Pratima Gautam - Academia.edu (original) (raw)
Papers by Pratima Gautam
International journal of health sciences
Many diseases are increasing day by day and it takes too much time to detect. In India after Covi... more Many diseases are increasing day by day and it takes too much time to detect. In India after Covid-19 pandemic so many diseases have been spread their era. Like Liver Disease, Lung cancer and Brain Stroke. They are among us and lethal diseases which need to predict earlier or in initial stage. Machine Learning (ML) is the subset of Artificial intelligent which can imitate like human intelligence and it can process the large information. The classification or prediction of those diseases can be done by classifiers. The disease prediction is the method which can predict future of Liver diseases, Lung Cancer and Brain Stroke possibilities based on the collection of historical dataset. In this paper we will use Hybrid Ensemble Classifier Model (HECM) which is the combination of Supervised Classifiers like LightGBM, Random Forest, KNN used as Ensemble Classifier then output given to Voting classifier for final output. Accuracy and time will be calculate
Microorganisms
Copper is an essential trace element for living cells. However, copper can be potentially toxic f... more Copper is an essential trace element for living cells. However, copper can be potentially toxic for bacterial cells when it is present in excess amounts due to its redox potential. Due to its biocidal properties, copper is prevalent in marine systems due to its use in antifouling paints and as an algaecide. Thus, marine bacteria must possess means of sensing and responding to both high copper levels and those in which it is present at only typical trace metal levels. Bacteria harbor diverse regulatory mechanisms that respond to intracellular and extracellular copper and maintain copper homeostasis in cells. This review presents an overview of the copper-associated signal transduction systems in marine bacteria, including the copper efflux systems, detoxification, and chaperone mechanisms. We performed a comparative genomics study of the copper-regulatory signal transduction system on marine bacteria to examine the influence of the environment on the presence, abundance, and diversit...
Journal of Microbiological Methods
A segment of 417 base pair(bp) for porcine circovirus type 2(PCV2) gene was amplified by PCR assa... more A segment of 417 base pair(bp) for porcine circovirus type 2(PCV2) gene was amplified by PCR assay from the tissues of a clinical diseased pig,and was cloned into the pGEM-T easy vector.Then,one recombinant plasmid with the 417 bp segment was constructed.The standard curve and the corresponding linear regression equation of PCV2 DNA level were obtained by real-time fluorescent quantitative PCR with the recombinant plasmid.The results showed that this method was easily reproducible and high specific while used to detect samples,its sensitivity was proved to be 102 copies/L.The present study indicated that the real-time PCR assay of PCV2 DNA was of high specificity,sensitivity and reproducibility,and provided a method to quantify PCV2.
International journal of health sciences
Internet users are increasing rapidly during the last decade, and after the Covid-19 outbreak, so... more Internet users are increasing rapidly during the last decade, and after the Covid-19 outbreak, social media platforms became the favorite source to express public responses. They are using Twitter, a free microblogging site, to express their thoughts, joys, and sorrows spontaneously. Researchers take great interest in analyzing public sentiments with the help of Data science techniques like natural language processing and machine learning methods, to predict public suggestions on topics of social concerns. In the proposed research article, we have collected public tweets during the third wave of Covid19 from 21st to 31st January 2022, and public sentiments are observed with 12 popular Machine Learning algorithms and commonly used words are represented as n-grams and here three n-grams (Unigram, Bigram, and Trigram) are collected and prediction is also observed on these data. It is observed that in all the cases LinearSVC presents the highest classification accuracy of approx. 96% fo...
arXiv (Cornell University), Sep 27, 2012
Discovering frequent itemset is a key difficulty in significant data mining applications, such as... more Discovering frequent itemset is a key difficulty in significant data mining applications, such as the discovery of association rules, strong rules, episodes, and minimal keys. The problem of developing models and algorithms for multilevel association mining poses for new challenges for mathematics and computer science. In this paper, we present a model of mining multilevel association rules which satisfies the different minimum support at each level, we have employed princer search concepts, multilevel taxonomy and different minimum supports to find multilevel association rules in a given transaction data set. This search is used only for maintaining and updating a new data structure. It is used to prune early candidates that would normally encounter in the top-down search. A main characteristic of the algorithms is that it does not require explicit examination of every frequent itemsets, an example is also given to demonstrate and support that the proposed mining algorithm can derive the multiple-level association rules under different supports in a simple and effective manner
Journal of Nobel Medical College
Intracranial aneurysms are sometimes associated with other vascular anomalies in extracranial loc... more Intracranial aneurysms are sometimes associated with other vascular anomalies in extracranial location. Coarctation of aorta, a congenital vascular lesion can be associated with intracranial aneurysms. In patients with coarctation, evaluation of intracranial vasculature is essential. We encountered a 12-year kid with subarachnoid hemorrhage. On further evaluation, she had anterior communicating artery aneurysm. On further evaluation for secondary causes of aneurysm, she had coarctation of aorta. She was surgically managed successfully by clipping of the aneurysm.
