Sentiment Analysis System for Mapping Hate Speech Against Women in Social Media using GIS System

This study aims to map hate speech against women in the Middle East using a Geographic Information System GIS and sentiment analysis with the goal of identifying patterns The hate speech terms that were utilized in the research were gathered from more than 3600 women in the study region according to the dat

Degree of Availability and Utilization of Information Technology by Jordanian School Principals

No doubt that the most striking features of this century is the technological revolution: Computers, communications and other electronic mediums are main components for any developing program in the educational field. Therefore, the Ministry of Education in Jordan adapted an educational reform program aimed at utilizing information technology in schools. The program was called “Educational reform for Knowledge Economy” ( ERFKE). School’s administration was one of (ERFKE) domains. For the importance of Information Technology, the Ministry Of Education did its best to broaden the use of computers in all Jordanian schools ( Al Heleh, 2001) as part of reaching its goal of an “electronic school.” Using technology by principals is one way of developing and enhancing their leadership performance by saving time and effort. In addition, the revolution in using communication networks and new information systems, which, of course, require the use of computers and advanced technology, may determine the competitive advantage of any country (Tesurey, 2006), and helps in closing the divide between developed and developing countries in what is called “digital divide.

Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis

The coronavirus (COVID-19) pandemic has caused severe adverse effects on the human life and the global economy affecting all communities and individuals due to its rapid spreading, increase in the number of affected cases and creating severe health issues and death cases worldwide. Since no particular treatment has been acknowledged so far for this disease, prompt detection of COVID-19 is essential to control and halt its chain. In this paper, we introduce an intelligent fuzzy inference system for the primary diagnosis of COVID-19. The system infers the likelihood level of COVID-19 infection based on the symptoms that appear on the patient. This proposed inference system can assist physicians in identifying the disease and help individuals to perform self-diagnosis on their own cases

A Comparative Study of Drug Affinities Determined by Thermofluor and Kinetic Analysis

Determining the binding affinity and potency in vitro is one of the significant steps that can give a clue for a new candidate drug during the drug discovery process. Thermofluor is a method used in measuring binding affinity (Kd) of protein-ligands interaction through determining the change in thermal denaturation temperature of protein using real time PCR (RT-PCR). Kinetic analysis assay is used to screen a library of compounds to calculate their potencies (IC50) and inhibition constants (Ki) and it can be performed by spectrophotometer technique. In this study, we used bovine carbonic anhydrase II (BCA II) enzyme, and four of its inhibitors as a model to compare drug affinities, which were determined either by Fluorescence Thermal Shift Assay (FTSA) using Sypro Orange dye or kinetic assay using 4-Nitrophenyl acetate as a substrate to measure the nonphysiologically esterase activity of CA. The inhibitors studied were Methazolamide, Brinzolamide, Dorzolamide HCl and Mafenide HCl. The Kd values were determined to be 5.4±0.085 μM, 1.2±0.44 μM, 2.08±0.63 μM, and IC50 values were 0.148±0.024 μM 0.129±0.015 μM 0.092±0.01 μM 1.715±0.16 μM whereas the Ki values were 4±0.55 nM, 3.5±0.5 nM, 2.5±0.5 nM and 46.5±6.5 nM for Methazolamide, Brinzolamide, Dorzolamide HCl and Mafenide HCl, respectively. The potencies (IC50) of the inhibitors were10-50 fold lower than that of the Kd values. In addition, Kd values were higher compared to Ki values. Therefore, kinetic analysis is a more sensitive technique and requires a lower amount of the enzyme to measure drug affinity than FTSA

A data mining approach to ontology learning for automatic content-related question-answering in MOOCs.

The advent of Massive Open Online Courses (MOOCs) allows massive volume of registrants to enrol in these MOOCs. This research aims to offer MOOCs registrants with automatic content related feedback to fulfil their cognitive needs. A framework is proposed which consists of three modules which are the subject ontology learning module, the short text classification module, and the question answering module. Unlike previous research, to identify relevant concepts for ontology learning a regular expression parser approach is used. Also, the relevant concepts are extracted from unstructured documents. To build the concept hierarchy, a frequent pattern mining approach is used which is guided by a heuristic function to ensure that sibling concepts are at the same level in the hierarchy. As this process does not require specific lexical or syntactic information, it can be applied to any subject. To validate the approach, the resulting ontology is used in a question-answering system which analyses students' content-related questions and generates answers for them. Textbook end of chapter questions/answers are used to validate the question-answering system. The resulting ontology is compared vs. the use of Text2Onto for the question-answering system, and it achieved favourable results. Finally, different indexing approaches based on a subject's ontology are investigated when classifying short text in MOOCs forum discussion data; the investigated indexing approaches are: unigram-based, concept-based and hierarchical concept indexing. The experimental results show that the ontology-based feature indexing approaches outperform the unigram-based indexing approach. Experiments are done in binary classification and multiple labels classification settings . The results are consistent and show that hierarchical concept indexing outperforms both concept-based and unigram-based indexing. The BAGGING and random forests classifiers achieved the best result among the tested classifiers