检索结果-内蒙古大学图书馆

Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia

ATMOSPHERIC RESEARCH 2020年 233卷

作者： Pour, Sahar Hadi Abd Wahab, Ahmad Khairi Shahid, Shamsuddin Univ Teknol Malaysia Fac Engn Sch Civil Engn Johor Baharu 81310 Malaysia Univ Teknol Malaysia Ctr Coastal & Ocean Engn COEI Kuala Lumpur 54100 Malaysia Univ Malaysia Terengganu Inst Oceanog & Environm INOS Kuala Terengganu 21300 Terengganu Malaysia

Reliable prediction of rainfall extremes is vital for disaster management, particularly in the context of increasing rainfall extremes due to global climate change. Physical-empirical models have been developed in this study using three widely used machine learning (ML) methods namely, Support Vector machines (SVM), Random Forests (RF), Bayesian Artificial Neural Networks (BANN) for the prediction of rainfall and rainfall related extremes during Northeast Monsoon (NEM) in Peninsular Malaysia from synoptic predictors. The gridded daily rainfall data of Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) was used to estimate four rainfall indices namely, rainfall amount, average rainfall intensity, days having > 95-th percentile rainfall, and total number of dry days in Peninsular Malaysia during NEM for the period 1951-2015. The National Centers for Environmental Prediction (NCEP) reanalysis sea level pressure (SLP) data was used for the prediction of rainfall indices with different lead periods. The recursive feature elimination (RFE) method was used to select the SLP at different NCEP grid points which were found significantly correlated with NEM rainfall indices. The results showed superior performance of BANN among the ML models with normalised root mean square error of 0.04-0.14, Nash-Sutcliff Efficiency of 0.98-1.0, and modified agreement index of 0.97-0.99 and Kling-Gupta efficient index 0.65-0.96 for one-month lead period prediction. The 95% confidence interval (CI) band for BANN was found narrower than the other ML models. Almost all the forecasted values by BANN were also found with 95% CI, and therefore, the p-factor and the r-factor for BANN in predicting rainfall indices were found in the range of 0.95-1.0 and 0.25-0.49 respectively. Application of BANN in prediction of rainfall indices with higher lead time was also found excellent. The synoptic pattern revealed that SLP over the north of

关键词： Extreme rainfall Climate forecasting Physical-empirical model machine learning algorithm Recursive feature elimination

来源：评论

学校读者我要写书评

暂无评论

Spectrum sensing in cognitive radio networks: threshold optimization and analysis

引用

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2020年第1期2020卷 1-19页

作者： Kockaya, Kenan Develi, Ibrahim Sivas Cumhuriyet Univ Dept Divrigi & Nuri Demirag Vocat High Sch TR-58300 Sivas Turkey Erciyes Univ Dept Elect & Elect Engn TR-38039 Kayseri Turkey

Cognitive radio is a technology developed for the effective use of radio spectrum sources. The spectrum sensing function plays a key role in the performance of cognitive radio networks. In this study, a new threshold determination method based on online learning algorithm is proposed to increase the spectrum sensing performance of spectrum sensing methods and to minimize the total error probability. The online learning algorithm looks for the optimum decision threshold, which is the most important parameter to decide the presence or absence of the primary user, using historical detection data. Energy detection- and matched filter-based spectrum sensing methods are discussed in detail. The performance of the proposed algorithm was tested over non-fading and different fading channels for low signal-to-noise ratio regime with noise uncertainty. In the conclusion of the simulation studies, improvement in spectrum sensing performance according to optimal threshold selection was observed.

