检索结果-内蒙古大学图书馆

Exploring the fresh and rheology properties of 3D printed concrete with fiber reinforced composites (3DP-FRC): a novel approach using machine learning techniques

引用

MATERIALS RESEARCH EXPRESS 2024年第12期11卷 125502-125502页

作者： Rasel, Risul Islam Hossain, Md Minaz Zubayer, Md Hasib Zhang, Chaoqun Tongji Univ Coll Civil Engn Dept Bridge Engn Shanghai Peoples R China Tongji Univ Coll Civil Engn Dept Disaster Mitigat Struct Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China Sun Yat Sen Univ Sch Mat Shenzhen 518107 Peoples R China

This study focuses on the prediction models for four parameters related to the fresh and rheological properties of 3DP-FRC: spreading diameters (SPD), dynamic yield stress (DYs), static yield stress (SYs) and plastic viscosity (PV), respectively. Five machine learning (ml) algorithms were employed, namely artificial neural network (ANN), random forest (RF), decision tree (DT), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost). An extensive dataset was compiled including 373 (SPD) and 219 (SYs, DYs, PV) from various literature comprising experimental results. Fifteen input parameters were identified as the most influential factors affecting the fresh and rheological properties. These parameters include OPC, W/B, W/S, FA, LP, SF, SP, VMA, W, hf, Ri, AR, tsf, Ft, and Stime/Rtime. This study found strong correlations between the developed ml models and the experimental outcomes from both the training and testing datasets. The models demonstrated exceptional accuracy and provided precise predictions for SPD, SYs, DYs, and PV. The correlation coefficients (R2) ranged from 0.94 to 0.99 for SPD, 0.93 to 0.99 for SYs, 0.98 to 0.99 for DYs, and 0.98 to 1.00 for PV, with consistent results observed across both the training and testing datasets. Moreover, the model's precision was assessed using different error metrics, including root mean square error (RMSE), mean square error (MSE), coefficient of variation in root-mean-square error (CVRMSE), and mean absolute error (MAE). Sensitivity analysis was performed to identify their impact. Additionally, fiber dependent analysis was conducted to assess the effectiveness of different fiber types on the fresh and rheological properties (SPD, SYs, DYs, and PV). In conclusion, the ml models were effectively trained and optimized, resulting in accurate and highly predictive capabilities for the parameters of interest.

关键词： fresh and rheology properties 3D printing concrete with fiber-based (3DP-FRC) ml algorithm fiber-type depended analysis sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Developing machine learning model to estimate the shear capacity for RC beams with stirrups using standard building codes

引用

INNOVATIVE INFRASTRUCTURE SOLUTIONS 2022年第3期7卷 1-20页

作者： Uddin, Md Nasir Yu, Kequan Li, Ling-zhi Ye, Junhong Tafsirojjaman, T. Alhaddad, Wael Tongji Univ Dept Disaster Mitigat Struct Collage Civil Engn Siping Rd 1239 Shanghai 200092 Peoples R China Univ Southern Queensland Sch Civil Engn & Surveying Toowoomba Qld Australia Tongji Univ Dept Struct Engn Shanghai 200092 Peoples R China

Shear failure in reinforced concrete (RC) beams with a brittle nature is a serious safety concern. Due to the inadequate description of the phenomenology of shear resistance (the shear behavior of RC beams), several of the existing shear design equations for RC beams with stirrups have high uncertainty. Therefore, the predicted models with higher accuracy and lower variability are critical for the shear design of RC beams with stirrups. To predict the ultimate shear strength of RC beams with stirrups, machine learning (ml)-based models are proposed in the present research. The models were created using a database of 201 experimental RC beams with stirrups gathered from earlier investigations for training and testing of the ml method, with 70% of the data being used for model training and the rest for testing. The performance of suggested models was evaluated using statistical comparisons between experimental results and state-of-the-art current shear design models (ACI 318-08, Canadian code, GB 510010-2010, NZS 3101, BNBC 2015). The suggested machine learning-based models are consistent with experimentally observed shear strength and current predictive models, but they are more accurate and impartial. To understand the model very well, sensitivity analysis is determining as input values for a specific variable affect the outcomes of a mathematical model. To compare the results with different machine learning models in training and testing R-2, RMSE and MSE are also established. Finally, proposed ml models such as gradient boost regressor and random forest give higher accuracy to evaluate the shear strength of the reinforcement concrete beam using stirrups.

