Academic Articles & Research Papers

Recent Advances on Federated Learning for Cybersecurity and Cybersecurity for Federated Learning for Internet of Things

Decentralized paradigm in the field of cybersecurity and machine learning (ML) for the emerging Internet of Things (IoT) has gained a lot of attention from the government, academia, and industries in recent years. Federated cybersecurity (F...

Cybersecurity, Data Privacy and Blockchain: A Review

In this paper, we identify and review key challenges to bridge the knowledge-gap between SME's, companies, organisations, businesses, government institutions and the general public in adopting, promoting and utilising Blockchain technology....

A Survey on Explainable Artificial Intelligence for Cybersecurity

The “black-box” nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create mac...

Graph neural networks for materials science and chemistry

Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials...

Computational linguistics and discourse complexology: Paradigms and research methods

The dramatic expansion of modern linguistic research and enhanced accuracy of linguistic analysis have become a reality due to the ability of artificial neural networks not only to learn and adapt, but also carry out automate linguistic ana...

Classifying Characteristics of Opioid Use Disorder From Hospital Discharge Summaries Using Natural Language Processing

BackgroundOpioid use disorder (OUD) is underdiagnosed in health system settings, limiting research on OUD using electronic health records (EHRs). Medical encounter notes can enrich structured EHR data with documented signs and symptoms of O...

Improving the trial efficiency of criminal cases with the assistance of artificial intelligence

Abstract This study explores the application of artificial intelligence (AI) in improving the trial efficiency of criminal cases. Using a dataset consisting of 500 criminal case records, including minor, ordinary, and complex cases, we appl...

Bias and Fairness in Natural Language Processing

Recent advances in data-driven machine learning techniques (e.g., deep neural networks) have revolutionized many natural language processing applications. These approaches automatically learn how to make decisions based on the statistics an...

Enhancing sentiment classification on small datasets through data augmentation and transfer learning

Abstract Small-scale sentiment classification often suffers from data scarcity, which limits the generalization ability of the models. This study evaluates and compares the effectiveness of three data augmentation strategies: Easy Data Augm...

Natural Language Processing Techniques for Text Classification of Biomedical Documents: A Systematic Review

The classification of biomedical literature is engaged in a number of critical issues that physicians are expected to answer. In many cases, these issues are extremely difficult. This can be conducted for jobs such as diagnosis and treatmen...

Python Natural Language Processing

Leverage the power of machine learning and deep learning to extract information from text dataAbout This BookImplement Machine Learning and Deep Learning techniques for efficient natural language processingGet started with NLTK and implemen...

Evolution of transfer learning in natural language processing

In this paper, we present a study of the recent advancements which have helped bring Transfer Learning to NLP through the use of semi-supervised training. We discuss cutting-edge methods and architectures such as BERT, GPT, ELMo, ULMFit amo...

A systematic review of natural language processing applied to radiology reports

BACKGROUND: Natural language processing (NLP) has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to r...

The SIK1-NR4A2 axis: a core driver of epilepsy identified by integrated machine learning and experimental validation.

Machine Unlearning: A Perspective, Taxonomy, and Benchmark Evaluation

Machine Learning (ML) models trained on large-scale datasets learn useful predictive patterns, but they may also memorize undesired information, leading to risks such as information leakage, bias, copyright violations, and privacy attacks. ...

Data-driven materials research enabled by natural language processing and information extraction

Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly hetero...

Economic Activity Forecasting Based on the Sentiment Analysis of News

The outbreak of war and the earlier and ongoing COVID-19 pandemic determined the need for real-time monitoring of economic activity. The economic activity of a country can be defined in different ways. Most often, the country’s economic act...

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 ...

Data augmentation techniques in natural language processing

Data Augmentation (DA) methods – a family of techniques designed for synthetic generation of training data – have shown remarkable results in various Deep Learning and Machine Learning tasks. Despite its widespread and successful adoption w...

Information Extraction From FDA Drug Labeling to Enhance Product-Specific Guidance Assessment Using Natural Language Processing

Towards the objectives of the UnitedStates Food and Drug Administration (FDA) generic drug science and research program, it is of vital importance in developing product-specific guidances (PSGs) with recommendations that can facilitate and ...