Regression News and Research

RSS

Novel Method for Realistic Evaluation of ML Models in Software Bug Assignments

Researchers detail a groundbreaking approach for creating realistic train-and-test datasets to evaluate machine learning models in software bug assignments. The novel method, based on time dependencies, addresses limitations in existing techniques, ensuring more reliable assessments in real-world scenarios. The proposed method offers potential applications in telecommunication, software quality prediction, and maintenance, contributing to the development of bug-free software applications.

10 Dec 2023

Displacement Effects of Automation: Urban vs. Non-Urban Perspectives

This article delves into the unequal impacts of automation on urban and non-urban labor markets. Analyzing Italian data, it reveals that automation universally displaces workers, challenging assumptions about urban immunity. The study emphasizes the need for ongoing research to understand the evolving landscape of automation, its impact on workforce composition, and the potential for exacerbating inequalities between privileged urban occupations and excluded low-skill workers.

10 Dec 2023

Understanding Oklahoma's Earthquake Insurance Landscape: Influential Factors and Predictive Modeling

This research investigates the determinants of earthquake insurance uptake in Oklahoma post-2011 seismic events. Through supervised machine learning, it identifies influential factors including age, gender, ethnicity, political affiliation, tenure, housing status, education, income, earthquake experience, and environmental attitudes. The study emphasizes the significance of awareness and advanced machine learning tools for predictive modeling in managing environmental risks and advocates for informed disaster management strategies.

8 Dec 2023

FakeStack: A Deep Learning Approach for Robust Fake News Detection

This research introduces FakeStack, a powerful deep learning model combining BERT embeddings, Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) for accurate fake news detection. Trained on diverse datasets, FakeStack outperforms benchmarks and alternative models across multiple metrics, demonstrating its efficacy in combating false news impact on public opinion.

8 Dec 2023

Using Deep Learning and XAI to Enhance El Niño-Southern Oscillation Forecast

A groundbreaking study introduces the IGP-UHM AI v1.0 model, utilizing deep learning and XAI to enhance El Niño-Southern Oscillation (ENSO) prediction. The 2023–2024 forecast reveals sustained yet weakening EN conditions, emphasizing the model's credibility through Layerwise Relevance Propagation (LRP) explanations. The research underscores the need for ongoing refinement, human oversight, and raises crucial questions about ENSO predictability limits in the context of climate change.

7 Dec 2023

AI and ML Analysis of Elite Junior Female Tennis Players: Predicting Tournament Outcomes and Career Trajectories

Employing AI and ML, this study analyzed elite junior female tennis players' game statistics to predict tournament outcomes and understand career trajectories. While accurately forecasting junior tournament results, predicting future careers faced challenges, emphasizing the role of non-game factors and junior tournament participation in shaping successful careers. The study recommends refining models, emphasizing serve improvement, and supporting young talents through international tournaments for a nuanced understanding of tennis dynamics and enhanced training programs.

6 Dec 2023

Metamodeling Neuronal Networks: Power Spectra Estimation and Parameter Inference

Utilizing deep Gaussian process regression (DGPR) and masked autoregressive flow (MAF) metamodeling techniques, researchers approximate simulations of neuronal networks, specifically a two-population recurrent spiking network model. The study demonstrates the effectiveness of metamodels in estimating power spectra of population spiking activities and local field potentials (LFPs), offering a computationally efficient method for understanding network dynamics and inferring parameter distributions.

6 Dec 2023

AI Fortification: Safeguarding IoT Systems Through Comprehensive Algorithmic Approaches

This research delves into the synergy of Artificial Intelligence (AI) and Internet of Things (IoT) security. The study evaluates and compares various AI algorithms, including machine learning (ML) and deep learning (DL), for classifying and detecting IoT attacks. It introduces a novel taxonomy of AI methodologies for IoT security and identifies LSTM as the top-performing algorithm, emphasizing its potential applications in diverse fields.

5 Dec 2023

MLpronto: Democratizing Machine Learning with a User-Friendly Interface

This paper introduces MLpronto, a user-friendly machine learning platform aimed at democratizing the field by providing accessibility without requiring programming skills. This web-based tool swiftly processes data, executes prevalent supervised machine learning algorithms, and generates corresponding programming code, catering to both novice users and those inclined towards programming.

5 Dec 2023

Integrating Neural Networks and Physical Modeling for Microclimate Precision

Researchers present a novel microclimate model for precision agriculture in Bergamo, Italy, blending neural networks and physical modeling. Assessing the impact of global (ERA5) versus local (ARPA) climate data, the model achieved high accuracy in temperature predictions, emphasizing the role of neural networks in capturing intricate variations. The study contributes valuable insights for optimizing input data in microclimate modeling, vital for informed decision-making in precision agriculture.

