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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Patrick Purucker Christian Reil Alfred Höß Björn W. Schuller Department of Electrical Engineering Media Technology and Computer Sciences University of Applied Sciences Amberg-Weiden Amberg Germany CHI MRI Technical University of Munich Germany Munich Data-Science Institute (MDSI) Germany Munich Center for Machine Learning (MCML) Germany GLAM Imperial College London UK

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Commercial Unmanned Aircraft Systems (UAS) have a wide range of applications, including package delivery, inspection and search and rescue missions. For the operation of Unmanned Aircraft Vehicles (UAV) Beyond Visual Line of Sight (BVLOS), reliable long-range communication is essential. The cellular network is one possible solution, but there are issues such as signal loss and frequent handovers at higher altitudes. To mitigate these issues, our work proposes the use of two cellular links from different providers prioritised according to Quality of Service (QoS) prediction. We evaluate multiple AI-based model architectures for the prediction, and find that the model consisting of Gated Recurrent Units (GRU) and convolutional layers outperforms the others. The models are trained and tested on real-world data and show a reduction in latency peaks, thereby increasing connection resilience. Moreover, the prediction pipeline is designed to be executable on the UAV side and is not limited to a specific geographical area, making it applicable to real-world scenarios. Finally, we present a pre-flight path planning algorithm that takes QoS into account when calculating the flight path in order to further improve communication. To support the research community, we publicly share the dataset used to obtain our results.

关键词： Adaptation models Heuristic algorithms Quality of service Computer architecture Predictive models Autonomous aerial vehicles Modems

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Uncertainty Quantification For Learned ISTA

Uncertainty Quantification For Learned ISTA

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IEEE Workshop on machine learning for Signal Processing

作者： Frederik Hoppe Claudio Mayrink Verdun Hannah Laus Felix Krahmer Holger Rauhut Chair of Mathematics of Information Processing RWTH Aachen University Aachen Germany Department of Mathematics Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Technical University of Munich Munich Germany

Model-based deep learning solutions to inverse problems have attracted increasing attention in recent years as they bridge state-of-the-art numerical performance with interpretability. In addition, the incorporated prior domain knowledge can make the training more efficient as the smaller number of parameters allows the training step to be executed with smaller datasets. Algorithm unrolling schemes stand out among these model-based learning techniques. Despite their rapid advancement and their close connection to traditional high-dimensional statistical methods, they lack certainty estimates and a theory for uncertainty quantification is still elusive. This work provides a step towards closing this gap proposing a rigorous way to obtain confidence intervals for the LISTA estimator.

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Distribution-free binary classification: Prediction sets, confidence intervals and calibration 34

Distribution-free binary classification: Prediction sets, co...

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34th Conference on Neural Information Processing Systems, NeurIPS 2020

作者： Gupta, Chirag Podkopaev, Aleksandr Ramdas, Aaditya Machine Learning Department Carnegie Mellon University United States Department of Statistics and Data Science Carnegie Mellon University United States

We study three notions of uncertainty quantification—calibration, confidence intervals and prediction sets—for binary classification in the distribution-free setting, that is without making any distributional assumptions on the data. With a focus towards calibration, we establish a ‘tripod’ of theorems that connect these three notions for score-based classifiers. A direct implication is that distribution-free calibration is only possible, even asymptotically, using a scoring function whose level sets partition the feature space into at most countably many sets. Parametric calibration schemes such as variants of Platt scaling do not satisfy this requirement, while nonparametric schemes based on binning do. To close the loop, we derive distribution-free confidence intervals for binned probabilities for both fixed-width and uniform-mass binning. As a consequence of our ‘tripod’ theorems, these confidence intervals for binned probabilities lead to distribution-free calibration. We also derive extensions to settings with streaming data and covariate shift. © 2020 Neural information processing systems foundation. All rights reserved.

关键词： Calibration

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Smart Finance: An overview of Artificial Intelligence Integration in Fintech

Smart Finance: An overview of Artificial Intelligence Integr...

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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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