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Dynamic Resource Optimization for Cloud Encryption: Integrating ACO and Key-Policy Attribute-Based Encryption 4

Dynamic Resource Optimization for Cloud Encryption: Integrat...

引用

4th international conference on Soft computing for Security Applications, ICSCSA 2024

作者： Atmakuri, Aparnarajesh Sahoo, Amiya Pallavi, M. Behera, Dayal Kumar Gourisaria, Mahendra Kumar Padhi, Sunita Centurion University of Technology and Management SoET-Dept.of CSE Odisha Bhubaneswar India Aryan Institute of Engineering and Technology Department of CSE Odisha Bhubaneswar India Presidency University Department of CSE Karnataka Bangalore India KIIT Deemed to Be University School of Computer Engineering Odisha Bhubaneswar751024 India Aryan Institute of Engineering and Technology Department of CSE Odisha Bhubaneswar India

ISBN: (纸本)9798331515720

Encryption and decryption are essential in cloud-based systems, and it's vital to ensure they run efficiently. This paper introduces a new method called CloudSecurePath (cSp) to optimize the execution paths of these tasks using Ant Colony Optimization (ACO) and Key-Policy Attribute-Based Encryption (KP-ABE). The ACO algorithm identifies the most efficient resource sequences (such as virtual machines, containers, serverless functions) for encryption and decryption. In the ACO algorithm, ants represent different resource paths, evaluating each path based on execution time and resource use. Pheromone trails are updated to guide future ants to the best paths, and the algorithm runs continuously to adapt to changes in the cloud environment. To further improve security, the approach integrates KP-ABE, which provides fine-grained access control, ensuring only authorized entities can access encrypted data. Combining ACO-based path optimization with KP-ABE results in a robust, adaptive solution for secure and efficient encryption/decryption in cloud systems. The paper details the ACO-based path optimization algorithm, covering its initialization, ant exploration, pheromone updating, and convergence checking. It also discusses about implementing the optimal path and the continuous optimization process. This approach is designed to be scalable and adaptable, making it suitable for various cloud applications needing secure and efficient encryption and decryption. © 2024 IEEE.

关键词： Ant colony optimization

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OCW: Enhancing Few-Shot Learning with Optimized Class-Weighting Methods

OCW: Enhancing Few-Shot Learning with Optimized Class-Weight...

引用

2024 IEEE international conference on Communications, computing, Cybersecurity and Informatics, CCCI 2024

作者： Kang, Jieui Lee, Subean Kim, Eunseo Choi, Soeun Sim, Jaehyeong Ewha Womans University Artificial Intelligence Convergence Seoul Korea Republic of Ewha Womans University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350349832

Few-shot learning, the capability of a machine learning model to comprehend and adapt to new classes with limited instances, has been a critical area of research in the realm of artificial intelligence. To effectively train a model on a new class, a sufficient quantity of diverse samples is required. However, in the case of datasets with low similarity among classes, there is a limitation in the availability of samples for each specific class. This scarcity or imbalance of samples makes it challenging for the model to generalize effectively. This study introduces an innovative weight optimization technique, the Optimized Class-Weighting (OCW) method, designed to improve model accuracy in these contexts. The OCW method, born from the necessity to ameliorate the challenges of few-shot learning, optimizes the allocation of weights to different classes in a dataset, thereby enhancing the model's discriminatory power. Our research systematically evaluates the efficacy of the OCW method across four state-of-The-Art models-MobileNetv2, Vision Transformer (ViT), ResNet50, and EfficientNet-under various shot scenarios, from 1-shot to 5-shot cases. Our findings demonstrate a consistent improvement in model performance upon the application of the OCW method across all models and shot scenarios. For instance, the MobileNetv2 model exhibited a substantial improvement in the 1-shot scenario, with accuracy increasing from 66 % to 85% upon the implementation of the OCW method. This study underscores the robustness of the OCW method in augmenting model performance in few-shot learning, providing a promising avenue for future research in this field. Our work contributes significantly to the ongoing discourse on weight optimization techniques, setting the stage for further enhancements in the performance of AI models. © 2024 IEEE.

