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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

共 1561 条记录，以下是381-390 订阅

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Design of Intelligent Translation English Corpus Based on Deep Learning

Design of Intelligent Translation English Corpus Based on De...

引用

2024 international conference on computing, Robotics and System Sciences, ICRSS 2024

作者： Zeng, Rong Zheng, Zhuorui School of Foreign Languages Wuhan Business University Wuhan430056 China Hubei Digital Industry Group Co. Ltd. Wuhan430000 China

ISBN: (纸本)9798331511296

In order to further improve the effect of English intelligent translation, this paper combines the idea of deep learning to build an intelligent corpus system. Aiming at the problem of a lot of noise in speech data, the speech data is preprocessed by Mel frequency cepstrum coefficient method, and then transformers are combined with mutual information to recognize speech emotions. Then, by integrating diverse data sources, a wide range of English text data is provided to support complex translation and processing tasks. During the data acquisition phase, English text is collected from online platforms and social media through automated tools. In the data processing stage, advanced text processing technology is used for text analysis and preprocessing to ensure the quality and consistency of data. Finally, this paper verifies the effectiveness of the model proposed in this paper with experimental analysis. In the future, intelligent English translation subjects should take a positive attitude towards science and technology, and regard intelligent translation technology as an effective tool for English translation. © 2024 ieee.

关键词： data assimilation

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AI-Powered Privacy Preservation: A Novel Framework for Adaptive data Protection 2

AI-Powered Privacy Preservation: A Novel Framework for Adapt...

引用

2nd international conference on computing and data Analytics, ICCDA 2024

作者： Kumar, R. Senthil Lokeshwari, J. Shanmugam, Selvanayaki Kolandapalayam Dr. N.G.P. Arts and Science College Department of Computer Science with Cognitive Systems Tamil Nadu Coimbatore India Ashland University Dept. of Mathematics and Computer Science AshlandOH United States

ISBN: (纸本)9798331542573

The enlargement of Artificial Intelligence (AI)-powered privacy preservation techniques leads to new research avenues and innovations. Moreover, in today's data driven world the privacy and data protection are increased. In order to balance both data utility and individual privacy rights. This research paper provides a unique AI-powered privacy preservation framework by using machine learning algorithm that adapts to diverse data scenarios. The proposed framework uses Adaptive Privacy-Preserving Ensemble Learning (APPEL) machine learning algorithms to ensure the optimal data protection while maintaining both data utility and also privacy settings dynamically. Additionally, this paper evaluates the efficacy of data driven applications like Healthcare, social media and Online Platforms, Internet of Things (IoT) and smart devices. This study collected and pre-processed the data from data driven applications to determine trends, insights, and highlighting key challenges and opportunities for improvement. This framework's efficacy in real-world applications demonstrate the experimental results and provides an accurate and promising solution for privacy-preserving data analysis. This research contributes to the development of privacy-preserving AI solutions to safeguard sensitive data in data driven environment. © 2024 ieee.

关键词： Adversarial machine learning

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6th ieee international conference on Cloud computing and Artificial Intelligence: Technologies and Applications, ieee CloudTech 2023

6th IEEE International Conference on Cloud Computing and Art...

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6th ieee international conference on Cloud computing and Artificial Intelligence: Technologies and Applications, ieee CloudTech 2023

ISBN: (纸本)9783031786976

The proceedings contain 15 papers. The special focus in this conference is on Cloud computing and Artificial Intelligence: Technologies and Applications. The topics include: CAReNet: A Promising AI Architecture for Low data Regime Mixing Convolutions and Attention;Designing Converged Middleware for HPC, AI, and Big data: Challenges and Opportunities;key Mechanisms and Emerging Issues in Cloud Identity Systems;Power Consumption in HPC-AI Systems;integrated Architecture for Cloud and IoT with Logical Sensors and Actuators - Logical IoT Cloud;The Need for HPC in AI Solutions;facts and Issues of Neural Networks for Numerical Simulation;scalable Deep Learning for Industry 4.0: Speedup with Distributed Deep Learning and Environmental Sustainability Considerations;multi-domain dataset for Moroccan Arabic Dialect Sentiment Analysis in Social Networks;on the Challenges of Migrating to Microservices Architectures for Better Cloud Solutions;missing data Imputation Approach for IoT Using Machine Learning;holistic Approach for Enhanced Object Recognition in complex Environments;analyzing Sentiment in Arabic Tweets: A Study Using Machine Learning and Deep Learning Techniques.

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Bi-Objective Scheduling Algorithm for Hybrid Workflow in JointCloud 15

Bi-Objective Scheduling Algorithm for Hybrid Workflow in Joi...

