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An Application of Localized Model Explainability: Identifying Key Disparities in Social Determinants of Health in Food Deserts

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An Application of Localized Model Explainability: Identifyin...

2022 IEEE International Conference on Big data, Big data 2022

作者： Alam, Md Shafiul Ishmakhametov, Namazbai Xie, Ying Chakravarty, Sumit Kennesaw State University Analytics and Data Science Kennesaw United States Kennesaw State University College of Computing and Software Engineering Kennesaw United States Kennesaw State University Department of Electronic Engineering Kennesaw United States

ISBN: (纸本)9781665480451

Food deserts are geographic areas where people have limited access to healthy and affordable food. Millions of people in the US are experiencing adverse effects stemming from living in such areas and it becomes an important challenge for federal and local authorities to improve the quality of life of people living in food deserts. The majority of studies in this field explicitly consider income level when analyzing problems of food deserts, thus shadowing other attributes that might provide deeper insights into the problems of the food desert's population. The current paper assesses the significance of subtler, non-income related characteristics of census tracts by using a new variable attribution technique Appley which approximates Shapley values in linear time. The results show that attributes of census tracts that fall into the set of the so called "social determinants of health"have similar classification power compared to models that explicitly use income variables. The results suggest that each food desert has its own unique set of problems with social determinants of health with varying levels of intensities of each problem and this allows to clearly distinguish not only food deserts from non-food desert areas but also distinguish food deserts from each other. Appley attributes localized importance scores to food deserts' social determinants of health, thus showing which issues are more severe in a given food desert compared to another one. This allows for devising food desert-specific solutions to the health issues of desert residents thus letting authorities improve the quality of life of people in a targeted and budget-efficient manner. © 2022 IEEE.

关键词： Budget control

Editorial: Synopsis of Core Concepts for Explainable-AI-Ready data and Models

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Editorial: Synopsis of Core Concepts for Explainable-AI-Re...

International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure, DCLXVI 2024

作者： Horsch, Martin Thomas Scholze, Sebastian Al Machot, Fadi Department of Data Science Faculty of Science and Technology Norwegian University of Life Sciences P.O. Box 5003 Ås1432 Norway Scientific Computing Department UKRI Science and Technology Facilities Council Daresbury Laboratory Keckwick Ln WarringtonWA4 4AD United Kingdom ATB – Institut für angewandte Systemtechnik Bremen GmbH Wiener Str. 1 Bremen28359 Germany

ISBN: (纸本)9783031892738

Reliable AI technology necessitates the development of robust validation infrastructures and data documentation schemas to ensure that AI systems become trustworthy and transparent. The International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure (DCLXVI 2024), held on 11th December 2024 in Kaiserslautern, Germany, surveyed the conceptual landscape of explainable-AI-ready (XAIR) models and data. The definition of concepts relevant to data integrity, algorithmic transparency, and user interpretability were explored, resulting in a synopsis of core concepts for "XAIR." © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Applied ontology explainable-AI-readiness FAIR data

Wireless Power Transfer Technologies: State-Of-The-Art and Future Prospects

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Wireless Power Transfer Technologies: State-Of-The-Art and F...

Communication, computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Vivek Justus S.Viveka M.Sumithra Purushothaman V V.Samuthira Pandi Desidi Narsimha Reddy Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Wireless Power Transfer (WPT) technologies represent a transformative approach to energy transmission, offering advantages over traditional wired systems such as convenience, flexibility, and reduced maintenance. This paper presents a comprehensive review of current advancements and future prospects in WPT. The review encompasses three primary technologies: electromagnetic induction, magnetic resonance, and microwave transmission, each offering unique benefits and challenges. Recent developments have significantly enhanced WPT efficiency, extended transmission distances, and enabled integration with emerging technologies like Internet of Things (IoT) devices and electric vehicle (EV) charging infrastructure. Looking forward, the paper identifies several key research directions and challenges. These include improving efficiency through advanced materials and metamaterials, standardization efforts to promote interoperability and safety, and exploration of new applications such as wireless charging in smart cities and space-based applications. The potential impact of WPT on reducing reliance on fossil fuels and enabling sustainable energy practices underscores its importance in future energy landscapes. By synthesizing current knowledge and highlighting future opportunities, this study aims to guide researchers, engineers, and policymakers in accelerating the development and adoption of WPT technologies, paving the way for a moreefficient and interconnected energy ecosystem.

