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Predicting blood glucose level using salivary glucose and other associated factors: A machine learning model selection and evaluation study

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Informatics in Medicine Unlocked 2024年 48卷

作者： Chopra, Aditi Rao, Rohini R. Kamath, Shobha U. Akhila Arun, Sanjana Shettigar, Laasya Department of Periodontology Manipal College of Dental Sciences Manipal Academy of Higher Education Manipal Manipal Pin: 576 104 Karnataka India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal Manipal Pin: 576 104 Karnataka India Department of Biochemistry Kasturba Medical College and Hospital Manipal Academy of Higher Education Manipal Manipal Pin: 576 104 Karnataka India

Introduction: There is a need for designing non-invasive methods to predict blood glucose levels to ensure timely diagnosis of Diabetes Mellitus. Needle anxiety and bleeding disorders preclude many from undertaking blood tests. Objectives: The primary objective of this study was to assess if biomarkers like saliva can be used to estimate blood glucose levels. The second objective was to develop and evaluate Machine Learning (ML) models to predict blood glucose levels based on salivary glucose and associated features. An insight into the patient's features, which was important for predicting blood glucose levels, was also required. Methods: A cross-sectional study was conducted, and blood and saliva samples, along with patient-related data, were collected from healthy and diabetic patients. ML techniques were applied to the data to develop a tool for predicting blood glucose levels using patient features. The prediction intervals were computed, clinical accuracy was assessed, and important features for the prediction were identified. Results: The Random Forest Regressor Model, with features identified using the wrapper method, was selected as the best, with an average RMSE of 43.28. The prediction intervals were computed for point estimate, MAE = 23.821, and coverage was 100 percent, the clinical accuracy was compared with that of glucometers and continuous monitoring systems. All predicted values are in Zones A and B of the Clarke error grid, and the bias was 6.41. The most important feature for predicting blood glucose level is salivary glucose level, followed by known risk factors like Family History, BMI, etc. The study found that salivary glucose levels are insufficient to classify blood glucose levels as high or normal. Conclusion: The study concluded that salivary glucose with associated patient features could be a potential non-invasive biomarker for predicting blood glucose levels in patients. The developed ML model could be deployed in a device that inputs

关键词： Blood glucose level Confirmatory prediction Diabetes mellitus Feature selection Machine learning model Non-invasive method Random forest regressor Salivary glucose

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Unextendible and Strongly Uncompletable Product Bases

arXiv

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arXiv 2024年

作者： Zhen, Xiao-Fan Zuo, Hui-Juan Shi, Fei Fei, Shao-Ming School of Mathematical Sciences Hebei Normal University Shijiazhuang050024 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Hebei Key Laboratory of Computational Mathematics and Applications Shijiazhuang050024 China Hebei International Joint Research Center for Mathematics and Interdisciplinary Science Shijiazhuang050024 China Department of Computer Science School of Computing and Data Science University of Hong Kong Hong Kong999077 Hong Kong School of Mathematical Sciences Capital Normal University Beijing10048 China

In 2003, DiVincenzo et al. put forward the question that whether there exists an unextendible product basis (UPB) which is an uncompletable product basis (UCPB) in every bipartition [DiVincenzo et al. Commun. Math. Phys. 238, 379-410(2003)]. Recently, Shi et al. presented a UPB in tripartite systems that is also a strongly uncompletable product basis (SUCPB) in every bipartition [Shi et al. New J. Phys. 24, 113-025 (2022)]. However, whether there exist UPBs that are SUCPBs in only one or two bipartitions remains unknown. We provide a sufficient condition for the existence of SUCPBs based on a quasi U-tile structure. We analyze all possible cases about the relationship between UPBs and SUCPBs in tripartite systems. In particular, we construct a UPB with smaller size d3 − 3d2 + 1 in Cd Cd Cd, which is an SUCPB in every bipartition and has a smaller cardinality than the existing one. Copyright © 2024, The Authors. All rights reserved.

