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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Unnati Gayaki Subodh Daronde Abhay Tale Aditya Barhate Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University)) Wardha Maharashtra India 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

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. This review article aims to explore the current and potential future roles of machine learning (ML) in diabetes management, focusing on progress, applications, and challenges. It discusses various ML techniques, including support vector machines (SVM), logistic regression, decision trees, ensemble methods, and advanced deep learning approaches such as long short-term memory (LSTM) networks and convolutional neural networks (CNN). These methods have demonstrated benefits in analytics, patient monitoring, decision support systems, personalised treatment, and patient engagement. However, significant challenges persist, such as computational complexity, data quality, limited interoperability of systems, and ethical concerns regarding AI use in healthcare. The article addresses these challenges by fostering collaboration between AI implementers and health care professionals. This collaboration is essential to develop long-term, legally compliant, and solution-oriented frameworks that enhance patient care and diabetes management and pave the way for more efficient and ethical AI applications in healthcare.

关键词： Deep learning Support vector machines Ethics machine learning algorithms Reviews Neural networks Medical services Diabetes Convolutional neural networks Long short term memory

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Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks

arXiv

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arXiv 2023年

作者： Linhardt, Lorenz Müller, Klaus-Robert Montavon, Grégoire Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data - BIFOLD Berlin10587 Germany Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of Max Planck Institute for Informatics Saarbrücken66123 Germany Google DeepMind Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin14195 Germany

Robustness has become an important consideration in deep learning. With the help of explainable AI, mismatches between an explained model's decision strategy and the user's domain knowledge (e.g. Clever Hans effects) have been identified as a starting point for improving faulty models. However, it is less clear what to do when the user and the explanation agree. In this paper, we demonstrate that acceptance of explanations by the user is not a guarantee for a machine learning model to be robust against Clever Hans effects, which may remain undetected. Such hidden flaws of the model can nevertheless be mitigated, and we demonstrate this by contributing a new method, Explanation-Guided Exposure Minimization (EGEM), that preemptively prunes variations in the ML model that have not been the subject of positive explanation feedback. Experiments demonstrate that our approach leads to models that strongly reduce their reliance on hidden Clever Hans strategies, and consequently achieve higher accuracy on new data. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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A Review on Soil Moisture Monitoring Methods using Satellite Images

A Review on Soil Moisture Monitoring Methods using Satellite...

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

作者： Prateek Verma Aahash Kamble Aditya Barhate Abhay Tale Amit Gudadhe Department of Artificial Intelligence and Machine Learning Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Department of Basic Sciences and Humanities Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Sawangi (Meghe) Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Soil moisture is an important parameter in the hydrological cycle and an important component for agricultural production and prediction. The regular monitoring of soil moisture plays a big role in the prediction and management of crops and droughts in a region. Climate change has led to irregular patterns in rainfall and extreme weather conditions worldwide. This has led to the farmers taking extreme steps. The traditional method is the best method of measuring soil moisture, but it requires a lot of time and is labor-intensive. Therefore, satellite remote sensing provides an alternative approach to measuring soil moisture covering a large area within less time. The application of satellite remote sensing provides comparative results to traditional methods. This study provides a discussion on spectral indices obtained from satellite data for measuring soil moisture. The current study also provides insights about the applications, challenges, and advantages of using satellite remote sensing for monitoring of soil moisture using remote sensing datasets. The study may be helpful to farmers and government agencies for planning and management of crops and water preservation over the drought prone regions of India.

关键词： Soil measurements Moisture measurement Soil moisture Droughts Crops Satellite images Monitoring Remote sensing Farming Vegetation mapping Indexes Climate change Normalized difference vegetation index

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Homotopy reconstruction via the CecH complex and the vietoris-rips complex 36

Homotopy reconstruction via the CecH complex and the vietori...

