Sistema bioinformático ganadero para la toma de decisiones en procesos de transferencia de embriones [Cattle’s bioinformatics system for decision making in embryo transfer processes]

2014 ◽  
Author(s):  
Nestor Jaime Castaño Pérez ◽  
Félix Antonio Céspedes Giraldo ◽  
Octavio Isaza Londoño ◽  
Jhon Fredy Betancur Pérez
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Embryo Transfer ◽  
Sql Server ◽  
Process Data ◽  
Data Mining Techniques ◽  
Analysis And Design ◽  
Biological Variables ◽  
Toma De Decisiones ◽  
Using Data

Resumen La transferencia de embriones al útero de hembras receptoras bovinas, ha sido demostrada como una práctica que aumenta la incidencia de gestaciones. El estudio tuvo como objetivo el desarrollo de un sistema informático que permite la captura, análisis y gestión de la información de los procesos en la transferencia de embriones para la toma de decisiones. Se recolectaron, monitorearon y analizaron los datos del proyecto de ‘súper-ovulación y transferencia de embriones en bovinos’, en relación a todas las variables biológicas antes, durante y después de un tratamiento, en hembras donantes y receptoras. Se digitalizaron datos físicos y, en paralelo, se realizó un análisis y diseño para la construcción de un sistema informático para la captura datos en tiempo real; utilizando las tecnologías de programación web en móviles; HTML5, Jquery Mobile, en servidor, Microsoft .NET, de sincronización con sistemas heterogéneos, AJAX, y de almacenamiento en base datos local en móviles, SQLite y en servidor Microsoft SQL Server. Se utilizaron técnicas de minería de datos con la herramienta MATLAB® 7.10.0.499 (R2010a), y así se obtuvo un modelo matemático predictivo y fiable en la toma de decisiones con respecto a la obtención de mayores índices de preñez en bovinos. Palabras clave: Minería de datos, transferencia de embriones bovinos, biotecnología bovina, arquitectura de software distribuida en dispositivos móviles.   Abstract Embryo transfer to receptor female cattle has been proved as a practice that raises the incidence of gestations. This study has the objective of developing an informatics system that allows for the capture, analysis and management of information related to the process of embryo transfer that facilitates decision making in this specific process. Data related to the ‘Super-ovulation and embryo transfer in cattle’ was collected, monitored and analyzed, considering all biological variables ex-ante and ex-post a treatment in female donors and receptors. Physical data was digitized and in parallel a process of analysis and design was developed in order to build an information system that allows for real time data collection; using web programming technologies in mobile, HTML5, jQuery Mobile, server, Microsoft. NET, synchronization with heterogeneous systems, AJAX, and storage in mobile local database, SQLite and Microsoft SQL Server. Stored data was analyzed using data mining techniques with MATLAB® 7.10.0.499 (R2010a). By using data mining techniques on biological variables, it was possible to obtain a reliable predictive mathematical model in decision making related to the embryo transfer process that allows to raise the pregnancy success rate in cattle. Keywords: Data mining, cattle embryo transfer, cattle biotechnology, distributed software architecture for mobile devices.

Using Data Mining Techniques to Discover Patterns in an Airline's Flight Hours Assignments

2021 ◽  
pp. 107-126
Author(s):  
Francisco Javier Villar Martín ◽  
Jose Luis Castillo Sequera ◽  
Miguel Angel Navarro Huerga
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information System ◽  
Predictive Modeling ◽  
Data Model ◽  
Free Software ◽  
Data Mining Techniques ◽  
Visualization Tools ◽  
Using Data

The quality of a company's information system is essential and also its physical data model. In this article, the authors apply data mining techniques in order to generate knowledge from the information system's data model, and also to discover and understand hidden patterns within data that regulate the planning of flight hours of pilots and copilots in an airline. With this objective, they use Weka free software which offers a set of algorithms and visualization tools geared to data analysis and predictive modeling of information systems. Firstly, they apply clustering to study the information system and analyze data model; secondly, they apply association rules to discover connection patterns in data; and finally, they generate a decision tree to classify and extract more specific patterns. The authors suggest conclusions according these information system's data to improve future decision making in an airline's flight hours assignments.

scholarly journals Perbandingan Algoritma K-Means dan EM untuk Clusterisasi Nilai Mahasiswa Berdasarkan Asal Sekolah

2015 ◽  
Vol 1 (4) ◽  
pp. 316 ◽  
Author(s):  
Mardiani Mardiani
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Spatial Clustering ◽  
Sql Server ◽  
Cleaning Process ◽  
Tabular Form ◽  
Management Information ◽  
Process Data ◽  
Knowledge Analysis ◽  
Analysis Of Results

