Academic Articles & Research Papers

Explainable Sentiment-Based Reputational Risk Modelling in Ugandan Megaprojects

Reputational risk reflected in digital public discourse can significantly influence stakeholder perceptions, public trust, and decision-making surrounding government megaprojects. Existing computational approaches primarily focus on sentime...

Natural Language Processing

This chapter uncovers some of the basic techniques to tackle text data using PySpark. Today's textual form of data is being generated at a lightning pace with multiple social media platforms offering users the options to share their opinion...

Study on Building Business-oriented Resource On-demand Resolution Model

To address the issue of re-analyzing and repeating development of natural language processing tools and resource ana-lysis plugins when new requirements arise during project development,this paper proposes a business-oriented on-demand reso...

Attention Pooling-Based Bidirectional GRU Model for Sentimental Classification

Recurrent neural network (RNN) is one of the most popular architectures for addressing variable sequence text, and it shows outstanding results in many natural language processing (NLP) tasks and remarkable performance in capturing long-ter...

Instance-Level Weighted Contrast Learning for Text Classification

With the explosion of information, the amount of text data has increased significantly, making text categorization a central area of research in natural language processing (NLP). Traditional machine learning methods are effective, but deep...

A systematic literature review on spam content detection and classification

The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e., Facebook, Twitter, YouTube, and E-mail. ...

Graph-based Natural Language Processing and Information Retrieval

Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different p...

An Introduction to Natural Language Processing

Electronically stored clinical documents may contain both structured data and unstructured data. The use of structured clinical data varies by facility, but clinicians are familiar with coded data such as International Classification of Dis...

Deep learning for natural language processing: advantages and challenges

Deep learning refers to machine learning technologies for learning and utilizing ‘deep’ artificial neural networks, such as deep neural networks (DNN), convolutional neural networks (CNN) and recurrent neural networks (RNN). Recently, deep ...

Natural Language Processing Recipes

Implement natural language processing applications with Python in this problem-solution book. Here, coding exercises will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identi...

Analysis and Classification of Word Co-Occurrence Networks From Alzheimer’s Patients and Controls

In this paper we construct word co-occurrence networks from transcript data of controls and patients with potential Alzheimer’s disease using the ADReSS challenge dataset of spontaneous speech. We examine measures of the structure of these ...

Collectively encoding protein properties enriches protein language models

Abstract Pre-trained natural language processing models on a large natural language corpus can naturally transfer learned knowledge to protein domains by fine-tuning specific in-domain tasks. However, few studies focused on enriching such p...

A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications

Abstract Due to the rapid advances in computer vision and deep learning, human action recognition has become one of the most important representative tasks for video understanding. Especially for human action recognition based on RGB-D data...

Automated Burr Quality Classification in Drilling of AA6013 Aluminum Alloy Using YOLOv10n

Burr formation during drilling of aluminum alloys can compromise hole quality and assembly accuracy, increasing the need for secondary deburring operations. The time-consuming and operator-dependent nature of manual visual inspection create...

Identification of camouflage military individuals with deep learning approaches DFAN and SINETV2

Abstract Camouflaged object detection particularly is considered as challenging and crucial because these objects are designed to either mimic their environment or be completely hidden within it. The goal of camouflage patterns utilization ...

Computer Vision

Abstract: Computer vision is a field of artificial intelligence that focuses on enabling computers to understand and interpret visual information from images or videos. It involves developing algorithms and techniques to extract meaningful ...

An Overview of the Attention Mechanisms in Computer Vision

Abstract Deep convolutional neural network (CNN) plays an important role in the field of computer vision and image processing. In order to further improve the performance of CNN, scholars have conducted a series of new explorations, such as...

Transformer in Computer Vision

Transformer is widely used in Natural Language Processing (NLP), in which numerous papers have been proposed. Recently, the transformer has been borrowed for many computer vision tasks. However, there are few papers to give a comprehensive ...

A hybrid CNN-BiLSTM network with additive attention for automated four-stage classification of Alzheimer's disease from brain MRI.

Computer vision methods and algorithms for automatic detection and classification of objects in decision support systems in agriculture

The paper examines aspects of developing and formalizing the task of applying computer vision methods and algorithms using OpenCV (implemented in Python version 3.13 notation) for automatic detection and classification of objects in decisio...