检索结果-内蒙古大学图书馆

Hybrid Water Cycle Optimization algorithm With Simulated Annealing for Spam E-mail Detection

IEEE ACCESS 2019年 7卷 143721-143734页

作者： Al-Rawashdeh, Ghada Mamat, Rabiei Abd Rahim, Noor Hafhizah Binti Univ Malaysia Terengganu Dept Comp Sci Kuala Terengganu 21300 Malaysia

Spam is defined as junk and unwanted e-mail. The implementation of a reliable spam email filter becomes more and more important for e-mail users since they have to face with the growing amount of uninvited e-mails. The faults of spam classifiers are characterized by being more and more insufficient to handle huge volumes of relevant emails and to identify and detect the new spam email as example with high performance. The problem in spam classifiers is a huge number of features. Feature selection is an important task in keyword content classification for being among the most popular and effective methods for feature reduction. Accordingly, irrelevant and redundant features that can impede performance would be eliminated. Meta-heuristic optimization is to choose the optimal solution between possible multi-solutions, which respect the aim of this research that is the performance. The other problem is related to ambiguity of the effect of optimization feature selection on multiple classifiers algorithm which are popular used by previous work namely;K-nearest Neighbor, Naive Bayesian and Support Vector Machine. Therefore, the aim of this research is to improve the accuracy of feature selection by applying hybrid Water Cycle and Simulated Annealing to optimize results and to evaluate the proposed Spam Detection. The methodology used in this study which consists of groundwork, induction, improvement, evaluation and comparison quality. The cross-validation was used for training and validation dataset and seven datasets were employed in testing the spam classification proposed. The results demonstrate that the meta-heuristic namely water cycle feature selection (WCFS) was employed and three ways of hybridization with Simulated Annealing as a feature selection employed. In comparison with other feature selection algorithms such as Harmony Search, Genetic algorithm, and Particle Swarm, the hybridization interleaved hybridization outperformed other feature selection algorithms

关键词： Water cycle algorithm classification algorithm spam email simulating annealing hybridization global search local search

来源：评论

学校读者我要写书评

暂无评论

Combination of NIR spectroscopy and algorithms for rapid differentiation between one-year and two-year stored rice

引用

SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023年第1期291卷 122343页

作者： Shi, Shijie Feng, Junheng Yang, Lichao Xing, Junyang Pan, Gaofeng Tang, Jichao Wang, Jing Liu, Juan Cao, Cougui Jiang, Yang Huazhong Agr Univ Coll Plant Sci & Technol Wuhan 430070 Peoples R China Huazhong Agr Univ Shuangshui Shuanglu Inst Wuhan 430070 Peoples R China Cangzhou Acad Agr & Forestry Sci Cangzhou 061001 Peoples R China Xiangyang Acad Agr Sci Xiangyang 441022 Peoples R China Huazhong Agr Univ Coll Econ & Management Wuhan 430070 Peoples R China

Storage is necessary for rice to ensure the year-round consumption of rice. With the increase in storage time, the taste quality and commercial value of rice gradually decrease. The accurate determination of the freshness of rice is critical to the rice trade. However, it is difficult to distinguish aging rice from fresh rice, so a quick and simple method is needed to identify the freshness of the rice. In this study, a combination of near-infrared spectroscopy (NIR) and various algorithms, such as partial least squares discriminant analysis (PLS-DA), support vector ma-chines (SVM), and classification and regression trees (CART), were used to differentiate the freshness of rice. PLS-DA and SVM demonstrated excellent classification ability in identifying the freshness of rice, with sensitivity and specificity of 1. The original spectra were used with 100% accuracy in the test set to determine the freshness of the rice. As a result, PLS-DA and SVM can be used to determine the freshness of the rice.

