检索结果-内蒙古大学图书馆

Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

引用

Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Talking Buildings: Interactive Human-Building Smart-Bot for Smart Buildings 25th

Talking Buildings: Interactive Human-Building Smart-Bot for...

引用

25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Suhas, Devmane Rana, Omer Lannon, Simon Perera, Charith School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Welsh School of Architecture Cardiff University Cardiff United Kingdom

ISBN: (纸本)9789819605781

Conventional conversational agents have a limited ability to respond to different user intents when interacting with smart buildings. The uniqueness of each building, coupled with the heterogeneity of built environments, makes it challenging to adapt communication methods universally. A possible solution is to develop a conversational agent capable of understanding physical, logical, and virtual assets in the built environment, with the aim of establishing a standardised method of human-building communication. Current smart building ontologies and metadata description schemas aim to give smart buildings a common language for the rapidly growing number of devices in smart buildings. This research paper focuses on developing a comprehensive smart building framework that integrates chatbot-driven natural language interactions into smart buildings using the SPARQL query language to query the smart building Knowledge Base (KB) and interact with buildings using a chatbot. An environmental sensor network testbed was set up using an ontology representing a smart building to evaluate the answer to the question. We have used transformer-based machine learning (ML) models to translate the natural language (NL) queries into SPARQL queries and summarise combined SPARQL and natural language queries, which produced promising performance. By integrating chatbots into smart building systems on the edge, users can interact in natural language, provide real-time information, and detect potential threats without the need for specialised knowledge. Our future work will be to extend this model to support heterogeneous building types represented by smart building ontologies. The source code and data sets are publicly available. https://***/suhasdevmane/abacws-chatbot. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Chatbots

来源：评论

学校读者我要写书评

暂无评论

Bias analysis of AI models for undergraduate student admissions

引用

Neural Computing and Applications 2025年第12期37卷 7785-7795页

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Computing and Engineering Indiana University Luddy School of Informatics Indiana University Indianapolis IndianapolisIN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the school of science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

RGB-D Video Mirror Detection

RGB-D Video Mirror Detection

引用

2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Xu, Mingchen Herbert, Peter Lai, Yu-Kun Ji, Ze Wu, Jing School of Computer Science and Informatics Cardiff University United Kingdom School of Engineering Cardiff University United Kingdom

ISBN: (纸本)9798331510831

Mirror detection aims to identify mirror areas in a scene, with recent methods either integrating depth information (RGB-D) or making use of temporal information (video). However, utilizing both data is still under-explored due to the lack of a high-quality dataset and an effective method for the RGB-D Video Mirror Detection (DVMD) problem. To the best of our knowledge, this is the first work to address the DVMD problem. To exploit depth and temporal information in mirror segmentation, we first construct a large-scale RGB-D Video Mirror Detection Dataset (DVMD-D), which contains 17977 RGB-D images from 273 diverse videos. We further develop a novel model, named DVMDNet, which can first locate the mirrors based on triple consistencies: local consistency, cross-modality consistency and global consistency, and then refine the mirror boundaries through content discontinuity, taking the temporal information within videos into account. We conduct a comparative study on the DVMD dataset, evaluating 12 state-of-the-art models (including single-image mirror detection, single-image glass detection, RGB-D mirror detection, video shadow detection, video glass detection, and video mirror detection methods). Code is available from https://***/UpChen/2025_DVMDNet. © 2025 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Simple One-step Multi-view Clustering with Fast Similarity and Cluster Structure Learning

引用

IEEE Signal Processing Letters 2025年 32卷 1850-1854页

作者： Xia, Xianxian Huang, Dong Yang, Chen-Min He, Chaobo Wang, Chang-Dong South China Agricultural University College of Mathematics and Informatics Guangzhou China South China Normal University School of Computer Science Guangzhou China Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China

Multi-view clustering (MVC) is essential for integrating heterogeneous data from multiple sources. However, many existing approaches are hindered by high computational complexity and the separate optimization of similarity and cluster structures. In light of these challenges, this paper presents a novel anchor-based MVC method termed simple one-step multi-view clustering with fast similarity and cluster structure learning (SONIC), which models adaptive anchor learning, multi-view similarity structure learning, and discrete cluster structure learning in a joint framework. In particular, we employ the anchor-based multi-view similarity learning to capture the consensus manifold structure latent in multiple views, thereby constructing a unified bipartite graph with adaptive anchor learning and view weighting. Then we impose a low-rank constraint on the bipartite graph structure to directly reveal the desired number of clusters without additional post-processing. An efficient alternating minimization algorithm is developed to optimize the model, resulting in a computational complexity that scales linearly with the number of samples. Extensive experiments on eight benchmark datasets demonstrate the superior performance of SONIC in both clustering quality and computational efficiency. © 1994-2012 IEEE.

