The present research studied fault diagnosis of composite sheets using vibration signal processin... more The present research studied fault diagnosis of composite sheets using vibration signal processing and artificial intelligence (AI)-based methods. To this end, vibration signals were collected from sound and faulty composite plates. Using different time-frequency signal analysis and processing methods, a number of features were extracted from these signals and the most effective features containing further information on these composite plates were provided as input to different classification systems. The output of these classification systems reveals the faults in composite plates. The different types of classification systems used in this research were the support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), k-nearest neighbor (k-NN), artificial neural networks (ANNs), Extended Classifier System (XCS) algorithm, and the proposed improved XCS algorithm. The research results were reflective of the superiority of ANFIS in terms of precision, while this method...
This paper examines two different yet related questions related to explainable AI (XAI) practices... more This paper examines two different yet related questions related to explainable AI (XAI) practices. Machine learning (ML) is increasingly important in financial services, such as pre-approval, credit underwriting, investments, and various front-end and back-end activities. Machine Learning can automatically detect non-linearities and interactions in training data, facilitating faster and more accurate credit decisions. However, machine learning models are opaque and hard to explain, which are critical elements needed for establishing a reliable technology. The study compares various machine learning models, including single classifiers (logistic regression, decision trees, LDA, QDA), heterogeneous ensembles (AdaBoost, Random Forest), and sequential neural networks. The results indicate that ensemble classifiers and neural networks outperform. In addition, two advanced post-hoc model agnostic explainability techniques-LIME and SHAP are utilized to assess ML-based credit scoring models using the open-access datasets offered by US-based P2P Lending Platform, Lending Club. For this study, we are also using machine learning algorithms to develop new investment models and explore portfolio strategies that can maximize profitability while minimizing risk.
2022 5th International Conference on Contemporary Computing and Informatics (IC3I), 2022
Banks and other financial institutions will be able to adopt innovative products and services, an... more Banks and other financial institutions will be able to adopt innovative products and services, and more importantly, endure disruptions to their customers' experience, thanks to advancements in artificial intelligence (AI). Financial institutions would struggle to thrive in today's mechanized economy without the aid of fintech companies, which use innovative innovation to enhance or try and supplant human specialists with complex calculations. To keep a critical upper hand, banking and monetary organizations should take on man-made intelligence and integrate it into their business system and tasks. This examination venture will look at the elements of computer based intelligence environments in the banking and money industry and how they are quickly arising as a key disruptor by focusing on probably the most basic perplexing business worries around here. Several viewpoints exist on the potential of AI in this field, with the majority centering on the effects and relevance it will have on employment in the banking and financial services industry.
The present research studied fault diagnosis of composite sheets using vibration signal processin... more The present research studied fault diagnosis of composite sheets using vibration signal processing and artificial intelligence (AI)-based methods. To this end, vibration signals were collected from sound and faulty composite plates. Using different time-frequency signal analysis and processing methods, a number of features were extracted from these signals and the most effective features containing further information on these composite plates were provided as input to different classification systems. The output of these classification systems reveals the faults in composite plates. The different types of classification systems used in this research were the support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), k-nearest neighbor (k-NN), artificial neural networks (ANNs), Extended Classifier System (XCS) algorithm, and the proposed improved XCS algorithm. The research results were reflective of the superiority of ANFIS in terms of precision, while this method...
This paper examines two different yet related questions related to explainable AI (XAI) practices... more This paper examines two different yet related questions related to explainable AI (XAI) practices. Machine learning (ML) is increasingly important in financial services, such as pre-approval, credit underwriting, investments, and various front-end and back-end activities. Machine Learning can automatically detect non-linearities and interactions in training data, facilitating faster and more accurate credit decisions. However, machine learning models are opaque and hard to explain, which are critical elements needed for establishing a reliable technology. The study compares various machine learning models, including single classifiers (logistic regression, decision trees, LDA, QDA), heterogeneous ensembles (AdaBoost, Random Forest), and sequential neural networks. The results indicate that ensemble classifiers and neural networks outperform. In addition, two advanced post-hoc model agnostic explainability techniques-LIME and SHAP are utilized to assess ML-based credit scoring models using the open-access datasets offered by US-based P2P Lending Platform, Lending Club. For this study, we are also using machine learning algorithms to develop new investment models and explore portfolio strategies that can maximize profitability while minimizing risk.
2022 5th International Conference on Contemporary Computing and Informatics (IC3I), 2022
Banks and other financial institutions will be able to adopt innovative products and services, an... more Banks and other financial institutions will be able to adopt innovative products and services, and more importantly, endure disruptions to their customers' experience, thanks to advancements in artificial intelligence (AI). Financial institutions would struggle to thrive in today's mechanized economy without the aid of fintech companies, which use innovative innovation to enhance or try and supplant human specialists with complex calculations. To keep a critical upper hand, banking and monetary organizations should take on man-made intelligence and integrate it into their business system and tasks. This examination venture will look at the elements of computer based intelligence environments in the banking and money industry and how they are quickly arising as a key disruptor by focusing on probably the most basic perplexing business worries around here. Several viewpoints exist on the potential of AI in this field, with the majority centering on the effects and relevance it will have on employment in the banking and financial services industry.
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Papers by Swati Tyagi