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Design and Implementation of Data-Intensive Application using Memory Expansion Device 14

Design and Implementation of Data-Intensive Application usin...

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14th international conference on Information and Communication technology convergence, ICTC 2023

作者： Ahn, HooYoung Kim, SeonYoung Park, Yoo-Mi Han, Woojong Electronics and Telecommunications Research Institute Supercomputing Technology Research Center Artificial Intelligence Computing Research Laboratory Korea Republic of

ISBN: (纸本)9798350313277

Modern intelligent applications utilizing data-driven analysis have gained significant attention. As these applications rely on analyzing large-scale data for improved performance, they demand substantial high-performance memory. However, current computer architectures often lack the capacity to provide enough fast memory, leading to performance degradation in such applications. To address these limitations, memory expansion devices are being developed that connect to host servers through the new cache coherent interconnects like CXL. These devices offer additional memory and hardware acceleration capabilities to enhance *** this paper, we propose a novel method to accelerate data-intensive MPI applications in a CPU-FPGA heterogeneous computing environment, employing memory expansion devices called MEX. Our approach leverages MEX and MPI to enable near-memory processing by utilizing MEX as shared computing resources. To the best of our knowledge, this is the first study to combine memory expansion devices and MPI for accelerating data-intensive MPI applications. We implement a content-based image similarity search system using MPI and MEX and verify the feasibility of our proposed methods. © 2023 IEEE.

关键词： Data-Intensive Application Memory Expansion Device Message Passing Interface

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Classification of Chest X-Ray Images Using CNN-Based Transfer Learning Models 6

Classification of Chest X-Ray Images Using CNN-Based Transfe...

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6th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Chen, Danrui Xu, Yu School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350374315

Pneumonia and COVID-19 are infectious and fatal. X-ray is the imaging modality for early detection of pneumonia. However, it is difficult to detect pneumonia due to the small target, low resolution, and insufficient image information content of the pneumonia in X-ray images. This study aims to address these problems by comparing manual identification and other traditional machine learning models with a transfer learning model based on convolutional neural network (CNN) to classify pneumonia from chest X-ray images. A transfer learning model based on a convolutional neural network (CNN) was developed to classify pneumonia from chest X-ray images. This model was pre-trained to accelerate the convergence rate and improve the classification accuracy, thereby enhancing its generalisation ability. To validate the model's effectiveness, experiments were conducted on two datasets. The transfer learning model based on ResNet significantly better than other models on the Chest X-ray 2017, with accuracy, accuracy, recall, kappa and F1 values of 89.99%, 90.70%, 85.94%, 75.66% and 87.77%, respectively. On the COVID-19 radiography database, the transfer learning model based on MobileNetV2 demonstrated significantly superior performance to other models. The accuracy, accuracy, recall, kappa, and F1 values of MobileNetV2 were 85.56%, 81.50%, 89.80%, 77.34%, and 84.65%, respectively. The results demonstrated that the CNN-based transfer learning model was more effective in classifying pneumonia X-ray images, which could reduce misdiagnosis and missed diagnosis and shorten the diagnostic time. © 2024 IEEE.

关键词： COVID-19

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An Intensive Review on 6G and its Future Technological Developments using New technology Integration 4

An Intensive Review on 6G and its Future Technological Devel...

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4th IEEE international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Sharma, Anil Deepa, R. Sowmiya, S. Kumar, Rakesh Kuppuraj, T. Pavaiyarkarasi, R. M. P Bhopal India Tamil Nadu T.M.Palayam India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering India Mathura India Karpagam Academy of Higher Education Department of Computer Applications TN Coimbatore India R.M.K. Engineering College Department of Ece Kavaraipettai Tamilnadu RSM Nagar India

