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Triboinformatic Approaches for Composite Coatings on Titanium Alloys 1

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11th international conference on Industrial Tribology, IndiaTrib-2022

作者： Naveen Kumar, K. Barman, Utpal Masset, Patrick J. Rao, T.V.V.L.N. Department of Metallurgical and Materials Engineering Indian Institute of Technology Madras Chennai600036 India Faculty of Computer Technology Assam down town University Gandhi Nagar SankarMadhab Path Assam Panikhaiti Guwahati781026 India Lukasiewicz Research Network Krakow Institute of Technology ul. Zakopiańska 73 Kraków30-418 Poland Faculty of Engineering Assam down town University Gandhi Nagar SankarMadhab Path Assam Panikhaiti Guwahati781026 India

ISBN: (数字)9789819992645

ISBN: (纸本)9789819992638

Ti-6Al-4 V is an (α + β) titanium alloy that has been most widely used in automotive, aerospace, and biomedical applications due to the extensive material properties of high strength, toughness, high strength-to-weight ratio, and biocompatibility. Machine learning (ML) algorithms of data-driven methods provide a better understanding of the correlation between material properties and tribological properties. Correlations of tribological test variables (sliding speed, sliding distance, and normal load) with the tribological properties (coefficient of friction and wear rate) were studied using machine learning algorithms. A total of 41 data points based on the Ti-6Al-4 V alloy coating were divided into training and testing sets in the ratio of 80:20. the ML-based algorithms, which include Decision Tree (DT) and Random Forest (RF) algorithms, have been studied to predict the wear rates and coefficient of friction. the evaluation metrics like MAE, MSE, and RMSE are used to find the best suitable algorithm for the predictions. Using the data analysis, the coefficient of friction and wear rates have been satisfactorily predicted from the considered data sets. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tribology

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the Hidden Troubles and Countermeasures of Perfect Education System for College Students Based on Big data Analysis 11th

The Hidden Troubles and Countermeasures of Perfect Education...

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11th international conference on Frontier Computing, FC 2021

作者： Zhang, Yan Bai, Xinwen Department of Psychology Chinese Academy of Science University Beijing100049 China

ISBN: (纸本)9789811680519

Perfectionist education refers to the excessive pursuit of the best and perfection by parents and teachers in their children’s growth education. Although the original intention of this idea is good, the pursuit of perfection is often counterproductive. On this basis, this article discusses and analyzes the hidden dangers of perfectionism education in the context of big data. In order to further analyze the impact of the perfectionist education concept on students, this article visited some schools in our city. through the survey, we learned about the basic situation of the students affected by the concept of perfectionism education, and tested and evaluated them. the results showed that the score of emotion test of students who were not affected by the concept of perfectionism education was 19.37, the score of interpersonal relationship was 8.53, the score of learning objective was 15.76, and the score of adaptability was 21.56. the scores of students affected by perfectionist education concepts were 15.42, 5.27, 11.32, and 11.32, respectively. It can be seen that the ideal of perfectionism education will have a certain negative impact on students’ psychology. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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learning heterogeneus cooperative linguistic fuzzy rules using local search: Enhancing the COR search space

Learning heterogeneus cooperative linguistic fuzzy rules usi...

引用

international conference on intelligent Systems Design and Applications (ISDA)

作者： Javier Cózar Luis delaOssa Jose A. Gámez Department of Computing Systems Intelligent Systems and Data Mining Laboratory Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain

the COR methodology allows the learning of Linguistic Fuzzy Rule-Based Systems by considering cooperation among rules. In order to do that, COR firstly finds the set of candidate fuzzy rules that can be fired by the examples in the training set, and then uses a search algorithm to find the final set of rules. In the algorithms proposed so far, all candidate rules have the same number of antecedents, which is the number of input variables. However, these rules could be too specific, and rules more generic are not considered. In this paper we study the effect of considering all possible rules, regardless of their number of antecedents. Experiments show that the rule bases obtained use simpler rules, and the results for the error of prediction improve upon those obtained by using classical COR methods.

关键词： Pragmatics Training intelligent systems Cities and towns Input variables Genetic algorithms Cybernetics

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User Behavior Analytics for Anomaly Detection Using LSTM Autoencoder-Insider threat Detection 20

User Behavior Analytics for Anomaly Detection Using LSTM Aut...

