检索结果-内蒙古大学图书馆

Dynamic Resource Utilization Prediction Model for Cloud Datacenter

IAENG International Journal of Applied Mathematics 2025年第2期55卷 307-331页

作者： Bliedy, Doaa Khafagy, Mohamed H. Badry, Rasha M. Department of Information Systems Faculty of Computers and Artificial Intelligence Fayoum University Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Fayoum University Egypt

Much research has been done on predicting resource utilization in the cloud to avoid over- and under-provisioning resources. Most existing systems focus on estimating the utilization of one or two resources at most, including memory, CPU, storage, network, or servers dedicated to cloud applications;they do not consider the correlation between resources. A maximum of one or two machine learning algorithms is employed for prediction purposes. Additionally, traditional prediction methods in the cloud provide a one-dimensional output. Most current solutions predict resources, such as memory and CPU utilization, as a single output. Unfortunately, a one-dimensional output in resource provision and usage cannot capture the relationship between the application requirements of different resources, such as CPU, memory, CPU cores, disk, and network;this results in incomplete and inaccurate information and prediction results. Efficient resource allocation and management require predicting several parameters using multivariate state variables. This study presents a multi-resource utilization prediction model that uses several machine learning approaches, such as support vector regression, random forests, MLP regression, neural networks (NN) using Adam and SGD optimizers, and decision tree regression. The prediction model is based on univariate and multivariate time series. Google cluster trace data is used to evaluate the work. Four experiments are executed on the dataset, seeking to predict the resources for different time series interval periods. The various algorithms’ mean absolute errors (MAE), root mean square error, mean absolute percentage error and R-squared are compared to determine which technique achieves the lowest error rate. The experimental results have shown that the prediction model yields better accuracy than previous research. © (2024), (International Association of Engineers). All rights reserved.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Predicting COVID-19 Cases in India Using ARIMA, Prophet, LSTM and Data Analysis Using Power BI 2nd

Predicting COVID-19 Cases in India Using ARIMA, Prophet, LST...

引用

2nd International Conference on Data Science and Network Engineering, ICDSNE 2024

作者： Das, Abhrajit Seidenberg School of Computer Science and Information Systems Pace University New York United States

ISBN: (纸本)9789819783359

India confronted a serious challenge in the initial throes of the COVID-19 pandemic. This study addressed this by leveraging machine learning to analyse confirmed cases and support better decision-making. We compared the efficacy of Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet and Long Short-Term Memory (LSTM) models on a Kaggle dataset, visualizing the results with Power BI to forecast future trends. The LSTM model, evaluated using Mean Absolute Percentage Error (MAPE), demonstrated superior accuracy in predicting case numbers. This research underlines the importance of data analysis during public health emergencies. Machine learning offers valuable insights for policymakers, empowering them to control outbreaks and allocate resources effectively. Ultimately, this paves the way for strengthened public health responses and preparedness for future pandemics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Forest Fire Prevention and Detection System 5

Forest Fire Prevention and Detection System

引用

5th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Jayakody, Lasitha Edirisinghe, Chamathka Senasinghe, Tharush Dissanayake, Shaminda Gamage, Narmada Pandithage, Dinithi Wijayaraja, Jaliya L. Sri Lanka Institute of Information Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331533694

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-Time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires. © 2025 IEEE.

关键词： Temperature sensors

来源：评论

学校读者我要写书评

暂无评论

A Fast Frequency-Space Transform-Invariant Template Matching using a "Pseudo-Normalizable"Fourier Transform (PNFT) 6

A Fast Frequency-Space Transform-Invariant Template Matching...

