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Research on Power Consumption Behavior Detection Technology based on Electricity data 4

Research on Power Consumption Behavior Detection Technology ...

引用

4th IEEE international conference on Power, intelligent computing and systems, ICPICS 2022

作者： Sikun, Gao North China University of Technology Beijing China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

With the increase of daily life and the continuous impact of the epidemic, not only the demand for electricity on the customer side is increasing, but also new patterns of electricity consumption behavior emerge, so it becomes important to identify the customer's electricity consumption behavior to better adapt to the customer's needs. Usually the electricity consumption data on the customer side contains rich data related to electricity consumption behavior, which is introduced into the electricity consumption behavior detection in this paper. In this paper, we also design a power behavior feature model to address the problem that the constructed power behavior features are not comprehensive, and present a power behavior feature construction method that integrates business-driven and data-driven features. After constructing the features, we propose a power behavior detection method for multivariate power sensing data, firstly, we give a feature importance assessment method based on the maximum information coefficient and variance, then we use a power behavior classification with an improved recursive feature elimination algorithm, and finally we get the user classification results. © 2022 IEEE.

关键词： Electric power utilization

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Understanding, Visualizing and Explaining XAI Through Case Studies 8

Understanding, Visualizing and Explaining XAI Through Case S...

引用

8th international conference on Advanced computing and Communication systems, ICACCS 2022

作者： Karpagam, G.R. Varma, Aditya Samrddhi, M. Shri Shivathmika, V. Psg College of Technology Coimbatore India

ISBN: (纸本)9781665408165

data-driven AI applications require Accountability, Responsibility, and Transparency. At the same time,users' expectations on the accuracy and precision of the predictions are unreasonable. This draws attention to - explainability, an inherent limitation in the traditional intelligent systems that are black box in nature. As Explainable AI(XAI) seeks to bridge this gap through the explanation of AI models, this paper focuses on the study of achieving Responsible AI through XAI. AIX360 is a robust popular tool that is used to provide explainability from various viewpoints. Hence, this work uses AIX 360 to bring explainability in the viewpoint of various stakeholders namely domain experts, service providers and customers. The uniqueness of the paper lies in designing a conceptual model for XAI based predictive analysis. This paper is an attempt to understand, visualize and explain the observations through two data-intensive intelligence systems as case studies HELOC(Home Equity Line Of Credit) and ISIC(international Skin Imaging Collaboration). © 2022 IEEE.

关键词： intelligent systems

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Towards a data-driven Smart Assembly Design: State-of-the-Art 1

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12th international conference on Integrated Design and Production, CPI 2022

作者： Allagui, Amal Belhadj, Imen Plateaux, Régis Hammadi, Moncef Penas, Olivia Aifaoui, Nizar Choley, Jean-Yves LGM ENIM University of Monastir 05 rue Ibn Eljazzar Monastir5019 Tunisia Laboratoire Quartz EA7393 ISAE-Supmeca 3 rue Fernand Hainaut Saint-Ouen93400 France

ISBN: (数字)9783031236150

ISBN: (纸本)9783031236143

With the increase of customer requirements and customized products needs, mechanical systems are becoming more sophisticated and equipped with intelligent parts. With this level of intelligence the mechanical systems become more and more complex and also their assembly processes becomes tedious. Therefore, the Industry 4.0 promotes efficient and smart assembly design to support commissioning automation and consequently customer satisfaction. This paper de-scribes the different challenges of assembly design, before presenting the advances on data analysis and Industry 4.0 technologies that can support intelligent assembly design. After analyzing these approaches, an innovative framework is proposed to provide the designer with a tool to address the different challenges of assemblies, based on the one hand on data from smart parts in operation (equipped with RFID sensors) transmitted by the Industrial Internet of Things and Cloud computing, and on the other hand on the analysis of these massive data and the use of deep learning algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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Deep Reinforcement Learning-based Joint Caching and computing Edge Service Placement for Sensing-data-driven IIoT Applications

Deep Reinforcement Learning-based Joint Caching and Computin...