ISBN: (纸本)9798350360165

As 5G deployment picks up steam, there's been a noticeable change in the field of wireless research toward imagining what 6G will look like for communication in the future. The purpose of this article is to present the anticipated communication requirements and technology for the 6G future. The creation of digital twin realms that accurately replicate the biological and physical worlds in real time is the key to the future of connectedness as it is currently envisioned. The goal of this integration is to bring experiences from the digital, biological, and physical realms together. The development of novel man-machine layouts through productive local devices, the ubiquitous distribution of general computing across individual devices and the cloud, as well as multi-sensory integration of data for the creation of multi-verse corresponds and exciting mixed-reality experiences, and accurate sensing and actuation for efficient control of the physical world are emerging themes that are shaping 6G system specifications and technologies. The 6G air link and network will be supported by artificial intelligence, which will use data, computation, and energy as key resources to achieve higher performance. This paper also explores other revolutionary technologies that will likely shape 6G: cognitive spectrum sharing, integrating localization and sensing into system design, meeting strict latency and reliability performance requirements, new network architecture concepts involving sub-networks while RAN-Core convergence, and creative security and privacy strategies. © 2024 IEEE.

关键词： 5G mobile communication systems

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Mesh Deformation Scheme for High Quality 3D Model Reconstruction

Mesh Deformation Scheme for High Quality 3D Model Reconstruc...

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2023 IEEE international conference on Metaverse computing, Networking and Applications, MetaCom 2023

作者： Park, Jung Suk Seo, Bong-Seok Kim, Dong Ho Seoul National University of Science and Technology It Media Engineering Seoul Korea Republic of Seoul National University of Science and Technology Information Technology and Media Engineering Seoul Korea Republic of Seoul National University of Science and Technology Department of Smart Ict Convergence Engineering Seoul Korea Republic of

ISBN: (纸本)9798350333336

This paper proposes a new mesh deformation scheme in a 3D reconstruction in which the performance deteriorates when an object of an unlearned category is input in a 3D model generation network. The proposed method uses a new loss function to generate appropriate 3D reconstruction outputs even for inputs of unlearned categories of 3D models. We compare the performance with the existing method so as to prove the validity of this method. The simulation results show that the proposed loss function improves the generalization ability of the 3D reconstruction with good performance even for inputs of unlearned categories. © 2023 IEEE.

关键词： 3D modeling

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Artificial Intelligence and Blockchain: The Future of Financial Services technology 15

Artificial Intelligence and Blockchain: The Future of Financ...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Shubhi Sharma, Meenakshi Khetwal, Neha Walia, Arpit Goyal, Lalit Ahmad, Vasim Dev Bhoomi Uttrakhand University School of Management and Commerce Dehradun India Uttaranchal Institute of Management Uttaranchal University Dehradun India Soban Singh Jeena University Pt B. D. Pandey Campus Bageshwar India Tula's Institute Graduate School of Business Dhulkot Mafi Dehradun India

ISBN: (纸本)9798350370249

This has in fact given rise to new digitalisation technologies, predominantly Artificial Intelligence and Blockchain, which are disrupting the traditional financial services. Fintechs are using AI and blockchain to disrupt and innovate all of their previous business models. The study utilizes conceptual and secondary data to examine how AI and blockchain are digitizing financial services. However, a critical shift in the technology landscape of financial services is in the trend of increasing use toward combining Artificial Intelligence (AI) with Blockchain technology, looking for better security, efficiency, and personalized delivery of financial services in a manner that continues to hold the core challenges associated with traditional financial ecosystems. AI and Blockchain will merge to introduce a new era of innovation, efficiency, and security to the financial sector. Thus, the complementarity of the capabilities of AI with the domain of Blockchain, in areas of resilience, accessibility, and, in fact, user-centeredness through computational, predictive, Json-based, and automated means. The partnership will result in more intelligent, safer operations, with all of these innovations also opening up avenues for innovation, financial inclusion, and better customer service. The unfolding of this technology convergence behoves industry leaders, policymakers, and technologists to further develop an ecosystem that now enables maximization of benefits and minimization of risks to easily provide financial services to all, making them clear and better suited for their needs. The study explores how AI and Blockchain can unite in the banking industry;it focuses on realizing the role of the two technologies in transforming the traditional financial infusion, processes, and customers' experiences. It also found that AI and Blockchain significantly influence the digitalization of financial services, particularly in security, customer support, and accessibility. © 2024 IEEE.