引用

6th IEEE International Conference on Image Processing, Applications and systems, IPAS 2025

作者： Zakaria, Nordin Universiti Teknologi Petronas Center for Cyber Physical Systems Computer and Information Science Department Malaysia

ISBN: (纸本)9798331506520

Given a template image T, the task in template matching is to search for T in a larger image region S. While simple in concept, much effort in recent years has been channeled on improving its robustness to complex transformation, with attention being largely on the similarity measures. Much lesser attention has been on the efficiency of the search computation itself. This paper attempts to fill the gap by proposing Pseudo-Normalizable Fourier Transform (PNFT), a novel complex transform that enables efficient transform-invariant, vectorized template matching. The concept of normalization is overloaded to denote the transformation of a function over an arbitrarily located patch to that about the origin, enabling the efficient use of image integral to perform template matching. The paper discusses on the 'normalizability' - whether the function can be normalized - and further introduces pseudo-normalization as an approximation. The approach proposed requires no learning and runs fast on lower-end CPU-only platform. Further, it is conceptually simple and can be implemented in approximately 200 lines of Python/Numpy code. The efficacy of the approach is empirically analyzed using a complex example image. © 2025 IEEE.

关键词： Template matching

来源：评论

学校读者我要写书评

暂无评论

Developing and Comparing Protocols for Low-Latency IoT Networking 2

Developing and Comparing Protocols for Low-Latency IoT Netwo...

引用

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

作者： Ebadinezhad, Sahar Abdillahi, Abdiwahab Yousuf Fareed, Ghufran Sabah Tuama, Yasir Abbas Maombe, Joyeuse Igwe, Ifeanyi Happy Systems Near East University Department of Computer Information Cyprus Near East University Nicosia99138 Cyprus Near East University Department of Computer Information Systems Nicosia99138 Cyprus

ISBN: (纸本)9798331505745

This paper presents the design and performance comparison study of MQTT and CoAP protocols of IoT that ensures low latency. The main motivation is to illustrate the performance, as well as limitations, of these protocols concerning key performance indicators that include latency, power consumption, and reliability. The work makes theoretical analysis while also conducting hands-on experiments on the test-bed environment, including tools for the simulation of real-world scenario software such as Wireshark and Python. The results show that CoAP is more suitable for applications that require minimum latency, while MQTT provides better reliability in message delivery. Finally, the research points out that appropriate protocol selection concerning specific application requirements is of paramount importance;this is a foundational step toward the development of more efficient IoT communication strategies for future innovations. © 2025 IEEE.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

uFOIL: An Unsupervised Fusion of Image Processing and Language Understanding

引用

IEEE Access 2025年 13卷 31683-31700页

作者： Rahman, Md. Abdur Hasan, Md. Tanzimul Farooq Howlader, Umar Kader, Md. Abdul Islam, Md. Motaharul Hung Pham, Phuoc Mehedi Hassan, Mohammad United International University Department of Computer Science and Engineering Dhaka1212 Bangladesh Providence College Department of Mathematics and Computer Science ProvidenceRI02918 United States King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia

In academic institutions, processing and evaluating documents such as exam scripts remains a labor-intensive process susceptible to human error. Traditional digitization systems face significant challenges in handling the complexities of mixed handwritten and printed text and varying document structures. These challenges are exacerbated by the absence of annotated datasets due to privacy concerns, particularly in contexts involving sensitive exam evaluations. To address these issues, this study introduces uFOIL, an unsupervised ensemble-based framework that integrates advanced image and language processing techniques to automate the extraction and validation of key information. The framework employs a majority voting mechanism that combines four state-of-the-art optical character recognition systems. Furthermore, a transformer architecture is incorporated to enhance contextual understanding and the structured formatting of extracted text that follows a post-processing confidence scoring mechanism. The proposed framework achieves high performance, with accuracies of 95.77% and 96.48% for student names and IDs, respectively;and 95.07% for total mark validation based on a dataset of exam script samples. Additionally, experiments on the benchmark ICDAR2013 dataset suggest the framework’s strong applicability achieving precision, recall, and F1-scores of 95.89%, 97.86%, and 96.87%, respectively. © 2025 The Authors.