引用

IEEE international conference on Communications (ICC)

作者： Chen, Yan Sun, Yanjing Yang, Bin Taleb, Tarik China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China Chuzhou Univ Sch Comp & Informat Engn Chuzhou Peoples R China Univ Oulu Fac Informat Technol & Elect Engn Oulu Finland

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

Edge computing (EC) is a promising technology to support a variety of performance-sensitive intelligent applications, especially in the Industrial Internet of Things (IIoT). The sensing-data-driven applications whose task processing requires sensing data from various sensors are typical applications in IIoT systems. The placement of caching and computing edge service functions for such applications is vital to ensure system performance and resource utilization in EC-enabled IIoT systems. Therefore, this paper investigates the joint caching and computing edge service placement (JCCESP) for multiple sensing-data-driven IIoT applications in an EC-enabled IIoT system. The JCCESP problem is formulated as a Markov Decision Process (MDP). Then, a deep reinforcement learning (DRL)-based approach is proposed to address the challenges like limited prior knowledge and the heterogeneity of such IIoT systems. Under such an approach, the policy network of the DRL agent is constructed based on an encoder-decoder model to tackle various applications requiring different numbers of service functions. A REINFORCE-based method is further employed to train the policy network. Simulation results indicate that the performances achieved by our proposed approach can converge after training and are significantly superior to benchmarks.

关键词： Edge computing IIoT sensing-data-driven application joint service placement and DRL

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e-SMARTS: A System to Simulate intelligent Traffic Management Solutions (Demo Paper) 22

e-SMARTS: A System to Simulate Intelligent Traffic Managemen...

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30th ACM SIGSPATIAL international conference on advances in Geographic Information systems (ACM SIGSPATIAL GIS)

作者： Gunarathna, Udesh Borovica-Gajic, Renata Karunasekera, Shanika Tanin, Egemen Univ Melbourne Melbourne Vic Australia

ISBN: (纸本)9781450395298

intelligent traffic management solutions that leverage machine learning have gained a lot of interest in recent years. These techniques, however, cannot be deployed in real-world settings at a desirable pace due to technological barriers. Thus, easily customizable, realistic simulation environments are needed to train and verify the effectiveness of machine learning algorithms for traffic control. We propose an easily extendable traffic simulation system named e-SMARTS to allow researchers to experiment with novel data-driven traffic management algorithms in a setup that mimics real-world traffic conditions. We demonstrate the flexibility of e-SMARTS using widely researched traffic management solutions for Autonomous Intersection Management (AIM). In the demonstration, we present several pluggable algorithms for AIM and show that these computationally efficient algorithms can achieve effective and safe results.

关键词： Simulations Spatial-temporal data Reinforcement learning

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international conference on advances in data Science and computing Technologies, ASDC 2022

International Conference on Advances in Data Science and Com...

引用

international conference on advances in data Science and computing Technologies, ASDC 2022

ISBN: (纸本)9789819936557

The proceedings contain 77 papers. The special focus in this conference is on international conference on advances in data Science and computing Technologies. The topics include: Effect of Dropout on Convolutional Neural Network for Hyperspectral Image Classification;state Derivative Optimal Control Law for Submersible Autonomous Robotic Vehicle in Steering Plane;ASR for Indian Regional Languages Using Fine-Tuned Wav2Vec2 Model;intelligent Online Voting System for Twenty-First Century and Smart Cities 5.0: An Empirical Approach through Blockchain with ML Techniques;an Empirical Study on Credit Risk Assessment Using Ensemble Classifiers;audio Based Text Summarization Using Natural Language Processing;blockchain-Enabled Security in Vehicular Ad Hoc Network;stock Selection Using Ontological Financial Analysis;Prediction of Future Career Course of Students Through RF Algorithm;components of Information Diffusion and Its Models in Online Social Networks;a Comparative Study;development of a Residual Neural Network Architecture for the Detection of Diabetic Retinopathy in Retinal Fundus Images;prediction Markets Using Machine Learning;evaluation of Optimum Batting Order Based on Partnership Analysis Using Ant Colony Optimization Technique;identification of Rock Images in Mining Industry: An Application of Deep Learning Technique;evaluation of Teachers’ Performance Using Fuzzy Inferences System;a Detailed Analysis on Intrusion Detection systems, datasets, and Challenges;object Detection Under Low-Lighting Conditions Using Deep Learning Architectures: A Comparative Study;terahertz Image Processing: A Boon to the Imaging Technology;developing a Talent Identification Model for Predicting Player Position in Football Using Machine Learning Algorithms;Survey of Various Machine Learning Techniques for Analyzing IoMT-Based Remote Patient Monitoring System;PSO-Based Traffic Signals in a Real-World City.