arXiv (Cornell University), Mar 22, 2010
Finding multilevel association rules in transaction databases is most commonly seen in is widely ... more Finding multilevel association rules in transaction databases is most commonly seen in is widely used in data mining. In this paper, we present a model of mining multilevel association rules which satisfies the different minimum support at each level, we have employed fuzzy set concepts, multi-level taxonomy and different minimum supports to find fuzzy multilevel association rules in a given transaction data set. Apriori property is used in model to prune the item sets. The proposed model adopts a topdown progressively deepening approach to derive large itemsets. This approach incorporates fuzzy boundaries instead of sharp boundary intervals. An example is also given to demonstrate and support that the proposed mining algorithm can derive the multiple-level association rules under different supports in a simple and effective manner.
In current years, the use of data mining techniques and related applications has enlarged a lot a... more In current years, the use of data mining techniques and related applications has enlarged a lot as it is used to extract important knowledge from large amount of data. Now a days the incredible growth of data in every field[1]. This increment of the data created lots of challenges in privacy. Privacy preserving in data mining becomes too essential due to share this data for our benefit purpose[2].This shared data may contain sensitive attributes, Database containing sensitive knowledge must be protected against illegal access. Therefore this it has become necessary to hide sensitive knowledge in database. Privacy preserving data mining (PPDM) try to conquer this problem by protecting the privacy of data without sacrificing the integrity of data. A number of techniques have been proposed for privacy-preserving data mining. To address this problem, Privacy Preservation Data Mining (PPDM) include association rule hiding method to protect privacy of sensitive data against association ru...
Abstract- In this paper an algorithm is proposed for mining multilevel association rules. A Boole... more Abstract- In this paper an algorithm is proposed for mining multilevel association rules. A Boolean Matrix based approach has been employed to discover frequent itemsets, the item forming a rule come from different levels. It adopts Boolean relational calculus to discover maximum frequent itemsets at lower level. When using this algorithm first time, it scans the database once and will generate the association rules. Apriori property is used in prune the item sets. It is not necessary to scan the database again; it uses Boolean logical operation to generate the multilevel association rules and also use top-down progressive deepening method.
Cloud computing has come to be on the top of any list of topics in the fields of computer because... more Cloud computing has come to be on the top of any list of topics in the fields of computer because of its cost effectiveness. Storage and maintenance of large amount of data used to be a nightmare of the end users, but the advent of cloud computing gave them breathers because of its third party computing capabilities, thereby cutting the cost of infrastructure and man power. Moreover cloud computing has greater flexibility and reliability. Since it has effected a paradigm shift in the whole field of computing, it has become an unavoidable concept with any government in the field of e-governance and rural development in the developing world, shifting the entire concept of computing from the user-owned network infrastructure to the third party computing capability, sourcing data from a server situated not in the user’s location but somewhere else. At the same time a word of caution becomes inevitable on the security aspect of the stored data which is kept in the open environment. This ...
In this paper an algorithm is proposed for mining multilevel association rules. A Boolean Matrix ... more In this paper an algorithm is proposed for mining multilevel association rules. A Boolean Matrix based approach has been employed to discover frequent itemsets, the item forming a rule come from different levels. It adopts Boolean relational calculus to discover maximum frequent itemsets at lower level. When using this algorithm first time, it scans the database once and will generate the association rules. Apriori property is used in prune the item sets. It is not necessary to scan the database again; it uses Boolean logical operation to generate the multilevel association rules and also use top-down progressive deepening method.
