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检索条件"任意字段=3rd International Multi-Conference on Computing in the Global Information Technology, ICCGI 2008"

共 162 条记录，以下是1-10 订阅

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Proceedings - The 3rd international multi-conference on computing in the global information technology, iccgi 2008 in Conjunction with ComP2P 2008: The 1st international Workshop on Computational P2P Networks: Theory and Practice

Proceedings - The 3rd International Multi-Conference on Comp...

引用

3rd international multi-conference on computing in the global information technology, iccgi 2008

ISBN: (纸本)9780769532752

The proceedings contain 48 papers. The topics discussed include: intelligent store - an innovative technological solution for retail activities with mobile access;on designing an asynchronous and dynamic platform for solving single task requests of remote applications;web based telematics application for robotics;how do people seek knowledge in information system projects: a new perspective from social network theory;case-based decision support for bridge monitoring;an empirical study of flexible business process based on modularity system theory;an agent-based multi-issue negotiation model;metadata extraction from semi-structured email documents;transparent connectivity for embedded system design;a contextual acceptance model of mobile commerce based on TAM;generator for multimedia presentations;and an algorithm for robust relative influence values elicitation (ARRIVE).

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Low Resolution Deepfake Face Detection using multi-Scale Discrete Cosine Transform and Vision Transformer 3

Low Resolution Deepfake Face Detection using Multi-Scale Dis...

引用

3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Uddin, Main Fu, Zhangjie Zhang, Xiang Arnob, Abu Bakor Hayat Nanjing University of Information Science and Technology School of Computer Science Nanjing China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China

ISBN: (纸本)9798331523893

Deepfake technology uses generative adversarial networks to produce highly authentic counterfeit images and videos, which raises a significant global security concern by disseminating fake information through online platforms, especially on social media. Researchers have developed sophisticated models for deepfake detection, demonstrating high performance on high-resolution images. However, their effectiveness significantly diminishes when applied to low-resolution images. We propose a simple framework that utilizes multi-scale discrete cosine transform and vision transformer for low-resolution deepfake face detection. This framework introduces a multi-scale frequency filter fusion approach to extract subtle frequency features in the frequency domain. Our proposed network has been tested on the FaceForensics++ and Celeb-DF datasets, and it outperforms existing models, achieving superior AUC and F1 scores. © 2025 IEEE.

关键词： Computer Vision Deepfake Face Detection Discrete Cosine Transform Vision Transformer

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An iterated greedy algorithm based on NSGA-II for distributed hybrid flow shop scheduling problem 3

An iterated greedy algorithm based on NSGA-II for distribute...

引用

3rd international conference on Communications, information System, and Data Science, CISDS 2024

作者： Liu, Dandan Liang, Xu Zou, Zhiyuan School of Computing Beijing Information Science and Technology University Beijing China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

The distributed hybrid flow shop scheduling problems (DHFSP) widely exist in various industrial production processes, and thus have received widespread attention. However, studies on HFSP considering green objective in distributed production environment are quite limited. Therefore, this paper investigated a distributed hybrid flow shop scheduling problem with objectives of minimization the makespan and total energy consumption (TEC). To solve it, an iterated greedy algorithm based on NSGA-II (NSGAIG) is developed. In the proposed algorithm, a random initialization strategy is used to generate the initial solution. Then, starting from the population initialization, a multi-objective local search is carried out for the current optimal solution in the population to obtain the global optimal solution. Next, random variation method is used to increase the exploration space of the algorithm. Finally, the proposed NSGAIG algorithm is compared with other multi-objective optimization algorithms. Experimental results indicate that the proposed NSGAIG outperforms its compared algorithms in solving this problem. © 2025 SPIE.

关键词： Optimization algorithms

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Hybrid Quantum Approximate Optimization Algorithm (HQAOA) for Efficient Blockchain Transaction Scheduling 3

Hybrid Quantum Approximate Optimization Algorithm (HQAOA) fo...