关键词： Artificial intelligence Blockchain Digitalization financial services

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Systematic Cognitive computing Framework Application Using Medical Information Processing 10

Systematic Cognitive Computing Framework Application Using M...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Kanna, R. Kishore Ambikapathy, A. Al-Hameed, Mazin Riyadh Sumalatha, Ippa Singh, Navdeep Jeruslaem College of Engineering Department of Biomedical Engineering Chennai India Galgotias College of Engineering and Technology Department of of Electrical and Electronics Engineering India Collage of Engineering National University of Science and Technology Dhi Qar Iraq Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350382471

In recent years, the convergence of cognitive computing and natural language processing (NLP) has emerged as a critical field of study, promising significant breakthroughs in medical imaging. This research digs into the integration of cognitive computing approaches with NLP to increase the interpretation and comprehension of complicated medical narratives. We offer a unique framework that harnesses the cognitive capacities of computers to process, analyze, and interpret huge quantities of unstructured medical material. Our technique combines deep learning architectures and semantic analysis to extract therapeutically important information from radiology reports, patient histories, and other textual data sources. Preliminary findings suggest a considerable increase in the accuracy and efficiency of medical picture annotations, leading to more accurate diagnostic insights. Furthermore, the system exhibits an adeptness in understanding sophisticated medical jargons, acronyms, and context-dependent interpretations. This discovery not only emphasizes the promise of cognitive computing in changing medical imaging but also establishes a precedent for its use in other sectors needing complex language interpretation. © 2023 IEEE.

关键词： Cognitive computing Medical Imaging Natural Language Processing Radiology Reports Semantic Analysis

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Innovative Computational Fluid Dynamics Techniques for Enhanced Predictive Modeling in Multiphase Flow and Heat Exchanger Optimization

Innovative Computational Fluid Dynamics Techniques for Enhan...

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2024 OPJU international technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Fakher, Ahmed Jalal Ramesh, B. Anandhi, R.J. Singla, Atul Chandra, Pradeep Kumar Nair, Rajit National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India LLloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Vit Bhopal University Bhopal India

ISBN: (纸本)9798350373783

An innovative technique to combine 'Innovative Computational Fluid Dynamics Techniques for Enhanced Predictive Modeling in Multiphase Flow and Heat Exchanger Optimization.' Five approaches tackle distinct predictive modeling problems in the framework. The hybrid CFD and Machine Learning Framework integrates CFD and ML without affecting each other. This enables the framework to be refined repeatedly until convergence. As solution mistakes occur, adaptive mesh refinement (AMR) adjusts the mesh. This improves multiphase flow models' spatial accuracy and flexibility. The Lattice Boltzmann Method (LBM) with Machine Learning Enhancement is quicker and more accurate. The data-driven calibration and validation technique is unique in that it updates model parameters using real-world data, allowing estimations to match testing findings. The Parallelized Algorithm for Large-Scale Simulations maximizes computer performance by partitioning the region into parallel jobs. The performance evaluation in tables and charts reveals that the recommended framework is better in accuracy, processing efficiency, scalability, flexibility, stability, and applicability. Since the approaches are iterative and adaptive, they can improve. This makes the system suitable for modeling complicated multiphase flows. This comprehensive and cutting-edge technique improves multiphase flows and heat exchanges, providing scientists and engineers with new prediction tools. Future testing and implementation in other industries might establish the framework as a versatile and dependable computational fluid dynamics solution. © 2024 IEEE.

关键词： Mesh generation

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Terrain Image Classification Based on Vision Transformer Deep Learning Algorithm 7

Terrain Image Classification Based on Vision Transformer Dee...

