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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291-300 订阅

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CyberShield Cloud: Fortifying data Exchange with Role-Based Encryption Mastery 1

CyberShield Cloud: Fortifying Data Exchange with Role-Based ...

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1st international conference on cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Abinaya, P. Senthil Kumar, J. Aakash, T. Roshan, Jerry J A Mepco Schlenk Engineering College Department of Computer Science and Engineering Sivakasi India Mepco Schlenk Engineering College Department of Electronics and Communication Engineering Sivakasi India

ISBN: (纸本)9798350361780

In today's digital landscape, when a myriad of products are widely available online, the critical necessity for authenticity, anonymity, and security above accessibility high-lights the importance of encryption with access management. This research paper proposes an advanced encryption approach designed to improve the security of overall resource admissions. In contrast to traditional techniques, our proposed solution makes use of Homomorphic Encryption-based approach combined with 'Role-Based Access Control' (RBAC) and 'eXtensible Access Connectivity Markup Language (XACML)'. This novel technology enables secure data storage within internet computers by utilizing cryptographic principles and a fast admission management mechanism. The suggested encryption approach combines classic homomorphic encryption procedures with a dynamic information distribution strategy, resulting in a strong barrier for sensitive data. Users gain the advantage of obtaining information in a secure and privacy-preserving manner by using this hybrid encryption technology. Notably, when compared to existing encryption approaches, the overall execution of our suggested solution is significantly more secure and efficient. This study adds to the expanding landscape of secure information storage and retrieval by seamlessly merging homomorphic encryption with RBAC, providing a more efficient and protective paradigm for managing online resources and protecting sensitive data. © 2024 ieee.

关键词： Sensitive data

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3D reconstruction and path planning of complex parts for laser cladding 9

3D reconstruction and path planning of complex parts for las...

引用

9th international conference on Image, Vision and computing, ICIVC 2024

作者： Zhu, Hao Wang, Shaopeng School of Information and Communication Engineering Nanjing Institute of Technology Nanjing China Jiangsu Advanced CNC and Electric Power Transmission Key Laboratory Nanjing Institute of Technology Nanjing China

ISBN: (纸本)9798350385991

When laser cladding is performed on a free surface, the accuracy of 3D reconstruction of the parts and the planning of the cladding path have a significant impact on the machining quality. This article studies the path planning method for laser cladding curved parts, using a 3D scanner to obtain point cloud data on the blade surface. By using point cloud denoising and simplification algorithms to appropriately process the collected point cloud data, computational efficiency can be improved. Perform NURBS surface fitting on the target. Calculate and analyze the surface curvature characteristics of the parts. Slice the model based on the curvature feature values, and finally plan the cladding path. The actual machining test results of the engineering prototype show that the algorithm proposed in this paper can meet the actual machining requirements. © 2024 ieee.

关键词： 3D reconstruction

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An Action Recognition Algorithm Based on Two-Stream Deep Learning for Metaverse Applications 20

An Action Recognition Algorithm Based on Two-Stream Deep Lea...

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20th ieee international Wireless Communications and Mobile computing (ieee IWCMC) - Green and Intelligent Communications

作者： Liu, Jiayue Mao, Tianqi Huang, Yicheng He, Dongxuan Beijing Inst Technol MIIT Key Lab Complex Field Intelligent Sensing Beijing 100081 Peoples R China Beijing Inst Technol Jiaxing Yangtze Delta Reg Acad Jiaxing 314019 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

ISBN: (纸本)9798350361261;9798350361278

Action recognition algorithms have gained significant attention in recent years, which can be indispensable for a plethora of cutting-edge applications like extended reality or Metaverse. These services often pose stringent requirement on immediate sensing and cognition of the surroundings, which necessitates immediate classifications of the captured actions (e.g., video data) that classical signal processing methods can hardly attain. In this paper, we introduced a residual artificial neural network with two-stream structure to further improve the accuracy of action recognition algorithm. Specifically, two residual networks (ResNet101) are trained separately, one by spatial RGB image streams, and another by optical flow streams. The two-strem network outputs are then fed into a fusion classifier, in which information extracted by spatial network and temporal network jointly determines the classification result. Moreover, in the training process, hyper-parameters setting and optimizer selection are performed numerically to achieve optimal performance. Finally, the recognition accuracy of the proposed algorithm has been compared to other existing widely-employed counterparts, where UCF101 data set is utilized for training and testing. Simulations validates aiming that the network can achieve higher recognition accuracy than traditional algorithms, and the two-stream method shows its superiority over the single-network counterpart.

关键词： Action recognition deep learning residual network ResNet101 two-stream method

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Spatiotemporal Analysis and Risk Profiling of Dengue in Lima and Callao: A data-Driven Approach for Tailored Prevention Policies 31

Spatiotemporal Analysis and Risk Profiling of Dengue in Lima...