关键词： RC beam with stirrups Shear capacity ml algorithm Standard building code Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Role of machine learning in changing social and business eco-system - a qualitative study to explore the factors contributing to competitive advantage during COVID pandemic

引用

WORLD JOURNAL OF ENGINEERING 2022年第2期19卷 238-243页

作者： Sanil, Hishan S. Singh, Deepmala Raj, K. Bhavana Choubey, Somya Bhasin, Narinder Kumar Kumar Yadav, Ranjeeta Gulati, Kamal Univ Teknol Malaysia Azman Hashim Int Business Sch Kuala Lumpur Malaysia LBEF Kathmandu Nepal Inst Publ Enterprise Hyderabad India Manipal Univ Jaipur Dept Commerce Jaipur Rajasthan India Amity Univ Asibas Noida India Amity Univ Noida India ABES Engn Coll Ghaziabad India

Purpose "Machine learning (ml)" in business aids in increasing company scalability and boosting company operations for businesses all over the world. "Artificial intelligence (AI)" technologies and several "ml" algorithms have grown in prominence in the business analytics sector. In the era of a huge quantum of data being generated by the virtue of the integration of the various software with the business operations, the relevance of "ml" is continuously increasing. As a result, companies may now profit from knowing how companies may use "ml" and incorporating it into their own operations. "ml" derives useful results from the data to address very dynamic and difficult social and business problems. ml helps in establishing a system that learns automatically and produces results in less time and effort, allowing machines to discover. ml is developing at a breakneck pace, fuelled mostly by new computer technology to competitive advantages during the COVID pandemic. Design/methodology/approach For firms all around the world, "ml" in business aids in expanding scalability and boosting operations. In the field of business analytics, artificial intelligence (AI) and machine learning (ml) algorithms have become increasingly popular. The importance of "ml" is growing in an era when a massive amount of data is generated as a result of the integration of various applications with company activities. As a result, businesses can now benefit from understanding how other businesses are using "ml" and adopting it into their own operations. In order to handle very dynamic and demanding societal and business challenges, machine learning (ml) extracts valuable results from data. Machine learning (ml) aids in the development of a system that learns automatically and generates outcomes with less time and effort, allowing machines to discover. ml is progressing at a dizzying pace, fueled primarily by new computer technology and used to gain competitive advantages during the COVID pandemi

关键词： Data Artificial intelligence Machine learning COVID Data-rich ml algorithm

来源：评论

学校读者我要写书评

暂无评论

A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System

引用

SENSORS 2023年第5期23卷 2545-2545页

作者： Yaro, Abdulmalik Shehu Maly, Filip Prazak, Pavel Univ Hradec Kralove Fac Informat & Management Dept Informat & Quantitat Methods Hradec Kralove 50003 Czech Republic Ahmadu Bello Univ Dept Elect & Telecommun Engn Zaria 810106 Nigeria

A receive signal strength (RSS) fingerprinting-based indoor wireless localization system (I-WLS) uses a localization machine learning (ml) algorithm to estimate the location of an indoor user using RSS measurements as the position-dependent signal parameter (PDSP). There are two stages in the system's localization process: the offline phase and the online phase. The offline phase starts with the collection and generation of RSS measurement vectors from radio frequency (RF) signals received at fixed reference locations, followed by the construction of an RSS radio map. In the online phase, the instantaneous location of an indoor user is found by searching the RSS-based radio map for a reference location whose RSS measurement vector corresponds to the user's instantaneously acquired RSS measurements. The performance of the system depends on a number of factors that are present in both the online and offline stages of the localization process. This survey identifies these factors and examines how they impact the overall performance of the 2-dimensional (2-D) RSS fingerprinting-based I-WLS. The effects of these factors are discussed, as well as previous researchers' suggestions for minimizing or mitigating them and future research trends in RSS fingerprinting-based I-WLS.

关键词： indoor localization RSS fingerprinting ml algorithm

来源：评论

学校读者我要写书评

暂无评论

A comprehensive evaluation of liposome/water partition coefficient prediction models based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method: Challenges from different descriptor dimension reduction methods and machine learning algorithms

引用

JOURNAL OF HAZARDOUS MATERIALS 2023年第Pt A期443卷 130181页

作者： Zhu, Tengyi Yu, Yan Tao, Tianyun Yangzhou Univ Sch Environm Sci & Engn Yangzhou 225127 Jiangsu Peoples R China Yangzhou Univ Coll Agr Yangzhou 225009 Jiangsu Peoples R China

The liposome/water partition coefficient (Klip/w) is a key parameter to evaluate the bioaccumulation potential of pollutants. Considering that it is difficult to determine the Klip/w values of all pollutants through experiments, researchers gradually developed models to predict it. However, there is currently no research on how to comprehensively evaluate prediction models and recommend a compelling optimal modeling method. To remedy the defect of single parameters in a traditional model comparison, the TOPSIS evaluation method, based on entropy weight, was first proposed. We use this method to comprehensively evaluate models from multiple angles in this study. Thirty QSPR models, including 3 descriptor dimension reduction methods and 10 algorithms (belonging to 4 tribes), were used to predict Klip/w and verify the effectiveness of the comprehensive assessment method. The results showed that RF (descriptor dimension reduction method), symbolism (tribes) and RF (algorithm) exhibited significant advantages in establishing the Klip/w value prediction model. At present, the application of TOPSIS in environmental model evaluations is almost absent. We hope that the proposed TOPSIS evaluation method can be applied to more chemical datasets and provide a more systematic and comprehensive basis for the application of the QSPR model in environmental studies and other fields.

