International Journal of Research in Engineering and Technology, 2018
Nowadays, as Population is increasing day by day this growth leads to many environmental problems... more Nowadays, as Population is increasing day by day this growth leads to many environmental problems such as Pollution, carbon emission, Environmental imbalance which further leads to Global warming. So, to Tackle with these problems and as awareness towards sustainable society is growing "Green energy" and Green materials for manufacturing is gaining predominant Recognition. Nanotechnology plays significant role in many fields such as medical, portable electronics and majorly in Electric Vehicles. So currently nanomaterials are gaining vital recognition in all manufacturing processes. Using these nanomaterials, improvement in performance and and proliferation in productivity is achieved and this overall impact enhanced the proliferation of industries. But these nanomaterials are not environmental friendly; as manufacturing process involves certain amount of chemical processes so nanomaterials produce some hazardous elements or pollutants which can make adverse impact on environment, so use of green nanomaterials is very significant for maintaining environmental balance. These green nanomaterials are based on green nanotechnology in which materials that used in nanomaterials are not harmful to environment. This green nanotechnology enables nanomaterials not only to ameliorate the overall performance but also in most environmental friendly way that is keeping environment clean. This green nanotechnology can be the one of the best approach towards clean sustainable development. This paper mainly focuses on role of green nanotechnology for obtaining green nanomaterials which makes possible to obtain environmental friendly nanomaterials. Green nanomaterials plays significant role in mitigating environmental problems and helps for keeping environment clean. Green energy plays vital role in energy security, sustainable development and overall social, economical development.
International journal of research in engineering and technology, Jun 25, 2018
Nowadays, as Population is increasing day by day this growth leads to many environmental problems... more Nowadays, as Population is increasing day by day this growth leads to many environmental problems such as Pollution, carbon emission, Environmental imbalance which further leads to Global warming. So, to Tackle with these problems and as awareness towards sustainable society is growing "Green energy" and Green materials for manufacturing is gaining predominant Recognition. Nanotechnology plays significant role in many fields such as medical, portable electronics and majorly in Electric Vehicles. So currently nanomaterials are gaining vital recognition in all manufacturing processes. Using these nanomaterials, improvement in performance and and proliferation in productivity is achieved and this overall impact enhanced the proliferation of industries. But these nanomaterials are not environmental friendly; as manufacturing process involves certain amount of chemical processes so nanomaterials produce some hazardous elements or pollutants which can make adverse impact on environment, so use of green nanomaterials is very significant for maintaining environmental balance. These green nanomaterials are based on green nanotechnology in which materials that used in nanomaterials are not harmful to environment. This green nanotechnology enables nanomaterials not only to ameliorate the overall performance but also in most environmental friendly way that is keeping environment clean. This green nanotechnology can be the one of the best approach towards clean sustainable development. This paper mainly focuses on role of green nanotechnology for obtaining green nanomaterials which makes possible to obtain environmental friendly nanomaterials. Green nanomaterials plays significant role in mitigating environmental problems and helps for keeping environment clean. Green energy plays vital role in energy security, sustainable development and overall social, economical development.
Machine learning algorithms have become increasingly prevalent in multiple domains, such as auton... more Machine learning algorithms have become increasingly prevalent in multiple domains, such as autonomous driving, healthcare, and finance. In such domains, data preparation remains a significant challenge in developing accurate models, requiring significant expertise and time investment to explore the huge search space of well-suited data curation and transformation tools. To address this challenge, we present AutoCure, a novel and configuration-free data curation pipeline that improves the quality of tabular data. Unlike traditional data curation methods, AutoCure synthetically enhances the density of the clean data fraction through an adaptive ensemble-based error detection method and a data augmentation module. In practice, AutoCure can be integrated with open source tools, e.g., Auto-sklearn, H2O, and TPOT, to promote the democratization of machine learning. As a proof of concept, we provide a comparative evaluation of AutoCure against 28 combinations of traditional data curation tools, demonstrating superior performance and predictive accuracy without user intervention. Our evaluation shows that AutoCure is an effective approach to automating data preparation and improving the accuracy of machine learning models. CCS CONCEPTS • Information systems → Data cleaning; • Computing methodologies → Machine learning; • Mathematics of computing → Information theory.
