检索结果-内蒙古大学图书馆

Design of 2D/2D β-Ni(OH)₂/ZnO heterostructures via photocatalytic deposition of nickel for sonophotocatalytic degradation of tetracycline and modeling with three supervised machine learning algorithms

引用

Chemosphere 2024年 352卷 141328页

作者： Ajami Yazdi, Aghil Ebrahimian Pirbazari, Azadeh Esmaeili Khalil Saraei, Fatemeh Esmaeili, Amin Ebrahimian Pirbazari, Ali Akbari Kohnehsari, Atena Derakhshesh, Ali Hybrid Nanomaterials & Environment Lab Fouman Faculty of Engineering College of Engineering University of Tehran Fouman 43581- 39115 Iran Data Mining Research Group Fouman Faculty of Engineering College of Engineering University of Tehran Fouman 43581-39115 Iran Department of Chemical Engineering College of Engineering Technology University of Doha for Science and Technology Arab League St Doha 24449 Qatar Environment Lab Eshtehard Industrial Park Alborz Eshtehard 31881336 Iran

Due to the expansive use of tetracycline antibiotics (TCs) to treat various infectious diseases in humans and animals, their presence in the environment has created many challenges for human societies. Therefore, providing green and cost-effective solutions for their effective removal has become an urgent need. Here, we will introduce 2D/2D p-n heterostructures that exhibit excellent sonophotocatalytic/photocatalytic properties for water-soluble pollutant removal. In this contribution, for the first time, β- Ni(OH)₂ nanosheets were synthesized through visible-light-induced photodeposition of different amounts of nickel on ZnO nanosheets (β-Ni(x)/ZNs) to fabricate 2D/2D p-n heterostructures. The PXRD patterns confirmed the formation of wurtzite phase for ZNs and the hexagonal crystal structure of β-Ni(OH)₂. The FESEM and TEM micrographs showed that the β-Ni(OH)₂ sheets were dispersed on the surface of ZNs and formed 2D/2D p-n heterojunction in β-Ni(x)/ZNs samples. With the photodeposition of β-Ni(OH)₂ nanosheets on ZNs, the surface area, pore volume, and pore diameter of β-Ni(x)/ZNs heterostructures have increased compared to ZNs, which can have a positive effect on the sonophotocatalytic/photocatalytic performance of ZNs. The degradation experiments showed that β-Ni(0.1)/ZNs and β-Ni(0.4)/ZNs have the highest degradation percentage in photocatalytic (51 %) and sonophotocatalytic (71 %) degradation of TC, respectively. Finally, the sonophotocatalytic/photocatalytic degradation process of TC was systematically validated through modeling with three powerful and supervised machine learning algorithms, including Support Vector Regression (SVR), Artificial Neural Networks (ANNs), and Stochastic Gradient Boosting (SGB). Five statistical criteria including R², SAE, MSE, SSE, and RMSE were calculated for model validation. It was observed that the developed SGB algorithm was the most reliable model for predicting the degr

关键词： 2D/2d p-n heterojunctions Data validation Nickel photodeposition Sonophotocatalytic/photocatalytic degradation supervised machine learning algorithms ZnO nanosheets

来源：评论

学校读者我要写书评

暂无评论

Predictive Analytics of Sensor Data Based on supervised machine learning algorithms

Predictive Analytics of Sensor Data Based on Supervised Mach...

引用

International Conference on Next Generation Computing and Information Systems (ICNGCIS)

作者： Gupta, Shreya Mittal, Mohit Padha, Anupama Guru Nanak Dev Univ Dept CET Amritsar Punjab India MIET Dept CSE Jammu India

ISBN: (纸本)9780769563619

Wireless sensor network (WSN) is one of emerging technologies in today's scenario. Due to progressive advancement in micro-electro-mechanical system (MEMS) technology it can easily deployed in harsh environment. Sensor node communicates with their neighboring sensor nodes via radio frequencies and has many notable capabilities like self-configurable, self-manageable and monitoring physical phenomenon. Wireless sensor network is gaining popularity due to presence of many characteristics like cheap, cost-effective, reliable etc. along with this it has one major challenge that is limited battery life. To overcome this challenge, many solutions have found till date such as improvising routing protocols, reduction in computation of data, modification in time-stamp synchronization etc but still need more work. In this paper, our major focus is on processing of sensor dataset using various machine learning algorithms. We have managed different range of datasets from hundreds to thousands values and processed with various supervised machine learning algorithms. Simulation result shows that Gaussian Naive Bayes algorithm prominently gives better results than other algorithms in terms of accuracy parameter.

