scholarly journals Predictive Modelling of Covid-19 Stimulus Funds Paid for Nursing Home Quality Incentive Program

2021 ◽  
Author(s):  
Omar Al-Azzam ◽  
Paul Court
Keyword(s):  
Data Mining ◽  
Nursing Home ◽  
Health Resources ◽  
Predictive Modelling ◽  
Government Agencies ◽  
Incentive Program ◽  
Nursing Home Quality ◽  
Quality Incentive ◽  
Using Data ◽  
Nursing Home Facilities

Painstaking measures should be taken to determine how federal dollars are spent. Proper justification for allocation of funds rooted in logic and fairness leads to trust and transparency. The COVID-19 pandemic has warranted rapid response by government agencies to provide vital aide to those in need. Decisions made should be evaluated in hindsight to see if they indeed achieve their objectives. In this paper, the data collected in the final four months of 2020 to determine funding for nursing home facilities via the Quality Incentive Program will be analysed using data mining techniques. The objective is to determine the relationships among numeric variables and formulae given. The dataset was assembled by the Health Resources and Services Administration. Results are given for the reader’s insight and interpretation. With the data collection and analytical process, new questions come to light. These questions should be pondered for further analysis.

scholarly journals Predictive Modelling for Quality Prediction and Assurance of Extrusion Blow Molding

2019 ◽  
Vol 8 (11) ◽  
pp. 1364-1368
Keyword(s):  
Data Mining ◽  
Predictive Modelling ◽  
Molding Process ◽  
K Nearest Neighbors ◽  
Blow Molding ◽  
Data Mining Approach ◽  
Complex Relationships ◽  
Using Data ◽  
Extrusion Blow Molding

Extrusion Blow Molding process plays an important role in manufacturing of hollow products with wide variety of materials like polyethylene (PE), polypropylene (PP), polyvinylchloride (PVC). Extrusion blow molded products are rejected due to the occurrence of defects such as die lines, blowouts, shrinkage, over weight of part. The complex relationships that exist between the process variables, and causes of defects are investigated for 1 litre container made of highdensity polyethylene (HDPE) using data mining techniques in order to reduce scrap. In this paper Data Mining approach is implemented by applying Decision Tree, k-Nearest Neighbors, Rule Induction and Vote techniques in RapidMiner for quality assurance and prediction of the quality of the extrusion blow molded product

Mining Complex Spatial Patterns: Issues and Techniques

2014 ◽  
Vol 13 (02) ◽  
pp. 1450019 ◽  
Author(s):  
Grace L. Samson ◽  
Joan Lu ◽  
Aminat A. Showole
Keyword(s):  
Data Mining ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Predictive Modelling ◽  
Spatial Data Mining ◽  
Data Types ◽  
Data Set ◽  
Spatial Systems ◽  
Spatial Attributes ◽  
Using Data

Spatial data mining is the quantitative study of phenomena that are located in space. This paper investigates methods of mining patterns of a complex spatial data set (which generally describes any kind of data where the location in space of object holds importance). We based this research on the analysis of some spatial characteristics of certain objects. We began with describing the spatial pattern of events or objects with respect to their attributes; we looked at how to describe the spatial nature/characteristics of entities in an environment with respect to their spatial and non-spatial attributes. We also looked at modelling (predictive modelling/knowledge management of complex spatial systems), querying and implementing a complex spatial database (using data structure and algorithms). Critically speaking, the presence of spatial auto-correlation and the fact that continuous data types are always present in spatial data makes it important to create methods, tools and algorithms to mine spatial patterns in a complex spatial data set. This work is particularly useful to researchers in the field of data mining as it contributes a whole lot of knowledge to different application areas of data mining especially spatial data mining. It can also be useful in teaching and likewise for other study purposes.

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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