Cytotoxicity Analysis of Nanoparticles by Association Rule Mining

2021 ◽  
Author(s):  
Gulsah Gul ◽  
Ramazan Yildirim ◽  
Nazar Ileri-Ercan
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Long Term Effects ◽  
Rule Mining ◽  
Efficient Delivery ◽  
Intracellular Targets

Understanding the toxicity behavior of NPs is of great importance to ensure efficient delivery to intracellular targets without causing cytotoxicity, to measure the long-term effects of nanoparticles (NPs), and to...

scholarly journals Detecting and Analyzing Fraudulent Patterns of Financial Statement for Open Innovation Using Discretization and Association Rule Mining

2021 ◽  
Vol 7 (2) ◽  
pp. 128
Author(s):  
Siriporn Sawangarreerak ◽  
Putthiporn Thanathamathee
Keyword(s):  
Association Rule ◽  
Open Innovation ◽  
Association Rule Mining ◽  
Financial Statements ◽  
Frequent Pattern ◽  
Financial Ratios ◽  
Rule Mining ◽  
Business Income ◽  
Fraudulent Financial Statements

Identifying fraudulent financial statements is important in open innovation to help users analyze financial statements and make investment decisions. It also helps users be aware of the occurrence of fraud in financial statements by considering the associated pattern. This study aimed to find associated fraud patterns in financial ratios from financial statements on the Stock Exchange of Thailand using discretization of the financial ratios and frequent pattern growth (FP-Growth) association rule mining to find associated patterns. We found nine associated patterns in financial ratios related to fraudulent financial statements. This study is different from others that have analyzed the occurrence of fraud by using mathematics for each financial item. Moreover, this study discovered six financial items related to fraud: (1) gross profit, (2) primary business income, (3) ratio of primary business income to total assets, (4) ratio of capitals and reserves to total debt, (5) ratio of long-term debt to total capital and reserves, and (6) ratio of accounts receivable to primary business income. The three other financial items that were different from other studies to be focused on were (1) ratio of gross profit to primary business profit, (2) ratio of long-term debt to total assets, and (3) total assets.

scholarly journals Medication Use and the Risk of Newly Diagnosed Diabetes in Patients with Epilepsy

2020 ◽  
Vol 32 (2) ◽  
pp. 93-108 ◽  
Author(s):  
Sheng-Feng Sung ◽  
Pei-Ju Lee ◽  
Cheng-Yang Hsieh ◽  
Wan-Lun Zheng
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Clinical Problem ◽  
Future Research ◽  
Newly Diagnosed ◽  
Rule Mining ◽  
Diagnosed Diabetes ◽  
Patients With Epilepsy ◽  
Newly Diagnosed Diabetes

Epilepsy is a common neurological disorder that affects millions of people worldwide. Patients with epilepsy generally require long-term antiepileptic therapy and many of them receive polypharmacy. Certain medications, including older-generation antiepileptic drugs, have been known to predispose patients to developing diabetes. Although data mining techniques have become widely used in healthcare, they have seldom been applied in this clinical problem. Here, the authors used association rule mining to discover drugs or drug combinations that may be associated with newly diagnosed diabetes. Their findings indicate in addition to the most common culprits such as phenytoin and valproic acid, prescriptions containing carbamazepine, oxcarbazepine, or lamotrigine may be related to the development of newly diagnosed diabetes. These mined rules are useful as guidance to both clinical practice and future research.

scholarly journals Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data

2019 ◽  
Vol 11 (14) ◽  
pp. 3967 ◽  
Author(s):  
Tosporn Arreeras ◽  
Mikiharu Arimura ◽  
Takumi Asada ◽  
Saharat Arreeras
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Accurate Information ◽  
Rule Mining ◽  
Large Dataset ◽  
Natural Landscape ◽  
The Sustainable Development ◽  
Digital Footprint ◽  
Mobility Tracking

The rise of radiofrequency scanner technology has led to its potential application in the observation of people’s movements. This study used a Wi-Fi scanner device to track tourists’ traveling behavior in Hokkaido’s tourism area, which occupies a large region that features a unique natural landscape. Inbound tourists have significantly increased in recent years; thus, tourism’s sustainability is considered to be important for maintaining the tourism atmosphere in the long term. Using internet-enabled technology to conduct extensive area surveys can overcome the limitations imposed by conventional methods. This study aims to use digital footprint data to describe and understand traveler mobility in a large tourism area in Hokkaido. Association rule mining (ARM)—a machine learning methodology—was performed on a large dataset of transactions to identify the rules that link destinations visited by tourists. This process resulted in the discovery of traveling patterns that revealed the association rules between destinations, and the attractiveness of the destinations was scored on the basis of visiting frequency, with both inbound and outbound movements considered. A visualization method was used to illustrate the relationships between destinations and simplify the mathematical descriptions of traveler mobility in an attractive tourism area. Hence, mining the attractiveness of destinations in a large tourism area using an ARM method integrated with a Wi-Fi mobility tracking approach can provide accurate information that forms a basis for developing sustainable destination management and tourism policies.

Prediksi Code Defect Perangkat Lunak Dengan Metode Association Rule Mining dan Cumulative Support Thresholds

2015 ◽  
Vol 6 (2) ◽  
Author(s):  
Rizal Setya Perdana ◽  
Umi Laili Yuhana
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Program Code

Kualitas perangkat lunak merupakan salah satu penelitian pada bidangrekayasa perangkat lunak yang memiliki peranan yang cukup besar dalamterbangunnya sistem perangkat lunak yang berkualitas baik. Prediksi defectperangkat lunak yang disebabkan karena terdapat penyimpangan dari prosesspesifikasi atau sesuatu yang mungkin menyebabkan kegagalan dalam operasionaltelah lebih dari 30 tahun menjadi topik riset penelitian. Makalah ini akandifokuskan pada prediksi defect yang terjadi pada kode program (code defect).Metode penanganan permasalahan defect pada kode program akan memanfaatkanpola-pola kode perangkat lunak yang berpotensi menimbulkan defect pada data setNASA untuk memprediksi defect. Metode yang digunakan dalam pencarian polaadalah memanfaatkan Association Rule Mining dengan Cumulative SupportThresholds yang secara otomatis menghasilkan nilai support dan nilai confidencepaling optimal tanpa membutuhkan masukan dari pengguna. Hasil pengujian darihasil pemrediksian defect kode perangkat lunak secara otomatis memiliki nilaiakurasi 82,35%.

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