to generate medical reports and to include semantic similarity metrics in the quantitative analysis of the generated reports