Diagnosis of liver infection at preliminary stage is important for better treatment. In todays sc... more Diagnosis of liver infection at preliminary stage is important for better treatment. In todays scenario devices like sensors are used for detection of infections. Accurate classification techniques are required for automatic identification of disease samples. In this context, this study utilizes data mining approaches for classification of liver patients from healthy individuals. Four algorithms (Naive Bayes, Bagging, Random forest and SVM) were implemented for classification using R platform. Further to improve the accuracy of classification a hybrid NeuroSVM model was developed using SVM and feed-forward artificial neural network (ANN). The hybrid model was tested for its performance using statistical parameters like root mean square error (RMSE) and mean absolute percentage error (MAPE). The model resulted in a prediction accuracy of 98.83%. The results suggested that development of hybrid model improved the accuracy of prediction. To serve the medicinal community for prediction ...
Bankruptcy is a legal procedure that claims a person or organization as a debtor. It is essential... more Bankruptcy is a legal procedure that claims a person or organization as a debtor. It is essential to ascertain the risk of bankruptcy at initial stages to prevent financial losses. In this perspective, different soft computing techniques can be employed to ascertain bankruptcy. This study proposes a bankruptcy prediction system to categorize the companies based on extent of risk. The prediction system acts as a decision support tool for detection of bankruptcy
International Journal of Business Information Systems, 2017
Movie marketing strategies have undergone a rapid metamorphosis over the years with the progress ... more Movie marketing strategies have undergone a rapid metamorphosis over the years with the progress in technological innovations and advent of social media. Social media gives a two way interacting platform and such interactions generate voluminous textual content which can be a source for deriving new insights into the customer behavioural dynamics and can also act as a handy tool for revenue enhancement. This study is designed to understand whether the polarity of the social media content of Bollywood movies can essentially reveal any insights about the potential box office revenues. The initial steps involved data collection from social media, followed by text mining to identify the sentiments about a movie. Furthermore, the relationship between the sentiments captured from social media and total revenue generated was explored in both pre-release and post-release scenarios and linear regression models were built. The model can be further improved by incorporating additional metrics.
Diagnosis of liver infection at preliminary stage is important for better treatment. In today’s s... more Diagnosis of liver infection at preliminary stage is important for better treatment. In today’s scenario devices like sensors are used for detection of infections. Accurate classification techniques are required for automatic identification of disease samples. In this context, this study utilizes data mining approaches for classification of liver patients from healthy individuals. Four algorithms (Naïve Bayes, Bagging, Random forest and SVM) were implemented for classification using R platform. Further to improve the accuracy of classification a hybrid NeuroSVM model was developed using SVM and feed-forward artificial neural network (ANN). The hybrid model was tested for its performance using statistical parameters like root mean square error (RMSE) and mean absolute percentage error (MAPE). The model resulted in a prediction accuracy of 98.83%. The results suggested that development of hybrid model improved the accuracy of prediction. To serve the medicinal community for prediction of liver disease among patients, a graphical user interface (GUI) has been developed using R. The GUI is deployed as a package in local repository of R platform for users to perform prediction.
Black cumin (Nigella sativa) is a spice having medicinal properties with pungent and bitter odour... more Black cumin (Nigella sativa) is a spice having medicinal properties with pungent and bitter odour. It is used since thousands of years to treat various ailments, including cancer mainly in South Asia and Middle Eastern regions. Substantial evidence in multiple research studies emphasizes about the therapeutic importance of bioactive principles of N. sativa in cancer bioassays; however, the exact mechanism of their anti-tumour action is still to be fully comprehended. The current study makes an attempt in this direction by exploiting the advancements in the Insilico reverse screening technology. In this study, three different Insilico Reverse Screening approaches have been employed for identifying the putative molecular targets of the bioactive principles in Black cumin (thymoquinone, alpha-hederin, dithymoquinone and thymohydroquinone) relevant to its anti-tumour functionality. The identified set of putative targets is further compared with the existing set of experimentally validated targets, so as to estimate the performance of insilico platforms. Subsequently, molecular docking simulations studies were performed to elucidate the molecular interactions between the bioactive compounds & their respective identified targets. The molecular interactions of one such target identified i.e. VEGF2 along with thymoquinone depicted one H-bond formed at the catalytic site. The molecular targets identified in this study need further confirmatory tests on cancer bioassays, in order to justify the research findings from Insilico platforms. This study has brought to light the effectiveness of usage of Insilico Reverse Screening protocols to characterise the un-identified target-ome of poly pharmacological bioactive agents in spices.
