Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

System of Memory01:23

System of Memory

6.6K
Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
6.6K
Chunking and Rehearsal in Sensory Memory01:22

Chunking and Rehearsal in Sensory Memory

339
Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
339
Manipulation and Analysis01:21

Manipulation and Analysis

110
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
110
Long-Term Memory01:18

Long-Term Memory

329
Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...
329

You might also read

Related Articles

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

Sort by
Same author

Disease progression in Chinese patients with hepatitis C virus RNA-positive infection via blood transfusion.

Experimental and therapeutic medicine·2016
Same author

The Safety and Efficacy of Dexmedetomidine vs. Sufentanil in Monitored Anesthesia Care during Burr-Hole Surgery for Chronic Subdural Hematoma: A Retrospective Clinical Trial.

Frontiers in pharmacology·2016
Same author

Cognitive Frailty and Adverse Health Outcomes: Findings From the Singapore Longitudinal Ageing Studies (SLAS).

Journal of the American Medical Directors Association·2016
Same author

Distribution differences of macular cones measured by AOSLO: Variation in slope from fovea to periphery more pronounced than differences in total cones.

Vision research·2016
Same author

Universal space-time scaling symmetry in the dynamics of bosons across a quantum phase transition.

Science (New York, N.Y.)·2016
Same author

High-Nuclear Organometallic Copper(I)-Alkynide Clusters: Thermochromic Near-Infrared Luminescence and Solution Stability.

Chemistry (Weinheim an der Bergstrasse, Germany)·2016

Related Experiment Video

Updated: Oct 19, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.9K

Optimization Algorithm of Tourism Security Early Warning Information System Based on Long Short-Term Memory (LSTM).

Lei Feng1, Yukai Hao1,2

  • 1School of Management, Wuhan University of Technology, Wuhan 400070, China.

Computational Intelligence and Neuroscience
|September 20, 2021
PubMed
Summary

This study introduces an optimized tourism security early warning system using Long Short-Term Memory (LSTM) networks. The system enhances real-time data analysis and prediction for improved tourist safety and sustainable tourism development.

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K

Related Experiment Videos

Last Updated: Oct 19, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.9K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K

Area of Science:

  • Tourism Management
  • Artificial Intelligence
  • Data Science

Background:

  • Tourism safety is critical for tourist well-being, social stability, and industry sustainability.
  • Current security systems often react to incidents rather than proactively warning of potential risks.
  • Limited staff awareness and analytical capabilities hinder timely information dissemination in many tourist destinations.

Purpose of the Study:

  • To develop an optimized tourism security early warning information system.
  • To enhance the processing and prediction capabilities for time-series security data.
  • To improve real-time monitoring and dynamic analysis of potential security issues in tourism.

Main Methods:

  • Utilized a Long Short-Term Memory (LSTM) recurrent neural network model.
  • Optimized the LSTM model architecture, specifically exploring the impact of three hidden layers.
  • Compared the LSTM-based system's performance against a traditional Backpropagation (BP) neural network system.

Main Results:

  • The LSTM model with three hidden layers reduced training time and improved system performance.
  • The proposed system demonstrated superior accuracy and stability compared to the BP neural network.
  • The system exhibits enhanced capabilities for processing and predicting time-series data for tourism security.

Conclusions:

  • The LSTM-based tourism security early warning system offers significant improvements in data analysis and prediction.
  • The optimized model facilitates scientific, real-time, and dynamic monitoring of tourism security data.
  • This approach contributes to proactive risk management, enhancing overall tourist safety and industry resilience.