Academic Articles & Research Papers

Data Science Methodology for Cybersecurity Projects

Cybersecurity solutions are traditionally static and signature-based.The traditional solutions along with the use of analytic models, machine learning and big data could be improved by automatically trigger mitigation or provide relevant aw...

Machine learning in cybersecurity: a comprehensive survey

Today’s world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices or services (e.g., cloud computing or online banking), and thereby each passing minut...

Machine learning in cybersecurity: A review

Machine learning technology has become mainstream in a large number of domains, and cybersecurity applications of machine learning techniques are plenty. Examples include malware analysis, especially for zero‐day malware detection, threat a...

Parallel and distributed computing for cybersecurity

Parallel and distributed data mining offer great promise for addressing cybersecurity. The Minnesota Intrusion Detection System can detect sophisticated cyberattacks on large-scale networks that are hard to detect using signature-based syst...

Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for NLP. It also benefit related domains such as machine learning, social network analysis, semantic Web, infor...

A neural network approach to ordinal regression

Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe an effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a gen...

Heterogeneous Graph Neural Network

Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This...

Using neural networks for data mining

The Graph Neural Network Model

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper,...

Spreading Social Influence with both Positive and Negative Opinions in Online Networks

Social networks are important media for spreading information, ideas, and influence among individuals. Most existing research focuses on understanding the characteristics of social networks, investigating how information is spread through t...

A Model to Predict Heart Disease Treatment Using Data Mining

Background and Aim: Nowadays heart disease is very common and is a major cause of mortality. Proper and early diagnosis of this disease is very important. Diagnostic methods and treatments of the disease are so expensive and have many side ...

Data Mining Techniques for Predictive Maintenance in Manufacturing Industries a Comprehensive Review

Predictive maintenance (PdM) is one of the major methods used in modern manufacturing to realize downtime minimization, lower the cost of maintenance and maximize machine service life by analyzing the collected data using data mining method...

Natural Language Processing Technologies in Radiology Research and Clinical Applications

The migration of imaging reports to electronic medical record systems holds great potential in terms of advancing radiology research and practice by leveraging the large volume of data continuously being updated, integrated, and shared. How...

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

BACKGROUND: Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides ...

Which factors influence public perceptions of urban attractions? — A comparative study

To better serve the public, urban attractions must understand the public's likes and dislikes and continually adapt and optimize their service in response. However, traditional methods for gauging public opinion, such as questionnaires and ...

Natural language processing

Abstract Approximately 40 years ago, the goal of endowing computers with the capacity to understand natural language began. These efforts were originally callednatural language understanding, which is now more frequently callednatural langu...

Natural language processing

Summary form only given. Natural language processing (NLP) is a major area of artificial intelligence research, which in its turn serves as a field of application and interaction of a number of other traditional AI areas. Until recently, th...

Applications of deep learning to MRI images: A survey

Deep learning provides exciting solutions in many fields, such as image analysis, natural language processing, and expert system, and is seen as a key method for various future applications. On account of its non-invasive and good soft tiss...

Deep Learning for Spatio-Temporal Data Mining: A Survey

With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temp...

Application of Deep Learning in Food: A Review

Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food sci...