检索结果-内蒙古大学图书馆

International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Abrar Ahmed Minhazul Islam Mahi M. Akhtaruzzaman Jamal Uddin Tanvin Fahim Shahryer Sachitra Halder Muhammad Towfiqur Rahman Md Shofiqul Islam Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh Department of Computer Science & Engineering University Asia Pacific (UAP) Dhaka Bangladesh Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Geelong Victoria

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Medication management poses significant challenges for many patients, particularly the elderlies, who often struggle with keeping track of their medication schedules and taking the correct dosages. To address this issue, this study aims to design an AI-integrated Medication Management and Assistive Unit (AMMAU) for elderlies with some important features like, schedule reminder, medicine recognition and count, early indication of shortage, sorted slots, and so on. Automated insulin-dose prediction and alert system makes the system unique and more demanding at these current scenarios. For this study, a suitable machine learning model is designed, analyzed, verified, and embedded in the proposed system so that the elderly diabetic patients can get alert, further compare with the current insulin-doses. Though, the proposed system is currently focusing on only the Basal insulin doses prediction, the system will definitely reduce the risks associated with wrong management of medication by degrading the chances of missed doses or taking wrong pills for elderlies at home.

关键词： Training Schedules Analytical models Prototypes Machine learning Predictive models Internet of Things Older adults Insulin Physical design

来源：评论

学校读者我要写书评

暂无评论

intelligent sensing devices and systems for personalized mental health

引用

Med-X 2025年第1期3卷 1-22页

作者： Xing, Yantao Yang, Yang Yang, Kaiyuan Lu, Albert Xing, Luyi Mackie, Ken Guo, Feng Department of Intelligent Systems Engineering Indiana University Bloomington Bloomington USA Culver Academies High School Culver USA Department of Computer Science Indiana University Bloomington Bloomington USA Department of Psychological and Brain Sciences Gill Institute for Neuroscience Indiana University Bloomington Bloomington USA

Mental disorders disturb the cognition, emotion, and behavior of a diverse patient population, and can reduce their quality of life and even cause death. Despite significant advances in the diagnosis and treatment of mental disorders, challenges remain in achieving objective understanding, accurate assessment, and timely intervention for personalized conditions. Here, we review the recent development of intelligent sensing devices and systems for advancing the diagnosing, monitoring, and managing of mental disorders, with a special emphasis on personalized mental healthcare. We first introduce the mechanisms and clinical symptoms of mental disorders and related diagnostic principles. Then, we discuss the working principle and application of wearable sensors and systems to track various physiological parameters and markers for long-term monitoring, early screening, and treatment evaluation. Furthermore, we highlight recent emerging advancements in Artificial Intelligence (AI) and digital health and give perspectives on their integration with sensing technologies to address the emergent challenges of personalized mental healthcare. We believe innovative intelligent sensing technologies may significantly improve the patient's quality of life, enhance the efficiency and robustness of current healthcare systems, and reduce the socioeconomic burden for mental disorders and other diseases. • Summary of innovative intelligent sensing devices and systems for personalized mental healthcare. • Highlight of recent advancement of physiological and biochemical sensing technologies for objective assessment of mental disorders. • Introduction of emergent Al and digital health for advancing long-term monitoring, early screening, and treatment evaluation of mental disorders. • Perspectives of data, privacy, and safety for personalized mental healthcare.

关键词：

来源：评论

学校读者我要写书评

暂无评论

System for Automating Medication Orders to the Hospital Pharmacy by Nurses in Peru Through Voice Recognition

System for Automating Medication Orders to the Hospital Phar...

