检索结果-内蒙古大学图书馆

An Ensemble Deep Learning Approach for Accurate Urinary Sediment Detection Using YOLOv9e and KD-YOLOX-ViT

IEEE Access 2025年 13卷 101934-101954页

作者： Mansura Naznine Abdus Salam Muhammad Mohsin Khan Sadia Farhana Nobi Muhammad E. H. Chowdhury Department of Electrical Engineering Qatar University Doha Qatar Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Electrical and Computer Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Neurology Hamad Medical Corporation Doha Qatar Department of Public Health and Informatics BSMMU Dhaka Bangladesh

Urinary Tract Infections (UTIs) are common medical diseases that occur from bacterial infections which can impact any area of the urinary system. Detecting urine particles is crucial for diagnosing UTIs and other renal abnormalities, as the presence of red blood cells (RBCs), white blood cells (WBCs), casts, fungi, and bacteria in urine can indicate disorders such as hematuria, kidney stones, and urinary tract malignancies. This study introduces an advanced deep learning method that utilizes ensemble of YOLOv9e and KD-YOLOX-ViT models. YOLOv9e model is built upon the Generalized Efficient Layer Aggregation Network (GELAN) and Programmable Gradient Information (PGI). The KD-YOLOX-ViT model integrates knowledge distillation and Vision Transformer (ViT) modules within the YOLOX framework to improve performance. This ensemble framework combines the strengths of YOLOv9e and KD-YOLOX-ViT models to automate the detection and classification of seven distinct urine sediment particles, effectively addressing challenges related to class imbalance, image resolution, and domain adaptation. The YOLOv9e model exhibited exceptional performance, obtaining precision, recall, and mean average precision (mAP50) scores of 88.5%, 88.1%, and 92.2%, respectively. The KD-YOLOX-ViT model also performed well, achieving precision, recall, mAP50, and mAP50-95 scores of 86%, 88%, 86.7%, and 53.3%, respectively. By ensembling YOLOv9e and KD-YOLOX-ViT using Weighted Box Fusion (WBF), the model achieved a final mAP50 of 94.18%. The model’s adaptability was further validated through rigorous external validation on a novel dataset, yielding in a mAP50 of 94.64%. Comparative analysis with state-of-the-art methods confirms the model’s real-time analytical capabilities. Moreover, the integration of eXplainable AI (XAI) enhances interpretability, offering valuable insights for confident clinical diagnosis. This comprehensive approach shows significant promise in advancing diagnostic accuracy and enabling

关键词： Sediments Accuracy Adaptation models Kidney Deep learning Microscopy White blood cells Medical diagnostic imaging Feature extraction Bacteria

来源：评论

学校读者我要写书评

暂无评论

Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems

引用

Mathematical Biosciences and engineering 2024年第7期21卷 6471-6492页

作者： Ali, Ahmed Salah, Ahmad Bekhit, Mahmoud Fathalla, Ahmed Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044 Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ad-Dhahirah Ibri Oman Zagazig University Department of Computer Science Faculty of Computers and Informatics Sharkeya Zagazig Egypt Sydney Australia Kaplan Business School Sydney Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user’s data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user’s age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest. © 2024 the Author(s).

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Location Aware and Drift Control Using a Smartphone On-board an Unmanned Aerial Vehicle

Location Aware and Drift Control Using a Smartphone On-board...

