Search results for: fake health news classification model

Authors: Ariyaratna Athugala

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Mass media play a significant role in democratic societies. The Political Economy of the Mass Media postulates that elite media interlock with other institutional sectors in ownership, and editorial management effectively circumscribing their ability to remain analytically detached from other dominant institutional sectors. The production of meaning in news discourse is not valued neutral, but part of a larger process of presenting a hegemonic understanding of the world to audiences as the “production of consent.” The horse race model argues that “the raw material of news” pressures six bands that ultimately shape the news audiences receive. The six bands are as follows: Crown piece (raw material), brow band (professionalism), throat latch (gatekeeper), a bit (construction), nose band (perception), and reins (ownership). dThe horse race model suggests that media ultimately serve to “manufacture consent” for a range of self-serving elite opinion options. These bands determine what events are deemed newsworthy, how they are covered, where they are placed within the media and how much coverage they receive. Highly descriptive in nature, the horse race model of communication is concerned with the question of whether media can be seen to play a hegemonic role in the society oriented towards legitimization, hegemonic pressures and ideological construction.

Keywords: hegemonic pressures, horse race, ideological construction, six bands

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Authors: Reza Mohammadi, Mahmod R. Sahebi, Mehrnoosh Omati, Milad Vahidi

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Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.

Keywords: Bag of Visual Words (BOVW), classification, feature extraction, land cover management, Polarimetric Synthetic Aperture Radar (PolSAR)

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Authors: Zhaoxin Luo, Michael Zhu

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In natural languages, there are always complex semantic hierarchies. Obtaining the feature representation based on these complex semantic hierarchies becomes the key to the success of the model. Several RNN models have recently been proposed to use latent indicators to obtain the hierarchical structure of documents. However, the model that only uses a single-layer latent indicator cannot achieve the true hierarchical structure of the language, especially a complex language like Chinese. In this paper, we propose a deep layered model that stacks arbitrarily many RNN layers equipped with latent indicators. After using EM and training it hierarchically, our model solves the computational problem of stacking RNN layers and makes it possible to stack arbitrarily many RNN layers. Our deep hierarchical model not only achieves comparable results to large pre-trained models on the Chinese short text classification problem but also achieves state of art results on the Chinese long text classification problem.

Keywords: nature language processing, recurrent neural network, hierarchical structure, document classification, Chinese

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Authors: Jonathan De Rothewelle

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A pandemic, such as that of COVID-19, can be a time of panic and stress; concerns about health supersede others such as work and leisure. With such concern comes the seeking of crucial information— information that, during a global health crisis, could mean the difference between life and death. Whether newspapers, cable news, or radio, media plays an important role in the transmission of medical information to the general public. Moreover, the news media in particular must uphold its obligation to the public to only disseminate factual, useful information. The circulation of misinformation, whether explicit or implicit, may profoundly impact global health. Using a discursive analytic framework founded in linguistics, the images and headlines of top coverage of COVID-19 from the most influential media outlets will be examined. Micro-analyses reveal what may be interpreted as evidence of sensationalism, which may be argued to a form of misinformation, and ultimately a departure from ethical media. Withdrawal from responsible reporting and publishing, expressly in times of epidemic, may cause further confusion and panic.

Keywords: public health, pandemic, public education, media

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Authors: Nurul Aini Abdul Wahab, Nor Syamim Halidin, Sayidatina Aisah Masnan, Nur Izzati Romli

