scholarly journals Mobile Information System of English Teaching Ability Based on Big Data Fuzzy K-Means Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yanfei Miao
Keyword(s):  
Time Series ◽  
Information System ◽  
Big Data ◽  
Information Fusion ◽  
Large Scale ◽  
Clustering Algorithms ◽  
Utilization Rate ◽  
English Teaching ◽  
Language Interpretation ◽  
Mobile Information

The implication of mobile English teaching is that English teachers and students use mobile devices for English teaching and communication at the same time. In order to accurately evaluate language interpretation skills, it is necessary to construct a mobile information system sampling model of the restrictive factors of language interpretation skills. Then, the nonlinear information fusion method is combined with the time series cognition method to make a statistical cognition of language interpretation skills. The parameter of language interpretation skills constraint is a set of nonlinear time series. To this end, this paper studies the language interpretation skills mobile information system, proposes language interpretation skills, and constructs the constraint parameters of the language interpretation skills evaluation and cognition using an indicator cognition model. The quantitative recursive cognition method analyzes the language interpretation ability evaluation model and the entropy feature of language interpretation ability and extracts the constraint feature information. The combination of large-scale data information fusion and K-means clustering algorithms provides indexing and integration of index parameters for language interpreting skills. On this basis, the corresponding allocation scheme of teaching resources is formulated to realize the assessment of language interpretation skills. The experimental results of related big data clustering algorithms show that the English teaching method proposed in this paper is highly effective, and the evaluation accuracy and teaching resource utilization rate have been increased by 5% and 6%, respectively.

scholarly journals A Novel Approach for Clustering Big Data based on MapReduce

2018 ◽  
Vol 8 (3) ◽  
pp. 1711 ◽  
Author(s):  
Gourav Bathla ◽  
Himanshu Aggarwal ◽  
Rinkle Rani
Keyword(s):  
Big Data ◽  
Categorical Data ◽  
Large Scale ◽  
Clustering Algorithms ◽  
Numerical Data ◽  
Large Data ◽  
Data Sets ◽  
Single Node ◽  
Novel Approach ◽  
Network Analytics

Clustering is one of the most important applications of data mining. It has attracted attention of researchers in statistics and machine learning. It is used in many applications like information retrieval, image processing and social network analytics etc. It helps the user to understand the similarity and dissimilarity between objects. Cluster analysis makes the users understand complex and large data sets more clearly. There are different types of clustering algorithms analyzed by various researchers. Kmeans is the most popular partitioning based algorithm as it provides good results because of accurate calculation on numerical data. But Kmeans give good results for numerical data only. Big data is combination of numerical and categorical data. Kprototype algorithm is used to deal with numerical as well as categorical data. Kprototype combines the distance calculated from numeric and categorical data. With the growth of data due to social networking websites, business transactions, scientific calculation etc., there is vast collection of structured, semi-structured and unstructured data. So, there is need of optimization of Kprototype so that these varieties of data can be analyzed efficiently.In this work, Kprototype algorithm is implemented on MapReduce in this paper. Experiments have proved that Kprototype implemented on Mapreduce gives better performance gain on multiple nodes as compared to single node. CPU execution time and speedup are used as evaluation metrics for comparison.Intellegent splitter is proposed in this paper which splits mixed big data into numerical and categorical data. Comparison with traditional algorithms proves that proposed algorithm works better for large scale of data.

Towards benefitting from diverse inferred features and foreseen behaviours of hydroclimatic variables in predictive modelling contexts

2021 ◽  
Author(s):  
Georgia Papacharalampous ◽  
Hristos Tyralis
Keyword(s):  
Time Series ◽  
Big Data ◽  
Time Series Analysis ◽  
Large Scale ◽  
Predictive Modelling ◽  
Global Scale ◽  
Multiple Models ◽  
Forecast Combinations ◽  
Hydroclimatic Variability ◽  
Hydroclimatic Variables

<p>We discuss possible pathways towards reducing uncertainty in predictive modelling contexts in hydrology. Such pathways may require big datasets and multiple models, and may include (but are not limited to) large-scale benchmark experiments, forecast combinations, and predictive modelling frameworks with hydroclimatic time series analysis and clustering inputs. Emphasis is placed on the newest concepts and the most recent methodological advancements for benefitting from diverse inferred features and foreseen behaviours of hydroclimatic variables, derived by collectively exploiting diverse essentials of studying and modelling hydroclimatic variability and change (from both the descriptive and predictive perspectives). Our discussions are supported by big data (including global-scale) investigations, which are conducted for several hydroclimatic variables at several temporal scales.</p>

scholarly journals About Scaling of Controlling Information System of Industrial Complex by Streamlining of Big Data Arrays in Compliance with Hierarchy of the Present Lifeworlds

2019 ◽  
Vol 4 (5) ◽  
pp. 1127-1139 ◽  
Author(s):  
D. Reut ◽  
S. Falko ◽  
E. Postnikova
Keyword(s):  
Information System ◽  
Big Data ◽  
Large Scale ◽  
Cumulative Effect ◽  
Industrial Complex ◽  
Data Array ◽  
Large Scale System ◽  
Analysis And Design ◽  
Control Information ◽  
New Type