Data mining and knowledge engineering, 2010
Data mining is having a vital role in many of the applications like market-basket analysis, in bi... more Data mining is having a vital role in many of the applications like market-basket analysis, in biotechnology field etc. In data mining, frequent itemsets plays an important role which is used to identify the correlations among the fields of database. The problem of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. In most of the studies, multilevel rules will be mined through repeated mining from databases or mining the rules at each individually levels, it affects the efficiency, integrality and accuracy. This paper proposes a hash based method for multilevel association rule mining, which extracting knowledge implicit in transactions database with different support at each level. The proposed algorithm adopts a top-down progressively deepening approach to derive large itemsets. This approach incorporates boundaries instead of sharp boundary intervals. An example is also given to demonstrate that the pro...
Journal of Nepal Health Research Council, 2020
Key informant method is an innovative technique for identifying people who are disabled in the co... more Key informant method is an innovative technique for identifying people who are disabled in the community, by training local volunteers to act as key informants. Key informants are the local native people include teachers, village doctors, local health workers, religious leaders, community leaders, students, traditional healers, police, NGO staffs, health professionals, local journalists, village councils etc. For them, host organization organized a training to train the key informants to identify and refer the suspected disable people. The study proved key informant method as a valid method for identification of disabling children. Key informant method had a high sensitivity (average 98%) for case detection in all groups but specificity was lower (average 44%), particularly for hearing impairment. Key Informant Method can be used to collect data on types of disabilities, cause, the magnitude of impairments, severity, quantify a need for disabled people, and making access to services...
Identifying and locating defects in software projects is a difficult work. In particular, when pr... more Identifying and locating defects in software projects is a difficult work. In particular, when project sizes grow, this task becomes expensive. The aim of this research is to establish a method for identifying software defects using data mining applications methods. In this work we used Synthetic data Program (SD).We used mining methods to construct a two step model that predicts potentially defected modules within a given set of software modules with respect to their metric data. The data set used in the experiments is organized in two forms for learning and predicting purposes; the training set and the testing set. The experiments show that the two step model enhances defect prediction performance. KeywordsFault Prediction, Hardware, Software, Mining, Fault Detection.
Data mining and knowledge engineering, 2011
We have extended the capacity of the learn of mining association rules from single level to multi... more We have extended the capacity of the learn of mining association rules from single level to multiple concept levels and studied methods for mining multiple-level association rules from large transaction databases. Mining multiple-level association rules may lead to progressive mining of refined knowledge from data and have interesting applications for knowledge discovery in transaction databases, as well as other business or engineering databases.Mining frequent patterns in huge transactional database is an extremely researched area in the field of data mining. Mining frequent itemsets is a basic problem for mining association rules. Taking out association rules at multiple levels helps in discovers more specific and applicable knowledge. Even as computing the number of occurrence of an item we require to scan the given database lots of times. Thus we used partition method and boolean methods for finding frequent itemsets at each concept levels which reduce the number of scans, I/O ...
We can use educational data mining to predict student' performance on the basis of different ... more We can use educational data mining to predict student' performance on the basis of different attribute. In this paper, the classification task is used to predict the result of students. Decision tree (DT) learning is one of the most famous practical methods for analytical study. We use DT as prediction method in this paper. In this paper we propose a model using fuzzy set to predict more accurate result.Fuzzy logic brings in an improvement of analysis aspects due to the elasticity of fuzzy sets formalism. Therefore, we proposed a decision tree on fuzzy set data, which combines ID3 with fuzzy theory.The results are compared to some other popular classification algorithms.
International Journal of Advanced Research in Computer Science, 2017
Cloud computing model are obtaining ubiquitous authorization due to the heterogeneous convenience... more Cloud computing model are obtaining ubiquitous authorization due to the heterogeneous convenience they provide. Although, the security & privacy problems are the main considerable encumbrance holding back the universal adoption of this new emerging technology. Various researches are concentrated on enhancing the security on Software as well as Hardware levels on the cloud. But these interpretations do not mainly furnish the complete security way and therefore the data security compute (measure) are still kept under the access control of service provider. Trusted Computing is another research concept. In actuality, these furnish a set of tools controlled by the third party technologies to secure the Virtual Machines from the cloud computing providers. These approaches provides the tools to its consumers to assess and monitor the aspects of security their data, they don’t allocate the cloud consumers with high control capability. While as the new emerging DCS approach aims to provide ...