关键词： Zero-shot learning

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Multi-UAV Cooperative Task Allocation Based on Fish Swarm Auction Hybrid Algorithm 3rd

Multi-UAV Cooperative Task Allocation Based on Fish Swarm Au...

引用

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

作者： Mo, Zijing Zhang, Tingting Zhou, Xuan Wang, Chang Wu, Lizhen Key Laboratory of Maritime Intelligent Cyberspace Technology Ministry of Education Hohai University Jiangsu Nanjing210024 China Chinese People’s Liberation Army Unit Beijing91977 China National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9789819624676

The multi-UAV cooperative task allocation problem is central to multi-UAV cooperative combat. To enhance operational efficiency, this paper establishes a multi-UAV cooperative task allocation model. The model considers the constraints such as the ability of UAV and task priority, and takes the shortest total distance and task execution time of UAV swarm to complete the task as the optimization goal. A fish swarm auction hybrid algorithm is proposed to solve the model. Initially, the auction algorithm provides an allocation scheme to initialize the fish swarm algorithm, enhancing early convergence speed. Then, an adaptive improvement strategy is proposed for the step size of artificial fish. Initially, the step size is increased to enhance global search and speed up iterations. Later, it is reduced to avoid getting trapped in local optima and improve solution accuracy. The simulation results indicate that the hybrid algorithm demonstrates superior convergence and effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed computer systems

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Research on air pollution index prediction strategy based on LSTM and ridge regression

Research on air pollution index prediction strategy based on...

引用

2024 international conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2024

作者： Fu, Yongliang Wang, Haihang Department of Computer Science and Technology Tongji University Shanghai201804 China

ISBN: (纸本)9781510685840

When evaluating the degree of air pollution, the PM2.5 index is often regarded as an important assessment indicator. The ability to accurately predict the future PM2.5 air pollution index at a certain time based on past data has significant research implications in areas such as public health protection and adjustments to production and economic activities. Based on this, a network model combining LSTM and ridge regression was proposed for predicting the air pollution index. Initially, operations such as handling missing data, preprocessing, data scaling, and standardizing data steps were carried out on the dataset to ensure better convergence during training of the neural network. It was observed that with an increase in the number of training epochs, overfitting occurred, leading to an increase in mean squared error. The use of ridge regression to assist in predicting with the LSTM model yielded better results. Emphasis was placed on the feature values of the air pollution index, with the feature values of the LSTM training model being extracted and used as input for the ridge regression model, resulting in improvements in the prediction errors (MSE, MAE, and SMAPE). This confirmed the high level of fitting of the model on the designated test set and actual data. Furthermore, it was found during the experimental process that in the absence of other auxiliary feature values (such as temperature, humidity, and precipitation conditions), more accurate results could be obtained by using ridge regression to assist the LSTM model. © 2024 SPIE.

关键词： Mean square error

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An Ameliorated Algorithm of Hybrid Vulture Optimization Based on Lens Reverse Learning Strategy 12

An Ameliorated Algorithm of Hybrid Vulture Optimization Base...

引用

12th international conference of Information and Communication technology, ICTech 2023

作者： Bu, Liping He, Shaojie Zhou, Te Shenyang Institute of Computing Technology Chinese Academy of Science Shenyang China University of Chinese Academy of Science Shenyang China Neusoft Group Co. Ltd. Shenyang China

ISBN: (纸本)9798350332919

Cloud platform usually allows multiple tasks to perform in parallel, and as an important cornerstone of cloud platforms, the quality of task scheduling strategies is related to the replication quality and system performance of the entire cloud analysis platform. Its scheduling efficiency directly affects the operating factor of cloud computing virtual machines and other software and hardware resources and the quality of service for users. To improve the productiveness of the process of assigning tasks to virtual computing resources in cloud platforms, there advanced a mixed meta heuristic algorithm algorithm, called LBE algorithm (LiL-Baldeagle-ESDA Optimization Algothrim), which combines the reflection imaging principle of lens, finds another position point in the solution space, broaden the solution domain, and prevent the solution from plung in the global suboptimal or part optimal solution. Combining the advantages of the Electrostatic Discharge Algorithm (ESDA) to address the shortcomings of the Bald Eagle Search Optimization Algorithm (BES) in global solution domain. This article uses 23 classic checkout functions from early CEC to trail the behavior of algorithm, and contrast to the results of several scriptures' algorithms. The test value on the function set indicate that the proposed LBE has advantages within the solution space, convergence rate and optimization ability. © 2023 IEEE.

