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An approach for crop recommendation with uncertainty quantification based on machine learning for sustainable agricultural decision-making

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Results in Engineering 2025年 26卷

作者： Md. Sakib Bin Alam Vatcharaporn Esichaikul Aiman Lameesa Shams Forruque Ahmed Amir H. Gandomi Department of Information Technology University of Information Technology and Sciences (UITS) Dhaka 1212 Bangladesh Computer Science and Information Management Program School of Engineering and Technology Asian Institute of Technology Pathum Thani 12120 Thailand Department of Computer Science American International University-Bangladesh Dhaka 1229 Bangladesh School of Mathematical Sciences Sunway University Bandar Sunway Petaling Jaya 47500 Selangor Darul Ehsan Malaysia Department of Mathematics & Physics North South University Dhaka 1229 Bangladesh Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia University Research and Innovation Center (EKIK) Óbuda University 1034 Budapest Hungary Department of Computer Science Khazar University Mahsati 41 Baku Azerbaijan

While machine learning (ML) models for crop recommendation have demonstrated high predictive accuracy, a critical gap persists in their practical reliability: the omission of uncertainty quantification. Existing studies predominantly deliver deterministic recommendations, neglecting inherent uncertainties arising from data noise. This raises concerns about the reliability of the decision support systems for crop recommendation. To address this, we propose an ensemble ML framework incorporating entropy-based uncertainty quantification. Trained on a publicly available Indian agricultural dataset with 2200 samples across seven agro-climatic features (nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall) and 22 crop classes, the model achieves a predictive accuracy of 99.54 %. By estimating prediction confidence using entropy, the framework offers probabilistic recommendations that support environmentally informed decision-making under uncertainty. These findings suggest that integrating uncertainty measures into ML-driven crop recommendation systems can enhance reliability and promote sustainable agricultural practices.

关键词： Crop recommendation Machine learning Uncertainty Precision agriculture

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An adaptive replica configuration mechanism based on predictive file popularity and queue balance in mobile edge computing environment

Research Square

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Research Square 2021年

作者： Chiang, Mao-Lun Hsieh, Hui-Ching Chang, Ting-Yi Lin, Wei-Ling Chen, Hong-Wei Department of Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taiwan Department of New Media Communication Hsing Wu University New Taipei City Taiwan Department of Industrial Education and Technology National Changhua University of Education Changhua City Taiwan Department of Information Management Chaoyang University of Technology Taichung City Taiwan

In the current era of the Internet of Things (IoT), various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause the cloud core overload and significant network delays. To overcome these problems, the Mobile Edge Computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal. Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, when there is a large amount of popular data accessed in a short period, the system must generate many replicas, which will not only reduce access efficiency but also cause additional traffic overhead. To improve the above problems, an Adaptive Replica Configuration Mechanism (ARCM) is proposed in this paper to predict the popularity of the file and make a replica to the low-blocking node. This method spreads the subsequent access workload by copying the popular file in advance to improve the overall performance of the system. © 2021, CC BY.

关键词： Mobile edge computing

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Overall mortality risk analysis for rectal cancer using deep learning-based fuzzy systems

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Computers in Biology and Medicine 2023年 157卷 106706-106706页

作者： Yang, Cheng-Hong Chen, Wen-Ching Chen, Jin-Bor Huang, Hsiu-Chen Chuang, Li-Yeh Department of Information Management Tainan University of Technology Tainan Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Ph. D. Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung Taiwan School of Dentistry Kaohsiung Medical University Kaohsiung Taiwan Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung Taiwan Department of Internal Medicine Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Kaohsiung Taiwan Department of Community Health Chia-Yi Christian Hospital Chia-Yi City Taiwan Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering I-Shou University Kaohsiung Taiwan

