scholarly journals Argumentation Mining: Techniques and Emerging Trends

2021 ◽  
Vol 23 (05) ◽  
pp. 116-128
Author(s):  
Shobhit Sinha ◽  
◽  
Bineet Kumar Gupta ◽  
Rajat Sharma ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Research Area ◽  
Computational Logic ◽  
Learning Methods ◽  
Survey Paper ◽  
Machine Learning Methods ◽  
Unstructured Text ◽  
Emerging Trends ◽  
Argumentation Mining ◽  
Computational Argumentation

By Argument we mean persuasion of a reason or reasons in support of a claim or evidence. In Artificial Intelligence computational argumentation is the field dealing with computational logic upon which many models of argumentation have been suggested. The goal of Argumentation Mining is to automatically extract structured arguments from the unstructured text. It has the potential of extracting information from web and social media, making it one of the most sought after research area. Some recent advances in computational logic and Machine Learning methods do provide a new insight to the applications for policy making, economic sciences, legal, medical and information technology. Different models have been proposed for argumentation mining with different machine learning methods applied on the argumentation frameworks proposed for this particular mining task. In this survey article we will review the existing systems and applications and will cover the three categories of argumentation models and a comparative table depicting the most frequently applied ML method. This survey paper will also cover the various challenges of the field with the new potential perspectives in this new emerging research area.

scholarly journals ML-Based Analysis of Particle Distributions in High-Intensity Laser Experiments: Role of Binning Strategy

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 21
Author(s):  
Yury Rodimkov ◽  
Evgeny Efimenko ◽  
Valentin Volokitin ◽  
Elena Panova ◽  
Alexey Polovinkin ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Quantum Electrodynamics ◽  
Strong Field ◽  
Experimental Studies ◽  
Research Area ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

When entering the phase of big data processing and statistical inferences in experimental physics, the efficient use of machine learning methods may require optimal data preprocessing methods and, in particular, optimal balance between details and noise. In experimental studies of strong-field quantum electrodynamics with intense lasers, this balance concerns data binning for the observed distributions of particles and photons. Here we analyze the aspect of binning with respect to different machine learning methods (Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Fully-Connected Neural Network (FCNN), Convolutional Neural Network (CNN)) using numerical simulations that mimic expected properties of upcoming experiments. We see that binning can crucially affect the performance of SVM and GBT, and, to a less extent, FCNN and CNN. This can be interpreted as the latter methods being able to effectively learn the optimal binning, discarding unnecessary information. Nevertheless, given limited training sets, the results indicate that the efficiency can be increased by optimizing the binning scale along with other hyperparameters. We present specific measurements of accuracy that can be useful for planning of experiments in the specified research area.

scholarly journals A Survey : Data Mining and Machine Learning Methods for Cyber Security

2021 ◽  
pp. 24-34
Author(s):  
Ashish Prajapati ◽  
Shital Gupta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Intrusion Detection ◽  
Survey Data ◽  
Cyber Security ◽  
Literature Survey ◽  
Learning Methods ◽  
Survey Paper ◽  
Machine Learning Methods ◽  
Number Of Citations

This survey paper describes the literature survey for cyber analytics in support of intrusion detection of machine learnings (ML) and data mining (DM) methods. Short ML/DM method tutorial details will be given. Documents representing each method were categorized, read and summarized based on the number of citations and significance of an evolving method. Since data is so important.

scholarly journals Insider threats and Insider Intrusion Detection

2019 ◽  
Vol 8 (2S5) ◽  
pp. 158-166
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection Methods ◽  
Learning Methods ◽  
Insider Threats ◽  
Survey Paper ◽  
Machine Learning Methods ◽  
Different Types ◽  
Critical Problems

this survey paper narrates insider threats and their detection types and methods. Insider threats are emerging nowadays, it is important to identify these threats as they are generating critical problems to the system. This paper pays particular attention to the categories of threats and different types of detection methods. Based on different strategies, statistical and machine learning methods for detecting these threats, are identified and summarized here.

scholarly journals Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey

2019 ◽  
Vol 9 (20) ◽  
pp. 4396 ◽  
Author(s):  
Hongyu Liu ◽  
Bo Lang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Detection System ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Learning Methods ◽  
Machine Learning Methods

Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that capitalize on machine learning methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine learning methods have strong generalizability, so they are also able to detect unknown attacks. Deep learning is a branch of machine learning, whose performance is remarkable and has become a research hotspot. This survey proposes a taxonomy of IDS that takes data objects as the main dimension to classify and summarize machine learning-based and deep learning-based IDS literature. We believe that this type of taxonomy framework is fit for cyber security researchers. The survey first clarifies the concept and taxonomy of IDSs. Then, the machine learning algorithms frequently used in IDSs, metrics, and benchmark datasets are introduced. Next, combined with the representative literature, we take the proposed taxonomic system as a baseline and explain how to solve key IDS issues with machine learning and deep learning techniques. Finally, challenges and future developments are discussed by reviewing recent representative studies.

scholarly journals Using Machine Learning in Burnout Prediction: A Survey

2021 ◽  
Author(s):  
Małgorzata Grządzielewska
Keyword(s):  
Machine Learning ◽  
Theoretical Models ◽  
Research Area ◽  
Economic Benefits ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Occupational Burnout ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Traditional Approaches

AbstractAccurate prediction provides a number of important benefits for research and decision-making. Occupational burnout is intertwined with individual, cultural, and social factors, the resolution of which requires methods that can deal with large amounts of data. The application of such methods capable of dealing with large datasets is a relatively novel research area in social science. For this purpose, this article presents insights into machine learning methods, mainly related to prediction tasks. A brief review of these techniques in burnout domain was applied. It is shown that the choice of a method depends on the presence of certain dependent variables. This paper also presents a comparison between novel and traditional approaches, which shows that the appropriateness of a technique depends on the aim of the research. The theoretical and practical implications of using machine learning methods in this context is also presented in the paper. It is found that a gap in the study of burnout exists which requires the attention of social work researchers. Through machine learning techniques, new theoretical models of burnout can be created. These algorithms can also provide new approaches to create data-driven interventions. Burnout monitoring systems supported by machine-learning algorithms can also be used in recruitment processes and to supervise employees. Applying machine learning methods in reducing burnout can also provide socio-economic benefits such as help to reduce employee turnover and improve general working conditions.

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