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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1897 条记录，以下是441-450 订阅

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A Hybrid Edge-Cloud Computing Approach for Energy-Efficient Surveillance Using Deep Reinforcement learning 3

A Hybrid Edge-Cloud Computing Approach for Energy-Efficient ...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Singh, Jagendra Dabas, Partibha Bhati, Sunita Kumar, Santosh Upreti, Kamal Shaik, Nazeer School of Computer Science Engineering and Technology Bennett University Greater Noida India Dr Akhilesh das Gupta Institute of Technology and Management Dept. of Computer Science & Engineering Delhi India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India Department of Computer Science Ghaziabad India SRIT-Autonomous Anantapur Department of Computer Science Engineering India

ISBN: (纸本)9798350342338

This paper explores the novel application of Deep Reinforcement learning (DRL) in designing a more efficient, scalable, and distributed surveillance architecture, which addresses concerns such as data storage limitations, latency, event detection accuracy, and significant energy consumption in cloud data centers. The proposed architecture employs edge and cloud computing to optimize video data processing and energy usage. The study further investigates the energy consumption patterns of such a system in detail. The implementation leverages machine learning models to identify optimal policies based on system interactions. The proposed solution is tested over an extensive period, resulting in a system capable of reducing latency, enhancing event detection accuracy, and minimizing energy consumption. © 2023 IEEE.

关键词： Cloud Data Centers Deep Reinforcement learning Edge Computing Energy Efficiency Smart Surveillance

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application research of plant leaf pests and diseases base on unsupervised learning 3

Application research of plant leaf pests and diseases base o...

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3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Pei, Mingjing Kong, Min Fu, Maosheng Zhou, Xiancun Li, Zusong Xu, Jieru School of Information Engineering West Anhui University Lu'an China

ISBN: (纸本)9781665459112

In agricultural productivity, detecting plant pests and diseases is extremely crucial. This research studies images of plant leaf pests and diseases from an unsupervised perspective to solve the problem that existing plant leaf disease datasets are difficult to acquire and include few types of diseases, and they cannot find the defective parts of leaves. This paper utilizes the idea of image restoration and uses a deep learning correlation model to detect and localize the abnormal regions of plant leaves. The experimental results show that the img_AUCROC and pixel_AUCROC level anomaly detection and localization achieve good results, which bring influence and reference to other peers. © 2022 IEEE.

关键词： Unsupervised learning

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A Comparison Between machine learning Models for Airticket Price Prediction 3

A Comparison Between Machine Learning Models for Airticket P...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Thilak, Saran Jaya Benny, Binnies Pallimyalil Paulose, Eldho Chittate, Arjun Raj Khan, Talha Ali Kouatly, Rand University of Europe for Applied Sciences Department of Tech & Software Potsdam Germany

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

Airline ticket prices fluctuate, even for the same flight. Airlines are focusing on implementing various algorithms to adjust prices to maximize revenue from system-generated fares, allowing them to fill the maximum number of seats available. Because of the fierce competition among airline services, these models are unavailable to the general public. Recent advances in machine learning (ML) make it possible to model price fluctuation. This study presents a new application based on two Kaggle datasets. To forecast the price of airline tickets, it employs well-known machine learning algorithms like Random Forest and Randomized Search CV. This predictive model will assist passengers in purchasing decisions by predicting how air ticket prices will fluctuate. © 2022 IEEE.

关键词： Forecasting

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application of Artificial Intelligence for the Diagnosis of Dementia (Alzheimer): A Systematic Evaluation 1

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3rd international conference on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Pandey, Purushottam Kumar Pruthi, Jyoti Bhatia, Surbhi Department of Computer Science Manav Rachna University Haryana Faridabad India School of Science Engineering and Environment University of Salford Salford United Kingdom

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

Advancement in technology is helping human mankind. Medical professionals are using technology for the understanding of the diseases that they were finding difficulty with. From Cancer to HIV to dementia all the doctors have been able to make some breakthrough. Technology is helping doctors at each stage of diseases from diagnosis to treatment. More than 70% of patients suffering from dementia have Alzheimer. Alzheimer is a neurological degenerative non curable disease. In recent past, researchers have been trying to use both the division of Artificial Intelligence which are machine and deep learning models for the disease diagnosis. The review purpose is to have a detailed understanding of the work that has been accomplished in the area and learn about different datasets available that can be used for the purpose of research. Finally highlighting the shortcoming in the research so that the same can be used as area of research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial Intelligence Cancer Dementia Medical Neurological

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Analysis of COVID-19 Data Using Advanced machine learning Models 3

Analysis of COVID-19 Data Using Advanced Machine Learning Mo...

