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3rd International Conference on Algorithms, Microchips, and Network Applications, AMNA 2024

作者： Zhang, Zhibo Wang, Huiqiang Niu, Shuangyue Lv, Hongwu College of Computer Science and Technology Harbin Engineering University Harbin China AVIC Chengdu Aircraft Design and Research Institute Chengdu China

ISBN: (纸本)9781510680098

Recently, the combination of a service function chain (SFC) with network function virtualization (NFV) and software-defined networking (SDN) has provided customers with flexible and efficient services. The emergence of multi-access edge computing (MEC) further enhances the level of service customization. However, achieving joint optimization of virtual network function (VNF) deployment and flow allocation in resource-constrained scenarios while meeting the diverse requirements of 5G verticals is challenging. Current research rarely addresses dedicated service provisioning for edge servers and considers the additional instantiation overhead introduced by adjusting cloud server parameters. In fact, this is a non-negligible issue during SFC deployment in 5G-MEC scenarios. Based on the above considerations, this paper constructs a joint SFC deployment problem for edge-cloud networks with the goal of maximizing network utility. We first propose a univariate modeling method based on meta-links that effectively avoids the variable coupling problem in traditional multivariate modeling approaches and reduce the problem size by at least half. Subsequently, to solve the NP-hard integer nonlinear problem (INLP), we propose a distributed computing architecture named SP-ADMM, which improves the speed and quality of SFC deployment in large-scale scenarios via convex combinatorial formulations and a Viterbi-based heuristic algorithm (PAC-GREP). Finally, we experimentally verify the convergence and approximation of the algorithms. Our solution demonstrates advantages in terms of network utility and convergence speed under the same network resources, increasing service capacity by at least 39%. © 2024 SPIE.

关键词： Network function virtualization

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Sign Language Vocabulary Recognition Only with Tactile Sensing Glove 17

Sign Language Vocabulary Recognition Only with Tactile Sensi...

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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Kagami, Motoki Yu, Zeping Ceng, Sim Teck Jing, Lei The University of Aizu School of Computer Science and Engineering Aizu-Wakamatsu Japan The University of Aizu Graduate School of Computer Science and Engineering Aizu-Wakamatsu Japan University of Aizu Junior College Division Department of Management and Design Aizu-Wakamatsu Japan

ISBN: (纸本)9798331530471

A significant challenge in communication arises between deaf individuals and hearing individuals. Sign language recognition is expected to be an important research field for eliminating barriers. In this field, data collection is primarily conducted using two methods: sensor-based and vision-based approaches. In this study, we adopted a sensor-based approach by using a tactile sensing glove due to its portability, costeffectiveness, and ability to capture fine finger movements. Subsequently, we applied LSTM and k-NN respectively to evaluate the recognition accuracy using this method. As a result, LSTM achieved a test accuracy of approximately 76%, while k-NN demonstrated an average accuracy of 87%. These findings highlight the potential of sensor-based SLR technology in reducing communication barriers and advancing inclusively in communication tools. © 2024 IEEE.

关键词： Audition

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Style Transfer of Chinese Opera Character Paintings

Style Transfer of Chinese Opera Character Paintings

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Gao, Xirong Binti Mohammad Noh, Liza Marziana Mu, Xian Wu, Chenhao Liu, Jiang University Teknologi Mara Faculty of Art and Design Selangor Malaysia School of Computer Science and Engineering Macau University of Science and Technology Taipa China Garduate School of Fundamental Science and Engineering Waseda University Tokyo Japan

ISBN: (纸本)9798350378214

Traditional opera character painting blends Chinese color ink painting with conventional opera, requiring both advanced opera art understanding and Chinese painting skills. However, creating these paintings demands specific artistic skills and the ability to integrate elements of opera art. Addressing these challenges is essential for the long-term development of opera character paintings and the promotion of traditional Chinese culture. Recent advancements in artificial intelligence (AI), particularly in image style transfer technology, offer new methods for generating opera character paintings. This paper uses our collected dataset of traditional opera character photographs and their paintings, employing an adversarial generative network (GAN) for implicit feature learning. Consequently, real opera character photographs are transformed into distinctive Chinese color ink paintings. To enhance the quality of the generated images, the encoding process incorporates feature mapping and attention module integration, which help reduce unimportant areas. Additionally, adaptive normalization techniques are used to improve neural network stability, accelerate convergence, and strengthen generalization capabilities. Simulation experiments demonstrate that the proposed method outperforms existing CNN and CycleGAN-based methods, indicates promising transfer performance and highlights the potential for the artistic creation of opera character paintings. © 2024 IEEE.

