scholarly journals Analysis of Behavioral Characteristics of Smartphone Addiction Using Data Mining

2018 ◽  
Vol 8 (7) ◽  
pp. 1191 ◽  
Author(s):  
MyungSuk Lee ◽  
MuMoungCho Han ◽  
JuGeon Pak
Keyword(s):  
Data Mining ◽  
Emotional Regulation ◽  
Smartphone Addiction ◽  
Behavioral Characteristics ◽  
World Population ◽  
Self Reports ◽  
Usage Patterns ◽  
Input Variables ◽  
Using Data ◽  
Reporting Method

In 2016, the number of mobile phone subscriptions worldwide had surpassed the total world population; moreover, the number of smartphone addicts is increasing each year. Thus, the objective of this study is to analyze smartphone addiction by considering the differences between smartphone usage patterns as well as cognition. Our proposed method involves automatically collecting and analyzing data through an app instead of using the existing self-reporting method, thereby improving the accuracy of data and ensuring data reliability from respondents. Based on the results of our study, we observed that there is a significant cognitive bias between the self-reports and automatically collected data. As a result of applying data mining, among the six criteria out of the total 24 items of the questionnaire, the higher the “recurrence” item, the higher the addiction; further, “forbidden” item 1 had the largest effect on addiction. In addition, the input variables that have the greatest influence on the high-risk users were the number of times the screen was turned on and real-use time/cognitive-use time. However, the amount of data and time of smartphone usage were not related to addiction. In the future, we will modify the app to obtain more accurate data, based on which, we can analyze the effects of smartphone addiction, such as depression, anxiety, stress, self-esteem, and emotional regulation, among others.

Implementation of Mining Techniques to Enhance Discovery in Service-Oriented Computing

2014 ◽  
pp. 249-271
Author(s):  
Chellammal Surianarayanan ◽  
Gopinath Ganapathy
Keyword(s):  
Data Mining ◽  
Service Discovery ◽  
Service Usage ◽  
Data Repositories ◽  
Huge Data ◽  
Usage Patterns ◽  
Service Oriented ◽  
Mining Methods ◽  
Using Data ◽  
Semantic Service

Web services have become the de facto platform for developing enterprise applications using existing interoperable and reusable services that are accessible over networks. Development of any service-based application involves the process of discovering and combining one or more required services (i.e. service discovery) from the available services, which are quite large in number. With the availability of several services, manually discovering required services becomes impractical and time consuming. In applications having composition or dynamic needs, manual discovery even prohibits the usage of services itself. Therefore, effective techniques which extract relevant services from huge service repositories in relatively short intervals of time are crucial. Discovery of service usage patterns and associations/relationships among atomic services would facilitate efficient service composition. Further, with availability of several services, it is more likely to find many matched services for a given query, and hence, efficient methods are required to present the results in useful form to enable the client to choose the best one. Data mining provides well known exploratory techniques to extract relevant and useful information from huge data repositories. In this chapter, an overview of various issues of service discovery and composition and how they can be resolved using data mining methods are presented. Various research works that employ data mining methods for discovery and composition are reviewed and classified. A case study is presented that serves as a proof of concept for how data mining techniques can enhance semantic service discovery.

Predicting Students’ Achievement in a Hybrid Environment Through Self-Regulated Learning, Log Data, and Course Engagement: A Data Mining Approach

2021 ◽  
pp. 073563312110561
Author(s):  
Amira D. Ali ◽  
Wael K. Hanna
Keyword(s):  
Data Mining ◽  
Log Data ◽  
Communication Course ◽  
Face To Face ◽  
Self Regulated Learning ◽  
Self Reports ◽  
Regulated Learning ◽  
Course Engagement ◽  
Engagement Factors ◽  
Using Data

