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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Parth Dhananjay Akre Utkarsha Pacharaney Warsha Siraskar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The role of FinTech in advancing contemporary economies, societies, and technologies is growing. Smart FinTech is the next wave of FinTech that leverages artificial intelligence and data science methodologies to drive major advancements in fields including blockchain, cryptocurrencies, InsurTech, PayTech, BankingTech, TradeTech, LendTech, InsurTech, WealthTech, and RiskTech. An overview of these domains, as well as the artificial intelligence and data science techniques applied, is given in this paper. These techniques include augmentation, optimization, privacy-preserving processing, deep learning, federated learning, and intelligent interactions, complex system methods, mathematical approaches, knowledgeable communication, recognition and responses, and data analytics. This study offers a thorough analysis of smart financial companies, the obstacles they face, the smart FinTech environment, the artificial intelligence and data science methods that make smart FinTech possible, and potential future research areas for the artificial intelligence and data science community. To improve comprehension and analysis, the work makes use of visual aids such as figures and tables. The relevance of data quality, including data augmentation and manipulation, as well as enhancing fairness and decision-support, is emphasized in the study for financial firms. It also covers ethical and explicable FinTech, with a focus on privacy preservation, accountability, openness, and objectivity in FinTech and business designs. The potential for more research and innovation at the heart of analytics, data science, and FinTech is highlighted in the paper's conclusion.

关键词： Ethics Visualization Technological innovation Fintech Education Companies data science Regulation Risk management Artificial intelligence

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On conditional versus marginal bias in multi-armed bandits 37

On conditional versus marginal bias in multi-armed bandits

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37th International Conference on machine learning, ICML 2020

作者： Shin, Jaehyeok Rinaldo, Alessandro Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States

ISBN: (纸本)9781713821120

The bias of the sample means of the arms in multiarmed bandits is an important issue in adaptive data analysis that has recently received considerable attention in the literature. Existing results relate in precise ways the sign and magnitude of the bias to various sources of data adaptivity, but do not apply to the conditional inference setting in which the sample means are computed only if some specific conditions are satisfied. In this paper, we characterize the sign of the conditional bias of monotone functions of the rewards, including the sample mean. Our results hold for arbitrary conditioning events and leverage natural monotonicity properties of the data collection policy. We further demonstrate, through several examples from sequential testing and best arm identification, that the sign of the conditional and marginal bias of the sample mean of an arm can be different, depending on the conditioning event. Our analysis offers new and interesting perspectives on the subtleties of assessing the bias in data adaptive settings. © 2020 37th International Conference on machine learning, ICML 2020. All rights reserved.

关键词： machine learning

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Exploring the Tangible Impact of Artificial Intelligence and machine learning: Bridging the Gap between Hype and Reality

Exploring the Tangible Impact of Artificial Intelligence and...

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Advanced Computing and Emerging Technologies (ACET), International Conference on

作者： Rahul Dattangire Ruchika Vaidya Divya Biradar Ashish Joon Data Engineering Publicis Sapient Houston Texas USA Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Computer Science University of Texas at Arlington Arlington Texas USA Software Developer Andhus Technologies Inc Pensacola FL

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

Beyond being merely trendy terms in technology, machine learning (ML) and artificial intelligence (AI) have directly influenced every industry. This article thus demystifies the true meaning of these technologies by sifting through the sultry descriptions. Here, we explore how AI and ML enhance efficiency, decision-making, and problem-solving in various sectors including health, Agriculture, finance, and production as we discover real-world applications. Continuing with the discussion of implementation challenges, the aspects of bias, ethical pitfalls, and report cards are examined. In addition, the article also covered how the integration of AI as well as ML remains a way of improving processes, creating better user experiences, and spurring innovation. This paper, which is also based on case studies, discusses the relationship between social welfare and AI and the importance of AI’s development and use being done responsibly. Considering all the discussed aspects, this volume provides a rather nuanced perspective on AI and ML, unmasking the myths around these concepts to reveal the fact that they have a highly significant impact on the current reality and its projected evolutions.

