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检索条件"任意字段=International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022"

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Roadmap on Industrial Knowledge System for data-Oriented intelligent Operation and Maintenance in Chinese Power Industry 11

Roadmap on Industrial Knowledge System for Data-Oriented Int...

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11th international conference on Networks, Communication and computing, ICNCC 2022

作者： Qiao, Lin Qu, Ruiting Liu, Shenglong Zhang, Yu Wang, Huiying Liu, Biqi Information and Communication Branch of State Grid Liaoning Electric Power Co. Ltd China

ISBN: (纸本)9781450398039

To effectively and efficiently manage the information of power industry, especially in the State Grid of China, data-oriented intelligent operation and maintenance have always been a crucial task. Hence, in this paper, the roadmap on industrial knowledge system is presented for data-oriented intelligent operation and maintenance in Chinese power industry. Firstly, the background of the data-oriented intelligent operation and maintenance is described, and it has been pointed out that the core problem is data explosion and lack of knowledge in the data-oriented intelligent operation and maintenance in the State Grid of China. This is important not only for the construction of smart grid, but also for global energy savings. Secondly, as for data-oriented intelligent operation and maintenance, the State Grid data-oriented knowledge graph can be constructed. Then, we study the knowledge-driven multi-scenario intelligent operation decision technologies and information deployment technologies. Finally, the possible research direction of industrial knowledge systems is briefly presented for data-oriented intelligent operation and maintenance in State Grid. © 2022 ACM.

关键词： Energy conservation

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An Integrated Development Of IoT Based Machine Learning For data-driven Travel Recommendations

An Integrated Development Of IoT Based Machine Learning For ...

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advances in data Engineering and intelligent computing systems (ADICS), international conference on

作者： Shakeel Rifath Sridhar Constance Xavier Brindasri S Computer Science and Engineering Department Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

An innovative methodology is introduced in this Research to enhance data-driven travel decision-making by integrating IoT technology and data science techniques. With a specific focus on air and sound pollution, the research utilizes real-time sensor data from various urban sources to develop predictive models for forecasting future pollution levels. These models serve as the backbone for decision support tools seamlessly integrated into open-source platforms like ThingSpeak and IFTTT, facilitating proactive travel planning for both the general public and patients. The core of this methodology lies in the real-time aggregation of IoT sensor data, offering users comprehensive insights into prevailing air pollution levels at their intended destinations. Augmented by predictive machine learning algorithms such as logistic regression and decision trees, the system's real-time monitoring capabilities are significantly enhanced, with the decision tree algorithm particularly noted for its accuracy in assessing destination safety for patients. The forecasting accuracy and reliability of the model are upheld through rigorous training on historical data. Notably, the synergy between ThingSpeak and IFTTT enables the delivery of personalized travel recommendations grounded in predictive analytics and real-time environmental monitoring. This research underscores the pivotal role of data-driven decision-making in travel planning, empowering patients with tailored guidance derived from robust data analysis.

关键词： Training Machine learning algorithms Decision making Predictive models Real-time systems data models Planning

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Privacy-Aware intelligent Healthcare Services with Federated Learning Architecture and Reinforcement Learning Agent 14th

Privacy-Aware Intelligent Healthcare Services with Federated...

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14th international conference on Computer Science and its Applications, CSA 2022 and the 16th KIPS international conference on Ubiquitous Information Technologies and Applications, CUTE 2022

作者： Tam, Prohim Song, Inseok Kang, Seungwoo Kim, Seokhoon Department of Software Convergence Soonchunhyang University Asan Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan Korea Republic of

ISBN: (纸本)9789819912513

The convergence of digital health systems, Internet of Healthcare Things (IoHT), and Deep Learning (DL) offers intelligent healthcare services to deploy in medical software development with extended decision-making modules. However, the communication reliability and information privacy of data-driven model integration remain a challenging topic to further discuss by researchers and standard organizations. From a real-time communication perspective, the standard of Quality-of-Service requirements in smart healthcare is within real-time services, which permits extremely low packet delay budget and error loss rate. To jointly generate a delay-aware, reliability-aware, and privacy-aware approaches in intelligent healthcare services (e.g. DL-based medical software), there are three key essential aspects, namely distributed edge learning, resource virtualization, and model slicing orchestration. This paper presents optimized Federated Learning (FL) architecture based on autonomous agent policies (resource placement and slicing orchestration) using reinforcement learning. The system architecture outlines large-scale IoHT deployment in wireless cellular networks, network functions virtualization-enabled edge computing placement, and converged FL components. The communication and computation models tackle the utilization efficiencies of bandwidth, energy, and computing capacities. This proposed optimization approach strengthens privacy and reliability by orchestrating through different weights of slicing prioritization in multi-intelligent healthcare services such as non-real-time/real-time modelling, telemedicine, and remote operation (e.g. telesurgery). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Proceedings of international conference on computing, Communication, Security and intelligent systems, IC3SIS 2022

Proceedings of International Conference on Computing, Commun...

