scholarly journals Effect of Non-Academic Parameters on Student’s Performance

2021 ◽  
Author(s):  
Shantanu Lokhande ◽  
Vedant Bahel
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Data Analysis ◽  
Knowledge Discovery In Databases ◽  
Academic Factors ◽  
Student’S Performance ◽  
Overall Performance ◽  
Key Features ◽  
Detailed Application ◽  
Better Than

With the exponential growth in today’s technology and its expanding areas of application it has become vital to incorporate it in education. One such application is Knowledge Discovery in Databases (KDD) which is a subset of data mining. KDD deals with extracting useful information and meaningful patterns from the database that were not known before. This study is a detailed application of KDD and focuses on analyzing why a particular set of students performed better than others and what factors influenced the results. The study is conducted on a dataset of 480 students and across 16 different features. The authors implemented 4 major classification techniques namely Logistic Regression, Decision Tree, Random Forest and XGB classifier. Obtaining the key features from the top performing ML algorithms that have a major impact on the performance of the student, the study takes these features as a baseline for further analysis. Further data analysis highlights patterns in the data. The study concludes that there are a lot of non-academic factors that influence the overall performance of a student and should be taken into consideration by universities and other relevant bodies.

Translating Advances in Data Mining in Business Operations

2008 ◽  
pp. 2734-2748
Author(s):  
Henry Dillon ◽  
Beverley Hope
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Analysis ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Business Context ◽  
Business Operations ◽  
Analysis Tools ◽  
It Managers ◽  
Tools And Techniques

Knowledge discovery in databases (KDD) is a field of research that studies the development and use of various data analysis tools and techniques. KDD research has produced an array of models, theories, functions and methodologies for producing knowledge from data. However, despite these advances, nearly two thirds of information technology (IT) managers say that data mining products are too difficult to use in a business context. This chapter discusses how advances in data mining translate into the business context. It highlights the art of business implementation rather than the science of KDD.

Landslide Susceptibility Mapping and Model Performance Assessment

2013 ◽  
Vol 353-356 ◽  
pp. 3487-3493 ◽  
Author(s):  
Chen Chao Xiao ◽  
Yuan Tian ◽  
Kang Ping Si ◽  
Ting Li
Keyword(s):  
Logistic Regression ◽  
Performance Assessment ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Model Performance ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Maps ◽  
Overall Performance ◽  
Better Than

In this paper landslide susceptibility mapping and model performance assessment was conducted using three models, logistic regression, GAM, and SVM, in a study area in Shenzhen, China. Ten factors, slope angle, aspect, elevation, plan and profile curvature of the slope, lithology, NDVI, building density, the distance to the river, and the distance to the fault were selected as influencing factors for the landslide occurrences. All three models were trained and the resulting susceptibility maps were created. The performances of the three models were then assessed by AUC values through a 10-fold cross-validation. It could be concluded that in the study area GAM had the best overall performance among the three models, while SVM was better than logistic regression. Based on the derived DPR values, the optimum thresholds between stable areas and risky areas for all three models were also determined.

scholarly journals EDM – survey of performance factors and algorithms applied

2018 ◽  
Vol 7 (2.6) ◽  
pp. 93 ◽  
Author(s):  
Deepali R Vora ◽  
Kamatchi Iyer
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Educational Data Mining ◽  
Knowledge Discovery In Databases ◽  
Useful Knowledge ◽  
Performance Factors ◽  
Student’S Performance

Educational Data Mining (EDM) is a new field of research in the data mining and Knowledge Discovery in Databases (KDD) field. It mainly focuses in mining useful patterns and discovering useful knowledge from the educational information systems from schools, to colleges and universities. Analysing students’ data and information to perform various tasks like classification of students, or to create decision trees or association rules, so as to make better decisions or to enhance student’s performance is an interesting field of research. The paper presents a survey of various tasks performed in EDM and algorithms (methods) used for the same. The paper identifies the lacuna and challenges in Algorithms applied, Performance Factors considered and data used in EDM.

Translating Advances in Data Mining in Business Operations

2011 ◽  
pp. 263-279
Author(s):  
Henry Dillon ◽  
Beverley Hope
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Analysis ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Business Context ◽  
Business Operations ◽  
Analysis Tools ◽  
It Managers ◽  
Tools And Techniques

Knowledge discovery in databases (KDD) is a field of research that studies the development and use of various data analysis tools and techniques. KDD research has produced an array of models, theories, functions and methodologies for producing knowledge from data. However, despite these advances, nearly two thirds of information technology (IT) managers say that data mining products are too difficult to use in a business context. This chapter discusses how advances in data mining translate into the business context. It highlights the art of business implementation rather than the science of KDD.

scholarly journals An Analysis on Ensemble Classifiers in Ensemble Classification Problems

2019 ◽  
Vol 8 (9S3) ◽  
pp. 382-384
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Ensemble Classifier ◽  
Ensemble Classification ◽  
Ensemble Classifiers ◽  
Classification Problems ◽  
Huge Amount ◽  
Combined Classifier ◽  
Improved Accuracy ◽  
Better Than

Day to Day the amount of data was increasing rapidly. Due to analyzing the huge amount of data various technologies are also introduced. Traditional data mining approaches can be used to perform data analysis through classification algorithms. In data mining a single classifier can be used to perform data analysis. Sometimes, multiple or combined classifier can also be used to perform data analysis. But, the performance of ensemble classifier is better than single classifier. Based on improved accuracy the various number of ensemble classifiers are introduced. Now, this paper can reviews various ensemble classifiers based on their accuracy.