关键词： Liposome water partition coefficient QSPR models ml algorithm algorithm Descriptor dimension reduction method Comprehensive evaluation method

来源：评论

学校读者我要写书评

暂无评论

Detection of ChatGPT fake science with the xFakeSci learning algorithm

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Hamed, Ahmed Abdeen Wu, Xindong SUNY Binghamton Complex Adapt Syst & Computat Intelligence Lab Binghamton NY 13902 USA Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ China Hefei 230009 Peoples R China China Elect Data Ind Grp Shenzhen 518057 Peoples R China

Generative AI tools exemplified by ChatGPT are becoming a new reality. This study is motivated by the premise that "AI generated content may exhibit a distinctive behavior that can be separated from scientific articles". In this study, we show how articles can be generated using means of prompt engineering for various diseases and conditions. We then show how we tested this premise in two phases and prove its validity. Subsequently, we introduce xFakeSci, a novel learning algorithm, that is capable of distinguishing ChatGPT-generated articles from publications produced by scientists. The algorithm is trained using network models driven from both sources. To mitigate overfitting issues, we incorporated a calibration step that is built upon data-driven heuristics, including proximity and ratios. Specifically, from a total of a 3952 fake articles for three different medical conditions, the algorithm was trained using only 100 articles, but calibrated using folds of 100 articles. As for the classification step, it was performed using 300 articles per condition. The actual label steps took place against an equal mix of 50 generated articles and 50 authentic PubMed abstracts. The testing also spanned publication periods from 2010 to 2024 and encompassed research on three distinct diseases: cancer, depression, and Alzheimer's. Further, we evaluated the accuracy of the xFakeSci algorithm against some of the classical data mining algorithms (e.g., Support Vector Machines, Regression, and Naive Bayes). The xFakeSci algorithm achieved F1 scores ranging from 80 to 94%, outperforming common data mining algorithms, which scored F1 values between 38 and 52%. We attribute the noticeable difference to the introduction of calibration and a proximity distance heuristic, which underscores this promising performance. Indeed, the prediction of fake science generated by ChatGPT presents a considerable challenge. Nonetheless, the introduction of the xFakeSci algorithm is a significant step

关键词： ChatGPT Generative AI Fake publication Human-generated publications ml algorithm Fake science

来源：评论

学校读者我要写书评

暂无评论

A Systematic Review of Effective Hardware and Software Factors Affecting High-Throughput Plant Phenotyping

引用

INFORMATION 2023年第4期14卷 214页

作者： Solimani, Firozeh Cardellicchio, Angelo Nitti, Massimiliano Lako, Alfred Dimauro, Giovanni Reno, Vito Natl Res Council Italy Inst Intelligent Ind Technol & Syst Adv Mfg Via Amendola 122 D-O I-70126 Bari Italy Polytech Univ Tirana Fac Civil Engn Blvd Deshmoret & Kombit 4 Tirana 1000 Albania Univ Bari Dept Comp Sci Via E Orabona 4 I-70125 Bari Italy

Plant phenotyping studies the complex characteristics of plants, with the aim of evaluating and assessing their condition and finding better exemplars. Recently, a new branch emerged in the phenotyping field, namely, high-throughput phenotyping (HTP). Specifically, HTP exploits modern data sampling techniques to gather a high amount of data that can be used to improve the effectiveness of phenotyping. Hence, HTP combines the knowledge derived from the phenotyping domain with computer science, engineering, and data analysis techniques. In this scenario, machine learning (ml) and deep learning (DL) algorithms have been successfully integrated with noninvasive imaging techniques, playing a key role in automation, standardization, and quantitative data analysis. This study aims to systematically review two main areas of interest for HTP: hardware and software. For each of these areas, two influential factors were identified: for hardware, platforms and sensing equipment were analyzed;for software, the focus was on algorithms and new trends. The study was conducted following the PRISMA protocol, which allowed the refinement of the research on a wide selection of papers by extracting a meaningful dataset of 32 articles of interest. The analysis highlighted the diffusion of ground platforms, which were used in about 47% of reviewed methods, and RGB sensors, mainly due to their competitive costs, high compatibility, and versatility. Furthermore, DL-based algorithms accounted for the larger share (about 69%) of reviewed approaches, mainly due to their effectiveness and the focus posed by the scientific community over the last few years. Future research will focus on improving DL models to better handle hardware-generated data. The final aim is to create integrated, user-friendly, and scalable tools that can be directly deployed and used on the field to improve the overall crop yield.

