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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"

共 558 条记录，以下是281-290 订阅

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Smart Tourism Development Strategy Under the Background of Big data

Smart Tourism Development Strategy Under the Background of B...

引用

international conference on Multi-modal Information Analytics, MMIA 2021

作者： Ren, Linlin Shanghai Xingjian College Shanghai200072 China

ISBN: (纸本)9783030748104

Under the background of big data, smart tourism emerges at the right moment and has become the focus of attention of tourism industry and tourism enthusiasts. This paper analyzes the status and problems on the development of Smart tourism in China, and explores effective approaches to the development of smart tourism under the background of big data. This paper proposes the construction of cloud service platform, management mode, information sharing mechanism and the strengthening of information security management. In order to provide suggestions for the innovation-driven development of smart tourism mode in the future. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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A new ensemble technique for recognize the long and shortest text 6th

A new ensemble technique for recognize the long and shortest...

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6th international conference on Information System Design and intelligent Applications, INDIA 2019

作者： Malyadri, M. Afrin, Najeema Anusha Reddy, M. CMR Technical Camus Hyderabad India

ISBN: (纸本)9789811553998

In data mining, shorter-text analysis is performed more widely for many applications. Based on the syntax of the language, it is very difficult to analyze the short text with several traditional tools of natural language processing, and this is not applied correctly either. In short text, it is known that there are rare and insufficient data available with this text, and it is difficult to identify semantic knowledge. With the great noise and ambiguity of short texts, it is very difficult to find semantic knowledge. In this paper, it was proposed to replace the coefficient of similarity of cosine with the measure of similarity of Jaro–Winkler to obtain the coincidence of similarity between pairs of text (source text and target text). Jaro–Winkler does a much better job of determining the similarity of the strings because it takes order into account when using positional indices to estimate relevance. It is presumed that the performance of CACT driven by Jaro–Winkler with respect to one-to-many data links offers optimized performance compared to the operation of CACT driven by cosine. An evaluation of our proposed concept is sufficient as validation. © Springer Nature Singapore Pte Ltd 2021.

关键词： data mining

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Boosting Accuracy of Fake Review Prediction Using Synthetic Minority Oversampling Technique

Boosting Accuracy of Fake Review Prediction Using Synthetic ...

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international conference on computing, Communication, and intelligent systems (ICCCIS)

作者： Bhawna Saxena Shruti Goyal Anjali Kumari Anushka Agarwal Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida Uttar Pradesh India

ISBN: (纸本)9781665462013

In recent times prior to making a purchase, the vast majority read reviews about that product, and their decision is largely driven by the reviews. Deceitful online sellers often gather fake or spam reviews for their products or services, thereby reducing the effectiveness of online reviews. The review data is often imbalanced such that the fake reviews greatly outnumber the genuine reviews. An imbalance leads to a bias, as the model tends to mostly predict the majority class. To attain a high-quality classification outcome, the issue of imbalanced data should be resolved before applying the classification algorithms. This paper studies the performance of supervised machine learning classifiers pertaining to fake review detection. The approach put forward in this paper aims to improve the prediction accuracy of popular supervised learning classifiers Random - Forest, LightGBM, XGBoost, Naive Bayes, and Decision Tree on an imbalanced review dataset For boosting the accuracy of these classifiers, the Synthetic Minority Oversampling Technique is used for addressing the class imbalance problem. The performance of the classifiers has been studied by changing the oversampling parameters. The application of SMOTE showed a significant improvement in the classifier’s prediction accuracy.

关键词： Forestry Predictive models Boosting data models Classification algorithms Decision trees intelligent systems

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Modelling IF Steels Using Artificial Neural Networks and Automated Machine Learning 20th

Modelling IF Steels Using Artificial Neural Networks and Aut...

