scholarly journals A Wide Survey on Data Mining Approach for Crop Diseases Detection and Prevention

2021 ◽  
Author(s):  
Vikas N Nirgude ◽  
Sandeep Malik
Keyword(s):  
Data Mining ◽  
Manufacturing Sector ◽  
Fruit Production ◽  
Disease Classification ◽  
Growing Up ◽  
Data Mining Approach ◽  
Crop Disease ◽  
Complete Survey ◽  
Area Unit

India is agriculture land and major revenue manufacturing sector. However, because of amendment in temporal parameters and uncertainty in climate directly have an effect on quality and amount of the assembly and maintenance of crops. Also, quality even a lot of degrade once the crops area unit infected by any malady. The main focus of this analysis in agriculture is to increment the crop quality and potency at lower price and gain profit as result of in India the majority of the population depends on agriculture. Big selection of fruits is growing up in India such as apple, banana, guava, grape, mango, pomegranate, orange is the main one. Fruit production gives around 20% of the country’s development. However, because of absence of maintenance, inappropriate development of fruits and manual investigation there has been scale back in generate the standard of fruits.So, Data Mining Approach used in the agriculture domain to resolve several agricultural issues of classification or prediction. During this paper complete survey of several data mining approach for crop disease management has been done. Detection of disease in early state will improve in quality of crop still as decrease the production cost. Also, we can improve the production of the particular crop. Several major parameters are used for the crop disease classification or prediction.

Fuzzy Miner

2011 ◽  
pp. 299-321 ◽  
Author(s):  
Nikos Pelekis ◽  
Babis Theodoulidis ◽  
Ioannis Kopanakis ◽  
Yannis Theodoridis
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Selection ◽  
Internet Routing ◽  
Data Mining Approach ◽  
New Information

QOSP Quality of Service Open Shortest Path First based on QoS routing has been recognized as a missing piece in the evolution of QoS-based services in the Internet. Data mining has emerged as a tool for data analysis, discovery of new information, and autonomous decision-making. This paper focuses on routing algorithms and their appli-cations for computing QoS routes in OSPF protocol. The proposed approach is based on a data mining approach using rough set theory, for which the attribute-value system about links of networks is created from network topology. Rough set theory offers a knowledge discovery approach to extracting routing-decisions from attribute set. The extracted rules can then be used to select significant routing-attributes and make routing-selections in routers. A case study is conducted to demonstrate that rough set theory is effective in finding the most significant attribute set. It is shown that the algorithm based on data mining and rough set offers a promising approach to the attribute-selection prob-lem in internet routing.

ADAPTIVE DATA MINING APPROACH FOR FLOW REDIRECTION IN MULTIHOMED MOBILE ROUTER

2010 ◽  
Vol 09 (05) ◽  
pp. 737-758 ◽  
Author(s):  
MIHUI KIM ◽  
YUKYONG JUNG ◽  
KIJOON CHAE
Keyword(s):  
Data Mining ◽  
Decision Models ◽  
Data Mining Approach ◽  
Efficient Resource ◽  
Mobile Router ◽  
Network Status ◽  
Interface Type ◽  
Physical Signal ◽  
Selection Of

On multihomed mobile router with several interfaces toward Internet, it is important to provide an efficient flow redirection (FR) method that changes the served interface according to each network status or user movement. However, currently as the mobile devices roams, the interface is selected as only the physical signal strength, thus efficient resource use or quality of service (QoS) support is not guaranteed. In this paper, we propose an adaptive data mining approach that provides the selection of influential attributes for FR and the proper FR decision model as the interface type. We analyze assuming network protocols in order to heuristically extract the FR candidate attributes for estimating the required QoS. We abstract theoretically the FR influential attributes through decision-tree algorithm, and finally obtain the proper FR decision models per each interface. Our simulation results show that our FR approach provides improved performances in comparison with current handover.

scholarly journals Parameterization of LSB in Self-Recovery Speech Watermarking Framework in Big Data Mining

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Shuo Li ◽  
Zhanjie Song ◽  
Wenhuan Lu ◽  
Daniel Sun ◽  
Jianguo Wei
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Least Significant Bit ◽  
Speech Watermarking ◽  
Big Data Mining ◽  
Data Mining Approach ◽  
Watermark Embedding ◽  
Data Infrastructures ◽  
Original Speech

The privacy is a major concern in big data mining approach. In this paper, we propose a novel self-recovery speech watermarking framework with consideration of trustable communication in big data mining. In the framework, the watermark is the compressed version of the original speech. The watermark is embedded into the least significant bit (LSB) layers. At the receiver end, the watermark is used to detect the tampered area and recover the tampered speech. To fit the complexity of the scenes in big data infrastructures, the LSB is treated as a parameter. This work discusses the relationship between LSB and other parameters in terms of explicit mathematical formulations. Once the LSB layer has been chosen, the best choices of other parameters are then deduced using the exclusive method. Additionally, we observed that six LSB layers are the limit for watermark embedding when the total bit layers equaled sixteen. Experimental results indicated that when the LSB layers changed from six to three, the imperceptibility of watermark increased, while the quality of the recovered signal decreased accordingly. This result was a trade-off and different LSB layers should be chosen according to different application conditions in big data infrastructures.