Advancement of unparalleled data in bioinformatics over the years is a major concern for storage ... more Advancement of unparalleled data in bioinformatics over the years is a major concern for storage and management. Such massive data must be handled efficiently to disseminate knowledge. Computational advancements in information technology present feasible analytical solutions to process such data. In this context, the paper is an attempt to highlight the influence of big data in bioinformatics. Some of the concepts emphasized are definition of big data; architectural platforms supporting data analytics; followed by the application of above mentioned analytical techniques towards complex problems in bioinformatics. The challenges and future prospects of big data analytics in bioinformatics are briefly discussed. This paper provides a comprehensive summary of several data analytical techniques available for bioinformatics researchers and computer scientists.
Diagnosis of liver infection at preliminary stage is important for better treatment. In todays sc... more Diagnosis of liver infection at preliminary stage is important for better treatment. In todays scenario devices like sensors are used for detection of infections. Accurate classification techniques are required for automatic identification of disease samples. In this context, this study utilizes data mining approaches for classification of liver patients from healthy individuals. Four algorithms (Naive Bayes, Bagging, Random forest and SVM) were implemented for classification using R platform. Further to improve the accuracy of classification a hybrid NeuroSVM model was developed using SVM and feed-forward artificial neural network (ANN). The hybrid model was tested for its performance using statistical parameters like root mean square error (RMSE) and mean absolute percentage error (MAPE). The model resulted in a prediction accuracy of 98.83%. The results suggested that development of hybrid model improved the accuracy of prediction. To serve the medicinal community for prediction ...
Bankruptcy is a legal procedure that claims a person or organization as a debtor. It is essential... more Bankruptcy is a legal procedure that claims a person or organization as a debtor. It is essential to ascertain the risk of bankruptcy at initial stages to prevent financial losses. In this perspective, different soft computing techniques can be employed to ascertain bankruptcy. This study proposes a bankruptcy prediction system to categorize the companies based on extent of risk. The prediction system acts as a decision support tool for detection of bankruptcy
International Journal of Business Information Systems, 2017
Movie marketing strategies have undergone a rapid metamorphosis over the years with the progress ... more Movie marketing strategies have undergone a rapid metamorphosis over the years with the progress in technological innovations and advent of social media. Social media gives a two way interacting platform and such interactions generate voluminous textual content which can be a source for deriving new insights into the customer behavioural dynamics and can also act as a handy tool for revenue enhancement. This study is designed to understand whether the polarity of the social media content of Bollywood movies can essentially reveal any insights about the potential box office revenues. The initial steps involved data collection from social media, followed by text mining to identify the sentiments about a movie. Furthermore, the relationship between the sentiments captured from social media and total revenue generated was explored in both pre-release and post-release scenarios and linear regression models were built. The model can be further improved by incorporating additional metrics.
Diagnosis of liver infection at preliminary stage is important for better treatment. In today’s s... more Diagnosis of liver infection at preliminary stage is important for better treatment. In today’s scenario devices like sensors are used for detection of infections. Accurate classification techniques are required for automatic identification of disease samples. In this context, this study utilizes data mining approaches for classification of liver patients from healthy individuals. Four algorithms (Naïve Bayes, Bagging, Random forest and SVM) were implemented for classification using R platform. Further to improve the accuracy of classification a hybrid NeuroSVM model was developed using SVM and feed-forward artificial neural network (ANN). The hybrid model was tested for its performance using statistical parameters like root mean square error (RMSE) and mean absolute percentage error (MAPE). The model resulted in a prediction accuracy of 98.83%. The results suggested that development of hybrid model improved the accuracy of prediction. To serve the medicinal community for prediction of liver disease among patients, a graphical user interface (GUI) has been developed using R. The GUI is deployed as a package in local repository of R platform for users to perform prediction.
Black cumin (Nigella sativa) is a spice having medicinal properties with pungent and bitter odour... more Black cumin (Nigella sativa) is a spice having medicinal properties with pungent and bitter odour. It is used since thousands of years to treat various ailments, including cancer mainly in South Asia and Middle Eastern regions. Substantial evidence in multiple research studies emphasizes about the therapeutic importance of bioactive principles of N. sativa in cancer bioassays; however, the exact mechanism of their anti-tumour action is still to be fully comprehended. The current study makes an attempt in this direction by exploiting the advancements in the Insilico reverse screening technology. In this study, three different Insilico Reverse Screening approaches have been employed for identifying the putative molecular targets of the bioactive principles in Black cumin (thymoquinone, alpha-hederin, dithymoquinone and thymohydroquinone) relevant to its anti-tumour functionality. The identified set of putative targets is further compared with the existing set of experimentally validated targets, so as to estimate the performance of insilico platforms. Subsequently, molecular docking simulations studies were performed to elucidate the molecular interactions between the bioactive compounds & their respective identified targets. The molecular interactions of one such target identified i.e. VEGF2 along with thymoquinone depicted one H-bond formed at the catalytic site. The molecular targets identified in this study need further confirmatory tests on cancer bioassays, in order to justify the research findings from Insilico platforms. This study has brought to light the effectiveness of usage of Insilico Reverse Screening protocols to characterise the un-identified target-ome of poly pharmacological bioactive agents in spices.
