The projected increase in PayLater utilization reaches up to five million people by 2025. To optimize the yearly profit from their PayLater service, fintech companies must examine all possible risks before a unanimous...
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The box office (BO) income had significantly declined up to 80% in 2020, as the COVID-19 pandemic emerged. To minimize further financial risks, multiplex (multiple cinema complexes) owners need to analyze their potent...
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Reduced alertness because of fatigue or drowsiness accounts for a major cause of road accidents globally. To minimize the likelihood of alertness reduction-related crashes, a video-based detection emerges as a non-int...
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NoSQL database has gained popularity in Big Data and other various applications for its simplicity and flexibility. The non-relational nature of NoSQL database such as MongoDB proves to improve development lifecycles ...
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ISBN:
(数字)9798331542313
ISBN:
(纸本)9798331542320
NoSQL database has gained popularity in Big Data and other various applications for its simplicity and flexibility. The non-relational nature of NoSQL database such as MongoDB proves to improve development lifecycles and resources efficiency. However, security challenges arise along with increasing usage of NoSQL database, and NoSQL database is no exception to injection attacks. Machine learning proved to be an efficient method, as much has been researched. However, in the future there may be an increasing complexity of features that may prove costly to the model’s performance. Therefore, this research aims to utilize principal component analysis as dimensionality reduction and deep neural network as the classification method, to improve the security of NoSQL database. The text query is converted to feature vectors then further processed to reduce the input dimension of the deep neural network using PCA. The features used are based on previous research and various sources, and some are added after analyzing the dataset.10-fold cross validation is also applied to ensure that the model does not overfit the data, attempting to reduce bias to the result. The 10-fold cross validation model accuracy result is in average 97.44% with a standard deviation of 1.7%, and the testing results are 97.5% in accuracy,95.65% in precision, 91.67% in recall, and 93.61% in F1 score. Thus, it can be concluded that the usage of PCA on injection feature vectors can reduce complexity of the model.
This study investigates the performance of Vision Transformer (ViT) variants—the Shifted Window Transformers (SWIN), Distillation with No Labels (DINO), and Data-efficient Image Transformers (DeIT)—in image captioni...
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ISBN:
(数字)9798331506490
ISBN:
(纸本)9798331506506
This study investigates the performance of Vision Transformer (ViT) variants—the Shifted Window Transformers (SWIN), Distillation with No Labels (DINO), and Data-efficient Image Transformers (DeIT)—in image captioning tasks using the Flickr8K dataset. While ViT architectures have shown promise in image classification, their effectiveness for image captioning, particularly with smaller datasets, remains unclear. The models' performance was evaluated using BLEU metrics, while training efficiency was analyzed through Pareto front analysis of computational time and accuracy. Among the tested variants, SWIN Transformers demonstrated superior performance (BLEU-1: 64.4, BLEU-2: 33.9, BLEU-3: 17.1, BLEU-4: 8.4), followed by DINO (BLEU-1: 63.1, BLEU-2: 32.7, BLEU-3: 16.4, BLEU-4: 7.5), while DeIT showed the weakest performance (BLEU-1: 61.6, BLEU-2: 31.1, BLEU-3: 14.7, BLEU-4: 6.5). SWIN Transformers achieved the shortest training time at 3 minutes 31 seconds per epoch, making it the most efficient model among ViT variants based on Pareto front analysis. While ViT variants achieved competitive BLEU-1 scores comparable to previous top models, they struggled with generating coherent, longer sentences, as evidenced by suboptimal BLEU-4 scores. These findings provide empirical evidence of how the lack of inductive bias in transformer architectures affects their ability to capture complex scene relationships, despite their strong feature detection capabilities, contributing to the understanding of transformer models' limitations in vision-language tasks, especially with limited data.
The expansion of deep learning techniques, as well as the availability of large audio/sound datasets, have fueled tremendous breakthroughs in audio/sound classification during the last several years. The transfer lear...
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The rapid growth of digital payment platforms like OVO, ShopeePay, and GoPay in Indonesia has driven the need for businesses to optimize marketing strategies by analyzing customer interactions through social media. Th...
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The purpose of this study is to find out and analyze what has been done by previous studies in knowing the problems faced by SMEs today and how the influence of Industry 4.0 technology in dealing with problems in Smal...
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The use of balanced medical datasets is essential to improve the precision and accuracy of machine learning models in the healthcare field. However, dataset imbalance often becomes an obstacle, especially in the diagn...
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This study examines the necessity of employing BERT2GPT for single-document summarization in the current age of escalating digital data. The primary focus of this work is on the abstractive technique, which tries to g...
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