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7th international conference on Algorithms, computing and Artificial Intelligence, ACAI 2024

作者： Ren, Shiqi Zhang, Yu College of information engineering College of artificial intelligence Yangzhou university Jiangsu Yangzhou225000 China College of Computing Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9798331529314

Terrain image classification is an important research direction in the field of remote sensing and computer vision, aiming to realize automatic recognition and classification of different landform features through the analysis and processing of terrain images. In this paper, a deep learning algorithm based on Vision Transformer (ViT) is used to classify terrain images, and the performance of the algorithm in this task is systematically evaluated. In the process of model construction, we first imported the Vision Transformer model and made corresponding parameter Settings to ensure its adaptability and effectiveness. After training, it is observed that the loss function of the training set decreases from the initial value of 2.84 to 0.35, a decrease of 2.49, indicating that the model tends to converge in continuous optimization. At the same time, the accuracy is also significantly improved, from 55.9% to 86.8%, an increase of 30.9%, showing the enhancement of the model's learning ability. For the validation set, loss also decreased from 0.78 to 0.47, a decrease of 0.31, while accuracy increased from 60.1% to 83.5%, an increase of 23.4%. These results further prove the good performance of the model on different data sets and its convergence trend. In addition, through the evaluation of the test set, we get more specific performance indicators: The accuracy of the terrain image classification model based on Vision Transformer on the test set reaches 89.9%, the Precision is 0.9615, the Recall is 0.9494, and the F1-score is 0.9554. These indicators show that the model not only has high classification accuracy, but also performs well in generalization ability. To sum up, this research demonstrates the effectiveness of Vision Transformer deep learning algorithm in terrain image classification, and provides a new solution idea and method for related fields. Through continuous optimization and adjustment, the algorithm is expected to achieve more extensive promotion in pract

关键词： Machine vision

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Survey on Advanced Video Streaming with Multi-Access Edge computing 13

Survey on Advanced Video Streaming with Multi-Access Edge Co...

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13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Kim, Heemin Kim, Moonbeom Paek, Jeongyeup Chung-Ang University Department of Computer Science & Engineering Seoul Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Video streaming market has been growing rapidly for the past two decades. YouTube and Netflix, for example, are part of everyday life for many of us. Although network bandwidth has increased with the development of 5G and fiber, user demand and expectations have also increased with 4K videos, larger screens, and COVID-19. Recent explosive increase in video traffic made it difficult for the network and applications to satisfy the user requirements of video streaming. Multi-access edge computing (MEC) is a promising technology to resolve this problem. MEC is a network architecture concept that offers cloud-computing capabilities and an IT service environment at the edge of the network. In this paper, we review the research efforts in video streaming with MEC over the past few years. We have clustered and categorized the literature according to the optimization criteria, with focus on cache management. We aim this survey to be a cornerstone for designing effective novel caching scheme in MEC for video streaming. © 2022 IEEE.

关键词： Video streaming

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Research on Graph Feature Aggregation Algorithm Based on GCN and GAT 6

Research on Graph Feature Aggregation Algorithm Based on GCN...

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6th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Zhou, Qi Yang, Shaohua China Electronics Technology Group Corporation 20th Research Institute Xi'an China

ISBN: (纸本)9798350374315

This paper focuses on the integration of Graph Convolution Network and Graph Attention Network, two cutting-edge models in graph neural networks, aiming to explore a new paradigm of graph feature learning that integrates their advantages efficiently. GCN uses graph convolution mechanism to achieve global aggregation of node features, while GAT introduces attention mechanism to optimize the feature selection process and enhance the recognition of local features. The core contribution of this study is to construct an innovative fusion framework, which cleverly combines the macro information integration capability of GCN with the micro feature refining capability of GAT, and realizes the dynamic fusion of model outputs by designing trainable weight allocation strategies. This framework has not only deepened the understanding of the complexity of graph structure, moreover, it has notably elevated the model performance across diverse graph data tasks, encompassing social network analysis, chemical molecular classification, and recommendation systems. This enhancement is particularly pronounced when addressing challenges associated with data heterogeneity and noise. In addition to the empirical analysis, the convergence characteristics, parameter sensitivity and computational efficiency of fusion algorithm are also discussed in this study, which provides solid support for theoretical deepening and practical application. Finally, through the prospect of potential application scenarios and the outline of future research paths, this paper not only deepens the academic community's understanding of GNN fusion strategies, but also opens up a new path for solving the feature learning challenges of large-scale and complex graph data. In summary, this study is not only a deep exploration of the theory and application potential of GCN and GAT, but also a promotion of the frontier of graph feature aggregation technology, which provides an important theoretical and practical referenc

关键词： Graph neural networks

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