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36th International Symposium on Computational Geometry, SoCG 2020

作者： Kim, Jisu Shin, Jaehyeok Chazal, Frédéric Rinaldo, Alessandro Wasserman, Larry Inria Saclay - Île-de-France Palaiseau France Department of Statistics and Data Science Carnegie Mellon University PittsburghPA United States Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9783959771436

We derive conditions under which the reconstruction of a target space is topologically correct via the Čech complex or the Vietoris-Rips complex obtained from possibly noisy point cloud data. We provide two novel theoretical results. First, we describe sufficient conditions under which any non-empty intersection of finitely many Euclidean balls intersected with a positive reach set is contractible, so that the Nerve theorem applies for the restricted Čech complex. Second, we demonstrate the homotopy equivalence of a positive µ-reach set and its offsets. Applying these results to the restricted Čech complex and using the interleaving relations with the Čech complex (or the Vietoris-Rips complex), we formulate conditions guaranteeing that the target space is homotopy equivalent to the Čech complex (or the Vietoris-Rips complex), in terms of the µ-reach. Our results sharpen existing results. © Jisu Kim, Jaehyeok Shin, Frédéric Chazal, Alessandro Rinaldo, and Larry Wasserman;licensed under Creative Commons License CC-BY 36th International Symposium on Computational Geometry (SoCG 2020).

关键词： Topology

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Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation

arXiv

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arXiv 2022年

作者： Chen, Shuai Garcia-Uceda, Antonio Su, Jiahang Van Tulder, Gijs Wolff, Lennard Van Walsum, Theo De Bruijne, Marleen The Biomedical Imaging Group Rotterdam Department of Radiology & Nuclear Medicine Erasmus Mc Rotterdam Netherlands The Data Science Group Faculty of Science Radboud University Nijmegen Netherlands Department of Radiology & Nuclear Medicine Erasmus Mc Rotterdam Netherlands The Machine Learning Section Department of Computer Science University of Copenhagen CopenhagenDK-2110 Denmark

Deep convolutional neural networks for image segmentation do not learn the label structure explicitly and may produce segmentations with an incorrect structure, e.g., with disconnected cylindrical structures in the segmentation of tree-like structures such as airways or blood vessels. In this paper, we propose a novel label refinement method to correct such errors from an initial segmentation, implicitly incorporating information about label structure. This method features two novel parts: 1) a model that generates synthetic structural errors, and 2) a label appearance simulation network that produces synthetic segmentations (with errors) that are similar in appearance to the real initial segmentations. Using these synthetic segmentations and the original images, the label refinement network is trained to correct errors and improve the initial segmentations. The proposed method is validated on two segmentation tasks: airway segmentation from chest computed tomography (CT) scans and brain vessel segmentation from 3D CT angiography (CTA) images of the brain. In both applications, our method significantly outperformed a standard 3D U-Net and other previous refinement approaches. Improvements are even larger when additional unlabeled data is used for model training. In an ablation study, we demonstrate the value of the different components of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Bringing closure to FDR control: beating the e-Benjamini-Hochberg procedure

arXiv

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arXiv 2025年

作者： Xu, Ziyu Fischer, Lasse Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Competence Center for Clinical Trials Bremen University of Bremen Germany Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University United States

False discovery rate (FDR) has been a key metric for error control in multiple hypothesis testing, and many methods have developed for FDR control across a diverse cross-section of settings and applications. We develop a closure principle for all FDR controlling procedures, i.e., we provide a characterization based on e-values for all admissible FDR controlling procedures. A general version of this closure principle can recover any multiple testing error metric and allows one to choose the error metric post-hoc. We leverage this idea to formulate the closed eBH procedure, a (usually strict) improvement over the eBH procedure for FDR control when provided with e-values. This also yields a closed BY (BY) procedure that dominates the Benjamini-Yekutieli (BY) procedure for FDR control with arbitrarily dependent p-values, thus proving that the latter is inadmissibile. We demonstrate the practical performance of our new procedures in simulations. Copyright © 2025, The Authors. All rights reserved.

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Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node data: Whole City Traffic and ETA from Stationary Vehicle Detectors 36

Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph...

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36th Annual Conference on Neural Information Processing Systems - Competition Track, NeurIPS 2022

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab. China Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023 M. Neun et al.

