SaaS (Software-as-a-Service) is a service model provided by cloud computing. It has a high requirement for QoS (Quality of Software) due to its method of providing software service. However, manual identification and ...
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SaaS (Software-as-a-Service) is a service model provided by cloud computing. It has a high requirement for QoS (Quality of Software) due to its method of providing software service. However, manual identification and diagnosis for performance issues is typically expensive and laborious because of the complexity of the application software and the dynamic nature of the deployment environment. Recently, substantial research efforts have been devoted to automatically identifying and diagnosing performance issues of SaaS software. In this survey, we comprehensively review the different methods about automatically identifying and diagnosing performance issues of SaaS software. We divide them into three steps according to their function: performance log generation, performance issue identification and performance issue diagnosis. We then comprehensively review these methods by their development history. Meanwhile, we give our proposed solution for each step. Finally, the effectiveness of our proposed methods is shown by experiments.
Machine learning (ML) with data analysis has many successful applications and is widely employed daily. Additionally, they have played a significant role in combating the global coronavirus (COVID-19) outbreak. Intern...
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The Internet of Things (IoT) has developed into a crucial component for meeting the connection needs of the current smart healthcare systems. The Internet of Medical Things (IoMT) consists of medical devices that are ...
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Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore th...
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Cantonese opera, a key facet of Chinese traditional opera, boasts profound cultural and artistic value and has been designated as intangible cultural heritage. The use of certain roles is a basic concept in Cantonese ...
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Synthetic data generation via Generative Artificial Intelligence (GenAI) is essential for enhancing cybersecurity and safeguarding privacy in the Internet of Medical Things (IoMT) and healthcare. We introduce Multi-Fe...
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Privacy-preserving statistical analysis enables the data center to analyze datasets from multiple data owners, extracting valuable insights while safeguarding privacy. However, the observation of microdata involvement...
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The proliferation of cooking videos on the internet these days necessitates the conversion of these lengthy video contents into concise text recipes. Many online platforms now have a large number of cooking videos, in...
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The proliferation of cooking videos on the internet these days necessitates the conversion of these lengthy video contents into concise text recipes. Many online platforms now have a large number of cooking videos, in which, there is a challenge for viewers to extract comprehensive recipes from lengthy visual content. Effective summary is necessary in order to translate the abundance of culinary knowledge found in videos into text recipes that are easy to read and follow. This will make the cooking process easier for individuals who are searching for precise step by step cooking instructions. Such a system satisfies the needs of a broad spectrum of learners while also improving accessibility and user simplicity. As there is a growing need for easy-to-follow recipes made from cooking videos, researchers are looking on the process of automated summarization using advanced techniques. One such approach is presented in our work, which combines simple image-based models, audio processing, and GPT-based models to create a system that makes it easier to turn long culinary videos into in-depth recipe texts. A systematic workflow is adopted in order to achieve the objective. Initially, Focus is given for frame summary generation which employs a combination of two convolutional neural networks and a GPT-based model. A pre-trained CNN model called Inception-V3 is fine-tuned with food image dataset for dish recognition and another custom-made CNN is built with ingredient images for ingredient recognition. Then a GPT based model is used to combine the results produced by the two CNN models which will give us the frame summary in the desired format. Subsequently, Audio summary generation is tackled by performing Speech-to-text functionality in python. A GPT-based model is then used to generate a summary of the resulting textual representation of audio in our desired format. Finally, to refine the summaries obtained from visual and auditory content, Another GPT-based model is used
Iris biometrics allow contactless authentication, which makes it widely deployed human recognition mechanisms since the couple of years. Susceptibility of iris identification systems remains a challenging task due to ...
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In the realm of medical diagnostics, particularly in differential diagnosis, where differentiating between illnesses or ailments with comparable symptoms is essential, deep learning has gained importance. Recent devel...
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