Colorectal cancer is a leading cause of cancer mortality worldwide, with an increasing incidence rate in developing countries. Integration of genetic information with cancer therapy guidance has shown promise in cancer treatment, indicating its potential as an essential tool in translation oncology. However, the high-throughput analysis and variability of genomic data poses a major challenge to conventional analytic approaches. In this study, we propose an advanced analytic approach, named "Fuzzy-based RNNCoxPH," incorporated fuzzy logic, recurrent neural networks (RNNs), and Cox proportional hazards regression (CoxPH) for detecting missense variants associated with high-risk of all-cause mortality in rectum adenocarcinoma. The test data set was downloaded from "Rectum adenocarcinoma, TCGA-READ" the Genomic Data Commons (GDC) portal. In this study, four model-based risk score models were derived using RNN, CoxPH, RNNCoxPHAddition, and RNNCoxPHMultiplication. The RNNCoxPHAddition and RNNCoxPHMultiplication models were obtained as the sum and product of the RNN risk degree matrix and the CoxPH risk degree matrix, respectively. Moreover, the fuzzy logic system was used to calculate the survival risk values of missense variants and classified their membership grade to improve the identification of high-risk gene variation locations associated with cancer mortality. The four models were integrated to develop an advanced risk estimation model. There were 20 028 variants associated with survival status, amongst 17 638 variants were associated with survival and 2390 variants associated with mortality. The proposed Fuzzy-based RNNCoxPH model obtained a balanced accuracy of 93.7%, which was significantly higher than that of the other four test methods. In particular, the CoxPH model is commonly used in medical researches and the XGBoost model is famous for its high accuracy in machine learning. The results suggest that the Fuzzy-based RNNCoxPH model exhibits a higher efficacy

关键词： Fuzzy logic

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Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study

arXiv

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arXiv 2023年

作者： Zhang, Ruichen Xiong, Ke Du, Hongyang Niyato, Dusit Kang, Jiawen Shen, Xuemin Poor, H. Vincent The Engineering Research Center of Network Management Technology High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Recognizing the tremendous improvements that the integration of generative AI can bring to intelligent transportation systems, this article explores the integration of generative AI technologies in vehicular networks, focusing on their potential applications and challenges. Generative AI, with its capabilities of generating realistic data and facilitating advanced decision-making processes, enhances various applications when combined with vehicular networks, such as navigation optimization, traffic prediction, data generation, and evaluation. Despite these promising applications, the integration of generative AI with vehicular networks faces several challenges, such as real-time data processing and decision-making, adapting to dynamic and unpredictable environments, as well as privacy and security concerns. To address these challenges, we propose a multi-modality semantic-aware framework to enhance the service quality of generative AI. By leveraging multi-modal and semantic communication technologies, the framework enables the use of text and image data for creating multi-modal content, providing more reliable guidance to receiving vehicles and ultimately improving system usability and efficiency. To further improve the reliability and efficiency of information transmission and reconstruction within the framework, taking generative AI-enabled vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning (DRL)-based approach is proposed for resource allocation. Finally, we discuss potential research directions and anticipated advancements in the field of generative AI-enabled vehicular networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Oxidative Stress and Natural Antioxidants: Back and Forth in the Neurological Mechanisms of Alzheimer’s Disease

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Journal of Alzheimer's Disease 2023年第3期96卷 877-912页

作者： Mani, Shalini Dubey, Rajni Lai, I-Chun Babu, M. Arockia Tyagi, Sakshi Swargiary, Geeta Mody, Deepansh Singh, Manisha Agarwal, Shriya Iqbal, Danish Kumar, Sanjay Hamed, Munerah Sachdeva, Punya Almutary, Abdulmajeed G. Albadrani, Hind Muteb Ojha, Shreesh Singh, Sandeep Kumar Jha, Niraj Kumar Centre for Emerging Diseases Department of Biotechnology Jaypee Institute of Information Technology UP Noida India Division of Cardiology Department of Internal Medicine Taipei Medical University Hospital Taipei Taiwan School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Division of Radiation Oncology Department of Oncology Taipei Veterans General Hospital Taipei Taiwan The Ph.D. Program for Translational Medicine College of Medical Science and Technology Taipei Medical University Academia Sinica Taipei Taiwan Institute of Pharmaceutical Research GLA University Mathura India Department of Molecular Sciences Macquarie University Sydney Australia Department of Health Information Management College of Applied Medical Sciences Buraydah Private Colleges Buraydah Saudi Arabia Department of Life Sciences School of Basic Sciences and Research (SBSR) Sharda University Uttar Pradesh Greater Noida India Department of Pathology Faculty of Medicine Umm Al-Qura University Makkah Saudi Arabia GloNeuro Vishwakarma Road Noida India Department of Biomedical Sciences College of Health Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Department of Clinical Laboratory Sciences College of Applied Medical Sciences Imam Abdulrahman Bin Faisal University Eastern Province Dammam Saudi Arabia Department of Pharmacology and Therapeutics College of Medicine and Health Sciences United Arab Emirates University Abu Dhabi United Arab Emirates Indian Scientific Education and Technology Foundation Lucknow India Department of Biotechnology School of Engineering & Technology (SET) Sharda University Uttar Pradesh Greater Noida India School of Bioengineering and Biosciences Lovely Professional University Punjab Phagwara India Department of Biotechnology School of Applied & Life Sciences (SALS) Uttaranchal University Uttarakhand Dehradun India Department of Biotechnology Engineering and Food Technology Chandigarh University Mohali India

