Industry 4.0 will bring not only transformation to the manufacturing technologies but also to the profile of the workforce. Education system should be revised to prepare the future graduates embracing the knowledge of...
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Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment cla...
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In the realm of artificial intelligence (AI), a notable challenge has surfaced: adversarial attacks, these attacks involve altering input data to mislead AI models. Developing defensive measures against adversarial at...
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ISBN:
(数字)9798350363104
ISBN:
(纸本)9798350363111
In the realm of artificial intelligence (AI), a notable challenge has surfaced: adversarial attacks, these attacks involve altering input data to mislead AI models. Developing defensive measures against adversarial attacks is necessary for a more reliable AI system to protect its users from potential harm. In response to the risk of adversarial attacks, this study aims to mitigate the risk of such attacks, especially in image classification tasks, by proposing Adversarial Detection Guided Input Transformation (ADGIT) as an architecture designed to handle such attacks. In this study, the author will experiment with creating such architecture and measure the quality of the proposed technique. ADGIT works by utilizing SafetyNet as an adversarial detector to detect and cleanse adversarial attacks. The author concludes that the proposed technique could improve robustness against adversarial attacks increasing consistent prediction accuracy from 43% to 60% and reconstructing adversarial input images to be more similar to their unperturbed version, with reconstructed images' PSNR score on 0.007 perturbation increased from 43.1182 to 89.0999. The technique proposed could be used as a new defensive measure and improving robustness against adversarial attacks. Although ADGIT is capable of handling adversarial samples, ADGIT has a drawback in performance speed due to the extra preprocessing step.
In the rapidly evolving beauty industry, consumers are often bombarded with an overwhelming array of skincare brands and products, making the quest for the perfect skincare regimen a daunting task. This saturation of ...
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ISBN:
(数字)9798350365351
ISBN:
(纸本)9798350365368
In the rapidly evolving beauty industry, consumers are often bombarded with an overwhelming array of skincare brands and products, making the quest for the perfect skincare regimen a daunting task. This saturation of the market not only confuses consumers but also poses the risk of resource wastage and potential skin damage due to incompatible ingredient combinations. To mitigate these challenges, our research presents an innovative recommendation system designed to streamline the product selection process. Utilizing the principle of cosine similarity, our methodology involves a detailed analysis of the ingredients contained in various skincare products. A quantitative foundation for evaluating ingredient lists of various skincare products is provided by cosine similarity, a mathematical metric that evaluates the similarity between two non-zero vectors by computing the cosine of the angle between them. Our algorithm generates customized product recommendations by thoroughly comprehending the intricate interactions among different constituents. This bespoke approach simplifies the decision-making process for consumers, enabling them to make well-informed choices that cater to their unique skin health needs. The effectiveness of our recommendation system is validated through comprehensive user feedback, demonstrating its potential to redefine the paradigm of personalized skincare recommendations within the beauty industry. Through providing customers with critical information and encouraging a culture of knowledgeable choice, we see a time when customized skincare products will not only increase customer satisfaction but also brand loyalty, which will be a big step toward the democratization of customized skincare.
Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model...
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Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subje...
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When I subjects answer questions regarding J variables K times, the data can be stored in a three-mode data set of size I× J× K. Among the various component analysis approaches to summarize such data, Three-...
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Currently, there are a lot of measurement data on different items collected over time. The GMANOVA model is appropriate for analyzing the trends in such data, in order to analyze some longitudinal data collected on di...
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In this work, we propose the development of a hybrid video-to-text summarization (VTS) framework on cascading the advanced and code-accessible extractive and abstractive (EA) approaches for supporting viewers' vid...
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This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colore...
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