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Digital Twin driven Blockchain Based Reliable and Efficient 6G Edge Network 18

Digital Twin Driven Blockchain Based Reliable and Efficient ...

引用

18th Annual international conference on Distributed computing in Sensor systems (DCOSS)

作者： Ozdogan, Mehmet Ozgen Carkacioglu, Levent Canberk, Berk Istanbul Tech Univ Dept Comp Engn Istanbul Turkey Istanbul Tech Univ Dept Artificial Intelligence & Data Engn Istanbul Turkey Aselsan Inc Ankara Turkey

ISBN: (纸本)9781665495127

With the rapid development of intelligent devices in wireless communication, the fifth generation (5G) mobile networks have limited high data rates, low latency, high availability demands. The sixth -generation (6G) mobile network can use Digital -twin (DT) techniques to meet these demands. DT is the virtual representation of physical aspects such as 6G edge nodes. DT optimize the 6G edge nodes parameters using artificial intelligence (AI) and especially machine learning (ML) algorithms. However, AI and ML bring along privacy and security concerns. Therefore, user data must be protected from unauthorized persons during the 6G edge network recovery and expansion phases. In this paper, we proposed a new reliable Digital Twin -based 6G edge network recovery framework using Blockchain technology. We applied the Transfer Learning (TL) technique to improve our proposed framework's performance. We ensured data privacy and security using TL and Blockchain.

关键词： digital twin networks blockchain 6G edge recovery transfer learning knowledge sharing

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Towards the Identification of Multiclass Lung Cancer-Related Genes: An Evolutionary and intelligent Procedure 17th

Towards the Identification of Multiclass Lung Cancer-Related...

引用

17th international Work-conference on Artificial Neural Networks (IWANN)

作者： Carlos Gomez-Lopez, Juan Castillo-Secilla, Daniel Gonzalez, Jesus Javier Herrera, Luis Rojas, Ignacio Univ Granada Dept Comp Engn Automat & Robot ICAR Granada Spain Univ Granada Res Ctr Informat & Commun Technol CITIC UGR Granada Spain Fujitsu Technol Solut SA CoE Data Intelligence Camino Cerro Gamos 1 Madrid Spain

ISBN: (纸本)9783031430848;9783031430855

The amount of available transcriptomic data from which relevant knowledge can be extracted has rapidly increased. Besides, with the advances in areas such as machine learning and high-performance computing, the time and computing efforts for analyzing those data are being reduced, leading to the design of Clinical-Decision Support systems (CDSS) for the precision medicine paradigm. As a result of this increase, the use of intelligent and evolutionary methods to study and classify cancer diseases has been proposed in the literature showing promising results. This study is aimed at identifying a set of genes able to distinguish between the following types of lung cancer: Adenocarcinoma (ACC), Squamous Cell Carcinoma (SCC), and healthy lung. The Differentially Expressed Genes (DEGs) analysis was carried out through RNA-seq data coming from The Cancer Genome Atlas (TCGA). An optimized evolutionary procedure has been developed with the purpose of finding the optimal combinations among the hundreds of candidate DEGs. Our custom method can maximize multiclass lung cancer recognition while minimizing the number of selected DEGs. The results show an outstanding classification rate with a significantly reduced number of DEGs biologically related to lung cancer.