关键词： Data assimilation

来源：评论

学校读者我要写书评

暂无评论

MLDA-Net: Multi-Level Deep Aggregation Network for 3D Nuclei Instance Segmentation

引用

IEEE Journal of Biomedical and Health informatics 2025年第5期29卷 3516-3525页

作者： Hu, Bin Ye, Zhiwei Wei, Zimei Snezhko, Eduard Kovalev, Vassili Ye, Mang Hubei University of Technology School of Computer Wuhan China Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network Wuhan China United Institute of Informatics Problems of the National Academy of Sciences of Belarus Belarus Wuhan University School of Computer Science Wuhan China

Segmentation of cell nuclei from three-dimensional (3D) volumetric fluorescence microscopy images is crucial for biological and clinical analyses. In recent years, convolutional neural networks have become the reliable 3D medical image segmentation standard. However, convolutional layers are limited by their finite receptive fields and weight-sharing mechanisms. Consequently, they struggle to effectively model long-range dependencies and spatial correlations, which may lead to inadequate nuclei segmentation. Moreover, the diversity in nuclear appearance and density poses additional challenges. This work proposes a lightweight multi-layer deep aggregation network, MLDA-Net, incorporating Wide Receptive Field Attention (WRFA). This module effectively simulates the large receptive field generated by self-attention in the Swin Transformer while requiring fewer model parameters. This design implements an extended global sensory field that enhances the ability to capture a wide range of spatial information. In addition, the multiple cross-attention (MCA) module in MLDA-Net enhances the output features of different resolutions from the encoder while maintaining global effectiveness. The Multi-Path Aggregation Feature Pyramid Network (MAFPN) receives multi-scale outputs from the MCA module, generating a robust hierarchical feature pyramid for the final prediction. MLDA-Net outperforms state-of-the-art networks, including 3DU-Net, nnFormer, UNETR, SwinUNETR, and 3DUXNET, on the 3D volumetric datasets NucMM and MitoEM. It achieves average performance improvements of 4% to 7% in F1 score, MIoU, and PQ metrics, thereby establishing new benchmark results. © 2013 IEEE.

关键词： Fluorescence microscopy

来源：评论

学校读者我要写书评

暂无评论

BeGrading: large language models for enhanced feedback in programming education

引用

Neural Computing and Applications 2025年第2期37卷 1027-1040页

作者： Yousef, Mina Mohamed, Kareem Medhat, Walaa Mohamed, Ensaf Hussein Khoriba, Ghada Arafa, Tamer Center for Informatics Science School of Information Technology and Computer Science Nile University Giza Egypt Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt

In recent years, large language models (LLMs) have gained significant traction across various domains, including education. This paper explores the application of LLMs in grading programming assignments. By leveraging data collected from existing programming assignments and their corresponding grades, we aim to develop a robust LLM-based grading system. We also incorporate augmented data representing various grading scenarios to enhance the model’s performance and ensure comprehensive coverage across all grading levels. Our approach involves training the LLM on this combined dataset to enable accurate and consistent evaluation of programming assignments. The proposed model, BeGrading, aims to reduce the grading burden on educators and provide timely and objective feedback to students. Compared to the Codestral model, our proposed model demonstrates an absolute difference rate of 19%, equivalent to ±0.95 out of 5. This is acceptable for using a small, fine-tuned model with optimized data. Additionally, the Codestral model compared to the dataset optimized score shows a difference of 15% equivalent to a margin of ±0.75 out of 5. Preliminary results demonstrate the potential of LLMs to perform grading tasks with a high degree of reliability, opening avenues for further research and practical applications in automated education systems. © The Author(s) 2024.

关键词： Digital elevation model

来源：评论

学校读者我要写书评

暂无评论

Does Adding a Modality Really Make Positive Impacts in Incomplete Multi-Modal Brain Tumor Segmentation?

