1,720,957 research outputs found
Phishing Website Detection Using Ensemble Algorithm Convolutional Neural Network and Bidirectional LSTM
Get PDFThis study focuses on phishing website detection by leveraging an ensemble of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) models. Phishing is a type of cyber attack where attackers disguise themselves as legitimate entities to trick individuals into providing sensitive information such as usernames, passwords, and credit card details. Given the escalating threat of phishing attacks and the limitations of traditional detection methods, this research explores the potential of machine learning techniques to enhance detection accuracy and robustness. By integrating CNN and BiLSTM models within an ensemble framework, the study demonstrates improved performance in identifying phishing websites through real-time analysis. The ensemble model benefits from the strengths of both CNN and BiLSTM architectures. CNNs are effective in feature extraction from input data, and capturing spatial hierarchies, while BiLSTMs excel at understanding sequential dependencies. The model achieves an accuracy of 89.5%, with training and validation accuracies converging to high values, and exhibits a consistent decrease in both training and validation losses, indicating robust performance without overfitting
Comparative Analysis of Graph Neural Network with SAGE Conv, GAT Conv, and GCN Conv Techniques for Fake News Detection
Get PDFIn today's rapidly evolving information landscape, the spread of fake news poses a critical challenge to the integrity of public information. Fake news, characterized by intentionally falsified or misrepresented information, can manipulate public opinion, disrupt political processes, and incite social instability. Consequently, the detection of fake news has become essential for maintaining media integrity and ensuring a healthy democratic function. Traditional methods for detecting fake news, such as decision trees and support vector machines, often fall short due to their inability to capture the relational and structural context of data. To address this, Graph Neural Networks (GNNs) have emerged as promising solutions, offering the ability to process data structured as graphs and retain topological information. This study investigates three GNN models—Graph Convolutional Network (GCN Conv), Graph Attention Network (GAT Conv), and GraphSAGE (SAGEConv)—each with unique strategies for handling graph data in the context of fake news detection. Our comparative analysis reveals that GAT Conv achieves the highest test accuracy of 0.9488 at epoch 86, demonstrating strong learning performance and efficient convergence. SAGE Conv, while slightly less effective, achieves a maximum accuracy of 0.9472 at epoch 93, indicating its potential in specific scenarios. GCN Conv offers a balanced performance with a maximum accuracy of 0.9482 at epoch 99, showcasing its robustness as an alternative approach. These findings underscore the importance of selecting suitable GNN models based on the characteristics of the network, optimizing fake news detection efforts, and contributing to enhanced media integrity and democratic stability.Keywords: GNN, Fake News Detection, Deep Learnin
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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