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Journal articles on the topic 'Artificial intelligence in entomology'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Mansouri, Vahid, Saber Gholizadeh, and Saeed Hosseinpoor. "Impact of artificial intelligence on medical entomology research." Health Science Monitor 3, no. 2 (2024): 113–19. http://dx.doi.org/10.61186/hsm.3.2.113.

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2

Sharma, Ram Prakash, Anasuya Boruah, Anam Khan, et al. "Exploring the Significance of Insects in Ecosystems: A Comprehensive Examination of Entomological Studies." International Journal of Environment and Climate Change 13, no. 11 (2023): 1243–52. http://dx.doi.org/10.9734/ijecc/2023/v13i113276.

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The dynamic and expansive field of entomology is poised for transformative changes, thanks to technological advancements and the emergence of new avenues of research. Traditional methodologies like specimen collection and observation are increasingly being complemented by state-of-the-art techniques such as environmental DNA (eDNA) analysis, CRISPR genome editing, remote sensing, and artificial intelligence. The eDNA methodology, for example, offers a non-invasive approach to monitor elusive or endangered insect species, thereby enriching biodiversity databases. Genome editing technologies lik

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3

Lefèvre, Thomas, and Laurent Tournois. "Artificial Intelligence and Diagnostics in Medicine and Forensic Science." Diagnostics 13, no. 23 (2023): 3554. http://dx.doi.org/10.3390/diagnostics13233554.

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Diagnoses in forensic science cover many disciplinary and technical fields, including thanatology and clinical forensic medicine, as well as all the disciplines mobilized by these two major poles: criminalistics, ballistics, anthropology, entomology, genetics, etc. A diagnosis covers three major interrelated concepts: a categorization of pathologies (the diagnosis); a space of signs or symptoms; and the operation that makes it possible to match a set of signs to a category (the diagnostic approach). The generalization of digitization in all sectors of activity—including forensic science, the a

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4

Fraiwan, Mohammad, Rami Mukbel, and Dania Kanaan. "Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species." PLOS ONE 20, no. 4 (2025): e0320224. https://doi.org/10.1371/journal.pone.0320224.

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Sandflies are vectors for several tropical diseases such as leishmaniasis, bartonellosis, and sandfly fever. Moreover, sandflies exhibit species-specificity in transmitting particular pathogen species, with females being responsible for disease transmission. Thus, effective classification of sandfly species and the corresponding sex identification are important for disease surveillance and control, managing breeding/populations, research and development, and conducting epidemiological studies. This is typically performed manually by observing internal morphological features, which maybe an err

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5

Veremme, Alexandre, Éric Lefevre, Gildas Morvan, Daniel Dupont, and Daniel Jolly. "Evidential calibration process of multi-agent based system: An application to forensic entomology." Expert Systems with Applications 39, no. 3 (2012): 2361–74. http://dx.doi.org/10.1016/j.eswa.2011.08.075.

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6

De, Mitu, and Santi Ranjan Dey. "An overview on Taxonomic Keys and Automated Species Identification (ASI)." INTERNATIONAL JOURNAL OF EXPERIMENTAL RESEARCH AND REVIEW 20 (December 30, 2019): 40–47. http://dx.doi.org/10.52756/ijerr.2019.v20.004.

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A core goal of taxonomy and systematics, entomology, field botany, horticulture, zoology and many agriculture courses involves learning to identify plants and animals. But current syllabi and time constraint allow students to see only a limited amount of taxonomic variability. Usually only experts such as taxonomists and trained technicians can identify taxa accurately because it requires special skills acquired through extensive experience. Taxonomic keys are essential tools for species identification, used by students and professionals. The development of computer-based, multi-media keys pro

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7

Fraiwan, Mohammad. "Efficient Convolutional Neural Network Model for the Taxonomy and Sex Identification of Three Phlebotomine Sandfly Species (Diptera, Psychodidae, and Phlebotominae)." Animals 14, no. 24 (2024): 3712. https://doi.org/10.3390/ani14243712.

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Sandflies, small insects primarily from the Psychodidae family, are commonly found in sandy, tropical, and subtropical regions. Most active during dawn and dusk, female sandflies feed on blood to facilitate egg production. In doing so, they can transmit infectious diseases that may cause symptoms such as fever, headaches, muscle pain, anemia, skin rashes, and ulcers. Importantly, sandflies are species-specific in their disease transmission. Determining the gender and species of sandflies typically involves examining their morphology and internal anatomy using established identification keys. H

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8

Espinosa-Chim, Alejandro D., Abigail Cetina-Romero, Héctor Iván Chumba-Poot, et al. "A Deep Learning Approach for Automated Identification of Triatoma infestans Using YOLOv8." Journal of Artificial Intelligence and Computing Applications 2, no. 2 (2024): 30–38. https://doi.org/10.5281/zenodo.14976415.