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There are many applications of principle component method for reducing the large set of variables in various fields. Fisher’s Discriminant function is also a popular tool for classification. In this research, the researcher focuses on studying the performance of Principle Component-Fisher’s Discriminant function in helping to classify rice kernels to their defined classes. The data were collected on the smells or odour of the rice kernel using odour-detection sensor, Cyranose. 32 variables were captured by this electronic nose (e-nose). The objective of this research is to measure how well a combination model, between principle component and linear discriminant, to be as a classification model. Principle component method was used to reduce all 32 variables to a smaller and manageable set of components. Then, the reduced components were used to develop the Fisher’s Discriminant function. In this research, there are 4 defined classes of rice kernel which are Aromatic, Brown, Ordinary and Others. Based on the output from principle component method, the 32 variables were reduced to only 2 components. Based on the output of classification table from the discriminant analysis, 40.76% from the total observations were correctly classified into their classes by the PC-Discriminant function. Indirectly, it gives an idea that the classification model developed has committed to more than 50% of misclassifying the observations. As a conclusion, the Fisher’s Discriminant function that was built on a 2-component from PCA (2-PC-D) is not satisfying to classify the rice kernels into its defined classes.

Keywords: classification model, discriminant function, principle component analysis, variable reduction

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

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Authors: Bethany Wood

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The world pandemic led to a change in the way social media was used by audiences, with young people spending more hours on the platform due to lockdown. Reports by Ofcom have demonstrated that the internet is the second most popular platform for accessing news after television in the UK with social media and the internet ranked as the most popular platform to access news for those aged between 16-24. These statistics are unsurprising considering that at the time of writing, 98 percent of Generation Z (Gen Z) owned a smartphone and the subsequent ease and accessibility of social media. Technology is constantly developing and with this, its importance is becoming more prevalent with each generation: the Baby Boomers (1946-1964) consider it something useful whereas millennials (1981-1997) believe it a necessity for day to day living. Gen Z, otherwise known as the digital native, have grown up with this technology at their fingertips and social media is a norm. It helps form their identity, their affiliations and opens gateways for them to engage with news in a new way. It is a common misconception that Gen Z do not consume news, they are simply doing so in a different way to their predecessors. Using a sample of 800 18-20 year olds whilst utilising Generational theory, Actor Network Theory and the Social Shaping of Technology, this research provides a critical analyse regarding how Gen Z’s news consumption and engagement habits are developing along with technology to sculpture the future format of news and its distribution. From that perspective, allied with the empirical approach, it is possible to provide research orientated advice for the industry and even help to redefine traditional concepts of journalism.

Keywords: journalism, generation z, digital, social media

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Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: Kunal Shah, Swati Patel

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Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also, comparative analysis of multi label classification methods on the basis of theoretical study, and then on the basis of simulation was done on various data sets.

Keywords: binary relevance, concept drift, data stream mining, MLSC, multiple window with buffer

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Authors: Shah Nister Kabir

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This study examines the construction of the controversy surrounding the cartoons of the Prophet Muhammad, as it appeared in three New Zealand newspapers; the Otago Daily Times (ODT), the New Zealand Herald (NZH) and the Press. It discursively argues that these mainstream newspapers promote the Orientalist perception of a clash of civilizations between Islam and the West in their news frame, but, in most cases, the perceived clash was not apparent in editorials. This study also argues that the uniformity in news frame conflicts with editorials’ construction of the issue. Furthermore, while the construction of the issue in news frame and editorials in the Press remained similar, the other two newspapers—the ODT and NZH—both contradict their own news frame in their editorials and contradict the editorials appearing in the Press.

Keywords: clash of culture, discourse, Islam, media agenda, New Zealand, orientalism, the West

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Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

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Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: evolutionary computation, feature selection, classification, clustering