Proceedings of the Sixth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management
Machine learning algorithms have become increasingly prevalent in multiple domains, such as auton... more Machine learning algorithms have become increasingly prevalent in multiple domains, such as autonomous driving, healthcare, and finance. In such domains, data preparation remains a significant challenge in developing accurate models, requiring significant expertise and time investment to explore the huge search space of well-suited data curation and transformation tools. To address this challenge, we present AutoCure, a novel and configuration-free data curation pipeline that improves the quality of tabular data. Unlike traditional data curation methods, AutoCure synthetically enhances the density of the clean data fraction through an adaptive ensemble-based error detection method and a data augmentation module. In practice, AutoCure can be integrated with open source tools, e.g., Auto-sklearn, H2O, and TPOT, to promote the democratization of machine learning. As a proof of concept, we provide a comparative evaluation of AutoCure against 28 combinations of traditional data curation tools, demonstrating superior performance and predictive accuracy without user intervention. Our evaluation shows that AutoCure is an effective approach to automating data preparation and improving the accuracy of machine learning models. CCS CONCEPTS • Information systems → Data cleaning; • Computing methodologies → Machine learning; • Mathematics of computing → Information theory.
Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
Explainable AI (XAI) is currently a vibrant research topic. However, the absence of ground truth ... more Explainable AI (XAI) is currently a vibrant research topic. However, the absence of ground truth explanations makes it difficult to evaluate XAI systems such as Explainable Search. We present an Explainable Search system with a focus on evaluating the XAI aspect of Trustworthiness along with the retrieval performance. We present SIMFIC 2.0 (Similarity in Fiction), an enhanced version of a recent [1] explainable search system. The system retrieves books similar to a selected book in a query-by-example setting. The motivation is to explain the notion of similarity in fiction books. We extract hand-crafted interpretable features for fiction books and provide global explanations by fitting a linear regression and local explanations based on similarity measures. The Trustworthiness facet is evaluated using user studies, while the ranking performance is compared by analysis of user clicks. Eye tracking is used to investigate user attention to the explanation elements when interacting with the interface. Initial experiments show statistically significant results on the Trustworthiness of the system, paving way for interesting research directions that are being investigated. CCS CONCEPTS • Computing methodologies → Artificial intelligence; • Information systems → Evaluation of retrieval results.
International Journal of Research in Engineering and Technology, 2018
Nowadays, as Population is increasing day by day this growth leads to many environmental problems... more Nowadays, as Population is increasing day by day this growth leads to many environmental problems such as Pollution, carbon emission, Environmental imbalance which further leads to Global warming. So, to Tackle with these problems and as awareness towards sustainable society is growing "Green energy" and Green materials for manufacturing is gaining predominant Recognition. Nanotechnology plays significant role in many fields such as medical, portable electronics and majorly in Electric Vehicles. So currently nanomaterials are gaining vital recognition in all manufacturing processes. Using these nanomaterials, improvement in performance and and proliferation in productivity is achieved and this overall impact enhanced the proliferation of industries. But these nanomaterials are not environmental friendly; as manufacturing process involves certain amount of chemical processes so nanomaterials produce some hazardous elements or pollutants which can make adverse impact on environment, so use of green nanomaterials is very significant for maintaining environmental balance. These green nanomaterials are based on green nanotechnology in which materials that used in nanomaterials are not harmful to environment. This green nanotechnology enables nanomaterials not only to ameliorate the overall performance but also in most environmental friendly way that is keeping environment clean. This green nanotechnology can be the one of the best approach towards clean sustainable development. This paper mainly focuses on role of green nanotechnology for obtaining green nanomaterials which makes possible to obtain environmental friendly nanomaterials. Green nanomaterials plays significant role in mitigating environmental problems and helps for keeping environment clean. Green energy plays vital role in energy security, sustainable development and overall social, economical development.
