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Investigating the Effects of Image Restoration Algorithms on Image Quality in Digital Image Processing 3

Investigating the Effects of Image Restoration Algorithms on...

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3rd international conference on Smart Generation computing, communication and Networking, SMART GENCON 2023

作者： Dhabliya, Dharmesh Saini, Rajat Yadav, Dhyan Geetha, G. Singh, Baldev Sripriya, T. Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350319125

This study examines the effect of various image recovery algorithms on photo great in digital photo processing. Especially, the consequences on the visual quality of the photographs are studied through diverse metrics along with contrast, brightness, sharpness, and color saturation. Additionally, the algorithms are compared to every other as a way to decide which algorithm produces the quality effects. The studies will use images from public picture databases and effects can be gathered the use of photo first-rate evaluation software. Moreover, diverse noise and distortion distortions are applied to the photos to be able to investigate the impact of the distinctive algorithms on them. By means of determining variations in photograph first-rate, beneficial insights at the performance of numerous image recuperation algorithms can be obtained. In conclusion, this studies will offer a complete evaluation of the performance of diverse picture recovery algorithms utilized in digital photograph processing, losing light on which algorithms satisfactory enhance the visual nice of snap shots. © 2023 IEEE.

关键词： Complete Losing Processing Satisfactory Visual

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Research on Automated IT Asset Data Updating Methods for Multi-Operating Platforms 7

Research on Automated IT Asset Data Updating Methods for Mul...

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7th international conference on Computer information Science and Application technology, CISAT 2024

作者： Fu, Hua Meng, Chunzhi Shi, Lei Nong, Chunkai Meng, Chunchen Nanning Power Supply Bureau Guangxi Nanning530028 China Information Communication Branch of Guangxi Grid Company Guangxi Nanning530028 China

ISBN: (纸本)9798350375107

Due to the differences in the interfaces and data formats of different platforms as well as the non-uniform management mechanism. In order to ensure the automated update of IT asset data on multiple operation platforms, a research on the automated update method of IT asset data on multiple operation platforms is proposed. Through data synchronization technology, real-time or timed updating of data between different platforms is realized;the process of converting data from different platforms to a standard format is unified to facilitate data parsing and transmission;through the cloud computing platform, centralized management and unified scheduling of IT asset data can be realized to improve the management efficiency and automation of data. Experimental data show that this method can significantly improve the speed and accuracy of data updating, reduce manual intervention and operation, and improve work efficiency. Among other things, the automated update method can realize automatic data transmission and synchronization between different platforms, which is convenient for enterprises to carry out cross-platform management and maintenance. © 2024 IEEE.

关键词： Data accuracy

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The Role of Cloud computing in Real-Time Big Data Analytics for Smart Cities and Infrastructure Automation 15

The Role of Cloud Computing in Real-Time Big Data Analytics ...

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

作者： Sarpal, Sumeet Singh Kumar, V. Mathan Pimplapure, Vivek Patil, Shrinivas Marathe, Vijay Henrietta, H. Mary Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Dr. Ambedkar Institute of Management Studies And Research Department of Management Nagpur India School of Mangement - Pg Karnataka Bangalore India Prince Shri Vishwakarma Institute of Technology Department of Electrical Pune India Saveetha Engineering College Department of Mathematics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350370249

With the enhancement of Big Data and Smart Cities, Cloud computing has emerged as an advanced technology. Innovative city operations require real-time processing of vast volumes of data from various sources like sensors, IOT devices, or social media so that it can be analyzed and acted on in real-time with better automation for its infrastructure against different parameters. This article examines the potential of cloud computing to enable real-time big data analytics for smart cities and infrastructure automation. The cloud allows for real-time big data analytics, collecting input from disparate sources across a business ecosystem and analyzing it immediately for actionable insights. The need for large amounts of data combined with the nature of many Iota devices necessitates processing and storage at scale, a task uniquely suited to cloud computing due to its ability to provide scalability, flexibility, and lower cost essential qualities in building intelligent cities or automated infrastructure. Cloud computing offers a space for shared data and collaborative analysis, allowing the city government to open up its vast browser-based datasets so that other agencies (both within government and outside it), researchers, and even citizens can access this information, in turn driving innovation or assisting with strategic planning. Moreover, the evolution not just in cloud-based machine learning and artificial intelligence algorithms but also device-side AI/ML allows for predictive analytics, which can be helpful to preemptively discern potential failures to prevent them pro-actively, thus augmenting city operations efficacy as well as resource utilization besides operational efficiency of infrastructural management. Cloud computing is integral for real-time and big data analytics and innovates cities, organizations, and infrastructure. © 2024 IEEE.