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Predicting the Impacts of the Environment on the Probability and Severity of Electrical Injuries by AIML

Predicting the Impacts of the Environment on the Probability...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Araddhana Arvind Deshmukh K Parish Venkata Kumar B.Anjanee Kumar Pramoda Patro Pavitar Parkash Singh N. Ashokkumar Department of Artificial Intelligence and Data science Marathwada Mitra Mandal College of Engineering Pune India Department of Computer Applications Velagapudi Ramakrishna Siddhartha Engineering College (Autonomous) Vijayawada India Electronics and Communication Engineering Dadi institute of engineering and technology Visakhapatnam India Department of Mathematics Koneru Lakshmaiah Education Foundation Hyderabad India Department of Management Lovely Professional University Phagwara India Department of ECE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The acceleration of global warming and environmental degradation has been attributed to industrial growth and fossil fuel utilization. Consequently, this phenomenon has resulted in a rise in the frequency and intensity of natural disasters. Due to their predominantly outdoor installation, generators are subject to significant impacts. The occurrence of natural disasters has led to an increase in accidents associated with electrical equipment. This paper analyses statistical data on electrical fires, electric shock accidents, and electricity-related accidents caused by climate change in domestic settings to determine the associated risk *** this study, we propose a risk assessment model for electrical fires, explicitly focusing on every month. Our approach involves analysing data on electrical hazards in different regional areas, particularly metropolitan cities, considering climatic conditions such as temperature and humidity. Additionally, we aim to evaluate the accident risk rating associated with natural disasters, specifically those related to low and high-voltage equipment. In this study, a risk analysis model has been developed to assess electrical hazards in different regions and equipment types. The model presented herein serves as a foundational predictive tool. Hence, offering electrical security amenities in the forthcoming period is feasible by giving a risk estimation map delineating potential dangers specific to each geographical area and wiring category via web platforms or mobile applications utilizing the investigation data provided. Additionally, it is imperative to enhance the resilience and dependability of electrical components as a proactive measure against electrocute ions resulting from climate change-induced emergencies.

关键词： Wiring Temperature distribution Electric potential Analytical models Disasters Urban areas Humidity

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Deep Learning Algorithms for Business Management: Ethical Considerations

Deep Learning Algorithms for Business Management: Ethical Co...

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Communication, computer sciences and Engineering (IC3SE), International Conference on

作者： Shiney Chib B Santhosh Kumar V Asha Navdeep Singh Melanie Lourens Dyuti Banerjee Management Datta Meghe Institute of Management Studies Nagpur Maharashtra Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Deputy Dean Faculty of Management Sciences Durban University of Technology South Africa Artificial intelligence & Data Science (AI&DS) Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Guntur District Andhra Pradesh

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

By applying the right method to extract both structured and unstructured data, data science may add value. Accelerated deep learning advancement has led to substantial changes in data science operations. Deep learning is a sophisticated machine learning algorithmic method. It differs significantly from other machine learning procedures in that it uses a straightforward method to provide data to the system. To tackle difficult issues and make wise business decisions, deep learning leverages cutting-edge analytics. As smart manufacturing initiatives expand, deep leaning is seen as a potential avenue to boost efficiency and sustainability even further. In this study, we look into how DL models can enhance the application of business intelligence by organisational management. The results show that DL models may be trained on diverse data sources to identify linkages and patterns in the data. These models can assist decision-makers in making well-informed decisions based on accurate and reliable data.

关键词： Deep learning Accuracy Computational modeling Soft sensors Decision making data science Prediction algorithms

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Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networks

arXiv

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arXiv 2025年

作者： Khot, Ayush Wang, Xiwei Roy, Avik Kindratenko, Volodymyr Neubauer, Mark S. Department of Physics University of Illinois Urbana-Champaign UrbanaIL61801 United States The Grainger College of Engineering Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Center for Artificial Intelligence Innovation National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States The Grainger College of Engineering Siebel School of Computing and Data Science University of Illinois Urbana-Champaign UrbanaIL61801 United States

