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Found 26 papers, 2 papers with code
Transforming Norm-based To Graph-based Spatial Representation for Spatio-Temporal Epidemiological Models
Our findings show that by leveraging agent-based simulations and heuristic algorithms for the graph node's location and population's spatial walk dynamics approximation one can use graph-based spatial representation without losing much of the model's accuracy and expressiveness.
Whose LLM is it Anyway? Linguistic Comparison and LLM Attribution for GPT-3.5, GPT-4 and Bard
Large Language Models (LLMs) are capable of generating text that is similar to or surpasses human quality.
Detecting LLM-Assisted Writing in Scientific Communication: Are We There Yet?
Large Language Models (LLMs), exemplified by ChatGPT, have significantly reshaped text generation, particularly in the realm of writing assistance.
Predicting Postoperative Nausea And Vomiting Using Machine Learning: A Model Development and Validation Study
Therefore, prognostic tools for the prediction of early and delayed PONV were developed in this study with the aim of achieving satisfactory predictive performance.
Exploration-Exploitation Model of Moth-Inspired Olfactory Navigation
During the experiments in the wind tunnel, we add disturbance to the airflow and analyze the effect of increased fluctuations on moth flights in the context of the proposed EE model.
Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks
In the realm of machine and deep learning regression tasks, the role of effective feature engineering (FE) is pivotal in enhancing model performance.
Individual Variation Affects Outbreak Magnitude and Predictability in an Extended Multi-Pathogen SIR Model of Pigeons Vising Dairy Farms
The model expands on the Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) framework and accounts for both within-species and cross-species transmission of pathogens, as well as the exploration-exploitation movement dynamics of pigeons, which play a critical role in the spread of infection agents.
Predicting Lung Cancer's Metastats' Locations Using Bioclinical Model
Lung cancer is a leading cause of cancer-related deaths worldwide.
Machine Learning Approaches to Predict and Detect Early-Onset of Digital Dermatitis in Dairy Cows using Sensor Data
The aim of this study was to employ machine learning algorithms based on sensor behavior data for (1) early-onset detection of digital dermatitis (DD); and (2) DD prediction in dairy cows.
The Scientometrics and Reciprocality Underlying Co-Authorship Panels in Google Scholar Profiles
In this work, we examine whether scientometrics and reciprocality can explain the observed selections.
Digitally-Enhanced Dog Behavioral Testing: Getting Help from the Machine
An unsupervised clustering of the dogs' trajectories revealed two main clusters showing a significant difference in the stranger-directed fear C-BARQ factor, as well as a good separation between (sufficiently) relaxed dogs and dogs with excessive behaviors towards strangers based on expert scoring.
BovineTalk: Machine Learning for Vocalization Analysis of Dairy Cattle under Negative Affective States
There is a critical need to develop and validate non-invasive animal-based indicators of affective states in livestock species, in order to integrate them into on-farm assessment protocols, potentially via the use of precision livestock farming (PLF) tools.
Temporal Graphs Anomaly Emergence Detection: Benchmarking For Social Media Interactions
Temporal graphs have become an essential tool for analyzing complex dynamic systems with multiple agents.
Can We Mathematically Spot Possible Manipulation of Results in Research Manuscripts Using Benford's Law?
The reproducibility of academic research has long been a persistent issue, contradicting one of the fundamental principles of science.
Improving Gradient-Trend Identification: Fast-Adaptive Moment Estimation with Finance-Inspired Triple Exponential Moving Average
Existing optimizers predominantly adopt techniques based on the first-order exponential moving average (EMA), which results in noticeable delays that impede the real-time tracking of gradients trend and consequently yield sub-optimal performance.
The Family Tree Graph as a Predictor of the Family Members' Satisfaction with One Another
To this end, this study examines the relationship between the family tree graph and family members' satisfaction with their nuclear and extended family.
A Computational Model For Individual Scholars' Writing Style Dynamics
A manuscript's writing style is central in determining its readership, influence, and impact.
Cancer-inspired Genomics Mapper Model for the Generation of Synthetic DNA Sequences with Desired Genomics Signatures
Genome data are crucial in modern medicine, offering significant potential for diagnosis and treatment.
Multi-Species Prey-Predator Dynamics During a Multi-Strain Pandemic
Small and large scale pandemics are a natural phenomenon repeatably appearing throughout history, causing ecological and biological shifts in ecosystems and a wide range of their habitats.
Economical-Epidemiological Analysis of the Coffee Trees Rust Pandemic
Coffee leaf rust is a prevalent botanical disease that causes a worldwide reduction in coffee supply and its quality, leading to immense economic losses.
Knowledge-integrated AutoEncoder Model
Data encoding is a common and central operation in most data analysis tasks.
Cost-optimal Seeding Strategy During a Botanical Pandemic in Domesticated Fields
In this work, we propose a novel epidemiological-economic mathematical model that describes the economic profit from a field of plants during a botanical pandemic.
High Resolution Spatio-Temporal Model for Room-Level Airborne Pandemic Spread
Airborne pandemics have caused millions of deaths worldwide, large-scale economic losses, and catastrophic sociological shifts in human history.
A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge
Discovering a meaningful symbolic expression that explains experimental data is a fundamental challenge in many scientific fields.
Rivendell: Project-Based Academic Search Engine
We posit that in searching for research papers, a combination of a life-time search engine with an explicitly-provided context (project) provides a solution to the concept drift problem.
SubStrat: A Subset-Based Strategy for Faster AutoML
It then employs the AutoML tool on the small subset, and finally, it refines the resulted pipeline by executing a restricted, much shorter, AutoML process on the large dataset.