关键词： Costs Reviews Smart cities Magnetic resonance Wireless power transfer Standardization Telecommunication computing Smart grids Internet of Things Tuning

Deep Graph Anomaly Detection: A Survey and New Perspectives

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arXiv 2024年

作者： Qiao, Hezhe Tong, Hanghang An, Bo King, Irwin Aggarwal, Charu Pang, Guansong School of Computing and Information Systems Singapore Management University Singapore Department of Computer Science University of Illinois Urbana-Champaign United States College of Computing and Data Science Nanyang Technological University Singapore Department of Computer Science & Engineering Chinese University of Hong Kong Hong Kong IBM T. J. Watson Research Center United States

Graph anomaly detection (GAD), which aims to identify unusual graph instances (e.g., nodes, edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its significance in a wide range of applications. Deep learning approaches, graph neural networks (GNNs) in particular, have been emerging as a promising paradigm for GAD, owing to its strong capability in capturing complex structure and/or node attributes in graph data. Considering the large number of methods proposed for GNN-based GAD, it is of paramount importance to summarize the methodologies and findings in the existing GAD studies, so that we can pinpoint effective model designs for tackling open GAD problems. To this end, in this work we aim to present a comprehensive review of deep learning approaches for GAD. Existing GAD surveys are focused on task-specific discussions, making it difficult to understand the technical insights of existing methods and their limitations in addressing some unique challenges in GAD. To fill this gap, we first discuss the problem complexities and their resulting challenges in GAD, and then provide a systematic review of current deep GAD methods from three novel perspectives of methodology, including GNN backbone design, proxy task design for GAD, and graph anomaly measures. To deepen the discussions, we further propose a taxonomy of 13 fine-grained method categories under these three perspectives to provide more in-depth insights into the model designs and their capabilities. To facilitate the experiments and validation of the GAD methods, we also summarize a collection of widely-used datasets for GAD and empirical performance comparison on these datasets. We further discuss multiple important open research problems in GAD to inspire more future high-quality research in this area. A continuously updated repository for GAD datasets, links to the codes of GAD algorithms, and empirical comparison is available at https://***/mala-lab/Awesome-Deep-G

关键词： Contrastive Learning

Image-Based Soil Health Analysis Using Deep Learning for Precision Agriculture

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Image-Based Soil Health Analysis Using Deep Learning for Pre...

International Conference on Communication and Electronics Systems (ICCES)

作者： V. S. Saranya Saikiran Mangali K Srinija Galeiah Medabalimi Meena Devi R Venkata Ramana N Ajanthaa Lakkshmanan Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Department of CSBS B V RAJU INSTITUTE OF TECHNOLOGY Medak Telangana Department: Data science MLRIT DUNDIGAL Sagi Ramakrishnam Raju Engineering College Sagi Ramakrishnam Raju Marg Bhimavaram Andhra Pradesh India Department of electrical and electronics engineering KCG college of technology Chennai Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Soil health is a critical component of agricultural productivity and sustainability. Traditional methods of assessing soil health, including physical sampling and laboratory testing, are labor-intensive, time-consuming, and costly. This paper proposes a novel image-based approach for soil health analysis using deep learning techniques. By utilizing aerial images captured by drones or satellites equipped with multispectral and thermal sensors, the system non-invasively assesses key soil health indicators such as moisture content, nutrient levels, organic matter, and erosion patterns. The proposed method integrates image processing techniques with machine learning models, particularly Convolutional Neural Networks (CNNs), to extract relevant soil features from the images. These CNNs are trained to predict soil health metrics, and the system's performance is evaluated through accuracy, mean squared error (MSE), and F1-score, showing significant improvements over traditional methods. This scalable solution offers a real-time tool for precision agriculture, aiding farmers and agronomists in making informed, data-driven decisions regarding soil health management.

关键词： Precision agriculture Deep learning Moisture Soil Thermal sensors Nutrients Feature extraction Real-time systems Convolutional neural networks Monitoring

Video-Based Detection of Epileptic Spasms in IESS:Modeling,Detection,and Evaluation

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Journal of Shanghai Jiaotong university(science) 2025年第1期30卷 1-9页

作者： DING Lihui FU Lijun YANG Guang WAN Lin CHANG Zhijun Shenyang Institute of Computing Technology Chinese Academy of SciencesShenyang110168China University of Chinese Academy of Sciences Beijing100049China Laboratory of Big Data and Artificial Intelligence Technology Shandong UniversityJinan250100China Senior Department of Pediatrics The Seventh Medical Center of Chinese PLA General HospitalBeijing100700China Department of Pediatrics The First Medical Center of Chinese PLA General HospitalBeijing100853China The Second School of Clinical Medicine Southern Medical UniversityGuangzhou510280China The National Science Library Chinese Academy of SciencesBeijing100190China

Behavioral scoring based on clinical observations remains the gold standard for screening,diagnosing,and evaluating infantile epileptic spasm syndrome(IESS).The accurate identification of seizures is crucial for clinical diagnosis and *** this study,we propose an innovative seizure detection method based on video feature recognition of patient *** capture the temporal characteristics of the spasm behavior presented in the videos effectively,we incorporate asymmetric convolutions and convolution–batch normalization–ReLU(CBR)*** within the 3D-ResNet residual blocks,we split the larger convolutional kernels into two asymmetric 3D convolutional *** kernels are connected in series to enhance the ability of the convolutional layers to extract key local features,both horizontally and *** addition,we introduce a 3D convolutional block attention module to enhance the spatial correlations between video frame channels *** improve the generalization ability,we design a composite loss function that combines cross-entropy loss with triplet loss to balance the classification and similarity *** train and evaluate our method using the PLA IESS-VIDEO dataset,achieving an average seizure recognition accuracy of 90.59%,precision of 90.94%,and recall of 87.64%.To validate its generalization capability further,we conducted external validation using six different patient monitoring videos compared with assessments by six human experts from various medical *** final test results demonstrate that our method achieved a recall of 0.6476,surpassing the average level achieved by human experts(0.5595),while attaining a high F1-score of *** findings have substantial significance for the long-term assessment of patients with IESS.

