Coal Characterization and Classification through IDNN Innovations Contrasted with LeNet-5 and CNN Techniques DOI

M. Praveena,

A Afsana,

T. Durga Devi

et al.

Published: Feb. 9, 2024

Coal remains the predominant energy source worldwide, yet presence of gangue, an unwanted byproduct from coal power plants, poses challenges. Effective removal gangue during coal's pre-processing phase is imperative. The advent advanced computing opens avenues for refining separation techniques. Our study introduces a novel deep neural network approach precise identification. This method builds upon discernible differences in grayscale and surface texture between to design coal-gangue filtration system. Leveraging this technique, machines can autonomously detect segregate minimizing human intervention. Should sector adopt innovative technology, it would catalyze transformative shift towards automated extraction. not only elevates raw processing efficiency but also enhances overall quality. Empirical tests underscore efficacy our method, recording remarkable accuracy improvement up 98.87% distinguishing materials compared existing practices.

Language: Английский

Coal Characterization and Classification through IDNN Innovations Contrasted with LeNet-5 and CNN Techniques DOI

M. Praveena,

A Afsana,

T. Durga Devi

et al.

Published: Feb. 9, 2024

Coal remains the predominant energy source worldwide, yet presence of gangue, an unwanted byproduct from coal power plants, poses challenges. Effective removal gangue during coal's pre-processing phase is imperative. The advent advanced computing opens avenues for refining separation techniques. Our study introduces a novel deep neural network approach precise identification. This method builds upon discernible differences in grayscale and surface texture between to design coal-gangue filtration system. Leveraging this technique, machines can autonomously detect segregate minimizing human intervention. Should sector adopt innovative technology, it would catalyze transformative shift towards automated extraction. not only elevates raw processing efficiency but also enhances overall quality. Empirical tests underscore efficacy our method, recording remarkable accuracy improvement up 98.87% distinguishing materials compared existing practices.

Language: Английский

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