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An initiative on digital nephrology: the Kidney Imageomics Project

National science Review 2025年第3期12卷 92-96页

作者： Fangxu Zhou Zehua Li Haifeng Li Yao Lu Linjia Cheng Ying Zhang Zichen Wang Jing Nie Heping Cheng Bin Dong Lei Ma Li Yang Renal Division Peking University Institute of Nephrology Peking University First Hospital Key Laboratory of Renal Disease—Ministry of Health of China Key Laboratory of CKD Prevention and Treatment (Peking University)—Ministry of Education of China Peking University First Hospital Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases—Chinese Academy of Medical Sciences Peking University First Hospital National Biomedical Imaging Center College of Future Technology Peking University Beijing International Center for Mathematical Research and the New Cornerstone Science Laboratory Peking University Academy for Advanced Interdisciplinary Studies Peking University State Key Laboratory of Membrane Biology Peking University Institute of Molecular Medicine College of Future Technology Peking University Peking-Tsinghua Center for Life Sciences Peking University Center for Machine Learning Research Peking University KIP Consortium

The kidney's complex structure and functions are essential for *** human kidney contains one million nephrons surrounded by a vast network of peritubular capillaries that support oxygen and nutrients, and an inter...

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Model and feature diversity for bayesian neural networks in mutual learning 23

Model and feature diversity for bayesian neural networks in ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Cuong Pham Cuong C. Nguyen Trung Le Dinh Phung Gustavo Carneiro Thanh-Toan Do Department of Data Science and AI Monash University Australia Australian Institute for Machine Learning University of Adelaide Australia Department of Data Science and AI Monash University Australia and VinAI Vietnam Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

Bayesian Neural Networks (BNNs) offer probability distributions for model parameters, enabling uncertainty quantification in predictions. However, they often underperform compared to deterministic neural networks. Utilizing mutual learning can effectively enhance the performance of peer BNNs. In this paper, we propose a novel approach to improve BNNs performance through deep mutual learning. The proposed approaches aim to increase diversity in both network parameter distributions and feature distributions, promoting peer networks to acquire distinct features that capture different characteristics of the input, which enhances the effectiveness of mutual learning. Experimental results demonstrate significant improvements in the classification accuracy, negative log-likelihood, and expected calibration error when compared to traditional mutual learning for BNNs.

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HealthGuardian: Artificial Intelligence System for Early Diagnosis and Clinical Support

HealthGuardian: Artificial Intelligence System for Early Dia...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： V Radhamani D. Sudha Devi E Chandra Blessie P J Isabella Priya R Tanushree Department of Computing (Decision and Computing Sciences) Coimbatore Institute of Technology Coimbatore Tamil Nadu India Department of Computing (Data Science) Coimbatore Institute of Technology Coimbatore Tamil Nadu India Department of Computing (Artificial Intelligence and Machine Learning) Coimbatore Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

In today's fast-paced world, access to timely and accurate healthcare information is critical, yet many individuals face challenges in assessing their symptoms, finding appropriate care, and understanding potential treatments. To address these issues, we propose HealthGuardian, an AI-powered clinical decision support system designed to assist users with symptom checking, personalized treatment recommendations, healthcare provider suggestions, online consultations, and early-stage diabetes prediction. HealthGuardian allows users to input their symptoms through natural language processing (NLP) and a recur-rent neural network (RNN) model, providing possible diagnoses and advising when to seek medical attention. The system offers personalized treatment plans based on medical history, suggests nearby healthcare providers using the Overpass API, and facilitates online consultations with doctors. Additionally, it features a predictive tool for early-stage diabetes risk assessment using various machine learning algorithms. The system leverages machine learning algorithms such as logistic regression, decision tree classifier, Naive Bayes, and random forest classifier to enhance diagnostic accuracy and prediction reliability. Our results demonstrate that HealthGuardian improves early diagnosis accuracy, provides actionable treatment guidance, and increases healthcare accessibility, helping users make informed decisions about their health.