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Triatoma infestans, a primary vector of Chagas disease, poses a significant public health risk in Latin America. Rapid and accurate identification of this insect is essential for both vector surveillance programs and individual-level decision-making after potential exposure. Traditional identification methods rely on manual inspection, which is time-consuming, error-prone, and dependent on expert knowledge. This study explores the feasibility of an AI-driven detection system based on the medium YOLOv8 model (YOLOv8m) to automate the identification of T. infestans from images. The model was tra

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9

Liu, Chao, Jing Liu, Li Xu, and Wei Xiang. "Recent achievements in bionic implementations of insect structure and functions." Kybernetes 43, no. 2 (2014): 307–24. http://dx.doi.org/10.1108/k-09-2013-0192.

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Purpose – Entomology is a useful tool when applied to engineering challenges that have been solved in nature. Especially when these special abilities of olfactory sensation, vision, auditory perception, fly, jump, navigation, chemical synthesis, exquisite structure and others were connected with mechanization, informationization and intelligentization of modern science and technology, and produced innumerable classical bionic products. The paper aims to discuss these issues. Design/methodology/approach – All kinds of special abilities of insects and application status have been described and d

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10

Salili-James, Arianna, Ben Scott, Laurence Livermore, et al. "AI-Accelerated Digitisation of Insect Collections: The next generation of Angled Label Image Capture Equipment (ALICE)." Biodiversity Information Science and Standards 7 (September 15, 2023): e112742. https://doi.org/10.3897/biss.7.112742.

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The digitisation of natural science specimens is a shared ambition of many of the largest collections, but the scale of these collections, estimated at at least 1.1 billion specimens (Johnson et al. 2023), continues to challenge even the most resource-rich organisations.The Natural History Museum, London (NHM) has been pioneering work to accelerate the digitisation of its 80 million specimens. Since the inception of the NHM Digital Collection Programme in 2014, more than 5.5 million specimen records have been made digitally accessible. This has enabled the museum to deliver a tenfold increase

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11

Dai, Peng, Mausam, and Daniel Weld. "Artificial Intelligence for Artificial Artificial Intelligence." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1153–60. http://dx.doi.org/10.1609/aaai.v25i1.8096.

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Crowdsourcing platforms such as Amazon Mechanical Turk have become popular for a wide variety of human intelligence tasks; however, quality control continues to be a significant challenge. Recently, we propose TurKontrol, a theoretical model based on POMDPs to optimize iterative, crowd-sourced workflows. However, they neither describe how to learn the model parameters, nor show its effectiveness in a real crowd-sourced setting. Learning is challenging due to the scale of the model and noisy data: there are hundreds of thousands of workers with high-variance abilities. This paper presents an en

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12

Kambur, Emine. "Emotional Intelligence or Artificial Intelligence?: Emotional Artificial Intelligence." Florya Chronicles of Political Economy 7, no. 2 (2015): 147–68. http://dx.doi.org/10.17932/iau.fcpe.2015.010/fcpe_v07i2004.

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13

Floridi, Luciano. "Artificial artificial intelligence." Philosophers' Magazine, no. 64 (2014): 22–23. http://dx.doi.org/10.5840/tpm2014647.

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14

Youm, Jung-seop. "Commercialization of Artificial Intelligence and Artificial Intelligence." Journal of Korean Association for Buddhist Studies 85 (March 31, 2018): 65–94. http://dx.doi.org/10.22255/jkabs.85.3.

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15

Parikh, Purvish M. "Artificial Intelligence: ChatGPT to Artificial Intelligence Washing." Journal of Mahatma Gandhi University of Medical Sciences and Technology 8, no. 1 (2024): 1–4. http://dx.doi.org/10.5005/jp-journals-10057-0231.

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16

Johnson, Jeffrey, Peter Denning, Andrew Odlyzko, et al. "Artificial Intelligence: Foundational Technologies of Artificial Intelligence." Ubiquity 2025, July (2025): 1–10. https://doi.org/10.1145/3747355.

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More than the 70 years since its emergence in the early 1950s, artificial intelligence (AI) is performing cognitive tasks traditionally considered the unique province of humans. This progress did not occur in a vacuum. AI emerged against a rich background of technologies from computer science and ideas about intelligence and learning from philosophy, psychology, logic, game theory, and cognitive science. We sketch out the enabling technologies for AI. They include search, reasoning, neural networks, natural language processing, signal processing and computer graphics, programming and conventio

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17

Bernatzeder, Petra. "Artificial Intelligence ... »mental intelligence«." Digitale Welt 4, no. 1 (2019): 15. http://dx.doi.org/10.1007/s42354-019-0224-5.

