Cost-Effective Autonomous Drone Navigation Using Reinforcement Learning: Simulation and Real-World Validation DOI Creative Commons

Tomasz Czarnecki,

Marek Stawowy,

Adam Kadłubowski

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 15(1), P. 179 - 179

Published: Dec. 28, 2024

Artificial intelligence (AI) is used in tasks that usually require human intelligence. The motivation behind this study the growing interest deploying AI public spaces, particularly autonomous vehicles such as flying drones, to address challenges navigation and control. primary challenge lies developing a robust, cost-effective system capable of real-world environments, handling obstacles, adapting dynamic conditions. To tackle this, we propose novel approach integrating machine learning (ML) algorithms, specifically, reinforcement (RL), with comprehensive simulation testing framework. Reinforcement algorithms designed solve problems requiring optimization solution for highest possible reward were used. It was assumed do not have be created from scratch, but they need well-defined training environment will appropriately or punish actions taken. This aims develop implement drone using algorithms. innovation integration ML control system, encompassing both simulations testing. A vital component creating multi-stage accurately replicates actual flight conditions progressively increases complexity scenarios, ensuring robust evaluation algorithm performance. research also introduces new optimizing cost accessibility. involves commercially available, drones open-source free tools, significantly reducing entry barriers potential users. critical aspect assess whether affordable components can provide sufficient accuracy stability without compromising quality. authors developed autonomously determining optimal paths controlling drone, allowing it avoid obstacles respond real time. performance trained confirmed through flights, which allowed assessing their usefulness practical scenarios.

Language: Английский

3D Bioprinting of Natural Materials and Their AI-Enhanced Printability: A Review DOI
Soumaya Grira, Mohammad Sayem Mozumder, Abdel‐Hamid I. Mourad

et al.

Bioprinting, Journal Year: 2025, Volume and Issue: unknown, P. e00385 - e00385

Published: Jan. 1, 2025

Language: Английский

Citations

2
The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry DOI Creative Commons

Lakshman P. Samaranayake,

Nozimjon Tuygunov, Falk Schwendicke

et al.

International Dental Journal, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Artificial intelligence (AI) holds immense promise in revolutionising dentistry, spanning, diagnostics, treatment planning and educational realms. This narrative review, two parts, explores the fundamentals multifaceted potential of AI dentistry. The current article profound impact encompassing diagnostic tools, planning, patient care. Part 2 delves into education, ethics FDI communique on review begins by elucidating historical context AI, outlining its recent widespread use various sectors, including medicine fundamental concepts which entails developing machines capable executing tasks that typically necessitate human intellect. In biomedical realm, has evolved from exploring computational models to constructing systems for clinical data processing interpretation, aiming enhance medical/dental decision-making. discussion pivotal role such as Large Language Models (LLM), Vision (LVM), Multimodality (MM), revolutionizing processes documentation planning. extends applications dental specialties periodontics, endodontics, oral pathology, restorative prosthodontics, paediatric forensic odontology, maxillofacial surgery, orthodontics, orofacial pain management. AI's improving outcomes, accuracy, decision-making is evident across these specialties, showcasing transforming concludes highlighting need continued validation, interdisciplinary collaboration, regulatory frameworks ensure seamless integration paving way enhanced outcomes evidence-based practice field.

Language: Английский

Citations

2
Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine DOI Open Access
Nikolaos Theodorakis, Georgios Feretzakis, Lazaros Tzelves

et al.

Journal of Personalized Medicine, Journal Year: 2024, Volume and Issue: 14(9), P. 931 - 931

Published: Aug. 31, 2024

Aging is a fundamental biological process characterized by progressive decline in physiological functions and an increased susceptibility to diseases. Understanding aging at the molecular level crucial for developing interventions that could delay or reverse its effects. This review explores integration of machine learning (ML) with multi-omics technologies-including genomics, transcriptomics, epigenomics, proteomics, metabolomics-in studying hallmarks develop personalized medicine interventions. These include genomic instability, telomere attrition, epigenetic alterations, loss proteostasis, disabled macroautophagy, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, dysbiosis. Using ML analyze big complex datasets helps uncover detailed interactions pathways play role aging. The advances can facilitate discovery biomarkers therapeutic targets, offering insights into anti-aging strategies. With these developments, future points toward better understanding process, aiming ultimately promote healthy extend life expectancy.

Language: Английский

Citations

12
Towards unbiased skin cancer classification using deep feature fusion DOI Creative Commons

Ali Atshan Abdulredah,

Mohammed A. Fadhel, Laith Alzubaidi

et al.

BMC Medical Informatics and Decision Making, Journal Year: 2025, Volume and Issue: 25(1)

Published: Jan. 31, 2025

Abstract This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple pathways, emphasizing width augmentation to enhance efficiency. The proposed model addresses potential biases associated with conditions, particularly in individuals darker tones or excessive hair, by incorporating fusion assimilate insights from diverse datasets. Extensive experiments were conducted using publicly accessible datasets evaluate SWNet’s effectiveness.This study utilized four datasets-Mnist-HAM10000, ISIC2019, ISIC2020, Melanoma Skin Cancer-comprising images categorized into benign classes. Explainable Artificial Intelligence (XAI) techniques, specifically Grad-CAM, employed interpretability model’s decisions. Comparative analysis was performed three pre-existing learning networks-EfficientNet, MobileNet, Darknet. results demonstrate superiority, achieving an accuracy 99.86% F1 score 99.95%, underscoring its efficacy gradient propagation capture across various levels. research highlights significant advancing classification, providing robust tool accurate early diagnosis. integration enhances mitigates hair tones. outcomes this contribute improved patient healthcare practices, showcasing exceptional capabilities classification.