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2022 international conference on computing, Communication, Security and intelligent systems, IC3SIS 2022

ISBN: (纸本)9781665468831

The proceedings contain 113 papers. The topics discussed include: malware detection using machine learning on edge devices;sentence similarity - a state of art approaches;speech emotion recognition using bagged support vector machines;dynamic service deployment strategy using reinforcement learning in edge networks;blockchain adaptation of remote patient monitoring with Internet of medical things;adaptive control of machines: a data driven model based design;enabling granular exploration of sequence diagrams with activity diagrams;spoken language identification of Indian languages in adversarial synthetic and noisy attacking environments;veterinary medication prescription analysis: a machine learning approach;optimized and secured utilization of infrastructure resources using VMware stretched cluster multi-site solutions;technological advancements in automated crop pest and disease detection: a review & ongoing research;personality prediction using machine learning;an efficient approach for sustainable agricultural trading using blockchain technology;detection, analysis and countermeasures for container based misconfiguration using docker and Kubernetes;combinatorial optimization for travelling salesman and vehicle routing problem;and here you go: an intelligent mobile application featuring immediate assistance for the people of Bangladesh.

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Blockchain-Based Cloud computing: Model-driven Engineering Approach

Blockchain-Based Cloud Computing: Model-Driven Engineering A...

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international conference on Advanced intelligent systems for Sustainable Development, AI2SD 2022

作者： Bentayeb, Youness Badir, Hassan En-Nahnahi, Noureddine ENSAT Abdelmalek Essaadi University Tangier Morocco Faculty of Sciences Dhar el Mehraz USMBA Fes Morocco

ISBN: (纸本)9783031263835

Blockchain has attracted attention as a next generation financial technology because of its security which is suitable for the era of computerization and storing information electronically in digital format. In particular, it provides security through authentication of peers who share Cryptocurrency without the need of a centralized third party, encryption and hash value generation. In addition, Blockchain can be applied beyond the Cryptocurrency environment to include Cloud computing, which has been widely adopted in all IT environments for its efficiency and availability. Blockchain is a representative technology to guarantee anonymity, if combined with the cloud computing and can be upgraded to a convenient service that provides enhanced security for Cloud computing environments. However, studies on the protection of privacy and anonymity in this area are not sufficient. In this paper, we present a systematic study that covers the security and privacy aspects of Blockchain, highlight its potential fusion with cloud computing. We provide an overview of Blockchain technology, followed by a detailed explanation of use cases of Blockchain with Cloud computing. Also, we highlight the various challenges to blockchain-based data in the cloud computing environment. Finally, we compare recently published literatures with our studies to draw insights for future research directions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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IoMT Enabled Prototype Edge computing Healthcare System for Isolated Patients 3

IoMT Enabled Prototype Edge Computing Healthcare System for ...

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3rd international conference on intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Shamna, P.A. Mohan, Priyanka C Kmct College of Engineering Dept. of Ece Kerala Kozhikode India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Continuous patient care and the use of multiple medical machines are two challenges facing today's healthcare sector in terms of patient's healthcare. During the pandemic situation, many people isolated in their home, such as covid-19 positive patients, elderly people living away from their families, bedridden patients, etc., need regular health checks and controls, but during this pandemic is lacking. Recent advances in the Internet of Medical Things (IoMT) has been able to give good results in collecting health data of patients at home environment. Deep learning (DL) applications can able to run on edge nodes, it locally processes, computes and analyzes data from IOMT devices to make inferences on patient health information. This ensures the privacy and security of the patient's physiological information and also and allows patient health information to remain at the patient's side. Send all this information to healthcare professionals and relatives of patients. This framework will provide safety for isolated patients and a health support systemas a whole. © 2022 IEEE.

关键词： Software prototyping

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Privacy and Security of Big data Architectures for Information Management System 13

Privacy and Security of Big Data Architectures for Informati...

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13th international conference on advances in computing, Control, and Telecommunication Technologies, ACT 2022

作者： Jain, Honey Benhal, Rohan Parbat, Tanmayee Maeer's MIT Pune India

ISBN: (纸本)9781713857938

The importance of data-driven decisions and support is increasing day by day in every management area. The constant access to volume, variety, and veracity of data has made big data an integral part of management studies. New sub-management areas are emerging day by day with the support of big data to drive businesses. This study takes a systematic literature review approach to uncover the emerging management areas supported by big data in contemporary times. For this, we have analyzed the research papers published in the reputed management journals in the last ten years, fir using network analysis followed by natural language processing summarization techniques to find the emerging new management areas which are yet to get much attention. Furthermore, we ran the same exercise in each of these management areas to uncover these areas better. This research will act as a reference for future information systems (IS) scholars who want to perform analysis that is deep-dive in nature on each of these management areas, which in the coming times will get all the due attention to become dedicated research domains in the management area. © Grenze Scientific Society, 2022.

