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检索条件"任意字段=2021 International Conference on Internet of Things and Machine Learning, IoTML 2021"

共 3781 条记录，以下是91-100 订阅

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A Transfer learning Framework for IoT-enabled Environments 21

A Transfer Learning Framework for IoT-enabled Environments

引用

6th ACM/IEEE international conference on internet-of-things Design and Implementation (IoTDI)

作者： Anjomshoaa, Amin Curry, Edward Natl Univ Ireland Lero Irish Software Res Ctr Galway Ireland Natl Univ Ireland Insight Ctr Data Analyt Galway Ireland

ISBN: (纸本)9781450383547

The major enabling technologies for creating smart building services are IoT and cognitive computing. While deploying IoT infrastructure during the construction and renovation processes is rather a straightforward practice, the implementation of cognitive components that benefit from generated data of IoT infrastructure is a complicated task that should be elaborated based on the specific attributes of target services and smart buildings. This research investigates the potential of communication and reuse of cognitive knowledge between smart environments for a seamless and automatic transfer of services and machine learning models.

关键词： Knowledge Graph Transfer learning internet of things

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Business Recommender System through Matchmaking with Supervised machine learning in Distributed Digital Platforms: Energy Complexity Analysis 8

Business Recommender System through Matchmaking with Supervi...

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8th international conference on Future internet of things and Cloud, FiCloud 2021

作者： Benramdane, Mustapha Kamal Maupas, Hanene Kornyshova, Elena Banerjee, Soumya MUST Chevreuse France CEDRIC Lab CNAM Paris France

ISBN: (纸本)9781665425742

Nowadays, digital platforms bring together different organizations belonging to the same market segment. With the presence of multiple actors and the increasing complexity of exchanged data, it is difficult to identify the best potential customers and suppliers and allocate resources. We propose an approach providing a business recommender system about the best allocation of the results. This approach is based on hybrid machine learning algorithms including matchmaking and supervised machine learning. In this paper, we present our approach and analyze the energy consumption of our approach on distributed digital platforms. Thus, we seek to improve the understanding of the energy complexity of our algorithms within a distributed system. The proposed approach contributes to the emergence and development of matchmaking systems involving diverse and distributed data sources oriented towards optimized energy consumption. © 2021 IEEE.

关键词： Energy utilization

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AI-based Models for Resource Allocation and Resource Demand Forecasting Systems in Aviation: A Survey and Analytical Study

AI-based Models for Resource Allocation and Resource Demand ...

引用

IEEE international conference on internet of things and Intelligence Systems (IoTaIS)

作者： Hejji, Dina Abu Talib, Manar Nassif, Ali Bou Nasir, Qassim Bouridane, Ahmed Univ Sharjah Comp Engn Sharjah U Arab Emirates Univ Sharjah Comp Sci Sharjah U Arab Emirates Univ Sharjah Elect Engn Sharjah U Arab Emirates

ISBN: (纸本)9781665420358

There is an increasing interest in developing Intelligent Decision Support Systems (IDSSs) for various aviation operations such as resource planning. Recently, with the significant advancements in machine learning (ML), it has been widely used to develop the core methods for IDSSs. Thus, researchers have broadly used ML to address Resource Allocation and Resource Demand Forecasting (RARDF) in aviation industry. This research paper reviews the resources that have been tackled by Artificial Intelligence (AI) based Intelligent Systems in aviation industry. In addition, it reviews the most recent ML-based work done in RARDF and analyzes the possibilities and challenges for this paradigm in aviation industry.

关键词： machine learning Intelligent Decision Support Systems Resource Allocation Resource Demand Forecasting

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A Comprehensive Insight on machine learning Enabled internet of things Recommender Systems (IoTRS) 3

A Comprehensive Insight on Machine Learning Enabled Internet...

引用

3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Ahlawat, Partibha Rana, Chhavi University Institute of Engineering and Technology Department of Computer Science and Engineering Maharishi Dayanand University Rohtak India

ISBN: (纸本)9781665438117

With the advancement in technologies, the opportunities for new integrated solutions are also unlocked. internet of things by connecting cyber and physical world introduce enormous paradigm change in computing and information technologies. Recommender system helps in filtering the data by taking care of inclinations of users to prevail the issues related to overdosing of IoT generated data. machine learning techniques can be applied for producing intelligent recommendation by exploiting IoT-based dynamic, scalable and heterogeneous data. This paper reviews the machine learning techniques applied for generating personalized recommendation in IoT-based services and give the brief introduction of IoT recommender systems and machine learning techniques. Personalization and recommendations services in different application domains are enhanced by the incorporation of machine learning algorithms. In IoT recommendations machine learning also leaving their remarkable presence with integrating automaticity and dynamicity. © 2021 IEEE.