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

scholarly journals Analisis Cluster Hasil Uji Kompetensi Lembaga Sertifikasi Profesi (LSP) Melalui Teknologi Data Mining

2021 ◽  
Vol 19 (2) ◽  
pp. 76-81
Author(s):  
Raditya Danar Dana ◽  
Ahmad Faqih
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Data Cleaning ◽  
Quality Of Education ◽  
Data Mining Approach ◽  
Collection Data ◽  
Competency Tests ◽  
Cluster 2 ◽  
Made In

The implementation of the Competency Test at the LSP institution in higher education is an effort to ensure that students have abilities in certain fields according to predetermined competency standards. Education providers are required to always strive to improve the quality and quality of education with the aim that the student's academic performance will always improve. From the results of observations made in the research location, it was found a problem with the high number of failures in the implementation of the competency test. This study aims to conduct cluster analysis of the data resulting from the implementation of competency tests with the Data Mining approach through several stages in the form of data collection, data cleaning, data transformation, data modeling and data evaluation. This study resulted in grouping the results of competency tests which were divided into 3 clusters, namely cluster 1 as much as 38%, cluster 2 as much as 32% and cluster 3 as much as 30%..

scholarly journals A Data Mining Approach for Cardiovascular Diagnosis

2017 ◽  
Vol 7 (1) ◽  
pp. 36-40 ◽  
Author(s):  
Joana Pereira ◽  
Hugo Peixoto ◽  
José Machado ◽  
António Abelha
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Data Sets ◽  
Complex Data ◽  
Data Set ◽  
Data Mining Approach ◽  
Degree Of Disability ◽  
Cardio Vascular ◽  
Cardiovascular Diagnosis

Abstract The large amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analysed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the prediction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hospital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD) unit in 2016 for having suffered a cardiovascular accident. To develop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA) machine learning Workbench since this one allows users to quickly try out and compare different machine learning methods on new data sets

Routing Attribute Data Mining Based on Rough Set Theory

2008 ◽  
pp. 3033-3048 ◽  
Author(s):  
Yanbing Liu ◽  
Shixin Sun ◽  
Menghao Wang ◽  
Hong Tang
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Selection ◽  
Attribute Data ◽  
Data Mining Approach ◽  
New Information

QOSPF(Quality of Service Open Shortest Path First)based on QoS routing has been recognized as a missing piece in the evolution of QoS-based services in the Internet. Data mining has emerged as a tool for data analysis, discovery of new information, and autonomous decision-making. This paper focuses on routing algorithms and their applications for computing QoS routes in OSPF protocol. The proposed approach is based on a data mining approach using rough set theory, for which the attribute-value system about links of networks is created from network topology. Rough set theory of-fers a knowledge discovery approach to extracting routing-decisions from attribute set. The extracted rules can then be used to select significant routing-attributes and make routing-selections in routers. A case study is conducted to demonstrate that rough set theory is effective in finding the most significant attribute set. It is shown that the algo-rithm based on data mining and rough set offers a promising approach to the attribute-selection problem in internet routing.

Routing Attribute Data Mining Based on Rough Set Theory

2011 ◽  
pp. 322-337
Author(s):  
Yanbing Liu ◽  
Menghao Wang ◽  
Jong Tang
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Selection ◽  
Attribute Data ◽  
Data Mining Approach ◽  
New Information

QOSPF (Quality of Service Open Shortest Path First) based on QoS routing has been recognized as a missing piece in the evolution of QoS-based services on the Internet. Data mining has emerged as a tool for data analysis, discovery of new information, and autonomous decision making. This article focuses on routing algorithms and their applications for computing QoS routes in OSPF protocol. The proposed approach is based on a data mining approach using rough set theory, for which the attribute-value system about links of networks is created from network topology. Rough set theory offers a knowledge discovery approach toextracting routing decisions from attribute set. The extracted rules then can be used to select significant routing attributes and to make routing selections in routers. A case study is conducted in order to demonstrate that rough set theory is effective in finding the most significant attribute set. It is shown that the algorithm based on data mining and rough set offers a promising approach to the attribute selection problem in Internet routing.

Cooperative Learning and Virtual Reality-Based Visualization for Data Mining

Data Mining ◽  
2011 ◽  
pp. 55-79
Author(s):  
Herna Viktor ◽  
Eric Paquet ◽  
Gys le Roux
Keyword(s):  
Data Mining ◽  
Virtual Reality ◽  
Knowledge Discovery ◽  
Large Data ◽  
Discovery Process ◽  
Visual Data Mining ◽  
Data Repositories ◽  
Data Mining Approach ◽  
The Individual

Data mining concerns the discovery and extraction of knowledge chunks from large data repositories. In a cooperative datamining environment, more than one data mining tool collaborates during the knowledge discovery process. This chapter describes a data mining approach used to visualize the cooperative data mining process. According to this approach, visual data mining consists of both data and knowledge visualization. First, the data are visualized during both data preprocessing and data mining. In this way, the quality of the data is assessed and improved throughout the knowledge discovery process. Second, the knowledge, as discovered by the individual learners, is assessed and modified through the interactive visualization of the cooperative data mining process and its results. The knowledge obtained from the human domain expert also forms part of the process. Finally, the use of virtual reality-based visualization is proposed as a new method to model both the data and its descriptors.

scholarly journals Survey on data mining approach for analysis and prediction of student performance

2018 ◽  
Vol 7 (4.5) ◽  
pp. 467 ◽  
Author(s):  
Chala Simon ◽  
Ybralem Bugusa
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Student Performance ◽  
Quality Of Education ◽  
Survey Paper ◽  
Data Mining Approach ◽  
Student Academic Performance

The quality of education is measured by the academic performance of students and the results they produce. Since the student academic performance is the made up of the environmental, psychological, socio-economic and other factors, it is challenging to measure the aca- demic performance of students. Such difficulties can be reduced by investigation of various factors that influence the student perfor- mance. Many researchers have been used different approaches to identifying the variables that help to predict students’ performance. This survey paper examines various data mining methodologies that have been used to analyze and predict students’ performance.   

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