关键词： Rice classification algorithm Storage time Near-infrared spectroscopy

来源：评论

学校读者我要写书评

暂无评论

Identifying Irrigated Areas in the Snake River Plain, Idaho: Evaluating Performance across Compositing algorithms, Spectral Indices, and Sensors (vol 9, pg 546, 2017)

引用

REMOTE SENSING 2017年第7期9卷

作者： Chance, Eric W. Cobourn, Kelly M. Thomas, Valerie A. Dawson, Blaine C. Flores, Alejandro N. Virginia Polytech Inst & State Univ Dept Forest Resources & Environm Conservat 310 West Campus Dr Blacksburg VA 24061 USA Boise State Univ Dept Geosci 1910 Univ Dr Boise ID 83725 USA

There are pressing concerns about the interplay between agricultural productivity, water demand, and water availability in semi-arid to arid regions of the world. Currently, irrigated agriculture is the dominant water user in these regions and is estimated to consume approximately 80% of the world's diverted freshwater resources. We develop an improved irrigated land-use mapping algorithm that uses the seasonal maximum value of a spectral index to distinguish between irrigated and non-irrigated parcels in Idaho's Snake River Plain. We compare this approach to two alternative algorithms that differentiate between irrigated and non-irrigated parcels using spectral index values at a single date or the area beneath spectral index trajectories for the duration of the agricultural growing season. Using six different pixel and county-scale error metrics, we evaluate the performance of these three algorithms across all possible combinations of two growing seasons (2002 and 2007), two datasets (MODIS and Landsat 5), and three spectral indices, the Normalized Difference Vegetation Index, Enhanced Vegetation Index and Normalized Difference Moisture Index (NDVI, EVI, and NDMI). We demonstrate that, on average, the seasonal-maximum algorithm yields an improvement in classification accuracy over the accepted single-date approach, and that the average improvement under this approach is a 60% reduction in county scale root mean square error (RMSE), and modest improvements of overall accuracy in the pixel scale validation. The greater accuracy of the seasonal-maximum algorithm is primarily due to its ability to correctly classify non-irrigated lands in riparian and developed areas of the study region. © 2017 by the authors.

关键词： Agriculture classification algorithm Irrigation Snake River Plain Water use

来源：评论

学校读者我要写书评

暂无评论

Critical Analysis of Risk Factors and Machine-Learning-Based Gastric Cancer Risk Prediction Models: A Systematic Review

引用

PROCESSES 2023年第8期11卷

作者： Fan, Zeyu He, Ziju Miao, Wenjun Huang, Rongrong Nanjing Tech Univ Sch Pharmaceut Sci Nanjing 211816 Peoples R China

The gastric cancer risk prediction model used for large-scale gastric cancer screening and individual risk stratification is an artificial intelligence tool that combines clinical diagnostic data with a classification algorithm. The ability to automatically make a quantitative assessment of complex clinical data contributes to increased accuracy for diagnosis with higher efficiency, significantly reducing the incidence of advanced gastric cancer. Previous studies have explored the predictive performance of gastric cancer risk prediction models, as well as the predictive factors and algorithms between each model, but have reached controversial conclusions. Thus, the performance of current machine-learning-based gastric cancer risk prediction models alongside the clinical relevance of different predictive factors needs to be evaluated to help build more efficient and feasible models in the future. In this systematic review, we summarize the current research progress related to the gastric cancer risk prediction model;discuss the predictive factors and methods used to construct the model;analyze the role of important predictive factors in gastric cancer, the preference of the selected classification algorithm, and the emphasis of evaluation criteria;and provide suggestions for the subsequent construction and improvement of the gastric cancer risk prediction model. Finally, we propose an improved approach based on the ethical issues of artificial intelligence in medicine to realize the clinical application of the gastric cancer risk prediction model in the future.

关键词： gastric cancer predictive factors risk prediction model machine learning classification algorithm

来源：评论

学校读者我要写书评

暂无评论

Heart rate variability feature selection method for automated prediction of sudden cardiac death

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年 65卷 102310-102310页

作者： Parsi, Ashkan Byrne, Dallan Glavin, Martin Jones, Edward Natl Univ Ireland NUI Galway Coll Engn & Informat Galway H91 TK33 Ireland

Sudden cardiac death is often caused by a major heart dysfunction with approximately 80% of occurrences attributed to ventricular arrhythmias. This paper proposes a novel ventricular arrhythmia prediction method to differentiate ventricular fibrillation and ventricular tachycardia from non-life-threatening cardiac activities, which may result in lower rates of unnecessary therapeutic intervention. Time series, frequency, bispectrum and nonlinear cardiac data can be analyzed on implantable devices such as implantable cardioverter defibrillators, to detect a life-threatening event. In order to reduce the computational overhead of the detection method, a feature selection technique is applied to reduce the number of features. Signal buffer lengths are examined, where one-minute and five-minute signal durations are processed. The features are then applied to classifiers and results from a selection of classification algorithms are compared. The proposed method achieves 88.8% sensitivity and 94.2% specificity using only six HRV features with a k-nearest neighbor classifier while processing a 5-minute window of R-R interval signals. Using a 1-minute window of R-R interval signals results in 86.9% sensitivity and 93.5% specificity, with a support vector machine classifier. The results show that the proposed method, using between four and eleven features, outperforms related work in the literature.

