Predictive Models in Cybercrime Investigation

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.

Predictive Models in Cybercrime Investigation

2010 ◽  
Vol 2 (3) ◽  
pp. 1-12 ◽  
Author(s):  
A. S. N. Murthy ◽  
Vishnuprasad Nagadevara ◽  
Rahul De'
Keyword(s):  
Data Mining ◽  
Law Enforcement ◽  
Success Rate ◽  
Predictive Models ◽  
Law Enforcement Agencies ◽  
Limited Success ◽  
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.

scholarly journals Crime Detection using Data Mining Techniques

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.

scholarly journals Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids

2020 ◽  
Author(s):  
Edy Irwansyah ◽  
Ebiet Salim Pratama ◽  
Margaretha Ohyver
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Principal Component Analysis ◽  
Data Reduction ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Data Mining Techniques ◽  
The World ◽  
Using Data

Cardiovascular disease is the number one cause of death in the world and Quoting from WHO, around 31% of deaths in the world are caused by cardiovascular diseases and more than 75% of deaths occur in developing countries. The results of patients with cardiovascular disease produce many medical records that can be used for further patient management. This study aims to develop a method of data mining by grouping patients with cardiovascular disease to determine the level of patient complications in the two clusters. The method applied is principal component analysis (PCA) which aims to reduce the dimensions of the large data available and the techniques of data mining in the form of cluster analysis which implements the K-Medoids algorithm. The results of data reduction with PCA resulted in five new components with a cumulative proportion variance of 0.8311. The five new components are implemented for cluster formation using the K-Medoids algorithm which results in the form of two clusters with a silhouette coefficient of 0.35. Combination of techniques of Data reduction by PCA and the application of the K-Medoids clustering algorithm are new ways for grouping data of patients with cardiovascular disease based on the level of patient complications in each cluster of data generated.

scholarly journals Development of predictive models using Data Mining techniques to detect borer infestation (Diatraea saccharalis) in sugarcane culture

2017 ◽  
Author(s):  
Nádia Vieira Ribeiro ◽  
Luiz Henrique Antunes Rodrigues ◽  
Monique Pires Gravina de Oliveira ◽  
FELIPE FERREIRA BOCCA
Keyword(s):  
Data Mining ◽  
Predictive Models ◽  
Data Mining Techniques ◽  
Diatraea Saccharalis ◽  
Using Data

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

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.

scholarly journals Building predictive models for MERS-CoV infections using data mining techniques

2016 ◽  
Vol 9 (6) ◽  
pp. 744-748 ◽  
Author(s):  
Isra Al-Turaiki ◽  
Mona Alshahrani ◽  
Tahani Almutairi
Keyword(s):  
Data Mining ◽  
Predictive Models ◽  
Data Mining Techniques ◽  
Using Data

scholarly journals Rain Fall Prediction using Data Mining Techniques with Modernistic Schemes and Well-Formed Ideas

2019 ◽  
Vol 9 (1) ◽  
pp. 258-263
Keyword(s):  
Data Mining ◽  
Weather Forecasting ◽  
Safety Measures ◽  
Data Mining Techniques ◽  
Flight Operations ◽  
Fall Prediction ◽  
The World ◽  
Rain Fall ◽  
Using Data ◽  
Work Done

Weather forecasting is essential because it helps to deal with the environment related future anomalies. Accurate and timely predications can contribute largely for taking safety measures in the ongoing projects such as agriculture tasks, flight operations, transportation tasks and many others. There are large number of meteorologist all over the world who are trying their level best to predict the aspects of environment using data mining techniques. This paper contains some of the best work done in rain fall prediction using data mining techniques. This paper helps the researchers to study the literature of this field in a crisp, summarized and encapsulated way.

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