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18

DELIO, ILIA. "IS ARTIFICIAL INTELLIGENCE “ARTIFICIAL”?" Researcher. European Journal of Humanities & Social Science 3, no. 2 (2020): 13–32. http://dx.doi.org/10.32777/r.2020.3.2.1.

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19

Singh, Sarbjeet, and Sukhvinder Singh. "Artificial Intelligence." International Journal of Computer Applications 6, no. 6 (2010): 21–23. http://dx.doi.org/10.5120/1081-1413.

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20

Meij, Jan Martijn. "Artificial Intelligence." Nature and Culture 15, no. 3 (2020): 318–28. http://dx.doi.org/10.3167/nc.2020.150305.

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21

Hou, Jean, and Cynthia C. Nast. "Artificial Intelligence." Clinical Journal of the American Society of Nephrology 15, no. 10 (2020): 1389–91. http://dx.doi.org/10.2215/cjn.13450820.

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22

Marinchak, Christina McDowell, Edward Forrest, and Bogdan Hoanca. "Artificial Intelligence." International Journal of E-Entrepreneurship and Innovation 8, no. 2 (2018): 14–24. http://dx.doi.org/10.4018/ijeei.2018070102.

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As marketers and consumers simultaneously adopt artificial intelligence (AI) services and applications, the dynamic of the process of exchange between the buyer and seller in the marketplace is being fundamentally altered. This article reviews the emerging patterns of adoption and rates of diffusion of AI applications by both marketers and consumers. On the marketers' side the authors will address the single most defining phenomenon that is affecting the marketer's role and function in the marketing process: the exponential increase in the number, variety and capability of marketing applicatio

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23

Smith, Adam Leon. "Artificial Intelligence." ITNOW 64, no. 3 (2022): 47. http://dx.doi.org/10.1093/combul/bwac093.

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24

Smith, Adam Leon. "Artificial Intelligence." ITNOW 64, no. 2 (2022): 65. http://dx.doi.org/10.1093/itnow/bwac065.

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25

Bali, Jatinder, and Shivam Nayak. "Artificial Intelligence." Journal of Clinical Ophthalmology and Research 8, no. 1 (2020): 1. http://dx.doi.org/10.4103/jcor.jcor_18_20.

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26

Daisy, Anjali. "Artificial Intelligence." International Journal of Social Ecology and Sustainable Development 13, no. 1 (2022): 1–9. http://dx.doi.org/10.4018/ijsesd.292075.

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Artificial Intelligence (AI) is gradually changing the practice of surgery with the advanced technological development of imaging, navigation, and robotic intervention. In this article, the recent successful and influential applications of AI in surgery are reviewed from pre-operative planning and intra-operative guidance to the integration of surgical robots. It ends with summarizing the current state, emerging trends, and major challenges in the future development of AI in surgery. Robotic surgery is the use of computer technologies working in conjunction with robot systems to perform medica

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27

Carroll, Whende M. "Artificial intelligence." Nursing Management 52, no. 11 (2021): 29–32. http://dx.doi.org/10.1097/01.numa.0000795584.59335.f7.

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28

Matveev, V. I. "ARTIFICIAL INTELLIGENCE." Kontrol'. Diagnostika, no. 275 (May 2021): 56–63. http://dx.doi.org/10.14489/td.2021.05.pp.056-063.

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Artificial intelligence is becoming the main direction of the development of science and technology, making progress at a new level. Automation of production, the implementation of operations in hazardous and harmful areas, the implementation of routine actions in the environment are inevitable in the modern world. A person creates an analogue for himself, realizing the possible consequences and limiting them to legislative acts. The article provides positive examples of the implementation of the artificial intelligence project and legislative measures that limit its impact on the social envir

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29

Commissariat, Tushna. "Artificial intelligence." Physics World 34, no. 5 (2021): 17. http://dx.doi.org/10.1088/2058-7058/34/05/24.

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30

Smith, Adam Leon. "Artificial Intelligence." ITNOW 64, no. 1 (2022): 41. http://dx.doi.org/10.1093/itnow/bwac021.

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31

Stephens, C. D. "Artificial intelligence." British Dental Journal 180, no. 8 (1996): 284. http://dx.doi.org/10.1038/sj.bdj.4809057.

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32

Gitomer, Jerry. "Artificial Intelligence." Journal of Information Systems Management 3, no. 1 (1986): 58–60. http://dx.doi.org/10.1080/07399018608965231.

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33

Gil, Yolanda. "Artificial intelligence." AI Matters 1, no. 1 (2014): 4–5. http://dx.doi.org/10.1145/2639475.2639477.