关键词： Ventricular arrhythmia Implantable cardioverter defibrillator classification algorithm Heart rate variability Cardiac signal analysis

来源：评论

学校读者我要写书评

暂无评论

An efficient channel recurrent Criss-cross attention network for epileptic seizure prediction

引用

MEDICAL ENGINEERING & PHYSICS 2024年 130卷 104213页

作者： Zhu, Lei Wang, Wentao Huang, Aiai Ying, Nanjiao Xu, Ping Zhang, Jianhai Hangzhou Dianzi Univ Sch Automat Hangzhou 310000 Peoples R China Hangzhou Dianzi Univ Sch Comp Sci Hangzhou 310000 Peoples R China Key Lab Brain Machine Collaborat Intelligence Zhej Hangzhou 310018 Peoples R China

Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurately predicting the onset of epilepsy can effectively improve the quality of life for patients with the condition. While there are many methods for detecting epilepsy, EEG is currently considered one of the most effective analytical tools due to the abundant information it provides about brain activity. The aim of this study is to explore potential time-frequency and channel features from multi-channel epileptic EEG signals and to develop a patientspecific seizure prediction network. In this paper, an epilepsy EEG signal classification algorithm called Channel Recurrent Criss-cross Attention Network (CRCANet) is proposed. Firstly, the spectrograms processed by the short-time fourier transform is input into a Convolutional Neural Network (CNN). Then, the spectrogram feature map obtained in the previous step is input into the channel attention module to establish correlations between channels. Subsequently, the feature diagram containing channel attention characteristics is input into the recurrent criss-cross attention module to enhance the information content of each pixel. Finally, two fully connected layers are used for classification. We validated the method on 13 patients in the public CHB-MIT scalp EEG dataset, achieving an average accuracy of 93.8 %, sensitivity of 94.3 %, and specificity of 93.5 %. The experimental results indicate that CRCANet can effectively capture the time-frequency and channel characteristics of EEG signals while improving training efficiency.

关键词： EEG Epilepsy prediction classification algorithm Spectrogram Short-time fourier transform Attention

来源：评论

学校读者我要写书评

暂无评论

EnzyMiner: automatic identification of protein level mutations and their impact on target enzymes from PubMed abstracts

引用

BMC BIOINFORMATICS 2009年第Sup8期10卷 S2-S2页

作者： Yeniterzi, Sueveyda Sezerman, U. Sabanci Univ Fac Engn & Nat Sci TR-34956 Istanbul Turkey

Background: A better understanding of the mechanisms of an enzyme's functionality and stability, as well as knowledge and impact of mutations is crucial for researchers working with enzymes. Though, several of the enzymes' databases are currently available, scientific literature still remains at large for up-to-date source of learning the effects of a mutation on an enzyme. However, going through vast amounts of scientific documents to extract the information on desired mutation has always been a time consuming process. In this paper, therefore, we describe an unique method, termed as EnzyMiner, which automatically identifies the PubMed abstracts that contain information on the impact of a protein level mutation on the stability and/or the activity of a given enzyme. Results: We present an automated system which identifies the abstracts that contain an amino-acid-level mutation and then classifies them according to the mutation's effect on the enzyme. In the case of mutation identification, MuGeX, an automated mutation-gene extraction system has an accuracy of 93.1% with a 91.5 F-measure. For impact analysis, document classification is performed to identify the abstracts that contain a change in enzyme's stability or activity resulting from the mutation. The system was trained on lipases and tested on amylases with an accuracy of 85%. Conclusion: EnzyMiner identifies the abstracts that contain a protein mutation for a given enzyme and checks whether the abstract is related to a disease with the help of information extraction and machine learning techniques. For disease related abstracts, the mutation list and direct links to the abstracts are retrieved from the system and displayed on the Web. For those abstracts that are related to non-diseases, in addition to having the mutation list, the abstracts are also categorized into two groups. These two groups determine whether the mutation has an effect on the enzyme's stability or functionality followed by displaying th