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15th ieee international conference on Joint Cloud computing (JCC)

作者： Li, Rui Wang, Huaimin Shi, Peichang Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci State Key Lab Complex & Crit Software Environm Changsha 410073 Peoples R China

ISBN: (纸本)9798350387339

Big data workflows are widely used in IoT, recommended systems, and real-time vision applications, and they continue to grow in complexity. These hybrid workflows consist of both resource-intensive batch jobs and latency-sensitive stream jobs. Examples include the data analytics workflow, which incorporates batch data transformations and low-latency querying, and the machine learning workflow, which processes stream data feature extraction before performing batch training and low-latency inference. However, existing research on workflow scheduling primarily focuses on either stream or batch workflows, neglecting the efficient scheduling of hybrid workflows that respect their diverse resource requirements and the costly data transfers between them. In this article, we propose a hybrid workflow model that defines the optimal placement of hybrid workflows (OHWP) as a bi-objective optimization problem. Our proposed model takes into account parameters related to inter-communication between stream and batch jobs, as well as the heterogeneous resources in JointCloud environment. Additionally, we present OHWP-PS (OHWP on a Pruned Space), a scheduling algorithm for hybrid workflows that minimizes both cost and latency by improving the initial population and dynamically updating the search space. The results demonstrate that the proposed OHWP-PS algorithm is effective and competitive across all experiments.

关键词： batch and stream workflow scheduling hybrid workflow JointCloud bi-object optimization

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Privacy-Preserving Access Control Model for E-Medical Systems 2

Privacy-Preserving Access Control Model for E-Medical System...

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2nd international conference on Big data and Privacy computing, BDPC 2024

作者： Cai, Rongsheng Chen, Lanxiang Zhu, Yizhao City University of Macau Faculty of Data Science 999078 China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (纸本)9798350307597

With the high speed development of information technology, the healthcare system increasingly relies on cloud servers for data storage and complex computation, patient data privacy issues stem from healthcare systems' vulnerability to breaches, despite data encryption emphasis, insufficient focus on preventing unauthorized access risks compromises patient information confidentiality. Consequently, the need to protect privacy within the healthcare system has gained significant attentions. However, existing research primarily concentrates on data encryption processes, but there is comparatively less attention given to addressing potential privacy leaks concerning data visitors. In this paper, we propose a privacy-preserving access control model for healthcare cloud data visitors. Our model combines the RBAC (Role-Based Access Control) model with the PSI (Private Set Intersection) technique to ensure the privacy and security of data visitors. The performance analysis of our proposed solution demonstrates its relative efficiency and practicality when applied to E-medical systems. © 2024 ieee.

关键词： Differential privacy

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A Dynamic Monitoring and Early Warning System for Industrial Markets Based on Computer Algorithms 3

A Dynamic Monitoring and Early Warning System for Industrial...

引用

3rd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Jin, Li China Electrical Equipment International Company Limited Shanghai China

ISBN: (纸本)9798331519032

With the deepening development of global economic integration, industrial market fluctuations are becoming increasingly frequent and complex, which puts higher demands on enterprises and policy makers to timely and accurately grasp market dynamics, accurately predict potential risks, and formulate effective response strategies based on this. Market risk is not only related to the success or failure of enterprise operations, but also an important factor affecting national economic stability. Therefore, establishing a comprehensive industrial market dynamic monitoring and early warning system to achieve real-time monitoring and early warning of various internal and external risk factors has become a key issue that enterprises and governments urgently need to solve. This article delves into the dynamic monitoring and early warning system of industrial markets based on computer algorithms. The system integrates multi-source data using big data processing technology, and uses advanced machine learning (ML) algorithms to explore hidden patterns behind the data, constructing accurate prediction models. The experimental results show that the system proposed in this article performs well in terms of prediction accuracy, real-time performance, and stability. This system can accurately capture market trends, provide early warning of potential risks, and provide strong support for enterprise decision-making. © 2024 ieee.

关键词： Commerce

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Enhancing Traffic Prediction via Spatial Multi-Granularity Co-Evolving Mechanism 11

Enhancing Traffic Prediction via Spatial Multi-Granularity C...

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11th ieee international conference on Behavioural and Social computing, BESC 2024

作者： Sui, Shanshan Han, Qilong Lu, Dan Wu, Shiqing Xu, Guandong Harbin Engineering University Harbin China University of Technology Sydney Sydney Australia The Education University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798331531904

Accurate traffic prediction is crucial for effective urban planning and congestion management, aiming to forecast future traffic conditions by analyzing historical and real-time data. Traditional deep learning approaches typically rely on a single spatial granularity of traffic network data, which limits their capacity to fully capture the intricate details of multi-granularity spatial information, leading to decreased prediction accuracy in complex real-world scenarios. More critically, existing methods have shown deficiencies in effectively capturing the co-evolving dynamics across different granular levels, a key aspect for enhancing predictive accuracy. In this work, we introduce a novel Spatial Multi-granularity Co-evolving Framework (SMCF) to tackle these challenges. This innovative framework strategically integrates multiple spatial representations - micro, meso, and macro - enabling a comprehensive encapsulation of traffic dynamics at varying granularities. Unique to our approach is a co-evolutionary strategy where data from coarser granularities guide the predictive modeling at finer scales, fostering a dynamic and synergistic interaction across different levels of data granularity. Our extensive evaluations of two real-world traffic datasets confirm that the SMCF outperforms existing baseline models and demonstrates superior performance in traffic prediction tasks. This enhanced accuracy indicates our framework's ability to model complex spatial interactions and its potential applicability in real-world traffic management and planning scenarios. © 2024 ieee.