关键词： Generative adversarial networks

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Collaborative Model for Sustainable Energy Utilization in Cloud Infrastructure 6

Collaborative Model for Sustainable Energy Utilization in Cl...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Rajagopal, Manikandan Kumar, K. Sathesh Prabagar, S. Sivasakthivel, Ramkumar Ramar, Gobinath Computer Science and Engineering Alliance College of Engineering and Design Alliance University - Central Campus Anekal Main Road Karnataka Bangalore562106 India CHRIST University Bangalore India

ISBN: (纸本)9798331522667

As the infrastructures of cloud computing provides paramount services to worldwide users, persistent applications are congregated using large scale data centres at the customer sides. For such wide platforms, virtualization technique has been incorporated for multiplexing the essential sources available. Due to the extensive application variations in the workloads, it is significant to handle the resource allocation methodologies of the virtual machines (VM) for assuring the Quality of Service (QoS) of cloud. On concentrating this, the paper proposed a Decentralized Energy-Aware Collaborative Model (DEACM) for effectively managing the data centres in cloud infrastructures. Initially, the optimal model for system management and power management are declared. Then, functions of workload vectors and data collection about workloads has been carried out for optimal selection of virtual machines to migrate for balancing loads efficiently. This can be further applied for Target-based VM Migration Algorithm for determining the migrating target for VM. Moreover, the algorithm involved in energy utilization with managed QoS. The developed DEACM is evaluated using CloudSim platform and the results are discussed. The results exemplify that the DEACM can balance the workload across variety of machines optimally and provide reduced energy consumption to the complete system efficiently. © 2025 IEEE.

关键词： Cloud platforms

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Innovative Skin Melanoma Diagnosis using Deep Learning and African Gorilla Troops Optimizer 1

Innovative Skin Melanoma Diagnosis using Deep Learning and A...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Parthasarathi, P. Nivedha, S. Balamurugan, M. Yuvaraj, D. Siamala Devi, S. Sangeethaa, S.N. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India Sona College of Technology Dpartment of Computer Science and Engineering Salem India Kongu Engineering College Department of Computer Science and Design Perundurai India Sri Eshwar College of Engineering Kuniamuthur Department of Information Technology Coimbatore India Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350352931

Skin melanoma, a dangerous kind of skin cancer, necessitates accurate identification and diagnosis to improve patient outcomes. The use of deep learning algorithms into medical imaging promises significant advancements in skin disease analysis, particularly melanoma diagnosis. This study looks at the usage of a novel optimisation technique, the African Gorilla Troops Optimizer (AGTO), to enhance deep learning models in the segmentation and analysis of skin images. The AGTO, which is based on African gorilla groups' social and hunting activities, is an effective approach of improving convolutional neural networks (CNNs) for accurate and reliable skin image segmentation. The proposed approach uses AGTO to distinguish melanoma lesions from benign skin disorders, which increases their identification. Extensive research on benchmark skin image datasets has confirmed the AGTO-enhanced deep learning model's durability and performance, emphasising its clinical use in dermatology and the proposed model shows 98 %accuracy. This work not only underlines the need of advanced optimisation techniques in medical image processing, but it also paves the way for more effective diagnostic tools for skin problems. © 2024 IEEE.

关键词： Image segmentation

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Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT

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Mathematical Biosciences and engineering 2024年第4期21卷 5735-5761页

作者： Zhang, Wencong Tao, Yuxi Huang, Zhanyao Li, Yue Chen, Yingjia Song, Tengfei Ma, Xiangyuan Zhang, Yaqin Department of Biomedical Engineering College of Engineering Shantou University Shantou China Department of Radiology The Fifth Affiliated Hospital of Sun Yat-sen University Zhuhai China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Biomedical Engineering College of Design and Engineering National University of Singapore Singapore

Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bidirectional feature interaction among multiple phases, thus maximally exploiting the available multiphase information. To enhance the model’s capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multiphase CT data exploitation. The proposed MI-TransSeg network has the potential to assist rad

关键词： Tumors

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A Convnet Model-Based Deep Learning Approach for Pneumonia Recognition from Chest X-ray Images 2

A Convnet Model-Based Deep Learning Approach for Pneumonia R...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Venkateswarulu Naik, B. Unhelkar, Bhuvan Chakrabarti, Prasun Ghantasala, G.S. Pradeep Vidyullatha, Pellakuri Pyla, Meghana Department of Information Systems and Decision Sciences Muma College of Business University of South Florida South Florida United States Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (纸本)9798350383867

