Classification of Tulungagung Batik Images in Comparison of Convolution Neural Network and Vision Transformer Algorithms

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis is a rapid and reliable method for bacterial identification. Classification algorithms, as a critical part of the MALDI-TOF MS analysis approach, have been developed using both traditional algorithms and machine learning algorithms. In this study, a method that combined helix matrix transformation with a convolutional neural network (CNN) algorithm was presented for bacterial identification. A total of 14 bacterial species including 58 strains were selected to create an in-house MALDI-TOF MS spectrum dataset. The 1D array-type MALDI-TOF MS spectrum data were transformed through a helix matrix transformation into matrix-type data, which was fitted during the CNN training. Through the parameter optimization, the threshold for binarization was set as 16 and the final size of a matrix-type data was set as 25 × 25 to obtain a clean dataset with a small size. A CNN model with three convolutional layers was well trained using the dataset to predict bacterial species. The filter sizes for the three convolutional layers were 4, 8, and 16. The kernel size was three and the activation function was the rectified linear unit (ReLU). A back propagation neural network (BPNN) model was created without helix matrix transformation and a convolution layer to demonstrate whether the helix matrix transformation combined with CNN algorithm works better. The areas under the receiver operating characteristic (ROC) curve of the CNN and BPNN models were 0.98 and 0.87, respectively. The accuracies of the CNN and BPNN models were 97.78 ± 0.08 and 86.50 ± 0.01, respectively, with a significant statistical difference (p < 0.001). The results suggested that helix matrix transformation combined with the CNN algorithm enabled the feature extraction of the bacterial MALDI-TOF MS spectrum, which might be a proposed solution to identify bacterial species.

This study employed a convolutional neural network (CNN) algorithm to develop an automated dental age estimation method based on the London Atlas of Tooth Development and Eruption. The primary objectives were to create and validate CNN models trained on panoramic radiographs to achieve accurate dental age predictions using a standardized approach. A dataset of 801 panoramic radiographs from outpatients aged 5 to 15 years was used. A CNN model for dental age estimation was developed using a 16-layer CNN architecture implemented in Python with TensorFlow and Scikit-learn, guided by the London Atlas of Tooth Development. The model included 6 convolutional layers for feature extraction, each followed by a pooling layer to reduce the spatial dimensions of the feature maps. A confusion matrix was used to evaluate key performance metrics, including accuracy, precision, recall, and F1 score. The proposed model achieved an overall accuracy, precision, recall, and F1 score of 74% on the validation set. The highest F1 scores were observed in the 10-year and 12-year age groups, indicating superior performance in these categories. In contrast, the 6-year age group demonstrated the highest misclassification rate, highlighting potential challenges in accurately estimating age in younger individuals. Integrating a CNN algorithm for dental age estimation represents a significant advancement in forensic odontology. The application of AI improves both the precision and efficiency of age estimation processes, providing results that are more reliable and objective than those obtained via traditional methods.

PurposeThe aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT).MethodsCombining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python.ResultsThe periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%–91.2%) for premolars and 73.4% (95% CI, 59.9%–84.0%) for molars.ConclusionsWe demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

Due to the frequent occurrence of local minimization, slow convergence speed, and inconsistent structural selection in traditional BP neural network models, it can have a certain degree of impact on the algorithm. To overcome this problem, this study Uses an convolutional neural network (CNN) algorithm model, financial data information is processed and reduced dimensionally, converting complex high-dimensional data that frequently occur into simplified and easily manageable low-dimensional data information, thus enhancing data information management capability. In order to improve the training ability of data information, this paper designs an auxiliary model tensor convolutional autoencoder neural network model to achieve the analysis and processing of multi-dimensional data in hospital finance. Among them, tensor convolutional autoencoder neural network is an auxiliary model of the main model. The main implementation of this algorithm model is the processing and analysis of multidimensional data, greatly improving the efficiency of financial data information processing and analysis. Experimental results demonstrate the effectiveness of the proposed method, achieving fault diagnosis and comprehensive management of financial data. From the perspectives of storage and traceability of financial information, a new model for enterprise financial information management is established, providing insights for the specific applications of blockchain in enterprise financial information management. However, the research conducted in this study is only an exploratory analysis of the integration of blockchain and enterprise financial information management, and further specific analysis is required to address more practical issues in real-world applications.