关键词： internet of things

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Transfer learning and CNN-based Framework for Intrusion Detection in Highway internet Toll Systems 24

Transfer Learning and CNN-based Framework for Intrusion Dete...

引用

4th international conference on internet of things and machine learning

作者： Li, Zhuo Zhong, Guoqing Hu, Yong Xu, Jianbin Jiang, Nan Zhang, Yuejin East China Jiaotong Univ Sch Informat & Software Engn Nanchang Peoples R China Jiangxi Transportat Res Inst Co Ltd Intelligent Transportat Dept Nanchang Peoples R China Traff Monitoring & Command Ctr Dept Transportat Jiangxi Prov Nanchang Peoples R China

ISBN: (纸本)9798400710353

Nowadays, the frequency and intensity of cyber attacks have escalated significantly. Traditional network security defenses, which predominantly rely on static, predefined rules to distinguish between legitimate and malicious network traffic, have proven inadequate in identifying complex and sophisticated network intrusions. The integration of artificial intelligence (AI) technologies offers a pathway to enhancing the reliability and effectiveness of these defenses. Convolutional neural networks (CNNs), a subset of deep learning models, have achieved notable advancements in image processing, thereby gaining considerable scholarly attention. By harnessing the capabilities of CNN models, complex network attacks can be efficiently detected through the transformation of network traffic datasets into image representations. This study proposes an intelligent Intrusion Detection System (IDS) model, designed to enhance the security of highway internet-based toll systems, which integrates optimized CNN architectures, transfer learning, and ensemble learning methodologies. The ensemble model is trained on the transformed data and validated using the CICIDS2019 network dataset. The experimental results indicate a detection accuracy of 99.91%, substantiating the feasibility and effectiveness of the proposed approach within this research.

关键词： IDS Convolutional Neural Networks Transfer learning Ensemble Model Data Transformation

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A Resource Management Based on Deep learning in Ubiquitous Power internet of things 17

A Resource Management Based on Deep Learning in Ubiquitous P...

引用

17th IEEE international Wireless Communications and Mobile Computing conference (IEEE IWCMC)

作者： Yao, Wenhao Peng, Huaide Peng, Huiyun Xiong, Yanfang Li, Xueting Kong, Qiang STATE GRID Jiangxi Econ Res Inst State Nanchang Jiangxi Peoples R China Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing Peoples R China

ISBN: (纸本)9781728186160

This paper proposes a resource allocation method based on deep learning in power internet of things. The method uses density-based spatial clustering of noisy applications (DBSCAN) to analyze data, and then trains deep neural networks (DNN) based on the results of DBSCAN data. The results show that our method can significantly improve the user's quality of service (QoS).

关键词： power internet of things resource management deep neural network machine learning

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An IoT-Based Real-Time Intelligent Irrigation System using machine learning 13

An IoT-Based Real-Time Intelligent Irrigation System using M...

引用

13th international conference on Information and Communication Technology and System (ICTS)

作者： Shahriar, Saleh Mohammed Peyal, Hasibul Islam Nahiduzzaman, Md Pramanik, Md Abu Hanif RUET Dept ECE Rajshahi 6204 Bangladesh

ISBN: (纸本)9781665440592

Irrigation is very important fact in the field of agriculture. A machine learning and internet of things (IoT) based irrigation system is proposed here to make irrigation process more efficient. Soil moisture and Temperature value are taken by the sensors in Raspberry Pi with the help of analog to digital converter (ADC). Serial peripheral interface (SPI) protocol is used here to do it. A machine learning model is trained with Naive Bayes algorithm and deployed in Raspberry Pi. The machine learning model controls the irrigation system with the sensor value with almost 98.33% accuracy. A prototype project of this irrigation system is also developed with a water pump and relay to show that how accurately the system works.