引用

International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： Adil Farooq Majda Sadaf Tayyaba Nosheen Narumol Chumuang Department of Electrical and Computer Engineering University of Cyprus Cyprus School of Electrical and Electronic Engineering Newcastle University United Kingdom Department of Biomedical Engineering Silesian University of Technology Poland Department of Digital Media Technology Muban Chombueng Rajabhat University Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

Smartphones and Unmanned Aerial Vehicles (UAVs), regarded as consumer-grade technologies, offer a remarkable price/performance ratio, making their integration a strategic approach for maximizing overall performance. Our work addresses the complex challenges of UAV navigation in GPS-denied and enclosed environments for estimating the exact relative position by proposing an integration of a smartphone-based aerial platform. The selected control strategy and estimation, demonstrates enhanced robustness in diverse indoor and outdoor environments, showcasing precision and managing uncertainty by reducing the sensory drift taking into account the motion dynamics of the UAV. This study underscores the challenges of UAV navigation in GPS-denied settings, emphasizing the significance of sensor fusion and advanced estimation techniques. Our devised strategies aim to keep a balanced tradeoff between the estimation and motion model of the aerial platform. This research could lay the groundwork for advancing UAV technology, providing solutions to real-world challenges in GPS-denied and enclosed spaces.

关键词： Uncertainty Navigation Estimation Sensor fusion Autonomous aerial vehicles Robustness Telecommunications

来源：评论

学校读者我要写书评

暂无评论

Navigating the impact of huge electrical demand on grid through intelligently controlled renewables and emerging V2G technology

Navigating the impact of huge electrical demand on grid thro...

引用

14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion (MEDPOWER 2024)

作者： Raheel Tariq Zunaib Ali Komal Saleem Nicholas Christofides School of Engineering London South Bank University London UK School of Architecture Computing and Engineering University of East London London UK Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (数字)9781837242689

Among the biggest initiatives to help the UK reach its Net Zero goal are the conversion of the power grid to renewable energy sources (RESs) and the switch to all-electric vehicles (EVs) by 2050. However, because of the advent of dispersed unconventional energy sources and electric vehicles (EVs), the full switch to renewable energy sources and EVs poses a possible risk to power system stability, power quality, and energy management. To intelligently regulate the flow of energy and enhance the current power system, it is crucial to employ appropriate energy management strategies. This paper aims to establish a microgrid that is grid-connected and utilises multiple energy sources, including solar, battery energy storage, and electric vehicles. The goal is to create an intelligent algorithm that efficiently controls these resources and mainly enable bi-directional EV operation for vehicle-to-grid (V2G) application, resulting in both financial and environmental advantages. The main objective is to maximise the microgrid's overall profitability while minimising carbon emissions and effectively managing energy demand and network stability. It has been shown that utilising EVs as storage units via beneficial V2G technology not only stabilises renewable energy generation but also provides economic advantages.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Leveraging LLM in Genetic Programming Hyper-heuristics for Dynamic Microservice Deployment 37th

Leveraging LLM in Genetic Programming Hyper-heuristics for...

引用

37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Fang, Zhengxin Ma, Hui Chen, Gang Hartmann, Sven Wang, Chen Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-Zellerfeld Germany National Institute of Water and Atmospheric Research Wellington New Zealand

ISBN: (纸本)9789819603473

Microservice deployment in cloud computing is a challenging combinatorial optimization problem due to the complex dependencies among microservices and the intricate trade-offs among different QoS requirements, e.g., minimizing Energy Consumption (EC) vs. minimizing Communication Overhead (CO). Recently, some hyper-heuristics methods, particularly Genetic Programming Hyper-Heuristics (GPHH), have been proposed to automatically generate heuristics for solving dynamic microservice deployment problems. Meanwhile, Large Language Models (LLMs) are becoming popular for solving various domain-specific problems thanks to their strong ability to learn problem-related knowledge. However, hybridizing GPHH with LLM by combining their abilities in solving complex optimization problems remains unexplored. In this paper, we propose an LLM-enhanced Genetic Programming Hyper-Heuristic (LLM-GPHH) algorithm to evolve heuristics for the dynamic deployment of applications composed of microservices, to jointly optimize EC and CO. Our experiments on real-world datasets demonstrate the effectiveness of the newly proposed LLM-GPHH. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

Measuring Credibility Level of E-commerce Sellers with Structural Equation Modeling and Naive Bayes on Tweets of Service Quality 38

Measuring Credibility Level of E-commerce Sellers with Struc...