引用

11th international conference on Advances in Information Technology, IAIT 2020

作者： Sharma, Balaram Pokharel, Prabhat Joshi, Basanta LogPoint Lalitpur Nepal Institute of Engineering Lalitpur Nepal

ISBN: (纸本)9781450377591

Identifying anomalies from log data for insider threat detection is practically a very challenging task for security analysts. User behavior modeling is very important for the identification of these anomalies. this paper presents unsupervised user behavior modeling for anomaly detection. the proposed approach uses LSTM based Autoencoder to model user behavior based on session activities and thus identify the anomalous data points. the proposed method follows a two-step process. First, it calculates the reconstruction error using the autoencoder on the non-anomalous dataset, and then it is used to define the threshold to separate the outliers from the normal data points. the identified outliers are then classified as anomalies. the CERT insider threat dataset has been used for the research work. For each user, the feature vectors are prepared by extracting key information from corresponding raw events and aggregating the data points based on users' actions within respective users' sessions. LSTM Autoencoder has been implemented for behavior learning and anomaly detection. For any unseen behavior or anomaly pattern, the model produces high reconstruction error which is an indication of an anomaly. the experimental results show that in the best case, the model produced an Accuracy of 90.17%, True Positives 91.03%, and False Positives 9.84%. thus, the results suggest that the proposed approach can be effectively used in automatic anomaly detection. © 2020 ACM.

关键词： Anomaly detection

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Hiding of Personal Information Areas through a Dynamic Selection Strategy 1

引用

11th international conference on Frontier Computing, FC 2021

作者： Lee, Sang-Hong Jang, Seok-Woo Anyang University Anyang14028 Korea Republic of

ISBN: (数字)9789811680526

ISBN: (纸本)9789811680519

Video content with exposed personal information is freely distributed to the general public through the Internet without user approval, which is a problem. In this paper, we propose a method of detecting areas representing personal information from continuous color images through an artificial neural network and hiding the detected target area appropriately for the surrounding environment. the proposed method first applies a color model and deep learning to robustly detect a target area representing personal information exposed from an image. the detected area is then tracked quickly based on position prediction and simultaneously blocked. In this paper, the hiding of the target object is performed by adaptively selecting one of the mosaic processing, image blurring, and virtual object insertion techniques in consideration of surrounding conditions. Experimental results show that the proposed method accurately extracts the personal information area, efficiently tracks the extracted area, and dynamically selects the hiding technique. the method proposed in this paper is expected to be useful in related fields such as object detection, big data analysis, and biometric recognition. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Winter wheat identification and area extraction based on hyperspectral/multispectral image fusion 11

Winter wheat identification and area extraction based on hyp...

引用

11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Lu, Qiu Ye Li, Yan Ling Yang, Yun Tao Liu, Fa Jun Shandong Agricultural University College of Information Science and Engineering Tai'an China The Fifth Geological Brigade of Shandong Provincial Bureau of Geology&Mineral Resources Tai'an China

ISBN: (纸本)9798350303513

Food is God for the people. Grain production has a bearing on the national economy and people's livelihood. this study takes the Daiyue District of Tai 'an City in Shandong Province as the research area. this paper uses multi-source remote sensing images to fuse Zhuhai-1 hyperspectral images ('OHS hyperspectral images') and GF-1 high-resolution images using a hyperspherical color spatial resolution merging algorithm. the feasibility of multi-source optical image fusion in winter wheat identification is explored. Meanwhile, this paper compares OHS hyperspectral image with the Landsat8 OLI image to explore the differences between hyperspectral and multispectral in extracting winter wheat areas. Firstly, this study combines land use data to select the optimal time phase and the best wave combination to extract the range of cultivated land in the study area. Secondly, the study extracts image features (texture features, spectral features, and index features). It builds Machine learning models (Random Forest classification, Support Vector Machine classification, and Maximum Likelihood classification) to decode and identify winter wheat and calculates its planted area using the Dimidiate Pixel Model. Finally, the extraction results of fused images are compared and analyzed with those of OHS and OLI images. the results show that the overall classification accuracy of the fusion images is the highest, and the accuracy of the OHS images is higher than that of the OLI images. the Random Forest classification accuracy of HCS fused images is as high as 98.15%, and the Maximum Likelihood classification is 96.13%. the Random Forest classification accuracy of OHS images is 94.62%, the Support Vector Machine accuracy is 93.87%, and the Maximum Likelihood classification is 91.49%. Combining the statistical data and calculating the relative error, it can be seen that the Random Forest classification of the fused images has the lowest relative error of 2.20%, and the Maximum Likelihoo

关键词： Extraction

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Dynamic construction of category hierarchy using fuzzy relational products

Dynamic construction of category hierarchy using fuzzy relat...

