Plant Classification Using Artificial Neural Networks

Pacífico, Luciano D. S.; Macario, Valmir; Oliveira, João Fausto Lorenzato de

doi:10.1109/ijcnn.2018.8489701

Cited by 22 publications

(10 citation statements)

References 17 publications

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“…The 500 objects have used to validate the proposed system, which is categorized as 70-30% documents for training as well as testing respectively. The second experiment evaluates the proposed system accuracy with some existing machine learning algorithms like NB [13], ANN [12], RF [11], etc. The propose RNN provides better accuracy for structured data classification.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed system provides around 97.5% average accuracy after the multiple experiments. The system was evaluated with three different classification algorithms like [11,12,13] respectively, and shows the document object classification system perform an effective solution to handle redundancy generated during multi-label classification using Recurrent Neural Network then existing classification algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…This technique has experimented with ten Arabic and English long texts. The result classifies the text into two classes as reliable or suspicious text.In [6] In [12] system used a hybrid text categorization model that combines both the Rocchio algorithm and the Random Forest algorithm to perform Multi-label text categorization. These methods find a correlation between training Data sets and only related specifications filter required Classification.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Document Classification using NLP and Recurrent Neural Network

Ghumade*,

Deshmukh

2019

IJEAT

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The classification technique is most important for supervised and semi supervised base machine learning task. Many classification algorithms has introduced already for existing systems. Class-label classification is an important machine learning task wherein one assigns a subset of candidate without label to an object. Classification of various document models based on short text, metadata, heading levels these are the existing techniques which are introduced in literature survey. Sometime whole data reading and processing might be take a much time for classification, so it increase the time complexity for entire system. We proposed a new document classification method based on deep learning using NLP and machine learning approach. In this work system has several attractive properties: it captures some metadata from entire abstract section and built the training set first. Once complete all document process, it deals with optimization algorithm. Recurrent Neural Network has used to categories the individual object according to their weights. And it provides final class label for entire test dataset. Based on the various experimental analysis system provides data classification accuracy as well as minimum time complexity than classical machine learning algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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A Document Classification using NLP and Recurrent Neural Network

Ghumade*,

Deshmukh

2019

IJEAT

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“…Document or text datasets usually include multiple different words. Pre-processing methods can slow due to memory and time complexity [21]. A standard solution to this problem is merely practicing cheap algorithms.…”

Section: B Dimension Reductionmentioning

confidence: 99%

“…Displays the performance analysis of the proposed system with a different flavor of current classification algorithms. according to RF, NB [20], ANN [21] statistics the RNN framework provides better reliability for the identification of structured data as it gives a minimum failure rate for the whole data set during the classification.…”

Section: Fig 5 System Processing Timementioning

confidence: 99%

Micro Clustering Methodology for Document Objects using Deep Learning Techniques

Mulla,

Patil

2019

IJEAT

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Large data clustering and classification is a very challenging task in data mining. Various machine learning and deep learning systems have been proposed by many researchers on a different dataset. Data volume, data size and structure of data may affect the time complexity of the system. This paper described a new document object classification approach using deep learning (DL) and proposed a recurrent neural network (RNN) for classification with a micro-clustering approach.TF-IDF and a density-based approach are used to store the best features. The plane work used supervised learning method and it extracts features set called as BK of the desired classes. once the training part completed then proceeds to figure out the particular test instances with the help of the planned classification algorithm. Recurrent Neural Network categorized the particular test object according to their weights. The system can able to work on heterogeneous data set and generate the micro-clusters according to classified results. The system also carried out experimental analysis with classical machine learning algorithms. The proposed algorithm shows higher accuracy than the existing density-based approach on different data sets.

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