Personalization of Learning: Machine Learning Models for Adapting Educational Content to Individual Learning Styles

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 121114 - 121130

Published: Jan. 1, 2024

Language: Английский

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Optimized Energy-Efficient Routing Protocol for Wireless Sensor Network Integrated with IoT: An Approach Based on Deep Convolutional Neural Network and Metaheuristic Algorithms

Journal of Nanoelectronics and Optoelectronics, Journal Year: 2023, Volume and Issue: 18(3), P. 367 - 379

Published: March 1, 2023

Wireless sensor networks (WSNs) have emerged as a significant architecture for data collection in various applications. However, the integration of WSNs with IoT poses energy-related challenges due to limited node energy, increased energy consumption wireless sharing, and necessity energy-efficient routing protocols reliable transmission reduced consumption. This paper proposes an optimized protocol integrated Internet Things. The aims improve network lifetime secure by identifying optimal Cluster Heads (CHs) network, selected using Tree Hierarchical Deep Convolutional Neural Network. To achieve this, introduces fitness function that takes into account cluster density, traffic rate, collision, delay throughput, distance from capacity node. Additionally, considers three factors, including trust, connectivity, QoS, determine best course action. also presents novel optimization approach, hybrid Marine Predators Algorithm (MPA) Woodpecker Mating (WMA), optimize QoS parameters path selection minimal delay. simulation process is implemented MATLAB, developed method’s efficiency evaluated several performance metrics. results demonstrate effectiveness proposed method, which achieved significantly lower (99.67%, 98.38%, 89.34%, 97.45%), higher delivery ratio (89.34%, 83.12%, 88.96%), packet drop (93.15%, 91.25%, 79.90%, 92.88%) comparison existing methods. These outcomes indicate potential ensure IoT.

Language: Английский

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Multimodal AI techniques for pain detection: integrating facial gesture and paralanguage analysis

Frontiers in Computer Science, Journal Year: 2024, Volume and Issue: 6

Published: July 29, 2024

Accurate pain detection is a critical challenge in healthcare, where communication and interpretation of often limit traditional subjective assessments. The current situation characterized by the need for more objective reliable methods to assess pain, especially patients who cannot effectively communicate their experiences, such as young children or critically ill individuals. Despite technological advances, effective integration artificial intelligence tools multifaceted accurate continues present significant challenges. Our proposal addresses this problem through an interdisciplinary approach, developing hybrid model that combines analysis facial gestures paralanguage using techniques. This contributes significantly field, allowing objective, accurate, sensitive individual variations. results obtained have been notable, with our achieving precision 92%, recall 90%, specificity 95%, demonstrating evident efficiency over conventional methodologies. clinical implications include possibility improving assessment various medical settings, faster interventions, thereby patients’ quality life.

Language: Английский

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Design a Secure Routing and Monitoring Framework Based on Hybrid Optimization for IoT-Based Wireless Sensor Networks

Journal of Nanoelectronics and Optoelectronics, Journal Year: 2023, Volume and Issue: 18(3), P. 338 - 346

Published: March 1, 2023

Wireless Sensor Networks (WSNs) have employed in recent years for many different applications and functions. But, it has the critical task to detect malicious node because attacks are dangerous attacks, concept of a attack is opponents enter network, search accidentally, capture one or more normal nodes. A lot research developed overcome this problem, but no precise results found. In paper, design Hybrid Vulture African Buffalo with Node Identity Verification (HVAB-NIV) model predict nodes WSN. The fitness functions HVAB-NIV operated recognize energy level each improve performance detection. replica includes three stages that monitor node, calculate node. More than 100 inputs were initialized proposed technique implemented MATLAB tool. suggested mechanism enhances detection gains good accuracy detecting also, saves running time power consumption. experimental validated other existing replicas time, False Prediction Rate (FPR), accuracy, True (TPR), methods achieve better by gaining high rate detection, less false

Language: Английский

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Improving Healthcare Facility Safety with Electronic Monitoring by a Machine Learning Framework Based on the Internet of Things

Journal of Nanoelectronics and Optoelectronics, Journal Year: 2023, Volume and Issue: 18(3), P. 347 - 356

Published: March 1, 2023

Hacks, unauthorised access, and other problems have increased the risk to healthcare system dependent on data analytics in recent years. When a is kept its factory settings, it provides an easier target for hackers who wish get access server steal data. In order protect privacy of patients, we use innovative encryption approach called Whale-based Random Forest (WbRF) Scheme this research. Furthermore, ciphertext made by layering micro-electronic sensors employing Identity-based Encryption (IBE) plaintext. The purpose surveillance ensure model’s continued health while keeping vigilant eye out threats. Therefore framework programmed into Python tool, trained more than 200 patient datasets. Medical records patients can be encrypted stored safely cloud using nano-electronic jargon, end. generated model subjected various attacks determine how secure effective really is. Energy consumption, execution time, latency, accuracy, decryption time are compared between created conventional methods.