Advancement of unparalleled data in bioinformatics over the years is a major concern for storage ... more Advancement of unparalleled data in bioinformatics over the years is a major concern for storage and management. Such massive data must be handled efficiently to disseminate knowledge. Computational advancements in information technology present feasible analytical solutions to process such data. In this context, the paper is an attempt to highlight the influence of big data in bioinformatics. Some of the concepts emphasized are definition of big data; architectural platforms supporting data analytics; followed by the application of above mentioned analytical techniques towards complex problems in bioinformatics. The challenges and future prospects of big data analytics in bioinformatics are briefly discussed. This paper provides a comprehensive summary of several data analytical techniques available for bioinformatics researchers and computer scientists.
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Papers by Amulya Shree
decision support tool for detection of bankruptcy
like sensors are used for detection of infections. Accurate classification techniques are required for automatic
identification of disease samples. In this context, this study utilizes data mining approaches for classification of
liver patients from healthy individuals. Four algorithms (Naïve Bayes, Bagging, Random forest and SVM) were
implemented for classification using R platform. Further to improve the accuracy of classification a hybrid
NeuroSVM model was developed using SVM and feed-forward artificial neural network (ANN). The hybrid
model was tested for its performance using statistical parameters like root mean square error (RMSE) and mean
absolute percentage error (MAPE). The model resulted in a prediction accuracy of 98.83%. The results
suggested that development of hybrid model improved the accuracy of prediction. To serve the medicinal
community for prediction of liver disease among patients, a graphical user interface (GUI) has been developed
using R. The GUI is deployed as a package in local repository of R platform for users to perform prediction.
(thymoquinone, alpha-hederin, dithymoquinone and thymohydroquinone) relevant to its anti-tumour functionality. The identified set of putative targets is further compared with the existing set of experimentally validated targets, so as to estimate the performance of insilico platforms. Subsequently, molecular docking simulations studies were performed to elucidate the molecular interactions between the bioactive compounds & their respective identified targets. The molecular interactions of one such target identified i.e. VEGF2 along with thymoquinone depicted one H-bond formed at the catalytic site. The molecular targets identified in this study need further confirmatory tests on cancer bioassays, in order to justify the research findings from Insilico platforms. This study has brought to light the effectiveness of usage of Insilico Reverse Screening protocols to characterise the un-identified target-ome of poly pharmacological bioactive agents in spices.
decision support tool for detection of bankruptcy
like sensors are used for detection of infections. Accurate classification techniques are required for automatic
identification of disease samples. In this context, this study utilizes data mining approaches for classification of
liver patients from healthy individuals. Four algorithms (Naïve Bayes, Bagging, Random forest and SVM) were
implemented for classification using R platform. Further to improve the accuracy of classification a hybrid
NeuroSVM model was developed using SVM and feed-forward artificial neural network (ANN). The hybrid
model was tested for its performance using statistical parameters like root mean square error (RMSE) and mean
absolute percentage error (MAPE). The model resulted in a prediction accuracy of 98.83%. The results
suggested that development of hybrid model improved the accuracy of prediction. To serve the medicinal
community for prediction of liver disease among patients, a graphical user interface (GUI) has been developed
using R. The GUI is deployed as a package in local repository of R platform for users to perform prediction.
(thymoquinone, alpha-hederin, dithymoquinone and thymohydroquinone) relevant to its anti-tumour functionality. The identified set of putative targets is further compared with the existing set of experimentally validated targets, so as to estimate the performance of insilico platforms. Subsequently, molecular docking simulations studies were performed to elucidate the molecular interactions between the bioactive compounds & their respective identified targets. The molecular interactions of one such target identified i.e. VEGF2 along with thymoquinone depicted one H-bond formed at the catalytic site. The molecular targets identified in this study need further confirmatory tests on cancer bioassays, in order to justify the research findings from Insilico platforms. This study has brought to light the effectiveness of usage of Insilico Reverse Screening protocols to characterise the un-identified target-ome of poly pharmacological bioactive agents in spices.