关键词： Wireless sensor network supervised machine learning algorithms MEMS

来源：评论

学校读者我要写书评

暂无评论

Site adaptation of global horizontal irradiance from the Copernicus Atmospheric Monitoring Service for radiation using supervised machine learning techniques

引用

RENEWABLE ENERGY 2022年 195卷 92-106页

作者： Salamalikis, Vasileios Tzoumanikas, Panayiotis Argiriou, Athanassios A. Kazantzidis, Andreas Univ Patras Dept Phys Lab Atmospher Phys Patras 26500 Greece

Satellite and reanalysis-derived solar products have gained great attention due to the inadequate number of radiometric stations worldwide, however, they are associated with considerable uncertainties. This study deals with the ground-based validation of Global Horizontal Irradiance from CAMS radiation service (GHICAMS) and the application of supervised machine learning algorithms (MLAs) to site-adapt GHICAMS. The validation of GHICAMS against measurements shows significant systematic and dispersion errors for all-sky (nMBE = 4.9% and nRMSE = 15.7%) and cloudy conditions (nMBE = 17.6% and nRMSE = 38.8%). Under clear skies, CAMS performs adequately (nMBE < 1% and nRMSE < 5%). All MLAs lead to reduced errors for the site-adapted irradiances. MBE is improved by more than 50%, accompanied by significant reductions in RMSE for various solar zenith angles and cloud fractions. The best results are revealed for the tree-based MLAs and especially for Random Forests. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Global horizontal irradiance CAMS-Rad Clear sky detection Site-adaptation supervised machine learning algorithms Goodness-of-fit statistics

来源：评论

学校读者我要写书评

暂无评论

Performance of the supervised learning algorithms in sex estimation of the proximal femur: A comparative study in contemporary Egyptian and Turkish samples

引用

SCIENCE & JUSTICE 2022年第3期62卷 288-309页

作者： Attia, MennattAllah H. Attia, Mohamed H. Farghaly, Yasmin Tarek Abulnoor, Bassam Ahmed El-Sayed Curate, Francisco Alexandria Univ Fac Med Forens Med & Clin Toxicol Alexandria Egypt Alexandria Univ Med Res Inst Biomed Engn Alexandria Egypt Deakin Univ Inst Intelligent Syst Res & Innovat Burwood Australia Alexandria Univ Fac Med Diagnost Radiol Alexandria Egypt Ain Shams Univ Fac Dent Fixed Prosthodont Cairo Egypt Univ Coimbra Res Ctr Anthropol & Hlth Dept Life Sci Coimbra Portugal Univ Coimbra Dept Life Sci Lab Forens Anthropol Coimbra Portugal

Sex estimation standards are population specific however, we argue that machine learning techniques (ML) may enhance the biological sex determination on trans-population application. Linear discriminant analysis (LDA) versus nine ML including quadratic discriminant analysis (QDA), support vector machine (SVM), Decision Tree (DT), Gaussian process (GPC), Naive Bayesian (NBC), K-Nearest Neighbor (KNN), Random Forest (RFM) and Adaptive boosting (Adaboost) were compared. The experiments involve two contemporary populations: Turkish (n = 300) and Egyptian populations (n = 100) for training and validation, respectively. Base models were calibrated using isotonic and sigmoid calibration schemes. Results were analyzed at posterior probabilities (pp) thresholds > 0.95 and > 0.80. At pp = 0.5, ML algorithms yielded comparable accuracies in the training (90% to 97%) and test sets (81% to 88%) which are not modified after employing the calibration techniques. At pp > 0.95, the raw RFM, LDA, QDA, and SVM models have shown the best performance however, calibration techniques improved the performance of various classifier especially NBC and Adaboost. By contrast, the performance of GPC, KNN, QDA models worsened by calibration. RFM has shown the best performance among all models at both thresholds whereas LDA benefited the best from using both calibration methods at pp > 0.80. Complex ML models are not necessarily achieving better performance metrics. LDA and QDA remain the fastest and simplest classifiers. We demonstrated the capability of enhancing sex estimation using ML on an independent population sample however, differences in the underlying probability distribution generated by models were detected which warranted more cautious application by forensic practitioners.

