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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Praveena, K.N. Mishra, Awakash Kamalraj, R. Belwal, S. Ankit School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India

ISBN: (纸本)9789819780426

This paper gives a predictive intelligence-enhanced fuzzy version for underwater network optimization. It combines predictive intelligence technology with fuzzy common sense, which can lessen the functionality gap of underwater community optimization. This proposed version is implemented in an underwater cooperative tracking community. With the proposed model, objectives can be tracked with sensors within the network, and their national statistics can also be appropriately anticipated. Similarly, the version has excessive scalability and robustness because of the mixing of predictive intelligence. In the end, simulation experiments suggest that the proposed model correctly improves the precision of tracking challenges and improves the scalability of the network. The Predictive Intelligence superior Fuzzy model (PIE-FUZMO) for underwater network optimization is a singular method developed to cope with the complex optimization issues confronted through underwater networks. The PIE-FUZMO uses a couple of laptops with imaginative and prescient sample recognition techniques and fuzzy good judgment to enhance the underwater community performance while decreasing the running fee. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scalability

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TRNet: Two-Tier Recursion Network for Co-Salient Object Detection

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5844-5857页

作者： Cong, Runmin Yang, Ning Liu, Hongyu Zhang, Dingwen Huang, Qingming Kwong, Sam Zhang, Wei Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi’an710129 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Lingnan University School of Data Science Hong Kong

Co-salient object detection (CoSOD) is to find the salient and recurring objects from a series of relevant images, where modeling inter-image relationships plays a crucial role. Different from the commonly used direct learning structure that inputs all the intra-image features into some well-designed modules to represent the inter-image relationship, we resort to adopting a recursive structure for inter-image modeling, and propose a two-tier recursion network (TRNet) to achieve CoSOD in this paper. The two-tier recursive structure of the proposed TRNet is embodied in two stages of inter-image extraction and distribution. On the one hand, considering the task adaptability and inter-image correlation, we design an inter-image exploration with recursive reinforcement module to learn the local and global inter-image correspondences, guaranteeing the validity and discriminativeness of the information in the step-by-step propagation. On the other hand, we design a dynamic recursion distribution module to fully exploit the role of inter-image correspondences in a recursive structure, adaptively assigning common attributes to each individual image through an improved semi-dynamic convolution. Experimental results on five prevailing CoSOD benchmarks demonstrate that our TRNet outperforms other competitors in terms of various evaluation metrics. © 1991-2012 IEEE.

关键词： HTTP

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RNN-EdgeQL: An auto-scaling and placement approach for SFC

RNN-EdgeQL: An auto-scaling and placement approach for SFC

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作者： Pandey, Suman Choi, Minji Yoo, Jae-Hyoung Hong, James Won-Ki School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Korea Republic of Graduate School of Artificial Intelligence Pohang University of Science and Technology Pohang Korea Republic of Department of Computer Science and Engineering Pohang University of Science and Technology Pohang Korea Republic of

This paper proposes a prediction-based scaling and placement of service function chains (SFCs) to improve service level agreement (SLA) and reduce operation cost. We used a variant of recurrent neural network (RNN) called gated recurrent unit (GRU) for resource demand prediction. Then, considering these predictions, we built an intuitive scale in/out algorithm. We also developed an algorithm that applies Q-Learning on Edge computing environment (EdgeQL) to place these scaled-out VNFs in appropriate locations. The integrated algorithm that combines prediction, scaling, and placement are called RNN-EdgeQL. RNN-EdgeQL (v2) is further improved to achieve application agnostic group level elasticity in the chain, independent of applications installed on the VNFs. We tested our algorithm on two realistic temporal dynamic load models including Internet traffic (Abilene) and an application specific traffic (Wiki) on an OpenStack testbed. The contribution of this article is threefold. First, prediction model prepares the target SFC for the upcoming load. Second, an application agnostic characteristics of the algorithm achieves the group-level elasticity in SFC. Finally, the EdgeQL placement model minimizes the end-to-end path of an SFC in multi-access edge computing (MEC) environment. As a result, RNN-EdgeQL (v2) gives the lowest overall latency, lowest SLA violations, and lowest VNFs requirement, compared to RNN-EdgeQL (v1) and Threshold-Openstack default placement. © 2022 John Wiley & Sons Ltd.

关键词： Recurrent neural networks

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Image analysis and machine learning-based malaria assessment system

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Digital Communications and Networks 2022年第2期8卷 132-142页

作者： Kyle Manning Xiaojun Zhai Wangyang Yu School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK Key Laboratory of Modern Teaching Technology Ministry of EducationSchool of Computer Science Shaanxi Normal UniversityXi'anChina

Malaria is an important and worldwide fatal disease that has been widely reported by the World Health Organization(WHO),and it has about 219 million cases worldwide,with 435,000 of those *** common malaria diagnosis approach is heavily reliant on highly trained experts,who use a microscope to examine the ***,there is a need to create an automated solution for the diagnosis of *** of the main objectives of this work is to create a design tool that could be used to diagnose malaria from the image of a blood *** this paper,we firstly developed a graphical user interface that could be used to help segment red blood cells and infected cells and allow the users to analyze the blood ***,a Feed-forward Neural Network(FNN)is designed to classify the cells into two *** achieved results show that the proposed techniques can be used to detect malaria,as it has achieved 92%accuracy with a database that contains 27,560 benchmark images.

