Shaveta Kalsi - Academia.edu (original) (raw)

Papers by Shaveta Kalsi

Research paper thumbnail of Naïve Bayes Approach for the Crime Prediction in Data Mining

International journal of computer applications, May 15, 2019

Research paper thumbnail of Analysis of Wheat Production using Naïve Bayes Classifier

International journal of computer applications, May 15, 2019

Data mining is defined as the process in which useful information is extracted from the raw data.... more 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.

Research paper thumbnail of Providing an efficient Customers Churn Prediction Model based on Improvised K-Means Clustering And Non Linear Support Vector Machine

Journal of emerging technologies and innovative research, Mar 1, 2021

Research paper thumbnail of Providing an efficient Customers Churn Prediction Model based on Improvised K-Means Clustering And Non Linear Support Vector Machine

This paper proposed a new approach to enhance the performance of existing base techniques includi... more This paper proposed a new approach to enhance the performance of existing base techniques including Neural networks, Logistic Regression, Linear Support vector machines and Non-Linear support vector machine with the proposed technique Improvised K-Means with NLSVM. The Improvised K-Means algorithm resolved the random selection problem of cluster centroid of K-Means by choosing the cluster centroid by taking the mean value of the data points. The Improvised K-Means algorithm clusters are then classified with Non-Linear Support vector machine classification algorithm. This enhanced approach is used for predicting customer churn. So that proactive measures could be taken by company for churn prevention. The experimental results show that the proposed technique performs better than the existing base techniques in terms of recall and f-measure. IndexTerms – Data Mining, Customer Churn Prediction, Clustering, Classification, K-Means, Non-Linear Support Vector Machine (NLSVM).

Research paper thumbnail of Clipping Based Hue Preserving Color Image Enhancement using Integrated 2-Dimensional Filter

The images, received from Black Lightening and Night vision, might go through from negative best ... more The images, received from Black Lightening and Night vision, might go through from negative best or inappropriate brightness. Therefore, Image enhancements play a pivotal position to improve the quality of the image. In this paper, the approach focal point on bettering the quality, normalize brightness, and enlarge contrast. An approach known as Clipping is used to separate the RGB pix into different block sizes n*n. The present strategies Guided filter based totally Sub Image Histogram Equalization (GSIHE) is used to amplify the visual quality, which is pursued with the aid of integrated 2–dimensional filter for enhance the excellent of the image. Simulation demonstrate that this proposed approach has higher contrast, Hue preservation, elevated Correlation Coefficient and Entropy, PSNR and reduced MSE.

Research paper thumbnail of Naïve Bayes Approach for the Crime Prediction in Data Mining

International Journal of Computer Applications, 2019

Prediction analysis is the analysis in which future trends and outcomes are predicted on the basi... more 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.

Research paper thumbnail of Analysis of Wheat Production using Naïve Bayes Classifier

International Journal of Computer Applications, 2019

Data mining is defined as the process in which useful information is extracted from the raw data.... more 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.

Research paper thumbnail of Naïve Bayes Approach for the Crime Prediction in Data Mining

International journal of computer applications, May 15, 2019

Research paper thumbnail of Analysis of Wheat Production using Naïve Bayes Classifier

International journal of computer applications, May 15, 2019

Data mining is defined as the process in which useful information is extracted from the raw data.... more 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.

Research paper thumbnail of Providing an efficient Customers Churn Prediction Model based on Improvised K-Means Clustering And Non Linear Support Vector Machine

Journal of emerging technologies and innovative research, Mar 1, 2021

Research paper thumbnail of Providing an efficient Customers Churn Prediction Model based on Improvised K-Means Clustering And Non Linear Support Vector Machine

This paper proposed a new approach to enhance the performance of existing base techniques includi... more This paper proposed a new approach to enhance the performance of existing base techniques including Neural networks, Logistic Regression, Linear Support vector machines and Non-Linear support vector machine with the proposed technique Improvised K-Means with NLSVM. The Improvised K-Means algorithm resolved the random selection problem of cluster centroid of K-Means by choosing the cluster centroid by taking the mean value of the data points. The Improvised K-Means algorithm clusters are then classified with Non-Linear Support vector machine classification algorithm. This enhanced approach is used for predicting customer churn. So that proactive measures could be taken by company for churn prevention. The experimental results show that the proposed technique performs better than the existing base techniques in terms of recall and f-measure. IndexTerms – Data Mining, Customer Churn Prediction, Clustering, Classification, K-Means, Non-Linear Support Vector Machine (NLSVM).

Research paper thumbnail of Clipping Based Hue Preserving Color Image Enhancement using Integrated 2-Dimensional Filter

The images, received from Black Lightening and Night vision, might go through from negative best ... more The images, received from Black Lightening and Night vision, might go through from negative best or inappropriate brightness. Therefore, Image enhancements play a pivotal position to improve the quality of the image. In this paper, the approach focal point on bettering the quality, normalize brightness, and enlarge contrast. An approach known as Clipping is used to separate the RGB pix into different block sizes n*n. The present strategies Guided filter based totally Sub Image Histogram Equalization (GSIHE) is used to amplify the visual quality, which is pursued with the aid of integrated 2–dimensional filter for enhance the excellent of the image. Simulation demonstrate that this proposed approach has higher contrast, Hue preservation, elevated Correlation Coefficient and Entropy, PSNR and reduced MSE.

Research paper thumbnail of Naïve Bayes Approach for the Crime Prediction in Data Mining

International Journal of Computer Applications, 2019

Prediction analysis is the analysis in which future trends and outcomes are predicted on the basi... more 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.

Research paper thumbnail of Analysis of Wheat Production using Naïve Bayes Classifier

International Journal of Computer Applications, 2019

Data mining is defined as the process in which useful information is extracted from the raw data.... more 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.