检索结果-南通市图书馆

Fusion of Activation Functions: An Alternative to Improving prediction accuracy in Artificial Neural Networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

World Journal of Engineering and Technology 2024年第4期12卷 836-850页

作者： Justice Awosonviri Akodia Clement K. Dzidonu David King Boison Philip Kisembe Accra Institute of Technology Advanced School of Systems and Data Studies (ASSDAS) Accra Ghana Accra Institute of Technology Accra Ghana Knowledge Web Centre Accra Ghana Department of Computer Engineering Ghana Communication Technology University Accra Ghana

The purpose of this study was to address the challenges in predicting and classifying accuracy in modeling Container Dwell Time (CDT) using Artificial Neural Networks (ANN). This objective was driven by the suboptimal outcomes reported in previous studies and sought to apply an innovative approach to improve these results. To achieve this, the study applied the Fusion of Activation Functions (FAFs) to a substantial dataset. This dataset included 307,594 container records from the Port of Tema from 2014 to 2022, encompassing both import and transit containers. The RandomizedSearchCV algorithm from Python’s Scikit-learn library was utilized in the methodological approach to yield the optimal activation function for prediction accuracy. The results indicated that “ajaLT”, a fusion of the Logistic and Hyperbolic Tangent Activation Functions, provided the best prediction accuracy, reaching a high of 82%. Despite these encouraging findings, it’s crucial to recognize the study’s limitations. While Fusion of Activation Functions is a promising method, further evaluation is necessary across different container types and port operations to ascertain the broader applicability and generalizability of these findings. The original value of this study lies in its innovative application of FAFs to CDT. Unlike previous studies, this research evaluates the method based on prediction accuracy rather than training time. It opens new avenues for machine learning engineers and researchers in applying FAFs to enhance prediction accuracy in CDT modeling, contributing to a previously underexplored area.

关键词： Artificial Neural Networks Container Dwell Time Fusion of Activation Functions Randomized Search CV Algorithm prediction accuracy

Using machine learning to improve the accuracy of genomic prediction of reproduction traits in pigs

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Animal Science and Biotechnology 2022年第5期13卷 1293-1304页

作者： Xue Wang Shaolei Shi Guijiang Wang Wenxue Luo Xia Wei Ao Qiu Fei Luo Xiangdong Ding Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture and Rural Affairs National Engineering Laboratory of Animal BreedingCollege of Animal Science and TechnologyChina Agricultural UniversityBeijingChina Hebei Province Animal Husbandry and Improved Breeds Work Station ShijiazhuangHebeiChina Zhangjiakou Dahao Heshan New Agricultural Development Co. LtdZhangjiakouHebeiChina

Background:Recently,machine learning(ML)has become attractive in genomic prediction,but its superiority in genomic prediction over conventional(ss)GBLUP methods and the choice of optimal ML methods need to be ***:In this study,2566 Chinese Yorkshire pigs with reproduction trait records were genotyped with the GenoBaits Porcine SNP 50 K and PorcineSNP50 *** ML methods,including support vector regression(SVR),kernel ridge regression(KRR),random forest(RF)and Adaboost.R2 were *** 20 replicates of fivefold cross-validation(CV)and one prediction for younger individuals,the utility of ML methods in genomic prediction was *** CV,compared with genomic BLUP(GBLUP),single-step GBLUP(ssGBLUP)and the Bayesian method BayesHE,ML methods significantly outperformed these conventional *** methods improved the genomic prediction accuracy of GBLUP,ssGBLUP,and BayesHE by 19.3%,15.0% and 20.8%,*** addition,ML methods yielded smaller mean squared error(MSE)and mean absolute error(MAE)in all *** yielded an improvement of 3.8% on average in accuracy compared to that of GBLUP,and the accuracy of BayesHE was close to that of *** genomic prediction of younger individuals,RF and Adaboost.R2_KRR performed better than GBLUP and BayesHE,while ssGBLUP performed comparably with RF,and ssGBLUP yielded slightly higher accuracy and lower MSE than Adaboost.R2_KRR in the prediction of total number of piglets born,while for number of piglets born alive,Adaboost.R2_KRR performed significantly better than *** ML methods,Adaboost.R2_KRR consistently performed well in our *** findings also demonstrated that optimal hyperparameters are useful for ML *** tuning hyperparameters in CV and in predicting genomic outcomes of younger individuals,the average improvement was 14.3% and 21.8% over those using default hyperparameters,***:Our findings demonstrated that ML methods had better