关键词： Space platforms

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Assessment of time performance of lightweight virtualization for edge computing applications 19

Assessment of time performance of lightweight virtualization...

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IEEE 19th international Workshop on Factory Communication Systems (WFCS)

作者： Sisinni, Emiliano Bellagente, Paolo Depari, Alessandro Flammini, Alessandra Gaffurini, Massimiliano Pasetti, Marco Rinaldi, Stefano Ferrari, Paolo Univ Brescia Dept Informat Engn Brescia Italy

ISBN: (纸本)9781665464321

The demand for enhanced flexibility in automation systems, dictated by initiatives as the Industry 4.0 in Europe or the Industrial Internet Consortium in the United States, can be satisfied inheriting solutions of the information technology (the so called IT-OT convergence). Other than communication and cloud technologies, virtualization will play a relevant role in the next future. The use of lightweight virtualization, e.g., in the form of containers for implementing the edge computing paradigm, is a promising tool to dynamically provide services depending on the actual production needs. However, requirements of industrial automation applications are different from those of office-like counterparts. For this reason, methodologies for evaluating obtainable performance are needed. In this work, an industrial-grade framework, the Siemens Industrial Edge, is considered. A reference testbed is setup to evaluate latencies when information is exchanged between two user applications. Experiments demonstrate that round trip time in the order of 10 ms is feasible, compatible with the requirements of both process control systems and supervision applications.

关键词： lightweight virtualization edge computing container docker

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Adaptive Extended State Observer-Based Tracking Control for PMSM Drive Systems With Uncertainties and Disturbances

Adaptive Extended State Observer-Based Tracking Control for ...

引用

2023 IEEE international conference on Real-Time computing and Robotics, RCAR 2023

作者： Zhang, Zhongjin Liu, Zhitai Li, Peng Cheng, Wei Lin, Weiyang Harbin Institute of Technology Research Institute of Intelligent Control and Systems Harbin150001 China Harbin Institute of Technology Shenzhen School of Mechanical Engineering and Automation Shenzhen518055 China Devol Advanced Automation Inc Shenzhen518101 China

ISBN: (纸本)9798350327182

With the aim of improving the robust tracking performance of permanent magnet synchronous motor (PMSM) drive systems, an indirect adaptive robust control strategy based on adaptive extended state observer (AESO) is developed in this paper. Firstly, the parameter estimation process is separated from the controller design, and the indirect adaptive law driven by the system input and output is adopted to guarantee the convergence and accuracy of parameter estimation. Secondly, an AESO is established to estimate the unmodeled characteristics and disturbances in the system, and the performance of disturbance estimation and compensation is improved by dynamically adjusting the observer gain. Finally, an adaptive tracking controller is synthesized using the backstepping method, and its stability is proven using Lyapunov analysis. Simulation results demonstrate the effective performance improvement of the synthesized controller for tracking control. © 2023 IEEE.

关键词： Controllers

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Diagnosis of Gastrointestinal Diseases Using Modern CNN Techniques 8

Diagnosis of Gastrointestinal Diseases Using Modern CNN Tech...