关键词： Travel time

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Likelihood-Free Frequentist Inference: Bridging Classical Statistics and machine learning for Reliable Simulator-Based Inference∗

arXiv

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arXiv 2021年

作者： Dalmasso, Niccolò Masserano, Luca Zhao, David Izbicki, Rafael Lee, Ann B. Department of Statistics and Data Science Carnegie Mellon University United States Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University United States Department of Statistics Federal University of São Carlos Brazil

Many areas of science rely on simulators that implicitly encode intractable likelihood functions of complex systems. Classical statistical methods are poorly suited for these so-called likelihood-free inference (LFI) settings, especially outside asymptotic and low-dimensional regimes. At the same time, popular LFI methods — such as Approximate Bayesian Computation or more recent machine learning techniques — do not necessarily lead to valid scientific inference because they do not guarantee confidence sets with nominal coverage in general settings. In addition, LFI currently lacks practical diagnostic tools to check the actual coverage of computed confidence sets across the entire parameter space. In this work, we propose a modular inference framework that bridges classical statistics and modern machine learning to provide (i) a practical approach for constructing confidence sets with near finite-sample validity at any value of the unknown parameters, and (ii) interpretable diagnostics for estimating empirical coverage across the entire parameter space. We refer to this framework as likelihood-free frequentist inference (LF2I). Any method that defines a test statistic can leverage LF2I to create valid confidence sets and diagnostics without costly Monte Carlo or bootstrap samples at fixed parameter settings. We study two likelihood-based test statistics (ACORE and BFF) and demonstrate their performance on high-dimensional complex data. Code is available at https://***/lee-group-cmu/lf2i. © 2021, CC BY.

关键词： Inference engines

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Large language models:game-changers in the healthcare industry

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science Bulletin 2025年第3期70卷 283-286页

作者： Bin Dong Li Zhang Jiajia Yuan Yang Chen Quanzheng Li Lin Shen Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Center for Machine Learning Research Peking UniversityBeijing 100871China Peking University Changsha Institute for Computing and Digital Economy Changsha 410205China Center for Data Science Peking UniversityBeijing 100871China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing)Peking University Cancer Hospital and InstituteBeijing 100142China Massachusetts General Hospital Boston 02114-2696USA Harvard Medical School Boston 02115USA

The healthcare industry faces core challenges,including increasingly complex operational processes and entities,the rapid development of medical knowledge,and the rising demand for interdisciplinary *** complexity of medical processes is evident in every aspect,from patient appointments,diagnoses,and treatments to follow-up tasks,all of which involve intricate data processing and decision-making procedures.

关键词： industry expertise evident

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Innovative Healthcare Advancements: Harnessing Artificial and Human Intelligence for Bionic Solutions

Innovative Healthcare Advancements: Harnessing Artificial an...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Samruddhi Sapkal Sulaxan Jadhav P Mallikarjun Rejuwan Shamim Atowar Ul Islam Kalyan Bamane Artificial Intelligence and Machine Learning D Y Patil International University Akurdi Pune India D Y Patil International University Akurdi Pune India Department of Electrical and Electronic Engineering Malla Reddy Engineering College JNTUH Hyderabad School of data science Maharishi University of Information Technology Noida India Department of Computer Science and Electronics University of Science and Technology Meghalaya Ri-Bhoi Techno city Kiling Road Baridua Meghalaya Department of Information Technology D Y Patil College of Engineering Akurdi Pune

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

According to initial data, individuals who have been diagnosed with type 2 diabetes (T2DM) appear to be at a more chances of evolving breast cancer compared to those who have not received a T2DM diagnosis. The primary goal of the research was to estimate the efficacy of three dissimilar methods in forecasting the probability of breast cancer in T2DM patients with diverse attributes. To achieve this objective, a danger expectation model was created for breast cancer in individuals with T2DM using the primary *** ensure a comprehensive analysis, we also gathered information on potential factors that may predict the growth of breast cancer. As the population sample size was restricted, we utilized Synthetic Minority Oversampling Technology to amplify the quantity of accessible data. Random assignment of data points to training or test sets was conducted at a ratio of roughly 39 to 1. Three distinct models, specifically Artificial Neural Network (ANN), Logistic Regression (LR), and Random Forest (RF), were assessed for effectiveness using a range of performance criteria, including as $F 1$ score, area under the receiver operation characteristic curves, recalled, and correctness. (AUC). AUC values for these models were as follows: LR had an AUC of 0.834, ANN had an AUC of 0.865, and RF had an AUC of 0.959, with RF having the greatest AUC. According to our study, the Random Forest model outperformed the LR and ANN models in correctly estimating the incidence of breast cancer in people with T2DM.

关键词： Radio frequency Training Technological innovation Biological system modeling Artificial neural networks Receivers Predictive models

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