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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20256 条记录，以下是611-620 订阅

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Secure and Efficient Deduplication for Multimedia Sharing in Privacy-Sensitive Cloud Ecosystems 3

Secure and Efficient Deduplication for Multimedia Sharing in...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Suresh, A. Murali, Erasappa Anjali Sri, V. Sumana, E. Sathwik, Gopaluni Venkata Sai Devendra Reddy, P. Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur India

ISBN: (纸本)9798331527549

Current cloud-based systems face the challenge of managing storage and eliminating redundant data associated with the exponentially growing multimedia content, where numerous semantically similar but nonidentical files are included. In this paper we introduce a novel secure deduplication framework for encrypted cloud storage. This proposed system employs similarity-preserving hashing, fuzzy PoW protocol, and attribute-based encryption to perform efficient deduplication without compromising security and providing ownership verification. Security and deduplication rate scalability are improved over brute-force and ownership-faking attacks by dynamically varying thresholds based on file characteristics. Experimental evaluation shows that a 48% average deduplication rate and resistance to common attack vectors make it suitable for real-world cloud storage scenarios. The framework fills critical gaps in existing approaches and provides an innovative solution for dealing with similarity-aware data deduplication in privacy-sensitive environments. © 2025 IEEE.

关键词： Cloud storage

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Advancements and Challenges in Quantum Machine learning for Medical Image Classification: A Comprehensive Review 3

Advancements and Challenges in Quantum Machine Learning for ...

引用

3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Shahriyar, Farhan Tanbhir, Gazi World University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331523893

Quantum technologies are rapidly advancing as image classification tasks grow more complex due to large image volumes and extensive parameter updates required by traditional machine learning models. Quantum Machine learning (QML) offers a promising solution for medical image classification. The parallelization of quantum computing can significantly improve speed and accuracy in disease detection and diagnosis. This paper provides an overview of recent studies on medical image classification through a structured taxonomy, highlighting key contributions, limitations and gaps in current research. It emphasizes moving from simulations to real quantum computers, addressing challenges like noisy qubits and suggests future research to enhance medical image classification using quantum technology. © 2025 IEEE.

关键词： Qubits

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Integrating Big data Analytics in Soil Quality Assessment: Techniques, Challenges, and Applications 3

Integrating Big Data Analytics in Soil Quality Assessment: T...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Hanif, Md Abu Lovely Professional University Department of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Department of Computer Science 365 5th Ave New YorkNY110016 United States

ISBN: (纸本)9798331515911

This review paper investigates the application of Big data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. Big data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for Big data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between Big data and conventional soil science practices.. © 2025 IEEE.

关键词： data quality

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Automatic audit system based on intelligent mining of audit work order text 3

Automatic audit system based on intelligent mining of audit ...

引用

3rd international conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Zhao, Guoyi Liu, Mingming Gu, Li Ren, Haiyang Chang, Le He, Jingsong Wang, Zongwei Jiang, Xinyue State Grid Customer Service Center State Grid Corporation of China Tianjin China School of Information Science and Technology University of Science and Technology of China Hefei China Marketing Service Center of Anhui Province State Grid Corporation of China Hefei China School of Microelectronics University of Science and Technology of China Hefei China

ISBN: (纸本)9781510680449

With the rapid development of electricity marketing business, the volume of business and data for power grid companies has significantly increased. Traditional manual auditing methods are unable to meet modern management demands. According to statistics, the manual misjudgment rate during the quality inspection process of audit work orders has reached 17.8%. This article establishes an intelligent audit model for audit work orders based on text mining technology. Firstly, pre-trained models are used to generate sentence vectors. Then, TextRank algorithm and MMR algorithm are integrated to generate summaries. Subsequently, using a Transformer model with incorporated sparse attention mechanism, the generated summaries are used as inputs for binary classification to obtain audit results. Experimental results demonstrate that the proposed audit model for work orders can achieve automated processing and fine-grained analysis of marketing audit monitoring tasks, effectively improving the efficiency and accuracy of the auditing process. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Commerce

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Lung Cancer Identification from CT Scans using a Soft-attention enabled Deep Transfer learning Model 3

Lung Cancer Identification from CT Scans using a Soft-attent...