引用

Software Engineering and computer science (CSECS), International Conference on

作者： Conrado Moreno-Fernández Fabian Colque-Fernández José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Perú Department of Computer Science Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

The management of nurses' orders to a Peruvian hospital pharmacy faces efficiency challenges, which affects time spent on patient care. This context highlights the need for a system that optimizes this process. In this sense, a web-based system is proposed for the automation of orders from the nurse's cell phone, which uses a customized model for automatic speech recognition. The development of the system was divided into three stages: first, data collection with 21 orders placed by three nurses and 978 audio recordings to validate the automatic speech recognition model; second, implementation of the system allowing agile order processing; and finally, evaluation of results in terms of time and accuracy. The findings indicate that the processing time ranged from 37.70 to 60.15 seconds, representing a 97%-time reduction in operational tasks. In addition, the Word Error Rate was 10.51%, significantly lower than the 25.05% of Whisper Large V2. These results demonstrate the system's potential for adoption in various hospital contexts.

关键词： Automation Accuracy Hospitals Error analysis Terminology Transforms Time measurement Artificial intelligence Automatic speech recognition Software engineering

来源：评论

学校读者我要写书评

暂无评论

On robust learning of memory attractors with noisy deep associative memory networks

引用

Neural Networks 2025年 188卷 107474页

作者： Rao, Xuan Zhao, Bo Liu, Derong School of Systems Science Beijing Normal University Beijing100875 China School of Automation and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518000 China Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL60607 United States

Developing the computational mechanism for memory systems is a long-standing focus in machine learning and neuroscience. Recent studies have shown that overparameterized autoencoders (OAEs) implement associative memory (AM) by encoding training data as attractors. However, the learning of memory attractors requires that the norms of all eigenvalues of the input–output Jacobian matrix are strictly less than one. Motivated by the observed strong negative correlation between the attractor robustness and the largest singular value of the Jacobian matrix, we develop the noisy overparameterized autoencoders (NOAEs) for learning robust attractors by injecting random noises into their inputs during the training procedure. Theoretical demonstrations show that the training objective of the NOAE approximately minimizes the upper bound of the weighted sum of the reconstruction error and the square of the largest singular value. Extensive experiments in terms of numerical and image-based datasets show that NOAEs not only increase the success rate of the training samples becoming attractors, but also improve the attractor robustness. Codes are available at https://***/RaoXuan-1998/neural-netowrk-journal-NOAE. © 2025

关键词： Associative storage

来源：评论

学校读者我要写书评

暂无评论

FediLive: A Framework for Collecting and Preprocessing Snapshots of Decentralized Online Social Networks 25

FediLive: A Framework for Collecting and Preprocessing Snaps...

引用

Companion Proceedings of the ACM on Web Conference 2025

作者： Shaojie Min Shaobin Wang Yaxiao Luo Min Gao Qingyuan Gong Yu Xiao Yang Chen Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Department of Information and Communications Engineering Aalto University Espoo Finland

ISBN: (纸本)9798400713316

Decentralized online social networks such as Mastodon have emerged quickly during the past years, and offer unique opportunities to investigate user behavior, moderation strategies, and community evolution. However, their decentralized nature imposes challenges for data collection and preprocessing, particularly for obtaining a real-time snapshot in a timely manner. This paper introduces FediLive, a framework designed to rapidly collect and preprocess the live feeds from Mastodon, generate a comprehensive snapshot in real-time, including user-generated contents, interaction networks, and users' demographic attributes. Such a snapshot could further be leveraged for data analysis from different angles, leading to a deeper understanding of user activities on Mastodon. Using FediLive, we collected a 13-day snapshot of Mastodon, covering the publicly-visible activities of all Mastodon users. Our study demonstrates the usefulness of FediLive, and reveals its potential in facilitating data-driven analysis for decentralized online social networks.

关键词： decentralized online social networks

来源：评论

学校读者我要写书评

暂无评论

Model for Endometriosis Detection Using Machine Learning Algorithms

Model for Endometriosis Detection Using Machine Learning Alg...

