Kalpdrum Passi received his Ph.D. in Parallel Numerical Algorithms from Indian Institute of Technology, Delhi, India in 1993. He is an Associate Professor, Department of Mathematics
Smart innovation, systems and technologies, Oct 6, 2022
Sentiment analysis is one of the major fields of research for any case regarding natural language... more Sentiment analysis is one of the major fields of research for any case regarding natural language processing. For this purpose, the data is often some form of review or a feedback so that the emotion and the main sentiment behind the feedback can be assessed using machine learning techniques. A similar approach is performed in this research, In this report, Whatsapp reviews of customers in Telugu language were analysed and the sentiment polarity was calculated using a rule based approach. Telugu language is from the southern part of India and uses different sets of fonts from the general sets. The strings are treated as similarly as they are in any other machine learning process since the meaning behind them is captured through the patterns that emerge from the text. All the text processing is carried oput similarly to most NLP scenarios. To find out the overall sentiment in the review that is collected from the internet, a manual rule-based algorithm is developed which can apply certain sets of rules to a sentence to check the polarity, which can be positive, negative, or neutral. These rules check the presence of words such as major negative words and major positive words, and even auxiliary verbs and their position with respect to the negative and positive words. This rule-based approach was then used to train a machine learning model using a few parametric classifiers like K-nearest neighbours (KNN), XGBoost and support vector machines (SVM). The classifiers also fetched a decent accuracy of 81%, 82% and 78% respectively, which indicated towards the good performance of the rule-based approach and its effectiveness with error counts of 0.296, 0.288 and 0.252 with TF-IDF and 0.285, 0.285 iv and 0.234 with Bag of Words. Along the process, manual observation was also used to compare the assigned sentiments to the sentence to find the errors in the method. The best performance with respect to results was given by SVM classifiers that returned an f1 score of 79% and the lowest error count of 0.23 which is better among all the classifiers. The metrics which were used to judge these classifiers were the precision, recall, f1 scores and the mean squared error.
Significances of bioengineering & biosciences, Feb 12, 2024
Epigenomics is the field of biology dealing with modifications of the phenotype that do not cause... more Epigenomics is the field of biology dealing with modifications of the phenotype that do not cause any alteration in the sequence of cell DNA. Epigenomics adds something to the top of DNA to change the properties, which eventually prohibits certain DNA behavior from being performed. Such modifications occur in cancer cells and are the sole cause of cancer. The main objective of this research is to perform prediction and survival analysis of Head and Neck Squamous Cell Carcinoma (HNSCC) which is one of the biggest reasons of death and accounts for more than 650,000 cases and 330,000 deaths annually worldwide. Tobacco use, alcohol consumption, Human Papillomavirus (HPV) infection (for oropharyngeal cancer) and Epstein-Barr Virus (EBV) infection are the main risk factors associated with head and neck cancer (for nasopharyngeal cancer). Males, with a proportion ranging from 2:1 to 4:1, are slightly more affected than females. Four different types of data are used in this research to predict HNSCC in patients. The data includes methylation, histone, human genome and RNA-Sequences. The data is accessed through open-source technologies in R and Python programming languages. The data is processed to create features and with the help of statistical analysis and advanced machine learning techniques, the prediction of HNSCC is obtained from the fine-tuned model. The optimal model was determined to be ResNet50 utilizing the Sobel feature selection method for image data and Relief F-based feature selection for clinical features, achieving a test accuracy of 97.9%. The model's precision score was 0.929, its recall score was 0.930 and its F1 score was 0.930. Additionally, the ResNet101 model demonstrated the best performance using the Histogram of Gradients feature selection method for image data and mutual informationbased feature selection for clinical features, yielding a test accuracy of 96.1%. Its precision score, recall score and F1 score were identical to the aforementioned ResNet50 model. The research also utilized Kaplan-Meier survival analysis to investigate the survival rates of patients based on various factors, including age, gender, smoking status, tumor size and location of site. The results obtained from this analysis yielded the effectiveness of the method in providing valuable insights for risk assessment.
A number of query languages have been proposed in recent times for processing queries on XML and ... more A number of query languages have been proposed in recent times for processing queries on XML and semistructured data. All these query languages make use of regular path expressions to query XML data. To optimize the processing of query paths a number of indexing schemes have also been proposed recently. XPath provides the basis for processing queries on XML data in the form of regular path expressions. In this paper, we propose two algorithms called Entry-point algorithm and Rest-tree algorithm that exploit different types of indexes, which we have defined to efficiently process XPath queries. We also discuss and compare two variations in implementing these algorithms; Root-first and Bottom-first.