Pneumonia diagnosis using chest X-ray (CXR) images shows a decisive part in early detection and handling of the disease. Within this research, proposes a convolutional neural network (ConvNet) model for automated pneumonia diagnosis based on CXR images. Our model leverages the deep learning capabilities of ConvNets to analyze features in CXR images and categorize them as pneumonia-positive or pneumonia-negative. We conducted experimentations on a comprehensive dataset of CXR images, achieving auspicious outcomes in terms of efficiency and accuracy. The proposed ConvNet model demonstrates prospective for an accurate and appropriate pneumonia diagnosis, contributing to improved healthcare outcomes and resource optimization in medical settings. ConvNet model is implemented using tensorflow keras with preprocessing using Image data generator. The models are deployed like sequential, Conv2D with tuning parameters of Relu, dropout, max pool 2d and adam optimizer. For training the images with 150, 150 pixels, train sample as are 1000 and input shape is 3, epochs are 50, batch size is 20. Applications of the categorical cross-entropy loss function for multi-class division with Adam optimizer employed for parameter optimization. The Model trained for a stable amount of epochs through early based on validation loss toward prevent overfitting and learning rate, batch size, and dropout rate optimized through hyperparameter tuning. © 2024 IEEE.

关键词： Convolutional neural networks

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Scalable IoT Security Framework Leveraging Ensemble Learning for Attestation 16

Scalable IoT Security Framework Leveraging Ensemble Learning...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Tt, Mini Behera, Santrupti Reddy, M Srikar Hota, Chittaranjan Celestica Design Engineering Chennai India Birla Institute of Technology and Science Pilani Department of Computer Science India Birla Institute of Technology and Science Pilani Department of Electronics & Communication India

ISBN: (纸本)9798331505264

Attestation involves providing verifiable evidence to support claims about the characteristics of a target device. Devices that manage sensitive data-such as wearables, Industrial IoT devices, and servers-are vulnerable to various threats, including edge-based attacks, denial-of-service (DoS) attacks, counterfeiting, and firmware tampering. Attestation plays a crucial role in ensuring the integrity of firmware and configurations, authenticating genuine hardware, and verifying that composite device assemblies remain unaltered. However, a review of existing solutions highlights significant deficiencies, particularly in comprehensive lifecycle management and support for large-scale networks. This paper introduces a novel attestation method based on an optimized ensemble learning, which achieves a predictive accuracy of 93%, addressing these gaps and enhancing security across the entire device lifecycle. © 2024 IEEE.

关键词： Firmware

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Removing/Minimising Gaussian noise from real time CCTV footages using GAN 1

Removing/Minimising Gaussian noise from real time CCTV foota...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Prashanth, B.S. Puneeth, B.H. Manoj Kumar, M.V. Sreenivasa, B.R. Suraj, Adyapadi Hegde, Akshay Prashant Pranava Aithal, K.S. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India Bapuji Institute of Engineering and Technology Department of Computer Science and Design Karnataka Davanagere577004 India

ISBN: (纸本)9798350376517

This paper describes a system that uses generative adversarial networks (GANs) to eliminate gaussian noise from CCTV of footages. Unwanted noise like gaussian noise frequently degrades the quality of images and makes them more difficult to interpret. In our approach, a discriminator network is used to direct the training of a generator network, whose job it is to produce denoised images from noisy inputs. This framework includes operations like picture enhancement, noise reduction, and evaluation with metrics like the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The resultant denoised images show enhanced visual quality and have potential uses in image analysis and computer vision. © 2024 IEEE.

关键词： Generative adversarial networks

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Automated CKD Detection via Image Processing Employing Delta-E Metric and CNN

Automated CKD Detection via Image Processing Employing Delta...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Espin Shalo, S.P. Jeeshan, R.J. Gunasekar, S. Pius Agbulu, G. Venkat Ramana, N. Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai602105 India Rajalakshmi Engineering College Department of Computer Science and Design Tamilnadu Chennai602105 India Chinhoyi University of Technology Department of Mechatronics Engineering Zimbabwe Indian Institute of Technology Department of Electrical Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331510022

Chronic Kidney Disease (CKD) is a global health concern that often progresses silently until advanced stages, making early detection critical. This paper presents a point-of-care system that automates the color comparison between a test strip and a reference strip to detect biomarkers like albumin associated with kidney dysfunction. The system leverages mobile-captured images, applying image processing techniques such as segmentation and color extraction, and compares the test strip's top color with reference colors on the parent strip using the Delta-E metric in the LAB color space. The closest match quantifies the biomarker concentration, providing a reliable diagnostic measure. To enhance diagnostic precision, this research incorporates Convolutional Neural Networks (CNNs) to refine image preprocessing and anomaly detection. The CNN model complements existing methods by mitigating environmental influences like lighting variability and improving feature extraction accuracy. This hybrid approach ensures consistency while adapting to diverse datasets and image conditions. The result is a cost-effective, fast, and accessible diagnostic tool that reduces manual errors and supports early CKD detection in resource-limited settings. Future work will focus on expanding the application of this system to detect other diseases and further optimize the integration of CNNs for enhanced scalability and robustness. © 2024 IEEE.

关键词： Convolutional neural networks

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