关键词： Lipase Amylase Protein Mutation classification algorithm Regular Expression

来源：评论

学校读者我要写书评

暂无评论

Identification of the Technical Condition of Induction Motor Groups by the Total Energy Flow

引用

ENERGIES 2021年第20期14卷 6677页

作者： Koteleva, N. I. Korolev, N. A. Zhukovskiy, Y. L. St Petersburg Min Univ Educ Res Ctr Digital Technol St Petersburg 191106 Russia

The paper discusses the method of identifying the technical condition of induction motors by classifying the energy data coming from the main common power bus. The work shows the simulation results of induction motor operation. The correlation between occurring defects and current diagrams is presented. The developed simulation model is demonstrated. The general algorithm for conducting experiments is described. Five different experiments to develop an algorithm for the classification are conducted: determination of the motors number in operation with different power;determination of the motors number in operation with equal power;determination of the mode and load of induction electric motor;determination of the fault and its magnitude with regard to operation and load of induction motor;determination of the fault and its magnitude with regard to operation and load of induction motor with regard to non-linear load in the flow. The article also presents an algorithm for preprocessing data to solve the classification problem. In addition, the classification results are shown and recommendations for testing and using the classification algorithm on a real object are made.

关键词： classification algorithm induction electric motor simulation model the coefficient of electromagnetic momentum ripple current harmonic distortion factor

来源：评论

学校读者我要写书评

暂无评论

Inferring ethnicity from mitochondrial DNA sequence

引用

BMC proceedings 2011年第2-Sup期5 Suppl 2卷 S11页

作者： Chih Lee Ion I Măndoiu Craig E Nelson Computer Science and Engineering Department University of Connecticut Storrs CT USA. chihlee@engr.uconn.edu.

BACKGROUND:The assignment of DNA samples to coarse population groups can be a useful but difficult task. One such example is the inference of coarse ethnic groupings for forensic applications. Ethnicity plays an important role in forensic investigation and can be inferred with the help of genetic markers. Being maternally inherited, of high copy number, and robust persistence in degraded samples, mitochondrial DNA may be useful for inferring coarse ethnicity. In this study, we compare the performance of methods for inferring ethnicity from the sequence of the hypervariable region of the mitochondrial genome. RESULTS:We present the results of comprehensive experiments conducted on datasets extracted from the mtDNA population database, showing that ethnicity inference based on support vector machines (SVM) achieves an overall accuracy of 80-90%, consistently outperforming nearest neighbor and discriminant analysis methods previously proposed in the literature. We also evaluate methods of handling missing data and characterize the most informative segments of the hypervariable region of the mitochondrial genome. CONCLUSIONS:Support vector machines can be used to infer coarse ethnicity from a small region of mitochondrial DNA sequence with surprisingly high accuracy. In the presence of missing data, utilizing only the regions common to the training sequences and a test sequence proves to be the best strategy. Given these results, SVM algorithms are likely to also be useful in other DNA sequence classification applications.

关键词： Support Vector Machine Linear Discriminant Analysis classification algorithm Hypervariable Region Published Dataset

来源：评论

学校读者我要写书评

暂无评论

Designing a Model to Study Data Mining in Distributed Environment

引用

Journal of Data Analysis and Information Processing 2021年第1期9卷 23-29页

作者： Md. Abadur Rahman Masud Karim School of Business & Tourism Southern Cross University Sydney Australia Department of Computer Science & Engineering Daffodil Institute of Science & Technology Dhaka Bangladesh

To make business policy, market analysis, corporate decision, fraud detection, etc., we have to analyze and work with huge amount of data. Generally, such data are taken from different sources. Researchers are using data mining to perform such tasks. Data mining techniques are used to find hidden information from large data source. Data mining is using for various fields: Artificial intelligence, Bank, health and medical, corruption, legal issues, corporate business, marketing, etc. Special interest is given to associate rules, data mining algorithms, decision tree and distributed approach. Data is becoming larger and spreading geographically. So it is difficult to find better result from only a central data source. For knowledge discovery, we have to work with distributed database. On the other hand, security and privacy considerations are also another factor for de-motivation of working with centralized data. For this reason, distributed database is essential for future processing. In this paper, we have proposed a framework to study data mining in distributed environment. The paper presents a framework to bring out actionable knowledge. We have shown some level by which we can generate actionable knowledge. Possible tools and technique for these levels are discussed.

关键词： Data Mining Distributed Database Knowledge Discovery classification 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：