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Selection of Cache Replacement Algorithm for PostgreSQL

Selection of Cache Replacement Algorithm for PostgreSQL

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Sustainable computing and data Communication Systems (ICSCDS), international conference on

作者： P. Yogendra Prasad Lakshmi Narayana Velayduam Srirama Sai Kumar Momin Shaikshavali V. Vamsidhar Reddy Nayudori Ajay Kumar Dept of CSSE Erstwhile Sree Vidyanikethan Engineering College Mohan babu university Tirupati India

To always ensure that the best memory blocks are available in the Cache Memory, several Cache Replacement algorithms are used like Least Recently Used, Most Frequently Used, etc. but each Cache Replacement algorithm has its own advantages and disadvantages. To avoid problems like scanning, and time complexity, this study intends to propose a cache replacement algorithm for PostgreSQL, which currently uses 2Q algorithm to keep the best memory block references needed by the processor to complete its task inside cache memory. The PostgreSQL database retrieves the data very slowly when compared to other databases. We will compare various cache replacement algorithms under the same workload and choose the best one among them. When we compare ARC, LRU, MRU, and LFU, Adaptive Replacement Cache (ARC) is the significant cache replacement policy. So, we check the PostgreSQL with the ARC and analyze the condition of the database.

关键词： Performance evaluation Adaptive systems databases Cache memory Servers data communication Time complexity

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data Mining-based Classification Methods for Mental Health Prediction

Data Mining-based Classification Methods for Mental Health P...

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Pervasive computing and Social Networking (ICPCSN), international conference on

作者： Roja Boina S Sangeethapriya Hemant Singh Pokhariya Ashish Sharma ALN Rao Surendra Singh Department of English H&S SA Engineering College (Autonomous) Chennai Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Engineering and Applications GLA University Mathura U. P. Department of CSE Lloyd Institute of Engineering and Technology Greater Noida India Quantum University Roorkee Uttarakhand India

The advancement of mental health education and prognosis is crucial since the mounting pressures of contemporary society have contributed to an increase in psychological issues. In order to assess mental health intelligence, this research work suggests a method that makes use of data mining and classification algorithms. An improved method is created to overcome the shortcomings of existing methods by integrating decision trees with Artificial Neural Network (ANN) algorithms. Using data mining-derived compound learning algorithms, the system analyses and classifies mental health IQ test data. To assess the system's effectiveness and superiority, novel simulation tests are carried out. In order to increase accuracy and efficacy, the research also presents new methods for assessing mental health intelligence. While ensuring stability, the suggested system seeks to address the drawbacks of the current approaches.

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

引用

29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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ZO-DARTS: Differentiable Architecture Search with Zeroth-Order Approximation

ZO-DARTS: Differentiable Architecture Search with Zeroth-Ord...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Lunchen Xie Kaiyu Huang Fan Xu Qingjiang Shi School of Software Engineering Tongji University Shanghai China Peng Cheng Laboratory Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

Neural Architecture Search (NAS) is a silver bullet in alleviating time consumption and human effort for deep neural network design. It is however challenging to search for good architectures with low consumption. In this paper, we propose a novel NAS framework to address the differentiable neural architecture search problem by inspecting the bi-level problem formulation from scratch. Combined with the Zeroth-Order (ZO) gradient descent technique and implicit gradients, the proposed algorithm can not only reduce search time for suitable architectures than existing works but maintain the final accuracy simultaneously. Experimental results show the efficacy of our proposed ZO-based NAS approach.

关键词： Deep learning Neural networks Signal processing algorithms Computer architecture Signal processing Search problems Approximation algorithms

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A Literature Review on AWS-Based Cloud computing: A Case in South Korea

A Literature Review on AWS-Based Cloud Computing: A Case in ...