Facial expression conveys the emotional state of human beings. Facial expressions are a common form of non-verbal communication that helps to transfer necessary information or data from one person to another. However, in today’s world with increasing demand for artificial intelligence, recognition of facial expressions is a challenging task in solving problems related to artificial intelligence, machine learning, and computer vision. In this paper, we present an approach that helps to classify different types of facial expressions using Convolutional Neural Network (CNN) algorithm. The proposed model is a Neural Network architecture that is based on sharing of weights and optimizing parameters using CNN algorithm. Two Models are designed using this algorithm which is named Simple CNN and Improved CNN models having different convolution layers. Architecture designs of these two models are different from each other. The input of our system is grayscale images which consist of expressions of different faces. Using Input as grayscale images, both CNN models are trained and parameters optimized in neural network. Output of system is seven common facial expressions such as happy, anger, sad, surprise, fear, disgust, and neutral. To achieve better experimental results of designed model, graph of loss and accuracy is plotted for both of the above models. The overall simulation results prove that Improved CNN model improves the accuracy of facial expression recognition as compared to Simple CNN model. As result, analysis of confusion matrix is also obtained for Simple CNN and Improved CNN model.

PurposeThis study was conducted to develop a convolutional neural network (CNN) algorithm that can diagnose cervical foraminal stenosis using oblique radiographs and evaluate its accuracy.Materials and MethodsA total of 997 patients who underwent cervical MRI and cervical oblique radiographs within a 3-month interval were included. Oblique radiographs were labeled as “foraminal stenosis” or “no foraminal stenosis” according to whether foraminal stenosis was present in the C2–T1 levels based on MRI evaluation as ground truth. The CNN model involved data augmentation, image preprocessing, and transfer learning using DenseNet161. Visualization of the location of the CNN model was performed using gradient-weight class activation mapping (Grad-CAM).ResultsThe area under the curve (AUC) of the receiver operating characteristic curve based on DenseNet161 was 0.889 (95% confidence interval, 0.851–0.927). The F1 score, accuracy, precision, and recall were 88.5%, 84.6%, 88.1%, and 88.5%, respectively. The accuracy of the proposed CNN model was significantly higher than that of two orthopedic surgeons (64.0%, p<0.001; 58.0%, p<0.001). Grad-CAM analysis demonstrated that the CNN model most frequently focused on the foramen location for the determination of foraminal stenosis, although disc space was also frequently taken into consideration.ConclusionA CNN algorithm that can detect neural foraminal stenosis in cervical oblique radiographs was developed. The AUC, F1 score, and accuracy were 0.889, 88.5%, and 84.6%, respectively. With the current CNN model, cervical oblique radiography could be a more effective screening tool for neural foraminal stenosis.

This study aims to develop a hero photo identification system in the Mobile Legends game using the Convolutional Neural Network (CNN) algorithm. The background of this study is based on the need to speed up and simplify the hero identification process which often requires time and special knowledge. The CNN algorithm was chosen because of its superior ability in pattern recognition and image classification. The research method includes several stages, namely collecting Mobile Legends hero image data, data preprocessing including resizing and normalization, and dividing the data into training, validation, and testing data. The CNN model used consists of several convolution and pooling layers for feature extraction, and a fully connected layer for the final classification. Model training is carried out using a dataset that has been processed with augmentation techniques to increase data variation. The results of this study are that the data used amount to 600 image data divided into 30 classes. By implementing the CNN method, researchers have succeeded in creating a system that can recognize images of mobile legends heroes. Based on the scenario created by the researcher, the highest accuracy is a combination of ReLu activation, Dropout 0.2 and using epoch 20, resulting in an accuracy of 62.17%.

Identifying and classifying fractures is an important task in the study of fractured oil and gas reservoirs. The most common solution is to identify it by artificial interpretation or synthetically probability methods, and to classify them according to the degree of fracture development. In order to improve the accuracy and reduce the man-made or computational errors, this study introduces the convolutional neural network (CNN) algorithm, one of the deep learning algorithms, to distinguish the degree of fracture development while constructing a new model which can automatically identify cracks and determine the category of fractured reservoirs in the meantime. Firstly, the logging curves with strong sensitivity to fractures are selected as the input data of convolution neural network, and the crack category is quantified as the output label of the network. A CNN model which is suitable for the classification of cracks is designed, whose parameters is continuously optimized through a small batch gradient descent method in the training stage. Then the trained convolutional neural network is applied to process the logging data of an oil field. The comparison of the result of crack classification by convolutional neural network with that by the traditional BP neural network indicates that the unique convolutional weight sharing structure of convolutional neural networks can extract the most effective features and greatly improve the accuracy of the fracture classification in dealing with complex nonlinear problems such as the classification of fractured reservoirs.