Dari beberapa fungsionalitas data mining, digunakan clustering untuk mengelompokkan mahasiswa berdasarkan nilai. Cluster dilakukan dengan menggunakan algoritma yang sudah ada yaitu K-Means dan EM (Expectation Maximation). Setelah sebelumnya melakukan proses pembersihan data dengan menggunakan aplikasi SQL Server 2008, kemudian data dalam bentuk tabel diolah dengan aplikasi WEKA (Waikato Environment for Knowledge Analysis) untuk mendapatkan hasilnya. Hasil dari penelitian berupa clustering informasi sekolah mana yang berpotensi menghasilkan lulusan dengan nilai yang baik. Pengelompokan terdiri atas 3 cluster dengan kategori nilai tinggi, sedang dan rendah. Pengelompokan tersebut juga berdasarkan lokasi yang disebut sebagai spatial clustering. Kemudian dilakukan analisis hasil setelah mendapatkan data yang sudah terkelompok. Informasi yang didapat selanjutnya dapat dimanfaatkan untuk pengambilan keputusan di bidang pendidikan bagi mahasiswa dan manajemen STMIK MDP. Bagi pihak manajemen STMIK MDP informasi berguna untuk mengetahui sekolah mana yang memberikan kontribusi mahasiswa dengan nilai tertinggi.From some of the functionality of data mining, clustering is used to group students based on the value. Clusters is done by using existing algorithms namely K-Means and EM (Expectation Maximation). Having previously done the cleaning process data using SQL Server 2008 applications, then the data in tabular form is processed by the WEKA (Waikato Environment for Knowledge Analysis) to get the result. Results from the study of clustering information which school has the potential to produce graduates with good grades. The grouping consists of three clusters with the category of high value, medium and low. Grouping is also referred to as a location based spatial clustering. Then performed the analysis of results after getting the data is already grouped. The information obtained can then be utilized for decision making in the field of education for students and management STMIK MDP. For the STMIK MDP management information useful to know which schools contribute to student with the highest score.

Structured Scheme of Data Mining Based on SQL Server 2005

2011 ◽  
Vol 179-180 ◽  
pp. 646-650
Author(s):  
Xiao Hong Han ◽  
Lei Wang ◽  
Pei Jun Zhang
Keyword(s):  
Data Mining ◽  
Sql Server ◽  
Data Mining Techniques ◽  
Application Program ◽  
Development Platform ◽  
Data Mining Algorithms ◽  
Sql Server 2005 ◽  
Using Data ◽  
Mining Model ◽  
Mining Algorithms

This paper highlights the data mining components of SQL Server 2005 and the building of data mining process, completes the creation, training, and the corresponding predictions of data mining model, implements the operation of data mining using data mining algorithms, so the application program, relationship database and data mining are seamless integrated. SQL Server 2005 provides data mining solution with a powerful design and development platform, without too much acquaintance with data mining techniques and data mining algorithms.

scholarly journals Heart Diseases Forecast using Data Mining Techniques and Tools

2019 ◽  
Vol 9 (2) ◽  
pp. 2726-2729
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Heart Diseases ◽  
Complex Data ◽  
Huge Amount ◽  
Data Mining Techniques ◽  
Statistical Knowledge ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Using Data

Data Mining have always been a field and combination of both computer science and statistical knowledge. From the beginning it is used to ascertain designs, patterns and arrangements which are formed in the information pool. The motive of the data mining development is to produce useful information from the pool of raw data and convert it into useful information which can be used for future arrangements. The tools which are used in data mining are helpful in predicting the future trends and predictions across the market, which also help in decision making and building the knowledge to make decisions. The “Healthcare Industry” is generally information rich. It has been collecting data to improve the continuing problems and help to identify the solutions for that problems. Data mining techniques can be used to predict heart conditions from the voluminous and complex data which are kept by the hospitals for decision making which are difficult to analyze by outmoded methods. Unfortunately, outmoded methods are less accurate in discovering hidden information from effective decision making. Data mining helps in altering the huge amount of data into knowledge driven which takes, as compared to others, less time and effort for the prediction and with greater accuracy. Our effort is to apply different data mining techniques that are used to solve the problem of biased forecasts and decision making and help in calculating the results with more accuracy.

scholarly journals Application and Analysis of Retail Inventory Using Data Mining Techniques

2020 ◽  
pp. 27-33
Author(s):  
MD Imtiaz Uddin Adnan ◽  
Redoyan Raz ◽  
Tanvir Ahmed ◽  
A. H. M Saiful Islam
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Inventory Management ◽  
21St Century ◽  
Research Paper ◽  
Retail Sales ◽  
Data Mining Techniques ◽  
Using Data ◽  
Almost All ◽  
Attention To Detail

Data mining is one of the most essential tools for gathering information from different datasets in almost all recent industries. In this 21st-century, data mining gained attention because of its significance in decision making, and it has become a key component in various industries such as retail. Inventory management requires pre-planned goals and attention to detail, and prioritizing items that require less attention can be a waste of time and resources. Learning indications about customers’ shopping patterns by showing associations among various provides significant value in managing retail inventory. In the present research paper, popular data mining techniques have been applied and analyzed for multi-item inventory management in retail sales stores to show how data mining techniques can optimize and organize the retail inventory.

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. 

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