scholarly journals A Review on Soil Property Detection using Machine Learning Approach

2018 ◽  
Vol 4 (8) ◽  
pp. 6
Author(s):  
Smriti Singhatiya Dr. Shivnath Ghosh
Keyword(s):  
Data Mining ◽  
Performance Prediction ◽  
Agricultural Sector ◽  
Crop Productivity ◽  
Indian Economy ◽  
Soil Database ◽  
Significant Relationships ◽  
Machine Learning Approach ◽  
Key Factor ◽  
Using Data

The agricultural sector is the backbone of the Indian economy. Although focused on industrialization, agriculture remains an important sector of the Indian economy, both in terms of contribution to gross domestic product (GDP) and jobs for millions of people across the country. One of the key factor for productive agriculture is soil. The purpose of the work is to predict the type of terrain using data mining classification methods. Agricultural properties and soil ownership play a crucial role in agricultural decision-making. This research sought to evaluate various mining association techniques and apply them to a soil database to determine if significant relationships could be created. Performance prediction is one of the applications that uses the concept of data mining to increase crop productivity. This makes the problem of crop productive performance is an interesting challenge. An earlier performance prediction was made taking into account the cultivator's experience with a particular crop and culture. This work introduces a system that uses data mining techniques to predict the category of analyzed soil datasets.

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. 

USING DATA MINING ALGORITHMS IN WEB PERFORMANCE PREDICTION

2009 ◽  
Vol 40 (2) ◽  
pp. 176-187 ◽  
Author(s):  
Leszek Borzemski ◽  
Marta Kliber ◽  
Ziemowit Nowak
Keyword(s):  
Data Mining ◽  
Performance Prediction ◽  
Web Performance ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

scholarly journals Sentiment Analysis and Summarization of Social Media Content using Topic Modeling

2020 ◽  
Vol 9 (3) ◽  
pp. 209-212
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Opinion Mining ◽  
Text Documents ◽  
Topic Identification ◽  
Key Factor ◽  
Polarity Classification ◽  
Using Data

Explosion of Web 2.0 had made different social media platforms like Facebook, Twitter, Blogs, etc a data hub for the task of Data Mining. Sentiment Analysis or Opinion mining is an automated process of understanding an opinion expressed by customers. By using Data mining techniques, sentiment analysis helps in determining the polarity (Positive, Negative & Neutral) of views expressed by the end user. Nowadays there are terabytes of data available related to any topic then it can be advertising, politics and Survey Companies, etc. CSAT (Customer Satisfaction) is the key factor for this survey companies. In this paper, we used topic modeling by incorporating a LDA algorithm for finding the topics related to social media. We have used datasets of 900 records for analysis. By analysis, we found three important topics from Survey/Response dataset, which are Customers, Agents & Product/Services. Results depict the CSAT score according to Positive, Negative and Neutral response. We used topic modeling which is a statistical modeling technique. Topic modeling is a technique for categorization of text documents into different topics. This approach helps in better summarization of data according to the topic identification and depiction of polarity classification of sentiments expressed.

scholarly journals Employee Performance Prediction Using EPP Framework

2020 ◽  
pp. 297-302
Author(s):  
M. Karthika ◽  
T. Meyyappan
Keyword(s):  
Data Mining ◽  
Performance Prediction ◽  
Employee Performance ◽  
Research Work ◽  
Prediction Methods ◽  
Internal Factors ◽  
Using Data ◽  
Proper Solutions

In the today's industrial world, every company’s growth is depends on their employees. The company achievements are completely based on the employees in the organization. The employees’ performances are measured by the targets and achievements. But some external and internal factors affect the employees’ goals and achievements. Hence, the company has to find the performance of every employee and make proper solutions to improve the performance. This research work proposes a fully automated framework which can perform deep analysis of employees’ performance and job fitness using data mining and prediction methods.

scholarly journals Malicious Threats Detection of Executable File

2020 ◽  
Vol 9 (3) ◽  
pp. 3257-3262
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Data File ◽  
Data Mining Algorithm ◽  
Instruction Set ◽  
Detection Algorithms ◽  
Client Machine ◽  
Machine Learning Approach ◽  
Executable File ◽  
Using Data

Malware is a general problems faced in the present day. Malware is a file that may be on the client machine. Malware can root an uncorrectable risk to the safety and protection of personal workstation clients as an expansion in the spiteful threats. In this paper explain a malware threats detection using data mining and machine learning. Malware detection algorithms with machine learning approach and data file. Also explained break executable files, create instruction set and take a look at different machine learning and data mining algorithm for feature extraction, reduction for detection of malware. In the system precisely distinguishes both new and known malware occurrences even though the double distinction among malware and real software is ordinarily little. There is a demand to present a skeleton which can come across latest, malicious executable files.

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