Context. Scientific interest in studying high-energy transient phenomena in the Universe has risen sharply over the last decade. At present, multiple ground-based survey projects have emerged to continuously monitor the optical (and multi-messenger) transient sky at higher image cadences and covering ever larger portions of the sky every night. These novel approaches are leading to a substantial increase in global alert rates, which need to be handled with care, especially with regard to keeping the level of false alarms as low as possible. Therefore, the standard transient detection pipelines previously designed for narrow field-of-view instruments must now integrate more sophisticated tools to deal with the growing number and diversity of alerts and false alarms. Aims. Deep machine learning algorithms have now proven their efficiency in recognising patterns in images. These methods are now used in astrophysics to perform different classification tasks such as identifying bogus from real transient point-like sources. We explore this method to provide a robust and flexible algorithm that could be included in any kind of transient detection pipeline. Methods. We built a convolutional neural network (CNN) algorithm in order to perform a ‘real or bogus’ classification task on transient candidate cutouts (subtraction residuals) provided by different kinds of optical telescopes. The training involved human-supervised labelling of the cutouts, which are split into two balanced data sets with ‘true’ and ‘false’ point-like source candidates. We tested our CNN model on the candidates produced by two different transient detection pipelines. In addition, we made use of several diagnostic tools to evaluate the classification performance of our CNN models. Results. We show that our CNN algorithm can be successfully trained on a large and diverse array of images on very different pixel scales. In this training process, we did not detect any strong over- or underfitting with the requirement of providing cutouts with a limited size no larger than 50 × 50 pixels. Tested on optical images from four different telescopes and utilising two different transient detection pipelines, our CNN model provides a robust ‘real or bogus’ classification performance accuracy from 93% up to 98% for well-classified candidates.

Anemia, often caused by internal parasites like Haemonchus contortus, presents significant health and productivity challenges for small ruminants. The primary goal of this study was to accurately distinguish between healthy and anemic goats using an image classification system focused on eye conjunctiva images. In the initial phase, 1,200 eye conjunctiva images from 75 goats were collected at Fort Valley State University farms over a two-week period using smartphone cameras. These images were randomly divided into training (70%) and testing (30%) datasets, with each group containing three subfolders corresponding to FAMACHA scores of 1, 2, and 3. The validation folder included unique images not found in the other folders. A Convolutional Neural Network (CNN) algorithm was utilized for image analysis, incorporating data augmentation techniques such as Resize, RandomHorizontalFlip, RandomVerticalFlip, and RandomRotation. The CNN model was built on the Google Colaboratory platform using CUDA 11.2 and the PyTorch machine learning framework, incorporating three ConvNet layers. The model training used the Adam Optimizer with a slower learning rate of 0.001 and a weight decay of 0.0001 to prevent exploding gradient issues, alongside ReLU and the cross-entropy loss function over 1000 epochs. Results demonstrate that the Convolutional Neural Network (CNN) model was highly effective in classifying eye conjunctiva images of goats to detect anemia based on FAMACHA scores. The overall precision of 93.9% indicates that the model was accurate in identifying true positive cases. The recall accuracy of 92.1% suggests that the model was successful in capturing most of the true anemic cases from the entire dataset, minimizing the number of false negatives. When examining the precision for each FAMACHA score, the CNN model displayed excellent performance. With a precision of 100% for FAMACHA score 1, the model perfectly identified healthy goats without any false positives. For score 2, the model achieved a precision of 95%, indicating a high level of accuracy in detecting goats with mild anemia. Lastly, for FAMACHA score 3, the precision of the model was 92.9%, demonstrating its effectiveness in identifying goats with more severe anemia. These results show that smartphone-derived images can be a powerful tool in creating an image classification model for monitoring animal health, particularly in detecting anemia in small ruminants. Utilizing smartphone cameras makes the process more accessible, cost-effective, and user-friendly for farmers and veterinary professionals. Despite the impressive performance of the CNN model, the research suggests that there is still room for improvement. By increasing the size of the training dataset, Refining the model development process, such as adjusting the architecture, hyperparameters, or data augmentation techniques, could also contribute to enhanced performance. These improvements would further increase the accuracy and reliability of the model in identifying anemic goats, ultimately leading to better animal health management.