关键词： Forensic anthropology supervised machine learning algorithms Femur sexual dimorphism Regional sex estimation standards Contemporary metapopulations skeletal database

来源：评论

学校读者我要写书评

暂无评论

Improved Precision Crop Yield Prediction Using Weighted-Feature Hybrid SVM: Analysis of ML algorithms

引用

IETE JOURNAL OF RESEARCH 2024年第3期70卷 2628-2640页

作者： Pavani, S. Beulet, Augusta Sophy P. Vellore Inst Technol VIT Sch Elect Engn Chennai 600127 India

As the world's population grows, the agricultural sectors are destined to increase crop production and security. Improving crop yield by using advanced technologies gives remarkable growth to the economy of the country. Agriculture provides over 20% of India's GDP. Using machine learning algorithms, the crop yield can be predicted which is useful to the farmers to plan the cultivation beforehand. In this work, various machine learning (ML) algorithms are applied to predict the yield of 'rice and sorghum (jowar)' and a novel weighted feature approach with a combination of Support Vector machine (SVM) and Random Forest (RF) is proposed for two Indian seasons. RF is used to select training data at random, and the learning rate approach from deep learning concepts is implemented to add random weights to each parameter;the SVM model is then trained using the weighted training data. The best weights are again applied for the whole data to implement the SVM and RF algorithms. The weighted feature hybrid model is compared with SVM, RF, Decision tree, Naive Bayes, and k-Nearest Neighbor algorithms. RF-based regression method is also implemented and its ability to predict the crop yield has been discussed based on its performance metrics. The results show that the proposed weighted feature hybrid SVM-RF model gives the best accuracy of 90% when compared with the traditional algorithms. Also, the performances of various ML algorithms for crop yield prediction are analysed and cross-validation of the models is performed and compared, which improved the accuracy by 8-10%.

关键词： Crop yield prediction performance metrics regression model supervised machine learning algorithms weighted features

来源：评论

学校读者我要写书评

暂无评论

supervised machine learning Approach for Modeling Hot Deformation Behavior of Medium Carbon Steel

引用

STEEL RESEARCH INTERNATIONAL 2023年第2期94卷

作者： Murugesan, Mohanraj Yu, Jae-Hyeong Jung, Kyu-Seok Cho, Sung-Min Bhandari, Krishna Singh Chung, Wanjin Lee, Chang-Whan Seoul Natl Univ Sci & Technol Dept Mech Syst Design Engn Seoul 01811 South Korea Seoul Natl Univ Sci & Technol Dept Mech Informat Engn Seoul 01811 South Korea Jeju Natl Univ Dept Mech Engn Jeju Do 63243 South Korea

Metal forming process parameters selection highly depends on the consistent and realistic characterization of material behavior under the combined effects of strain, strain rate, and temperature on the material flow stress. Hot deformation tensile tests are performed for AISI 1045 steel at deformation temperatures and strain rates ranges from 650 to 950 degrees C and 0.05 to 1.0 s(-1), respectively. The received flow curves indicate that flow stress increases with a decrease in deformation temperature and an increase in strain rate. In this study, it is investigated the supervised machine learning techniques such as support vector regression, single decision tree, and random forest regression (RFR) models to characterize material-flow behavior during hot deformation. Overall, the proposed RFR model results are in good agreement with the experimental observations. Besides, the proposed model's predictability is assessed using graphical and numerical validations. The numerical quantification confirms that the RFR models perform significantly better with a higher coefficient of determination (R (2)), 0.9983, and low prediction error, 1.021%. Furthermore, it is revealed through the comparison with previous findings, that the proposed machine learning models can precisely calculate flow stress better than conventional models.

关键词： AISI 1045 steels flow stresses random forest regression supervised machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection Systems using supervised machine learning Techniques: A survey

Intrusion Detection Systems using Supervised Machine Learnin...

引用

13th International Conference on Ambient Systems, Networks and Technologies, ANT 2022 / 5th International Conference on Emerging Data and Industry 4.0, EDI40 2022

作者： Abdallah, Emad E. Eleisah, Wafa' Otoom, Ahmed Fawzi Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University P.O. Box 330127 Zarqa13133 Jordan

In this paper, we investigate the subject of intrusion detection using supervised machine learning methods. The main goal is to provide a taxonomy for linked intrusion detection systems and supervised machine learning algorithms. For this purpose, we provide a deep discussion of the concepts of intrusion detection systems, supervised machine learning techniques, and cyber-security attacks. Then, concerning the application of supervised learning for intrusion detection, we cover relevant efforts. Finally, a taxonomy is provided based on these related works. Based on this taxonomy, we can conclude that the classification performance of supervised learning algorithms is high and promising based on a study of four popular data sets in this domain: KDD'99, NSL-KDD, CICIDS2017, and UNSW-NB15. Moreover, feature selection is important and, in many cases, is needed for an enhancement in performance. Furthermore, data imbalance can be a concern, and sampling approaches can help resolve the issue. Finally, for good performance, large intrusion detection data sets necessitate a deep learning technique. © 2022 Elsevier B.V.. All rights reserved.