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3rd international conference on Pattern Recognition and machine learning, PRML 2022

作者： Noak, Callan Wang, Sujing Lamar University Department of Computer Science BeaumontTX United States

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

The outbreak of COVID-19 has dramatically changed peoples' lives over the past two years. The goal of this project is to analyze COVID-19 data from John Hopkins University for the USA, Brazil, India, and Iran between January 23, 2020 and January 21, 2022, and to develop prediction models for the new daily cases and new daily deaths related to COVID-19 for these four countries. Two supervised learning techniques, i.e., Holt's double exponential smoothing and Autoregressive Integrated Moving Averages (ARIMA), were developed to generate the prediction models for each country. Based on the results of this project, both Holt's double exponential smoothing and ARIMA techniques can produce good models that fit the data well for all four countries and help to predict the patterns of the spread of COVID-19. © 2022 IEEE.

关键词： COVID-19

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Music Stream Analysis for the Prediction of Song Popularity using machine learning and Deep learning Approach 3

Music Stream Analysis for the Prediction of Song Popularity ...

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3rd international conference on Computing, Analytics and Networks, ICAN 2022

作者： Arora, Shruti Rani, Rinkle Saxena, Nitin Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (纸本)9781665499446

Music streaming services have increased in popularity due to ubiquitous demand of web and smart devices. Music lovers are inclined towards a variety of music styles and leave a different digital trail. The emotional responses that music can evoke in its listeners can result in both physiological adjustment and cognitive benefits. The world's largest streaming service, Spotify, is the subject of this research. In this study, a model to predict whether a music would be featured on Hit-50 list of Spotify charts. This study employs the plotly for exploratory data analysis, offer descriptive statistical analysis, and compare machine learning approach with deep learning approach utilising Spotify data of eight months. Further, the study also identified several important features and correlation between them. © 2022 IEEE.

关键词： Music

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Mobile learning Based Teaching Language Skill Training Mode for Normal College Students 3

Mobile Learning Based Teaching Language Skill Training Mode ...

引用

3rd international conference on Educational Technology, ICET 2023

作者： Yang, Kaifang Wang, Hanxi Gong, Yanchao Jiang, Xiaowen Shaanxi Institute of Teacher Development School of Computer Science Shaanxi Normal University Xi'an China Shaanxi Normal University School of Computer Science Xi'an China School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China Xian Yang Cai Hong Middle School Shaanxi China

ISBN: (纸本)9798350302332

Based on grasping the essential characteristics of teaching language, constantly improving teachers' language skills is conducive to the effective transmission of teaching content and guarantees to teach performance, and then speeds up the professional development of teachers. Targeting the existing problems of teaching language skill training, combined with the advantages of mobile learning and the WeChat, this paper proposed a teaching language skill training mode with six core modules, i.e., theoretical knowledge, situational training, self-test training, mutual evaluation, classroom interaction and after-class expansion. It introduced the evaluation and practice efficiency of the training mode. The application results have shown that the mobile learning based training method provides teachers with a progressive learning process, flexible training method, rich training resources and training opportunities, which gives a new way for effectively improving the teaching language skill of normal college students. © 2023 IEEE.

关键词： Students

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Researches Advanced in Medical Detection based on Deep learning 3

Researches Advanced in Medical Detection based on Deep Learn...

