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International Conference on Inventive Computation Technologies (ICICT)

作者： Praisy Petra C Sujitha Juliet Anitha J Division of Data Science and Cybersecurity Karunya Institute of Technology and Sciences Coimbatore India Division of Arificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Accurately predicting a child's height based on genetic and environmental factors is a longstanding challenge in medical and data science research. Traditional models, such as linear regression, SVM, and Random Forest, have been widely used. However, their accuracy varies due to the complexity of the environment, genetics which affects the height. This paper investigates the possibility of machine learning techniques, including deep learning techniques, accompanied by traditional models, to improve prediction accuracy for height. By combining more features and systematically partitioning datasets, the study compares various algorithms perform in predicting child height. These findings provide a comparative evaluation of these models, demonstrating the impact of feature selection and model architecture on prediction performance. The study aims to offer insights into selecting optimal models for practical applications in genetics, health analytics, and predictive modelling.

关键词： Deep learning Support vector machines Pediatrics Analytical models Accuracy Predictive models Epigenetics Complexity theory Cultural differences Random forests

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Leveraging Patient Flow data to Reduce Hospital Readmissions: A Study Using Medical Dashboards

Leveraging Patient Flow Data to Reduce Hospital Readmissions...

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

作者： Santoshi Deshmukh Utkarsha Pacharaney Department of Artificial Intelligence and Data Science Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Transparent reporting of patient outcomes is becoming more and more common as the inequalities in health outcomes amongst groups are being more widely acknowledged. In addition, documenting health outcomes over time across subgroups will be necessary for pay-for-equity programs like those put forth by the Centers for Medicare and Medicaid. One way to visualize data in the context of healthcare is through dashboards, which may be used to identify important differences in clinical outcomes, direct focused efforts toward quality improvement, and eventually enhance health equity. We assess every study that has documented the effective creation of a disparity dashboard in this viewpoint, and we also publish the information gathered and any unexpected repercussions that have been mentioned. We offer a framework for the systematic enhancement of equity via reward systems to lessen health inequalities and incentives for gathering and reporting health data. essential elements of efficient hospital management. A sophisticated approach is necessary to address the impact that hospital leadership has on patient outcomes. Because they embody patient-centered care in their everyday lives, these executives serve as the company's role models. The beliefs, vision, and leadership style of healthcare professionals greatly influence their behavior and performance. Competent leaders who inspire and empower their staff foster a culture of responsibility, creativity, and cooperation. They exhort healthcare professionals to put their patients' safety, satisfaction, and well-being first. Strategic planning, resource allocation, and decision-making are all components of good leadership that support the hospital's goal of offering the highest quality of care. essential elements of efficient hospital management. A sophisticated approach is necessary to address the impact that hospital leadership has on patient outcomes. Because they embody patient-centered care in their everyday lives, these ex

关键词： Leadership Sentiment analysis Systematics Hospitals Decision making Strategic planning Safety Resource management Predictive analytics Creativity

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A Minimax Optimal Control Approach for Robust Neural ODEs

A Minimax Optimal Control Approach for Robust Neural ODEs

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European Control Conference (ECC)

作者： Cristina Cipriani Alessandro Scagliotti Tobias Wöhrer Department of Mathematics Technical University Munich (TUM) Munich Germany Munich Data Science Institute (MDSI) Munich Germany Munich Center for Machine Learning (MCML) Munich Germany

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this paper, we address the adversarial training of neural ODEs from a robust control perspective. This is an alternative to the classical training via empirical risk minimization, and it is widely used to enforce reliable outcomes for input perturbations. Neural ODEs allow the interpretation of deep neural networks as discretizations of control systems, unlocking powerful tools from control theory for the development and the understanding of machine learning. In this specific case, we formulate the adversarial training with perturbed data as a minimax optimal control problem, for which we derive first order optimality conditions in the form of Pontryagin's Maximum Principle. We provide a novel interpretation of robust training leading to an alternative weighted technique, which we test on a low-dimensional classification task.