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31st ieee international conference on Electronics, Electrical Engineering and computing, INTERCON 2024

作者： Alcocer, Rodrigo Alonso Bisetti Llerena, Soledad Espezua Universidad del Pacífico Facultad de Ingeniería Lima Peru

ISBN: (纸本)9798350378344

The recent atypical increase in dengue cases in Lima and Callao, Peru, underscores the need for a more sophisticated approach to controlling this disease in urban settings. This study employs advanced data mining techniques to conduct a comprehensive spatio-temporal analysis of dengue cases in these regions, integrating climatic, sanitation, and demographic factors at the district level. Using k-means clustering, two distinct risk profiles were identified among the districts, revealing spatial patterns related to port proximity and exposure to geological risks. Sensitivity analysis using Shapley Additive Explanations (SHAP) quantified the contribution of various variables to dengue incidence, highlighting the importance of exposure to geological risks and water management practices. The results indicate that, contrary to previous assumptions, coastal humidity is not a determining factor in the spread of dengue in the region. Instead, factors such as wind speed, landslide exposure, and water conservation practices emerged as significant predictors. Based on these findings, the study proposes specific recommendations for dengue prevention policies tailored to district risk profiles. This data-driven approach to understanding and mitigating dengue in complex urban areas offers a potentially applicable model to other regions facing similar challenges in vector-borne disease control. © 2024 ieee.

关键词： Sensitivity analysis

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Chart Career Using AI and data Analytics 1

Chart Career Using AI and Data Analytics

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1st international conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Yogitha Ramani, Roja Nayak, Vaishnavi Sirisha, M. New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350365405

In the realm of educational research, a comprehensive analysis of student performance unfolds through the application of diverse machine learning algorithms. These algorithms serve as advanced analytical tools, distilling raw data into nuanced skills analysis and showcasing the symbiosis of artificial intelligence and educational insights. Beyond algorithmic processes, the efficacy of this journey lies in the subsequent visualization of analyzed data using visualization tools. These visuals elucidate complex patterns, providing us with a clear understanding of academic and cognitive landscapes. This serves as a conduit for machine learning algorithms, to interpret academic destinies and guides the trajectory towards valuable insights for career guidance. © 2024 ieee.

关键词： Adversarial machine learning

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cognitive Dissonance in Banking Employees: Exploring Factors Amid the Artificial Intelligence Revolution

Cognitive Dissonance in Banking Employees: Exploring Factors...

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2024 ASU international conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Sachdeva, Channi Gangwar, Veer P Grover, Veena Gochhait, Saikat Mittal School of Business Lovely Professional University Jalandhar India School of Management Noida Institute of Engineering and Technology Greater Noida India Symbiosis Institute of Digital and Telecom Management Symbiosis International Deemed University India Neurosciences Research Institute Russia

ISBN: (纸本)9798350372229

This research explores the complex terrain of cognitive dissonance that banking industry employees encounter while negotiating the changing environment of artificial intelligence (AI) integration. In a time when artificial intelligence (AI) is revolutionizing banking processes, it is critical to comprehend the elements that lead to cognitive dissonance among employees. The study takes a multimodal approach, integrating quantitative techniques to investigate the complex interactions between factors that affect cognitive dissonance in the workplace. data were gathered from 344 bank employees via online questionnaires. The influence of AI -driven changes on job positions, workplace relationships, and employee expectations is examined in this study. It examines how individuals' flexibility, organizational culture changes, and communication gaps affect their capacity to align their views with the quickly changing technology world. The study also looks at the ethical implications of using AI in banking organizations, taking into account how moral issues might make employees feel more cognitively disoriented. The purpose of this study's findings is to offer useful information to human resource specialists, policymakers, and stakeholders in the banking sector who are attempting to manage the difficulties caused by cognitive dissonance while integrating AI. This research helps to design solutions that promote employee well-being, adaptation, and resilience in the face of revolutionary technology breakthroughs by elucidating the multiple dynamics at play. The purpose of this study is to investigate the many aspects of cognitive dissonance that affect employees in banking. © 2024 ieee.

关键词： AI banking cognitive dissonance employees

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Towards Precision Dementia Detection: Integrating ML and Clinical data 2

Towards Precision Dementia Detection: Integrating ML and Cli...

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2nd international conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： Kandula, Niravange Chakravarty, Sujata Kandula, Aksha Mohapatra, Sunil Kumar Centurion University of Technology & Management Computer Science & Engineering Vizianagaram India Centurion University of Technology & Management Computer Science & Engineering Bhubaneswar India Andhra University College of Engineering Computer Science & Engineering Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798350370188

The dominant pathology theory holds that dementia results from amyloid buildup, followed by tau protein aggregation and neurodegeneration. Current anti-amyloid and anti-tau therapies, however, show varied therapeutic success. Atypical Alzheimer's disease has radiologic and clinical symptoms with other medical conditions, making accurate diagnosis even more challenging. Our approach employs machine learning algorithms to analyze a dataset that includes clinical evaluations, neuropsychological assessment scores, and biomarker measures. The information was gathered from a broad group of people with varying cognitive states, including those in the preclinical and moderate cognitive impairment (MCI) phases as well as AD patients. This study has important implications for clinical practice, permitting prompt therapies, and advancing our understanding of the progression of Alzheimer's disease. When integrated with Boruta as a feature selector, random forest outperformed upon fine-tuning on both datasets. © 2024 ieee.