关键词： Technological innovation Ethics Decision making Finance machine learning Production Pressing Medical services Writing Problem-solving

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Predicting House Price Model : A Comprehensive Analysis with Random Forest and Decision Tree Method

Predicting House Price Model : A Comprehensive Analysis with...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Mansi Sharma Deepak Sharma Rushikesh Burle Pawan Patil Isha Joge Chetan Puri Department of Computer Science and Design Datta Meghe Institute of Higher Education and Research Wardha India Department of Commerce & Management Sciences Datta Meghe Institute of Higher Education and Research(DU) Wardha Maharashtra Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Data Science Datta Meghe Institute of Higher Education and Research Wardha India

This paper explores the field of predicting property prices through a thorough comparison of several regression techniques. Linear regression, decision tree regression, random forest regression, K-neighbors regression, gradient boosting regression, and XGBoost regression are the regression models that are being examined. Finding the best model to reliably estimate house prices based on a set of features is the *** dataset used in this study includes location, bedroom count, square footage, and other relevant parameters that are known to affect property values. Each regression model is trained and tested on this dataset using a methodical evaluation procedure, and its performance is evaluated using important metrics including R-squared, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and overall correctness. predicting house prices mainly use harlfoxem (Owner) repository of housing dataset will contain the homes sold between May 2014 and May 2015 with 21613 entries and 21 attribute were used to evaluate the performance of machine learning prediction model. For practitioners and scholars working in real estate, housing market analysis, and related sectors, this study offers insightful information. It emphasizes how crucial it is to properly choose regression techniques depending on the particulars of the prediction task and the distinct qualities of the dataset.

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An Adaptive Modelling Approach to Employee Burnout in the Context of the Big Five Personality Traits

SSRN

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SSRN 2022年

作者： Bashkirova, Anna Compagner, Annelies Henningsen, Diana M. Treur, Jan King's College London Institute of Psychiatry Psychology and Neuroscience United Kingdom Vrije Universiteit Amsterdam Department of Computer Science Social AI Group Netherlands University of Copenhagen Department of Computer Science Machine Learning and Data Science Denmark

Burnout has been on the rise in the past decade, especially amongst the younger working generation. While work environmental aspects play an important role in predicting burnout, variations in personality traits are integral for understanding the syndrome's risk factors, processes, and outcomes. This paper studies the complex interaction of personality factors on the one hand and work environment aspects on the other through the relatively novel adaptive causal network modelling paradigm. Due to the adaptive nature of the model, it can investigate the effects of changes in particular job demands and resources on the symptoms of burnout and their dependence on different personality traits. The model can also demonstrate how an individual’s personality traits, environmental perception, and burnout symptoms can adaptively be altered by individual therapy, in this case, mindfulness-based cognitive therapy. Using the dedicated software environment to simulate the designed adaptive causal network model, two main scenarios were explored, focusing on the neuroticism personality *** demonstrate that neuroticism increases due to interpersonal conflict, indicating that neuroticism can be treated as an adaptive trait. Furthermore, when mindfulness-based cognitive therapy was introduced into the simulation, the likelihood of developing burnout decreased because the perception of the work environment was positively changed due to the therapy. This model contributes to the field of burnout modelling by representing personality traits as adaptive factors that can be changed through individual interventions. More detailed research is needed to understand how organisational-level interventions can also impact burnout development through changes in environmental perception and personality. © 2022, The Authors. All rights reserved.

关键词： Employment

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Using voice analysis as an early indicator of risk for depression in young adults

arXiv

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arXiv 2024年

作者： Scherer, Klaus R. Burkhardt, Felix Reichel, Uwe D. Eyben, Florian Schuller, Björn W. Department of Psychology University of Geneva Geneva Switzerland audEERING GmbH Gilching Germany Technical University of Munich MRI Munich Germany University of Augsburg Augsburg Germany Imperial College London United Kingdom Munich Center for Machine Learning Munich Germany Munich Data Science Institute Munich Germany

Increasingly frequent publications in the literature report voice quality differences between depressed patients and controls. Here, we examine the possibility of using voice analysis as an early warning signal for the development of emotion disturbances in young adults. As part of a major interdisciplinary European research project in four countries (ECoWeB), examining the effects of web-based prevention programs to reduce the risk for depression in young adults, we analyzed a large number of acoustic voice characteristics in vocal reports of emotions experienced by the participants on a specific day. We were able to identify a number of significant differences in acoustic cues, particularly with respect to the energy distribution in the voice spectrum, encouraging further research efforts to develop promising non-obtrusive risk indicators in the normal speaking voice. This is particularly important in the case of young adults who are less likely to exhibit standard risk factors for depression such as negative life experiences. © 2024, CC BY.

