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Estimation and inversion of soil heavy metal arsenic (As) based on UAV hyperspectral platform

MICROCHEMICAL JOURNAL 2024年 207卷

作者： Feng, Yue Wang, JingLi Tang, YuLan Shenyang Jianzhu Univ Shenyang 110168 Peoples R China

Hyperspectral detection technology, as a novel technique, has become an effective means of assessing heavy metal contamination in polluted areas. However, due to the vast amount of hyperspectral data, how to filter out critical information to build more accurate models and create high-precision, high-resolution prediction maps has become a key issue in current research. To address this problem, this study focuses on an abandoned nonferrous metal plant in Yinzhou District, Tieling City, Liaoning Province. We constructed both an indoor imaging spectroscopy platform and an outdoor UAV hyperspectral platform. In the indoor experiment, soil samples were collected on-site, and Visible-Near Infrared (VNIR) imaging spectroscopy was obtained using a PikaL lens, followed by chemical analysis of arsenic content in the samples. In the outdoor experiment, the UAV equipped with the same sensor was used to collect hyperspectral images of the experimental site. The indoor spectral data were resampled based on the outdoor UAV spectral data, and the Direct Standardization(DS) algorithm was used to correct the UAV spectral data to enhance spectral consistency under different environmental conditions. Subsequently, the resampled indoor spectral bands were preprocessed, and methods such as Stable Competitive Adaptive Reweighted Sampling (sCARS), Successive Projections Algorithm (SPA), and Interval Optimization Algorithm (ICO) and their respective coupling algorithms were introduced to obtain the sensitive band combinations of heavy metal arsenic (As). Multiple 2D band spectral indices (2D_BSIs) and multiple 3D band spectral indices (3D_BSIs) were added to different combinations of sensitive bands to explore whether integrating these indices into new datasets could improve model accuracy. During the model construction process, Partial Least Squares Regression (PLSR), Support Vector Machine Regression (SVR), Extreme Learning Machine (ELM), and Random Forest (RF) models were utilized, and

关键词： Visible-Near Infrared Spectroscopy UAV Hyperspectral Imaging feature selection algorithms Spectral Indices

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Experimental Study of different FSAs in Classifying Protein Function

Experimental Study of different FSAs in Classifying Protein ...

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International Conference of Soft Computing and Pattern Recognition

作者： Rahman, Shuzlina Abdul Hussein, Zeti Azura Mohamed Abu Bakar, Azuraliza Univ Kebangsaan Malaysia CAIT Fac Informat Sci & Technol Bangi 43600 Selangor Malaysia Univ Kebangsaan Malaysia Sch Biosci & Biotechnol Fac Sci & Technol Bangi 43600 Selangor Malaysia

ISBN: (纸本)9781424453306

This paper addresses one of the challenges of machine learning in improving performance through feature selection algorithms (FSAs). Application of FSAs in the bioinformatics domain has become a necessity due to enormous growth of public sequence databases. This paper provides an experimental framework on the use of Rough Set Theory (RST) as FSAs in finding minimal feature subsets for classifying protein function. In experimenting RST, three different recent models are explored;Correlation feature selection (CFS), FCBF (Fast Correlation-Based Filter) and Artificial Immune System (AIS). The experimental study for these FSAs are based on four criteria: the accuracy (AC), the area under ROC graph (ROC), the length of the reducts (ARL), and the time taken (TT). Classification was performed on the reduced feature set using the Support Vector Machine algorithm. The results demonstrate that CFS and FCBF performs better if the main objectives are to measure the accuracy and ROC, however in terms of duration and rule length, RST is a better choice.

关键词： classification feature selection algorithms protein function protein sequences

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Research on Cloud Computing Based on Industrial Internet Platform for Human Motion Recognition algorithms 24

Research on Cloud Computing Based on Industrial Internet Pla...