关键词： data accuracy

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Optimizing Spectral Graph Kernels for Time-Delay Embedding of Multichannel EEG data 24

Optimizing Spectral Graph Kernels for Time-Delay Embedding o...

引用

8th international conference on Algorithms, computing and Systems, ICACS 2024

作者： Muralinath, Rashmi Nagawara Pathak, Vishwambhar Mahanti, Prabhat K. Department of Computer Science University of New Brunswick Saint JohnNB Canada Department of Computer Science Birla Institute of Technology Mesra Rajasthan Jaipur India

ISBN: (纸本)9798400718304

The task of determining the dynamic patterns of causal connectivity among brain regions from EEG data is challenging due to inherent noise in EEG signals, sparse connections. High volume and dimensionality of data add substantial computational demands. Koopman embedding kernels offer a promising approach for capturing the low-dimensional linear embeddings of complex nonlinear system dynamics. This study focuses on leveraging the Graph Kernel Koopman Embedding (GKKE) method to extract latent factors, a novel application within the realm of EEG analysis. Specifically, the research pioneers the adaptation of the GKKE algorithm to estimate the inherent connectivity substructures, that enable investigations of the neuro-markers of cognitive load states and disorders like seizures from EEG signals. The graph for each segment was constructed by computing the correlation coefficients among the channel pairs. The algorithmic workflow was applied to a labelled cognitive load dataset from an online repository. The features were extracted in terms of Koopman-spectral decomposed clusters of eigenfunctions of the gram matrices produced by two different graph kernels applied for approximation of the Koopman operator over each EEG-segment. These features, representing the latent metastable states, were fed into machine learning classifiers, including SVM and Random Forest, and Decision Tree classifiers. Weisfeiler-Lehman Kernel (WL)-Kernel yielded an average accuracy of around 84% for all the classifiers and outperformed the Random-walk kernel. The significance of this work lies in its pioneering use of graph Koopman spectral decomposition-based low-dimensional embedding of the metastable structures of connectivity to provide an algorithm that enables neuro-marker estimation. Specific contribution is the workflow that determines the optimal combination of the graph kernel and classifier ensemble. This equips deeper understanding of the connectivity dynamics of brain regions related

关键词： Decision trees

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A Conceptual Design for a New Collaborative Mapping System using Odometry and IMU data from a Modular Morphing Tracked Robot 29

A Conceptual Design for a New Collaborative Mapping System u...

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29th international conference on Automation and computing (ICAC)

作者： Adams, Rob Murray, John Manzoor, Umar Univ Sunderland Dept Comp Sci Sunderland England

ISBN: (纸本)9798350360882;9798350360899

Self-reconfigurable and morphing robots are used in numerous domains due to their adaptability to a variety of scenarios and environments which makes them ideal for exploration. While capable of adapting to an environment, many of these robots are incapable of successfully navigating an unknown environment without the use of expensive, complex and range limiting equipment. In addition, while Morphing Tracked Robots are excellent navigators, their reliance on computationally intensive components and substantial power demands contributes to their bulky design and restricted versatility, constraining their applicability across scenarios. We propose a new kind of robot (the MMTR) featuring the modularity from modular reconfigurable robots and the morphing ability found in flipper tracked robots. Combined with a simplified topological map generator using only encoders and an IMU. Through experiments, we have shown that the MMTR can navigate rough terrain using its morphing flippers and build a map using only inexpensive components.

关键词： Reconfigurable Morphing Robots Dynamic Unknown Environments Topological Map Generation

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E-commerce Strategies in the Digital Age Enhancing Customer Experience and Market Reach 3

E-commerce Strategies in the Digital Age Enhancing Customer ...

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international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Kumar, Thinesh M. Kumar, N. Sha, Nazim S. Kennedy, Noble E. Ilankadhir, M. SRM Inst Sci & Technol Dept Corp Secretaryship & Accounting & Finance Chennai Tamil Nadu India SRM Inst Sci & Technol Fac Management Chennai India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

The paper highlights the pressing demand for E-commerce innovation, outlining flaws in existing systems and emphasizing the necessary development to match modern consumer expectations. Existing systems need to capture complex user behavior, necessitating the implementation of a comprehensive technique. The proposed e-commerce system combines advanced data analytics, machine learning, augmented reality (AR), and virtual reality (VR) technology. Results and analytical metrics show significant progress, with marketing campaign accuracy rising from 70% to 90% and personalization efficiency shifting from low to high. The switch to real-time user data analysis after a two-week delay demonstrates the system's efficiency. Improvements in brand identification, customer loyalty, and repurchase rates highlight the system's ability to improve brand-consumer relationships. Despite these advances, obstacles such as privacy concerns and accessibility issues are highlighted, emphasizing the necessity for ongoing refinement to remain relevant in the dynamic digital commerce world. The study proposes implementing such new methods to address existing limitations and improve the E-commerce scene.

关键词： E-commerce data Analytics Augmented Reality Virtual Reality Omnichannel Optimization Consumer Experience Collaborative Filtering Brand Recognition

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