关键词： Risk assessment

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Enhanced Vision Transformer with Lion Optimizer for Accurate Classification of Monkeypox Skin Images

Enhanced Vision Transformer with Lion Optimizer for Accurate...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Roheet Bhatnagar Esraa Hassan Abeer Saber Mahmoud Y. Shams Samar Elbedwehy Department of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Information Technology Dept. Faculty of Computers and Artificial Intelligence Damietta University Damietta Egypt Dept. of Data Science Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Monkeypox is a contagious viral infection caused by the monkeypox virus (Mpoxvirus). Characterized by skin lesions, fever, respiratory difficulties, swollen lymph nodes, and neurological complications, MPX can be fatal. Recent outbreaks have spread the disease across Europe, Australia, the United States, and Africa. Traditionally diagnosed through PCR testing of skin lesions, this process poses significant risks to medical personnel due to potential exposure to the highly contagious virus. However, the usage artificial intelligence (AI) is revolutionizing diagnostics, providing a safer and more efficient approach to MPX detection. In this paper, a new AI technique based on vision transformer and lion optimizer for classifying Monkeypox Skin Images. The presented model used for classifying the dataset and results reached accuracy of 99.6%.

关键词： Computer vision Accuracy Measles Feature extraction Transformers Skin Lesions Artificial intelligence Viruses (medical) Testing

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Improving the Quality of Diabetic data with Large Language Model-driven Cleaning Techniques

Improving the Quality of Diabetic Data with Large Language M...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Divya Biradar Rahul Dattangire Ruchika Vaidya NagaSuryaShivani Inti Computer Science University of Texas at Arlington Arlington Texas USA Data Engineering Independent Researcher Houston Texas USA Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Computer and Information Science University of Texas at Arlington Arlington Texas USA

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by looking up and dealing with the inaccuracies encountered during the project like missing or duplicated records, outliers, and data that will not add any value to the project. The “Diabetes” data set from Kaggle is divided into implementing the combined approach data-cleaning process such as removing duplicate entries, and emptying fields by a Large Language Model, to improve the accuracy is assessed. Three components will increase the reliability of the collected data: 1. Gateway, 2. Storage 3. Cleaning. The outcomes demonstrated the LLMbased approach’s effective impact in correcting and consistent data within the healthcare dataset.

关键词： Adaptation models Large language models Noise Medical services Cleaning data models Diabetes Safety data mining Reliability

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Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees

arXiv

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arXiv 2025年

作者： Lin, Jiahe Zhang, Yikai Michailidis, George Machine Learning Research Morgan Stanley United States Department of Statistics and Data Science UCLA United States

Graphical models are widely used in diverse application domains to model the conditional dependencies amongst a collection of random variables. In this paper, we consider settings where the graph structure is covariate-dependent, and investigate a deep neural network-based approach to estimate it. The method allows for flexible functional dependency on the covariate, and fits the data reasonably well in the absence of a Gaussianity assumption. Theoretical results with PAC guarantees are established for the method, under assumptions commonly used in an Empirical Risk Minimization framework. The performance of the proposed method is evaluated on several synthetic data settings and benchmarked against existing approaches. The method is further illustrated on real datasets involving data from neuroscience and finance, respectively, and produces interpretable results. © 2025, CC BY.

关键词： Graphic methods

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Implicit Regularization for Tubal Tensor Factorizations via Gradient Descent

arXiv

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arXiv 2024年

作者： Karnik, Santhosh Veselovska, Anna Iwen, Mark Krahmer, Felix Department of Mathematics Northeastern University Boston United States Department of Computational Mathematics Science and Engineering Michigan State University East Lansing United States TUM School of Computation Information and Technology Munich Data Science Institute Technical University of Munich Garching Germany Munich Center for Machine Learning Munich Germany Department of Mathematics Michigan State University East Lansing United States

We provide a rigorous analysis of implicit regularization in an overparametrized tensor factorization problem beyond the lazy training regime. For matrix factorization problems, this phenomenon has been studied in a number of works. A particular challenge has been to design universal initialization strategies which provably lead to implicit regularization in gradient-descent methods. At the same time, it has been argued by [7] that more general classes of neural networks can be captured by considering tensor factorizations. However, in the tensor case, implicit regularization has only been rigorously established for gradient flow or in the lazy training regime. In this paper, we prove the first tensor result of its kind for gradient descent rather than gradient flow. We focus on the tubal tensor product and the associated notion of low tubal rank, encouraged by the relevance of this model for image data. We establish that gradient descent in an overparametrized tensor factorization model with a small random initialization exhibits an implicit bias towards solutions of low tubal rank. Our theoretical findings are illustrated in an extensive set of numerical simulations show-casing the dynamics predicted by our theory as well as the crucial role of using a small random initialization. © 2024, CC BY-SA.

关键词： Matrix factorization

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