Language: Английский

Citations

1
Machine learning for medical image classification DOI Creative Commons
Gulam Mohammed Husain, Jonathan Mayer,

Molly Bekbolatova

et al.

Academia Medicine, Journal Year: 2024, Volume and Issue: 1(4)

Published: Dec. 23, 2024

This review article focuses on the application of machine learning (ML) algorithms in medical image classification. It highlights intricate process involved selecting most suitable ML algorithm for predicting specific conditions, emphasizing critical role real-world data testing and validation. navigates through various methods utilized healthcare, including Supervised Learning, Unsupervised Self-Supervised Deep Neural Networks, Reinforcement Ensemble Methods. The challenge lies not just selection an but identifying appropriate one a task as well, given vast array options available. Each unique dataset requires comparative analysis to determine best-performing algorithm. However, all available is impractical. examines performance recent studies, focusing their applications across different imaging modalities diagnosing conditions. provides summary these offering starting point those seeking select conditions modalities.

Language: Английский

Citations

4
Optimizing Robotic Arm Learning: Curiosity-Driven Deep Deterministic Policy Gradient DOI Creative Commons
Jiarun Liu

ITM Web of Conferences, Journal Year: 2025, Volume and Issue: 73, P. 01007 - 01007

Published: Jan. 1, 2025

This study explores the application of Reinforcement Learning (RL) in training robotic arms, particularly using Deep Deterministic Policy Gradient (DDPG) algorithm enhanced by a curiosity- driven mechanism. Robotic arms have various real-life applications, such as surgeries and assistive technologies. However, collecting large- scale real-world data is costly impractical, making simulation environments essential for optimization. The DDPG, well-suited continuous action spaces, was employed to improve arm’s precision adaptability. Integrating curiosity mechanism allowed system explore learn more efficiently, significantly improving time success rate. results demonstrate 12% reduction an 18% increase rate when exploration. These findings suggest that DDPG not only accelerates learning but also enables better task execution, offering promising approach applications.

Language: Английский

Citations

0
Artificial intelligence and pediatric acute kidney injury: a mini-review and white paper DOI Creative Commons
Jieji Hu, Rupesh Raina

Frontiers in Nephrology, Journal Year: 2025, Volume and Issue: 5

Published: Feb. 18, 2025

Acute kidney injury (AKI) in pediatric and neonatal populations poses significant diagnostic management challenges, with delayed detection contributing to long-term complications such as hypertension chronic disease. Recent advancements artificial intelligence (AI) offer new avenues for early detection, risk stratification, personalized care. This paper explores the application of AI models, including supervised unsupervised machine learning, predicting AKI, improving clinical decision-making, identifying subphenotypes that respond differently interventions. It discusses integration existing scores biomarkers enhance predictive accuracy its potential revolutionize nephrology. However, barriers data quality, algorithmic bias, need transparent ethical implementation are critical considerations. Future directions emphasize incorporating biomarkers, expanding external validation, ensuring equitable access optimize outcomes AKI

Language: Английский

Citations

0
Lights and Shadows on Artificial Intelligence in Glaucoma: Transforming Screening, Monitoring, and Prognosis DOI Open Access
Alessio Martucci, Gabriele Gallo Afflitto,

Giulio Pocobelli

et al.

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(7), P. 2139 - 2139

Published: March 21, 2025

Background/Objectives: Artificial intelligence (AI) is increasingly being integrated into medicine, including ophthalmology, owing to its strong capabilities in image recognition. Methods: This review focuses on the most recent key applications of AI diagnosis and management of, as well research on, glaucoma by performing a systematic latest papers literature. Results: In glaucoma, can help analyze large amounts data from diagnostic tools, such fundus images, optical coherence tomography scans, visual field tests. Conclusions: technologies enhance accuracy diagnoses could provide significant economic benefits automating routine tasks, improving accuracy, enhancing access care, especially underserved areas. However, despite these promising results, challenges persist, limited dataset size diversity, class imbalance, need optimize models for early detection, integration multimodal clinical practice. Currently, ophthalmologists are expected continue playing leading role managing glaucomatous eyes overseeing development validation tools.

Language: Английский

Citations

0
Advanced deep learning and large language models: Comprehensive insights for cancer detection DOI
Yassine Habchi, Hamza Kheddar, Yassine Himeur

et al.

Image and Vision Computing, Journal Year: 2025, Volume and Issue: unknown, P. 105495 - 105495

Published: March 1, 2025

Language: Английский

Citations

0
Artificial intelligence and its application in clinical microbiology DOI
Assia Mairi,

Lamia Hamza,

Abdelaziz Touati

et al.

Expert Review of Anti-infective Therapy, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

Traditional microbiological diagnostics face challenges in pathogen identification speed and antimicrobial resistance (AMR) evaluation. Artificial intelligence (AI) offers transformative solutions, necessitating a comprehensive review of its applications, advancements, integration clinical microbiology. This examines AI-driven methodologies, including machine learning (ML), deep (DL), convolutional neural networks (CNNs), for enhancing detection, AMR prediction, diagnostic imaging. Applications virology (e.g. COVID-19 RT-PCR optimization), parasitology malaria detection), bacteriology automated colony counting) are analyzed. A literature search was conducted using PubMed, Scopus, Web Science (2018-2024), prioritizing peer-reviewed studies on AI's accuracy, workflow efficiency, validation. AI significantly improves precision operational efficiency but requires robust validation to address data heterogeneity, model interpretability, ethical concerns. Future success hinges interdisciplinary collaboration develop standardized, equitable tools tailored global healthcare settings. Advancing explainable federated frameworks will be critical bridging current implementation gaps maximizing potential combating infectious diseases.

Language: Английский

Citations

0