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: B. N. Emenyeonu

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Initially identified as sensationalism in the eras of yellow journalism and tabloidization, the inclusion of news which shocks or provokes strong emotional responses among readers, viewers, and browsers has not only remained a persistent feature of journalism but has also seemingly escalated in the current climate of digital and social media. Whether in the relentless revelation of scandals in high places, profiles on people displaced by sporadic wars or natural disasters, gruesome accounts of trucks plowing into pedestrians in a city centre, or the coverage of mourners paying tributes to victims of a mass shooting, mainstream, and digital media are often awash with tragedy, tears, and trauma. While it may aim at inspiring sympathy, outrage, or even remedial reactions, it would appear that the deluge of grief and misery in the news merely generates in the audience a feeling that borders on hearing or seeing too much to care or act. This feeling also appears to be accentuated by the dizzying diffusion of social media news and views, most of whose authenticity is not easily verifiable. Through a survey of 400 regular consumers of news and an in-depth interview of 10 news managers in selected media organizations across the Middle East, this study therefore investigates public attitude to the profusion of bad news in mainstream and digital media. Among other targets, it examines whether the profusion of bad news generates empathy fatigue among the audience and, if so, whether there is any association between biographic variables (profession, age, and gender) and an inclination to empathy fatigue. It also seeks to identify which categories of bad news and media are most likely to drag the audience into indifference. In conclusion, the study discusses the implications of the findings for mass-mediated advocacies such as campaigns against conflicts, corruption, nuclear threats, terrorism, gun violence, sexual crimes, and human trafficking, among other threats to humanity.

Keywords: digital media, empathy fatigue, media campaigns, news selection

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Authors: R. L. Ujjwal, Abhishek Jain

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In the field of computer vision, most facial variations such as identity, expression, emotions and gender have been extensively studied. Automatic age estimation has been rarely explored. With age progression of a human, the features of the face changes. This paper is providing a new comparable study of different type of algorithm to feature extraction [Hybrid features using HAAR cascade & HOG features] & classification [KNN & SVM] training dataset. By using these algorithms we are trying to find out one of the best classification algorithms. Same thing we have done on the feature selection part, we extract the feature by using HAAR cascade and HOG. This work will be done in context of age group classification model.

Keywords: computer vision, age group, face detection

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Authors: Jiahe Liu, Hongyang Yu, Feng Qian, Miao Luo

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SMPL-X was a powerful parametric human body model that included male, neutral, and female models, with significant gender differences between these three models. During the process of 3D human body reconstruction, the correct selection of standard templates was crucial for obtaining accurate results. To address this issue, we developed an efficient gender classification algorithm to automatically select the appropriate template for 3D human body reconstruction. The key to this gender classification algorithm was the precise analysis of human body features. By using the SMPL-X model, the algorithm could detect and identify gender features of the human body, thereby determining which standard template should be used. The accuracy of this algorithm made the 3D reconstruction process more accurate and reliable, as it could adjust model parameters based on individual gender differences. SMPL-X and the related gender classification algorithm have brought important advancements to the field of 3D human body reconstruction. By accurately selecting standard templates, they have improved the accuracy of reconstruction and have broad potential in various application fields. These technologies continue to drive the development of the 3D reconstruction field, providing us with more realistic and accurate human body models.

Keywords: gender classification, joint detection, SMPL-X, 3D reconstruction

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Authors: Somaiyeh Dehghan, Mohammad Reza Kheirkhahan Haghighi

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The rapid growth of domains that have data with a large number of features, while the number of samples is limited has caused difficulty in constructing strong classifiers. To reduce the dimensionality of the feature space becomes an essential step in classification task. Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers that each base learner in ensemble has subset of features. In the present paper, we introduce Random Subspace Ensemble of CMAC neural network (RSE-CMAC), each of which has training with subset of features. Then we use this model for classification task. For evaluation performance of our model, we compare it with bagging algorithm on 36 UCI datasets. The results reveal that the new model has better performance.

Keywords: classification, random subspace, ensemble, CMAC neural network

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Authors: Karim Abouelmehdi, Loubna Dali, Elmoutaoukkil Abdelmajid, Hoda Elsayed, Eladnani Fatiha, Benihssane Abderahim

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The security of cloud services is the concern of cloud service providers. In this paper, we will mention different classifications of cloud attacks referred by specialized organizations. Each agency has its classification of well-defined properties. The purpose is to present a high-level classification of current research in cloud computing security. This classification is organized around attack strategies and corresponding defenses.

Keywords: cloud computing, classification, risk, security

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Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury

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Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.

Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification

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Authors: Supaporn Wimonchailerk

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This research aims to critical analyze the feminine violent within Thai daily newspaper. This study was qualitative base; content analysis from two popular newspapers (Thairath and Dailynews) two qualitative newspapers (Thaipost and Mathichon). Purposive sampling was used to select eleven specialize news reporters to do in-depth interview. The result found that, popular newspapers, Thairath and dailynews have presented feminine violent news in their paper more than Thaipost and Mathichon the qualitative newspaper. Beside, majority of sample present the feminine violent within news under the code of ethic, The National Press Council of Thailand. Interesting, the age of feminine violent victim was the information that has been focused most. The popular newspaper have illustrated crime scene photo on their first-page while qualitative newspaper used only headline to present the same news.

Keywords: ethic, feminine, journalism, newspaper, violent victim

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Authors: Annette Davies

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Celebrity entertainment TV news shows such as Access Hollywood, Entertainment Tonight, E! News, etc., have remained extremely popular. While the effects of these shows have been examined in academic literature on various sample populations, the effects have rarely been examined on a sample population of performing artists in the entertainment field. The present research investigates how the consumption of these shows influence the desire for fame and the perception of fame among active performing artists. Two separate chi-square analyses were conducted to yield the following results: 1) as the viewing of these shows increases, the overall desire for fame also tends to increase; 2) performers who believe that these celebrity entertainment TV news shows frame fame positively are more likely to perceive fame as a positive achievement overall. Thus, the results of the second finding are consistent with second-level agenda-setting theory. Limitations and suggestions for future research are also discussed.

Keywords: celebrity, entertainment, fame, performers

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

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Authors: Aleksandra Dragojlov

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Since the election of Aleksandar Vučić and the Progressives, Serbia has witnessed a slow decline in media freedom, which has been worse than in the 1990s. Although the government adopted a package of three laws in August 2014 to bring the media landscape up to European standards, the implementation of the laws has been limited and marginal, with the progressives engaging in fake compliance. The adoption of the new media strategy for 2020-2025 in 2020 has not led to genuine domestic reform and compliance with EU conditionality. In fact, the EU Commission and journalists’ associations in Serbia have criticised the decline in Serbia’s media freedom citing continued attacks on journalists and indirect political and economic control through advertising and project co-financing, which continue to be features of the Serbian media landscape. In the absence of clear and credible EU conditionality, the decline of media freedom is in the eye of the beholder, where the gap between public engagements with Serbian politicians and the critical stance of progress reports regarding the degradation of the media have enabled Serbian elites to exploit this ambiguity to continue their strategy of fake compliance vis-a-vis rule of law. This study used a mixed methods approach combining both primary and secondary sources with those semi-structured interviews via Zoom, email, and in person with EU and Serbian officials and journalists. Our findings add to the studies where the lack of clear and credible conditionality has allowed Serbia politicians to exploit them in a manner that would suit their own interests, finding new means to retain their control over the media. We argued and concluded that it is this discrepancy between public engagements with Serbia and the progress reports in the area of freedom of expression that has not led to genuine domestic media reforms in Serbia and instead allowed Serbian elites to engage in a strategy of fake and even non-compliance towards media freedom conditionality.

Keywords: media freedom, EU conditionality, Serbia, fake compliance, EU integration, Chapter 23, justice and fundamental rights