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8th IEEE international conference for convergence in technology, I2CT 2023

作者： Varalaxmi, Guduru Baddam, Sahithi Reddy Yalamarthi, Emmanuel Suhas Swaraja, K. Madhavi, K. Reddy Sujatha, Cn. ECE GRIET Telangana India Mohan Babu University School of Computing Tirupati India Sreenidhi Institute of Science and Technology ECE Ghatkesar India

ISBN: (纸本)9798350334012

Endoscopy is used by the doctors in detecting the gastrointestinal diseases. Endoscopy is a procedure which helps in detection of many gastric diseases like polyps, cancer infections, celiac disease, Crohn's disease, ulcerative colitis, diverticulitis, malabsorption and so on. As in recent days use of endoscopy is increasing drastically because of high increase in gastrointestinal related issues. As this method consumes more time and repeated trials on the patient, it may affect the patient. Therefore, detection of diseases using deep learning helps in detecting the gastric diseases without affecting the patients. By using modern Convolutional Neural Network (CNN) techniques we can acquire accurate results within no time, so that in case of emergencies the treatment can be started immediately without any delays. In this work ResNet50 is implemented which is the subset of Residual Network. RESNET (a neural network), uses the complementary clues of spectral and spatial features to improve the classification. The proposed framework attained an accuracy of 88.05%, sensitivity of 87.05% and specification of 92.33%. © 2023 IEEE.

关键词： Endoscopy

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ASPFL: Efficient Personalized Federated Learning for Edge Based on Adaptive Sparse Training

ASPFL: Efficient Personalized Federated Learning for Edge Ba...

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2023 IEEE international conference on Web Services, ICWS 2023

作者： Jiang, Yuhui Lu, Xingjian Zheng, Haikun Mao, Wei East China Normal University School of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350304855

One of the primary challenges in cloud-edge environments is efficiently utilizing significant amounts of data on edge devices for machine learning tasks, enabling adaptation to increasingly complex computing and service scenarios. Federated Learning (FL) is a machine learning paradigm that enables collaborative training of models involving multiple data warehouses in a privacy-preserving manner. However, classical federated learning has poor convergence on highly heterogeneous data, which limits its performance of global model on each edge device. The emergence of Personalized Federated Learning (PFL) effectively alleviates data heterogeneity, but learning a personalized model may incur greater overheads. In this paper, we propose an efficient FL framework named as ASPFL, which uses dynamic sparse training for personalized federated learning to maintain model performance while reducing computational and communication overheads in cloud-edge environments. By adaptively allocating the dynamic sparsity from a global perspective to explore sparse network structure during training, ASPFL improves the independent parameter exploration process of local sparse training to adapt to various heterogeneous situations and solves the Non-IID challenge of FL. The abundant experimental results show that ASPFL outperforms state-of-the-art methods in performance, overheads, and convergence speed in PFL. © 2023 IEEE.

关键词： Edge computing

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Trust Model Based on Recommendation Filtering in Internet of Vehicles 2

Trust Model Based on Recommendation Filtering in Internet of...

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2nd international conference on computing, Communication, Perception and Quantum technology, CCPQT 2023

作者： Sun, Sheng Fan, Xiumei Xiao, Yan Xi'An University of Technology School of Automation and Information Engineering Xi'an China

ISBN: (纸本)9798350342697

Trust management can help vehicles judge whether the received messages are reliable and identify malicious nodes in the network. To solve the problem that the current trust model fails to select appropriate recommendation nodes and dynamically adjust the weight of recommendation trust according to the environment, this paper proposes a trust model based on recommendation value filtering based on the topology structure of the Internet of Vehicles. The model is divided into three layers: local trust management, cluster trust management and global trust management. The three concepts of time decay function, sliding window mechanism and penalty factor are introduced to calculate the direct trust value, which effectively reduces the timeliness of trust and the influence of switch attack. Before the recommendation trust calculation, the Fuzzy C-Means algorithm is used to filter out the malicious recommendation value, and then the similarity and difference degree are used to calculate the reliability of the recommendation trust value. Then, according to the actual dynamic environment of the Internet of Vehicles, the adaptive weight is used to conduct the comprehensive evaluation of the nodes to improve the adaptability of the model. The simulation findings demonstrate that this model exhibits quicker convergence and reduced recommendation errors compared to alternative models, thereby enhancing the precision of trust evaluation. © 2023 IEEE.

关键词： Vehicles

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