With the spread of the Covid-19 pandemic, many universities adopted a hybrid learning model as a substitute for a traditional one. Predicting students’ performance in hybrid environments is a complex task because it depends on extracting and analyzing different types of data: log data, self-reports, and face-to-face interactions. Students must develop Self-Regulated Learning (SRL) strategies to monitor their learning in hybrid contexts. This study aimed to predict the achievement of 82 undergraduates enrolled in a hybrid English for Business Communication course using data mining techniques. While clustering techniques were used to understand SRL patterns through classifying students with similar SRL data into clusters, classification algorithms were utilized to predict students' achievement by integrating the log files and course engagement factors. Clustering results showed that the group with high SRL achieved higher grades than the groups with medium SRL and low SRL. Classification results revealed that log data and engagement activities successfully predicted students’ academic performance with more than 88% accuracy. Therefore, this study contributes to the literature of SRL and hybrid classrooms by interpreting the predictive power of log data, self-reports, and face-to-face engagement to predict students’ achievement, a relatively unexplored area. This study recommended practical implications to promote students’ SRL and achievement in hybrid environments.

Implementation of Mining Techniques to Enhance Discovery in Service-Oriented Computing

2016 ◽  
pp. 338-358
Author(s):  
Chellammal Surianarayanan ◽  
Gopinath Ganapathy
Keyword(s):  
Data Mining ◽  
Service Discovery ◽  
Service Usage ◽  
Data Repositories ◽  
Huge Data ◽  
Usage Patterns ◽  
Service Oriented ◽  
Mining Methods ◽  
Using Data ◽  
Semantic Service

Web services have become the de facto platform for developing enterprise applications using existing interoperable and reusable services that are accessible over networks. Development of any service-based application involves the process of discovering and combining one or more required services (i.e. service discovery) from the available services, which are quite large in number. With the availability of several services, manually discovering required services becomes impractical and time consuming. In applications having composition or dynamic needs, manual discovery even prohibits the usage of services itself. Therefore, effective techniques which extract relevant services from huge service repositories in relatively short intervals of time are crucial. Discovery of service usage patterns and associations/relationships among atomic services would facilitate efficient service composition. Further, with availability of several services, it is more likely to find many matched services for a given query, and hence, efficient methods are required to present the results in useful form to enable the client to choose the best one. Data mining provides well known exploratory techniques to extract relevant and useful information from huge data repositories. In this chapter, an overview of various issues of service discovery and composition and how they can be resolved using data mining methods are presented. Various research works that employ data mining methods for discovery and composition are reviewed and classified. A case study is presented that serves as a proof of concept for how data mining techniques can enhance semantic service discovery.

scholarly journals Evaluating Evapotranspiration By Using Data Mining Instead of The Physical Based Model In Remote Sensing

2021 ◽  
Author(s):  
Lamya Neissi ◽  
Mona Golabi ◽  
Mohammad Albaji ◽  
Abd Ali Naseri
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Decision Tree ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Plant Production ◽  
Tree Model ◽  
Water Index ◽  
Input Variables ◽  
Using Data

Abstract Precise calculations for plant water requirements and evapotranspiration is very crucial in determining the volume of water consumption for plant production. In order to estimate evapotranspiration in the extended area, different remote sensing algorithms required many climatological variables. Climatological variable measurements will cover small limited areas which can cause an error in extended areas. By using data mining and remote sensing, the evapotranspiration process can be modeled. In this research, the physical-based SEBAL evapotranspiration algorithm was modeled by M5 decision tree equations in GIS. Input variables of the M5 decision tree consisted of albedo, emissivity, and Normalized Difference Water Index (NDWI) which are represented as absorbed light, transformed light, and plant moisture, respectively. After extracting the best equations in the M5 decision tree model for 8 April 2019, these equations were modeled in GIS by using python scripts for 8 April 2019 and 3 April 2020. The calculated correlation coefficient (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for 8 April 2019 were 0.92, 0.54, and 0.42 and for 3 April 2020 were 0.95, 0.31, and 0.23, respectively. Also, sensitivity and uncertainty analysis were considered for more model evaluation. Those analysis revealed that evapotranspiration is sensitive to albedo more than the two other model inputs and the estimated evapotranspiration obtained by data mining is in acceptable range of certainty.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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