引用

Software Engineering and computer science (CSECS), International Conference on

作者： Alexis Bautista Jahir Tardillo José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Endometriosis is a chronic disease that affects a considerable percentage of women of reproductive age and is characterized by the presence of endometrial tissue outside the uterine cavity, leading to symptoms such as pelvic pain and dysmenorrhea. The aim of this study is to develop a predictive model for the classification of endometriosis using four Machine Learning algorithms: Random Forest, LASSO, SVM, and Naive Bayes. For this purpose, a dataset from the Global Health Data Exchange was utilized, consisting of 1,000 cases of patients with endometriosis. The methodology included data cleaning and preprocessing, as well as the evaluation of each algorithm's performance using four metrics: precision, recall, F1-Score, and accuracy. The findings revealed that the Random Forest algorithm was the most effective in identifying endometriosis, outperforming the other algorithms with a precision of 0.99 for the “endometriosis” class and an overall accuracy of 0.98.

关键词： Support vector machines Measurement Training Machine learning algorithms Accuracy Software algorithms Predictive models Bayes methods Random forests Software engineering

来源：评论

学校读者我要写书评

暂无评论

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers

arXiv

引用

arXiv 2025年

作者： Singh, Siddharth Singhania, Prajwal Ranjan, Aditya Kirchenbauer, John Geiping, Jonas Wen, Yuxin Jain, Neel Hans, Abhimanyu Shu, Manli Tomar, Aditya Goldstein, Tom Bhatele, Abhinav Department of Computer Science University of Maryland College Park United States ELLIS Institute Tübingen Max Planck Institute for Intelligent Systems Tübingen Germany Department of Electrical Engineering and Computer Sciences University of California Berkeley United States

Training and fine-tuning large language models (LLMs) with hundreds of billions to trillions of parameters requires tens of thousands of GPUs, and a highly scalable software stack. In this work, we present a novel four-dimensional hybrid parallel algorithm implemented in a highly scalable, portable, open-source framework called AxoNN. We describe several performance optimizations in AxoNN to improve matrix multiply kernel performance, overlap non-blocking collectives with computation, and performance modeling to choose performance optimal configurations. These have resulted in unprecedented scaling and peak flop/s (bf16) for training of GPT-style transformer models on Perlmutter (620.1 Petaflop/s), Frontier (1.381 Exaflop/s) and Alps (1.423 Exaflop/s). While the abilities of LLMs improve with the number of trainable parameters, so do privacy and copyright risks caused by memorization of training data, which can cause disclosure of sensitive or private information at inference time. We highlight this side effect of scale through experiments that explore "catastrophic memorization," where models are sufficiently large to memorize training data in a single pass, and present an approach to prevent it. As part of this study, we demonstrate fine-tuning of a 405-billion parameter LLM using AxoNN on Frontier. Copyright © 2025, The Authors. All rights reserved.

关键词： Supercomputers

来源：评论

学校读者我要写书评

暂无评论

HS-Gen: Human-Like Handwriting Synthetic Generation—A Preliminary Investigation 27th

HS-Gen: Human-Like Handwriting Synthetic Generation—A Preli...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Bensalah, Asma Parziale, Antonio Coccaro, Rosanna Marcelli, Angelo Fornés, Alicia Lladós, Josep Computer Vision Center Universitat Autònoma de Barcelona Barcelona Spain Computer Science Department Universitat Autònoma de Barcelona Barcelona Spain DIEM University of Salerno Via Giovanni Paolo II 132 SA Fisciano84084 Italy AI3S Unit CINI National Laboratory of Artificial Intelligence and Intelligent Systems University of Salerno SA Fisciano Italy