引用

3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Dev, Shubhro Roy, Pragyadipta Sinha Chakraborty, Neelotpal Chakraborty, cNeelotpal Sarkar, Ram Rajiv Gandhi Institute of Petroleum Technology Department of Computer Science & Engineering Uttar Pradesh 229305 India KIIT University Department of Computer Science & Engineering Bhubaneswar751024 India RCC Institute of Information Technology Department of Computer Science & Engineering Kolkata700015 India Jadavpur University Department of Computer Science & Engineering Kolkata700032 India

ISBN: (纸本)9798331523893

Being a major contributor to the global mortality, lung cancer, owing to its fatality, calls for early and efficient diagnosis. This requires the employment of efficient computer aided diagnostics for determining the computed tomography (CT) scans as indicating lung cancer or not. Most such systems leverage the high efficiency of deep learning models to identify the lung cancer type from an input CT scan. In this work, a deep transfer learning model equipped with a soft attention block is framed to classify CT scans as either lung cancer affected or normal. Public datasets such as IQ-OTH/NCCD along with LIDC-IDRI are adopted for evaluation of the proposed technique which scored accuracies of 99.55% and 95.41% respectively, that are comparable with those of some recent standard techniques. © 2025 IEEE.

关键词： Deep learning

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Inducing a Realistic Surface Roughness onto 3D Mesh data Using Conditional Generative Adversarial Network (cGAN) 3rd

Inducing a Realistic Surface Roughness onto 3D Mesh Data Usi...

引用

3rd international conference on Advanced Surface Enhancement (INCASE)

作者： Mutiargo, Bisma Lou, Shan Wong, Zheng Zheng Adv Remfg & Technol Ctr A STAR Singapore Singapore Univ Huddersfield EPSRC Future Metrol Hub Huddersfield W Yorkshire England

ISBN: (纸本)9789819986422;9789819986439

In the age of machine learning, data-driven approaches with hybrid data (a mixture of real images and simulation images) are getting increasingly popular. One major issue with creating a realistic simulation for surface engineering is that the surface of the mesh model used in the simulation is smooth. Often, this mesh does not contain information on surface texture;thus, simulating an object based on these meshes may not represent an actual surface texture of a real component. This article presents a novel technique for introducing surface roughness onto a smooth mesh object to facilitate engineering simulation by using a conditional Generative-Adversarial Network (cGAN) that is trained on real height maps to generate random 2D height maps that represents a realistic texture of a typical upskin and downskin surface of an additively manufactured (AM) part. This approach extracted the past scans of AM components from the Focus Variation microscopy. The 3D surface deviation is extracted as height maps and used as the training data for the generative network. This paper will also discuss the structural similarities between the synthetic and real data using standard descriptors for surface texture characterisation, such as S-a, S-q and S-dq.

关键词： Surface roughness Deep learning GAN Additive manufacturing Synthetic data generation

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Advancing path of digital empowerment in the reform of intelligent teaching design 24

Advancing path of digital empowerment in the reform of intel...

引用

3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Wang, Jing Zhang, Fan Shu, Chang Chen, Qidi Li, Yang College of Information Engineering Wuchang Institute of technology Hubei Wuhan China School of Business Administration Zhongnan University of Economics and Law Hubei Wuhan China

ISBN: (纸本)9798400712692

As the digitization of education informatization continues to advance, increasingly stringent demands are placed on the systematization, intelligence and functionality. It is known that the intelligent teaching design plays a crucial role in the process, therefore, in this study, a smart teaching design with data-driven was employed to improve teaching effect. Through precise control of personalized learning paths and application of technology integration, the challenges in resource support and imprecision in learning effectiveness evaluation were effectively mitigated during the teaching process. The study exemplified by application-oriented courses and the results demonstrate that intelligent teaching design significantly enhances teaching efficiency and students' learning effectiveness. It offers practical guidance for the digital transformation of education. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Teaching