关键词： high-throughput phenotyping platform sensor plant traits DL ml algorithm image acquisition technique

来源：评论

学校读者我要写书评

暂无评论

Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation

引用

RESPIRATORY RESEARCH 2021年第1期22卷 94-94页

作者： Chen, Chaojin Yang, Dong Gao, Shilong Zhang, Yihan Chen, Liubing Wang, Bohan Mo, Zihan Yang, Yang Hei, Ziqing Zhou, Shaoli Sun Yat Sen Univ Dept Anesthesiol Affiliated Hosp 3 600 Tianhe Rd Guangzhou 10630 Guangdong Peoples R China Guangzhou AID Cloud Technol Co LTD Guangzhou Peoples R China Sun Yat Sen Univ Dept Informat Affiliated Hosp 3 Guangzhou Peoples R China Sun Yat Sen Univ Dept Hepat Surg Affiliated Hosp 3 600 Tianhe Rd Guangzhou 510630 Guangdong Peoples R China Sun Yat Sen Univ Liver Transplantat Ctr Affiliated Hosp 3 600 Tianhe Rd Guangzhou 510630 Guangdong Peoples R China

Background: Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pneumonia in OLT patients using machine learning (ml) methods. Methods: Data of 786 adult patients underwent OLT at the Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2019 was retrospectively extracted from electronic medical records and randomly subdivided into a training set and a testing set. With the training set, six ml models including logistic regression (LR), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost) and gradient boosting machine (GBM) were developed. These models were assessed by the area under curve (AUC) of receiver operating characteristic on the testing set. The related risk factors and outcomes of pneumonia were also probed based on the chosen model. Results: 591 OLT patients were eventually included and 253 (42.81%) were diagnosed with postoperative pneumonia, which was associated with increased postoperative hospitalization and mortality (P<0.05). Among the six ml models, XGBoost model performed best. The AUC of XGBoost model on the testing set was 0.734 (sensitivity: 52.6%;specificity: 77.5%). Pneumonia was notably associated with 14 items features: INR, HCT, PLT, ALB, ALT, FIB, WBC, PT, serum Na+, TBIL, anesthesia time, preoperative length of stay, total fluid transfusion and operation time. Conclusion: Our study firstly demonstrated that the XGBoost model with 14 common variables might predict postoperative pneumonia in OLT patients.

关键词： Liver transplantation Postoperative pneumonia Machine learning Postoperative pulmonary complications Disease prediction Risk factors Early intervention Deep learning ml algorithm Extreme gradient boosting

来源：评论

学校读者我要写书评

暂无评论

An Improved OFDM Timing Synchronization algorithm with Variable Length of Cyclic Prefix 6th

An Improved OFDM Timing Synchronization Algorithm with Varia...

引用

6th International Conference on Communications, Signal Processing, and Systems (ICCSPS)

作者： Li, Yanping Song, Qichao Gao, Hui Wu, Dongyan Zhang, Lingzhi Heilongjiang Inst Technol Harbin Heilongjiang Peoples R China

The influence of the length of cyclic prefix to ml algorithm synchronization performance has been studied. On the basis, an improved algorithm with variable length of cyclic prefix is proposed. The proposed algorithm ... 详细信息

ISBN: (纸本)9789811065712;9789811065705

关键词： Cyclic prefix ml algorithm Vary with length Timing synchronization OFDM

来源：评论

学校读者我要写书评

暂无评论

Performance analysis of partial support recovery and signal reconstruction of compressed sensing

引用

IET SIGNAL PROCESSING 2014年第2期8卷 188-201页

作者： Xu, Wenbo Lin, Jiaru Niu, Kai He, Zhiqiang Wang, Yue Beijing Univ Posts & Telecommun Minist Educ Key Lab Univ Wireless Commun Beijing 100876 Peoples R China Huawei Technol Co Ltd Res Dept Hisilicon Beijing 100095 Peoples R China

Recent work in the area of compressed sensing mainly focuses on the perfect recovery of the entire support for sparse signals. However, partial support recovery, where a part of the signal support is correctly recovered, may be adequate in many practical scenarios. In this study, in the high-dimensional and noisy setting, the authors develop the probability of partial support recovery of the optimal maximum-likelihood (ml) algorithm. When a large part of the support is available, the asymptotic mean-square-error (MSE) of the reconstructed signal is further developed. The simulation results characterise the asymptotic performance of the ml algorithm for partial support recovery, and show that there exists a signal-to-noise ratio (SNR) threshold, beyond which the increase of SNR cannot bring any obvious MSE gain.

关键词： maximum likelihood estimation mean square error methods probability signal reconstruction compressed sensing partial support recovery signal reconstruction compressed sensing sparse signals optimal maximum-likelihood algorithm asymptotic mean-square-error ml algorithm SNR MSE gain

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：