关键词： Malaria assessment system Image analysis Image segmentation Artificial intelligence

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QoS-Constrained,Reliable and Energy-Efficient Task Deployment in Cloud Computing

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计算机科学与技术汇刊（中英文版） 2024年第1期11卷 22-31页

作者： Zhenghui Zhang Yuqi Fan School of Computer Science and Information Engineering Hefei University of TechnologyHefeiAnhui230000China

Reliability,QoS and energy consumption are three important concerns of cloud service *** of the current research on reliable task deployment in cloud computing focuses on only one or two of the three ***,these three factors have intrinsic trade-off *** existing studies show that load concentration can reduce the number of servers and hence save *** this paper,we deal with the problem of reliable task deployment in data centers,with the goal of minimizing the number of servers used in cloud data centers under the constraint that the job execution deadline can be met upon single server *** propose a QoS-Constrained,Reliable and Energy-efficient task replica deployment(QSRE)algorithm for the problem by combining task replication and *** each task in a job that cannot finish executing by re-execution within deadline,we initiate two replicas for the task:main task and task *** main task runs on an individual *** associated task replica is deployed on a backup server and completes part of the whole task load before the main task *** from the main tasks,multiple task replicas can be allocated to the same backup server to reduce the energy consumption of cloud data centers by minimizing the number of servers required for running the task ***,QSRE assigns the task replicas with the longest and the shortest execution time to the backup servers in turn,such that the task replicas can meet the QoS-specified job execution deadline under the main task *** conduct experiments through *** experimental results show that QSRE can effectively reduce the number of servers used,while ensuring the reliability and QoS of job execution.

关键词： Cloud Computing Task Deployment Reliability Quality of Service Energy Consumption

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Interpretation Mode of Spaceborne SAR Based on Analysis of Cognitive Needs 11th

Interpretation Mode of Spaceborne SAR Based on Analysis of C...

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11th China Conference on Command and Control, C2 2023

作者： Lanyu, Li Shouwei, Yang Nanjing Research Institute of Electronic Technology Nanjing210049 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210004 China

ISBN: (纸本)9789819990207

The intelligent interpretation of spaceborne SAR images is an important technical field for future remote sensing applications. However, due to the imaging defects of SAR itself, it is very difficult to divide the image interpretation samples. Command and control, as an important application field of image interpretation, has many cognitive scenarios. Based on the analysis of image usage requirements, this article calculates the information value of corresponding target samples under different application purposes through text analysis of relevant SAR image scientific literature, technical reports, etc., and uses this as the selection threshold for image interpretation to improve the efficiency of image interpretation. At the same time, this paper also designed a SAR image interpretation mode based on graph neural network, replacing the attention mechanism module with knowledge calibration, and providing technical support for the in-depth application of satellite-borne SAR. © Chinese Institute of Command and Control 2024.

关键词： Synthetic aperture radar

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Domain Adaptation Using Generative Adversarial Networks for Medical Image Synthesis 5th

Domain Adaptation Using Generative Adversarial Networks for ...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Ganesh, D. Verma, Upendra K. Patil, Smita Alam, Intekhab Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India 3School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Latest advancements in state-of-the-art scientific photograph evaluation depend heavily on the availability of large new datasets of cutting-edge annotated scientific photographs for version training. Unfortunately, obtaining massive samples of cutting-edge categorized scientific picas is ultra-modern high-priced, or impossible. Area edition is a way to generalize a version educated on a selected dataset to a brand new area, wherein the model turned into not directly educated. Generative adverse Networks (GANs) were used to generate realistic artificial photographs. It has enabled the opportunity present day the usage of GANs to bridge the domain hole via generating artificial medical photos, which could then be used for education fashions. On this paper;we evaluate the latest progress in making use of area edition the use of GANs for scientific photosynthesis, with an emphasis on applications including pathology, scientific imaging, and optical acceptable imaging. We talk about the benefits of ultra-modern using GANs for area adaptation and present processes for each present day of the above programs. We also speak about various challenges and future directions in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Dynamical Selective Image Encryption Using Chaos 9

Dynamical Selective Image Encryption Using Chaos

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9th International Conference on Integrated Circuits, Design, and Verification, ICDV 2024

作者： Hoang, Thang Manh Hoang, Manh-Hai Pham, Quang-Anh Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi Viet Nam University of Alberta Department of Computer Science Edmonton Canada