Player selection is one the most important tasks for any sport and cricket is no exception. The p... more Player selection is one the most important tasks for any sport and cricket is no exception. The performance of the players depends on various factors such as the opposition team, the venue, his current form etc. The team management, the coach and the captain select 11 players for each match from a squad of 15 to 20 players. They analyze different characteristics and the statistics of the players to select the best playing 11 for each match. Each batsman contributes by scoring maximum runs possible and each bowler contributes by taking maximum wickets and conceding minimum runs. This paper attempts to predict the performance of players as how many runs will each batsman score and how many wickets will each bowler take for both the teams. Both the problems are targeted as classification problems where number of runs and number of wickets are classified in different ranges. We used naïve bayes, random forest, multiclass SVM and decision tree classifiers to generate the prediction models for both the problems. Random Forest classifier was found to be the most accurate for both the problems.
The explosive growth in the amount of data in the field of biology, education, environmental rese... more The explosive growth in the amount of data in the field of biology, education, environmental research, sensor network, stock market, weather forecasting and many more due to vast use of internet in distributed environment has generated an urgent need for new techniques and tools that can intelligently automatically transform the processed data into useful information and knowledge. Hence data mining has become a research are with increasing importance. Since continuation in collection of more data at this scale, formalizing the process of big data analysis will become paramount. Given the vast amount of data are geographically spread across the globe, this means a very large number of models is generated, which raises problems on how to generalize knowledge in order to have a global view of the phenomena across the organization. This is applicable to web-based educational data. In this chapter, the new dynamic and scalable data mining approach has been discussed with educational data.
Communications in Computer and Information Science, 2016
The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The network transformation from physical network function to Virtual Network Function (VNF) requi... more The network transformation from physical network function to Virtual Network Function (VNF) requires a fundamental design change in the way applications and services are tested and assured in a hybrid virtual network. Once the VNFs are onboarded in a cloud network infrastructure, operators need to automatically test VNFs in real-time at the time of instantiation. This paper explicitly analyses the problem of adaptive self-healing of a Virtual Machine (VM) allocated by the VNF with the Deep Reinforcement Learning (DRL) approach. The DRL-based big data collection and analytics engine performs aggregation to probe data and analyze it for troubleshooting and performance management. This engine helps to determine corrective actions (self-healing), such as scaling or migrating VNFs. Hence, we proposed a Deep Queue Learning (DQL) based Deep Queue Networks (DQN) mechanism for self-healing VNFs in the virtualized infrastructure manager. Virtual network probes of closed-loop orchestration perform the automation of the VNF and provide analytics for real-time, policy-driven orchestration in an open networking automation platform through the stochastic gradient descent method for VNF service assurance and network reliability. The proposed DQN/DDQN mechanism optimizes the price and lowers the cost by 18% for resource usage without disrupting the Quality of Service (QoS) provided by the VNF. The outcome of adaptive self-healing of the VNFs enhances the computational performance by 27% compared to other state-of-the-art algorithms.