The problems of developing models and algorithms for multilevel association mining pose for new c... more The problems of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. These problems become more challenging when some form of uncertainty in data or relationships in data exists. In this paper, we present a partition technique for the multilevel association rule mining problem. Taking out association rules at multiple levels helps in discovering more specific and applicable knowledge. Even in computing, for the number of occurrence of an item, we require to scan the given database a lot of times. Thus we used partition method and boolean methods for finding frequent itemsets at each concept levels which reduce the number of scans, I/O cost and also reduce CPU overhead. In this paper, a new approach is introduced for solving the abovementioned issues. Therefore, this algorithm above all fit for very large size databases. We also use a top-down progressive deepening method, developed for efficient mining of mu...
In the contemporary world, cybercrime has provided ample space for propagating the unethical usag... more In the contemporary world, cybercrime has provided ample space for propagating the unethical usage of internet. In pursuance to same, the aim of the study was to explore the cybercrime awareness among male and female adolescents of Anantnag District of central Kashmir. Cybercrime awareness scale developed by Shalom Saini, Parminder Kaur (2008) was used for data collection. 300 male and female adolescents were selected from different educational institutions of Anantnag District. The collected data was put to suitable statistical treatment by using Frequency Distribution, Percentage, Mean, S.D and ‘t’ value. The results of the study indicate that there is significant difference between male and female adolescents on their level of cybercrime awareness. Male adolescents were observed with high level of cybercrime awareness as compared to female adolescents.
International Journal of Scientific & Technology Research, 2019
Web usage mining is used to identify and understand patterns that are taken from web data. Web us... more Web usage mining is used to identify and understand patterns that are taken from web data. Web usage mining is one of the applications of Data mining procedures to perceive and comprehend the requirements of electronic uses. In other words, the quick development of web-based business has incited the item to be over-burden, where the clients on the web are not ready to successfully choose the items they are presented to. To overcome these issues, web usage mining can be used to generate patterns. Web usage mining is an effective technique for extracting knowledge from unstructured data. In our research work, we are using HMM ranking and Expectation Minimization-Gaussian Mixture Model (EM-GMM) clustering for generating better patterns for the ease of web based services. The research work is performed on MATLAB simulation tool for generating rules for clusters first and second.
International journal of health sciences
Many diseases are increasing day by day and it takes too much time to detect. In India after Covi... more Many diseases are increasing day by day and it takes too much time to detect. In India after Covid-19 pandemic so many diseases have been spread their era. Like Liver Disease, Lung cancer and Brain Stroke. They are among us and lethal diseases which need to predict earlier or in initial stage. Machine Learning (ML) is the subset of Artificial intelligent which can imitate like human intelligence and it can process the large information. The classification or prediction of those diseases can be done by classifiers. The disease prediction is the method which can predict future of Liver diseases, Lung Cancer and Brain Stroke possibilities based on the collection of historical dataset. In this paper we will use Hybrid Ensemble Classifier Model (HECM) which is the combination of Supervised Classifiers like LightGBM, Random Forest, KNN used as Ensemble Classifier then output given to Voting classifier for final output. Accuracy and time will be calculate
Microorganisms
Copper is an essential trace element for living cells. However, copper can be potentially toxic f... more Copper is an essential trace element for living cells. However, copper can be potentially toxic for bacterial cells when it is present in excess amounts due to its redox potential. Due to its biocidal properties, copper is prevalent in marine systems due to its use in antifouling paints and as an algaecide. Thus, marine bacteria must possess means of sensing and responding to both high copper levels and those in which it is present at only typical trace metal levels. Bacteria harbor diverse regulatory mechanisms that respond to intracellular and extracellular copper and maintain copper homeostasis in cells. This review presents an overview of the copper-associated signal transduction systems in marine bacteria, including the copper efflux systems, detoxification, and chaperone mechanisms. We performed a comparative genomics study of the copper-regulatory signal transduction system on marine bacteria to examine the influence of the environment on the presence, abundance, and diversit...