关键词： internet of things (IoT) machine learning (ML) Raspberry Pi Analog to digital converter (ADC) Serial peripheral interface (SPI) Naive Bayes classifier

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Explore the Review Area: Research on Factors Influencing the Review Helpfulness for Fresh Agricultural Products Based on machine learning Methods-A Case Study of JD E-commerce Platform 24

Explore the Review Area: Research on Factors Influencing the...

引用

4th international conference on internet of things and machine learning

作者： Qi, Yiru Zhejiang Gongshang Univ Sch Business Adm Hangzhou Peoples R China

ISBN: (纸本)9798400710353

Online reviews contribute to mitigating information uncertainty and enhancing consumer purchasing efficiency. Early research on the review helpfulness in the fresh agricultural products largely treated fresh agricultural products as a single category, with limited attention paid to different types of such products. Consequently, the focus of this paper lies in exploring the mechanisms that influence the helpfulness of reviews for various types of fresh agricultural products. Drawing on the Elaboration Likelihood Model, this paper establishes predictive models for review helpfulness using Random Forest, Backpropagation Neural Network, Support Vector machine, Naive Bayes, XGBoost, and Catboost algorithms. Furthermore, this paper delves into the feature importance of each product category. The findings reveal that: (1) Random Forest is the best predictive model for meat and egg products, while XGBoost excels in predicting the helpfulness of seafood, vegetable, and fruit reviews;(2) In terms of feature importance scores, this paper observes that consumers tend to be highly involved when purchasing these five categories of fresh agricultural products online, and the central route factors play a significant role in predicting the helpfulness of reviews across all product types;(3) Regarding specific feature preferences, consumers attach the highest importance to "scoring objectivity" when purchasing meat, egg, and seafood products, while "review timeliness" is the most important feature for vegetable and fruit purchases.

关键词： Review helpfulness Fresh agricultural products machine learning methods JD e-commerce platform

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Research on the Application Practice of the internet of things in the Smart learning Environment 18

Research on the Application Practice of the Internet of Thin...

引用

18th international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2021

作者： Wang, Jianjun Hunan First Normal University School of Electronic Information Changsha410205 China

ISBN: (纸本)9781665413640

Smart education is the fundamental means to realize 'student-centered', and the smart learning environment is the technical support and condition guarantee for smart education. Explains how to build a smart learning environment based on the three-tier architecture of the internet of things, that is, the perception layer, network layer, and application layer, to achieve the deep integration of human, machine, and things, to create a contextual learning environment for learners to satisfy learners Personalized learning needs. © 2021 IEEE.

关键词： internet of things

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Bad data filtering algorithm of power internet of things based on artificial intelligence technology

Bad data filtering algorithm of power internet of things bas...

引用

2021 international conference on machine learning and Intelligent Systems Engineering, MLISE 2021

作者： Zhang, Xin Zhang, Rui Chen, Fengchao Dongguan Power Supply Bureau Guangdong Power Grid Corporation China

ISBN: (纸本)9781665417365

With the rapid development of artificial intelligence technology (AIT) and communication technology, China's traditional power grid (TPG) is developing in the direction of smart grid (SG). The application of power internet of things based on internet of things in SG is the proof of the development of information technology to a certain level. The main function of China's power grid is to monitor the daily operation of power consumption, collect and integrate the power consumption data of each household. However, with the rapid development of the current society, a large number of power consumption data makes the TPG start to operate under load, so the SG comes into being. After the reasonable operation of data collection and integration, how to screen bad data has become a new challenge for power internet of things. The purpose of this paper is to study the bad data screening algorithm of power internet of things based on AIT. This paper mainly expounds the types of power internet of things data classification processing and analysis, and understands its batch processing and analysis, near real-time analysis and stream processing analysis. At the same time, this paper describes the process of data processing screening, analyzes the data screening process and the problems that the algorithm will encounter, makes algorithm analysis for the data screening of power internet of things, and proves the reliability of the bad data screening algorithm. At the same time, the processing time and efficiency of TPG and SG are analyzed. It is proved that under the same workload, the efficiency of power internet of things data algorithm based on AIT is much higher than that of TPG. The experimental results show that the working time of the power internet of things is less than that of the TPG, but the working efficiency is far better than that of the TPG. When processing 500000 pieces of data, the working time of the TPG is 574 seconds, and the working efficiency is 1.52, while th

关键词： internet of things

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