Alzheimer’s disease (AD) is characterized by the progressive degeneration of neuronal cells. With the increase in aged population, there is a prevalence of irreversible neurodegenerative changes, causing a significant mental, social, and economic burden globally. The factors contributing to AD are multidimensional, highly complex, and not completely understood. However, it is widely known that aging, neuroinflammation, and excessive production of reactive oxygen species (ROS), along with other free radicals, substantially contribute to oxidative stress and cell death, which are inextricably linked. While oxidative stress is undeniably important in AD, limiting free radicals and ROS levels is an intriguing and potential strategy for deferring the process of neurodegeneration and alleviating associated symptoms. Therapeutic compounds from natural sources have recently become increasingly accepted and have been effectively studied for AD treatment. These phytocompounds are widely available and a multitude of holistic therapeutic efficiencies for treating AD owing to their antioxidant, anti-inflammatory, and biological activities. Some of these compounds also function by stimulating cholinergic neurotransmission, facilitating the suppression of beta-site amyloid precursor protein-cleaving enzyme 1, α-synuclein, and monoamine oxidase proteins, and deterring the occurrence of AD. Additionally, various phenolic, flavonoid, and terpenoid phytocompounds have been extensively described as potential palliative agents for AD progression. Preclinical studies have shown their involvement in modulating the cellular redox balance and minimizing ROS formation, displaying them as antioxidant agents with neuroprotective abilities. This review emphasizes the mechanistic role of natural products in the treatment of AD and discusses the various pathological hypotheses proposed for AD. © 2023 – IOS Press. All rights reserved.

关键词： Alzheimer’s disease antioxidants neuroprotection oxidative stress therapeutics vitamins

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A Software Factory in the Cloud for Pandemics and other Disasters - Initial Results and Future Directions

A Software Factory in the Cloud for Pandemics and other Disa...

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IEEE Cloud Summit

作者： Amjad Umar Information Systems Engineering and Management Program Harrisburg University of Science and Technology UN Partnership on Small Islands and Developing States (ICT4SIDS) Harrisburg USA

ISBN: (数字)9781728182667

ISBN: (纸本)9781728182674

Pandemics such as COVID-19 and other disasters in this age require a wide range of cyber-physical solutions such as web portals, mobile apps, IoTs, robots and drones. These solutions need to be customized for different geographical locations with different capabilities and should be produced quickly and at massive scales to meet the specific demands of the disasters. To complicate matters further, several government policies and industry guidelines regulate the deployment and use of such solutions. It is virtually impossible to handcraft the needed solutions individually and manually. A factory model is needed to rapidly build highly customized solutions very much like the auto factories that build millions of highly customized cars to satisfy needed safety requirements. In the same vein, this paper presents initial results from a software factory in the cloud that has produced a Smart Global Village (SGV) with more than 700 smart hubs for 130 countries that span 12 sectors, including disaster management. These highly customized smart hubs, created by using the SPACE Factory in less than 20 minutes per hub, also collaborate with each other that leads to highly creative B2B collaborations at global level. The paper discusses initial results from creating the SGV sandbox by using a factory model and outlines future research directions.