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3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Paul, Soumyadip Chakraborty, Sarit Government College of Engg. & Ceramic Technology Dept. Of Information Technology WB Kolkata 700010 India Government College of Engineering and Leather Technology Dept. of Computer Science and Engineering WB Kolkata 700106 India

ISBN: (纸本)9798331523893

Quantum technology offers a transformative approach to solving complex computational challenges in decentralized systems, particularly in blockchain transaction scheduling. Efficient transaction scheduling is critical in distributed ledger systems, which often face challenges like dynamic transaction loads. This work proposes a novel approach namely Hybrid Quantum Approximate Optimization Algorithm (HQAOA) which combines multi-Agent QAOA (MA-QAOA) and Adaptive Layer QAOA (AL-QAOA) to provide a robust solution to such issues. HQAOA leverages a decentralized optimization framework where multiple agents, each representing a node in the network, optimize local transaction scheduling while considering global constraints. Additionally, HQAOA adapts to varying transaction loads by dynamically adjusting the number of quantum layers for each agent, ensuring computational efficiency under different conditions. Such dynamic layer adjustment mechanisms mitigate the adverse effects of quantum noise and ensures optimal performance in scenarios with fluctuating transaction loads. In contrast to traditional QAOA, which struggles with noise and scaling issues, HQAOA provides improved performance by balancing the complexity of the problem with available quantum resources. Experimental results signify the future potential of HQAOA to outperform QAOA, particularly in noisy environments and irregular transaction conditions. This work demonstrates the potential of HQAOA to optimize blockchain transaction scheduling in decentralized systems. © 2025 IEEE.

关键词： AL-QAOA Blockchain HQAOA MA-QAOA Quantum computing

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multi-scale Feature Fusion for Enhanced Person Re-identification 3rd

Multi-scale Feature Fusion for Enhanced Person Re-identific...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Wang, XiaoHui Sun, Yulin Zou, Zhipeng Liang, Xiaoyang Sun, Zhenyu Liu, Wei Shandong Business Institute Yantai China Shandong Xuewen Information Technology Co. Ltd. Jining China Technology of Computer Application Yantai Engineering and Technology College Yantai China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

ISBN: (纸本)9789819624676

In the realm of surveillance and security, person re-identification (Re-ID) stands as a critical challenge, particularly across non-overlapping camera networks. The essence of this challenge lies in the accurate and efficient identification of individuals by leveraging features captured from multiple perspectives. Addressing this, we introduce the Contextual multi-Scale Network (Asy-CMSNet), a novel framework designed to enhance the Re-ID process through the integration of asymmetrical convolution and an innovative loss function, the Enhanced TriHard (E-TriHard). Unlike traditional approaches that rely on the stacking of convolutional layers, Asy-CMSNet employs a sophisticated mechanism that allows for the nuanced extraction and integration of multi-scale features, thereby optimizing the identification process across diverse pedestrian appearances. Central to our framework is the notion that effective person Re-ID necessitates the consideration of both global and localized pedestrian details, ranging from broad clothing characteristics to subtle accessories, which our model adeptly captures and utilizes. Through extensive experimentation across several benchmark datasets, Asy-CMSNet demonstrates superior performance in person Re-ID challenges, highlighting a substantial progression in the area. Our findings not only set new performance benchmarks but also pave the way for future research into more efficient, scalable, and robust person Re-ID systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Benchmarking

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Proc. - The 3rd Int. multi-Conf. computing in the global information technology, iccgi 2008 in Conjunction with ComP2P 2008: The 1st Int. Workshop on Computational P2P Networks: Theory and Practice: Preface

Proc. - The 3rd Int. Multi-Conf. Computing in the Global Inf...

引用

Proc. - The 3rd Int. multi-Conf. computing in the global information technology, iccgi 2008 in Conjunction with ComP2P 2008: The 1st Int. Workshop on Computational P2P Networks: Theory and Practice 2008年 x-xi页

作者： Paleologu, Constantin El-Bawab, Tarek S. Noll, Josef Antonopoulos, Nick Bi, Jun Jiang, Guorui Delic, Kemal A. Roman, Dumitru Dantas, Simone Serrelis, Emmanouil Karwaczynski, Piotr Badica, Costin Wei, Hoo Chong Shah, Rahul Pentikousis, Kostas Alachaher, Leila Nemmiche Boyle, David Liotta, Antonio Dini, Oana

No abstract available

ISBN: (纸本)9780769532752

No abstract available

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U-SwinTrans: Automated Skin Lesion Segmentation Using Swin Transformer 3

U-SwinTrans: Automated Skin Lesion Segmentation Using Swin T...