Cancer is a complex disease associated with the alteration in DNA methylation, histone modificati... more Cancer is a complex disease associated with the alteration in DNA methylation, histone modification, post transcriptional modifications with epigenetic changes in genes and pathways at different level, leading to generate the malignant phenotypes. However, with plethora of datasets for epigenetic status an accurate statistical insight is obtained which could provide the shreds of evidences for phenotypic alteration and certain behavior of DNA is still missing in breast cancer. In this paper, four different types of data viz. Methylation, Histone, Human Genome and RNA-Seq data have been used to discriminate between cancerous and non-cancerous cells in breast cancer. The data has been pre-processed with in-house developed R-Script and The Weka tool deployed for the feature selection and classification. Four different types of feature selection methods viz., PCA, Gain Ratio, ReliefF, and CFS and eight different types of classifiers with 10-fold cross validation have been used for accurate classification of data. Different combinations/ratio of data sizes for training and testing were used for achieving optimized classification accuracy. The entire data processing is done for the prediction and testing of breast cancer. With the help of the machine learning method, the epigenetics data shows the prediction of breast cancer in the given set of cells. The results are supported by various data tables and graphs which demonstrate how CFS is the best feature selection method and linear SVM is the best classifier out of all the selected ones since it gives 100% accuracy the greatest number of times with the combination of different feature selection methods. However, for the CFS feature selection, Random Forest classifier selects the best model for different characteristics giving 100% accuracy the greatest number of times. The total number of features is 1564 spanning the four categories of CpG methylation, Histone H3 data, nucleotide composition and RNA-seq data. CFS feature selection method selects the best 245 features. CFS and Linear SVM classification technique selects the best overall model with various characteristics, with accuracy of 100% using 10-fold cross-validation. With the help of various tests and methods, it was observed that different classifiers exhibit different results when tested under various training-testing data. Also, the outcome varies with the variations in dataset and data processing techniques. However, feature selection method plays a vital role in driving the results. With the help of various tests and methods, it was observed that different classifiers exhibit different results
The year 2020 was a challenging year known mainly as the pandemic year. However, the notable even... more The year 2020 was a challenging year known mainly as the pandemic year. However, the notable event of George Floyd's killing broke many humans' hearts and made them protest on social media and the streets as well. In this research, we studied the hashtag "BlackLivesMatter," and some of its adversary contentions regarding George Floyd's demise in 2020 on Twitter. Based on the extensive aftermath of protests in the United States, we considered an area analysis to compare tweet rates in different groups to some previously studied statistics. The purpose is to investigate how racism content is correlated with life expectancy, poverty, and education. Findings revealed a significant relationship between online color-based contents and some physical world indicators.
This Research presents an innovative approach towards detecting fraudulent credit card transactio... more This Research presents an innovative approach towards detecting fraudulent credit card transactions. A commonly prevailing yet dominant problem faced in detection of fraudulent credit card transactions is the scarce occurrence of such fraudulent transactions with respect to legitimate (authorized) transactions. Therefore, any data that is recorded will always have a stark imbalance in the number of minority (fraudulent) and majority (legitimate) class samples. This imbalanced distribution of the training data among classes makes it hard for any learning algorithm to learn the features of the minority class. In this thesis work, we analyze the impact of applying class-balancing techniques on the training data namely oversampling (using SMOTE algorithm) for minority class and under sampling (using CMTNN) for majority class. The usage of most popular classification algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Logistic Regression (LR), Random Forest (RF) are processed on balanced data and which results to quantify the performance improvement provided by our approach. The experiments show that the hybrid approach which integrates Complementary Neural Network and Synthetic Minority Oversampling Technique gives a Quantitative performance in terms of Accuracy of 99% and 99.7% of AUC with Random Forest Classification Algorithm compared to simple undersampling and oversampling.
Player selection is one the most important tasks for any sport and cricket is no exception. The p... more Player selection is one the most important tasks for any sport and cricket is no exception. The performance of the players depends on various factors such as the opposition team, the venue, his current form etc. The team management, the coach and the captain select 11 players for each match from a squad of 15 to 20 players. They analyze different characteristics and the statistics of the players to select the best playing 11 for each match. Each batsman contributes by scoring maximum runs possible and each bowler contributes by taking maximum wickets and conceding minimum runs. This paper attempts to predict the performance of players as how many runs will each batsman score and how many wickets will each bowler take for both the teams. Both the problems are targeted as classification problems where number of runs and number of wickets are classified in different ranges. We used naïve bayes, random forest, multiclass SVM and decision tree classifiers to generate the prediction models for both the problems. Random Forest classifier was found to be the most accurate for both the problems.