Design a Mechanism for Opinion Mining

Ajani, Samir N.; Bhanarkar, Parul

doi:10.1007/978-981-19-4687-5_35

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…Last but not least, Otsu's method should be used to section the pieces. 81 of the photos from the publication [20] were recalled for the data set, and the analysis was carried out in the L*a*b variety space. The k-implies grouping method was used to communicate the division of the leaf disease, and the DL-CNN algorithm was used to organize the infection.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Design and Implementation of Deep Learning Method for Disease Identification in Plant Leaf

Mulla¹,

Pawar²,

Banait

et al. 2023

IJRITCC

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In the whole agriculture plays a very important in country’s economic condition specially in Indian agriculture has a crucial role for raising the Indian economic structure and its level. India’s frequent changing climatic situation, various bacterial disease is much normal that drastically decreases the productivity of crop productivity. Most of the researcher is moving towards into this topic to find the early detection technique to identify the disease in small green leaves plants. A single, micro bacterial infectious disease can destroy all the agricultural small green leaves plants get damaged overnight and hence must be prevented and cured as earliest as possible so that agriculture production. In this research work, we had tried to developed a green small green leaves plants bacterial disease early detection system based on the deep learning network system which will detect the disease at very earlier state of symptoms observed. Deep learning technique is has various algorithms to detect the earliest stage of any of the procedural processing of any bacterial infections or disease. This paper consists of investigations and analysis of latest deep learning techniques. Initially we will explore the deep learning architecture, its various source of data and different types of image processing method that can be used for processing the images captured of leaf for data processing. Different DL architectures with various data visualization’s tools has recently developed to determine symptoms and classifications of different type of plant-based disease. We had observed some issue that was un identified in previous research work during our literature survey and their technique to resolve that issue in order to handle the functional auto-detection system for identifying the certain plant disease in the field where massive growth of green small green leaves plants production is mostly done. Recently various enhancement has been done in techniques in CNN (convolution neural network) that generates much accurate images classification of any object. Our research work is based on deep learning network that will observe and identifies the symptoms generated in leaflet of plant and identifies the type of bacterial infection in progress in that with the help of plant classification stated in the plant dataset. Our research work represents the implementation DCGAN and Hybrid Net Model using Deep learning algorithm for early-stage identification of green plant leaves disease in various environmental condition. Our result obtained shows that it has DCGAN accuracy 96.90% when compared withHybrid Net model disease detection methodologies.

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Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Design and Implementation of Deep Learning Method for Disease Identification in Plant Leaf

Mulla¹,

Pawar²,

Banait

et al. 2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…Through the usage of end-on-end multilayers, deep neural networks are able to include high-, medium-, and low-level classifiers and features, with the latter two tiers' enhancements corresponding to the number of hidden layers. As the field of CNN has advanced recently [12], it has allowed for significant performance gains in many image classification and object recognition tasks. The following table classifies the most common CNN subcategories.…”

Section: Related Workmentioning

confidence: 99%

An Improved VGG16 and CNN-LSTM Deep Learning Model for Image Forgery Detection

Shelar

Sharma

Rawat³

2023

IJRITCC

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As the field of image processing and computer vision continues to develop, we are able to create edited images that seem more natural than ever before. Identifying real photos from fakes has become a formidable obstacle. Image forgery has become more common as the multimedia capabilities of personal computers have developed over the previous several years. This is due to the fact that it is simpler to produce fake images. Since image object fabrication might obscure critical evidence, techniques for detecting it have been intensively investigated for quite some time. The publicly available datasets are insufficient to deal with these problems adequately. Our work recommends using a deep learning based image inpainting technique to create a model to detect fabricated images. To further detect copy-move forgeries in images, we use an CNN-LSTM and Improved VGG adaptation network. Our approach could be useful in cases when classifying the data is impossible. In contrast, researchers seldom use deep learning theory, preferring instead to depend on tried-and-true techniques like image processing and classifiers. In this article, we recommend the CNN-LSTM and improved VGG-16 convolutional neural network for intra-frame forensic analysis of altered images.

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An Analytical Review on Classification of IoT Traffic and Channel Allocation Using Machine Learning Technique

Lavate¹,

Srivastava

2023

2023 International Conference on Emerging Smart Computing and Informatics (ESCI)

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Design a Mechanism for Opinion Mining

Cited by 3 publications

References 20 publications

Design and Implementation of Deep Learning Method for Disease Identification in Plant Leaf

Design and Implementation of Deep Learning Method for Disease Identification in Plant Leaf

An Improved VGG16 and CNN-LSTM Deep Learning Model for Image Forgery Detection

An Analytical Review on Classification of IoT Traffic and Channel Allocation Using Machine Learning Technique

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