引用

38th IEEE Region 10 Conference, TENCON 2023

作者： Aji Bawono, Marastika Wicaksono Purwitasari, Diana Purnomo, Mauridhi Hery Sepuluh Nopember Institute of Technology Telkom University Departement of Technology Management West Java Bogor Indonesia Sepuluh Nopember Institute of Technology Departement of Informatics Engineering East Java Surabaya Indonesia Sepuluh Nopember Institute of Technology Departement of Electrical Engineering Departement of Computer Engineering East Java Surabaya Indonesia

ISBN: (纸本)9798350302196

In digital development 4.0, store brands are very important. The problem in this research is the lack of consumer trust to buy quality goods in e-commerce store accounts so that it affects consumer satisfaction. This study aims to address this question feedback from the problems of the customer, then on the other hand a questionnaire with the PLS-SEM (Partial Least Squares Structural Equation Modeling) model to determine the dimensions of the variables selected according to customer experience. To achieve this aim, both negative and positive customer comments were compiled to assess customer satisfaction, employing a comparative analysis method through Naive Bayes algorithm. The overarching goal was to achieve optimal results and extract valuable insights regarding the determinants that influenced customer satisfaction within the domain of online transactions. This research also has an impact on buyers so they can have an understanding of the factors that support trust in customer satisfaction, so that individuals do not hesitate in making purchasing decisions to shop online. The results showed that the algorithm initially recorded a modest accuracy score of 0.37. Meanwhile, after implementing hyperparameter tuning, the accuracy increased significantly to 0.62. In the aspect of Smart PLS questionnaire analysis, a standardized Normed Fit Index (NFI) of 0.707 was recorded, which was slightly below the established threshold of 0.90. The standardized root mean square residual (SRMR) was measured at 0.071, falling below the specified value of 0.08, indicating a commendable model fit. However, the RMS theta value at 0.240 exceeded the threshold of 0.102. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

An Intelligent E-Pharmacopoeia Retrieval System Using Responsive Web Design

引用

International Journal of engineering and technology Innovation 2024年第2期14卷 177-188页

作者： Huang, Tian-Hsiang Chao, Wan-Ling Hwang, Kao-Shing Chen, Yao-Mei Ho, Wen-Hsien Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Information Management Office Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Healthcare Administration and Medical Informatics Taiwan School of Nursing Kaohsiung Medical University Kaohsiung Taiwan Superintendent Office Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Taiwan College of Professional Studies National Pingtung University of Science and Technology Pingtung Taiwan

To develop an e-pharmacopeia retrieval mobile system aimed at assisting medical personnel in efficiently assessing precise drug information and ensuring patient safety, this study utilized responsive web design. The research framework incorporated the convergence of the technology acceptance model (TAM) and the information systems success model (ISSM) to investigate user satisfaction and continuance intention. A survey was administered to 151 pharmacy students who had utilized the system for over 30 minutes. The findings revealed that the TAM-ISSM model significantly influenced user satisfaction, surpassing the individual impacts of TAM and ISSM. Furthermore, user satisfaction exhibited a positive and significant impact on continuance intention, which, in turn, demonstrated a positive and significant influence on both individual and organizational performance. This suggests that the proposed system is proficient in accurately identifying and processing drugs, thereby mitigating the pain induced by diseases in patients. Copyright © by the authors. Licensee TAETI, Taiwan. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC) license (https://***/licenses/by-nc/4.0/).

关键词： Search engines

来源：评论

学校读者我要写书评

暂无评论

AI Based Prediction for Heart Disease: A Comparative Analysis and an Improved Machine Learning Approach 6

AI Based Prediction for Heart Disease: A Comparative Analysi...

