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Related Concept Videos

Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

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When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Predicting Products: SN1 vs. SN202:27

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Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Deep Neural Networks for Image-Based Dietary Assessment
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Visual and buying sequence features-based product image recommendation using optimization based deep residual

D N V S L S Indira1, Babu Rao Markapudi2, Kavitha Chaduvula1

  • 1Department of Information Technology, Gudlavalleru Engineering College, Gudlavalleru, 521356, Andhra Pradesh, India.

Gene Expression Patterns : GEP
|July 11, 2022
PubMed
Summary

This study introduces an optimized deep residual network for e-commerce product recommendations. The novel Elephant Herding Feedback Artificial Optimization (EHFAO) method improves recommendation accuracy and user satisfaction.

Keywords:
CNN featuresDeep residual networkFuzzy k-nearest neighborProduct image recommendationSentiment classification

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • E-commerce Technology

Background:

  • E-commerce platforms face challenges in managing product catalogs and user preferences.
  • Conventional recommendation systems often struggle with complexities and limitations.
  • Deep learning offers potential for enhanced product recommendation systems.

Purpose of the Study:

  • To develop an innovative optimization-driven deep residual network for product recommendation.
  • To enhance the accuracy and efficiency of e-commerce recommendation systems.
  • To leverage Convolutional Neural Network (CNN) features and a novel optimization algorithm for improved recommendations.

Main Methods:

  • Utilized Convolutional Neural Network (CNN) for image feature extraction.
  • Employed a deep residual network trained with Elephant Herding Feedback Artificial Optimization (EHFAO).
  • Integrated K-means clustering, Cosine similarity, and sentiment analysis for refined product recommendations.

Main Results:

  • The proposed EHFAO-based deep residual network achieved a maximal F-measure of 84.061%, 84.061% precision, and 87.845% recall.
  • Demonstrated a minimal Mean Squared Error (MSE) of 0.216.
  • Outperformed conventional recommendation techniques in performance metrics.

Conclusions:

  • The developed EHFAO-based deep residual network provides a superior approach to product recommendation in e-commerce.
  • Sentiment analysis effectively refines recommendations, enhancing user satisfaction.
  • The system offers a significant advancement over existing recommendation methodologies.