Journal of Microbiological Methods
A segment of 417 base pair(bp) for porcine circovirus type 2(PCV2) gene was amplified by PCR assa... more A segment of 417 base pair(bp) for porcine circovirus type 2(PCV2) gene was amplified by PCR assay from the tissues of a clinical diseased pig,and was cloned into the pGEM-T easy vector.Then,one recombinant plasmid with the 417 bp segment was constructed.The standard curve and the corresponding linear regression equation of PCV2 DNA level were obtained by real-time fluorescent quantitative PCR with the recombinant plasmid.The results showed that this method was easily reproducible and high specific while used to detect samples,its sensitivity was proved to be 102 copies/L.The present study indicated that the real-time PCR assay of PCV2 DNA was of high specificity,sensitivity and reproducibility,and provided a method to quantify PCV2.
International journal of health sciences
Internet users are increasing rapidly during the last decade, and after the Covid-19 outbreak, so... more Internet users are increasing rapidly during the last decade, and after the Covid-19 outbreak, social media platforms became the favorite source to express public responses. They are using Twitter, a free microblogging site, to express their thoughts, joys, and sorrows spontaneously. Researchers take great interest in analyzing public sentiments with the help of Data science techniques like natural language processing and machine learning methods, to predict public suggestions on topics of social concerns. In the proposed research article, we have collected public tweets during the third wave of Covid19 from 21st to 31st January 2022, and public sentiments are observed with 12 popular Machine Learning algorithms and commonly used words are represented as n-grams and here three n-grams (Unigram, Bigram, and Trigram) are collected and prediction is also observed on these data. It is observed that in all the cases LinearSVC presents the highest classification accuracy of approx. 96% fo...
arXiv (Cornell University), Sep 27, 2012
Discovering frequent itemset is a key difficulty in significant data mining applications, such as... more Discovering frequent itemset is a key difficulty in significant data mining applications, such as the discovery of association rules, strong rules, episodes, and minimal keys. The problem of developing models and algorithms for multilevel association mining poses for new challenges for mathematics and computer science. In this paper, we present a model of mining multilevel association rules which satisfies the different minimum support at each level, we have employed princer search concepts, multilevel taxonomy and different minimum supports to find multilevel association rules in a given transaction data set. This search is used only for maintaining and updating a new data structure. It is used to prune early candidates that would normally encounter in the top-down search. A main characteristic of the algorithms is that it does not require explicit examination of every frequent itemsets, an example is also given to demonstrate and support that the proposed mining algorithm can derive the multiple-level association rules under different supports in a simple and effective manner
Journal of Nobel Medical College
Intracranial aneurysms are sometimes associated with other vascular anomalies in extracranial loc... more Intracranial aneurysms are sometimes associated with other vascular anomalies in extracranial location. Coarctation of aorta, a congenital vascular lesion can be associated with intracranial aneurysms. In patients with coarctation, evaluation of intracranial vasculature is essential. We encountered a 12-year kid with subarachnoid hemorrhage. On further evaluation, she had anterior communicating artery aneurysm. On further evaluation for secondary causes of aneurysm, she had coarctation of aorta. She was surgically managed successfully by clipping of the aneurysm.
arXiv (Cornell University), Mar 22, 2010
Finding multilevel association rules in transaction databases is most commonly seen in is widely ... more Finding multilevel association rules in transaction databases is most commonly seen in is widely used in data mining. In this paper, we present a model of mining multilevel association rules which satisfies the different minimum support at each level, we have employed fuzzy set concepts, multi-level taxonomy and different minimum supports to find fuzzy multilevel association rules in a given transaction data set. Apriori property is used in model to prune the item sets. The proposed model adopts a topdown progressively deepening approach to derive large itemsets. This approach incorporates fuzzy boundaries instead of sharp boundary intervals. An example is also given to demonstrate and support that the proposed mining algorithm can derive the multiple-level association rules under different supports in a simple and effective manner.
In current years, the use of data mining techniques and related applications has enlarged a lot a... more In current years, the use of data mining techniques and related applications has enlarged a lot as it is used to extract important knowledge from large amount of data. Now a days the incredible growth of data in every field[1]. This increment of the data created lots of challenges in privacy. Privacy preserving in data mining becomes too essential due to share this data for our benefit purpose[2].This shared data may contain sensitive attributes, Database containing sensitive knowledge must be protected against illegal access. Therefore this it has become necessary to hide sensitive knowledge in database. Privacy preserving data mining (PPDM) try to conquer this problem by protecting the privacy of data without sacrificing the integrity of data. A number of techniques have been proposed for privacy-preserving data mining. To address this problem, Privacy Preservation Data Mining (PPDM) include association rule hiding method to protect privacy of sensitive data against association ru...