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4th international conference on intelligent data engineering and automated learning, IDEAL 2003

作者： Choi, Bumghi Lee, Ju-Hong Park, Sun Quark Co. Ltd Seoul Korea Republic of School of Computer Science and Engineering Inha University Incheon Korea Republic of

ISBN: (纸本)354040550X

Overwhelming search results often daunt web surfers on the web search engine. there have been many systems to try to solve this problem by constructing more specific search methods. Auto categorization and clustering have been presented. However, an efficient way of constructing the hierarchy of generated or pre-existing categories has not been suggested. We provide a dynamic category hierarchy structuring algorithm to reinforce the categorization and the clustering with using the fuzzy relational products. In this paper, we also propose a novel search method using this algorithm to complement the conventional directory search or category browsing and enhance the efficiency of search. Results from our evaluation show that our method helps users find categories more quickly and easily than conventional directory searching methods. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Search engines

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Machine learning Methods in Energy Consumption Optimization Assessment in Food Processing Industry

Machine Learning Methods in Energy Consumption Optimization ...

引用

IEEE international Workshop on intelligent data Acquisition and Advanced Computing Systems: Technology and Applications

作者： Piotr Milczarski Zofia Stawska Artur Hłobaż Bartosz Zieliński Paweł Maślanka Piotr Kosiński Faculty of Physics and Applied Informatics University of Lodz Lodz Poland

ISBN: (纸本)9781665426060

In the paper, the study of the carbon footprint optimization process is shown in order to receive low-carbon products. A short description of the Carbon Footprint assessment standards is provided. Basing on the conducting project CFOOD subsided by Polish R&D Agency the optimization boundaries are discussed and presented. In the paper, the methods of carbon footprint are discussed. the life cycle assessment (LCA) model for a carbon footprint assessment is presented and discussed. In the next step, the clusterization methods are used to the frozen vegetables production line in order to derive the models to assess the carbon footprint. three results of the clusterization by three chosen methods are assessed by five classification methods.

关键词： Energy consumption Clustering methods Production Machine learning Manufacturing Carbon footprint Optimization

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Edge-AI Addresses the Big data Challenges of IoT in the Cloud Edge 1

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8th international conference on Innovative Technologies in intelligent Systems and Industrial Applications, CITISIA 2023

作者： Jenkins, Christopher Linton Prasad, P.W.C. Charles Sturt University BathurstNSW2795 Australia Australian Institute of Higher Education Sydney Australia

ISBN: (数字)9783031717734

ISBN: (纸本)9783031717727

Academic research is appearing in a new disciplinary field through the amalgamation of Cloud Edge and Artificial Intelligence named Edge-AI. this amalgamation of technologies is promising to accelerate insights to end-user devices and remove the reliance on the Cloud for central data collection and analysis. IoT devices and their data will exponentially grow to 75 billion devices by 2025, which is 2.5 times the amount of IoT data processed in 2020. With this expected growth, the ability to transmit and process IoT sensor data in a time-sensitive and meaningful manner will challenge the current central Cloud architecture. this research aims to provide an overview of how Edge-AI deployed in the Cloud Edge will elevate IoT life-critical systems and improve privacy and security in analytics data. this research uses the Systematic Literature Review methodology to discuss collaborative learning frameworks, Machine learning distribution techniques, data consensus models and security and privacy in the context of Edge-AI. A keyword search was conducted, and the material was selected for review. the research concludes that Edge-AI is a promising architectural concept encompassing many subcomponents. Edge-AI will be required as IoT devices increase in numbers to support the efficient use of life-critical systems like autonomous vehicles and health monitors. there is significant fragmentation in research into the major subcomponents of Edge-AI, which hamper the ability to adopt widely. the Efficient Edge-AI Framework proposed in this research aims to progress this field by presenting a holistic framework encompassing the gaps in previous research. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Collaborative learning

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Nested state-transition graph model of user behaviors

Nested state-transition graph model of user behaviors

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4th international conference on intelligent data engineering and automated learning, IDEAL 2003

作者： Heh, Jia-Sheng Cheng, Shein-Yun Ma, Nan-Chao Department of Information and Computer Engineering Chung Yuan Christian University Chung Li Taiwan

ISBN: (纸本)354040550X

this paper proposes a systematic data mining approach to study users’ Internet resource access actions for finding out behavior models as statetransition graphs. A series of Internet resource access actions are stored in a database of [user, resource-access-action, time] records. Such access actions are treated as basic behavior elements and form an action hierarchy which possesses different levels of radix codes. For every user, the data sequence is divided into a series of transactions and all the actions in a transaction constitute a special behavior pattern, called (inter-transaction) behavior. the behavior codes can be aggregated as behavior hierarchy also. Accordingly, each user can possess his/her own behavior model, formulated as a state-transition graph with behavior states and transition probability between behaviors. the overall mining process is computerized and validated by experiment. the example uses simulated sequential data to show how to combine AprioriAll algorithm and the proposed algorithm to construct a set of nested state-transition graphs. © Springer-Verlag Berlin Heidelberg 2003.

关键词： data mining

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