引用

IEEE Transactions on Medical Imaging 2025年第5期44卷 2194-2205页

作者： Qiu, Yansheng Jiang, Kui Yao, Hongdou Wang, Zheng Satoh, Shin'ichi Wuhan University School of Computer Science Wuhan430072 China Shenzhen518000 China National Institute of Informatics Tokyo101-8430 Japan The University of Tokyo Graduate School of Information Science and Technology Department of Information and Communication Engineering Tokyo113-8654 Japan

Previous incomplete multi-modal brain tumor segmentation technologies, while effective in integrating diverse modalities, commonly deliver under-expected performance gains. The reason lies in that the new modality may cause confused predictions due to uncertain and inconsistent patterns and quality in some positions, where the direct fusion consequently raises the negative gain for the final decision. In this paper, considering the potentially negative impacts within a modality, we propose multi-modal Positive-Negative impact region Double Calibration pipeline, called PNDC, to mitigate misinformation transfer of modality fusion. Concretely, PNDC involves two elaborate pipelines, Reverse Audit and Forward Checksum. The former is to identify negative regions impacts of each modality. The latter calibrates whether the fusion prediction is reliable in these regions by integrating the positive impacts regions of each modality. Finally, the negative impacts region from each modality and miss-match reliable fusion predictions are utilized to enhance the learning of individual modalities and fusion process. It is noted that PNDC adopts the standard training strategy without specific architectural choices and does not introduce any learning parameters, and thus can be easily plugged into existing network training for incomplete multi-modal brain tumor segmentation. Extensive experiments confirm that our PNDC greatly alleviates the performance degradation of current state-of-the-art incomplete medical multi-modal methods, arising from overlooking the positive/negative impacts regions of the modality. The code is released at PNDC. © 2025 IEEE.

关键词： Multicarrier modulation

来源：评论

学校读者我要写书评

暂无评论

Direction-of-Arrival Estimation with Arbitrary Linear Array: A Turbo-HS Approach 5

Direction-of-Arrival Estimation with Arbitrary Linear Array:...

引用

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

作者： Chen, Zhendong Huang, Yongfeng Li, Dingzhao Lu, Xicheng Sun, Haixin School of Informatics Xiamen University Xiamen China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

ISBN: (纸本)9798331533694

Conventional subspace-based direction-of-arrival (DOA) estimation algorithms require optimal environments to achieve satisfactory estimation accuracy. With the advancement of sparse signal recovery theory, sparse optimization-based DOA estimation algorithms have exhibited commendable performance. One of the popular sparse optimization method is sparse Bayesian learning (SBL), due to the fact that it does not require regularization parameters. However, existing SBL algorithms do not adequately address the issue of noise modeling. In order to more accurately model the received signal covariance matrix under finite snapshots, we transform the signal covariance matrix into the form of sparse representation, normalize the variance of the observation matrix and vectorize it. Based on this, we introduce the Turbo-CS framework, which divides the DOA estimation problem into two subproblems: LMMSE estimation for external noise separation and a novel prior for internal SBL DOA estimation. As demonstrated by the experimental results, the proposed algorithm performs optimally in a variety of scenarios. © 2025 IEEE.

关键词： Direction of arrival

来源：评论

学校读者我要写书评

暂无评论

Visualizing NASDAQ Composite Index Historical Data Using Tableau 6th

Visualizing NASDAQ Composite Index Historical Data Using Tab...

引用

6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Safuan, Halawati Abd Jalil Nyein, Chit Ko Ko Riskhan, Basheer Ashfaq, Farzeen School of Computing and Informatics Albukhary International University Alor Setar Malaysia School of Computer Science SCS Taylors University Subang Jaya Malaysia

ISBN: (纸本)9789819780891

The study explores the visualization and analysis of historical data for the NASDAQ Composite Index (^IXIC) using Tableau. It examines trends in daily closing prices and volume distribution and compares performance with the NYSE index. Findings reveal significant fluctuations in the NASDAQ index over 22 years, suggesting potential future price changes. Forecasted graphs offer insights into potential economic decline and recovery, highlighting the dynamic nature of the market. Volume distribution analysis identifies periods of heightened trading activity. Comparative performance analysis emphasizes different trends observed in the NASDAQ and NYSE indices. Due to market unpredictability and external influences, additional research is crucial for validation. This study showcases the value of data visualization and analysis in finance through Tableau. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inflation

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：