Identification of Reptile Species Using Convolutional Neural Networks (CNN)

Annesa, Olvy Diaz; Kartiko, Condro; Prasetiadi, Agi

doi:10.29207/resti.v4i5.2282

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…CNN is a unique type of neural network that differs from other types of neural networks. It is specifically designed for feature extraction from images using a systematic extraction process based on multiconvolution [16]- [18]. Compared to regular neural networks, CNN is more efficient in image recognition since only image features are processed in hidden layers, rather than millions of rows of data as in regular NNs.…”

Section: Methods 21 Cnnmentioning

confidence: 99%

Fragmented-cuneiform-based convolutional neural network for cuneiform character recognition

Prasetiadi,

Saputra

2024

IJ-AI

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Cuneiform has been a widely used writing system in one of the human history phases. Although there are millions of tablets, have been excavated today, only around 100,000 tablets have been read. The difficulty in translating also increased if the tablet has damaged areas resulting in some of its characters become fragmented and hard to read. This paper investigates the possibility of reading fragmented cuneiform characters from Noto Sans Cuneiform font based on convolutional neural network (CNN). The dataset is built on extracted 921 characters from the font. These characters are then intentionally being damaged with specific patterns, resulting set of fragmented characters ready to be trained. The model produced by this training phase then being used to read the unseen fragmented pattern of cuneiform sets. The model also being tested for reading normal characters set. From the simulation, 83.86% accuracy of reading fragmented characters are obtained. Interestingly, 96.42% accuracy is obtained while the model is being tested for reading normal characters.

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Section: Methods 21 Cnnmentioning

confidence: 99%

Fragmented-cuneiform-based convolutional neural network for cuneiform character recognition

Prasetiadi,

Saputra

2024

IJ-AI

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“…By Using object detection and a machine learning method focused on Mask Region-based Convolutional Neural Network (Mask R-CNN) has been implemented for snake species classification [7]. To discern reptile species from images, deep learning, specifically CNN, has been used [5]. Snake sting marks images were also used to identify poisonous and non-poisonous snakes using the CNN method [20].…”

Section: Related Workmentioning

confidence: 99%

A CNN Based Model for Venomous and Non-venomous Snake Classification

Progga

Hossain

Islam

et al. 2021

Communications in Computer and Information Science

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Snakes are curved, limbless, warm blooded reptiles of the phylum serpents. Any characteristics, including head form, body shape, physical appearance, texture of skin and eye structure, might be used to individually identify nonvenomous and venomous snakes, that are not usual among non-experts peoples. A standard machine learning methodology has also been used to create an automated categorization of species of snake dependent upon the photograph, in which the characteristics must be manually adjusted. As a result, a Deep convolutional neural network has been proposed in this paper to classify snakes into two categories: venomous and non-venomous. A set of data of 1766 snake pictures is used to implement seven Neural network with our proposed model. The amount of photographs even has been increased by utilizing various image enhancement techniques. Ultimately, the transfer learning methodology is utilized to boost the identification process accuracy even more. Five-fold cross-validating for SGD optimizer shows that the proposed model is capable of classifying the snake images with a high accuracy of 91.30%. Without Cross validation the model shows 90.50% accuracy.

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“…Introducing or identifying species using a technological approach can significantly advance human performance in various fields. The deep learning method has been applied in various research areas to ease human work, including in the medical field [6]- [8], economy [9], farm [10], history [11], [12], language [13], and animals fields [14].…”

Section: Introductionmentioning

confidence: 99%

Identification of mangrove tree species using deep learning method

Asnur

Kosasih

Madenda

et al. 2023

IJAAS

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Artificial intelligence can help classify plants to make identification easier for everyone. This technology can be used to classify mangrove trees. The degradation of mangrove forests has resulted in a 20% loss of biodiversity, an 80% loss of microbial decomposers, reduced C-organic soil, and fish spawning grounds, resulting in estimated losses in the ecological and economic fields for up to IDR 39 billion. The identification of different mangrove species is the first step in ensuring the preservation of these forests. Therefore, this research aimed to develop algorithms and a convolutional neural network (CNN) architecture to classify mangrove tree species with the highest possible accuracy using Python software. The architecture selection for this model includes a batch size of 32, an input image size of 128x128 pixels, four classes, four convolution layers, four rectified linear unit (ReLU) layers, 2x2 max-pooling, and two fully connected layers (FCL). The finding showed that the resulting accuracy from the test was 97.50%, while the validation test was 81.25%, applied to four types of mangrove leaves, including <em>Avicenia marina, Avicenia officialis, Rizophora apiculata, </em>and <em>Soneratia caseolaris</em>.

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Identification of Reptile Species Using Convolutional Neural Networks (CNN)

Cited by 3 publications

References 1 publication

Fragmented-cuneiform-based convolutional neural network for cuneiform character recognition

Fragmented-cuneiform-based convolutional neural network for cuneiform character recognition

A CNN Based Model for Venomous and Non-venomous Snake Classification

Identification of mangrove tree species using deep learning method

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