Academic Articles & Research Papers

Outdoor access practices in the Canadian dairy industry

ABSTRACT: Dairy cows are highly motivated to access pasture and have a partial preference for alternative forms of outdoor access (e.g., deep-bedded outdoor sand or wood-chip packs). In addition, Canadians value the provision of outdoor acc...

Revenue-sensitive evaluation of AI-assisted ICD-10-CM coding and human-AI collaboration under dual DRG payment systems

Abstract Automated ICD-10-CM coding is critical for hospital reimbursement under Diagnosis-Related Group (DRG) payment systems, yet standard metrics weight all errors equally. This study evaluated 11 models on MIMIC-IV under heterogeneous c...

Digital footprints as a tool to evaluate the spatiotemporal environmental impacts of grocery shopping across Great Britain

Introduction & Background Climate change represents one of the most pressing challenges of our time. Within this context, the food sector is a critical domain for intervention, accounting for approximately one-third of global greenhouse gas...

A natural language processing based technique for sentiment analysis of college english corpus

The college English corpus can help us better master English, but how to obtain the desired information from a large number of English corpus has become the focus of information technology. Based on the natural language processing (NLP) tec...

Weighted DAG Automata for Semantic Graphs

Graphs have a variety of uses in natural language processing, particularly as representations of linguistic meaning. A deficit in this area of research is a formal framework for creating, combining, and using models involving graphs that pa...

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...

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...

Target reindexing and residual reconstruction enable reliable retrieval augmented few shot recognition

Abstract Retrieval-augmented few-shot recognition supplements sparsely annotated target classes with external visual samples, but open retrieval results may exhibit unstable quality, semantic mismatch, and cross-domain feature shifts. Direc...

A unified architecture for natural language processing

We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the lik...

Link Predictions with Bi-Level Routing Attention

Background/Objectives: Knowledge Graphs (KGs) are often incomplete, which can significantly impact the performance of downstream applications. Manual completion of KGs is time-consuming and costly, emphasizing the importance of developing a...

GDG-YOLO: A dual-branch lightweight network based on global context augmentation for laryngeal disease detection.

Segmentation of muscle tissue in computed tomography images at the level of the L3 vertebra

With the increasing routine workload on radiologists associated with the need to analyze large numbers of images, there is a need to automate part of the analysis process. Sarcopenia is a condition in which there is a loss of muscle mass. T...

RGB-D Tracking and Optimal Perception of Deformable Objects

Addressing the perception problem of texture-less objects that undergo large deformations and movements, this article presents a novel RGB-D learning-free deformable object tracker in combination with a camera position optimisation system f...

Bridging the performance–efficiency gap: a resource-efficient hybrid DL-ML framework for jackfruit disease detection

Complex environment variability and limited computational resources in agricultural settings create challenges in automated detection in tropical regions, where jackfruit diseases pose major threats to harvest productivity and food security...

Attack-Aware Deepfake Detection Under Counter-Forensic Manipulations

The rapid proliferation of AI-generated and manipulated visual content requires forensic detectors that remain reliable after routine processing and deliberate counter-forensic modification. This paper presents an attack-aware deepfake dete...

Automated estimation of cementitious sorptivity via computer vision

Monitoring water uptake in cementitious systems is crucial to assess their durability against corrosion, salt attack, and freeze-thaw damage. However, gauging absorption currently relies on labor-intensive and infrequent weight measurements...

Advanced Swine Management: Infrared Imaging for Precise Localization of Reproductive Organs in Livestock Monitoring

Traditional methods for predicting sow reproductive cycles are not only costly but also demand a larger workforce, exposing workers to respiratory toxins, repetitive stress injuries, and chronic pain. This occupational hazard can even lead ...

Multi‐object tracking using dominant sets

Multi‐object tracking is an interesting but challenging task in the field of computer vision. Most previous works based on data association techniques merely take into account the relationship between detection responses in a locally limite...

TransCat: a hybrid CNN-transformer network with KAN for medical image segmentation

Medical image segmentation is a fundamental task in computer vision and plays an important role in clinical diagnosis and treatment planning. Existing methods are mainly built on either convolutional neural networks (CNNs) or Transformer-ba...

A Complete System for Automated Semantic–Geometric Mapping of Corrosion in Industrial Environments

Corrosion, a naturally occurring process leading to the deterioration of metallic materials, demands diligent detection for quality control and the preservation of metal-based objects, especially within industrial contexts. Traditional tech...