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Papers by Nikita Bhandari

Research paper thumbnail of A Review of Audio Fingerprinting and Comparison of Algorithms

International journal of computer applications, May 17, 2013

An audio finger print is a small set of features that uniquely identifies a song. An audio finger... more An audio finger print is a small set of features that uniquely identifies a song. An audio fingerprint can be used for broadcast monitoring, audience measurement, meta-data collection. The general framework for building an audio fingerprint includes a front-end and a finger print modeling block. This paper details various uses and properties of an audio fingerprint and also the various stages included in the front end. Two algorithms namely-PRH and MLH have been discussed.

Research paper thumbnail of A comprehensive survey on computational learning methods for analysis of gene expression data

Frontiers in Molecular Biosciences, Nov 7, 2022

Computational analysis methods including machine learning have a significant impact in the fields... more Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.

Research paper thumbnail of Integrative gene expression analysis for the diagnosis of Parkinson’s disease using machine learning and explainable AI

Computers in Biology and Medicine

Research paper thumbnail of A comprehensive survey on computational learning methods for analysis of gene expression data

Frontiers in Molecular Biosciences

Computational analysis methods including machine learning have a significant impact in the fields... more Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction...

Research paper thumbnail of Comprehensive survey of computational learning methods for analysis of gene expression data in genomics

Computational analysis methods including machine learning have a significant impact in the fields... more Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of the gene expression data. However, more complex analysis for classification and discovery of feature genes or sample observations requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though, the methods are discussed in the context of expression microarray data, they can also be applied for the analysis of RNA sequencing or quantitative proteomics datasets. We specifically discuss methods for missing value (gene expression) imputation, feature gene scaling, selection and extraction of features for dimensionality reduction, and learning and ana...

Research paper thumbnail of A survey on computational learning methods for analysis of gene expression data in genomics

Computational analysis methods including machine learning have a significant impact in the fields... more Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of the gene expression data. However, more complex analysis for classification and discovery of feature genes or sample observations requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though, the methods are discussed in the context of expression microarray data, they can also be applied for the analysis of RNA sequencing or quantitative proteomics datasets. We specifically discuss methods for missing value (gene expression) imputation, feature gene scaling, selection and extraction of features for dimensionality reduction, and learning and ana...

Research paper thumbnail of Nisha Nair

An audio finger print is a small set of features that uniquely identifies a song. An audio finger... more An audio finger print is a small set of features that uniquely identifies a song. An audio fingerprint can be used for broadcast monitoring, audience measurement, meta-data collection. The general framework for building an audio fingerprint includes a front- end and a finger print modeling block. This paper details various uses and properties of an audio fingerprint and also the various stages included in the front end. Two algorithms namely- PRH and MLH have been discussed.

Research paper thumbnail of A Review of Audio Fingerprinting and Comparison of Algorithms

International Journal of Computer Applications, 2013

An audio finger print is a small set of features that uniquely identifies a song. An audio finger... more An audio finger print is a small set of features that uniquely identifies a song. An audio fingerprint can be used for broadcast monitoring, audience measurement, meta-data collection. The general framework for building an audio fingerprint includes a front-end and a finger print modeling block. This paper details various uses and properties of an audio fingerprint and also the various stages included in the front end. Two algorithms namely-PRH and MLH have been discussed.

Research paper thumbnail of Colorization of Grayscale Images using LBG VQ Codebook for different Color Spaces

The paper presents an innovative technique for colorization of grayscale images. Here the colors ... more The paper presents an innovative technique for colorization of grayscale images. Here the colors from some source color image are picked up and sprayed into the to be colored grayscale image. The color palette used in colorization technique discussed here is generated using the Linde Buzo and Gray (LBG) codebook. The technique is tested using various VQ codebook sizes like 32, 64, 128, 256 and 512. Also various color spaces like RGB, Kekre's LUV, YCbCr, YUV, YIQ, and Kekre's Biorthogonal color spaces are considered for colorization of grayscale images. In all 5 VQ codebook sizes and 8 color spaces give total 40 versions of proposed colorization method. All proposed versions are implemented and tested for colorization of grayscale images from different classes.

Research paper thumbnail of Colorization of Greyscale Images using Kekre‟ s Biorthogonal Color Spaces and Kekre‟ s Fast Codebook Generation

Research paper thumbnail of A comprehensive survey on computational learning methods for analysis of gene expression data

arXiv (Cornell University), Feb 7, 2022

Computational analysis methods including machine learning have a significant impact in the fields... more Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.