关键词： data Analytics

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A Novel CNN-Based Approach for intelligent Garbage Detection and Localization 4

A Novel CNN-Based Approach for Intelligent Garbage Detection...

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4th international conference on advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Sanjana Patel, G.M. Suraj, K.C. The National Institute of Engineering Department of Computer Science and Engineering Mysore India

ISBN: (纸本)9781665474368

In India, the problem of waste management is one of the most critical concern as tonnes of waste is generated every year of which only a very minute percentage is treated. The rest of it is either are not treated properly or dumped in landfills. Governments around the world have extensively tried to develop intelligent waste or garbage management techniques. The potential threat to sanitation, public health and safety caused by improper waste management is immense. In light of rapid increase in computational efficiency as neural networks became more capable of processing deeper data, deep learning was inevitable for garbage management systems. This paper attempts to provide an end-to-end solution to automating the process of garbage detection and alerts, beginning right at the source. The solution proposed comprises employing a highly accurate training strategy using the acclaimed VGG-16 network using a subset of the COCO dataset and also includes an accompanying Android app that makes the process of garbage localization and alerts seamless and effective. Finally, we guide you through our mobile application that is designed to assist municipalities in remotely monitoring garbage accumulation areas which help to take appropriate measures to keep surrounding areas clean and hygienic. The experimental accuracy of this system was over 95.92% which is much greater than that required for real-world scenarios. © 2022 IEEE.

关键词： Health risks

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AI-Based Smart Education System to Enhanced the Learning of Students

AI-Based Smart Education System to Enhanced the Learning of ...

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advances in computing Research on Science Engineering and Technology (ACROSET), international conference on

作者： Aaryan Barde Raj Thakur Shreyansh Patel Neelesh Sinah Snehlata Barde Dept.of CSE-AIML LNCT Group of college Bhopal India Dept. of AIDS SAGE University Indore India Inst. of Advanced Computing SAGE University Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

The education sector is poised for a significant shift propelled by Artificial Intelligence (AI) technologies. This paper explores the various uses of AI in education, such as data-driven decision assistance for instructors, adaptive assessment tools, intelligent tutoring, and personalized learning systems. It looks at how chatbots and virtual assistants driven by AI can improve learning by giving students instant, tailored support and direction. This paper also emphasizes how AI may improve student engagement and gamification of course materials, changing the educational environment into one that is dynamic, engaging, and pleasant.

关键词： Learning systems COVID-19 Distance learning Virtual assistants Educational technology Propulsion Turning Chatbots Mobile learning Artificial intelligence

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OntoHDClass: Ontology driven Approach for High Dimensional data Classification Integrating Semantic Measures and Recurrent Neural Network 1

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1st international conference on Advanced Network Technologies and intelligent computing (ANTIC)

作者： Ramanathan, N. Deepak, Gerard Santhanavijayan, A. SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Natl Inst Technol Dept Comp Sci & Engn Tiruchirappalli Tamil Nadu India

ISBN: (数字)9783030960407

ISBN: (纸本)9783030960407;9783030960391

Clustering the high-dimensional data has remained a burdensome job. Existing clustering algorithms developed are significantly inept. Frequently, in high dimensional data many noises and disturbances are formed and data is masked in existing clusters. Subsequently, we have presented an Ontology driven Approach for High Dimensional data Classification integrating Semantic Measures and Recurrent Neural Network to solve the above problem in this current Paper. The first phase OntoHDClass identifies the problem domain and formulate domain ontologies, accomplishes subspace relevance analysis by computing concept similarities. The second phase, after detection of dense regions elimination of outliers takes place and inliers are extracted. The extracted inliers then yield elite group by Cross entropy and Pearson's correlation. The third phase involves classification of the space using Recurrent Neural Network. The Result are found to be high that the OntoHDClass system reaches an overall Precision, Accuracy, and False Negative Rate of 97.32%, 96.37%, and 0.05% respectively. Elimination of outliers advances the subspace clustering with more accuracy and the elite subspace that are produced is indicated by our result. OntoHDClass can learn the clusters in very subspaces with Lower fault rate besides higher accuracy.

关键词： Concept similarity Cross entropy High dimensional data Ontology Subspace clustering

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