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international conference on Big data and Smart computing (BIGCOMP)

作者： Byeongcheon Lee Jinyeong Oh Woojin Shon Jihoon Moon Department of AI and Big Data Soonchunhyang University Asan South Korea

A collection of information technology (IT) services known as “cloud computing” is offered to customers across a network on a subscription basis with the flexibility to scale up or down based on their service needs. The emergence of cloud computing technology, which has tremendous advantages, is one of the major advancements in recent times. Many computers and servers are specifically devoted to meeting the demands of businesses in a cloud computing system for internal communications. Users can access their services via an internet connection. Registered users have remote access to both hardware and software, thanks to the cloud service, which has made essential adjustments to how information is stored and made accessible. This paper investigates the use of Amazon Web Services (AWS) for big data processing and analytics in South Korea. We collected several domestic journal and conference papers that studied local cloud services based on AWS to introduce distributed systems and cloud computing technologies. This study can provide researchers with a compact version of the extensive AWS-based data processing literature and potential future insights. It can also provide stakeholders tailored services, information about cutting-edge solutions that can influence academics, and details about current research needs.

关键词： Computers Cloud computing Web services Smart cities Standards organizations Transforms Big data

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Classifying Internet of Things (IoT) devices and non-IoT devices in data center edge network by one dimensional convolutional neural network using behavioral data 27

Classifying Internet of Things (IoT) devices and non-IoT dev...

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27th international conference on Advanced computing and Communications, ADCOM 2022

作者： Ramamurthy, Sharath Dutta, Dipankar Upgrad Education Pvt. Ltd. Mumbai India Liverpool John Moores University London United Kingdom CSE Department University Institute of Technology The University of Burdwan Burdwan India

ISBN: (纸本)9781839539107

Now a days it is important to distinguish IoT and non-IoT devices from their network traffic in real-time for providing differential treatment to meet quality of service (QoS) requirements. This paper focuses on the classification of IoT and non-IoT devices based on their network traffic at edge data centers. We proposed a method using One-Dimensional Convolutional Neural Networks (1D-CNNs) which are simple, compact, and computationally less expensive for real-time processing needs. We used a dataset, which contain information from 22 IoT and 7 non-IoT devices that included both User datagram Protocol (UDP) and Transport Control Protocol (TCP) flows. The classifier is constructed using 8 consecutive timesteps of payload exchanges carrying network payload data up to 96 bytes. Using 10-CV, the classifier achieves an accuracy of classification of about 98% on the test datasets. © ADCOM 2022. All rights *** right reserved.

关键词： Internet of things

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Research on water extraction method from remote sensing images of lakes in cold and arid regions based on deep learning 3

Research on water extraction method from remote sensing imag...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2023

作者： Wang, Xin Fu, Xueliang Hu, Hua Li, Honghui College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research Application of Agriculture and Animal Husbandry Hohhot China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Semantic segmentation technology is an important research direction in the field of computer vision, with the continuous improvement of computer GPU and other hardware arithmetic power, as well as the more clear and accurate remote sensing images. In recent years, deep learning technology has developed rapidly in the field of semantic segmentation, and the level of semantic segmentation technology has improved significantly. Based on unsupervised, automated, end-to-end deep learning networks have gradually become the object of research in scene object segmentation, pre-person background segmentation, face recognition and other related fields. In this paper, we conduct research on deep learning semantic segmentation algorithms for water body extraction, focusing on specific analysis of typical problems such as long freezing period of lakes in cold and arid regions, low precipitation, complex seasonal changes of lakes, and fragile water bodies. The cold and arid zone lakes distributed in Inner Mongolia region of China are selected as the research objects, and the water body extraction experiments are conducted on the remote sensing images of Hulun Lake and Wuoliangsuhai Lake. Propose a SER34AUnet model, the model was improved based on the Unet network model of Encode-Decode architecture, and finally achieved good results in the water extraction experiments. The main work of this paper is as follows: (1) Establishing image segmentation dataset: remote sensing images of several lakes in cold and arid regions in different seasons, different satellites and various remote sensing images with specific representative features typical of cold and arid regions are used to enhance the generalization ability of the dataset. (2) Improving the Unet model: Firstly, the Resnet34 network, which is more capable of extracting image features, is used in the Encode part. Secondly, the SE (Queeze and-Excitation) module based on the channel attention mechanism is added to the BasicBlock mo

关键词： Lakes

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Analyzing The Security of e-Health data Based on a Hybrid Federated Learning Model

Analyzing The Security of e-Health Data Based on a Hybrid Fe...

