URL ATTACKS: Classification of URLs via Analysis and Learning

Social Networks such as Twitter, Facebook play a remarkable growth in recent years. The ratio of tweets or messages in the form of URLs increases day by day. As the number of URL increases, the probability of fabrication also gets increased using their HTML content as well as by the usage of tiny URLs. It is important to classify the URLs by means of some modern techniques. Conditional redirection method is used here by which the URLs get classified and also the target page that the user needs is achieved. Learning methods also introduced to differentiate the URLs and there by the fabrication is not possible. Also the classifiers will efficiently detect the suspicious URLs using link analysis algorithm.

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Interpreting HVEM of muscle-cell impulse networks

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010012103x ◽

1992 ◽

Vol 50 (1) ◽

pp. 126-127

Author(s):

Paul DeCosta ◽

Kyugon Cho ◽

Stephen Shemlon ◽

Heesung Jun ◽

Stanley M. Dunn

Keyword(s):

Structural Analysis ◽

Muscle Cell ◽

Electron Micrographs ◽

Relative Size ◽

Automatic Classification ◽

Nerve Fibers ◽

Analysis Algorithm ◽

Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

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Index of suicide risk in Mexico using Twitter

Journal of Social Researches ◽

10.35429/jsr.2019.15.5.1.13 ◽

2019 ◽

pp. 1-13

Author(s):

Luz Judith Rodríguez-Esparza ◽

Diana Barraza-Barraza ◽

Jesús Salazar-Ibarra ◽

Rafael Gerardo Vargas-Pasaye

Keyword(s):

Social Networks ◽

Analytic Hierarchy Process ◽

Suicide Risk ◽

Opinion Mining ◽

Real Data ◽

Analytic Hierarchy ◽

Specialized Care ◽

The Analytic Hierarchy Process ◽

Hierarchy Process

Objectives: To identify early suicide risk signs on depressive subjects, so that specialized care can be provided. Various studies have focused on studying expressions on social networks, where users pour their emotions, to determine if they show signs of depression or not. However, they have neglected the quantification of the risk of committing suicide. Therefore, this article proposes a new index for identifying suicide risk in Mexico. Methodology: The proposal index is constructed through opinion mining using Twitter and the Analytic Hierarchy Process. Contribution: Using R statistical package, a study is presented considering real data, making a classification of people according to the obtained index and using information from psychologists. The proposed methodology represents an innovative prevention alternative for suicide.

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A Brief Survey of Machine Learning Methods in Identification of Mitochondria Proteins in Malaria Parasite

Current Pharmaceutical Design ◽

10.2174/1381612826666200310122324 ◽

2020 ◽

Vol 26 (26) ◽

pp. 3049-3058

Author(s):

Ting Liu ◽

Hua Tang

Keyword(s):

Machine Learning ◽

Computational Methods ◽

Future Development ◽

Malaria Parasite ◽

Mitochondrial Proteins ◽

Learning Methods ◽

Machine Learning Methods ◽

Effective Drugs ◽

Construction Strategies ◽

Day By Day

The number of human deaths caused by malaria is increasing day-by-day. In fact, the mitochondrial proteins of the malaria parasite play vital roles in the organism. For developing effective drugs and vaccines against infection, it is necessary to accurately identify mitochondrial proteins of the malaria parasite. Although precise details for the mitochondrial proteins can be provided by biochemical experiments, they are expensive and time-consuming. In this review, we summarized the machine learning-based methods for mitochondrial proteins identification in the malaria parasite and compared the construction strategies of these computational methods. Finally, we also discussed the future development of mitochondrial proteins recognition with algorithms.

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Multiclass Classification of Modulation Formats in the presence of Rayleigh and Rician Channel Noise using Deep Learning Methods

2020 3rd International Conference on Information and Communications Technology (ICOIACT) ◽

10.1109/icoiact50329.2020.9332035 ◽

2020 ◽

Author(s):

Rahim Khan ◽

Yang Qiang ◽

Ahsan Bin Tufail ◽

Alam Noor

Keyword(s):

Deep Learning ◽

Multiclass Classification ◽

Channel Noise ◽

Learning Methods ◽

Modulation Formats ◽

Rician Channel

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Classification of electrocardiogram (ECG) data using deep learning methods

2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) ◽

10.1109/ismsit50672.2020.9255000 ◽

2020 ◽

Author(s):

Fatma Bozyigit ◽

Fatih Erdemir ◽

Murat Sahin ◽

Deniz Kilinc

Keyword(s):

Deep Learning ◽

Learning Methods ◽

Electrocardiogram Ecg ◽

Ecg Data

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Comparison of machine learning methods for the classification of cardiovascular disease

Informatics in Medicine Unlocked ◽

10.1016/j.imu.2021.100606 ◽

2021 ◽

pp. 100606

Author(s):

Rachael Hagan ◽

Charles J. Gillan ◽

Fiona Mallett

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Learning Methods ◽

Machine Learning Methods

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An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

Big Data and Cognitive Computing ◽

10.3390/bdcc5010005 ◽

2021 ◽

Vol 5 (1) ◽

pp. 5

Author(s):

Ninghan Chen ◽

Zhiqiang Zhong ◽

Jun Pang

Keyword(s):

Machine Learning ◽

Social Networks ◽

Online Social Networks ◽

Exploratory Study ◽

Representation Learning ◽

Data Driven ◽

Learning Methods ◽

Twitter Users ◽

Over Time

The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.

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