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Today's Information Society demands complete access to available information, which is often hete... more Today's Information Society demands complete access to available information, which is often heterogeneous and distributed. A key challenge in building the Semantic Web is integrating heterogeneous data sources. This paper presents an incremental algorithm for maintaining integration in evolving ontologies. For example, an increased number of smaller, task oriented ontologies, are emerging across the Bioinformatics domain to represent domain knowledge; integrating these heterogeneous ontologies is crucial for applications utilizing multiple ontologies. Most ontologies share a core of common knowledge allowing them to communicate, but no single ontology contains complete domain knowledge. Recent papers examined integrating ontologies using bipartite graph matching techniques. However, they do not address the issue of incrementally maintaining the matching in evolving ontologies. In this paper we present an incremental algorithm, OntoMaintain, which incrementally calculates the perfect matching among evolving ontologies and simultaneously updates the labels of the concepts of ontologies. We show that our algorithm has a complexity of O(n 2) compared to complexity O(n 3) of traditional matching algorithms. Experimental results prove that our algorithm maintains the correctness of a 'brute force method' while significantly reducing the time needed to find a perfect matching in evolving ontologies.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Smart innovation, systems and technologies, Oct 6, 2022
Sentiment analysis is one of the major fields of research for any case regarding natural language... more Sentiment analysis is one of the major fields of research for any case regarding natural language processing. For this purpose, the data is often some form of review or a feedback so that the emotion and the main sentiment behind the feedback can be assessed using machine learning techniques. A similar approach is performed in this research, In this report, Whatsapp reviews of customers in Telugu language were analysed and the sentiment polarity was calculated using a rule based approach. Telugu language is from the southern part of India and uses different sets of fonts from the general sets. The strings are treated as similarly as they are in any other machine learning process since the meaning behind them is captured through the patterns that emerge from the text. All the text processing is carried oput similarly to most NLP scenarios. To find out the overall sentiment in the review that is collected from the internet, a manual rule-based algorithm is developed which can apply certain sets of rules to a sentence to check the polarity, which can be positive, negative, or neutral. These rules check the presence of words such as major negative words and major positive words, and even auxiliary verbs and their position with respect to the negative and positive words. This rule-based approach was then used to train a machine learning model using a few parametric classifiers like K-nearest neighbours (KNN), XGBoost and support vector machines (SVM). The classifiers also fetched a decent accuracy of 81%, 82% and 78% respectively, which indicated towards the good performance of the rule-based approach and its effectiveness with error counts of 0.296, 0.288 and 0.252 with TF-IDF and 0.285, 0.285 iv and 0.234 with Bag of Words. Along the process, manual observation was also used to compare the assigned sentiments to the sentence to find the errors in the method. The best performance with respect to results was given by SVM classifiers that returned an f1 score of 79% and the lowest error count of 0.23 which is better among all the classifiers. The metrics which were used to judge these classifiers were the precision, recall, f1 scores and the mean squared error.
Significances of bioengineering & biosciences, Feb 12, 2024
Epigenomics is the field of biology dealing with modifications of the phenotype that do not cause... more Epigenomics is the field of biology dealing with modifications of the phenotype that do not cause any alteration in the sequence of cell DNA. Epigenomics adds something to the top of DNA to change the properties, which eventually prohibits certain DNA behavior from being performed. Such modifications occur in cancer cells and are the sole cause of cancer. The main objective of this research is to perform prediction and survival analysis of Head and Neck Squamous Cell Carcinoma (HNSCC) which is one of the biggest reasons of death and accounts for more than 650,000 cases and 330,000 deaths annually worldwide. Tobacco use, alcohol consumption, Human Papillomavirus (HPV) infection (for oropharyngeal cancer) and Epstein-Barr Virus (EBV) infection are the main risk factors associated with head and neck cancer (for nasopharyngeal cancer). Males, with a proportion ranging from 2:1 to 4:1, are slightly more affected than females. Four different types of data are used in this research to predict HNSCC in patients. The data includes methylation, histone, human genome and RNA-Sequences. The data is accessed through open-source technologies in R and Python programming languages. The data is processed to create features and with the help of statistical analysis and advanced machine learning techniques, the prediction of HNSCC is obtained from the fine-tuned model. The optimal model was determined to be ResNet50 utilizing the Sobel feature selection method for image data and Relief F-based feature selection for clinical features, achieving a test accuracy of 97.9%. The model's precision score was 0.929, its recall score was 0.930 and its F1 score was 0.930. Additionally, the ResNet101 model demonstrated the best performance using the Histogram of Gradients feature selection method for image data and mutual informationbased feature selection for clinical features, yielding a test accuracy of 96.1%. Its precision score, recall score and F1 score were identical to the aforementioned ResNet50 model. The research also utilized Kaplan-Meier survival analysis to investigate the survival rates of patients based on various factors, including age, gender, smoking status, tumor size and location of site. The results obtained from this analysis yielded the effectiveness of the method in providing valuable insights for risk assessment.
A number of query languages have been proposed in recent times for processing queries on XML and ... more A number of query languages have been proposed in recent times for processing queries on XML and semistructured data. All these query languages make use of regular path expressions to query XML data. To optimize the processing of query paths a number of indexing schemes have also been proposed recently. XPath provides the basis for processing queries on XML data in the form of regular path expressions. In this paper, we propose two algorithms called Entry-point algorithm and Rest-tree algorithm that exploit different types of indexes, which we have defined to efficiently process XPath queries. We also discuss and compare two variations in implementing these algorithms; Root-first and Bottom-first.