关键词： Spectrum sensing Energy detection Threshold machine learning algorithm Online learning algorithm

来源：评论

学校读者我要写书评

暂无评论

machine learning Methods for Detecting Internet-of-Things (IoT) Malware

引用

INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2021年第4期15卷

作者： Yaokumah, Winfred Appati, Justice Kwame Kumah, Daniel Univ Ghana Dept Comp Sci Accra Ghana Univ Ghana Accra Ghana Hightel Consults Ltd Accra Ghana

This study aims to analyze the performance of machine learning models for detecting internet of things malware utilizing a recent IoT dataset. Experiments on the IoT dataset were conducted with nine well-known machine learning techniques, consisting of logistic regression (LR), naive bayes (NB), decision tree (DT), k-nearest neighbors (KNN), support vector machines (SVM), neural networks (NN), random forest (RF), bagging (BG), and stacking (ST). The results show that the proposed model attains 100% accuracy in detecting IoT malware for DT, SVM, RF, BG;about 99.9% percent for LR, NB, KNN, NN;and only 28.16% for ST classifier. This study also shows higher performance than other proposed machine learning models evaluated on the same dataset. Therefore, the results of this study can help both the researchers and application developers in designing and building intelligent malware detection systems for IoT devices.

关键词： Classification Data Theft Distributed Denial of Service Internet of Things IoT Dataset Keylogging machine learning algorithm Malware OS Fingerprinting Reconnaissance Service Scanning

来源：评论

学校读者我要写书评

暂无评论

Reflections on gaze data in statistics education

引用

TEACHING STATISTICS 2023年第Sup1期45卷 S40-S51页

作者： Boels, Lonneke Univ Appl Sci Utrecht Utrecht Netherlands Univ Utrecht Utrecht Netherlands

Gaze data are still uncommon in statistics education despite their promise. Gaze data provide teachers and researchers with a new window into complex cognitive processes. This article discusses how gaze data can inform and be used by teachers both for their own teaching practice and with students. With our own eye-tracking research as an example, background information on eye-tracking and possible applications of eye-tracking in statistics education is provided. Teachers indicated that our eye-tracking research created awareness of the difficulties students have when interpreting histograms. Gaze data showed details of students' strategies that neither teachers nor students were aware of. With this discussion paper, we hope to contribute to the future usage and implementation of gaze data in statistics education by teachers, researchers, educational and textbook designers, and students.

关键词： eye-tracking histogram machine learning algorithm teaching statistics

来源：评论

学校读者我要写书评

暂无评论

A Review on Negative Road Anomaly Detection Methods

引用

IEEE ACCESS 2020年 8卷 57298-57316页

作者： Dib, Jihad Sirlantzis, Konstantinos Howells, Gareth Univ Kent Sch Engn & Digital Arts Jennison Bldg Canterbury CT2 7NT Kent England

The main limitation to obstacle avoidance nowadays has been negative road anomalies which is the term we used to refer to potholes and cracks due to their negative drop from the surface of the road. This has for long been a limitation because of the fact that they exist in different, random and stochastic shapes. Today & x2019;s technology lacks the presence of sensors capable of detecting negative road anomalies efficiently as the latter surpasses the sensor & x2019;s limitations rendering the sensing technique inaccurate. A significant amount of research has been focused on the detection of negative road anomalies due to the fact that this topic is becoming a hot research topic. In this paper, the existing techniques will be reviewed. Their limitations will be highlighted and they will be assessed via certain performance indicators and via some chosen criteria which will be introduced.

关键词： Convolutional neural networks computer vision crack detection deep learning image processing image classification image texture analysis machine learning algorithm multi-layer neural networks negative road anomalies detection pothole detection real-time

来源：评论

学校读者我要写书评

暂无评论

Classification of arc fault between broken conductor and high-impedance surface: an empirical mode decomposition and Stockwell transform-based approach

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第22期14卷 5277-5286页

作者： Lala, Himadri Karmakar, Subrata VIT Univ Sch Elect Engn Vellore 632014 Tamil Nadu India NIT Rourkela Dept Elect Engn Rourkela 769008 Odisha India

The high-impedance arc fault (HIAF) is a result of the interaction between the current-carrying conductor and the high-impedance surface. This study presents a detailed and comparative time-frequency based analysis for the classification of different arcs, generated by the interaction of a broken conductor and different arcing surfaces. The real-time arcing voltage signals are considered as the basis of the whole time-frequency based analysis from a medium voltage distribution line. In this proposed approach, empirical mode decomposition (EMD) is used to study the real-time arc voltage signals of various surfaces. The intrinsic mode functions obtained by the application of the EMD technique is used as the input to various machine learning techniques, which later successfully classifies different HIAFs from their harmonic footprints. The Stockwell transform (ST) is additionally applied to the same test cases, and the classification of the arc is performed using similar learning algorithms to get a quantitative view of both outcomes of EMD and ST towards the selection of appropriate signal processing technique and machine learning algorithm for the same.