3 Dec 2023

Innovative Food Weight Estimation from Images Using Boosting Algorithms

Researchers unveil a pioneering method for accurately estimating food weight using advanced boosting regression algorithms trained on a vast Mediterranean cuisine image dataset. Achieving remarkable accuracy with a mean weight absolute error of 3.93 g, this innovative approach addresses challenges in dietary monitoring and offers a promising solution for diverse food types and shapes.

3 Dec 2023

Deep Learning-Based Label-Free Identification of Neurodegenerative Disease-Associated Aggregates

This groundbreaking study introduces a deep learning (DL)-based approach for Label-Free Identification of Neurodegenerative Disease (NDD)-associated Aggregates (LINA). Addressing limitations of fluorescently tagged proteins, the method accurately identifies unaltered and unlabeled protein aggregates in living cells, focusing on Huntington's disease (HD) as a model.

1 Dec 2023

Smartphone and AI-Based Estimation of Body Composition in Youth Soccer Players

This pioneering study investigated the accuracy of smartphone-based estimation of body composition in youth soccer players, utilizing a novel app (Mobile Fit) for digital anthropometric assessments. Researchers evaluated its validity against dual-energy X-ray absorptiometry (DXA) and developed population-specific equations for appendicular lean mass and body fat percentage estimation.

30 Nov 2023

Intelligent Forecasting of Free Atmospheric Corrosion in Marine Steel Structure

Researchers present an intelligent framework, integrating a Group Method of Data Handling (GMDH) neural network and Shapley Additive Explanations (SHAP) analysis, to predict free atmospheric corrosion in marine steel structures. Leveraging historical sensor data, the framework demonstrates high forecasting accuracy, with optimal parameter selection enhancing performance. The SHAP analysis reveals the impact of environmental factors on corrosion, providing valuable insights into the dynamics of atmospheric corrosion in marine settings.

29 Nov 2023

Revolutionizing Dyslexia Detection: Unveiling the Power of Multi-Source Data and AI Models

Researchers present an innovative approach to dyslexia identification using a multi-source dataset incorporating eye movement, demographic, and non-verbal intelligence data. Experimenting with various AI models, including MLP, RF, GB, and KNN, the study demonstrates the efficacy of a fusion of demographic and fixation data in accurate dyslexia prediction. The insights gained, including the significance of IQ, age, and gender, pave the way for enhanced dyslexia detection, while challenges like data imbalance prompt considerations for future improvements.

28 Nov 2023

Machine Learning for Precision Broiler Weight Estimation

This article introduces a novel machine learning approach for non-invasive broiler weight estimation in large-scale production. Utilizing Gaussian mixture models, Isolation Forest, and OPTICS algorithm in a two-stage clustering process, the researchers achieved accurate predictions of individual broiler weights. The comprehensive methodology, combining polynomial fitting, gray models, and adaptive forecasting, offers a promising and cost-effective solution for precise broiler weight monitoring in large-scale farming setups, as evidenced by considerable accuracy in evaluations across 111 datasets.

26 Nov 2023

Machine Learning Enhances Bulk Modulus Prediction in High Entropy Alloys

Researchers introduce machine learning (ML) models for predicting the bulk modulus in High Entropy Alloys (HEA), a crucial property for aerospace and high-pressure applications. The Gradient Boosting Classifier (GBC) excels in HEA classification, while the LASSO Regression model predicts bulk modulus values, accelerating the discovery and design of HEAs with superior mechanical traits. This pioneering study addresses a significant gap in HEA research and offers a pathway for optimized alloy compositions in diverse applications.

24 Nov 2023

Expertise Framing Effects on User Behavior: A Study on Algorithm Aversion in AI Advisory

This study investigates the impact of expertise framing on user behavior in distinguishing between human and algorithmic advisors. Experiments on regression tasks revealed a significant increase in advice utilization when human advisors were framed as experts, while a comparable effect was not observed for algorithmic advisors.

23 Nov 2023

Enhancing AR Glasses Adaptability in Aviation: A Multi-Modal IoT Approach

Researchers introduce a pioneering framework leveraging IoT and wearable technology to enhance the adaptability of AR glasses in the aviation industry. The multi-modal data processing system, employing kernel theory-based design and machine learning, classifies performance, offering a dynamic and adaptive approach for tailored AR information provision.

22 Nov 2023

Cigna's StressWaves Test Assessment: Reliability and Validity Concerns in AI-Based Stress Management

This study critically evaluates the Cigna StressWaves Test (CSWT), an AI-based tool integrated into Cigna's stress management toolkit, claiming 'clinical grade' assessment. The research, conducted with 60 participants, reveals significant concerns about CSWT's reliability and validity, challenging its efficacy. The study underscores the importance of stringent validation processes for AI-driven health tools, particularly in mental health assessment, and highlights challenges associated with speech-based health measures.

21 Nov 2023