Academic Articles & Research Papers

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...

DATA MINING AND MACHINE LEARNING IN ASTRONOMY

We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding ...

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...

Machine learning and deep learning—A review for ecologists

Abstract The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI) has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML and DL algorithms are often perceived as opa...

A Sensor-to-API Data Infrastructure for Building Digital Twins: Harmonizing Multi-Rate IoT Energy and Indoor Data Streams for Proactive Analytics

Digital twins are increasingly used in smart buildings to monitor energy use. This requires reliable sensor data, geospatial information, and semantically rich 3D building geometry. However, building sensor data often has inconsistent sampl...

Machine Learning in Healthcare

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, among...

Epistemic frontiers and the distinction between causality, information, and predictability in pattern recognition

Abstract High predictive accuracy is frequently misinterpreted as evidence of causal understanding or population-level signal. Models can exploit spurious correlations, confounding, or protocol-induced artefacts, while post-hoc explanations...

Multidimensional Correlates of Childhood Stunting in India: A Spatial Machine Learning and Explainable AI Approach

Childhood stunting remains a major public health challenge in India and is influenced by multiple socioeconomic and environmental factors. This ecological study examined district-level correlates of childhood stunting, including Crimes Agai...

The challenge of studying perovskite solar cells’ stability with machine learning

Perovskite solar cells are the most dynamic emerging photovoltaic technology and attracts the attention of thousands of researchers worldwide. Recently, many of them are targeting device stability issues–the key challenge for this technolog...

Acceptance of Machine Learning for Medication Selection in Epilepsy to Inform Clinical Trial Design: Co-Design Survey Study

Abstract BackgroundAntiseizure medications (ASMs) are the mainstay of epilepsy treatment; however, there is currently no reliable way to predict which medication will be most effective for an individual patient. Machine learning...

Machine learning applications in epilepsy

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recogn...

Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study

Abstract Background HIV/TB co-infection presents substantial public-health challenges, showing greater treatment-failure and mortality rates than tuberculosis alone. Recent advances in machine learning (ML) provide a robust means of identif...

Interpretable Intersection Control by Reinforcement Learning Agent With Linear Function Approximator

ABSTRACT Reinforcement learning (RL) is a promising machine‐learning solution to traffic signal control problems, which have been extensively studied. However, variants of non‐linear, deep artificial neural network (ANN) function approximat...

Hands-On Machine Learning with R

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process a...

Hybrid machine learning and 3D numerical modeling-based multi-objective optimization of geosynthetic-reinforced pile-supported embankments

Efficient and reliable design of Geosynthetic Reinforced Pile-Supported (GRPS) embankments on soft ground remains challenging due to the complex nonlinear soil–structure interaction and the high computational cost of repeated numerical anal...

Evaluating Model Resilience to Data Poisoning Attacks: A Comparative Study

Machine learning (ML) has become a cornerstone of critical applications, but its vulnerability to data poisoning attacks threatens system reliability and trustworthiness. Prior studies have begun to investigate the impact of data poisoning ...

Machine Learning

A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing wo...

Artificial Intelligence and Machine Learning in Anesthesiology

Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis, and self-lea...

The Integration of Cone Beam Computed Tomography, Artificial Intelligence, Augmented Reality, and Virtual Reality in Dental Diagnostics, Surgical Planning, and Education: A Narrative Review

(1) Background: Advancements in dental imaging technologies have significantly transformed diagnostic and surgical practices. The integration of cone beam computed tomography (CBCT), artificial intelligence (AI), augmented reality (AR), and...

A clinician aligned vision language framework for stepwise interpretation in fundus fluorescein angiography.