Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 26, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

A Fire Detection Method Based on a Mind-Linked Continuous-Coupled Neural Network.

Kangrong Liu¹, Ji Wang², Wei Yang²

¹School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.

Biomimetics (Basel, Switzerland)

|June 25, 2026

Summary

Related Concept Videos

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Prediction Model of Lymph Node Metastasis in Cervical Cancer Based on MRI Habitat Radiomics.

Cancers·2026

Same author

Revealing the mechanisms of semantic satiation with deep learning models.

Communications biology·2024

Same author

Infrared UAV Target Detection Based on Continuous-Coupled Neural Network.

Micromachines·2023

Same author

FDNet: An end-to-end fusion decomposition network for infrared and visible images.

PloS one·2023

Same author

An Approach for the Generation of an Nth-Order Chaotic System with Hyperbolic Sine.

Entropy (Basel, Switzerland)·2020

Same author

Complex reconstruction technique applied in advanced head and neck cancer.

Medicine·2020

Same journal

Multiphysics Investigation on Thermal Characteristics of Internal Bio-Inspired V-Ribbed Cooling Channels for Outer Rotor PMSM.

Biomimetics (Basel, Switzerland)·2026

Same journal

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.

Biomimetics (Basel, Switzerland)·2026

Same journal

A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions.

Biomimetics (Basel, Switzerland)·2026

Same journal

Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness.

Biomimetics (Basel, Switzerland)·2026

Same journal

Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis.

Biomimetics (Basel, Switzerland)·2026

Same journal

A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications.

Biomimetics (Basel, Switzerland)·2026

See all related articles

This summary is machine-generated.

This study introduces an improved mind-linked continuous-coupled neural network (ML-CCNN) for enhanced fire detection. The novel approach significantly reduces false alarms and missed detections in Internet of Things (IoT) systems.

Area of Science:

Computer Science
Artificial Intelligence
Sensor Networks

Background:

Internet of Things (IoT) fire detection systems face challenges with false alarms and missed detections due to environmental noise and sensor heterogeneity.
Existing machine learning methods show limited performance in improving classification accuracy for these systems.

Purpose of the Study:

To develop an advanced neural network model inspired by human cognitive mechanisms to improve the accuracy and reliability of IoT-based fire detection systems.
To address data imbalance and enhance feature representation for more robust fire detection.

Main Methods:

Developed an improved mind-linked continuous-coupled neural network (ML-CCNN) based on the existing CCNN architecture.
Implemented a parameter adaptation mechanism for modulating neural activations via a global threshold.

Keywords:

ML-CCNN brain-inspired computing fire detection smoke alarm

Related Experiment Videos

Last Updated: Jun 26, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Utilized Synthetic Minority Oversampling Technique (SMOTE) for data balancing and transformed feature vectors into matrices for training.

Main Results:

Achieved a high accuracy of 99.96% on a custom dataset.
Attained 99.97% accuracy on the public Smoke Detection Dataset (SDD).
Demonstrated significant reduction in false alarms and missed detections compared to existing methods.

Conclusions:

The ML-CCNN model shows significant potential for improving the performance of IoT fire detection systems.
The proposed methods effectively address data imbalance and enhance model robustness.
This advancement contributes to enhanced public safety through more reliable fire detection infrastructure.