关键词： infantile epileptic spasm syndrome video-based seizure analysis asymmetric convolution 3D-ResNet attention mechanism

Leveraging GPT for the Generation of Multi-Platform Social Media datasets for Research

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arXiv 2024年

作者： Tari, Henry Khan, Danial Rutten, Justus Othman, Darian Kaushal, Rishabh Bertaglia, Thales Iamnitchi, Adriana Department of Advanced Computing Sciences Maastricht University Netherlands Institute of Data Science Maastricht University Netherlands Department of Information Technology Indira Gandhi Delhi Technical University for Women India Utrecht University Netherlands

Social media datasets are essential for research on disinformation, influence operations, social sensing, hate speech detection, cyberbullying, and other significant topics. However, access to these datasets is often restricted due to costs and platform regulations. As such, acquiring datasets that span multiple platforms—which are crucial for a comprehensive understanding of the digital ecosystem—is particularly challenging. This paper explores the potential of large language models to create lexically and semantically relevant social media datasets across multiple platforms, aiming to match the quality of real datasets. We employ ChatGPT to generate synthetic data from two real datasets, each consisting of posts from three different social media platforms. We assess the lexical and semantic properties of the synthetic data and compare them with those of the real data. Our empirical findings suggest that using large language models to generate synthetic multi-platform social media data is promising. However, further enhancements are necessary to improve the fidelity of the outputs. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions

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arXiv 2024年

作者： Cheng, Sibo Min, Jinyang Liu, Che Arcucci, Rossella CEREA École des Ponts and EDF R&D Île-de-France France Data Science Instituite Department of Computing Imperial College London United Kingdom Department of Earth Science & Engineering Imperial College London United Kingdom

data assimilation techniques are often confronted with challenges handling complex high dimensional physical systems, because high precision simulation in complex high dimensional physical systems is computationally expensive and the exact observation functions that can be applied in these systems are difficult to obtain. It prompts growing interest in integrating deep learning models within data assimilation workflows, but current software packages for data assimilation cannot handle deep learning models inside. This study presents a novel Python package seamlessly combining data assimilation with deep neural networks to serve as models for state transition and observation functions. The package, named TorchDA, implements Kalman Filter, Ensemble Kalman Filter (EnKF), 3D Variational (3DVar), and 4D Variational (4DVar) algorithms, allowing flexible algorithm selection based on application requirements. Comprehensive experiments conducted on the Lorenz 63 and a two-dimensional shallow water system demonstrate significantly enhanced performance over standalone model predictions without assimilation. The shallow water analysis validates data assimilation capabilities mapping between different physical quantity spaces in either full space or reduced order space. Overall, this innovative software package enables flexible integration of deep learning representations within data assimilation, conferring a versatile tool to tackle complex high dimensional dynamical systems across scientific domains. © 2024, CC BY-NC-SA.

关键词： Kalman filters

Crystalline Material Discovery in the Era of Artificial Intelligence

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arXiv 2024年

作者： Wang, Zhenzhong Hua, Haowei Lin, Wanyu Yang, Ming Tan, Kay Chen The Hong Kong Polytechnic University Department of Computing Hong Kong The Hong Kong Polytechnic University Department of Data Science and Artificial Intelligence Hong Kong The Hong Kong Polytechnic University Department of Applied Physics Hong Kong

Crystalline materials, with symmetrical and periodic structures, exhibit a wide spectrum of properties and have been widely used in numerous applications across electronics, energy, and beyond. For crystalline materials discovery, traditional experimental and computational approaches are time-consuming and expensive. In these years, thanks to the explosive amount of crystalline materials data, great interest has been given to data-driven materials discovery. Particularly, recent advancements have exploited the expressive representation ability of deep learning to model the highly complex atomic systems within crystalline materials, opening up new avenues for fast and accurate materials discovery. These works typically focus on four types of tasks, including physicochemical property prediction, crystalline material synthesis, aiding characterization, and accelerating theoretical computations. Despite the remarkable progress, there is still a lack of systematic investigation to summarize their distinctions and limitations. To fill this gap, we systematically investigated the progress made in recent years. We first introduce several data representations of the crystalline materials. Based on the representations, we summarize various fundamental deep learning models and their tailored usages in various material discovery tasks. Finally, we highlight the remaining challenges and propose future directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Crystalline materials