Abstract- In this paper an algorithm is proposed for mining multilevel association rules. A Boole... more Abstract- In this paper an algorithm is proposed for mining multilevel association rules. A Boolean Matrix based approach has been employed to discover frequent itemsets, the item forming a rule come from different levels. It adopts Boolean relational calculus to discover maximum frequent itemsets at lower level. When using this algorithm first time, it scans the database once and will generate the association rules. Apriori property is used in prune the item sets. It is not necessary to scan the database again; it uses Boolean logical operation to generate the multilevel association rules and also use top-down progressive deepening method.
Cloud computing has come to be on the top of any list of topics in the fields of computer because... more Cloud computing has come to be on the top of any list of topics in the fields of computer because of its cost effectiveness. Storage and maintenance of large amount of data used to be a nightmare of the end users, but the advent of cloud computing gave them breathers because of its third party computing capabilities, thereby cutting the cost of infrastructure and man power. Moreover cloud computing has greater flexibility and reliability. Since it has effected a paradigm shift in the whole field of computing, it has become an unavoidable concept with any government in the field of e-governance and rural development in the developing world, shifting the entire concept of computing from the user-owned network infrastructure to the third party computing capability, sourcing data from a server situated not in the user’s location but somewhere else. At the same time a word of caution becomes inevitable on the security aspect of the stored data which is kept in the open environment. This ...
In this paper an algorithm is proposed for mining multilevel association rules. A Boolean Matrix ... more In this paper an algorithm is proposed for mining multilevel association rules. A Boolean Matrix based approach has been employed to discover frequent itemsets, the item forming a rule come from different levels. It adopts Boolean relational calculus to discover maximum frequent itemsets at lower level. When using this algorithm first time, it scans the database once and will generate the association rules. Apriori property is used in prune the item sets. It is not necessary to scan the database again; it uses Boolean logical operation to generate the multilevel association rules and also use top-down progressive deepening method.
Data mining and knowledge engineering, 2010
Data mining is having a vital role in many of the applications like market-basket analysis, in bi... more Data mining is having a vital role in many of the applications like market-basket analysis, in biotechnology field etc. In data mining, frequent itemsets plays an important role which is used to identify the correlations among the fields of database. The problem of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. In most of the studies, multilevel rules will be mined through repeated mining from databases or mining the rules at each individually levels, it affects the efficiency, integrality and accuracy. This paper proposes a hash based method for multilevel association rule mining, which extracting knowledge implicit in transactions database with different support at each level. The proposed algorithm adopts a top-down progressively deepening approach to derive large itemsets. This approach incorporates boundaries instead of sharp boundary intervals. An example is also given to demonstrate that the pro...
Journal of Nepal Health Research Council, 2020
Key informant method is an innovative technique for identifying people who are disabled in the co... more Key informant method is an innovative technique for identifying people who are disabled in the community, by training local volunteers to act as key informants. Key informants are the local native people include teachers, village doctors, local health workers, religious leaders, community leaders, students, traditional healers, police, NGO staffs, health professionals, local journalists, village councils etc. For them, host organization organized a training to train the key informants to identify and refer the suspected disable people. The study proved key informant method as a valid method for identification of disabling children. Key informant method had a high sensitivity (average 98%) for case detection in all groups but specificity was lower (average 44%), particularly for hearing impairment. Key Informant Method can be used to collect data on types of disabilities, cause, the magnitude of impairments, severity, quantify a need for disabled people, and making access to services...
Identifying and locating defects in software projects is a difficult work. In particular, when pr... more Identifying and locating defects in software projects is a difficult work. In particular, when project sizes grow, this task becomes expensive. The aim of this research is to establish a method for identifying software defects using data mining applications methods. In this work we used Synthetic data Program (SD).We used mining methods to construct a two step model that predicts potentially defected modules within a given set of software modules with respect to their metric data. The data set used in the experiments is organized in two forms for learning and predicting purposes; the training set and the testing set. The experiments show that the two step model enhances defect prediction performance. KeywordsFault Prediction, Hardware, Software, Mining, Fault Detection.