关键词： Genomic prediction Machine learning Pig prediction accuracy

Stock prediction Based on Technical Indicators Using Deep Learning Model

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第1期70卷 287-304页

作者： Manish Agrawal Piyush Kumar Shukla Rajit Nair Anand Nayyar Mehedi Masud Department of Computer Science&Engineering University Institute of TechnologyRajiv Gandhi Proudyogiki VishwavidyalayaBhopal-462033Madhya PradeshIndia Faculty of Department of Computer Science&Engineering University Institute of TechnologyRajiv Gandhi Proudyogiki VishwavidyalayaBhopal-462033Madhya PradeshIndia Faculty of Department of Computer Science&Engineering School of Engineering and TechnologyJagran Lakecity UniversityBhopal462026Madhya PradeshIndia Graduate School Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable *** stock data is usually non-stationary,and attributes are non-correlative to each *** traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket *** study the stock market characteristics using STIs and make efficient trading decisions,a robust model is *** paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using *** proposed model has implemented the Deep Learning(DL)model to establish the concept of *** analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is *** datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov *** work also conducted exhaustive experiments to study the correlation of various STIs with stock price *** model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning *** proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,*** indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.

关键词： Long short term memory evolutionary deep learning model national stock exchange stock technical indicators predictive modelling prediction accuracy

prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Energy Engineering 2024年第4期121卷 1007-1026页

作者： Zhuo Chen Ningning Wang Wenbo Jin Dui Li The Second Oil Production Plant Sinopec Northwest Oilfield CompanyUrumqi830011China Yingmaili Oil and Gas Production Management Area PetroChina Tarim Oilfield CompanyKorla841001China College of Petroleum Engineering Xi’an Shiyou UniversityXi’an710065China

A hard problem that hinders the movement of waxy crude oil is wax deposition in oil *** ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil *** at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is *** validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil *** two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,***,it shows a higher prediction accuracy compared to the ENN prediction *** fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.

关键词： Waxy crude oil wax deposition rate chaotic map improved reptile search algorithm Elman neural network prediction accuracy

prediction on the relative permittivity of energy storage composite dielectrics using convolutional neural networks:A fast and accurate alternative to finite-element method

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

iEnergy 2022年第4期1卷 463-470页

作者： Shao-Long Zhong Di-Fan Liu Lei Huang Yong-Xin Zhang Qi Dong Zhi-Min Dang State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijing 100084China

The relative permittivity is one of the essential parameters determines the physical polarization behaviors of the nanocomposite dielectrics in many applications,particularly for capacitive energy *** the relative permittivity of particle/polymer nanocomposites from the microstructure is of great ***,the classical effective medium theory and physics-based numerical calculation represented by finite element method are time-consuming and cumbersome for complex structures and nonlinear *** work explores a novel architecture combining the convolutional neural network(ConvNet)and finite element method(FEM)to predict the relative permittivity of nanocomposite dielectrics with incorporated barium titanite(BT)particles in polyvinylidene fluoride(PVDF)*** ConvNet was trained and evaluated on big datasets with 14266 training data and 3514 testing data generated form a programmatic *** numerical experiments,we demonstrate that the trained network can efficiently provide an accurate agreement between the ConvNet model and FEM by virtue of the significant evaluation metrics R2,which reaches as high as 0.9783 and 0.9375 on training and testing data,*** strong universality of the presented method allows for an extension to fast and accurately predict other properties of the nanocomposite dielectrics.

关键词： Relative permittivity nanocomposite dielectrics convolutional neural networks finite element method prediction accuracy

A New Method for Quarterly-Data predictions Based on the Extended Grey Model GM(1,1,exp×sin,exp×cos)and Its Application in China's Quarterly GDP prediction

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Systems Science and Information 2023年第1期11卷 109-123页

作者： Maolin CHENG Bin LIU School of Mathematical Sciences Suzhou University of Science and TechnologySuzhou 215009China School of Business Suzhou University of Science and TechnologySuzhou 215009China

Grey prediction is vital in statistical prediction with wide ***,most grey prediction methods focus on annual predictions of the monotonic time series instead of the seasonal time *** paper uses the extended model of the grey GM(1,1)model to predict the seasonal time *** improvements have been made in two aspects to improve the prediction accuracy of the model.1)We introduce seasonal multiple factors to transform the original time series,which improves the adaptability of the seasonal data to the *** transformed series conforms to the law presented by the model.2)The seasonal data are in superimposed sine and cosine fluctuations with ***,the paper extends the grey action quantity of the traditional GM(1,1)*** newly extended grey model is called the GM(1,1,exp×sin,exp×cos)model,which is provided with the parameter optimization methods and time response *** to the proposed modeling method,we establish a GM(1,1,exp×sin,exp×cos)model for China's quarterly gross domestic product(GDP)with high accuracy.