关键词： Training Weight measurement Robust control Risk minimization Perturbation methods Optimal control Artificial neural networks

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Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation

Task Specific Pretraining with Noisy Labels for Remote Sensi...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Chenying Liu Conrad M Albrecht Yi Wang Xiao Xiang Zhu Data Science in Earth Observation Technical University of Munich Germany German Aerospace Center Remote Sensing Technology Institute Germany Munich Center for Machine Learning (MCML) Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Compared to supervised deep learning, self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations. While image-level information for unsupervised pretraining efficiently works for various classification downstream tasks, the performance on pixel-level semantic segmentation lags behind in terms of model accuracy. On the contrary, many easily available label sources (e.g., automatic labeling tools and land cover land use products) exist, which can provide a large amount of noisy labels for segmentation model training. In this work, we propose to exploit noisy semantic segmentation maps for model pretraining. Our experiments provide insights on robustness per network layer. The transfer learning settings test the cases when the pretrained encoders are fine-tuned for different label classes and decoders. The results from two datasets indicate the effectiveness of task-specific supervised pretraining with noisy labels. Our findings pave new avenues to improved model accuracy and novel pretraining strategies for efficient remote sensing image segmentation.

关键词： Training Accuracy Semantic segmentation Transfer learning Land surface Robustness Noise measurement

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Parkinson's Disease Detection using Hand Drawn Spirals and Waves

Parkinson's Disease Detection using Hand Drawn Spirals and W...

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2023 International Conference on Recent Advances in science and Engineering Technology, ICRASET 2023

作者： Shashidhara, H.S. Kalbag, Saish K Mathias, Tanya Srinivasa, K.G. Niranjanamurthy, M. Affiliated to Visvesvaraya Technological University M S Ramaiah Institute of Techology and Engineering Department of Informaition Science and Engineering Belgaum Bangalore India International Institute of Information Technology Department of Data Science and Artififial Intelligece Chhattisgarh Naya Raipur India Affiliated to Visvesvaraya Technological University B M S Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Belgaum Bangalore India

ISBN: (纸本)9798350306927

Parkinson's disease, a neurodegenerative disorder, affects millions of people worldwide. Early detection is critical for effective treatment and management. Early detection of Parkinson's disease is challenging due to the lack of accessible and non-invasive screening methods. Current diagnostic techniques often require specialized equipment and invasive procedures, limiting their feasibility for widespread screening. There is a need for cost-effective and accessible approaches that can accurately detect early stages of Parkinson's disease. The model requires a high degree of precision in distinguishing between these patterns, which is a significant challenge to overcome. In this research work, we proposed a novel approach for detecting early stages of Parkinson's disease by analyzing hand-drawn spirals and waves in photographs. © 2023 IEEE.

关键词： Diagnosis

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Asymptotic and compound e-values: multiple testing and empirical Bayes

arXiv

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arXiv 2024年

作者： Ignatiadis, Nikolaos Wang, Ruodu Ramdas, Aaditya Department of Statistics and Data Science Institute University of Chicago United States Department of Statistics and Actuarial Science University of Waterloo Canada Departments of Statistics & Machine Learning Carnegie Mellon University United States

We explicitly define the notions of (exact, approximate or asymptotic) compound p-values and e-values, which have been implicitly presented and extensively used in the recent multiple testing literature. While it is known that the e-BH procedure with compound e-values controls the FDR, we show the converse: every FDR controlling procedure can be recovered by instantiating the e-BH procedure with certain compound e-values. Since compound e-values are closed under averaging, this allows for combination and derandomization of FDR procedures. We then connect compound e-values to empirical Bayes. In particular, we use the fundamental theorem of compound decision theory to derive the log-optimal simple separable compound e-value for testing a set of point nulls against point alternatives: it is a ratio of mixture likelihoods. We extend universal inference to the compound setting. As one example, we construct approximate compound e-values for multiple t-tests, where the (nuisance) variances may be different across hypotheses. Finally, we provide connections to related notions in the literature stated in terms of p-values. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision theory

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Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation Graph Convolutional Networks and Structural MRI data 5

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Gunasundari, C. Senthamarai, M. Pandiaraja, P. Nithin, P. Chitra, K. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Trichy India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India Kongu Engineering College Department of Computer Applications Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Alzheimer's disease (AD) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MRI data and Multi-Relation Graph Convolutional Networks (MR-GCN) to improve AD diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MRI features. Using a large dataset, the resusts are evaluated and contrasted in MR-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of AD. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how AD progresses. © 2024 IEEE.