关键词： signal processing fault diagnosis learning (artificial intelligence) time-frequency analysis arcs (electric) feature extraction power distribution faults power engineering computing transforms broken conductor high-impedance surface empirical mode decomposition Stockwell transform-based approach high-impedance arc fault time-frequency based analysis different arcing surfaces real-time arcing voltage signals medium voltage distribution line real-time arc voltage signals intrinsic mode functions HIAF appropriate signal processing technique machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

A New Concept of Multiple Neural Networks Structure Using Convex Combination

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2020年第11期31卷 4968-4979页

作者： Wang, Yu Deng, Yue Shen, Yilin Jin, Hongxia Samsung Res Amer Mountain View CA 94043 USA Beihang Univ Sch Astronaut Beijing 100083 Peoples R China

In this article, a new concept of convex-combined multiple neural networks (NNs) structure is proposed. This new approach uses the collective information from multiple NNs to train the model. Based on both theoretical and experimental analyses, the new approach is shown to achieve faster training convergence with a similar or even better test accuracy than a conventional NN structure. Two experiments are conducted to demonstrate the performance of our new structure: the first one is a semantic frame parsing task for spoken language understanding (SLU) on the Airline Travel Information System (ATIS) data set and the other is a handwritten digit recognition task on the Mixed National Institute of Standards and Technology (MNIST) data set. We test this new structure using both the recurrent NN and convolutional NNs through these two tasks. The results of both experiments demonstrate a 4x-8x faster training speed with better or similar performance by using this new concept.

关键词： Artificial neural networks Training machine learning Task analysis machine learning algorithms Backpropagation Artificial neural networks boosting image recognition machine learning algorithm natural language processing (NLP) neural networks (NNs)

来源：评论

学校读者我要写书评

暂无评论

Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture

引用

APPLIED MICROBIOLOGY AND BIOTECHNOLOGY 2020年第22期104卷 9449-9485页

作者： Hesami, Mohsen Jones, Andrew Maxwell Phineas Univ Guelph Gosling Res Inst Plant Preservat Dept Plant Agr Guelph ON N1G 2W1 Canada

Artificial intelligence (AI) models and optimization algorithms (OA) are broadly employed in different fields of technology and science and have recently been applied to improve different stages of plant tissue culture. The usefulness of the application of AI-OA has been demonstrated in the prediction and optimization of length and number of microshoots or roots, biomass in plant cell cultures or hairy root culture, and optimization of environmental conditions to achieve maximum productivity and efficiency, as well as classification of microshoots and somatic embryos. Despite its potential, the use of AI and OA in this field has been limited due to complex definition terms and computational algorithms. Therefore, a systematic review to unravel modeling and optimizing methods is important for plant researchers and has been acknowledged in this study. First, the main steps for AI-OA development (from data selection to evaluation of prediction and classification models), as well as several AI models such as artificial neural networks (ANNs), neurofuzzy logic, support vector machines (SVMs), decision trees, random forest (FR), and genetic algorithms (GA), have been represented. Then, the application of AI-OA models in different steps of plant tissue culture has been discussed and highlighted. This review also points out limitations in the application of AI-OA in different plant tissue culture processes and provides a new view for future study objectives.