Player selection is one the most important tasks for any sport and cricket is no exception. The p... more Player selection is one the most important tasks for any sport and cricket is no exception. The performance of the players depends on various factors such as the opposition team, the venue, his current form etc. The team management, the coach and the captain select 11 players for each match from a squad of 15 to 20 players. They analyze different characteristics and the statistics of the players to select the best playing 11 for each match. Each batsman contributes by scoring maximum runs possible and each bowler contributes by taking maximum wickets and conceding minimum runs. This paper attempts to predict the performance of players as how many runs will each batsman score and how many wickets will each bowler take for both the teams. Both the problems are targeted as classification problems where number of runs and number of wickets are classified in different ranges. We used naïve bayes, random forest, multiclass SVM and decision tree classifiers to generate the prediction models for both the problems. Random Forest classifier was found to be the most accurate for both the problems.
The explosive growth in the amount of data in the field of biology, education, environmental rese... more The explosive growth in the amount of data in the field of biology, education, environmental research, sensor network, stock market, weather forecasting and many more due to vast use of internet in distributed environment has generated an urgent need for new techniques and tools that can intelligently automatically transform the processed data into useful information and knowledge. Hence data mining has become a research are with increasing importance. Since continuation in collection of more data at this scale, formalizing the process of big data analysis will become paramount. Given the vast amount of data are geographically spread across the globe, this means a very large number of models is generated, which raises problems on how to generalize knowledge in order to have a global view of the phenomena across the organization. This is applicable to web-based educational data. In this chapter, the new dynamic and scalable data mining approach has been discussed with educational data.
Communications in Computer and Information Science, 2016
The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The network transformation from physical network function to Virtual Network Function (VNF) requi... more The network transformation from physical network function to Virtual Network Function (VNF) requires a fundamental design change in the way applications and services are tested and assured in a hybrid virtual network. Once the VNFs are onboarded in a cloud network infrastructure, operators need to automatically test VNFs in real-time at the time of instantiation. This paper explicitly analyses the problem of adaptive self-healing of a Virtual Machine (VM) allocated by the VNF with the Deep Reinforcement Learning (DRL) approach. The DRL-based big data collection and analytics engine performs aggregation to probe data and analyze it for troubleshooting and performance management. This engine helps to determine corrective actions (self-healing), such as scaling or migrating VNFs. Hence, we proposed a Deep Queue Learning (DQL) based Deep Queue Networks (DQN) mechanism for self-healing VNFs in the virtualized infrastructure manager. Virtual network probes of closed-loop orchestration perform the automation of the VNF and provide analytics for real-time, policy-driven orchestration in an open networking automation platform through the stochastic gradient descent method for VNF service assurance and network reliability. The proposed DQN/DDQN mechanism optimizes the price and lowers the cost by 18% for resource usage without disrupting the Quality of Service (QoS) provided by the VNF. The outcome of adaptive self-healing of the VNFs enhances the computational performance by 27% compared to other state-of-the-art algorithms.
Cancer is a complex disease associated with the alteration in DNA methylation, histone modificati... more Cancer is a complex disease associated with the alteration in DNA methylation, histone modification, post transcriptional modifications with epigenetic changes in genes and pathways at different level, leading to generate the malignant phenotypes. However, with plethora of datasets for epigenetic status an accurate statistical insight is obtained which could provide the shreds of evidences for phenotypic alteration and certain behavior of DNA is still missing in breast cancer. In this paper, four different types of data viz. Methylation, Histone, Human Genome and RNA-Seq data have been used to discriminate between cancerous and non-cancerous cells in breast cancer. The data has been pre-processed with in-house developed R-Script and The Weka tool deployed for the feature selection and classification. Four different types of feature selection methods viz., PCA, Gain Ratio, ReliefF, and CFS and eight different types of classifiers with 10-fold cross validation have been used for accurate classification of data. Different combinations/ratio of data sizes for training and testing were used for achieving optimized classification accuracy. The entire data processing is done for the prediction and testing of breast cancer. With the help of the machine learning method, the epigenetics data shows the prediction of breast cancer in the given set of cells. The results are supported by various data tables and graphs which demonstrate how CFS is the best feature selection method and linear SVM is the best classifier out of all the selected ones since it gives 100% accuracy the greatest number of times with the combination of different feature selection methods. However, for the CFS feature selection, Random Forest classifier selects the best model for different characteristics giving 100% accuracy the greatest number of times. The total number of features is 1564 spanning the four categories of CpG methylation, Histone H3 data, nucleotide composition and RNA-seq data. CFS feature selection method selects the best 245 features. CFS and Linear SVM classification technique selects the best overall model with various characteristics, with accuracy of 100% using 10-fold cross-validation. With the help of various tests and methods, it was observed that different classifiers exhibit different results when tested under various training-testing data. Also, the outcome varies with the variations in dataset and data processing techniques. However, feature selection method plays a vital role in driving the results. With the help of various tests and methods, it was observed that different classifiers exhibit different results
The year 2020 was a challenging year known mainly as the pandemic year. However, the notable even... more The year 2020 was a challenging year known mainly as the pandemic year. However, the notable event of George Floyd's killing broke many humans' hearts and made them protest on social media and the streets as well. In this research, we studied the hashtag "BlackLivesMatter," and some of its adversary contentions regarding George Floyd's demise in 2020 on Twitter. Based on the extensive aftermath of protests in the United States, we considered an area analysis to compare tweet rates in different groups to some previously studied statistics. The purpose is to investigate how racism content is correlated with life expectancy, poverty, and education. Findings revealed a significant relationship between online color-based contents and some physical world indicators.
