Jove
Visualize
Contact Us

Related Concept Videos

Habitat Fragmentation02:31

Habitat Fragmentation

21.2K
Habitat fragmentation describes the division of a more extensive, continuous habitat into smaller, discontinuous areas. Human activities such as land conversion, as well as slower geological processes leading to changes in the physical environment, are the two leading causes of habitat fragmentation. The fragmentation process typically follows the same steps: perforation, dissection, fragmentation, shrinkage, and attrition.
21.2K
The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

The Sense of Self: Reflected Self-Appraisal and Social Comparison

55.5K
According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
55.5K
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

225
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
225
Introduction to Special Senses01:26

Introduction to Special Senses

7.3K
Sensory receptors play an integral part in comprehending our external and internal environments. They receive diverse stimuli, converting them into the nervous system's electrochemical signals. This conversion occurs as the stimulus alters the sensory neuron's cell membrane potential, instigating the generation of an action potential. This action potential is subsequently transmitted to the central nervous system (CNS), which integrates with other sensory data or higher cognitive...
7.3K
Tactile and Chemical Senses01:27

Tactile and Chemical Senses

732
Tactile senses encompass touch, temperature, and pain, each mediated by specific receptors. Touch receptors detect mechanical energy or pressure against the skin. Sensory fibers from these receptors enter the spinal cord and relay information to the brain stem. Here, most fibers cross over to the opposite side of the brain. The touch information then moves to the thalamus, which projects a map of the body's surface onto the somatosensory areas of the parietal lobes in the cerebral cortex.
732
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.5K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.5K

You might also read

Related Articles

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

Sort by
Same author

Reconstruction of septin higher-order nano-size structures in ovarian cancer cells uncover susceptibility to the septin-targeting small molecule UR214-9.

bioRxiv : the preprint server for biology·2026
Same author

scCAPReSE: detection of large-scale genomic rearrangements from single-cell Hi-C based on few-shot learning.

Genomics & informatics·2026
Same author

Trimodal single-cell profiling of transcriptome, epigenome and 3D genome in complex tissues with scHiCAR.

Nature biotechnology·2026
Same author

The NF-κB-HE4 axis: A novel regulator of HE4 secretion in ovarian cancer.

PloS one·2024
Same author

Identification of circulating tumor cells captured by the FDA-cleared Parsortix<sup>®</sup> PC1 system from the peripheral blood of metastatic breast cancer patients using immunofluorescence and cytopathological evaluations.

Journal of experimental & clinical cancer research : CR·2024
Same author

Forchlorfenuron-Induced Mitochondrial Respiration Inhibition and Metabolic Shifts in Endometrial Cancer.

Cancers·2024
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 Experiment Video

Updated: Jan 23, 2026

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field
10:49

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Published on: March 16, 2019

9.0K

A Deep Learning-Based Automatic Mosquito Sensing and Control System for Urban Mosquito Habitats.

Kyukwang Kim1, Jieum Hyun2, Hyeongkeun Kim3

  • 1Department of Biological Sciences, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Korea. kkim0214@kaist.ac.kr.

Sensors (Basel, Switzerland)
|June 26, 2019
PubMed
Summary

This study introduces an automated mosquito detection system using deep learning, achieving 84% accuracy. The system efficiently targets mosquito larvae with a biopesticide, offering a safer alternative to adult mosquito control.

Keywords:
deep learningdrug spraymosquitourban habitatvector control

More Related Videos

Preventing the Spread of Malaria and Dengue Fever Using Genetically Modified Mosquitoes
17:50

Preventing the Spread of Malaria and Dengue Fever Using Genetically Modified Mosquitoes

Published on: July 4, 2007

12.9K
Mosquito-Associated Virus Isolation from Field-Collected Mosquitoes
06:41

Mosquito-Associated Virus Isolation from Field-Collected Mosquitoes

Published on: August 31, 2022

2.4K

Related Experiment Videos

Last Updated: Jan 23, 2026

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field
10:49

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Published on: March 16, 2019

9.0K
Preventing the Spread of Malaria and Dengue Fever Using Genetically Modified Mosquitoes
17:50

Preventing the Spread of Malaria and Dengue Fever Using Genetically Modified Mosquitoes

Published on: July 4, 2007

12.9K
Mosquito-Associated Virus Isolation from Field-Collected Mosquitoes
06:41

Mosquito-Associated Virus Isolation from Field-Collected Mosquitoes

Published on: August 31, 2022

2.4K

Area of Science:

  • Computer Science
  • Entomology
  • Public Health

Background:

  • Mosquitoes are significant vectors of infectious diseases, necessitating effective control strategies.
  • Traditional mosquito control methods can be inefficient and may harm non-target species.

Purpose of the Study:

  • To develop and evaluate an automated system for mosquito detection and control using artificial intelligence.
  • To assess the efficiency and accuracy of deep learning models in identifying mosquitoes.
  • To implement a targeted larvicide delivery system for mosquito population management.

Main Methods:

  • Utilized multiple deep learning networks, including Fully Convolutional Network (FCN) and neural network-based regression, for image processing-based mosquito detection.
  • Compared the performance of a multi-network system against a single image classifier.
  • Implemented an automated larvicide injection system using *Bacillus thuringiensis* israelensis (Bti) in static water bodies.

Main Results:

  • The automated system achieved an accuracy of 84% in detecting mosquitoes.
  • The single image classifier showed a lower accuracy of 52%.
  • Processing time was significantly reduced from 4.64 s to 2.47 s compared to conventional methods.

Conclusions:

  • The developed automated system demonstrates high efficiency and accuracy in mosquito detection.
  • The integrated approach of AI-driven detection and targeted larvicide application is effective in controlling mosquito proliferation.
  • This method offers a more efficient and environmentally conscious alternative to hunting adult mosquitoes, minimizing harm to beneficial insects.