Research paper thumbnail of A Review of Audio Fingerprinting and Comparison of Algorithms

International journal of computer applications, May 17, 2013

An audio finger print is a small set of features that uniquely identifies a song. An audio finger... more An audio finger print is a small set of features that uniquely identifies a song. An audio fingerprint can be used for broadcast monitoring, audience measurement, meta-data collection. The general framework for building an audio fingerprint includes a front-end and a finger print modeling block. This paper details various uses and properties of an audio fingerprint and also the various stages included in the front end. Two algorithms namely-PRH and MLH have been discussed.

Research paper thumbnail of A comprehensive survey on computational learning methods for analysis of gene expression data

Frontiers in Molecular Biosciences, Nov 7, 2022

Computational analysis methods including machine learning have a significant impact in the fields... more Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.

Research paper thumbnail of Integrative gene expression analysis for the diagnosis of Parkinson’s disease using machine learning and explainable AI

Computers in Biology and Medicine

Research paper thumbnail of A comprehensive survey on computational learning methods for analysis of gene expression data

Frontiers in Molecular Biosciences

Computational analysis methods including machine learning have a significant impact in the fields... more Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction...

Research paper thumbnail of Comprehensive survey of computational learning methods for analysis of gene expression data in genomics

Computational analysis methods including machine learning have a significant impact in the fields... more Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of the gene expression data. However, more complex analysis for classification and discovery of feature genes or sample observations requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though, the methods are discussed in the context of expression microarray data, they can also be applied for the analysis of RNA sequencing or quantitative proteomics datasets. We specifically discuss methods for missing value (gene expression) imputation, feature gene scaling, selection and extraction of features for dimensionality reduction, and learning and ana...

Research paper thumbnail of A survey on computational learning methods for analysis of gene expression data in genomics

Computational analysis methods including machine learning have a significant impact in the fields... more Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of the gene expression data. However, more complex analysis for classification and discovery of feature genes or sample observations requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though, the methods are discussed in the context of expression microarray data, they can also be applied for the analysis of RNA sequencing or quantitative proteomics datasets. We specifically discuss methods for missing value (gene expression) imputation, feature gene scaling, selection and extraction of features for dimensionality reduction, and learning and ana...

Research paper thumbnail of Nisha Nair

An audio finger print is a small set of features that uniquely identifies a song. An audio finger... more An audio finger print is a small set of features that uniquely identifies a song. An audio fingerprint can be used for broadcast monitoring, audience measurement, meta-data collection. The general framework for building an audio fingerprint includes a front- end and a finger print modeling block. This paper details various uses and properties of an audio fingerprint and also the various stages included in the front end. Two algorithms namely- PRH and MLH have been discussed.

Research paper thumbnail of A Review of Audio Fingerprinting and Comparison of Algorithms

International Journal of Computer Applications, 2013

An audio finger print is a small set of features that uniquely identifies a song. An audio finger... more An audio finger print is a small set of features that uniquely identifies a song. An audio fingerprint can be used for broadcast monitoring, audience measurement, meta-data collection. The general framework for building an audio fingerprint includes a front-end and a finger print modeling block. This paper details various uses and properties of an audio fingerprint and also the various stages included in the front end. Two algorithms namely-PRH and MLH have been discussed.

Research paper thumbnail of Colorization of Grayscale Images using LBG VQ Codebook for different Color Spaces

The paper presents an innovative technique for colorization of grayscale images. Here the colors ... more The paper presents an innovative technique for colorization of grayscale images. Here the colors from some source color image are picked up and sprayed into the to be colored grayscale image. The color palette used in colorization technique discussed here is generated using the Linde Buzo and Gray (LBG) codebook. The technique is tested using various VQ codebook sizes like 32, 64, 128, 256 and 512. Also various color spaces like RGB, Kekre's LUV, YCbCr, YUV, YIQ, and Kekre's Biorthogonal color spaces are considered for colorization of grayscale images. In all 5 VQ codebook sizes and 8 color spaces give total 40 versions of proposed colorization method. All proposed versions are implemented and tested for colorization of grayscale images from different classes.

Research paper thumbnail of Colorization of Greyscale Images using Kekre‟ s Biorthogonal Color Spaces and Kekre‟ s Fast Codebook Generation

Research paper thumbnail of A comprehensive survey on computational learning methods for analysis of gene expression data

arXiv (Cornell University), Feb 7, 2022

Computational analysis methods including machine learning have a significant impact in the fields... more Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.