This article discusses the problem of scaling the control information system. Some new type of horizontal scaling of big data array is offered. It consists in structuring of this array in compliance with hierarchy of lifeworlds (Lebenswelt), which become distinguishable in the paradigm of large-scale system. Processes of the "lower" lifeworlds can be so slow that won't be caught by strategic analysis and design instruments of "top" lifeworlds. However, these processes can give some delayed cumulative effect called "black swan effect". The complex formation algorithms recreating the logic of the interconnection of industrial enterprise subsystems are presented. The bases of the choice of temporary horizon extension for large-scale system strategic planning are given.

scholarly journals Online and Offline Mixed Intelligent Teaching Assistant Mode of English Based on Mobile Information System

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Miao Yanfei
Keyword(s):  
Information System ◽  
Teaching Methods ◽  
Rapid Development ◽  
Teaching Assistant ◽  
Internet Technology ◽  
Teaching Resources ◽  
Good Opportunity ◽  
English Teaching ◽  
Teacher Guidance ◽  
Mobile Information

In order to actively explore the English teaching methods in the “Internet+” era, by exploring the teaching effects of online and offline mixed teaching English courses, it is found that the online and offline mixed teaching methods are very popular among students. Autonomous learning can enable students to acquire knowledge with greater flexibility and efficiency and make teacher guidance more targeted and interactive in offline classrooms, thereby improving teaching effects. In the new era, the rapid development and progress of Internet technology, information technology, and multimedia provide a good opportunity for China to continue to promote the reform of education and teaching. This article aims to study the online and offline mixed intelligent teaching assistant mode of English based on the mobile information system, expounds the overall design strategy of the online and offline mixed teaching mode, and proposes to strengthen the online and offline English mixed intelligent teaching assistant mode. And this article proposes an English hybrid intelligent teaching assistant model based on mobile information system development, which integrates massive English teaching resources. According to each student’s learning progress, tailor-made online learning courses and materials, combined with offline auxiliary teaching, achieve double education guarantee. The experimental results in this article show that if 30 people in the exam refer to the results, the teacher wants to analyze the results of these 10 questions. Please enter these 10 questions and corresponding rules of the user interface into the database, and use the system for reasoning and analysis, and the knowledge points involved in selecting 10 questions on the test paper are divided into 9 different grammatical points. When the entire class has conquered them and they are all deterministic answers, the weighted value is the current lowest value, which is 0. This shows that the system is very convenient.

A Utilization Method of Big Sensor Data to Detect Tool Anomaly in Machining Process

2016 ◽  
Vol 719 ◽  
pp. 122-126
Author(s):  
Yasuo Kondo ◽  
Sho Mizunoya ◽  
Satoshi Sakamoto ◽  
Kenji Yamaguchi ◽  
Tsuyoshi Fujita ◽  
...  
Keyword(s):  
Time Series ◽  
Big Data ◽  
Large Scale ◽  
Machining Process ◽  
Detection Sensitivity ◽  
Sensor Data ◽  
Cutting Power ◽  
Effective Power ◽  
Power Sensor ◽  
Graphic Pattern

The essential features and scale of sensor data was discussed to monitor the tool anomaly in the machining process from the pattern variation of large scale sensor data such as vibration and effective power. The cycle data, the time series sensor data collected with an acceleration or power sensor in one periodical machining of the given groove shape, had been measured periodically. In this study, the graphic pattern formed by overwriting the time series cycle data on a specific coordinate system was treated as the “big sensor data”. The big data from the effective power sensor can stably respond to the cutting power changes and showed a strong possibility as a detecting device for tool anomaly such as abrasive wear and chipping. While the big data from the acceleration sensor only responded to a big event like the chattering vibration. The number of cycle data needed to generate the big sensor data also affected on the detection sensitivity for tool anomaly. It had been required a family of time series sensor data enough to represent the cutting power change as a visual graphic pattern.

scholarly journals Using Big Data Fuzzy K-Means Clustering and Information Fusion Algorithm in English Teaching Ability Evaluation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chen Zhen
Keyword(s):  
Big Data ◽  
Information Fusion ◽  
Clustering Algorithm ◽  
Measurement Data ◽  
Weighted Average ◽  
Weighting Factor ◽  
Fusion Algorithm ◽  
English Teaching ◽  
Teaching Ability ◽  
Ability Evaluation

Aiming at the problem of inaccurate classification of big data information in traditional English teaching ability evaluation algorithms, an English teaching ability evaluation algorithm based on big data fuzzy K-means clustering and information fusion is proposed. Firstly, the author uses the idea of K-means clustering to analyze the collected original error data, such as teacher level, teaching facility investment, and policy relevance level, removes the data that the algorithm considers unreliable, uses the remaining valid data to calculate the weighting factor of the modified fuzzy logic algorithm, and evaluates the weighted average with the node measurement data and gets the final fusion value. Secondly, the author integrates the big data information fusion and K-means clustering algorithm, realizes the clustering and integration of the index parameters of English teaching ability, compiles the corresponding English teaching resource allocation plan, and realizes the evaluation of English teaching ability. Finally, the results show that using this method to evaluate English teaching ability has better information fusion analysis ability, which improves the accuracy of teaching ability evaluation and the efficiency of teaching resources application.

Survey on Partition based Clustering Algorithms in Big Data

2017 ◽  
Vol 5 (12) ◽  
pp. 323-325
Author(s):  
E. Mahima Jane ◽  
◽  
◽  
E. George Dharma Prakash Raj
Keyword(s):  
Big Data ◽  
Clustering Algorithms
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