With the rapid development of the Internet, face recognition technology is widely used, which makes the protection of face database especially important. To protect the recognized faces, a multi-face image compression...
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作者：
Indumathi, V.Ashokkumar, C.School of Computing
College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India
This research presents an innovative deep learning-based predictive maintenance model designed for smart automotive systems, utilizing the EnsembleAE-Boost (EAE-Boost) algorithm. The primary objective of the proposed ...
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Feature selection is a crucial step in EEG emotion recognition. However, it was often used as a single objective problem to either reduce the number of features or maximize classification accuracy, while neglecting th...
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Feature selection is a crucial step in EEG emotion recognition. However, it was often used as a single objective problem to either reduce the number of features or maximize classification accuracy, while neglecting their balance. To address the issue, we proposed Improved Multi-objective Grey Wolf Optimization Feature Selection (IMGWOFS). Firstly, we designed a population initialization operator via discriminability and independence of features to accelerate search speed. Secondly, we employed a two-stage update strategy to improve the global search capabilities of the EEG feature subsets. Finally, we incorporated an adaptive mutation operator to escape the local optima. We conducted experiments on SEED and DEAP datasets, and the accuracy were 86.87$\pm$1.62 % and 60.65$\pm$1.51 % in the beta band using a smaller number of EEG features. In addition, the frontal lobe was related to emotion processing. In conclusion, IMGWOFS is an effective and feasible feature selection method for EEG-based emotion recognition. IEEE
Cross-speed bearing fault diagnosis based on multiple source domains and their data enables high-performance condition monitoring for variable-speed equipment, such as engines and turbines. Current multi-source method...
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The Internet of Things (IoT) is pivotal in transforming the way we live and interact with our surroundings. To cope with the advancement in technologies, it is vital to acquire accuracy with the speed. A phase frequen...
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Virtual consultation systems in healthcare also known as telemedicine a two-way technologically driven platforms or tools that enable healthcare providers to connect with patients virtually or remotely to deliver medi...
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Music classification is a fundamental task in the field of Music Information Retrieval. This paper focuses on composer classification, a specific task within music classification. Compressive techniques are commonly e...
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Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly promin...
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Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly prominent.A piece of code,known as a Webshell,is usually uploaded to the target servers to achieve multiple *** Webshell attacks has become a hot spot in current ***,the traditional Webshell detectors are not built for the cloud,making it highly difficult to play a defensive role in the cloud ***,a Webshell detection system based on deep learning that is successfully applied in various scenarios,is proposed in this *** system contains two important components:gray-box and neural network *** gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision *** neural network analyzer transforms suspicious code into Operation Code(OPCODE)sequences,turning the detection task into a classification *** experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate,which indicate its capability to provide good protection for the cloud environment.
Time series data plays a crucial role in intelligent transportation *** flow forecasting represents a precise estimation of future traffic flow within a specific region and time *** approaches,including sequence perio...
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Time series data plays a crucial role in intelligent transportation *** flow forecasting represents a precise estimation of future traffic flow within a specific region and time *** approaches,including sequence periodic,regression,and deep learning models,have shown promising results in short-term series ***,forecasting scenarios specifically focused on holiday traffic flow present unique challenges,such as distinct traffic patterns during vacations and the increased demand for long-term ***,the effectiveness of existing methods diminishes in such ***,we propose a novel longterm forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic *** model comprises three components:the similar scene matching module,responsible for extracting Similar Scene Features;the long-short term representation fusion module,which integrates scenario embeddings;and a simple fully connected layer at the head for making the final *** results on real datasets demonstrate that our model outperforms other methods,particularly in medium and long-term forecasting scenarios.
In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems,...
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In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.
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