关键词： RNA-Seq Evolutionary Algorithm Feature Selection Gene Expression Transcriptomic Technologies

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4th international conference on advances in Information Communication Technology and computing, AICTC 2022

4th International Conference on Advances in Information Comm...

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4th international conference on advances in Information Communication Technology and computing, AICTC 2022

ISBN: (纸本)9789811998874

The proceedings contain 51 papers. The special focus in this conference is on advances in Information Communication Technology and computing. The topics include: Internet of Things and Retail Performance in an Emerging Market: A Qualitative Analysis;the Impact of Social Media on Consumer Purchase Behaviour;the Contribution of the Internet of Things to Enhance the Brands of Small and Medium-Sized Enterprises in Iraq;trust Chain for Managing Trust in Blockchain-Associated IoT-Enabled Supply-Chains;supply Chain Management Using Blockchain Security Enhancement;a Novel Image Encryption Algorithm Using Logistic and Henon Map;an Analysis of data Sparsity Resolution Algorithms Used in Recommender systems;recent Trends for Practicing Machine Learning in Brain Tumors: A Survey;voice-Based intelligent Virtual Assistant;The Impact of IT Capabilities on Competitive Advantage;Identification of Autism Spectrum Disorder from Functional MRI Using Deep Learning;conversion of Sign Language to Text and Audio Using Deep Learning Techniques;security Attacks and Key Challenges in Blockchain Technology: A Survey;advancing from Manual to Automatic Telecast of News for Deaf;study of Nutrition-Based Recommender System for Diabetes and Cardiovascular Patients Based on Various Machine Learning Techniques: A Systematic Review;comparing Fish Finding Techniques using Satellite and Indigenous data based on Different Machine Learning Algorithms;implementation of E-Governance Framework for Rural Areas of India;opinion Summarization from Online Mobile Phone Reviews Using Feature Based Association Rule Mining;unconventional to Automated Attendance Marking Using Image Processing;synthetic Animations Based Dictionary for Indian Sign Language;start of Telemedicine in Uzbekistan. Technological Availability;review of Feature Extraction Techniques for Fake News Detection.

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Context-driven Policies Enforcement for Edge-based IoT data Sharing-as-a-Service

Context-driven Policies Enforcement for Edge-based IoT Data ...

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IEEE international conference on Services computing (IEEE SCC)

作者： Huu-Ha Nguyen Phung, Phu H. Nguyen, Phu H. Hong-Linh Truong Univ Dayton Dept Comp Sci Intelligent Syst Secur Lab Dayton OH 45469 USA SINTEF Oslo Norway Aalto Univ Espoo Finland

ISBN: (数字)9781665481465

ISBN: (纸本)9781665481465

Sharing real-time data originating from connected devices is crucial to real-world intelligent Internet of Things (IoT) applications, i.e., based on artificial intelligence/machine learning (AI/ML). Such IoT data sharing involves multiple parties for different purposes and is usually based on data contracts that might depend on the dynamic change of IoT data variety and velocity. It is still an open challenge to support multiple parties (aka tenants) with these dynamic contracts based on the data value for their specific contextual purposes. This work addresses these challenges by introducing a novel dynamic context-based policy enforcement framework to support IoT data sharing (on-Edge) based on dynamic contracts. Our enforcement framework allows IoT data Hub owners to define extensible rules and metrics to govern the tenants in accessing the shared data on the Edge based on policies defined with static and dynamic contexts. We have developed a proof-of-concept prototype for sharing sensitive data such as surveillance camera videos to illustrate our proposed framework. The experimental results demonstrated that our framework could soundly and timely enforce context-based policies at runtime with moderate overhead. Moreover, the context and policy changes are correctly reflected in the system in nearly real-time.

关键词： IoT Access Control Edge computing IoT Hub data sharing-as-a-service Policy Enforcement Dynamic Context

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