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

Natural and Artificial Concepts01:24

Natural and Artificial Concepts

265
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
265
Nomenclature of Alkanes02:22

Nomenclature of Alkanes

23.2K
In the late 19th-century, the number of new chemical compounds discovered increased tremendously. Hence, the necessity arose to develop a naming system for the systematic nomenclature of these newly discovered compounds. IUPAC (International Union for Pure and Applied Chemistry), established in 1919, sets rules for the nomenclature.
The alkane nomenclature considers the length of the carbon chain, the number, and the location of the substituent to arrive at its systematic name. The IUPAC...
23.2K
Nomenclature of Aromatic Compounds with a Single Substituent01:23

Nomenclature of Aromatic Compounds with a Single Substituent

8.6K
Benzene is the simplest aromatic hydrocarbon or arene. The IUPAC names for simple monosubstituted benzene derivatives are derived by adding the substituent's name as a prefix to the parent benzene. For example, halobenzene, where the halogen could be fluoro (F), chloro (Cl), bromo (Br), and iodo (I).
8.6K
Language Development01:22

Language Development

447
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
447
Nomenclature of Alkenes02:29

Nomenclature of Alkenes

12.9K
The IUPAC naming system for alkenes replaces -an- with -en- in the corresponding parent alkanes. Accordingly, a simple alkene replaces the -ane suffix of the alkane with -ene.
As per the IUPAC rules, the longest carbon chain containing the maximum number of double bonds is identified as the parent chain and is numbered such that the doubly bonded carbon atoms receive the lowest possible numbers. The location of the double bond is indicated by the number of its first carbon atom. In branched...
12.9K
Air-entraining Agents01:27

Air-entraining Agents

106
Air-entraining agents improve the durability and workability of concrete in climates with frequent freezing and thawing. These agents prevent cracks by introducing small air bubbles into the mix, creating spaces accommodating water expansion when temperatures drop. The air-entraining agents lower the surface tension of water, forming stable, small air bubbles. This method is more effective than having accidental large voids, as the intentional, smaller, and evenly distributed air voids improve...
106

You might also read

Related Articles

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

Sort by
Same author

Correction: Dual-functional Ni and Co oxide-doped carbon nanocomposite: an effective catalyst for electrochemical water splitting and CO<sub>2</sub> utilization.

RSC advances·2026
Same author

A vision-based MobileNet-GRU framework for continuous monitoring of student engagement and emotional states.

Scientific reports·2026
Same author

Use of Prokinetic Agents in Adult ICU Patients: An International Inception Cohort Study (PATIENCE).

Acta anaesthesiologica Scandinavica·2026
Same author

Deep learning integrated plasmonic electrochemical sensing for fast and accurate pathogen detection.

Scientific reports·2026
Same author

Secure task offloading framework for industrial edge computing using reconfigurable intelligent surfaces and spectrum agility.

Scientific reports·2026
Same author

EUS point shear-wave elastography: A novel approach for noninvasive liver fibrosis assessment.

Endoscopic ultrasound·2026

Related Experiment Video

Updated: Sep 10, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.6K

Unveiling Arabic named entity recognition using natural language processing with artificial intelligence approach on

Wala Bin Subait1, Nazir Ahmad2, Muhammad Swaileh A Alzaidi3

  • 1Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Scientific Reports
|August 22, 2025
PubMed
Summary

This study introduces a novel AI technique for Arabic Named Entity Recognition (NER) in Moroccan dialect, significantly improving accuracy. The Northern Goshawk Optimization with Artificial Intelligence (NGOAI-ANER) method enhances precision in extracting entities from Arabic text.

Keywords:
Arabic textArtificial intelligenceDeep learningNamed entity recognitionNorthern goshawk optimization

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

405

Related Experiment Videos

Last Updated: Sep 10, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.6K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

405

Area of Science:

  • Natural Language Processing (NLP)
  • Artificial Intelligence (AI)
  • Deep Learning (DL)

Background:

  • Named Entity Recognition (NER) is crucial for NLP applications like data retrieval.
  • Arabic NER presents unique challenges due to linguistic complexity, especially in dialects.
  • Existing deep learning models often focus on Modern Standard Arabic (MSA), neglecting dialects.

Purpose of the Study:

  • To introduce a novel technique, Northern Goshawk Optimization with Artificial Intelligence for Arabic Named Entity Recognition (NGOAI-ANER), for Moroccan dialect.
  • To enhance the precision and efficiency of Arabic NER systems.
  • To address the complexities of dialectal Arabic NER using advanced AI.

Main Methods:

  • Word embedding techniques to represent text semantically.
  • Stacked Attention Long Short-Term Memory (SALSTM) for accurate entity identification.
  • Northern Goshawk Optimization (NGO) model for hyperparameter tuning of DL models.

Main Results:

  • The NGOAI-ANER technique demonstrated superior performance on the DarNERcorp dataset.
  • Achieved an accuracy of 97.86%, outperforming existing Arabic NER approaches.
  • Validated the efficacy and scalability of the proposed AI-driven NER model.

Conclusions:

  • The NGOAI-ANER technique offers a significant advancement in dialectal Arabic NER.
  • The integration of optimization algorithms with deep learning effectively addresses NER challenges.
  • This approach provides a scalable and accurate solution for Arabic Named Entity Recognition.