Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation. Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annotating the text, feature engineering is performed using techniques like term frequency/inverse document frequency (TF/IDF) and Bag of words (BOW). The relevant features are supplied to support vector machine (SVM) and Multinomial Naïve Bayesian (MNB) classifiers. The fine tuning of SVM is being done by taking kernel Linear, Poly and RBF. SVM showed better results than MNB by having precision of 70%, recall of 76.5%, F1 Score of 69.5% and overall Accuracy of 69.2%.
Education specialists have differed about determining the best ways to detect the
talented. Since the appearance of the mental and psychological measurement movement, some
scholars adopted intelligence ratios as a criterion to identify the talented and others went to
rely on the degree of academic achievement. Each of these two methods has its own flaws and
mistakes and a large number of talented children were victims of these two methods.
Therefore the need to use other scales for the purpose of detection of talented children
appeared because they provide valuable information which may not be obtained easily
through objective tests and these scales are derived from consecutive studies of gifted andtalented children
Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To
... Show MoreMost dinoflagellate had a resting cyst in their life cycle. This cyst was developed in unfavorable environmental condition. The conventional method for identifying dinoflagellate cyst in natural sediment requires morphological observation, isolating, germinating and cultivating the cysts. PCR is a highly sensitive method for detecting dinoflagellate cyst in the sediment. The aim of this study is to examine whether CO1 primer could detect DNA of multispecies dinoflagellate cysts in the sediment from our sampling sites. Dinoflagellate cyst DNA was extracted from 16 sediment samples. PCR method using COI primer was running. The sequencing of dinoflagellate cyst DNA was using BLAST. Results showed that there were two clades of dinoflag
... Show MoreDiabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att
... Show MoreMost vegetation’s are Land cover (LC) for the globe, and there is an increased attention to plants since they represent an element of balance to natural ecology and maintain the natural balance of rapid changes due to systematic and random human uses, including the subject of the current study (Bassia eriophora ) Which represent an essential part of the United Nations system for land cover classification (LCCS), developed by the World Food Organization (FAO) and the world Organization for environmental program (UNEP), to observe basic environmental elements with modern techniques. Although this plant is distributed all over Iraq, we found that this plant exists primarily in the middle
... Show MoreFrance attached great importance to public funds Article 15 of the Declaration on Human and Citizen's Rights (DDHC) stipulates that society has the right to hold any public official accountable for his or her administration. For that reason, the French legislature has established a body specialized in the control of public funds, which it calls the Court of Accounting. It has established the Court of Budget and Financial Discipline to assist it. The courts are run by judges who cannot be dismissed. The courts are also given jurisdiction, administrative jurisdiction and the role and purpose for which they have been assigned
COVID-19 affected the entire world due to the unavailability of the vaccine. The social distancing was a contributing factor that gave rise to the usage of Online Social Networks. It has been seen that people share the information that comes to them without verifying its source . One of the common forms of information that is disseminated that have a radical purpose is propaganda. Propaganda is organized and conscious method of molding conclusions and impacting an individual's contemplations to accomplish the ideal aim of proselytizer. For this paper, different propagandistic tweets were shared in the COVID-19 Era. Data regarding COVID-19 propaganda was extracted from Twitter. Labelling of data was performed manually using diffe
... Show MoreIn this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A
... Show MoreInternal Audit is one of the most important backers of corporate governance, the researcher expanded his interest in this subject to examine the efficiency of Internal Auditors at the Arab Bank and its branches in Jordan to achieve Accountability which enhances the Corporate Governance and to identify the effect of the International Internal Audit Standards in strengthening the role of Internal Auditors in Accountability, and the effect of Attribute and Performance Standards in Accountability. The researcher applied descriptive analysis method to define the role of Internal Audit in the Arab Bank in achieving one of the basic principles of Corporate Governance assimilated in Accountability. The researcher’s sources include
... Show More