Advances in Science, Technology and Engineering Systems Journal 

About: Advances in Science, Technology and Engineering Systems Journal is an academic journal published by Advances in Science, Technology and Engineering Systems Journal (ASTESJ). The journal publishes majorly in the area(s): Computer science & Engineering. It has an ISSN identifier of 2415-6698. It is also open access. Over the lifetime, 2046 publications have been published receiving 5871 citations. The journal is also known as: ASTES journal & A imonthly peer-review journal.

A survey of text mining in social media: Facebook and Twitter perspectives

Abstract: Text mining has become one of the trendy fields that has been incorporated in several research fields such as computational linguistics, Information Retrieval (IR) and data mining Natural Language Processing (NLP) techniques were used to extract knowledge from the textual text that is written by human beings Text mining reads an unstructured form of data to provide meaningful information patterns in a shortest time period Social networking sites are a great source of communication as most of the people in today’s world use these sites in their daily lives to keep connected to each other It becomes a common practice to not write a sentence with correct grammar and spelling This practice may lead to different kinds of ambiguities like lexical, syntactic, and semantic and due to this type of unclear data, it is hard to find out the actual data order Accordingly, we are conducting an investigation with the aim of looking for different text mining methods to get various textual orders on social media websites This survey aims to describe how studies in social media have used text analytics and text mining techniques for the purpose of identifying the key themes in the data This survey focused on analyzing the text mining studies related to Facebook and Twitter; the two dominant social media in the world Results of this survey can serve as the baselines for future text mining research

A multilingual system for cyberbullying detection: Arabic content detection using machine learning

Abstract: A R T I C L E I N F O A B S T R A C T Article history: Received: 12 November, 2017 Accepted: 03 December, 2017 Online: 23 December, 2017 With the abundance of Internet and electronic devices bullying has moved its place from schools and backyards into cyberspace; to be now known as Cyberbullying. Cyberbullying is affecting a lot of children around the world, especially Arab countries. Thus, concerns from cyberbullying are rising. A lot of research is ongoing with the purpose of diminishing cyberbullying. The current research efforts are focused around detection and mitigation of cyberbullying. Previously, researches dealt with the psychological effects of cyberbullying on the victim and the predator. A lot of research work proposed solutions for detecting cyberbullying in English language and a few more languages, but none till now covered cyberbullying in Arabic language. Several techniques contribute in cyberbullying detection, mainly Machine Learning (ML) and Natural Language Processing (NLP). This journal extends on a previous paper to elaborate on a solution for detecting and stopping cyberbullying. It first presents a thorough survey for the previous work done in cyberbullying detection. Then a solution that focuses on detecting cyberbullying in Arabic content is displayed and assessed.

Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images

Abstract: Analysis and classification of lung diseases using X-ray images is a primary step in the procedure of pneumonia diagnosis, especially in a critical period as pandemic of COVID-19 that is type of pneumonia Therefore, an automatic method with high accuracy of classification is needed to perform classification of lung diseases due to the increasing number of cases Convolutional Neural Networks (CNN) based classification has gained a big popularity over the last few years because of its speed and level of accuracy on the image’s classification tasks Through this article, we propose an implementation a CNN-based classification models using transfer learning technique to perform pneumonia detection and compare the results in order to detect the best model for the task according to certain parameters As this has become a fast expanding field, there are several models but we will focus on the best outperforming algorithms according to their architecture, length and type of layers and evaluation parameters for the classification tasks Firstly, we review the existing conventional methods and deep learning architectures used for segmentation in general Next, we perform a deep performance and analysis based on accuracy and loss function of implemented models A critical analysis of the results is made to highlight all important issues to improve © 2020 ASTES Publishers All rights reserved

Medical imbalanced data classification

Abstract: A R T I C L E I N F O A B S T R A C T Article history: Received: 19 March, 2017 Accepted: 04 April, 2017 Online: 15 April, 2017 In general, the imbalanced dataset is a problem often found in health applications. In medical data classification, we often face the imbalanced number of data samples where at least one of the classes constitutes only a very small minority of the data. In the same time, it represent a difficult problem in most of machine learning algorithms. There have been many works dealing with classification of imbalanced dataset. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS) algorithm that penalizes errors of different samples with different weights and some rules of thumb to determine those weights. After the balancing phase, we apply the different techniques (Support Vector Machine [SVM], KNearest Neighbor [K-NN] and Multilayer perceptron [MLP]) for the balanced datasets. We have also compared the obtained results before and after balancing method. We have obtained best results compared to literature with a classification accuracy of 100%.