检索结果-内蒙古大学图书馆

Monitoring Earth Hazard with Remote Sensing Techniques

Photogrammetric Engineering and Remote Sensing 2022年第2期88卷 92-92页

作者： Malarvizhi Kumar, Priyan Afghah, Fatemeh Arunmozhi, Manimuthu Department of Computer Science and Engineering Kyung Hee University Korea Republic of School of Informatics Computing and Cyber Systems Northern Arizona University United States Nanyang Technological University Singapore

来源：评论

学校读者我要写书评

暂无评论

Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins

arXiv

引用

arXiv 2022年

作者： Zhang, Nan Bahsoon, Rami Tziritas, Nikos Theodoropoulos, Georgios Shenzhen China School of Computer Science University of Birmingham Birmingham United Kingdom Research Institute for Trustworthy Autonomous Systems Shenzhen China Department of Informatics and Telecommunications University of Thessaly Greece

Digital Twins (DT) are essentially dynamic data-driven models that serve as real-time symbiotic "virtual replicas" of real-world systems. DT can leverage fundamentals of Dynamic Data-Driven Applications systems (DDDAS) bidirectional symbiotic sensing feedback loops for its continuous updates. Sensing loops can consequently steer measurement, analysis and reconfiguration aimed at more accurate modelling and analysis in DT. The reconfiguration decisions can be autonomous or interactive, keeping human-in-the-loop. The trustworthiness of these decisions can be hindered by inadequate explainability of the rationale, and utility gained in implementing the decision for the given situation among alternatives. Additionally, different decision-making algorithms and models have varying complexity, quality and can result in different utility gained for the model. The inadequacy of explainability can limit the extent to which humans can evaluate the decisions, often leading to updates which are unfit for the given situation, erroneous, compromising the overall accuracy of the model. The novel contribution of this paper is an approach to harnessing explainability in human-in-the-loop DDDAS and DT systems, leveraging bidirectional symbiotic sensing feedback. The approach utilises interpretable machine learning and goal modelling to explainability, and considers trade-off analysis of utility gained. We use examples from smart warehousing to demonstrate the approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Privacy Preserving Large Language Models: ChatGPT Case Study Based Vision and Framework

arXiv

引用

arXiv 2023年

作者： Ullah, Imdad Hassan, Najm Gill, Sukhpal Singh Suleiman, Basem Ahanger, Tariq Ahamed Shah, Zawar Qadir, Junaid Kanhere, Salil S. The School of Computer Science Faculty of Engineering The University of Sydney SydneyNSW2006 Australia The Higher Colleges of Technology United Arab Emirates The School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom The Management Information Systems Department College of Business Administration Prince Sattam bin Abdulaziz University Al-Kharj16278 Saudi Arabia The Department of Information Technology Sydney International School of Technology and Commerce SydneyNSW2000 Australia The Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Sydney Australia

The generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical information such as, context, specific details, identifying information, use this information in the training process, and generate responses for the requested queries. The extracted data also contain sensitive information, which have raised serious threats to user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy-preserving model for LLMs that consists of two main components i.e., preserving user privacy during the data curation/pre-processing together with preserving private context and the private training process for large-scale data. To demonstrate the applicability of PrivChatGPT, we show how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy, specifically, we employed differential privacy and private training using Reinforcement Learning (RL). We measure the privacy loss and evaluate the measure of uncertainty or randomness once differential privacy is applied. It further recursively evaluate the level of privacy guarantees and the measure of uncertainty of public database and resources, during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, we hypothetically compared other mechanisms such as Blockchain, private information retrieval, randomisation, obfuscation, anonymisation, and the use of ToR for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility, training latency, vulnerability to attacks, and resource consumption. We conclude that the differential privacy, randomisation, and obfuscation can impact the utility and performance of the trained models, conversely, the use of ToR, Blockchain, and PIR may introduce ad

关键词： Large dataset

来源：评论

学校读者我要写书评

暂无评论

Advanced Interoperable Framework for Real-Time Predictive Analysis Leveraging Machine Learning and IoT in Smart Health Monitoring systems

Advanced Interoperable Framework for Real-Time Predictive An...

