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Related Concept Videos

Transformers01:26

Transformers

1.1K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
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Types Of Transformers01:16

Types Of Transformers

1.0K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
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A Lightweight Double-Stage Scheme to Identify Malicious DNS over HTTPS Traffic Using a Hybrid Learning Approach.

Sensors (Basel, Switzerland)·2023
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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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Enhancing Spam Message Classification and Detection Using Transformer-Based Embedding and Ensemble Learning.

Abdallah Ghourabi1,2, Manar Alohaly3

  • 1Department of Computer Science, Jouf University, Sakaka 72388, Saudi Arabia.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary

This study introduces a new SMS spam detection model using advanced AI. The model achieves 99.91% accuracy, significantly improving protection against malicious messages and data loss.

Keywords:
Ensemble LearningGPTSMS classificationTransformerspam detection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Short Message Service (SMS) is a primary communication channel.
  • SMS spam poses risks like credential theft and data loss.
  • Existing SMS spam detection methods require improvement.

Purpose of the Study:

  • To propose a novel model for effective SMS spam detection.
  • To leverage pre-trained Transformers and Ensemble Learning for enhanced accuracy.
  • To mitigate the threat of malicious SMS messages.

Main Methods:

  • Utilized a text embedding technique based on GPT-3 Transformer advancements.
  • Implemented an Ensemble Learning method combining four machine learning models.
  • Evaluated the model using the SMS Spam Collection Dataset.

Main Results:

  • Achieved state-of-the-art performance in SMS spam detection.
  • Reached an accuracy of 99.91%, surpassing previous benchmarks.
  • Demonstrated superior performance compared to individual constituent models.

Conclusions:

  • The proposed model offers a highly effective solution for SMS spam detection.
  • Advanced AI techniques like Transformers and Ensemble Learning significantly boost detection accuracy.
  • This approach provides robust protection against SMS-related security threats.