Data mining and knowledge engineering, 2011
We have extended the capacity of the learn of mining association rules from single level to multi... more We have extended the capacity of the learn of mining association rules from single level to multiple concept levels and studied methods for mining multiple-level association rules from large transaction databases. Mining multiple-level association rules may lead to progressive mining of refined knowledge from data and have interesting applications for knowledge discovery in transaction databases, as well as other business or engineering databases.Mining frequent patterns in huge transactional database is an extremely researched area in the field of data mining. Mining frequent itemsets is a basic problem for mining association rules. Taking out association rules at multiple levels helps in discovers more specific and applicable knowledge. Even as computing the number of occurrence of an item we require to scan the given database lots of times. Thus we used partition method and boolean methods for finding frequent itemsets at each concept levels which reduce the number of scans, I/O ...
We can use educational data mining to predict student' performance on the basis of different ... more We can use educational data mining to predict student' performance on the basis of different attribute. In this paper, the classification task is used to predict the result of students. Decision tree (DT) learning is one of the most famous practical methods for analytical study. We use DT as prediction method in this paper. In this paper we propose a model using fuzzy set to predict more accurate result.Fuzzy logic brings in an improvement of analysis aspects due to the elasticity of fuzzy sets formalism. Therefore, we proposed a decision tree on fuzzy set data, which combines ID3 with fuzzy theory.The results are compared to some other popular classification algorithms.
International Journal of Advanced Research in Computer Science, 2017
Cloud computing model are obtaining ubiquitous authorization due to the heterogeneous convenience... more Cloud computing model are obtaining ubiquitous authorization due to the heterogeneous convenience they provide. Although, the security & privacy problems are the main considerable encumbrance holding back the universal adoption of this new emerging technology. Various researches are concentrated on enhancing the security on Software as well as Hardware levels on the cloud. But these interpretations do not mainly furnish the complete security way and therefore the data security compute (measure) are still kept under the access control of service provider. Trusted Computing is another research concept. In actuality, these furnish a set of tools controlled by the third party technologies to secure the Virtual Machines from the cloud computing providers. These approaches provides the tools to its consumers to assess and monitor the aspects of security their data, they don’t allocate the cloud consumers with high control capability. While as the new emerging DCS approach aims to provide ...
The problems of developing models and algorithms for multilevel association mining pose for new c... more The problems of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. These problems become more challenging when some form of uncertainty in data or relationships in data exists. In this paper, we present a partition technique for the multilevel association rule mining problem. Taking out association rules at multiple levels helps in discovering more specific and applicable knowledge. Even in computing, for the number of occurrence of an item, we require to scan the given database a lot of times. Thus we used partition method and boolean methods for finding frequent itemsets at each concept levels which reduce the number of scans, I/O cost and also reduce CPU overhead. In this paper, a new approach is introduced for solving the abovementioned issues. Therefore, this algorithm above all fit for very large size databases. We also use a top-down progressive deepening method, developed for efficient mining of mu...
In the contemporary world, cybercrime has provided ample space for propagating the unethical usag... more In the contemporary world, cybercrime has provided ample space for propagating the unethical usage of internet. In pursuance to same, the aim of the study was to explore the cybercrime awareness among male and female adolescents of Anantnag District of central Kashmir. Cybercrime awareness scale developed by Shalom Saini, Parminder Kaur (2008) was used for data collection. 300 male and female adolescents were selected from different educational institutions of Anantnag District. The collected data was put to suitable statistical treatment by using Frequency Distribution, Percentage, Mean, S.D and ‘t’ value. The results of the study indicate that there is significant difference between male and female adolescents on their level of cybercrime awareness. Male adolescents were observed with high level of cybercrime awareness as compared to female adolescents.
International Journal of Scientific & Technology Research, 2019
Web usage mining is used to identify and understand patterns that are taken from web data. Web us... more Web usage mining is used to identify and understand patterns that are taken from web data. Web usage mining is one of the applications of Data mining procedures to perceive and comprehend the requirements of electronic uses. In other words, the quick development of web-based business has incited the item to be over-burden, where the clients on the web are not ready to successfully choose the items they are presented to. To overcome these issues, web usage mining can be used to generate patterns. Web usage mining is an effective technique for extracting knowledge from unstructured data. In our research work, we are using HMM ranking and Expectation Minimization-Gaussian Mixture Model (EM-GMM) clustering for generating better patterns for the ease of web based services. The research work is performed on MATLAB simulation tool for generating rules for clusters first and second.