Academic Articles & Research Papers

A synergistic enhancement of the Ivy algorithm for GAN-based imbalanced classification

Abstract The Ivy Algorithm (IVYA), a swarm intelligence algorithm inspired by plant growth, presents a novel framework for optimization. To unlock its full potential in complex, high-dimensional problems, it is crucial to address the fundam...

Student Modeling and Machine Learning

After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge neces...

Machine Learning in Healthcare Communication

Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelli...

Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials

Teaching an assistive robotic manipulator to move objects in a cluttered table requires demonstrations from expert operators, but what if the experts are individuals with motor disabilities? Batzianoulis et al. propose a learning approach w...

Human behavior analysis on political retweets using machine learning algorithms

The exponential rise in the use of social media has resulted in a massive increase in the volume of unstructured text created. This content is presented through messages, conversations, postings, and blogs. Microblogging has become a popula...

Ultra-broadband and polarization-insensitive metasurface absorber with behavior prediction using machine learning

The solar spectrum energy absorption is very important for designing any solar absorber. The need for absorbing visible, infrared, and ultraviolet regions is increasing as most of the absorbers absorb visible regions. We propose a metasurfa...

RDDA: Rectified Discriminatory Delta-Adjust

This paper introduces Rectified Discriminatory Delta-Adjust (RDDA), a novel methodology that enhances interpolation-based predictive modeling through adaptive sensitivity parameters. Building upon the foundational Delta-Adjust algorithm, RD...

Can machine learning be secure?

Machine learning systems offer unparalled flexibility in dealing with evolving input in a variety of applications, such as intrusion detection systems and spam e-mail filtering. However, machine learning algorithms themselves can be a targe...

Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning Techniques

By integrating advanced technologies and data-driven systems in smart grids, there has been a significant revolution in the energy distribution sector, bringing a new era of efficiency and sustainability. Nevertheless, with this advancement...

Life prediction model of feedback rod in electro-hydraulic servo valve

Abstract Metallic elastic elements serve as critical structural components in advanced electro mechanical assemblies, where cyclic loading induces progressive rigidity reduction. Within electro-hydraulic servo valves (EHSV), the feedback me...

Precision water quality indices forecasting through an optimized hybrid SMW-LSSVM-R model enhanced by SATLDE and uncertainty analysis

Precise forecasting of water quality indices (WQI) is essential for safeguarding ecosystems, human health, and sustainable water resource management. This study presents an innovative approach for evaluating river Water Quality Indices usin...

Contribution of model organism phenotypes to the computational identification of human disease genes

Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype–phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past d...

Generative-Adversarial-Network-Based Image Reconstruction for the Capacitively Coupled Electrical Impedance Tomography of Stroke

This study investigated the potential of machine-learning-based stroke image reconstruction in capacitively coupled electrical impedance tomography. The quality of brain images reconstructed using the adversarial neural network (cGAN) was e...

Practical Machine Learning with Python

This book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, desi...

Machine learning in pain research

1. Introduction Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence.14 It has been accepted that pain is a complex phenomenon.2,32,72 Contemporary methods of compu...

Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?

In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images i...

Interpretable machine learning for predicting compression index of clays using SHAP and gradient boosting models

Abstract This study introduces a novel, interpretable machine learning framework for predicting the compression index (Cc) of clay soils by integrating three advanced gradient boosting algorithms—XGBoost, CatBoost, and LightGBM—with SHapley...

Simulation of Flow-Induced Vibration and Dynamic Performance of Circular-Arc Helical Gear Pump under Background of Machine Learning

At present, with the continuous development and great improvement of mechanical manufacturing, processing, and assembly technology, mechanical flow-induced vibration (FIV) with a relatively concentrated frequency domain can be controlled by...

Automated speech-fluency explanations for schizophrenia diagnosis

Abstract Schizophrenia is a chronic and severe mental disorder that still relies on time-intensive, clinician-administered assessments. Although several automated approaches have been proposed to support diagnosis, these systems often lack ...

Machine learning for a sustainable energy future