Motilitas dan Vabilitas Spermatozoa Ayam Kampung pada Suhu 5°C Menggunakan Pengencer dan Lama Simpan yang Berbeda

2018 ◽  
Vol 5 (2) ◽  
pp. 102
Author(s):  
Enike Dwi Kusumawati ◽  
Selvinus Lawu Woli ◽  
Aju Tjatur Nugroho Krisnaningsih ◽  
Waluyo Edi Susanto ◽  
Syam Rahadi
Keyword(s):  
Data Analysis ◽  
Storage Temperature ◽  
Coconut Water ◽  
Laboratory Research ◽  
Ringer Lactate ◽  
Randomized Design ◽  
Native Chicken ◽  
Lactate Solution ◽  
Better Than

ABSTRAKPenelitian ini dilakukan untuk mengetahui motilitas dan viabilitas spermatozoa ayam kampung pada suhu 5oC menggunakan pengencer dan lama simpan yang berbeda. Metode yang digunakan dalam penelitian ini adalah penelitian laboratorium menggunakan Rancangan Acak Lengkap (RAL) Faktorial dengan pengencer ringer lactat solution, air kelapa dan tanpa pengencer serta lama simpan 0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, masing-masing diulang 10 kali. Variabel yang diamati yaitu motilitas dan viabilitas spermatozoa. Analisa data yang digunakan adalah analisis varian. Hasil analisis data menunjukkan bahwa motilitas dan viabilitas spermatozoa menggunakan pengencer ringer lactat solution lebih tinggi (P<0,01) serta dapat bertahan sampai lama simpan 24 jam dibandingkan air kelapa dan tanpa pengencer. Adapun nilai motilitas ringer lactat solution, air kelapa dan tanpa pengencer pada lama simpan 24 jam masing-masing sebesar 43,5±17,17%; 8±4,83%; 6,5±2,4%, sedangkan nilai viabilitasnya sebesar 83,2±7,25%; 64,6±3,20%; dan 63,1±2,33%. Kesimpulan dari hasil penelitian ini adalah ringer lactat solution lebih baik dibandingkan air kelapa dan tanpa pengencer dalam mempertahankan kualitas semen ayam kampung pada suhu simpan 5oC sampai lama simpan 24 jam.Kata Kunci : air kelapa, ayam kampung, motilitas, spermatozoa, viabilitas  ABSTRACTThis study was conducted to determine the motility and viability of spermatozoa of Native chickens at 5oC using different diluents and time storage. The method used in this study was laboratory research using Factorial Completely Randomized Design with ringer lactate solution, coconut water and without diluent at 0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30 hours of time storage each repeated 10 times. The variables observed were motility and viability of sperm. Data analysis used is variance analysis. The results of data analysis showed that the motility and viability of spermatozoa using ringer lactate solution diluents was higher (P <0.05) than coconut water and without diluents. The motility values of ringer lactat solution, coconut water and without diluents were 43,5±17,17%; 8±4,83%; 6,5±2,4% respectively, while the viability values were 83,2±7,25%; 64,6±3,20% and 63,1±2,33%. The conclusion of this study is that ringer lactat solution is better than coconut water an without diluents in maintaining the quality of Native chicken semen at a storage temperature of 5oC until 24 hours.Keywords: coconut water, motility, native chicken, sperm, viability

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

scholarly journals Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 687
Author(s):  
Salman Sakib ◽  
Dawit Ghebreyesus ◽  
Hatim O. Sharif
Keyword(s):  
Stage Iv ◽  
Tropical Storm ◽  
Average Correlation ◽  
Acceptable Range ◽  
Precipitation Estimates ◽  
Overall Performance ◽  
Early Late ◽  
Statistical Metrics ◽  
Better Than

Tropical Storm Imelda struck the southeast coastal regions of Texas from 17–19 September, 2019, and delivered precipitation above 500 mm over about 6000 km2. The performance of the three IMERG (Early-, Late-, and Final-run) GPM satellite-based precipitation products was evaluated against Stage-IV radar precipitation estimates. Basic and probabilistic statistical metrics, such as CC, RSME, RBIAS, POD, FAR, CSI, and PSS were employed to assess the performance of the IMERG products. The products captured the event adequately, with a fairly high POD value of 0.9. The best product (Early-run) showed an average correlation coefficient of 0.60. The algorithm used to produce the Final-run improved the quality of the data by removing systematic errors that occurred in the near-real-time products. Less than 5 mm RMSE error was experienced in over three-quarters (ranging from 73% to 76%) of the area by all three IMERG products in estimating the Tropical Storm Imelda. The Early-run product showed a much better RBIAS relatively to the Final-run product. The overall performance was poor, as areas with an acceptable range of RBIAS (i.e., between −10% and 10%) in all the three IMERG products were only 16% to 17% of the total area. Overall, the Early-run product was found to be better than Late- and Final-run.

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

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