关键词： Cloud computing Pandemics Service robots Veins Production facilities Software Safety

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Precision Oral Medicine: A DPR Segmentation and Transfer Learning Approach for Detecting Third Molar Compress Inferior Alveolar Nerve

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IEEE Journal of Translational Engineering in Health and Medicine 2025年

作者： Lin, Yuan-Jin Chen, Shih-Lun Mao, Yi-Cheng Chen, Tsung-Yi Peng, Cheng-Hao Tsai, Tzu-Hsiang Li, Kuo-Chen Chen, Chiung-An Tu, Wei-Chen Abu, Patricia Angela R. National Cheng Kung University Department of Program on Semiconductor Manufacturing Technology Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan City701401 Taiwan Chung Yuan Christian University Department of Electronic Engineering Taoyuan City32023 Taiwan Taoyuan Chang Gang Memorial Hospital Department of Operative Dentistry Taoyuan City33305 Taiwan Feng Chia University Department of Electronic Engineering Taichung city40724 Taiwan Chung Yuan Christian University Taoyuan City32023 Taiwan Chung Yuan Christian University Department of Information Management Taoyuan City32023 Taiwan Ming Chi University of Technology Department of Electronic Engineering New Taipei City243303 Taiwan National Cheng Kung University Department of Electrical Engineering Tainan city701401 Taiwan Ateneo de Manila University Department of Information Systems and Computer Science Quezon City Philippines

Objective: Extraction of the third molar of the mandible is one of the most common oral surgical procedures. Preoperative monitoring and assessment are crucial to mitigate neurological risks. Identifying whether the third molar in the mandible compresses the inferior alveolar nerve still relies on dental professionals, a task that is repetitive and time-consuming. Thus, the primary objective is to utilize dental panoramic radiography for image processing and classify whether the third molar compresses the inferior alveolar nerve, aiming to reduce the demand for CT images in symptom diagnosis and mitigate the risks associated with high-dose radiation. technology or Method: This study proposes an innovative dental panoramic radiography segmentation technique to locate the third molar position. Subsequently, an innovative edge masking enhancement method is used to extract features of the inferior alveolar nerve and the third molar. Moreover, a transformer-based image detection model to consider whether the third molar compresses the inferior alveolar nerve. Results: The third molar position localization method achieved an accuracy rate of 97.92%, compared to recent research at least improved by 3.6% accuracy. Subsequently, innovative edge masking and image enhancement methods improve classification accuracy by 4.3%, when supplemented with computed tomography scan images for further evaluation, the maximum accuracy reached 98.45%, representing a 4.5% improvement compared to previous studies. Conclusions: The third molar position detection results will impact the identification of the inferior alveolar nerve compressed by the third molar. Through the innovative edge region segmentation algorithm can effectively distinguish this object, and the overall evaluation accuracy can be improved by approximately 3.8%. © 2013 IEEE.

关键词： Neurophysiology

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, Computer Retrospective Study

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A System Dynamics Computer Model to Assess the Effects of Action Games on the Development of Children with Special Need 4

A System Dynamics Computer Model to Assess the Effects of Ac...

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4th International Conference on information management and technology, ICIMTech 2019

作者： Satria, Thomas Galih Sardjono, Wahyu Panggabean, Francisco Maruli Game Application Technology Program School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information Systems Management Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Special needs children have a tendency to play more games than children with development in general. Game play activities are often carried out by children with special needs can be a support learning and therapy due to his players the game has interaction without having to meet with his co-star in person. Starting from these data, this study aims to create a model of the System Dynamics approach. Data collected will be simulated by variables other support. The simulation results of the model will be seen autistic children therapy behavior. The results are then analyzed to create an alternative to the existing model. The benefit of this research is to know the description of game play to treatment of children with special needs. The results of simulation showed that playing games in an average time children with autism play therapy can help improve the quality of children with autism if children with autism play the game in a long time actually showed a decrease. © 2019 IEEE.

关键词： System theory

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National science Review 2025年第5期 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

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