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20th international conference on Hybrid intelligent systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired computing, NaBIC 2020

作者： Sgrott, Douglas Macedo Cerqueira, Fabricio Moreira Miranda, Fabiano J. F. Filho, José F. S. Parpinelli, Rafael S. UDESC Graduate Program in Applied Computing Santa Catarina State University JoinvilleSC Brazil Global Research and Development Group ArcelorMittal São Francisco do SulSC Brazil

ISBN: (纸本)9783030730499

The development of steels requires multiples manufacturing steps and involves a high number of variables and non-linear processes. Although there are data-driven models used to predict such steels’ mechanical properties, their design often requires specialized Machine Learning knowledge, hampering the application of such models. In this paper, an Automated Machine Learning (AutoML) framework, namely Auto-Keras, was employed to generate and train a data-driven model on a steel data-set through Neural Architecture Search. This paper proposes predicting the mechanical properties of steels applying Artificial Neural Networks (ANN) as a multivariate regression problem using AutoML. The results show that even with a vast parameter search space, it is possible to generate ANNs with different architectures that provide high performance with free and open-source AutoML. Three ANNs with different activation functions were optimized using Auto-Keras and compared. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Roadmap on Industrial Knowledge System for data-Oriented intelligent Operation and Maintenance in Chinese Power Industry 22

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

引用

Proceedings of the 2022 11th international conference on Networks, Communication and computing

作者： Lin Qiao Ruiting Qu Shenglong Liu Yu Zhang Huiying Wang Biqi Liu Information and communication branch of State Grid Liaoning Electric Power Co. Ltd China Department of Library and Information Science 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.

关键词： data-oriented intelligent maintenance data-oriented intelligent operation Industrial knowledge system Knowledge Graph

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Design of Personalized intelligent Learning Assistant System Under Artificial Intelligence Background

Design of Personalized Intelligent Learning Assistant System...

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international conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT 2020

作者： Xia, Ping Guangzhou College of Technology and Business Guangdong China

ISBN: (纸本)9783030627423

In the context of the era of artificial intelligence, integration and innovation of big data, cloud computing, Internet of Things, and 5G and other cutting-edge technologies in the field of higher education have the development of smart education. It provides a valuable opportunity for fully implementing adaptive and personalized learning based on teaching according to aptitude. This article is driven by AI and student-centered, starting from the personalized needs of learning. Analyze human-computer interaction technology founded on natural language processing, and design anthropomorphic learning assistance and communication functions. Collect and process educational big data, mining quantitative data on learning behavior characteristics, learning status and learning effects, and build a staged student portrait model. intelligently generate personalized learning programs and plans, recommend high-quality learning resources, and implement adaptive personalized learning program updates based on learning effect feedback. Through the research and design of the framework and hierarchical functions of the intelligent learning assistant system, we are committed to providing students with anthropomorphic learning assistance functions and an ecological learning environment, promoting students’ personalized learning and sustainable development, and realizing the function of intelligent tutoring. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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Enabling Big data Analytics at Manufacturing Fields of Farplas Automotive

Enabling Big Data Analytics at Manufacturing Fields of Farpl...

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international conference on intelligent and Fuzzy systems, INFUS 2020

作者： Akın, Özgün Deniz, Halil Faruk Nefis, Doğukan Kızıltan, Alp Çakır, Altan Farplas Otomotiv Kocaeli41400 Turkey Istanbul Technical University Istanbul34467 Turkey

ISBN: (纸本)9783030511555

Digitization and data-driven manufacturing process is needed for today’s industry. The term Industry 4.0 stands for the today industrial digitization which is defined as a new level of organization and control over the entire value chain of the life cycle of products;it is geared towards increasingly individualized customer’s high-quality expectations. However, due to the increase in the number of connected devices and the variety of data, it has become difficult to store and analyze data with conventional systems. The motivation of this paper is to provide an overview of understanding of the big data pipeline, providing a real-time on-premise data acquisition, data compression, data storage and processing with Apache Kafka and Apache Spark implementation on Apache Hadoop cluster, and identifying the challenges and issues occurring with implementation the Farplas manufacturing company, which is one of the biggest Tier 1 automotive suppliers in Turkey, to study the new trends and streams related to topics via Industry 4.0. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Industry 4.0

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State of Charge Estimation Using data-driven Techniques for Storage Devices in Electric Vehicles 3rd

State of Charge Estimation Using Data-Driven Techniques for ...