Current methods commonly used for uncertainty quantification (UQ) in deep learning (DL) models utilize Bayesian methods which are computationally expensive and time-consuming. In this paper, we provide a detailed study of UQ based on evidential deep learning (EDL) for deep neural network models designed to identify jets in high energy proton-proton collisions at the Large Hadron Collider and explore its utility in anomaly detection. EDL is a DL approach that treats learning as an evidence acquisition process designed to provide confidence (or epistemic uncertainty) about test data. Using publicly available datasets for jet classification benchmarking, we explore hyperparameter optimizations for EDL applied to the challenge of UQ for jet identification. We also investigate how the uncertainty is distributed for each jet class, how this method can be implemented for the detection of anomalies, how the uncertainty compares with Bayesian ensemble methods, and how the uncertainty maps onto latent spaces for the models. Our studies uncover some pitfalls of EDL applied to anomaly detection and a more effective way to quantify uncertainty from EDL as compared with the foundational EDL setup. These studies illustrate a methodological approach to interpreting EDL in jet classification models, providing new insights on how EDL quantifies uncertainty and detects out-of-distribution data which may lead to improved EDL methods for DL models applied to classification tasks. © 2025, CC BY.

关键词： Adversarial machine learning

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Evaluating Large Language Models on Spatial Tasks: A Multi-Task Benchmarking Study

arXiv

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arXiv 2024年

作者： Xu, Liuchang Zhao, Shuo Lin, Qingming Chen, Luyao Luo, Qianqian Wu, Sensen Ye, Xinyue Feng, Hailin Du, Zhenhong School of Mathematics and Computer Science Zhejiang Agriculture and Forestry University Hangzhou311300 China School of Earth Sciences Zhejiang University Hangzhou310058 China Department of Landscape Architecture and Urban Planning Center for Geospatial Sciences Applications and Technology Texas A&M University College StationTX77843 United States Financial Big Data Research Institute Sunyard Technology Co. Ltd Hangzhou310053 China

The advent of large language models such as ChatGPT, Gemini, and others has underscored the importance of evaluating their diverse capabilities, ranging from natural language understanding to code generation. However, their performance on spatial tasks has not been comprehensively assessed. This study addresses this gap by introducing a novel multi-task spatial evaluation dataset, designed to systematically explore and compare the performance of several advanced models on spatial tasks. The dataset encompasses twelve distinct task types, including spatial understanding and simple route planning, each with verified, accurate answers. We evaluated multiple models, including OpenAI’s gpt-3.5-turbo, gpt-4-turbo, gpt-4o, ZhipuAI’s glm-4, Anthropic’s claude-3-sonnet-20240229, and MoonShot’s moonshot-v1-8k, through a two-phase testing approach. Initially, we conducted zero-shot testing, followed by categorizing the dataset by difficulty and performing prompt tuning tests. Results indicate that gpt-4o achieved the highest overall accuracy in the first phase, with an average of 71.3%. Although moonshotv1-8k slightly underperformed overall, it surpassed gpt-4o in place name recognition tasks. The study also highlights the impact of prompt strategies on model performance in specific tasks. For example, the Chain-of-Thought (CoT) strategy increased gpt-4o’s accuracy in simple route planning from 12.4% to 87.5%, while a one-shot strategy enhanced moonshot-v1-8k’s accuracy in mapping tasks from 10.1% to 76.3%. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Linear attention coupled Fourier neural operator for simulation of three-dimensional turbulence

arXiv

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arXiv 2022年

作者： Peng, Wenhui Yuan, Zelong Li, Zhijie Wang, Jianchun Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and Technology Shenzhen518055 China Department of Computer Engineering Polytechnique MontrealH3T1J4 Canada