关键词： Logistic regression Accuracy machine learning algorithms Neural networks Medical services Diabetes Reliability Risk management Medical diagnostic imaging Random forests

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The Age and Gender Detection using Deep Convolution Neural Network

The Age and Gender Detection using Deep Convolution Neural N...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Shweta Chauhan Rahul Kumar Bibek Kumar Department of artificial intelligence and Machine Learning Madhav Institute of science and technology Gwalior Madhya Pradesh India ECE Department GLA University Mathura Uttar Pradesh India School of Data Science Symbiosis University of Applied Sciences Indore Madhya Pradesh India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Automatic age and gender classification has become important researcher since the rise of social platforms and social media. The main advancements in face recognition are the ability to determine a person's age and gender. This paper makes a significant contribution by providing a survey of different face recognition methods for determining age and gender. A deep neural network (DNN) known as a convolutional neural network is frequently used for natural language processing and image recognition and processing. The proposed method significantly enhances three elements: face alignment, face recognition, and face recognition accuracy. All weight vectors of people in the same age group within a given database are averaged together. To predict gender and age, picture classification and regression models have been discussed in this paper respectively. Although training the proposed network to make accurate predictions takes a lot of time, it significantly improves age and gender categorization precision. From the experimental results we compared and analyzed the work to the most recent UTK-Face benchmark for estimating age and gender.

关键词： Training Surveys Accuracy Image recognition Social networking (online) Face recognition Predictive models Vectors Natural language processing Convolutional neural networks

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THE RANDOM FEATURE METHOD FOR TIME-DEPENDENT PROBLEMS

arXiv

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arXiv 2023年

作者： Chen, Jingrun Weinan, E. Luo, Yixin School of Mathematical Sciences University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China AI for Science Institute Beijing and Center for Machine Learning Research School of Mathematical Sciences Peking University China University of Science and Technology of China Hefei230026 China

We present a framework for solving time-dependent partial differential equations (PDEs) in the spirit of the random feature method. The numerical solution is constructed using a space-time partition of unity and random feature functions. Two different ways of constructing the random feature functions are investigated: feature functions that treat the spatial and temporal variables (STC) on the same footing, or functions that are the product of two random feature functions depending on spatial and temporal variables separately (SoV). Boundary and initial conditions are enforced by penalty terms. We also study two ways of solving the resulting least-squares problem: the problem is solved as a whole or solved using the block time-marching strategy. The former is termed "the space-time random feature method" (ST-RFM). Numerical results for a series of problems show that the proposed method, i.e. ST-RFM with STC and ST-RFM with SoV, have spectral accuracy in both space and time. In addition, ST-RFM only requires collocation points, not a mesh. This is important for solving problems with complex geometry. We demonstrate this by using ST-RFM to solve a two-dimensional wave equation over a complex domain. The two strategies differ significantly in terms of the behavior in time. In the case when block time-marching is used, we prove a lower error bound that shows an exponentially growing factor with respect to the number of blocks in time. For ST-RFM, we prove an upper bound with a sublinearly growing factor with respect to the number of subdomains in time. These estimates are also confirmed by numerical *** Codes 65M20, 65M55, 65M70 Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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A Comprehensive Review of Mojo: A High-Performance Programming Language

A Comprehensive Review of Mojo: A High-Performance Programmi...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Parth Dhananjay Akre Utkarsha Pacharaney 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)9798331522667

ISBN: (纸本)9798331522674

As artificial intelligence continues to advance at an unprecedented pace, the selection of programming languages significantly affects development processes, workflows, and outcomes. Mojo, a novel programming language, has gained attention through its integration of Python-like syntax, Rust-inspired safety features, and superior performance characteristics. This paper evaluates Mojo across four critical dimensions: distinctive features, performance metrics, ecosystem maturity, and practical applications, while examining potential adoption barriers. Positioned at the intersection of high-level programming simplicity and low-level performance optimization, Mojo demonstrates particular utility for artificial intelligence developers requiring both flexibility and computational efficiency. Through analysis of prominent libraries, including Endia, Lightbug HTTP, Basalt, and ***, this study examines Mojo's capabilities and limitations. By presenting a comparative analysis of Mojo's features, emerging ecosystem, and applicability to artificial intelligence development, this research addresses the current literature gap and provides substantive insights into this emerging programming language.