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3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Sharma, Aaditya Lochan Sharma, Kalpana Srivastava, Utkarsh Prakash Ghosal, Palash Sikkim Manipal University Sikkim Manipal Institute of Technology Department of Computer Science and Engineering Sikkim India New York University Dept of Electrical and Computer Engineering New York United States Sikkim Manipal University Sikkim Manipal Institute of Technology Department of Information Technology Sikkim India

ISBN: (纸本)9798331523893

Skin lesions are difficult to segment because of their different shapes, sizes, colors, and textures. While Convolutional Neural Networks (CNNs) have proven effective at medical image segmentation, typical U-shaped networks that employ convolutional operations may struggle to capture global features successfully. To overcome these challenges, we incorporated Swin Transformer Blocks into a U-shaped architecture, enhancing the network's capability to capture global features more effectively. We also used traditional image enhancement techniques such as DCT, DWT, and FFT to preprocess input images for better model performance. Our method entailed tokenizing preprocessed pictures into patches and putting them into the U-SwinTransformer encoder-decoder architecture, which learned both local and global semantic characteristics required for successful lesion segmentation. A comprehensive analysis of the ISIC 2017 dataset proves the effectiveness of our approach in skin lesion segmentation. © 2025 IEEE.

关键词： DCT-DWT-FFT Segmentation Shifted-Windows multi Self Attention Skin Lesion Swin Transformer

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Low Resolution Deepfake Face Detection using multi-Scale Discrete Cosine Transform and Vision Transformer

Low Resolution Deepfake Face Detection using Multi-Scale Dis...

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international Symposium on Advanced computing and Communication (ISACC)

作者： Main Uddin Zhangjie Fu Xiang Zhang Abu Bakor Hayat Arnob School of Computer Science Nanjing University of Information Science and Technology Nanjing China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

关键词： Deepfakes Image resolution Social networking (online) Frequency-domain analysis Transformers Feature extraction Discrete cosine transforms Face detection Security Intelligent systems

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Leveraging Cloud-Based Deep Learning for Cardiovascular Diseases Prediction: Challenges and Solutions

Leveraging Cloud-Based Deep Learning for Cardiovascular Dise...

引用

Advancements in Electrical, Electronics, Communication, computing and Automation (ICAECA), international conference on

作者： S. Kanagaraj Department of Information Technology Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Cardiovascular diseases CVDs are a growing threat to global mortality therefore the need for accurate early prediction of diseases is imperative to improve patient outcome. The cloud-based deep learning model provides powerful CVD prediction solutions using large volumes of multi-structured medical data. But even then there are considerable challenges regarding data privacy, model interpretability, and effectiveness which inhibit effective application in clinics. The objective of this paper is to evaluate the role of explainable AI in the prediction of cardiovascular diseases using cloud based deep learning. Important shortcomings such as lack of model interpretability and trust are also discussed along with solutions to counter them. Here, XAI methods, for example SHAP and LIME may be used to enhance model interpretability. Clarifying the basis for AI predictions can enhance clinician's trust in AI-based decision making systems. Utilizing data augmentation techniques, multi-modal data integration, transfer learning, and other approaches can overcome data challenges and improve prediction outcomes. The paper further addresses the issues of data confidentiality and time multi-structured prediction by exploring federated learning and hybrid cloud models. This paper offers a new perspective in building XAI enhanced cardiovascular disease (CVD) predictive Deep Learning models through the integration of interpretability techniques and cloud infrastructure that will ultimately lead to better results, greater access to the systems, all while allowing the use of more personalized and effective healthcare methods.

关键词： Deep learning Accuracy Federated learning Explainable AI Transfer learning Data integration Medical services Predictive models Data models Cardiovascular diseases

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Hybrid Quantum Approximate Optimization Algorithm (HQAOA) for Efficient Blockchain Transaction Scheduling

Hybrid Quantum Approximate Optimization Algorithm (HQAOA) fo...

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international Symposium on Advanced computing and Communication (ISACC)

作者： Soumyadip Paul Sarit Chakraborty Dept. Of Information Technology Government College of Engg. & Ceramic Technology Kolkata WB India Dept. of Computer Science and Engineering Government College of Engineering and Leather Technology Kolkata WB India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

关键词： Quantum computing Distributed ledger Processor scheduling Heuristic algorithms Scalability Noise Dynamic scheduling Approximation algorithms Blockchains Complexity theory

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