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34

Hillman, Donald J. "Artificial Intelligence." Human Factors: The Journal of the Human Factors and Ergonomics Society 27, no. 1 (1985): 21–31. http://dx.doi.org/10.1177/001872088502700103.

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35

Wetzel, Randall C., Melissa Aczon, and David R. Ledbetter. "Artificial Intelligence." Pediatric Critical Care Medicine 19, no. 10 (2018): 1004–5. http://dx.doi.org/10.1097/pcc.0000000000001700.

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36

Kamaleswaran, Rishikesan, Oguz Akbilgic, Madhura A. Hallman, Alina N. West, Robert L. Davis, and Samir H. Shah. "Artificial Intelligence." Pediatric Critical Care Medicine 20, no. 4 (2019): 399. http://dx.doi.org/10.1097/pcc.0000000000001874.

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37

Sadiku, M. N. O. "Artificial intelligence." IEEE Potentials 8, no. 2 (1989): 35–39. http://dx.doi.org/10.1109/45.31596.

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38

Butterfield, William H. "Artificial Intelligence." Computers in Human Services 3, no. 1-2 (1988): 23–35. http://dx.doi.org/10.1300/j407v03n01_03.

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39

Fluke, John D., and Gregory N. O'Beirne. "Artificial Intelligence." Computers in Human Services 4, no. 1-2 (1989): 101–9. http://dx.doi.org/10.1300/j407v04n01_14.

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40

Scotti, Veronica. "Artificial intelligence." IEEE Instrumentation & Measurement Magazine 23, no. 3 (2020): 27–31. http://dx.doi.org/10.1109/mim.2020.9082795.

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41

CACM Staff. "Artificial intelligence." Communications of the ACM 60, no. 2 (2017): 10–11. http://dx.doi.org/10.1145/3034429.

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42

Weinstein, James N. "Artificial Intelligence." SPINE 44, no. 1 (2019): 1–4. http://dx.doi.org/10.1097/brs.0000000000002913.

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43

Hilton, Alexander D. "Artificial intelligence." AI Matters 5, no. 3 (2019): 70–76. http://dx.doi.org/10.1145/3362077.3362088.

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44

Raj, Anish. "Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 646–55. http://dx.doi.org/10.22214/ijraset.2024.64695.

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Artificial intelligence is a science that aims to perform tasks that require human intelligence. For the past two years, it has been used as a development tool in many areas such as prediction, health, security, and also improves the performance of production and services. Since artificial intelligence and its operations are based on too much data, algorithms, and scientific data, users cannot understand and comprehend the content and do not have the skills necessary to use this technology. Since artificial intelligence is controlled by machines and algorithms, it is difficult to determine the

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45

Pontieri-Lewis, Vittoria (Vicky). "Artificial Intelligence." Journal of Wound, Ostomy & Continence Nursing 51, no. 6 (2024): 437. http://dx.doi.org/10.1097/won.0000000000001134.

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46

Muspratt, Murray A. "Artificial Intelligence." Journal of Professional Issues in Engineering 112, no. 3 (1986): 158–69. http://dx.doi.org/10.1061/(asce)1052-3928(1986)112:3(158).

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47

Clarke, John R., David P. Cebula, and Bonnie L. Vebber. "ARTIFICIAL INTELLIGENCE." Journal of Trauma: Injury, Infection, and Critical Care 27, no. 7 (1987): 818. http://dx.doi.org/10.1097/00005373-198707000-00035.

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48

McGrow, Kathleen. "Artificial intelligence." Nursing 49, no. 9 (2019): 46–49. http://dx.doi.org/10.1097/01.nurse.0000577716.57052.8d.

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49

LAWRENCE, DAVID R., CÉSAR PALACIOS-GONZÁLEZ, and JOHN HARRIS. "Artificial Intelligence." Cambridge Quarterly of Healthcare Ethics 25, no. 2 (2016): 250–61. http://dx.doi.org/10.1017/s0963180115000559.

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Abstract:It seems natural to think that the same prudential and ethical reasons for mutual respect and tolerance that one has vis-à-vis other human persons would hold toward newly encountered paradigmatic but nonhuman biological persons. One also tends to think that they would have similar reasons for treating we humans as creatures that count morally in our own right. This line of thought transcends biological boundaries—namely, with regard to artificially (super)intelligent persons—but is this a safe assumption? The issue concerns ultimate moral significance: the significance possessed by hu

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50

Sheikh, Ahmad Y., and James I. Fann. "Artificial Intelligence." Thoracic Surgery Clinics 29, no. 3 (2019): 339–50. http://dx.doi.org/10.1016/j.thorsurg.2019.03.011.

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