Lilies are popular in the global flower market, but consumers often lack information about specific varieties. To address this issue, this paper proposes a computer recognition platform based on the Vision Transformer...
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ISBN:
(数字)9798331521165
ISBN:
(纸本)9798331521172
Lilies are popular in the global flower market, but consumers often lack information about specific varieties. To address this issue, this paper proposes a computer recognition platform based on the Vision Transformer (ViT) architecture. The proposed platform uses an improved vision transformer (ViT) architecture to classify different types of lilies, allowing consumers to access information and names of various Lilium species. The experimental results show that the proposed lily classification model achieved a 96.4% accuracy rate in classifying six lily species.
Currently, online project-based learning is one of the methodologies used in university student assessment. Furthermore, this study is supported by several factors and current conditions applied to learning, such as t...
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Recently,Industrial Control Systems(ICSs)have been changing from a closed environment to an open environment because of the expansion of digital transformation,smart factories,and Industrial Internet of Things(IIoT).S...
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Recently,Industrial Control Systems(ICSs)have been changing from a closed environment to an open environment because of the expansion of digital transformation,smart factories,and Industrial Internet of Things(IIoT).Since security accidents that occur in ICSs can cause national confusion and human casualties,research on detecting abnormalities by using normal operation data learning is being actively *** single technique proposed by existing studies does not detect abnormalities well or provide satisfactory *** this paper,we propose a GRU-based Buzzer Ensemble for AbnormalDetection(GBE-AD)model for detecting anomalies in industrial control systems to ensure rapid response and process *** newly proposed ensemble model of the buzzer method resolves False Negatives(FNs)by complementing the limited range that can be detected in a single model because of the internal models composing *** the internal models remain suppressed for False Positives(FPs),GBE-AD provides better *** addition,we generated mean prediction error data in GBE-AD and inferred abnormal processes using soft and hard *** confirmed that the detection model’s Time-series Aware Precision(TaP)suppressed FPs at 97.67%.The final performance was 94.04%in an experiment using anHIL-basedAugmented ICS(HAI)Security Dataset(ver.21.03)among public datasets.
Convolutional Neural network is state of the art of image recognition or image classification. However to build the robust model using CNN needs many parameters adjusted, and choosing the good combination hyperparamet...
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The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously....
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Emotion recognition can help human-computer interactions by enabling systems to respond empathetically and adapt to users' emotional conditions. This capability improves user experience, supporting the development...
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ISBN:
(数字)9798331508579
ISBN:
(纸本)9798331508586
Emotion recognition can help human-computer interactions by enabling systems to respond empathetically and adapt to users' emotional conditions. This capability improves user experience, supporting the development of a more intuitive and emotionally responsive communication system. This study analyzes a bimodal approach based on gender (male and female) to recognize emotions without contextual information in dialogue analysis. Utilizing the Multimodal EmoryNLP dataset extracted from the TV series Friends with acted speech, we focused on four primary emotions: Angry, Neutral, Joy, and Scared. The model used in this study for text classification is RoBERTa, and wav2vec 2.0 is used for audio feature extraction with the Bi-LSTM model for classification. The experiment results using weighted F1-score reveal that data augmentation enhanced the performance of analyzing the original dataset from 0.46% to 0.52% and the male dataset from 0.43% to 0.51 %. In comparison, the female dataset remained consistent at 0.46%. The weighted F1-score and Unweighted Averaged Recall (UAR) from the male dataset are higher, 51 % and 48%, respectively, than those from the female dataset, 46% and 47%, respectively. Gender-based analysis indicated that male and female datasets exhibited distinct performance patterns, highlighting variations in emotional expression and recognition between genders. These findings underscore the effectiveness of multimodal strategies in emotion recognition and suggest that gender-specific factors play a significant role in enhancing classification performance. While these results highlight performance trends, further validation through repeated trials and statistical analyses could provide stronger generalizations and insights into gender-based differences.
Medical image analysis is a challenging and complex field these days. This discipline focuses especially on the processing of MRI (Magnetic Resonance Imaging) images. It offers multiple methods for locating brain tumo...
Medical image analysis is a challenging and complex field these days. This discipline focuses especially on the processing of MRI (Magnetic Resonance Imaging) images. It offers multiple methods for locating brain tumors in MRI brain images and compares the precision of all the findings. Convolutional neural networks (CNN) and ResNet architectures are used to train the model. As deep learning models are highly efficient and correctly identify whether the MRI picture of a tumor is healthy or unhealthy. In this work, high-level features are extracted from the input images using the CNN architecture, which has multiple pooling layers. To create the final classification model, fully connected layers are then routed through the extracted characteristics. However, CNN has some drawbacks, and to overcome these issues, a ResNet based architecture has been used. Additionally, U-Net-based MRI brain tumor segmentation algorithms have gained popularity because they significantly improve segmentation accuracy by infusing high-level and low-level feature information via skip connections. The suitability of an attention module called Attention Gate, which was recently developed, for tasks involving the segmentation of brain tumors has also been explored in this work.
Autism spectrum disorders (ASD) are neurodevelopmental disorders that are marked by enduring difficulties with speech, nonverbal communication, and restricted or repetitive behaviors. Early detection and intervention ...
Autism spectrum disorders (ASD) are neurodevelopmental disorders that are marked by enduring difficulties with speech, nonverbal communication, and restricted or repetitive behaviors. Early detection and intervention can greatly improve outcomes for people with ASD. Recently, deep learning algorithms have been applied to aid in the early detection of ASD using facial images. In this work, modifications of the commonly used VGG16 and VGG19 models for image recognition tasks are proposed to improve the performance of detecting ASD from a child’s frontal face image. The proposed model is unique, as it alters the architecture of existing models, adds an attentional mechanism, and applys transfer learning. These changes are intended to decrease the chance of overfitting and enhance the model’s capacity to capture subtle face characteristics. The performance of the updated model is assessed through accuracy, which is 82.55% for VGG19 and 80% for VGG16 model, and contrasted the outcomes of the original model. Performance of the modified model is also compared with that of the original model. The obtained results show that the modified model outperforms in detecting ASD from facial images, suggesting that the proposed modification is non-invasive for early detection of ASD and has the potential to contribute to the development of efficient tools.
Marketing and consumer behavior predictions are two areas where Artificial Intelligence (AI) is finding widespread use. Evaluating the accuracy of AI -based consumer behavior prediction is the focus of this article. E...
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Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspec...
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