关键词： In vitro culture Data-driven model machine learning algorithm Plant biotechnology Computational approach Shoot proliferation Organogenesis Embryogenesis Androgenesis Rhizogenesis

来源：评论

学校读者我要写书评

暂无评论

An entropy-based classification of breast cancerous genes using microarray data

引用

NEURAL COMPUTING & APPLICATIONS 2020年第7期32卷 2397-2404页

作者： Mondal, Mausami Semwal, Rahul Raj, Utkarsh Aier, Imlimaong Varadwaj, Pritish Kumar Indian Inst Informat Technol Allahabad Dept Bioinformat & Appl Sci Prayagraj 211015 India

Gene expression levels obtained from microarray data provide a promising technique for doing classification on cancerous data. Due to the high dimensionality of the microarray datasets, the redundant genes need to be removed and only significant genes are required for building the classifier. In this work, an entropy-based method was used based on supervised learning to differentiate between normal tissue and breast tumor based on their gene expression profiles. This work employs four widely used machine learning techniques for breast cancer prediction, namely support vector machine (SVM), random forest, k-nearest neighbor (KNN) and naive Bayes. The performance of these techniques was evaluated on four different classification performance measurements which result in getting more accuracy in case of SVM as compared to other machine learning algorithms. Classification accuracy of 91.5% was achieved by support vector machine with 0.833 F1 measures. Furthermore, these techniques were evaluated on the basis of performance by ROC curve and calibration graph.

关键词： Support vector machine k-Nearest neighbor Random forest Naive Bayes Classification machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Reconstruction of high spatial resolution surface air temperature data across China: A new geo-intelligent multisource data-based machine learning technique

引用

SCIENCE OF THE TOTAL ENVIRONMENT 2019年 665卷 300-313页

作者： Zhu, Xiudi Zhang, Qiang Xu, Chong-Yu Sun, Peng Hu, Pan Beijing Normal Univ Key Lab Environm Change & Nat Disaster Minist Educ Beijing 100875 Peoples R China Beijing Normal Univ Acad Disaster Reduct & Emergency Management Fac Geog Sci Beijing 100875 Peoples R China Beijing Normal Univ State Key Lab Earth Surface Proc & Resources Ecol Beijing 100875 Peoples R China Univ Oslo Dept Geosci & Hydrol N-0316 Oslo Norway Anhui Normal Univ Coll Terr Resource & Tourism Wuhu 241002 Anhui Peoples R China

Good knowledge of the surface air temperature (SAT) is critical for scientific understanding of ecological environment changes and land-atmosphere thermodynamic interactions. However, sparse and uneven spatial distribution of the temperature gauging stations introduces remarkable uncertainties into analysis of the SAT pattern. From a geo-intelligent perspective, here we proposed a new SAT reconstruction method based on the multi-source data and machine learning technique which was developed by considering autocorrelation of the in situ observed SAT in both space and time, or simply STAML, i.e. Geoi-SVM (Geo-Intelligent Support Vector machine), Geoi-BPNN (Geo-Intelligent Back Propagation Neural Network) and Geoi-RF (Geo-Intelligent Random Forest). The multisource data used in this study include the in situ observed SAT and multisource remotely sensed data such as MODIS land surface temperature, NDVI (Normalized Difference Vegetation Index) data. Intermodel comparisons amidst reconstructed SAT data were done to evaluate reconstructing performance of abovementioned models. Besides, the SAT reconstructed by CART (Classification and Regression Tree) was also included to evaluate the reconstructing performance of the models considered in this study when compared to SAT data by CART algorithm. We found that the estimation error of the reconstructed SAT by the STAML is smaller than 0.5 K (Kelvin). In addition, it is interesting to note that the Geoi-RF performs better with Mean Absolute Error (MAE) of lower than 0.25 K, and Root Mean Squared Error (RMSE) and Standard Deviation (SD) of lower than 0.5 K respectively. Correlation coefficients between the reconstructed SAT by Geoi-RF and the observed SAT are close to 1. Besides, the estimation accuracy of the SAT by the Geoi-RF technique is 18.51-63.17% higher than that by the other techniques considered in this study. This study provides a new idea and technique for reconstruction of SAT over large spatial extent at regional a

关键词： Surface air temperature Multisource data Spatiotemporal autocorrelation Spatial resolution machine learning algorithm

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：