ISBN: (纸本)9798350371864

Image data has been increasingly generated by cameras as well as acquisition modalities. Image data is diverse and has different sensitive levels. Encryption algorithms for massive image data are required not only high confidential but also high speed. Sometimes, the trade-off between the speed and confidentiality of an encryption occurs for image data, and it can be obtained with selective image encryption. However, most of existing selective image encryption algorithms are with fixed values of parameters, as a consequence, the confidentiality is threatened from cryptanalysis methods. In this paper, the scheme of selective image encryption using chaos is proposed, in which its parameters are changed dynamically. Specifically, the diffusion is carried out with dynamically selected pixels, and on its varying number of significant bits. The permutation performs on blocks of selected pixels with varying size. Intuitively, the security of the proposed scheme is improved by dynamical selective of data for the encryption. The exemplar simulation shows the effectiveness of the proposed scheme with security analysis by means of testing entropy, and correlation between neighbor pixels. The amount of data to be encrypted is also measured in compared with existing selective image encryption. © 2024 IEEE.

关键词： Pixels

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RPF-ELD: Regional Prior Fusion using Early and Late Distillation for Breast Cancer Recognition in Ultrasound Images

RPF-ELD: Regional Prior Fusion using Early and Late Distilla...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Haosen Zhang, Gengyuan Zhao, Yingnan Lai, Fang Cui, Wenwei Xue, Jiexiao Wang, Qihang Zhang, Hao Lin, Yi Beijing Jiaotong University School of Computer Science and Information Technology Beijing China Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Harbin Engineering University School of Computer Science and Technology Harbin China University of Denver Department of Computer Science Denver United States University of York Wentworth College York United Kingdom Institute of Heilongjiang The Second Surveying and Mapping Harbin China Southeast University School of Information Science and Engineering Nanjing China Harbin Institute of Technology Faculty of Computing Harbin China Harbin Medical University School of Interdisciplinary Medicine and Engineering Harbin China

ISBN: (纸本)9798350386226

Breast cancer is one of the main factors responsible for the deaths of women worldwide. Ultrasound imaging is a key method for early detection of breast cancer, which can help patients gain valuable treatment time and improve their chances of survival. The computer-aided system of breast cancer recognition has started to receive attention due to the lack of experienced sonographers. Presently, most breast cancer recognition methods typically suffer from uncertain locations and proportions of tumor regions in ultrasound images. In this paper, we propose a novel Regional Prior Fusion framework using Early and Late Distillation (RPF-ELD), inspired by the knowledge distillation of the teacher-student framework, for breast cancer recognition in ultrasound images. Firstly, to enhance the concentration of the tumor regions, a high-performing prior-fused model is trained as the teacher model using ultrasound images with the corresponding regional prior information. Next, a diagnostic model is trained as the student model under the prior-fused model distillation using early and late features to implicitly obtain the regional prior knowledge. Finally, the diagnostic model recognizes the categories of breast cancer from only ultrasound images using the experience from distilled prior knowledge. Two publicly released datasets are used to evaluate the proposed RPF-ELD framework. Experimental results demonstrate that the proposed RPF-ELD surpasses current state-of-the-art methods. © 2024 IEEE.

关键词： Ultrasonic imaging

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BiEfficient: Bidirectionally Prompting Vision-Language Models for Parameter-Efficient Video Recognition 17th

BiEfficient: Bidirectionally Prompting Vision-Language Model...

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17th Asian Conference on computer Vision, ACCV 2024

作者： He, Haichen Liu, Weibin Xing, Weiwei School of Computer Science and Technology Beijing Jiaotong University Beijing China School of Software Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9789819609079

Vision-language models (VLMs) pre-trained on large-scale image-text pairs have shown great success in various image tasks. However, how to efficiently transfer such powerful VLMs into video domain is still an open problem. Given that full finetuning VLMs for video tasks could be computationally expensive, recent studies turn their focus on parameter-efficient finetuning (PEFT). The great potential of VLMs lies in leveraging the bidirectional semantic connections between the two modalities of vision and language. Nevertheless, most current PEFT methods use the vision-only framework and usually ignore the semantic connections between vision and language. In this paper, we propose a novel method called BiEfficient, which use bidirectional prompting schemes to efficiently transfer the VLM to video recognition task with a small number of tunable parameters: 1) Vision-to-Language: we propose two prompt mechanisms, Pre-Prompt and Post-Prompt, which act before and after the text encoder respectively to generate discriminative video-level text representation for each input video. 2) Language-to-Vision: we propose Word-Guided Visual-Prompt, which enhances the temporal modeling of videos using textual knowledge in an almost parameter-free manner. Experiments on Kinetics-400, UCF-101, HMDB-51 demonstrate that the proposed method can achieve comparable or even better performance to the full finetuning methods with much fewer tunable parameters across closed-set and zero-shot video recognition benchmarks. Code is available here: https://***/WbLiuBJTULab/BiEfficient. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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