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International Conference on algorithms, Software Engineering, and Network Security (ASENS)

作者： Tang, Xiaodong Xu, Gaofeng Shanghai Urban Construct Vocat Coll Shanghai 201415 Peoples R China

ISBN: (纸本)9798400709784

Based on Cloud computing's high-performance computing and cloud computing platform capabilities, this article proposes a feature sorting algorithm based on multi-information fusion for actual data features by analyzing existing classic feature selection algorithms. It not only considers the distribution characteristics within features, the correlation between features and category labels, but also takes into account the redundancy between features. The article provides a detailed process for feature sorting and compares its effectiveness on different datasets and classifiers. And for the practical application scenarios of this article, based on the coherence of human motion and the misjudgment model of posture recognition, a posture recognition correction algorithm is proposed. Under the comparison of experimental data, both the filtering algorithm and attitude correction algorithm proposed in this paper can achieve good results, which has played a promoting role in promoting the application of inertial sensors based on attitude recognition in the future.

关键词： Cloud computing feature selection algorithms attitude correction algorithm

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The Integration among Disambiguation Lexical Resources for more Effective Phrase-Level Contextual Polarity Recognition 9

The Integration among Disambiguation Lexical Resources for m...

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9th International Conference on Computer Engineering & Systems (ICCES)

作者： Abdelrahman, Samir E. Sayed, Ebtsam Abdelhakam Bahgat, Reem Cairo Univ Fac Comp & Informat Dept Comp Sci Giza Egypt Menia Univ Fac Comp & Informat Dept Comp Sci Al Minya Egypt

ISBN: (纸本)9781479965946

Phrase-level polarity disambiguation recently became attractive. Nowadays, most corners of the sentiment analysis research have been investigated while the core and hard parts are not yet intensively explored, among which polarity disambiguation is one. In this research, we propose the integration between two SentiWordNet-based resources on phrase-level polarity disambiguation: (1) its score for the best word sense identified by a word sense disambiguation algorithm and (2) its examples having the non-zero scores of the prior polarities for the related subjectivity lexicon words. The integration of subjectivity lexicon word prior polarities together with Senti-WordNet scores and examples of word contexts were presented as classifier features to reduce the classification disambiguation. Our empirical evaluation is twofold. First, an experimental analysis was intrinsically conducted using various lexical resource settings coupled with feature selection algorithms to improve the classifier contextual subjective and sentiment polarity recognition. Second, the integration was extrinsically applied to improve the opinion question answering ranking task. These evaluations prove the superiority of the proposed integration in comparison with the state-of-the-art seminal work and baselines.

关键词： feature selection natural language processing pattern classification question answering (information retrieval) SentiWordNet-based resources classification disambiguation reduction classifier contextual subjective recognition disambiguation lexical resources feature selection algorithms lexical resource settings opinion question answering ranking task phrase-level contextual polarity recognition phrase-level polarity disambiguation sentiment analysis sentiment polarity recognition subjectivity lexicon word prior polarities word contexts word sense disambiguation algorithm Blogs Filtering blogs Natural Language Processing Pattern recognition feature extraction polarity Filtration selection algorithm Sentiment analysis DNA Integration integration Word Classifiers

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The Effect of the Characteristics of the Dataset on the selection Stability

The Effect of the Characteristics of the Dataset on the Sele...

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23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Alelyani, Salem Liu, Huan Wang, Lei Arizona State Univ Dept Comp Sci & Engn Tempe AZ 85287 USA Univ Wollongong Sch Comp Sci & Software Engn Wollongong NSW 2522 Australia

ISBN: (纸本)9780769545967

feature selection is an effective technique to reduce the dimensionality of a data set and to select relevant features for the domain problem. Recently, stability of feature selection methods has gained increasing attention. In fact, it has become a crucial factor in determining the goodness of a feature selection algorithm besides the learning performance. In this work, we conduct an extensive experimental study using verity of data sets and different well-known feature selection algorithms in order to study the behavior of these algorithms in terms of the stability.

关键词： feature selection algorithms stability dimensionality reduction data distribution Jaccard Index sample size

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A Fuzzy Classification System for Prediction of the Results of the Basketball Games

A Fuzzy Classification System for Prediction of the Results ...

