Synchronization, Optimization, and Adaptation of Machine Learning Techniques for Computer Vision in Cyber-Physical Systems: A Comprehensive Analysis

Published: Jan. 7, 2025

Cyber-Physical Systems (CPS) seamlessly integrate computers, networks, and physical devices, enabling machines to communicate, process data, respond real-world conditions in real-time. By bridging the digital worlds, CPS ensures operations that are efficient, safe, innovative, controllable. As smart cities autonomous become more prevalent, understanding is crucial for driving future progress. Recent advancements edge computing, AI-driven vision, collaborative systems have significantly enhanced capabilities. Synchronization, optimization, adaptation intricate processes impact performance across different domains. Therefore, identifying emerging trends uncovering research gaps essential highlight areas require further investigation improvement. A systematic review facilitates this by allowing researchers benchmark compare various techniques, evaluate their effectiveness, establish best practices. It provides evidence-based insights into optimal strategies implementation while addressing potential trade-offs performance, resource usage, reliability. Additionally, such reviews help identify widely accepted standards frameworks, contributing development of standardized approaches.

Language: Английский

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HFSA: hybrid feature selection approach to improve medical diagnostic system

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2764 - e2764

Published: May 6, 2025

Thanks to the presence of artificial intelligence methods, diagnosis patients can be done quickly and accurately. This article introduces a new diagnostic system (DS) that includes three main layers called rejection layer (RL), selection (SL), (DL) accurately diagnose cases suffering from various diseases. In RL, outliers removed using genetic algorithm (GA). At same time, best features selected by feature method hybrid approach (HFSA) in SL. next step, filtered data is passed naive Bayes (NB) classifier DL give accurate diagnoses. this work, contribution represented introducing HFSA as composed two stages; fast stage (FS) (AS). FS, chi-square, filtering methodology, applied select while Hybrid Optimization Algorithm (HOA), wrapper AS features. It concluded better than other methods based on experimental results because enable different classifiers NB, K-nearest neighbors (KNN), neural network (ANN) provide maximum accuracy, precision, recall values minimum error value. Additionally, proved DS, including GA an outlier method, selection, NB mode, outperformed models.

Language: Английский

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Machine learning for brain tumor classification: evaluating feature extraction and algorithm efficiency

Discover Artificial Intelligence, Journal Year: 2024, Volume and Issue: 4(1)

Published: Dec. 19, 2024

Language: Английский

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Review on Automated Brain Tumor Segmentation using Advanced Deep Learning Techniques: Enhancing Precision and Clinical Applicability

Published: Dec. 4, 2024

Language: Английский

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