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Authors: Hossein Sabouri

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News stories as one of the branches of political discourse has always been regarded a sensitive and challenging area. Political translators often encounter some struggles that are vitally important when it comes to deal with the political tension between the source culture and the target one. Translating news stories is of prime importance since it has widespread availability and power of defining or even changing the facts. News translation is usually more than straight transfer of source text. Like original text endeavoring to manipulate the readers’ minds with imposing their ideologies, translated text seeking to change these ideologies influenced by ideological power. In other words, translation product is not considered more than a recontextualisation of the source text. The present study examines possible criteria for occurring changes in the translation process of news stories based on the ideological and political stance of translator using theories of ‘critical discourse analysis’and ‘translation and power. Fairclough investigates the ideological issues in (political) discourse and Tymoczko studies the political and power-related engagement of the translator in the process of translation. Incorporation of Fairclough and Gentzler and Tymoczko’s theories paves the way for the researcher to looks at the ideological power position of the translator. Data collection and analysis have been accomplished using 17 political-text samples taken from online news agencies which are related to the ‘Iran’s Nuclear Program’. Based on the findings, recontextualisation is mainly observed in terms of the strategies of ‘substitution, omissions, and addition’ in the translation process. The results of the study suggest that there is a significant relationship between the translation of political texts and ideologies of target culture.

Keywords: news translation, recontextualisation, ideological power, political discourse

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Authors: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on HSI dataset Indian Pines. The results confirm the capability of the proposed method.

Keywords: continual learning, data reconstruction, remote sensing, hyperspectral image segmentation

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Authors: Nidal F. Shilbayeh, Sameh T. Khuffash, Mohammad H. Allymoun, Reem Al-Saidi

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There is a real threat on the VIPs personal pages on the Social Network Sites (SNS). The real threats to these pages is violation of privacy and theft of identity through creating fake pages that exploit their names and pictures to attract the victims and spread of lies. In this paper, we propose a new secure architecture that improves the trusting and finds an effective solution to reduce fake pages and possibility of recognizing VIP pages on SNS. The proposed architecture works as a third party that is added to Facebook to provide the trust service to personal pages for VIPs. Through this mechanism, it works to ensure the real identity of the applicant through the electronic authentication of personal information by storing this information within content of their website. As a result, the significance of the proposed architecture is that it secures and provides trust to the VIPs personal pages. Furthermore, it can help to discover fake page, protect the privacy, reduce crimes of personality-theft, and increase the sense of trust and satisfaction by friends and admirers in interacting with SNS.

Keywords: social network sites, online social network, privacy, trust, security and authentication

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Authors: Taher Bahrani

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Armed with technological development, the present study aimed at gauging the effectiveness of exposure to news and movies as two types of audio-visual programs on improving language learners’ listening comprehension at the intermediate level. To this end, a listening comprehension test was administered to 108 language learners and finally 60 language learners were selected as intermediate language learners and randomly divided into group one and group two. During the experiment, group one participants had exposure to audio-visual news stories to work on in-and out-side the classroom. On the contrary, the participants in group two had only exposure to a sample selected utterances extracted from different kinds of movies. At the end of the experiment, both groups took another sample listening test to find out to what extent the participants in each group could enhance their listening comprehension. The results obtained from the post-test were indicative of the fact that the participants who had exposure to news outperformed the participants who had exposure to movies. The findings of the present research seem to indicate that the language input embedded in the type of audio-visual programs which language learners are exposed to is more important than the amount of exposure.

Keywords: audio-visual news, movies, listening comprehension, intermediate level

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Authors: Suzan Wedyan

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Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases.

Keywords: associative classification, classification, data mining, learning, rule ranking, rule pruning, prediction

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Authors: Fabio Fabris, Alex A. Freitas

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Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation.

Keywords: algorithm recommendation, meta-learning, bioinformatics, hierarchical classification

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Authors: Jaiden X. Schraut

Abstract:

Medical Imaging Segmentation of Chest X-rays is used for the purpose of identification and differentiation of lung cancer, pneumonia, COVID-19, and similar respiratory diseases. Widespread application of computer-supported perception methods into the diagnostic pipeline has been demonstrated to increase prognostic accuracy and aid doctors in efficiently treating patients. Modern models attempt the task of segmentation and classification separately and improve diagnostic efficiency; however, to further enhance this process, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional CNN module for auxiliary classification output. The proposed model achieves a final Jaccard Index of .9634 for image segmentation and a final accuracy of .9600 for classification on the COVID-19 radiography database.

Keywords: chest X-ray, deep learning, image segmentation, image classification

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