引用

Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： D. Antony Pradeesh Y. Syamala P. Priya Madhavi Sripathi Srilakshmi Kaza Rohith Bala Jaswanth B Department of Electronics and Communication Systems KG College of Arts and Science Coimbatore India Department of IoT Seshadri Rao Gudlavalleru Engineering College Vijayawada Andhra Pradesh Department of Computer Science The Quaide Milleth College for Men Chennai Tamilnadu Department of Business and Management Studies Seshadri Rao Gudlavalleru Engineering College Vijayawada Andhra Pradesh Department of Electronics and Communication Engineering Seshadri Rao Gudlavalleru Engineering College Vijayawada Andhra Pradesh

As health care evolves rapidly, devices enabled by the Internet of Things, and algorithms driven by Machine Learning have surfaced as transformative elements. However, these technologies often fall short due to a lack of interoperability between existing systems, real-time predictive capabilities, and strong security measures. This paper presents a novel framework designed to overcome these challenges and optimize Internet of Things applications in health monitoring systems. Our framework has been specifically created to work seamlessly across the Internet of Things devices while monitoring a wide array of health parameters. It integrates advanced machine learning models, including Extreme Gradient Boosting, Light Gradient Boosting Machine, Categorical Boosting, Random Forest and Neural Network to improve prediction accuracy. The framework also allows real-time analysis and prediction services with real-time updates available through an Internet portal. Furthermore, we employ robust security and privacy measures to safeguard patient data. Experimental results illustrate our framework's superior performance over existing systems, demonstrating its promise for creating efficient, secure smart health monitoring systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach to Menstrual Cycle Prediction Using GAN-Generated Data and Transformer Model for Tabular Data

A Novel Approach to Menstrual Cycle Prediction Using GAN-Gen...

引用

IEEE International WIE Conference on Electrical and computer Engineering (WIECON-ECE)

作者： Md Younus Ahamed Md Asif Bin Syed Mafuza Nasrin Tani Tanpia Tasnim Al Zadid Sultan Bin Habib Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown WV USA Department of Industrial and Management Systems Engineering West Virginia University Morgantown WV USA Army Medical College Rangpur Rangpur Bangladesh Green University of Bangladesh Narayanganj Dhaka Bangladesh

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535483

Accurate menstrual cycle prediction is crucial for women's health and fertility management. While prior studies have utilized machine learning models to predict cycle length and classify regularity, they often struggled with the low Observationto-Variable Ratio (OVR) inherent in menstrual cycle data. This study tackles this issue by employing Generative Adversarial Networks (GANs) to generate synthetic data and Transformerbased models for both classification a nd r egression t asks. We evaluated multiple machine learning models for menstrual cycle phase classification a nd l ength p rediction. F or t he classification of regular versus irregular cycles, TabNet significantly outperformed baseline models, including Logistic Regression, Random Forest, and LSTM, achieving 99.67% accuracy, an AUC of 0.96, an F1 score of 0.99, 92.37% specificity, a nd 99.66% sensitivity, with CTGAN-generated synthetic data enhancing its performance. In the regression task of predicting cycle length, TabNet demonstrated strong predictive power, with an MSE of 1.29, RMSE of 1.13, and an R 2 score of 0.87. Although Random Forest performed slightly better with an MSE of 0.958 and an R 2 score of 0.90, TabNet remained competitive. These results highlight the potential of GAN-generated data and advanced models like TabNet in overcoming low OVR challenges and improving menstrual cycle prediction. Future research may incorporate personalized health data to further enhance model accuracy and generalizability.

关键词： Logistic regression Accuracy Sensitivity Medical services Predictive models Transformers Data models Random forests Long short term memory Synthetic data

来源：评论

学校读者我要写书评

暂无评论

An equivalent reformulation and multi-proximity gradient algorithms for a class of nonsmooth fractional programming

arXiv

引用

arXiv 2023年

作者： Zhou, Junpeng Zhang, Na Li, Qia School of Computer Sciences and Engineering Sun Yat-Sen University Guangzhou510006 China Department of Applied Mathematics College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China School of Computer Sciences and Engineering Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University Guangzhou510006 China

In this paper, we consider a class of structured fractional programs, where the numerator part is the sum of a block-separable (possibly nonsmooth nonconvex) function and a locally Lipschitz differentiable (possibly nonconvex) function, while the denominator is a convex (possibly nonsmooth) function. We first present a novel reformulation for the original problem and show the relationship between optimal solutions, critical points and KL exponents of these two problems. Inspired by the reformulation, we propose a flexible framework of multi-proximity gradient algorithms (MPGA), which computes the proximity operator with respect to the Fenchel conjugate associated with the convex denominator of the original problem rather than evaluating its subgradient as in the existing methods. Also, MPGA employs a nonmonotone linear-search scheme in its gradient descent step, since the smooth part in the numerator of the original problem is not globally Lipschitz differentiable. Based on the framework of MPGA, we develop two specific algorithms, namely, cyclic MPGA and randomized MPGA, and establish their subsequential convergence under mild conditions. Moreover, the sequential convergence of cyclic MPGA with the monotone line-search (CMPGA ML) is guaranteed if the extended objective associated with the reformulated problem satisfies the Kurdyka-Lojasiewicz (KL) property and some other mild assumptions. In particular, we prove that the corresponding KL exponents are 12 for several special cases of the fractional programs, and so, CMPGA ML exhibits a linear convergence rate. Finally, some preliminary numerical experiments are performed to demonstrate the efficiency of our proposed algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Gradient methods