This Research presents an innovative approach towards detecting fraudulent credit card transactio... more This Research presents an innovative approach towards detecting fraudulent credit card transactions. A commonly prevailing yet dominant problem faced in detection of fraudulent credit card transactions is the scarce occurrence of such fraudulent transactions with respect to legitimate (authorized) transactions. Therefore, any data that is recorded will always have a stark imbalance in the number of minority (fraudulent) and majority (legitimate) class samples. This imbalanced distribution of the training data among classes makes it hard for any learning algorithm to learn the features of the minority class. In this thesis work, we analyze the impact of applying class-balancing techniques on the training data namely oversampling (using SMOTE algorithm) for minority class and under sampling (using CMTNN) for majority class. The usage of most popular classification algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Logistic Regression (LR), Random Forest (RF) are processed on balanced data and which results to quantify the performance improvement provided by our approach. The experiments show that the hybrid approach which integrates Complementary Neural Network and Synthetic Minority Oversampling Technique gives a Quantitative performance in terms of Accuracy of 99% and 99.7% of AUC with Random Forest Classification Algorithm compared to simple undersampling and oversampling.
Player selection is one the most important tasks for any sport and cricket is no exception. The p... more Player selection is one the most important tasks for any sport and cricket is no exception. The performance of the players depends on various factors such as the opposition team, the venue, his current form etc. The team management, the coach and the captain select 11 players for each match from a squad of 15 to 20 players. They analyze different characteristics and the statistics of the players to select the best playing 11 for each match. Each batsman contributes by scoring maximum runs possible and each bowler contributes by taking maximum wickets and conceding minimum runs. This paper attempts to predict the performance of players as how many runs will each batsman score and how many wickets will each bowler take for both the teams. Both the problems are targeted as classification problems where number of runs and number of wickets are classified in different ranges. We used naïve bayes, random forest, multiclass SVM and decision tree classifiers to generate the prediction models for both the problems. Random Forest classifier was found to be the most accurate for both the problems.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Today's Information Society demands complete access to available information, which is often hete... more Today's Information Society demands complete access to available information, which is often heterogeneous and distributed. A key challenge in building the Semantic Web is integrating heterogeneous data sources. This paper presents an incremental algorithm for maintaining integration in evolving ontologies. For example, an increased number of smaller, task oriented ontologies, are emerging across the Bioinformatics domain to represent domain knowledge; integrating these heterogeneous ontologies is crucial for applications utilizing multiple ontologies. Most ontologies share a core of common knowledge allowing them to communicate, but no single ontology contains complete domain knowledge. Recent papers examined integrating ontologies using bipartite graph matching techniques. However, they do not address the issue of incrementally maintaining the matching in evolving ontologies. In this paper we present an incremental algorithm, OntoMaintain, which incrementally calculates the perfect matching among evolving ontologies and simultaneously updates the labels of the concepts of ontologies. We show that our algorithm has a complexity of O(n 2) compared to complexity O(n 3) of traditional matching algorithms. Experimental results prove that our algorithm maintains the correctness of a 'brute force method' while significantly reducing the time needed to find a perfect matching in evolving ontologies.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
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