关键词： Reviews Ecosystems Syntactics Programming Libraries Real-time systems Safety Artificial intelligence Optimization Python

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Enhancing Interpretability: The Role of Explainable AI in Healthcare Diagnostics

Enhancing Interpretability: The Role of Explainable AI in He...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Nikita Zade Meher Langote Prateek Verma Department of Artificial Intelligence & Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education (DU) Sawangi Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering & Technology Datta Meghe Institute of Higher Education (DU) Sawangi Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

XAI is now transforming the use of AI in diagnosing diseases by overcoming some of the problems inherent in most black-box approaches. In time-sensitive speciality areas like computer-aided diagnosis, image analysis, and predictive modelling, SHAP, LIME, and attention-based XAI techniques explain how AI decides to help doctors and other healthcare professionals trust AI. Therefore, this paper aims to clarify why XAI is needed in healthcare, introduce techniques, and show their examples in cancer diagnosis and disease prognosis. XAI improves diagnostic performance and satisfies regulatory and ethical requirements for increased usage in clinical practice. Because of XAI, clinicians can use the best of artificial intelligence to complement their knowledge in enhancing the treatment of patients. That is why the outlook of XAI in healthcare includes the next steps in achieving higher scalability, managing bias, and working with diverse regulatory frameworks, which will define the future of AI solutions in healthcare.

关键词： Renewable energy sources Explainable AI Scalability Closed box Medical services Predictive models Regulation Artificial intelligence Prognostics and health management Cancer

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Comparative Analysis of Time Series Forecasting Models for Weather Prediction: ARIMA vs. STL

Comparative Analysis of Time Series Forecasting Models for W...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Aahash Kamble Sanika Belsare Purva Chaudhari Palash Gourshettiwar Swapnil Gundewar Department of Artificial Intellegence and Data Science Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intellegence and Data Science Datta Meghe Institute of Higher Education and Research(DU) Wardha Maharashtra Department of Computer Science and Medical Engineering Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intellegence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

This research compares two well-known models for predicting weather: ARIMA (Auto Regressive Integrated Moving Average) and STL (Seasonal-Trend Decomposition using Loess). Accurate weather forecasts are crucial for businesses and daily life, making strong forecasting methods necessary. ARIMA finds linear patterns and time dependencies in data, while STL splits time series into seasonal, trend, and random parts. Using real-world weather data, we train and test these models. We compare their performances using MSE (Mean Squared Error) and R2 (R-squared) metrics. The research demonstrates that the STL model performs better than the ARIMA model at accurately predicting weather patterns and fitting to the data. This superior performance makes the STL model a more dependable choice for weather forecasting. This study enhances techniques for weather prediction and offers valuable information to those making decisions in weather-sensitive businesses.

关键词： Visualization Accuracy Time series analysis Weather forecasting Predictive models Prediction algorithms Market research data models Forecasting Business

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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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TOPOGRAPH: AN EFFICIENT GRAPH-BASED FRAMEWORK FOR STRICTLY TOPOLOGY PRESERVING IMAGE SEGMENTATION

arXiv

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arXiv 2024年

作者： Lux, Laurin Berger, Alexander H. Weers, Alexander Stucki, Nico Rueckert, Daniel Bauer, Ulrich Paetzold, Johannes C. School of Computation Information and Technology Technical University of Munich Germany Department of Computing Imperial College London United Kingdom Munich Center for Machine Learning Germany Munich Data Science Institute Technical University of Munich Munich Germany

Topological correctness plays a critical role in many image segmentation tasks, yet most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological accuracy. Existing topology-aware methods often lack robust topological guarantees, are limited to specific use cases, or impose high computational costs. In this work, we propose a novel, graph-based framework for topologically accurate image segmentation that is both computationally efficient and generally applicable. Our method constructs a component graph that fully encodes the topological information of both the prediction and ground truth, allowing us to efficiently identify topologically critical regions and aggregate a loss based on local neighborhood information. Furthermore, we introduce a strict topological metric capturing the homotopy equivalence between the union and intersection of prediction-label pairs. We formally prove the topological guarantees of our approach and empirically validate its effectiveness on binary and multi-class datasets. Our loss demonstrates state-of-the-art performance with up to fivefold faster loss computation compared to persistent homology methods. © 2024, CC BY.

关键词： Image segmentation

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