引用

6th Asian Conference on Artificial Intelligence technology, ACAIT 2022

作者： Raval, Jay Verma, Jai Prakash Islam, Sardar N.M. Jain, Rachna Thakur, Narina Institute of Technology Nirma University Computer Science Engineering Department Ahmedabad India Victoria University Applied Informatics Research Melbourne Australia Bhagwan Parshuram Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665453110

Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in predicting heart disease-related issues. Finding the best accurate machine learning classifiers for various diagnostic uses by data mining and machine learning techniques aids in predicting whether or not the heart disease-related issue will occur. To predict heart disease, a number of supervised machine-learning algorithms are used and their effectiveness are evaluated. With the exceptionof MLP and KNN, all applied algorithms had their estimated feature significance scores for each feature. This helps to find the main factors affecting heart disease and the accuracy of the model, which helps to get the best prediction. At the end of the research the support vector machine gives us 87.91 % highest testing accuracy compare with all applied machine learning algorithm. © 2022 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Examining the economic feasibility of hybrid Photovoltaic – Battery Energy Storage System in public building with flexible demand: cyprus case study

Examining the economic feasibility of hybrid Photovoltaic –...

引用

International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Nikolas G. Chatzigeorgiou George Makrides Spyros Theocharides George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Photovoltaic – Battery Energy Storage Systems (PVBESS) are a promising solution against technical bottlenecks of high PV penetration. Public buildings are suitable for installation, although currently underexploited, while high BESS costs limit technology diffusion. This paper assesses the economic feasibility of PV-BESS in public buildings with flexible demand, through simulations showcasing BESS subsidisation impact and self-sufficiency contribution. The results derived that smaller PV-BESS sizes are more attractive investments, as larger systems are characterised by reduced profitability, while BESS subsidisation enhances system profitability. Shifting a small portion of load demand improves economic feasibility by decreasing BESS size, while combining PV production, energy storage, and energy flexibility is the optimal solution for public buildings against electricity prices. Finally, a trade-off between economic feasibility and energy self-sufficiency is observed. The results can be utilised by policymakers to design strategies for BESS promotion, while professionals can perform accurate system sizing, thus avoiding excess system capacities.

关键词： Photovoltaic systems Costs Accuracy Profitability Battery energy storage system Electricity Buildings Tariffs Production Investment

来源：评论

学校读者我要写书评

暂无评论

A fast parallel classification model for the diagnosis of cancer

Recent Advances in Computer Science and Communications

引用

Recent Advances in computer Science and Communications 2021年第1期14卷 291-301页

作者： Jain, Divya Singh, Vijendra Department of Computer Science and Engineering School of Engineering and Technology The Northcap University Gurugram India Department of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun India

Background: In this era of voluminous data, there is a need to process data speedily in less time. Moreover, it is also essential to diminish the dimensionality of data as well as to apply parallel computations for classification. SVM is a prominent classification tool and is currently one of the most popular state-of-the-art models for solving various classification problems that makes use of parallel computations to speed up its processing. Objective: To develop a fast, promising classification system using optimized SVM classifier with hybridized dimensionality reduction for the diagnosis of cancer disease. Methods: The proposed approach comprises of two stages – the first stage presents a hybrid approach to reduce the dimensionality of cancer datasets, and the second stage presents an efficient classification method to optimize the SVM parameters and improve its accuracy. To lessen the execution time, the proposed approach uses GPUs to concurrently run different processes on machine workers. Results: The proposed method with the combination of dimensionality reduction & parallel classification using optimized SVM classifier is found to give excellent results based on ‘Classification Accuracy’, ‘Selected Features’ and ‘Execution Time’. Conclusion: The experimental findings with benchmark datasets indicate that the proposed diagnostic model yields significant improvement in terms of execution time when compared to the conventional approach. The proposed model can assist doctors and medical professionals in the quick selection of most significant risk factors for the diagnosis and prognosis of cancer diseases. © 2021 Bentham Science Publishers.

关键词： Classification (of information)

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：