关键词： Optical character recognition

来源：评论

学校读者我要写书评

暂无评论

Reliable Task Offloading for 6G-Based IoT Applications

引用

computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

来源：评论

学校读者我要写书评

暂无评论

Algorithmic Bias from the Perspectives of Healthcare Professionals

Algorithmic Bias from the Perspectives of Healthcare Profess...

引用

18th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Xu, Jennifer Babaian, Tamara Department of Computer Information Systems Bentley University WalthamMA United States

This paper focuses on algorithmic bias of machine learning and artificial intelligence applications in healthcare information systems. Based on the quantitative data and qualitative comments from a survey and interviews with healthcare professionals, who have different job roles (e.g., clinical vs. administrative), this study provides findings about the relationships between algorithmic bias, perceived fairness, and the intended acceptance and adoption of ML algorithms and algorithm generated outcomes. The results suggest that the opinions of healthcare professionals toward the causes of algorithmic bias, the criteria of algorithm assessment, the perceived fairness, and bias mitigation approaches may vary depending on their job roles, perspectives, tasks, and the algorithm characteristics. More research is needed to investigate algorithmic bias to ensure fairness and equality in healthcare. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

State-of-the-art in human activity recognition based on inertial measurement unit sensors: survey and applications

引用

International Journal of computers and Applications 2025年第1期47卷 1-16页

作者： Sepahvand, Majid Meqdad, Maytham N. Abdali-Mohammadi, Fardin Department of Computer Engineering Arak University Markazi Iran Intelligent Medical Systems Department Al-Mustaqbal University Babil Hillah Iraq Department of Computer Engineering and Information Technology Razi University Kermanshah Iran

Human activity recognition systems using wearable sensors is an important issue in pervasive computing, which applies to various domains related to healthcare, context aware and pervasive computing, sports, surveillance and monitoring, and the military. Three approaches can be considered for activity recognition: video sensor-based, physical sensor-based, and environmental sensor-based. This paper investigates the related work regarding the physical sensor-based approaches to motion processing. In this paper, a wide range of artificial intelligence models, from single classifications to methods based on deep learning, have been reviewed. The human activity detection accuracy of different methods, under natural and experimental conditions poses several challenges. These challenges cause problems regarding the accuracy and applicability of the proposed methods. This paper analyzes the methods, challenges, approaches, and future work. The goal of this paper is to establish a clear distinction in the field of motion detection using inertial sensors. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Active learning

来源：评论

学校读者我要写书评

暂无评论

Leveraging Convolutional Neural Network and Explainable AI for Enhanced Pneumonia Detection 1

Leveraging Convolutional Neural Network and Explainable AI f...

引用

1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Murugan, Ramalingam Patel, Harshita Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India

ISBN: (纸本)9798331520540

Advances in machine learning and computer vision have significantly improved the diagnostic capabilities of medical imaging. Convolutional Neural Networks (CNNs) have emerged as a crucial tool for image classification, particularly in detecting pneumonia from chest X-ray images. Despite their high accuracy, CNNs face challenges in clinical adoption due to their lack of interpretability. Medical practitioners require a clear understanding of the decision-making process to trust AI-generated diagnoses. To address the aforementioned challenge, we propose pneumonia classification model incorporating CNN with a combination of the model and Grad-CAM, for enhanced interpretability. Grad-CAM visually outlines critical regions, influencing the prediction, so the clinician can easily confirm AI-based diagnoses with a more confident and credible approach. It is exclusively for pediatric pneumonia, an accurate and explainable machine for early interventions that can also work in resource-scarce regions. Our research uses a set of 5,863 pediatric chest X-ray images, divided into Pneumonia and Normal classes. The dataset was acquired from Guangzhou Women and Children's Medical Center and divided into three subsets: training, validation, and test. By improving the accuracy and transparency of the diagnosis, this model can help bridge the gap between AI and the real world. This can help facilitate quicker and more reliable pneumonia diagnostics. © 2025 IEEE.

关键词： Convolutional neural networks

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：