关键词： GM(1,1,exp×sin,exp×cos)model parameter estimation time-response equation prediction accuracy

Effects of Mapping Methods on accuracy of Protein Coding Regions prediction

在线全文

学校读者我要写书评

暂无评论

Agricultural Science & Technology 2011年第12期12卷 1802-1806,1860页

作者：马玉韬张成杨泽林李琦杨婷宁夏大学物理电气信息学院宁夏银川750021 天津大学电子信息工程学院天津300072

[Objective] To discuss the effects of major mapping methods for DNA sequence on the accuracy of protein coding regions prediction,and to find out the effective mapping methods.[Method] By taking Approximate Correlation（AC） as the full measure of the prediction accuracy at nucleotide level,the windowed narrow pass-band filter（WNPBF） based prediction algorithm was applied to study the effects of different mapping methods on prediction accuracy.[Result] In DNA data sets ALLSEQ and HMR195,the Voss and Z-Curve methods are proved to be more effective mapping methods than paired numeric（PN）,Electron-ion Interaction Potential（EIIP） and complex number methods.[Conclusion] This study lays the foundation to verify the effectiveness of new mapping methods by using the predicted AC value,and it is meaningful to reveal DNA structure by using bioinformatics methods.

关键词： prediction accuracy Protein coding regions Mapping method Windowed Narrow pass-band filter

Credit Scoring Based on the Set-Valued Identification Method

在线全文

学校读者我要写书评

暂无评论

Journal of Systems Science & Complexity 2020年第5期33卷 1297-1309页

作者： WANG Ximei HU Min ZHAO Yanlong DJEHICHE Boualem Department of Mathematics KTH Royal Institute of TechnologyStockholm 10044Sweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and SystemsScienceChinese Academy of SciencesBeijing 100190China School of Mathernatical Sciences University ofChinese Academy of SciencesBeijing 100190China Department of Mathematics KTH Rogal Institute of TechnologyStockholm 10044Sweden

Credit scoring is one of the key problems in financial risk *** paper studies the credit scoring problem based on the set-valued identification method,which is used to explain the relation between the individual attribute vectors and classification for the credit worthy and credit worthless *** particular,system parameters are estimated by the set-valued identification algorithm based on a given recognition *** order to illustrate the efficiency of the proposed method,practical experiments are conducted for credit card applicants of Australia and credit card holders from Taiwan,*** empirical results show that the set-valued model has a higher prediction accuracy on both small and large numbers of data set compared with logistic regression ***,parameters estimated by the set-valued identification method are more stable,which provide a meaningful and logical explanation for extracting factors that influence the borrowers’credit scorings.

关键词： Credit scoring logistic regression model prediction accuracy set-valued model

Evaluation of RNA secondary structure prediction for both base-pairing and topology

在线全文

学校读者我要写书评

暂无评论

Biophysics Reports 2018年第3期4卷 123-132页

作者： Yunjie Zhao Jun Wang Chen Zeng Yi Xiao Institute of Biophysics School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of EducationHuazhong University of Science and Technology Institute of Biophysics and Department of Physics Central China Normal University Department of Physics The George Washington University

Secondary structures of RNAs are crucial to the understanding of their tertiary structures and functions. At present, many theoretical methods are widely used to predict RNA secondary structures. The performance of these methods has been evaluated but only for their ability of base-pairing ***, the topology of a RNA secondary structure is more important for understanding its tertiary structure and function, especially for long RNAs. In this paper, we constructed a new non-redundant RNA database containing 73 RNA with lengths of 50–300 nucleotides and benchmarked four popular algorithms for both base pairing and topology. The results show that the prediction accuracy of secondary structure topology is only 38%, in contrast to 70% for that of base pairing. Furthermore, the topological consistency is not strongly correlated to the base-pairing consistency. Our results will be helpful to understand the limitations of RNA secondary structure prediction methods from a different point of view and also to their improvements in future.

关键词： RNA secondary structure prediction Base pairs Structural topology prediction accuracy