ISBN: (纸本)9783031876592

The application of handwriting analysis in the health field for early detection and diagnosis is limited by a lack of data, which presents a significant challenge for the implementation of deep learning-based models. To address this issue, numerous studies have focused on generating synthetic data. Current methods for generating synthetic handwriting from offline images, including generative models and geometrical techniques, fail to account for the kinematics of handwriting movements, which is critical for achieving more human-like results. To address this limitation, we propose a novel, human-like approach for the synthetic generation of handwriting or drawing from offline images. This method creates new samples by incorporating a trajectory recovery algorithm and a human-like time law generator, as well as the extraction and manipulation of kinematic parameters. The evaluation of the proposed method from both visual and kinematic perspectives demonstrates its potential applicability across a wide range of devices and handwriting styles. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Detection of Alzheimer’s Disease progression from Structural MRIs using ConvMixer based Deep Learning Model

引用

Procedia computer science 2025年 258卷 420-429页

作者： Mahak Lashkary Kanika Bansal Samyak Jain Praveen Kumar Shukla Hitesh Tekchandani Vijaypal Singh Dhaka School of Computing and Intelligent Systems Manipal University Jaipur Jaipur-303007 Rajasthan India School of Computer Science and Engineering Manipal University Jaipur Jaipur-303007 Rajasthan India Department of artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Hyderabad-500075 Telangana India

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive functions and quality of life. Affecting over 50 million people worldwide, early and accurate detection of AD progression is crucial for timely intervention and management. This paper presents a reliable method for classifying Alzheimer’s stages using Deep Learning based ConvMixer model on structural MRI data. The proposed deep learning based convmixer model significantly utilizes the patch based learning with depthwise separable convolution to achieve improved performance. The dataset used in this work has been extracted from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and comprises of three classes: Alzheimer’s Disease (AD), Cognitive Impairment (CI), and Cognitively Normal (CN). The proposed approach demonstrated exceptional performance, achieving an accuracy of 99.88%, specificity of 99.87%, sensitivity of 99.90 and precision of 99.95%. The achieved results demonstrate the proposed model’s accuracy in distinguishing Alzheimer’s stages, making it a promising tool for early diagnosis and disease progression monitoring.

关键词： Alzheimer’s Disease Cognitive Impairment (CI) Cognitively Normal (CN) ConvMixer deep learning structural MRI neuroimaging Classification early detection machine learning

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Framework for Real-time Oil Spill Detection and Classification 2

A Deep Learning Framework for Real-time Oil Spill Detection ...

引用

2nd Workshop on Artificial Intelligence for Perception and Artificial Consciousness, AIxPAC 2024

作者： Patel, Pranshu Desai, Kedar Jain, Rinkal Bhatt, Chintan Vanlanduit, Steve Bruno, Alessandro Mazzeo, Pier Luigi Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India InViLab Research Group Universiteit Antwerpen Groenenborgerlaan Antwerpen171 2020 Belgium Department of Business Law Economics and Consumer Behaviour "Carlo A. Ricciardi" IULM AI Laboratory IULM University Milan20143 Italy ISASI Institute of Applied Sciences Intelligent Systems-CNR Lecce73100 Italy

Efficient detection of oil spills is critical for minimizing environmental damage. This study introduces a novel approach utilizing deep learning, specifically the YOLOv8 architecture, augmented with advanced computer vision techniques for oil spill detection. Through meticulous dataset curation and model training, the YOLOv8 model achieved an impressive overall accuracy (R-score) of 0.531 and a Mean Average Precision (mAP) of 0.549. Performance varied across different spill types, with the model demonstrating notable accuracy in distinguishing between oil spills and natural features, achieving precision and recall rates of up to 0.75 and 0.68, respectively, for sheen detection. Visualizations such as box loss, class loss, and confusion matrices provide insights into the model’s performance dynamics, revealing a steady decrease in losses and an improvement in accuracy over epochs. In this dataset, the measurements are drone measurements performed by Port of Antwerp Bruges. Furthermore, practical applications showcase the model’s versatility in detecting various oil spill types in both image and video data, affirming its potential for real-world deployment in environmental monitoring and disaster response scenarios. This research represents a significant stride towards more effective oil spill detection, contributing to environmental sustainability and resilience efforts. © 2024 Copyright for this paper by its authors.

关键词： Oil spills

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：