关键词： Automation Infrastructure Intelligent Organizations Parameters

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Intelligent Agricultural Water and Fertilizer Irrigation System based on ZigBee technology and STM32 12

Intelligent Agricultural Water and Fertilizer Irrigation Sys...

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12th international conference on Networks, communication and computing (ICNCC)

作者： Zhu, Shilin Lin, Fanqiang Chengdu Univ Technol Coll Mech & Elect Engn Chengdu Peoples R China

ISBN: (纸本)9798400709265

With the continuous development of China's Internet of Things technology, the application of this technology in agriculture is becoming more and more extensive. China's agricultural irrigation has shown a trend of automation, but the current automatic irrigation system cannot automatically control the amount of water irrigation, and it is not intelligent enough. The irrigation water closely related to the development of modern green agriculture is far from meeting the development requirements of China's agriculture. When the irrigation amount is too large, it will cause serious waste of water resources, and when the irrigation amount is too small, it will affect the growth of crops. Based on this situation, this paper designs an intelligent agricultural irrigation system, which combines the communication module with the single-chip microcomputer, and monitors the data of the crop growth environment in real time with the help of ZigBee wireless communication and sensors. According to different crop growth models and soil nutrient content and the characteristics and laws of fertilizers needed by crops, an intelligent irrigation system platform model for real-time monitoring of crop growth environment information based on the Internet of Things is constructed. After testing, the model can predict the amount of water and fertilizer required in the process of crop growth, so as to make timely and appropriate irrigation decisions scientifically, so as to improve the level of irrigation management. The system can greatly alleviate the problem of waste of water resources and soil compaction. It can not only realize water saving and energy saving, but also promote agricultural production and income increase. It provides a technical basis and support platform for the follow-up development of intelligent agriculture.

关键词： ZigBee technology intelligent water and fertilizer irrigation DHT11

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Comparison of Image Quality Estimation Methods for JPEG Compression 15

Comparison of Image Quality Estimation Methods for JPEG Comp...

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

作者： Khurana, Jatin Chitra, S. Khurana, Shivam Akkur, Malatesh Kshirsagar, Pranali S. Ravivarman, G. Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Sciences Physics & Electronics Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India

ISBN: (纸本)9798350370249

This paper gives a comparative check of modern-day photograph pleasant estimation techniques for JPEG Compression. The techniques in comparison consist of No-Reference (NR) and full-Reference (FR) best metrics. For the FR approach, the Structural Similarity (SSIM), peak sign to Noise Ratio (PSNR), and Mutual statistics (MI) are evaluated. For the NR method, the Blind/Reference less photo spatial first-class Evaluator (BRISQUE) and visible information constancy (VIF) are taken into consideration. The overall performance of the FR and NR methods are as compared on 3 synthetic photograph datasets and an unbiased database containing natural snap shots. The outcomes received from the evaluation of those databases display that the SSIM yields the most correct effects for each synthetic images and actual natural photos. In contrast, the NR strategies carry out better for artificial photos but yield poorer results for herbal pictures in comparison to FR strategies. The paper concludes with an advice that SSIM can be used for each FR and NR image estimation. © 2024 IEEE.

关键词： Consideration Databases Estimation information Strategies Structural Techniques Unbiased

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communication-Aware Optimization of Microservice Scheduling on Edge computing 7th

Communication-Aware Optimization of Microservice Scheduling...

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7th international conference on Innovative computing, IC 2024

作者： Park, Hokun Yu, Heonchang Korea University Seoul Korea Republic of

ISBN: (纸本)9789819741816

Edge computing is well suited to environments that send massive data over the network, such as the Internet of Things. Kubernetes is used to manage services reliably in geographically distributed edge environments. However, the default Kubernetes placement policy does not consider the edge environment, which may lead to the degradation of service. In this paper, we propose a placement and replacement method that considers the edge environment. Compared to the default placement policy, proposed method improves the service performance by reducing the data transfer delay and the number of replacements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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A Novel Transformed Nested Array with Enhanced DOFs Based on Sum-Difference Coarray 6

A Novel Transformed Nested Array with Enhanced DOFs Based on...

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6th IEEE international conference on Electronic information and communication technology, ICEICT 2023

作者： Xin, Wei Li, Yingsong Harbin Engineering University China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350399059

Sum-difference driven coarray (SDCA) was paid great attention in array-signal-processing (ASP). By considering SDCA, the degrees of freedom (DOFs) for sparse arrays can be further improved. Here, a new transformed nested-array (TNA) is constructed, which reduces the element redundancy via rearranging the density of sub-arrays of the constructed new TNA. Compared to former TNAs, the constructed TNA gets higher DOFs and maximization signals, numerical simulation are given for getting its superior behaviors. © 2023 IEEE.

