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Related Concept Videos

Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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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.
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Classification of Systems-II01:31

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Related Experiment Videos

An intelligent transformer based framework for bilingual financial complaint classification.

Parnika Jain1, Srishti Tripathi1, Tushar Garg1

  • 1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.

Scientific Reports
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an automated system for classifying bilingual financial complaints, outperforming traditional methods. The fine-tuned XLM-RoBERTa model achieved 88.38% accuracy, improving efficiency in handling consumer financial data.

Keywords:
Bilingual NLPConsumer complaintsMachine learningNLPText classificationXLM RoBERTa

Related Experiment Videos

Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Financial Technology

Background:

  • Manual processing of bilingual consumer complaints is inefficient.
  • Operational challenges include delayed resolutions and poor resource use.

Purpose of the Study:

  • To develop and evaluate an intelligent automated system for classifying bilingual financial customer complaints.
  • To compare the performance of traditional machine learning models with a multilingual transformer model.

Main Methods:

  • A synthetic bilingual dataset of 25,000 records was created.
  • Traditional models (Logistic Regression, LightGBM) with TF-IDF were used.
  • A pre-trained multilingual transformer model (XLM-RoBERTa) was fine-tuned.

Main Results:

  • The fine-tuned XLM-RoBERTa model achieved 88.38% accuracy.
  • XLM-RoBERTa significantly outperformed traditional machine learning models.
  • The automated system demonstrated effectiveness in bilingual complaint classification.

Conclusions:

  • Contextualized multilingual transformer models are effective for complex bilingual NLP tasks.
  • This approach enhances efficiency in financial complaint classification.
  • The study addresses operational challenges in managing bilingual consumer data.