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An Ensemble Model of Dual Learning for Gambling and Pornographic Websites Classification
(2025-01-01)
Thianphan, Sirapat
;
Jearanaitanakij, Kietikul
The rapid proliferation of pornographic and gambling websites poses significant challenges, as these platforms increasingly employ sophisticated techniques to evade detection. Traditional classification approaches that rely on a single feature often fail to achieve high detection rates due to the diverse strategies these websites use to bypass detection systems. To address this limitation, this study introduces an ensemble model for classifying pornographic and gambling websites by integrating two key features: URLs and textual content. A webscraping script was developed to extract textual data from HTML elements of 3,000 websites, evenly distributed among benign, pornographic, and gambling categories, specifically curated for Thai users. The URLs undergo preprocessing to capture their meaningful semantic properties, which reflect the characteristics of the corresponding websites. Separate classifiers were then trained on each feature before being integrated into an ensemble model for final prediction. This approach achieved an outstanding accuracy of 96.83%, significantly surpassing single-feature classifiers. Moreover, the findings demonstrate the proposed model's robustness against obfuscation techniques and anti-crawling mechanisms, underscoring its potential for effective automated detection.
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Convergence property of Nesterov-accelerated adaptive moment estimation with safety helmet detection and classification in smart industry application
(2024-11-15)
Jirakitpuwapat, Wachirapong
;
Dubey, Premnath
;
Prasertsuk, Narachata
;
Phanthong, Chaowarit
;
Tritham, Chatchai
We propose a technique for first-order gradient-based optimization of stochastic objective functions called Nesterov-accelerated adaptive moment assessment, which makes use of dynamic evaluations of lower-order moments. The adaptive moment assessment and the Nesterov acceleration gradient are combined. Consequently, it has perks, and this technique is convenient to use, numerically economical, memory-light, and very well-suited for challenges with massive amounts of information and characteristics. Additionally, we investigate the algorithm's convergence characteristics and propose a conservative constraint on the convergence rate. Finally, we employ this technique for the detection and classification of safety helmets.
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The Comparison of Deep Learning Model Efficiency for Classification of Oral White Lesions
(2022-01-01)
Phosri, Kunchidsong
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Treebupachatsakul, Treesukon
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Chomkwah, Wanwalee
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Tanpatanan, Tananan
;
Thanathornwong, Bhornsawan
Oral cancer is one of the top health problems globally. Some white lesions of the oral cavity can develop into oral cancer if not screened and treated immediately. Modern screening technologies are popular for applying deep learning knowledge to screen and classify images. In this study, we used deep convolution neural network (CNN) to classify oral white lesions, ulcers, and normal anatomy using transfer learning, which can reduce training time. Ten pre-trained model of transfer learning including DenseNet121, DenseNet169, DenseNet201, Xception, ResNet50, InceptionResNetV2, InceptionV3, VGG16, VGG19, and EfficientNetB7 are implemented and evaluated. The evaluation of accuracy, precision, F1score, recall, sensitivity, confusion matrix, and AUC-ROC curve are discussed. The trained models of DenseNet169, DenseNet201, and Xception showed the highest testing accuracy of more than 90% and recall of 0.8833. In addition to the precision, F1score, and specificity, the DenseNet169 outperforms at 0.9034, 0.884, and 0.9417, respectively.
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Automatic Lymph Node Classification with Convolutional Neural Network
(2022-01-01)
Uthatham, Ason
;
Yodrabum, Nutcha
;
Sinmaroeng, Chanya
;
Titijaroonroj, Taravichet
Manual lymph node classification is a tedious and time-consuming task. It requires a histopathologist to discriminate a lymph node from other look-alike kinds of tissues. The lymph node is easily misunderstood with other tissues because its shape and color might be similar to the others tissue around it. To automate this task, we present an automatic lymph node classification with convolutional neural network (CNN). In addition, we compared eight existing CNNs to ensure that we discover the best architecture for discriminating lymph node. DenseNet architecture provided the highest performance among AlexNet, VGG, GoogLeNet, ResNet, SqueezeNet, MobileNet, and EfficientNet, the highest accuracy at 0.994 and an F1score of 0.996. DenseNet accomplished the highest performance from two advantages: (i) fewer parameters and (ii) Dense connectivity.
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Municipal solid waste segregation with CNN
(2019-07-01)
Srinilta, Chutimet
;
Kanharattanachai, Sivakorn
Pollution from municipal solid waste has been a problem in Thailand for a long time. People generate waste in every minute. Ineffective waste segregation does increase difficulties in solid waste management. The Pollution Control Department of Thailand provides segregation guideline for municipal solid waste. Household wastes should be separated into four types-general waste, compostable waste, recyclable waste and hazardous waste. This paper explored performance of CNN-based waste-type classifiers (VGG-16, ResNet-50, MobileNet V2 and DenseNet-121) in classifying waste types of 9,200 municipal solid waste images. Waste type can be identified directly from waste-type classifier or derived from waste-item class. Derived classifiers outperformed their corresponding direct classifiers in the experiment. The highest waste-type classification accuracy was 94.86% from the derived ResNet-50 classifier.

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