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Watermarking scheme based K-Means for Medical Image

引用

Transportation Research Procedia 2025年 84卷 51-58页

作者： Mohammed El Habib Kahla Mounir Beggas Abdelkader Laouid Mohammad Hammoudeh Lamri Laouamer LIAP Laboratory University of El Oued PO Box 789 El Oued and 39000 Algeria Information & Computer Science Department King Fahd University of Petroleum & Minerals Academic Belt Road Dhahran 31261 Saudi Arabia Department of Management Information Systems & Production Management College of Business & Economics Qassim University P.O. Box 6633 Buraidah 51452 KSA

In recent years, there has been substantial growth in multimedia and visual data management. This increase in data volume presents several challenges, notably protecting multimedia content from tampering or manipulation. Images, as a prevalent form of multimedia content, often require protective techniques such as watermarking and blockchain, especially in medical contexts. Addressing challenges like watermark size and manipulations without third parties in multimedia data, including medical images, is crucial. This paper proposes a medical image protection scheme using a combination of watermark and blockchain techniques based on the k-means algorithm. This scheme utilizes k-means to compress the watermark image into an actual binary image, which is then embedded into the medical image using the least significant bit (LSB) technique, and then stored on the blockchain network. also, employing a bitwise XOR operation is applied to detect any manipulations by comparing it with the originally embedded binary image. The technique’s efficacy is evaluated using the Peak Signal-to-Noise Ratio (PSNR) and subjected to various attacks, including different types of noise. Experimental and analytical results demonstrate that the proposed scheme effectively detects image manipulations and reduces the watermark size, enhancing security for medical images without third parties.

关键词： Watermarking blockchain Fingerprint medical-images LSB k-means

来源：评论

学校读者我要写书评

暂无评论

Continual Reinforcement Learning for Digital Twin Synchronization Optimization

引用

IEEE Transactions on Mobile Computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

引用

Computing (ICOCO), IEEE International Conference on

作者： Mahayaudin M. Mansor Nurain Ibrahim Noor Fadhilah Ahmad Radi Nadirah Abdul Rahim Syarul Heiry Yahaya Mohd Aftar Abu Bakar Roslinazairimah Zakaria School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA (Penang Campus) Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability.

关键词： Visualization Technological innovation Ethics Emotion recognition Bibliometrics Decision making Focusing Electronic healthcare Object recognition Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

MediVault DApp: A Electronic Health Record Application Using Blockchain Technology 2

MediVault DApp: A Electronic Health Record Application Using...

引用

2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Peddu, Lingeswara Reddy Mishra, Aditya Padmaja, Amirineni Rama L Rekha, G. Palanikumar, S. Pongiannan, R.K. SRM Institute of Science and Technology Dept. of Computing Technologies Tamil Nadu Kattankulathur603 203 India Geethanjali College of Engg. And Tech. Department of ECE Hyderabad India Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engg. College Avadi Dept. of Computer science and business systems Tamilnadu Chennai India

ISBN: (纸本)9798331505745

The healthcare industry stands to benefit greatly from blockchain technology due to its robust security, privacy protection, confidentiality, and decentralized nature. This study outlines how blockchain can revolutionize current medical data systems and proposes a framework for its implementation. The primary objective of this framework is to ensure secure storage of electronic records by establishing comprehensive access protocols for users. When patients seek medical care, hospitals generate and store patient records in a decentralized application (DApp), which is a blockchain- based platform for maintaining electronic medical records. Medical professionals can modify patient records, while patients can retrieve their medical information from the DApp using public keys. Electronic Health Records (EHRs) allow healthcare providers to monitor information necessary for compliance with insurance companies and government regulations. EHRs serve as a centralized data repository for storing doctors' orders related to laboratory tests, x-rays, and other diagnostic procedures. The proposed system aims to optimize individual medical information management, minimize redundant testing, reduce treatment delays, and equip patients with adequate information to make informed decisions. © 2025 IEEE.

关键词： Electronic health record

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：