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Next-Generation computing, IoT and Machine Learning (NCIM), international conference on

作者： Md. Nasimuzzaman Sabrin Akhter Mohammad Shafkat Hasan Shakila Zaman Muhammad Iqbal Hossain Computer Science and Engineering Brac University Dhaka Bangladesh Computer Science and Engineering University of North Texas Texas USA

This research aims to provide an approach for analyzing the security of the e-health care system through the pre-processing of different deep learning models and the usage of federated learning. Infrastructural support for e-healthcare services is being gradually deployed by the health sector. This method increased the safety of patients and doctors through a protected platform. As a result, it is going to replace the current health service. Even if this technology is becoming more and more widespread, a number of data security threats need to be tackled. In this research, a CNN and MLP architecture with a classification-focused approach using a number of pre-trained feature extractors such as ResNet-50, VGG16, and Inception- v3 have been implemented. Additionally, various machine learning classification algorithms and Other methods of picture classification besides neural networks have been tried and compared, including Random Forest and Logistic Regression. Federated learning has also been incorporated to increase the security of healthcare data because it does not transmit actual data but models. The objective is to develop a hybrid federated learning model to analyze the security of e-health data. The core premise is to utilize a methodology like federated learning, which enables a technique for creating machine learning models while safeguarding user privacy and can maintain e-health data security without transferring real-world data.

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Real-Time data processing and Optimization Research of Edge computing in Industrial Automation

Real-Time Data Processing and Optimization Research of Edge ...

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Industrial IoT, Big data and Supply Chain (IIoTBDSC), international conference on

作者： Xin Liu Yue Zhang Yefeng Zhang Yingxue Sun State Grid Information & Telecommunication Co. Ltd. Beijing China

ISBN: (数字)9798350341690

ISBN: (纸本)9798350341706

At present, the manufacturing industry is in the process of intelligent transformation and upgrading. With the rapid expansion of the connection scale of manufacturing terminals, the amount of data generated by network edge devices such as intelligent terminals is also growing rapidly, this puts forward higher requirements for the industrial transmission broadband, the new manufacturing mode has higher requirements for the real-time performance of data processing, and the existing centralized cloud computing processing mode is difficult to solve these problems efficiently. In order to meet the demand of real-time supervision and quality supervision in the field of industrial automation, it is necessary to process and analyze the class of industrial automation material production test data, explore the data processing technology based on edge computing, establish the cloud collaborative data processing method, the factory production inspection data identification, correlation and preliminary analysis, and upgrade the automation equipment. So as to improve the efficiency of industrial Internet data collection and processing, and relieve the operating pressure of industrial Internet automation equipment.

关键词： Cloud computing Automation Urban areas Collaboration data processing Real-time systems Internet

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data processing Optimization of Power Grid Dispatching Control Cloud Based on Edge-Cloud Collaborative computing

Data Processing Optimization of Power Grid Dispatching Contr...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Lei Sheng Chunying Wang Yan Liu Ningning Zhang Zhaohe Wang Information & Telecommunication Company State Grid Henan Electric Power Company Zhengzhou China

Cloud computing technology is widely used in power grid systems. In response to the data with strict delay requirements and a large amount of data generated by the operation of the power grid system. New challenges are raised to the delay of business processing. In order to achieve the goal of minimizing the system average latency time of task processing, based on the power grid dispatching cloud architecture, a delay optimization method based on SDN edge-cloud collaborative computing is proposed. The goal is to minimize the system average delay of task processing. Introduce continuously variable edge and cloud and task distribution coefficients. The delay optimization problem is transformed into the task allocation coefficient optimization problem. And finally get the task allocation optimization strategy based on the model calculation. The experimental results show that when the edge node tasks are reasonably loaded into the cloud center for processing, the system delay of the business can be effectively reduced. Compared with the previous methods, the edge-cloud collaborative computing method proposed in this paper reduces the system delay and utilizes the control mechanism of SDN that can coordinate cloud computing and edge computing. The algorithm can be used in the power system cloud platform system based on SDN.

关键词： Cloud computing Simulation Collaboration Power grids Dispatching Delays Resource management

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