Shaveta Kalsi

Bio: Shaveta Kalsi is an academic researcher. The author has contributed to research in topics: Naive Bayes classifier. The author has an hindex of 2, co-authored 2 publications receiving 9 citations.

Naïve Bayes Approach for the Crime Prediction in Data Mining

Abstract: Prediction analysis is the analysis in which future trends and outcomes are predicted on the basis of assumption. It is the analysis in which future trends and outcomes are predicted on the basis of assumption. Machine learning techniques and regression techniques are the two approaches that have been utilized in order to conduct predictive analytics. In the conducting predictive analytics, machine learning techniques are widely utilized and become popular as large scale datasets handled by it is effective manner and provide high performance. It provides the results with uniform characteristics and noisy data. The KNN is the popular technique which is applied in the prediction analysis. To improve accuracy of crime prediction technique of Naïve Bayes is applied in this research work. It is evaluated that Naïve Bayes give higher accuracy as compared to KNN for the crime prediction.

Analysis of Wheat Production using Naïve Bayes Classifier

Abstract: Data mining is defined as the process in which useful information is extracted from the raw data. In order to acquire essential knowledge it is essential to extract large amount of data. This process of extraction is also known as misnomer. Currently in every field, large amount of data is present and analyzing whole data is very difficult as well as it consumes a lot of time. The prediction analysis is most useful type of data which is performed today. To perform the prediction analysis the patterns needs to generate from the dataset with the machine learning. The prediction analysis can be done by gathering historical information to generate future trends. So, the knowledge of what has happened previously is used to provide the best valuation of what will happen in future with predictive analysis. Crop production analysis is one of the applications of prediction analysis. In this research work, the Naïve Bayes classifier is applied for the wheat production prediction. The Naïve Bayes classifier is compared with SVM and KNN. The Naïve Bayes performs well for the wheat production analysis.

Predicting Crime Using Time and Location Data

Abstract: To have a better response towards criminal activity, it is very important that one should understand the patterns in crime. We analyze this pattern by taking crime datasets from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. This dataset includes different blocks of the city of Chicago. The major aim of this mission is to expect which category of crime is most probably to take place at a detailed time and places in Chicago. Finally, this paper uses a different algorithm like Random Forest, Decision Tree and different ensemble methods such as Extra Trees, Bagging and AdaBoost to evaluate the accuracy given by each algorithm.

Spatiotemporal Analysis of Web News Archives for Crime Prediction

Abstract: In today’s world, security is the most prominent aspect which has been given higher priority. Despite the rapid growth and usage of digital devices, lucrative measurement of crimes in under-developing countries is still challenging. In this work, unstructural crime data (900 records) from the news archives of the previous eight years were extracted to predict the behavior of criminals’ networks and transform it into useful information using natural language processing (NLP). To estimate the next move of criminals in Pakistan, we performed hotspot-based spatial analysis. Later, this information is fed to two different classifiers for possible identification and prediction. We achieved the maximum accuracy of 92% using K-Nearest Neighbor (KNN) and 62% using the Random Forest algorithm. In terms of crimes, the results showed that the most prevalent crime events are robberies. Thus, the usage of digital information archives, spatial analysis, and machine learning techniques can open new ways of handling a peaceful and sustainable society in eradicating crimes for countries having paucity of financial resources.

Predictive Modelling of Crime Data Set Using Data Mining

Abstract: With a substantial increase in crime across the globe, there is a need for analyzing the crime data to lower the crime rate. This helps the police and citizens to take necessary actions and solve the crimes faster. In this paper, data mining techniques are applied to crime data for predicting features that affect the high crime rate. Supervised learning uses data sets to train, test and get desired results on them whereas Unsupervised learning divides an inconsistent, unstructured data into classes or clusters. Decision trees, Naive Bayes and Regression are some of the supervised learning methods in data mining and machine learning on previously collected data and thus used for predicting the features responsible for causing crime in a region or locality. Based on the rankings of the features, the Crimes Record Bureau and Police Department can take necessary actions to decrease the probability of occurrence of the crime.

Rainfall Forecasting to Recommend Crops Varieties Using Moving Average and Naive Bayes Methods

Abstract: Indonesia has been known as an agrarian country because of its fertile soil and is very suitable for agricultural land, including rice. Yogyakarta is one of the most significant granary regions in Indonesia, especially in the Sleman region. However, one of the main challenges in rice planting in recent years is the erratic rainfall patterns caused by climate anomalies due to the El Nino and La Nina phenomena. As a result of this phenomenon, farmers have difficulty determining planting time and harvest time and planting other plants. Therefore, we make rainfall predictions to recommend planting varieties with Moving Average and Naive Bayes Methods in Sleman District. The results showed that moving averages well use in predicting rainfall. From these results, we can estimate that in 2020 rice production will below. That can saw from the calculation of the probability of naive Bayes on rice plants being low at 0.999 and 0.923. So that the recommended intercrops planted in 2020 are corn and peanuts. We also find that rainfall prediction with Moving Average using data from several previous years in the same month is more accurate than using data from four past months or periods.

An overview of crime analysis, prevention and prediction using data mining based on real time and location data

Abstract: Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. The system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. With the increasing advent of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. Using the concept of data mining, real time and location data, the system can extract unknown, useful information from an unstructured data. Here we have an approach between computer science and criminal justice to develop a data mining, real time and location data procedure that can help solve crimes faster. Instead of focusing on causes of crime occurrence like criminal background of offender, political enmity etc. we are focusing mainly on crime factors of each day. To have a better response towards criminal activity, it is very important that one should understand the patterns in crime. I analyses these patterns by taking crime datasets from the Nigeria Police zone six (6) Calabar, Cross River State and town planning bodies. This dataset includes different streets of the city of Calabar. The major aim of this mission is to expect which category of crime is most probably to take place at a detailed time and places in