关键词： Degrees of freedom (mechanics)

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Leveraging Machine Learning to Predict Obesity Trends: Insights from Lifestyle and Biometric Data

Leveraging Machine Learning to Predict Obesity Trends: Insig...

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2025 IEEE international conference on Computational, communication and information technology, ICCCIT 2025

作者： Adapala, Rishi Manikanta Kancharla, Yaswanth Shaik, Baba Ameer Jyotsna, C. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Bangalore India

ISBN: (纸本)9798331512965

This is increasingly important as global obesity rates continue to rise: the causes need to be understood, and the ability to predict trends determined. In this study, we use machine learning techniques to predict obesity trends given lifestyle and biometric data from a large obesity dataset. Our methodology starts with data preprocessing that includes transforming categorical variables into numerical variables and standardizes the numerical variables into such a form so that machine learning algos can work with them properly. Supervised machine learning models, such as tree based methods, neural networks and various ensemble techniques are applied, and evaluated. For determining the most effective predictive approach, these models are evaluated by several performance metrics such as accuracy, precision, recall, and F1 score;the best model is chosen based on these metrics. In addition, the study includes explainable AI technique (SHAP - SHapley Additive exPlanations;LIME - Local Interpretable Model-Agnostic Explanations) to make the results more interpretable. The results from these methods provide insight into how different lifestyle and biometric factors contribute to the prediction of obesity;such as physical activity, diet patterns, and body mass index. The results from this work demonstrate not only how machine learning can improve prediction accuracy but also how these methods can provide actionable insights into the many contributing factors to obesity. © 2025 IEEE.

关键词： Self-supervised learning

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An Efficient Method for Water Quality Prediction for Ungauged River Catchment Under Dual Scenarios Based on CNN-BiRNN-A Approach 1

An Efficient Method for Water Quality Prediction for Ungauge...

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1st IEEE international conference on Electronics, computing, communication and Control technology, ICECCC 2024

作者： Vipin, V. Nerlekar, Tanaya Mishra, Nitin Ravitheja, A. Kiruthika, S. Hemamalini, U. Biochemistry Malabar Institute of Medical Sciences Affiliated to Kerala University of Health Sciences Kerala Thrissur India Dr. D. Y. Patil Institute of Technology Pimpri Department of Civil Engineering Pune India Department of Civil Engineering Graphic Dehradun India SVR Engineering College Department of Civil Engineering Andhra Pradesh Nandyala India Department of M Tech Tamilnadu Perundurai India Department of Information Technology India

ISBN: (纸本)9798350371802

All life on Earth depends on water, which makes up more than 80% of its surface. Harmful water pollution has emerged as a result of the fast deterioration of water quality caused by increasing industrialization and urbanization. Most water quality estimations need time-consuming and costly statistical calculations, rendering current real-time monitoring efforts useless. There is a predetermined sequence in which to carry out preprocessing, feature extraction, and model training. An important part of the preprocessing phase of data analysis is data normalization using the z-score approach. This step is vital for getting high-quality findings. The suggested method improved the accuracy of water pollution process classification by employing VGG for feature extraction. The model was trained using a CNN-BiRNN-A technique. In comparison to state-of-the-art CNN and BiRNN, our approach seems fresh. With a 97.63 % accuracy percentage, the numbers demonstrated a notable enhancement. © 2024 IEEE.

关键词： Water quality

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computing Force Network self-intelligence Based on Intent Driven and Digital Twin 3

Computing Force Network self-intelligence Based on Intent Dr...

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3rd international conference on Electronic information Engineering and Computer Science, EIECS 2023

作者： Li, Xiangning Cai, Yuqian Tian, Jingyue China Telecom Beijing Research Institude 6G Research Centre Beijing China

ISBN: (纸本)9798350316674

Introducing intent-driven network technology and digital twin network technology into the computing force network can solve the problem of unified management and scheduling of the computing force network and the high delay of data routing. Intent-driven network, based on technologies such as AI (artificial intelligence) and NFV (network function virtualization), can translate the service, performance and networking intents expected by users or operators into actual networking policies, so as to realize efficient and flexible reconfiguration of computing force networks. Digital twin network realizes efficient analysis, diagnosis and control of the computing force network by high-fidelity virtual real-time interaction. Combining intent-driven and digital twin, a self-intelligent optimization process of computing force network is proposed, which realizes the low delay self-organizing closed-loop control of user intent-based computing force network and the cross-domain regulation of computing force resources. © 2023 IEEE.

关键词： computing force network digital twin intent-driven

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