Aksara Sunda becomes one of the cultures of sundanese land that needs to be preserved. Currently, not all people know Aksara Sunda because of the shift in cultural values and there is a presumption that Aksara Sunda is difficult to learn because it has a unique and complicated shape. The use of deep learning has been widely used, especially in the field of computer vision to classify images, one of the commonly used algorithms is the Convolutional Neural Network (CNN). The application of The Convolutional Neural Network (CNN) algorithm on sundanese handwriting imagery can make it easier for people to learn Sundanese script, this study aims to find out how accurate the neural network convolutional algorithm is in classifying Aksara Sunda imagery. Data collection techniques are done by distributing questionnaires to respondents. System testing using accuracy tests, testing on CNN models using data testing get 97.5% accuracy and model testing using applications get 98% accuracy. So based on the results of the trial, the implementation of deep learning methods using neural network convolution algorithms was able to classify the handwriting image of Aksara Sunda well.

Tunnel boring machine vibration-based deep learning for the ground identification of working faces

This study discusses the reliability analysis of voice recognition security using the deep learning convolutional neural network (CNN) algorithm. The CNN algorithm has learning advantages in that it is safer, faster, and more accurate. CNN also can solve user identification problems in large amounts of data. The measured voice input is ten types of user's voice with the number of iterations of 6000, 12000, and 15000 sound files. Furthermore, voice extraction features are performed to recognize conversations and retain information that is very much needed. After that, the voice file iteration data is trained to register the user's voice so that a trained model is obtained. These results measure performance (confusion matrix) to analyze the actual value compared to the predicted value in the CNN algorithm. The results obtained are that the best accuracy is obtained at 15000 sound file iterations, 96.87%, 12000 sound file iterations get 96.30%, and 6000 sound file iterations get 95.77%. CNN's performance data shows that 15000 iterations of voice files produce high accuracy. Voice recognition security helps provide high security and maintain the privacy of one's identity.

Currently, there are many limitations to classify images of small objects. In addition, there are limitations such as error detection due to external factors, and there is also a disadvantage that it is difficult to accurately distinguish between various objects. This paper uses a convolutional neural network (CNN) algorithm to recognize and classify object images of very small moths and obtain precise data images. A convolution neural network algorithm is used for image data classification, and the classified image is transformed into image data to learn the topological structure of the image. To improve the accuracy of the image classification and reduce the loss rate, a parameter for finding a fast-optimal point of image classification is set by a convolutional neural network and a pixel image as a preprocessor. As a result of this study, we applied a convolution neural network algorithm to classify the images of very small moths by capturing precise images of the moths. Experimental results showed that the accuracy of classification of very small moths was more than 90%.

BackgroundThe purpose of this investigation was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the accuracy and usefulness of this system for the detection of alveolar bone loss in periapical radiographs in the anterior region of the dental arches. We also aimed to evaluate the usefulness of the system in categorizing the severity of bone loss due to periodontal disease.MethodA data set of 1724 intraoral periapical images of upper and lower anterior teeth in 1610 adult patients were retrieved from the ROMEXIS software management system at King Saud bin Abdulaziz University for Health Sciences. Using a combination of pre-trained deep CNN architecture and a self-trained network, the radiographic images were used to determine the optimal CNN algorithm. The diagnostic and predictive accuracy, precision, confusion matrix, recall, F1-score, Matthews Correlation Coefficient (MCC), Cohen Kappa, were calculated using the deep CNN algorithm in Python.ResultsThe periapical radiograph dataset was divided randomly into 70% training, 20% validation, and 10% testing datasets. With the deep learning algorithm, the diagnostic accuracy for classifying normal versus disease was 73.0%, and 59% for the classification of the levels of severity of the bone loss. The Model showed a significant difference in the confusion matrix, accuracy, precision, recall, f1-score, MCC and Matthews Correlation Coefficient (MCC), Cohen Kappa, and receiver operating characteristic (ROC), between both the binary and multi-classification models.ConclusionThis study revealed that the deep CNN algorithm (VGG-16) was useful to detect alveolar bone loss in periapical radiographs, and has a satisfactory ability to detect the severity of bone loss in teeth. The results suggest that machines can perform better based on the level classification and the captured characteristics of the image diagnosis. With additional optimization of the periodontal dataset, it is expected that a computer-aided detection system can become an effective and efficient procedure for aiding in the detection and staging of periodontal disease.