Enhanced Preprocessing Algorithm of Information System for Law Enforcement Using Data mining Techniques

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.

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Systematic Review on the Suspicious Profiles Detection on Online Social Media Data

Oriental journal of computer science and technology ◽

10.13005/ojcst/10.03.13 ◽

2017 ◽

Vol 10 (3) ◽

pp. 644-652

Author(s):

Asha Asha ◽

Dr. Balkishan

Keyword(s):

Systematic Review ◽

Data Mining ◽

Social Media ◽

Law Enforcement ◽

Social Media Data ◽

Data Mining Techniques ◽

Online Social Media ◽

Using Data ◽

Media Data ◽

Mining Tools

Escalating crimes on digital facet alarms the law enforcement bodies to keep a gaze on online activities which involve massive amount of data. This will raise a need to detect suspicious activities on online available social media data by optimizing investigations using data mining tools. This paper intends to throw some light on the data mining techniques which are designed and developed for closely examining social media data for suspicious activities and profiles in different domains. Additionally, this study will categorize the techniques under various groups highlighting their important features, challenges and application realm.

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Crime Detection using Data Mining Techniques

Computer Science & Engineering An International Journal ◽

10.5121/cseij.2020.10501 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1-5

Author(s):

Md. Sumon Rony ◽

Sagor Chandra Bakchy ◽

Hadisur Rahman

Keyword(s):

Data Mining ◽

Law Enforcement ◽

Criminal Activity ◽

Law Enforcement Agencies ◽

Data Mining Techniques ◽

Data Mining Approach ◽

Collection Data ◽

Crime Detection ◽

Phase Data ◽

Using Data

As crime rates keep spiraling each day, new challenges are faced by law enforcement agencies. They have to keep their on the lookout for any signs criminal activity. The law enforcement agencies should therefore be able to predict such increase or decrees or trends in crime. Such as theft, Killing. Crime that may occur in a particular area in a particular month, year, any timespan. Data mining is defined as a process of discovering hidden valuable knowledge by analyzing large amounts of data, which is stored in databases or data warehouse, using various data mining techniques such as machine learning, artificial intelligence, statistical. Many algorithms for data mining approach to help detect the crimes patterns. Data Collection, Data Preprocessing Phase, Data Filtering, Linier Regression. Wekasoft are used for collection of data analyzing. Visualization finally get results. The advantage of using this tool is that clustering will be performed automatically.

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Predictive Models in Cybercrime Investigation

Advancing the Service Sector with Evolving Technologies ◽

10.4018/978-1-4666-0044-7.ch011 ◽

2012 ◽

pp. 166-177

Author(s):

A. S. N. Murthy ◽

Vishnuprasad Nagadevara ◽

Rahul De’

Keyword(s):

Data Mining ◽

Law Enforcement ◽

Success Rate ◽

Predictive Models ◽

Law Enforcement Agencies ◽

Data Mining Techniques ◽

Investigation Methods ◽

The World ◽

Policy Measures ◽

Using Data

With increased access to computers across the world, cybercrime is becoming a major challenge to law enforcement agencies. Cybercrime investigation in India is in its infancy and there has been limited success in prosecuting the offenders; therefore, a need to understand and strengthen the existing investigation methods and systems for controlling cybercrimes is greatly needed. This study identifies important factors that will enable law enforcement agencies to reach the first step in effective prosecution, namely charge-sheeting of the cybercrime cases. Data on 300 cybercrime cases covering a number of demographic, technical and other variables related to cybercrime was analyzed using data mining techniques to identify and prioritize various factors leading to filing of the charge-sheet. These factors and the respective priority rankings are used to suggest various policy measures for improving the success rate of prosecution of cybercrimes.

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

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2017040103 ◽

2017 ◽

Vol 13 (2) ◽

pp. 45-62 ◽

Cited By ~ 1

Author(s):

Francisco Javier Villar Martín ◽

Jose Luis Castillo Sequera ◽

Miguel Angel Navarro Huerga

Keyword(s):

Data Mining ◽

Information System ◽

Data Model ◽

Free Software ◽

Data Mining Techniques ◽

Visualization Tools ◽

Hidden Patterns ◽

Using Data ◽

Analyze 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.

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Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i11.468 ◽

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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