Modeling three-dimensional (3D) turbulence by neural networks is difficult because 3D turbulence is highly-nonlinear with high degrees of freedom and the corresponding simulation is memory-intensive. Recently, the attention mechanism has been shown as a promising approach to boost the performance of neural networks on turbulence simulation. However, the standard self-attention mechanism uses O(n2) time and space with respect to input dimension n, and such quadratic complexity has become the main bottleneck for attention to be applied on 3D turbulence simulation. In this work, we resolve this issue with the concept of linear attention network. The linear attention approximates the standard attention by adding two linear projections, reducing the overall self-attention complexity from O(n2) to O(n) in both time and space. The linear attention coupled Fourier neural operator (LAFNO) is developed for the simulation of 3D isotropic turbulence and free shear turbulence. Numerical simulations show that the linear attention mechanism provides 40% error reduction at the same level of computational cost, and LAFNO can accurately reconstruct a variety of statistics and instantaneous spatial structures of 3D turbulence. The linear attention method would be helpful for the improvement of neural network models of 3D nonlinear problems involving high-dimensional data in other scientific domains. © 2022, CC0.

关键词： Degrees of freedom (mechanics)

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Inference-optimized AI and high performance computing for gravitational wave detection at scale

arXiv

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arXiv 2022年

作者： Chaturvedi, Pranshu Khan, Asad Tian, Minyang Huerta, E.A. Zheng, Huihuo Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Chicago ChicagoIL60637 United States

We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 hours. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaGPU supercomputer at Argonne Leadership computer Facility to conduct distributed inference. Using the entire ThetaGPU supercomputer, consisting of 20 nodes each of which has 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, our NVIDIA TensorRT-optimized AI ensemble processed an entire month of advanced LIGO data (including Hanford and Livingston data streams) within 50 seconds. Our inference-optimized AI ensemble retains the same sensitivity of traditional AI models, namely, it identifies all known binary black hole mergers previously identified in this advanced LIGO dataset and reports no misclassifications, while also providing a 3X inference speedup compared to traditional artificial intelligence models. We used time slides to quantify the performance of our AI ensemble to process up to 5 years worth of advanced LIGO data. In this synthetically enhanced dataset, our AI ensemble reports an average of one misclassification for every month of searched advanced LIGO data. We also present the receiver operating characteristic curve of our AI ensemble using this 5 year long advanced LIGO dataset. This approach provides the required tools to conduct accelerated, AI-driven gravitational wave detection at *** Codes 68T10, 85-08, 83C35, 83C57 Copyright © 2022, The Authors. All rights reserved.

关键词： Stars

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A defected ground structure based ultra-compact wider bandwidth terahertz multiple-input multiple-output antenna for emerging communication systems

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Heliyon 2024年第17期10卷 e36842页

作者： Kumar, Praveen Sivakumar, V. Rao, Vidya George, Criss Tom Awadhiya, Bhaskar Huchegowda, Yogeshwary Bommenahalli Nanjappa, Yashwanth Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India Department of Information & Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India Department of Electronics and Communication Engineering Shri Madhwa Vadiraja Institute of Technology and Management Visvesvaraya Technological University Bantakal 574115 India

This work presents a quad-port Multiple Input Multiple Output (MIMO) wideband antenna that operates in the terahertz (THz) frequency range is designed and analyzed. To improve MIMO performance, a defective ground structure (DGS) is used. A rectangular metallic patch was altered by adding parasitic elements to the radiator with the lowered ground plane on a polyamide substrate in order to achieve the wideband THz operating frequency. The THz antenna was turned into a MIMO antenna by replicating horizontally and vertically with a spacing of 0.05 λ and 0.002 λ, respectively (λ calculated at 1.7 THz). The designed THz MIMO antenna, comprising 40 μm × 46 μm × 2 μm, operates across the 1.7–10.4 THz frequency region. The THz MIMO antenna provides isolation of more than 20 dB in the frequency range of 2.8–10.4 THz and more than 10 dB for the frequency of 1.7–2.7 THz. Isolation augmentation is accomplished by establishing different local current channels using the antenna's DGS. The MIMO diversity properties of the proposed THz MIMO antenna are analyzed and found to be ECC<0.004, DG ~ 10 dB, TARC < −10 dB, MEG < −3 dB, and CCL<0.23 bps/Hz over the antenna's operating frequency. © 2024 The Author(s)

关键词： DGS MIMO THz antenna Wideband

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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