关键词： Pathology

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Enhanced Cloud Security Through DNA-based Authentication for data Storage and Transactions 5

Enhanced Cloud Security Through DNA-based Authentication for...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Yuvarani, R. Mahaveerakannan, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamilnadu Chennai602105 India

ISBN: (纸本)9798350379945

Cloud computing has experienced rapid growth, yet data breaches and unauthorized account access continue to be significant threats. Traditional cryptographic authentication methods, although mathematically complex, often fall short in computational security. This research study integrates deoxyribonucleic acid (DNA) computing to enhance cloud security through biological authentication protocols, making it suitable for widespread use. This study introduces a DNA-based cryptographic key system that exploits synthetic DNA's dense information capacity. This system integrates biochemically secure DNA signatures with current cloud architectures, providing multi-factor authentication (MFA) and encrypted access to sensitive data and transactions. The DNA cryptography protocols developed offer robust defense against common attacks, such as password leaks. Additionally, the inherent massive parallelism in biochemical reactions supports efficient processing of authentication requests, even under heavy cloud workloads. © 2024 ieee.

关键词： Cloud computing architecture

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cognitive Differences in Network Security Based on Mathematical Modeling Based on Ensemble Learning Algorithm 2

Cognitive Differences in Network Security Based on Mathemati...

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2nd international conference on data Science and Information System, ICDSIS 2024

作者： Wang, Weiwei Shandong Polytechnic Teaching Department Of Humanities And Basic Courses Jinan China

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381665

With the rapid development of network technology and digital economy, network security issues are increasingly receiving widespread attention. People's awareness of network security has attracted the attention of many scholars. This study obtained a large amount of relevant data through questionnaire surveys and practical task tests, and conducted in-depth analysis of the original data using ensemble learning algorithms, including feature extraction, model training, result prediction, and other steps. This study chose random forest as the ensemble learning algorithm because it has high training speed, good parallel computing ability, good scalability, and efficient memory management in mathematical modeling. In addition, this study also designed mathematical modeling tasks, including illegal access behavior in network intrusion, and trained a classification model using random forest algorithm. Finally, the performance of the model was comprehensively evaluated by dividing the data into 10 test sets and conducting independent performance tests. Research has found significant differences in the cognitive aspects of mathematical modeling of cybersecurity among different groups, mainly reflected in their understanding of cybersecurity issues, selection of response strategies, and risk assessment. In addition, the study also found that factors such as educational background, work experience, and cybersecurity awareness have a 30%, 36%, and 21% impact on people's cognitive performance, respectively. Among them, educational background and work experience determine people's understanding and response ability to network security issues, while network security awareness directly affects people's level of attention to network security issues and willingness to take preventive measures © 2024 ieee.

关键词： Learning algorithms

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Achieving High-Accuracy Human Activity Recognition Using BERT-Based Classwise Ensemble Models

Achieving High-Accuracy Human Activity Recognition Using BER...

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2024 ieee international conference on computing, Applications and Systems, COMPAS 2024

作者： Rahman Fuad, K.M Nafiur Ahmed, Mumtahina Rabbani Abir, Md. Golam Anisur Rahman, Md Hossain Zamil, Md Zakir Mridha, M.F. Shin, Jungpil Bangladesh University of Business and Technology Dhaka Bangladesh Western Illinois University IL United States American International University - Bangladesh Dhaka Bangladesh The University of Aizu School of Computer Science and Engineering Aizuwakamatsu Japan

ISBN: (纸本)9798331529765

Human Activity Recognition (HAR) plays a vital role in ubiquitous computing and has a wide range of applications in healthcare, sports, and human-computer interaction. The study presents an innovative way of leveraging the BERT (Bidirectional Encoder Representations from Transformers) architecture to enhance the accuracy of HAR systems. By utilizing a classwise ensemble method, we effectively harness the capabilities of BERT for sequence classification, adapting it to the multi-dimensional time-series data characteristic of HAR tasks. Our model was trained and evaluated on the UCI HAR dataset, which consists of data collected with sensors from accelerometers and gyroscopes which recorded activities like walking, walking upstairs, sitting, walking downstairs, sitting, standing, and lying down from 30 participants. Our approach achieved outstanding performance through extensive experimentation, attaining 98% accuracy, 97% precision, 98% recall, and 97% F1-score. The results portray the robustness of the BERT-based classwise ensemble method in capturing complex patterns of human activities, outperforming traditional HAR models. This progress opens the door to more precise and dependable HAR systems, carrying substantial potential to improve user experiences across diverse application areas. © 2024 ieee.

关键词： Ubiquitous computing

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