关键词： Convolutional neural networks

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Predicting Indian Sign Language Gestures Using Deep learning and machine learning Techniques

Predicting Indian Sign Language Gestures Using Deep Learning...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Bhushan Fulkar Rushikesh Burle Pramod Mahale Utkrsha Pacaharaney Department of Artificial intelligence And Data science Datta Meghe institute of Higher Education and Research Wardha India Department of Artificial Intelligence And Machine Learning 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

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Indian Sign Language is the language used by people with disabilities in India. Unfortunately, the general public's ignorance of sign language makes it difficult for the especially abled and the general public to communicate. We are releasing a strategy to reduce this difference. We have adopted two distinct strategies to deal with this problem. first by employing the depth+RGB data acquired with a Microsoft Kinect to make gesture predictions in real-time. To segment the hand region from the RGB-D camera data, we used affine transformation and 3D reconstruction to map the RGB and depth information. Convolutional neural networks were trained with inputs that were segmented hand images or videos. With 50 static Indian Sign Language hand gestures used for training, a 97.18% classification accuracy was achieved on test data. Furthermore, this model achieved a 97.71 % classification accuracy when we transferred learning American Sign Language. A convolutional kernel and an LSP were used to train twenty dynamic gestures. This model's classification accuracy was 98.88%. Nevertheless, as soon as we implemented this system, we found that the methodology was flawed. Essentially, it is absurd to need to carry around both a bulky Microsoft Kinect and a device capable of performing the computation required for human-to-person interaction. We attempted to address this issue by using semantic segmentation of the hands. U-Net and ResNet 101 were combined.

关键词： Training Sign language Accuracy Three-dimensional displays Webcams Speech recognition Auditory system Speech enhancement Real-time systems Videos

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Strictly positive definite kernels on compact Riemannian manifolds

arXiv

引用

arXiv 2023年

作者： Guella, Jean Carlo Jäger, Janin Unicamp Institute of Mathematics Statistics and Scientific Computing Campinas Brazil KU Eichstätt-Ingolstadt Mathematisches Institute for machine learning and data science Ingolstadt Germany

The paper studies strictly positive definite kernels on compact Riemannian manifolds. We state new conditions to ensure strict positive definiteness for general kernels and kernels with certain convolutional structure. We also state conditions for such kernels on product manifolds. As an example conditions for products of two-point homogeneous spaces are *** Codes 33B10, 33C20, 42A16, 46E22, 65D05 Copyright © 2023, The Authors. All rights reserved.

关键词： Geometry

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Multi-Stage Bidirectional Cross-Attention Model for Predicting Prognosis in Multiple Peritoneum Lesions with Clinical Information

Multi-Stage Bidirectional Cross-Attention Model for Predicti...

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IEEE International Symposium on Biomedical Imaging

作者： Haoshen Li Jieyuan Cai Yiyuan Wei Zifan Chen Heyun Chen Jie Zhao Yanjie Shi Bin Dong Lei Tang Xiaotian Zhang Li Zhang Center for Data Science Peking University China Peking University Cancer Hospital & Institute China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Peritoneal metastasis occurs when cancer cells spread from the primary tumor to the peritoneum, leading to morphological alterations that significantly impact patient survival. The specific changes across multiple peritoneal sites can effectively indicate a patient's risk level and prognosis. However, the considerable variation in peritoneal shape, size, and location poses significant challenges for feature extraction and prognostic analysis. Traditional multi-instance learning approaches typically fuse instance features extracted by the backbone network at the final stage, but their lack of intermediate interaction limits the ability to capture both common and distinct features. To address this limitation, we propose a Multi-Stage BiDirectional Cross-Attention framework (MSBDCA). Our approach enhances feature extraction and prognostic analysis by facilitating interaction among different peritoneal instances and incorporating clinical information. Experimental comparisons with five baseline models demonstrate our method's superiority, achieving improvements of 8.7% in Area Under Curve (AUC) and 5.9% in Concordance Index (C_index). These results suggest a promising direction for survival and prognostic analysis using peritoneal lesions. Our code is available at https://***/HaoshenLi/MSBDCA.

关键词： learning systems Shape Predictive models Feature extraction Metastasis Lesions Clinical diagnosis Prognostics and health management Abdomen Biomedical imaging

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