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ASBO-MAT-BiLSTM: Social Media Network Anomaly Detection using Optimized Multihead Attention Transformer based Model 3

ASBO-MAT-BiLSTM: Social Media Network Anomaly Detection usin...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Lunawat, Sonali Rao, Jyoti Patil, Pramod Dr. D. Y. Patil Institute of Technology Pimpri Pune India

ISBN: (纸本)9798331527549

Currently, attacks in the networks are the most vital issue in modern society and the networks from minor to huge networks are susceptible to network threats. Various approaches exist with several advantages and limitations in detecting anomaly, such as computational complexity, overfitting, low level of accuracy in detection, and so on. To overcome these issues and detect network anomaly effectively Adaptive Secretary Bird Optimization algorithm (ASBO) is integrated with the RAnked Minority Oversampling in Boosting (RAMOB) model to resolve the issue of data imbalance through making the data in a balanced manner. The Multihead Attention Transformer (MAT) mechanism and ASBO algorithm enable the BiLSTM model to effectively detect anomaly by solving the issues of overfitting and complexities in terms of both time and computation. The experimental outcomes display that the ASBO-MAT-BiLSTM model reaches an accuracy of 97.116% using NSL-KDD dataset and accuracy of 96.54%, using CIC-IDS2017 dataset, which reveals that our model attains higher efficiency in detecting anomalies in the networks. © 2025 IEEE.

关键词： Deep learning

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Machine learning Techniques for Task Offloading in Connected Autonomous Vehicles 3

Machine Learning Techniques for Task Offloading in Connected...

引用

3rd IEEE international conference on Power Electronics, intelligent Control and Energy Systems, ICPEICES 2024

作者： Kumar, Bhoopendra Bhardwaj, Aditya Sahu, Dinesh School of Scset Bennett University Greater Noida India

ISBN: (纸本)9798350387193

Connected Autonomous Vehicles (CAVs) can revolutionize the haulage industry by improving safety, efficiency, and comfort. However, real-time data processing within latency constraints is crucial for autonomous vehicles to perform well. Due to the limited computational resources available in CAVs, it is important to utilize them efficiently. One way to address this challenge is task offloading, which involves delegating computationally intensive tasks to remote servers or edge devices. In this context, a machine learning-based task offloading system is proposed for CAV s' edge computing networks. This system would dynamically determine the optimal offloading decisions regarding the vehicle's context & network conditions. Additionally, the paper provides a critical review and future roadmap for ML-driven task offloading in CAVs' edge computing environments. Finally, future directions for task offloading in the CAV s system are explored to provide valuable insights for researchers seeking to contribute to advancing the task offloading system. © 2024 IEEE.

关键词： Federated learning

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HL-Net: Zero-shot image denoising algorithm based on hybrid learning 24

HL-Net: Zero-shot image denoising algorithm based on hybrid ...

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3rd international conference on Artificial Intelligence and intelligent Information Processing, AIIIP 2024

作者： Wang, Yi Luo, Xiaohui School of Computer and Software Engineering Xihua University Sichuan ChengDu China

ISBN: (纸本)9798400707308

Deep neural networks trained on large datasets have achieved good results in image denoising. However, networks trained on specific datasets often have poor generalization, which is not conducive to practical applications. In addition, most denoisers often ignore the detail differences between image texture areas and smooth areas during the denoising process, resulting in the loss of details in texture areas. In this paper, we propose a zero-shot denoising network (HL-Net) with a Texture Noise Attention module (TNA) and a hybrid learning architecture. TNA allows the network to better extract noise information from texture areas without destroying their details, while the hybrid learning architecture uses two denoising methods for image smooth areas and texture areas: content learning LC (learning non-noise information in noisy images) and noise learning LN (learning noise information in noisy images), which can better preserve the image details in complex texture areas while extracting content information from smooth areas of noisy images. Additionally, the loss function proposed for the zero-sample hybrid learning network uses residual loss, consistency loss, and mean loss, enabling the network to better perform content learning and noise learning. Experiments show that our method outperforms most zero-sample denoising methods on various types of synthetic noise datasets and real-world noise datasets. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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