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3rd international conference on Innovative computing and Communication, ICICC 2020

作者： Singh, Rupam Khan, Mohammed Ali Kurukuru, V. S. Bharath Department of Electrical Engineering Delhi Technological University New Delhi India New Delhi India

ISBN: (纸本)9789811551123

This paper develops a state of charge (SOC) estimation mechanism for electric vehicle batteries using machine learning technique. Conventionally, the battery SOC is estimated using a model-based approach which requires extensive modeling, experimentation, and validation before accurate SOC estimation. The technique proposed in this research is unique and does not require the battery temperature or capacity as inputs which are essential for model-based estimation. Initially, the battery data is simulated, and a machine learning model is developed using support vector data descriptor and validated using the simulated battery data. The developed classification algorithm can estimate the state of charge of the battery and is suitable for online and real-time applications. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Electric vehicles

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data-driven Shoreline Change Forecasting on Eretan Beach Using Random Forest

Data-driven Shoreline Change Forecasting on Eretan Beach Usi...

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Advanced Creative Networks and intelligent systems (ICACNIS), international conference on

作者： Putu Harry Gunawan Iryanto Ahamd Lubis Ghozali Eka Ismantohadi Z. K.Abdurahman Baizal Ari Satrio School of Computing Telkom University Bandung Indonesia Informatics Department Politeknik Negeri Indramayu Indramayu Indonesia

Shoreline change is critical information in coastal management. Eretan Beach located in Kandanghaur, Indramayu, has severe abrasion. In this research, we study the shoreline change of Eretan beach by using numerical experiments. Here, we use the Random Forest algorithm to forecast the shoreline change from historical data. This research data is obtained from the Google Earth Engine with CoastSat. The computational results show that the algorithm performs well in predicting actual data. The best MAE and RMSE at position 195 are obtained 3.77 and 6.91, respectively. Moreover, the standard deviation by MAE and RMSE is achieved at 0.05 and 0.03, respectively.

关键词： Radio frequency Earth Sea measurements Forestry Prediction algorithms Internet Forecasting

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Fog computing in the Age of Big Healthcare data: Powering the Medical Internet of Things 1st

Fog Computing in the Age of Big Healthcare Data: Powering th...

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1st international conference of Advanced computing and Informatics, ICACIN 2020

作者： Khaloufi, Hayat Abouelmehdi, Karim Beni-Hssane, Abederrahim Department of Computer Science LAROSERI Laboratory Faculty of Science El Jadida Chouaib Doukkali University El Jadida Morocco Laboratory of Applied Mathematics and Information System Faculty Polydisciplinary of Nador Mohamed First University Nador Morocco

ISBN: (纸本)9789811560477

Today’s global marketplace for Internet of Things (IoT) remains fiercely competitive within the healthcare sector. This can be attributed to many exciting achievements. It is expected to have a positive influence on all aspects of healthcare such as reducing prevalence and incidence of chronic conditions, improving the patient treatment and lowering in per capita healthcare costs, resulting from the forecasted increases in the adoption of remote monitoring devices. These smart devices along with wearables and Internet-connected sensors are expected to generate a great amount of data every second. However, the present model used to handle these unprecedented data named as cloud computing becomes inadequate. The solution must be seen in fog computing. The strong need of this model was mainly driven by a growing demand for real-time analysis and low latency. Motivated from these facts, this paper defines fog computing for IoT-driven smart healthcare while comparing it with cloud computing. It also presents an architecture of fog-based healthcare system containing three layers: edge, fog, and cloud. Finally, this paper highlights opportunities and top challenges facing smart healthcare organizations in the coming years. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog computing

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