Language: Английский

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Enhanced Nanoelectronic Detection and Classification of Motor Imagery Electroencephalogram Signal Using a Hybrid Framework

Journal of Nanoelectronics and Optoelectronics, Journal Year: 2023, Volume and Issue: 18(10), P. 1254 - 1263

Published: Oct. 1, 2023

Motor imagery-based electroencephalogram (MI-EEG) signal classification plays a vital role in the development of brain-computer interfaces (BCIs), particularly providing assistance to individuals with motor disabilities. In this study, we introduce an innovative and optimized hybrid framework designed for robust MI-EEG signals. Our approach combines power Deep Convolutional Neural Network (DCRNN) efficiency Ant Lion Optimization (ALO) algorithm. This consists four key phases: data acquisition, pre-processing, feature engineering, classification. To enhance quality, our work incorporates adaptive filtering independent component analysis (ICA) during pre-processing phase. Feature extraction is carried out using deep autoencoder. For classification, employ DCRNN, further its performance ALO algorithm optimize training processes. The study implemented MATLAB evaluated PhysioNet dataset. Experimental results demonstrate effectiveness proposed method, achieving impressive accuracy 99.32%, precision 99.41%, recall 99.29%, f-measure 99.32%. These surpass existing strategies, highlighting potential various BCI applications.

Language: Английский

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Nanoscale Optical Sensing Approaches for Quantitative Evaluation of Acupuncture and Moxibustion Therapeutic Efficacy

Journal of Nanoelectronics and Optoelectronics, Journal Year: 2024, Volume and Issue: 19(1), P. 69 - 74

Published: Jan. 1, 2024

Acupuncture and moxibustion, integral components of traditional medicine, encounter challenges in achieving objective stable quantitative assessments. This study delves into the utilization nanoscale optical sensing technology, with a particular emphasis on graphene materials, to quantitatively analyze therapeutic efficacy acupuncture moxibustion. Initially, we examine properties synthesis methods followed by comprehensive characterization these materials. Subsequently, effectiveness graphene-based quantifying impact moxibustion is evaluated through meta-analysis, drawing upon data obtained from diverse literature databases. The findings reveal high level measurement accuracy, an Odds Ratio (OR) 53 within 95% Confidence Interval (CI) 27 76 P -value 0.75. These results underscore significant potential nanotechnologies, specifically sensing, enhancing objectivity precision assessments medicine practices.

Language: Английский

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Utilizing Machine Learning for Predictive Analysis in Cardiac Healthcare: A Detailed Survey

Published: June 5, 2024

Language: Английский

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Enhancement of Optical Coherence Tomography for Early Diagnostics Through Ag-Decorated ZnO Quantum Dots-Induced Motion Analysis

Journal of Nanoelectronics and Optoelectronics, Journal Year: 2023, Volume and Issue: 18(12), P. 1451 - 1457

Published: Dec. 1, 2023

Optical Coherence Tomography (OCT) stands as a pivotal imaging modality in medical diagnostics, providing intricate insights into microstructural alterations within biological tissues. This research delves the augmentative impact of nanostructures on OCT, with specific emphasis their potential applications early diagnostic scenarios. The article introduces novel composite material, Silver-Zinc Oxide (Ag-ZnO) nano-structures, synthesized through amalgamation zinc oxide (ZnO) quantum dots and silver (Ag) particles. study scrutinizes enhancement effect these depth capability precision OCT. Employing finite difference time domain method, simulates calculates extinction spectrum Ag-ZnO Comparative analyses are conducted to evaluate effectiveness accuracy OCT when enhanced against Magnetic Resonance Imaging (MRI) technology. outcomes manifest noteworthy improvement integration underscoring efficacy heightening for applications. not only accentuates role played by amplifying capabilities but also paves way advancement sophisticated tools realm imaging.

Language: Английский

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Utilization of Near Infrared Spectroscopy Imaging at Nanoscale for the Identification of Biomarkers in Sports-Induced Muscle Fatigue

Journal of Nanoelectronics and Optoelectronics, Journal Year: 2023, Volume and Issue: 18(12), P. 1517 - 1526

Published: Dec. 1, 2023

This study integrates Near Infrared Spectroscopy (NIRS) and nanoscale imaging technologies to discern alterations in muscle tissue biomarkers, thereby enhancing the precision of non-invasive monitoring fatigue. Experimental investigations were carried out on biceps brachii 12 subjects, categorized into mild, moderate, severe fatigue groups. Concurrently, a specific wavelength Laser Diode (NIR-LD) was employed acquire spectral data. The application Atomic Force Microscopy (AFM) conjunction with NIRS facilitated attainment high-resolution images tissue. absorption characteristics distinct biomarkers tissue, responsive near-infrared light, captured calculate concentration variations evaluate levels. findings revealed substantial concentrations Oxy-hemoglobin (HbO), Deoxy-hemoglobin (HbR), Lactic Acid (LA), Phosphocreatine (PCr), Troponin (Tn), Creatine Kinase (CK), Glutamine (Gln) across different Muscle assessment exhibited an average sensitivity, accuracy, specificity, F1 score 0.96, 0.95, respectively, for subjects. Area Under Curve (AUC) values detecting 0.98, respectively. method demonstrates notable accuracy identification rendering it suitable sports-related assessment.

Language: Английский

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