International journal of research in engineering and technology, Jun 25, 2018
Nowadays, as Population is increasing day by day this growth leads to many environmental problems... more Nowadays, as Population is increasing day by day this growth leads to many environmental problems such as Pollution, carbon emission, Environmental imbalance which further leads to Global warming. So, to Tackle with these problems and as awareness towards sustainable society is growing "Green energy" and Green materials for manufacturing is gaining predominant Recognition. Nanotechnology plays significant role in many fields such as medical, portable electronics and majorly in Electric Vehicles. So currently nanomaterials are gaining vital recognition in all manufacturing processes. Using these nanomaterials, improvement in performance and and proliferation in productivity is achieved and this overall impact enhanced the proliferation of industries. But these nanomaterials are not environmental friendly; as manufacturing process involves certain amount of chemical processes so nanomaterials produce some hazardous elements or pollutants which can make adverse impact on environment, so use of green nanomaterials is very significant for maintaining environmental balance. These green nanomaterials are based on green nanotechnology in which materials that used in nanomaterials are not harmful to environment. This green nanotechnology enables nanomaterials not only to ameliorate the overall performance but also in most environmental friendly way that is keeping environment clean. This green nanotechnology can be the one of the best approach towards clean sustainable development. This paper mainly focuses on role of green nanotechnology for obtaining green nanomaterials which makes possible to obtain environmental friendly nanomaterials. Green nanomaterials plays significant role in mitigating environmental problems and helps for keeping environment clean. Green energy plays vital role in energy security, sustainable development and overall social, economical development.
Machine learning algorithms have become increasingly prevalent in multiple domains, such as auton... more Machine learning algorithms have become increasingly prevalent in multiple domains, such as autonomous driving, healthcare, and finance. In such domains, data preparation remains a significant challenge in developing accurate models, requiring significant expertise and time investment to explore the huge search space of well-suited data curation and transformation tools. To address this challenge, we present AutoCure, a novel and configuration-free data curation pipeline that improves the quality of tabular data. Unlike traditional data curation methods, AutoCure synthetically enhances the density of the clean data fraction through an adaptive ensemble-based error detection method and a data augmentation module. In practice, AutoCure can be integrated with open source tools, e.g., Auto-sklearn, H2O, and TPOT, to promote the democratization of machine learning. As a proof of concept, we provide a comparative evaluation of AutoCure against 28 combinations of traditional data curation tools, demonstrating superior performance and predictive accuracy without user intervention. Our evaluation shows that AutoCure is an effective approach to automating data preparation and improving the accuracy of machine learning models. CCS CONCEPTS • Information systems → Data cleaning; • Computing methodologies → Machine learning; • Mathematics of computing → Information theory.
Proceedings of the Sixth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management
Machine learning algorithms have become increasingly prevalent in multiple domains, such as auton... more Machine learning algorithms have become increasingly prevalent in multiple domains, such as autonomous driving, healthcare, and finance. In such domains, data preparation remains a significant challenge in developing accurate models, requiring significant expertise and time investment to explore the huge search space of well-suited data curation and transformation tools. To address this challenge, we present AutoCure, a novel and configuration-free data curation pipeline that improves the quality of tabular data. Unlike traditional data curation methods, AutoCure synthetically enhances the density of the clean data fraction through an adaptive ensemble-based error detection method and a data augmentation module. In practice, AutoCure can be integrated with open source tools, e.g., Auto-sklearn, H2O, and TPOT, to promote the democratization of machine learning. As a proof of concept, we provide a comparative evaluation of AutoCure against 28 combinations of traditional data curation tools, demonstrating superior performance and predictive accuracy without user intervention. Our evaluation shows that AutoCure is an effective approach to automating data preparation and improving the accuracy of machine learning models. CCS CONCEPTS • Information systems → Data cleaning; • Computing methodologies → Machine learning; • Mathematics of computing → Information theory.
Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
Explainable AI (XAI) is currently a vibrant research topic. However, the absence of ground truth ... more Explainable AI (XAI) is currently a vibrant research topic. However, the absence of ground truth explanations makes it difficult to evaluate XAI systems such as Explainable Search. We present an Explainable Search system with a focus on evaluating the XAI aspect of Trustworthiness along with the retrieval performance. We present SIMFIC 2.0 (Similarity in Fiction), an enhanced version of a recent [1] explainable search system. The system retrieves books similar to a selected book in a query-by-example setting. The motivation is to explain the notion of similarity in fiction books. We extract hand-crafted interpretable features for fiction books and provide global explanations by fitting a linear regression and local explanations based on similarity measures. The Trustworthiness facet is evaluated using user studies, while the ranking performance is compared by analysis of user clicks. Eye tracking is used to investigate user attention to the explanation elements when interacting with the interface. Initial experiments show statistically significant results on the Trustworthiness of the system, paving way for interesting research directions that are being investigated. CCS CONCEPTS • Computing methodologies → Artificial intelligence; • Information systems → Evaluation of retrieval results.
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