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3rd international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2022

作者： Tang, Xuanbo Yu, Haomin Faculty of Computer Science and Technology Changchun University of Science and Technology Changchun510000 China School of International Education Nanjing Institute of Technology Nanjing211167 China

ISBN: (纸本)9781510661301

Medical image analysis based on computer vision technology has always been a research hotspot in the community, which aims to assist doctors in diagnosis by accurately analyzing pathological images to divide the patient's condition and the patient's lesions. Thanks to the rapid development of deep learning, the application of computer image recognition technology in medicine is becoming more and more widespread, while still facing a series of challenges such as low data set data, insufficient performance of algorithms and fine delineation of lesions. In order to solve these problems, based on extensive literature research, this paper first compares the algorithms in the application for Corona Virus Disease 2019, skin cancer and liver cancer. Then we introduce the improvement of these algorithms by expanding the number of data sets, optimizing the algorithms, and fitting the neural networks and models, whcih can improve the accuracy of image recognition technology to assist doctors in identifying lesions in clinical practice. The algorithms are further compared quantitatively on the basis of the training set in clinical diseases, and the difficulties to be overcome in image recognition and the future development trend are explained and predicted from the analysis of the comparison. Many new algorithms and excellent models are being gradually improved with the development of the times, and image recognition technology will also develop towards more research fields in the future. © 2023 SPIE.

关键词： Deep learning

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Towards the Development of Disaster Management Tailored machine learning Systems 3

Towards the Development of Disaster Management Tailored Mach...

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3rd IEEE India Council international Subsections conference, INDISCON 2022

作者： Mishra, Swaroop Jena, Manas Kumar Kumar Tripathy, Ashok Arizona State University Department of Computer Science Tempe United States Iit Palakkad Palakkad India Silicon Institute of Technology Bhubaneswar India

ISBN: (数字)9781665466011

ISBN: (纸本)9781665466011

Natural disasters such as cyclones and floods recur frequently in certain parts of the world. In this work, we provide a framework to build an easily deployable disaster management application, over five stages. First, we interview four categories of people to understand current problems and approaches in disaster management. Next, we analyze responses and establish that identified disaster management efforts are hitherto unable to effectively harness existing technology. We accordingly build a guided recommendation toolbox of existing machine learning (ML), Internet of Things (IOT), and NLP technologies, that satisfies critical system requirements for the identified efforts;this is qualitatively evaluated by senior data scientists and disaster management researchers, and found to reduce development time, as well as increase reliability and user-friendliness. Finally we provide a model to decentralize disaster management. Our work promotes the development of NLP systems tailored for disaster management, bridging the gap between research and real world applications. © 2022 IEEE.

关键词： Internet of things

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3rd international conference on Recent Trends in machine learning, IoT, Smart Cities and applications, ICMISC 2022

3rd International Conference on Recent Trends in Machine Lea...

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3rd international conference on Recent Trends in machine learning, IoT, Smart Cities and applications, ICMISC 2022

ISBN: (纸本)9789811960871

The proceedings contain 63 papers. The special focus in this conference is on Recent Trends in machine learning, IoT, Smart Cities and applications. The topics include: Early-Onset Identification of Stomach Cancer Using CNN;multifold Secured Bank application Authentication Service Using Random Visual Cryptography and Multimodal Steganography with Blockchain Technology;role of Blockchain in Health Care: A Comprehensive Study;a Novel Three Dense Layered Deep Fully Connected Neural Network for Hyperspectral Image Classification;research Trends in Artificial Intelligence and Nature Inspired Techniques;comparative Analysis of Software Defect Prediction Using Dimensionality Reduction;an Approach to Learn Structural Similarity Between Decision Trees Using Hungarian Algorithm;design and Development of IoT-Based Intelligent Cattle Shed Management;review Paper on Technologies to Curb Noise Pollution in No Honking Zones;security Threats in Healthcare Systems—A Bibliometric Study;news Channel Debate Analysis: A Detailed Insight;disease Recognization of Plant Using Different Image Processing Algorithm;a Detailed Survey on Sentimental Analysis on Social Media;perceiving Correlation Among Spatiotemporal Gait Parameters and Verifying Its Relation Using machine learning Classification Technique Pilot Study for Indian Population;radial Basis Neural Network Trained Minimum Snap Trajectory for Quadrotor;REMICARE—Medicine Intake Tracker and Healthcare Assistant;Detection of Fraudulent Credit Card Transactions in Real Time Using SparkML and Kafka;real-Time Face Detection and Face Recognition: Study of Approaches;ASIC Implementation of AI Edge Network on Chip (NoC) at 28 nm Technology Node and Its Various Timing Optimization Techniques at Each Stage;3D IC Integration Using Blockchain;Impact of COVID-19 on Import and Export of Petroleum Products and Crude Oil in India.

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