关键词： cyber security intrusion detection systems supervised machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Predicting Forced Blower Failures Using machine learning algorithms and Vibration Data for Effective Maintenance Strategies

引用

JOURNAL OF FAILURE ANALYSIS AND PREVENTION 2023年第5期23卷 2191-2203页

作者： Salem, Khaled AbdelGwad, Ebtisam Kouta, Hanan Port Said Univ Prod Engn & Mech Design Dept Fac Engn Port Said Egypt

The emergence of Industry 4.0, also known as the fourth industrial revolution, has brought forth the concept of prognostics and health management (PHM) as an inevitable trend in the realm of industrial big data and smart manufacturing. This study aims to present a proof-of-concept that illustrates how machine learning can be employed to analyze industrial facility data and anticipate the condition of industrial machines. Specifically, a comprehensive case study focusing on vibration monitoring is conducted. The proposed models aim to predict maintenance requirements for the forced blower of a chemical plant by utilizing vibration data obtained during the manufacturing process. To validate the methodology, five different machine learning algorithms, namely logistic regression (LR), support vector machine (SVM), K-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and random forest (RF), are employed. The evaluation metrics used include Matthews correlation coefficient (MCC) and receiver operator characteristic curve (ROC). This study aims to establish a relationship between machine failures caused by vibration and the prediction of both healthy and faulty bearings using the machine learning approaches. The findings indicate that the XGBoost algorithm outperforms other approaches with an MCC of 0.800 and a higher area under the ROC curve.

关键词： Industrial machine supervised machine learning algorithms Vibration data Case study Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Network Intrusion Detection Using machine learning

Performance Evaluation of Network Intrusion Detection Using ...

引用

2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Gnanasivam, Sajepan Tveter, Daniel Dinh, Nga Faculty of Computer Science Halden Norway

ISBN: (纸本)9798350324136

The development of 5G network and beyond has led to an explosion of data generation. It is therefore crucial to have an intrusion detection system (IDS) to detect and remove malicious packets from entering network. This paper therefore presents an IDS based on a Feature Selection approach which applies the Recursive Feature Elimination and Random Forest Classifier with 10-fold Cross Validation to classify malicious and benign traffic on a publicly available UNSW-NB15 dataset. Most existing Feature Selection approaches on this dataset directed to enhance the performance of a limited number of algorithms used. Our proposed Feature Selection approach was tested on six well-known supervised machine learning (ML) algorithms including Artificial Neural Network (ANN), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector machines (SVM) and Logistic Regression (LR) performing binary classification. In addition, we performed hyperparameter tuning to get the best possible parameters for each ML algorithm. Unlike hyperparameter tuning in most studies, we perform both Manual Search and Grid Search. The performance of the selected ML algorithms are evaluated based on Accuracy, Recall, Precision, and F1 score. The results from our experiments indicate that the most robust algorithm is ANN whereas the weakest performing algorithm is LR. RF is the second-best performing algorithm, however, its runtime is much lower than that of ANN. In particular, ANN excels with (testing accuracy, F1 score) of (88.62%, 96.473%), RF with (87.40%, 89.60%), DT with (87.266%, 89.414%), KNN with (87.11%, 88.7%), SVM with (81.835%, 86.959%) and LR with (81.835%, 85.632%). In addition, the over-fitting problems are eliminated based on our proposed Feature Selection and Hyperparameter turning. Compared with existing works with the same ML algorithms on UNSW-NB15 dataset, our proposed Feature Selection approach achieved better results in most cases and more stable among different

关键词： feature selection hyperparameter tuning Intrusion detection supervised machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Ensemble-Based Network Attack Prediction in WSN 15

Ensemble-Based Network Attack Prediction in WSN

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Tejaswini, B. Suruthi, V. Safia Naveed, S. Kcg College of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Computer network security and integrity are severely impacted by network attacks. The ability to predict and prevent these attacks is crucial for maintaining a secure network environment. supervised ML (machine learning) techniques have emerged as effective tools for network attack prediction because of their ability to analyze large amounts of network data and identify patterns indicative of malicious activity. We present a comprehensive analysis of supervised machine-learning techniques for the prediction of network attacks. We gather and preprocess the data, extracting pertinent features and converting them into a format suitable for machine learning algorithms. We assess the performance of these algorithms. We investigate the interpretability of the trained models to gain insights into the underlying patterns and characteristics of network attacks. This allows network administrators to understand the nature of attacks and develop appropriate defense strategies. Additionally, we discuss the challenges and limitations associated with the application of supervised ML techniques in the domain of network attack prediction, such as the need for real-time analysis and the emergence of sophisticated evasion techniques. © 2024 IEEE.

关键词： Network attacks prediction Patterns recognition Performance evaluation Real-time Analysis supervised machine learning algorithms

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：