Understanding of Convolutional Neural Network (CNN): A Review

Purwono, Purwono; Ma’arif, Alfian; Rahmaniar, Wahyu; Fathurrahman, Haris Imam Karim; Frisky, Aufaclav Zatu Kusuma; Haq, Qazi Mazhar ul

doi:10.31763/ijrcs.v2i4.888

Cited by 17 publications

(10 citation statements)

References 35 publications

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“…In this study, varied deep learning methods were implemented to perform the classification tasks. Artificial neural network (ANN) [59], recurrent neural network (RNN) [60], convolutional neural network (CNN) [61] and long short-term memory(LSTM) [62], were among these methods. To build the DL-based models, we have used the Keras framework [63] and at the back-end we used the TensorFlow library [64].…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria

Bajiya,

Choudhury,

Dhall

et al. 2024

Antibiotics

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Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics.

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Section: Deep Learning Algorithmsmentioning

confidence: 99%

AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria

Bajiya,

Choudhury,

Dhall

et al. 2024

Antibiotics

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“…The convolutional layer, as the core of CNN, plays a crucial role in extracting image features. By scanning the image using convolutional kernels, the convolutional layer can utilize information from neighboring regions in the image to extract relevant features [15]. Commonly used CNN architectures include ResNet-18 [16], MobileNet [17], VGG16 [18], AlexNet [19], and InceptionV3 [20], among others [21].…”

Section: Research Stepsmentioning

confidence: 99%

DenseNet Architecture for Efficient and Accurate Recognition of Javanese Script Hanacaraka Character

Sudewo,

Biddinika,

Fadlil

2024

matrik

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This study introduced a specifically optimized DenseNet architecture for recognizing Javanese Hanacaraka characters, focusing on enhancing efficiency and accuracy. The research aimed to preserve and celebrate Java’s rich cultural heritage and historical significance through the development of precise character recognition technology. The method used advanced techniques within convolutional neural networks (CNN) to integrate feature extraction across densely connected layers efficiently. The result of this study was that the developed model achieved a training accuracy of 100% and a validation accuracy of approximately 99.50% after 30 training epochs. Furthermore, when tested on previously unseen datasets, the model exhibited exceptional accuracy, precision, recall, and F1-score, reaching 100%. These findings underscored the remarkable capability of DenseNet architecture in character recognition, even across novel datasets, suggesting significant potential for automating Javanese Hanacaraka text processing across various applications, ranging from text recognition to digital archiving. The conclusion drawn from this study suggests that optimizing DenseNet architecture can be a significant step in preserving and developing character recognition technology for Javanese

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“…Convolutional neural network (CNN) is a type of deep learning model that is widely used in computer vision and image processing [30]. The CNN module used in this study consisted of convolutional layers and pooling layers [31]. The convolutional layers extracted features from images to adjust the training weight and bias of the neural network to generate the output feature maps of the input image [32].…”

Section: Cnn Module With Positional Encodingmentioning

confidence: 99%

DysDiTect: Dyslexia Identification Using CNN-Positional-LSTM-Attention Modeling with Chinese Dictation Task

Liu,

Wang,

Tong

2024

Brain Sciences

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Handwriting difficulty is a defining feature of Chinese developmental dyslexia (DD) due to the complex structure and dense information contained within compound characters. Despite previous attempts to use deep neural network models to extract handwriting features, the temporal property of writing characters in sequential order during dictation tasks has been neglected. By combining transfer learning of convolutional neural network (CNN) and positional encoding with the temporal-sequential encoding of long short-term memory (LSTM) and attention mechanism, we trained and tested the model with handwriting images of 100,000 Chinese characters from 1064 children in Grades 2–6 (DD = 483; Typically Developing [TD] = 581). Using handwriting features only, the best model reached 83.2% accuracy, 79.2% sensitivity, 86.4% specificity, and 91.2% AUC. With grade information, the best model achieved 85.0% classification accuracy, 83.3% sensitivity, 86.4% specificity, and 89.7% AUC. These findings suggest the potential of utilizing machine learning technology to identify children at risk for dyslexia at an early age.

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Understanding of Convolutional Neural Network (CNN): A Review

Cited by 17 publications

References 35 publications

AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria

AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria

DenseNet Architecture for Efficient and Accurate Recognition of Javanese Script Hanacaraka Character

DysDiTect: Dyslexia Identification Using CNN-Positional-LSTM-Attention Modeling with Chinese Dictation Task

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