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2010 IEEE World Congress on Computational Intelligence

作者： Trawinski, Krzysztof European Ctr Soft Comp Unit Applicat Fuzzy Log & Evolutionary Algorithms Mieres 33600 Asturias Spain

ISBN: (纸本)9781424469208

Prediction of the sports game results is an interesting topic that has gained attention lately. Mostly there are used stochastical methods of uncertainty description. In this work it is presented a preliminary approach to build a fuzzy model to basketball game results prediction. Ten fuzzy rule learning algorithms are selected, conducted and compared against standard linear regression with use of the KEEL system. feature selection algorithms are applied and a majority voting is used in order to select the most representative features.

关键词： KEEL system basketball game result prediction feature selection algorithms fuzzy classification system fuzzy model fuzzy rule learning algorithms fuzzy set theory learning (artificial intelligence) linear regression pattern classification regression analysis sport sports game stochastic processes stochastical methods uncertainty description

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Using Bio-inspired features selection algorithms in Software Effort Estimation: A Systematic Literature Review 45

Using Bio-inspired Features Selection Algorithms in Software...

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45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) / 22nd Euromicro Conference on Digital System Design (DSD)

作者： Ali, Asad Gravino, Carmine Univ Salerno Dept Comp Sci Via Giovanni Paolo II I-84084 Fisciano SA Italy

ISBN: (纸本)9781728134215

feature selection algorithms select the best and relevant set of features of the datasets which leads to an increase in the accuracy of predictions when employed with the machine learning techniques. Different feature selection algorithms are used in the domain of Software Development Effort Estimations (SDEE) and recently the use of bio-inspired feature selection algorithms got the attention of the researchers, which provided the best results in terms of the accuracy measures. In this paper, we manage to systematically evaluate and assess different bio-inspired feature selection algorithms which have been employed and investigated in the studies related to SDEE with the aim of increasing the accuracy of estimations. To the best of our knowledge, there is no Systematic Literature Review (SLR) which investigated the use of bio-inspired algorithms in SDEE. Since, the use of bio-inspired algorithms in the area of SDEE started in the late 2000, we have considered the studies published between 2007-2018. We have selected about 30 different studies from five digital libraries, i.e., IEEE explore, Springer, ScienceDirect, ACM digital library, and Google Scholar, after the filtering of inclusion/exclusion and quality assessment criteria. The main findings of our SLR are that Genetic algorithms (GA) and Particle Swarm Optimizations (PSO) are widely used bio-inspired algorithms. Moreover, GA and PSO are the algorithms which outperform baseline estimation techniques (estimation techniques employed without any feature selection algorithms) in more number of experiments, in terms of prediction accuracy.

关键词： Effort estimation feature selection algorithms Bio-inspired algorithms Systematic Literature Review

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Component-based Visual Evolutionary Computation 10

Component-based Visual Evolutionary Computation

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10th Computing Colombian Conference (10CCC)

作者： Garzon, Leidy Rodriguez, Nestor Orjuela, Henry Rojas-Galeano, Sergio DreamGis SAS Bogota Colombia CHECK24 Vergleichsportal GmbH Munich Area Germany Univ Distrital Sch Engn Bogota Colombia

ISBN: (纸本)9781467394642

Component-based computation advocates for building systems by assembling prefabricated self-contained software modules within a formally specified composition model. Furthermore, visual components allow users to perform such assemblage in a friendly graphical board were components are connected visually with virtually no coding needed. Recently, an Evolutionary Computation (EC) suite of components called Goldenberry complying with such premises has been released. In this paper we demonstrate its feasibility in several EC tasks: optimisation of continuous-valued and discrete-valued costs functions using Genetic algorithms and Estimation of Distribution algorithms as well as feature selection algorithms and relevance estimation in data mining tasks. We discuss the advantages and weaknesses of the approach and the toolbox, its differences with other existing software tools and proposing avenues of future development. Goldenberry is open-source under the New BSD License and it is available at: http://***

关键词： data mining distributed algorithms feature selection formal specification genetic algorithms object-oriented programming public domain software BSD license EC suite Goldenberry component-based visual evolutionary computation continuous-valued cost function optimisation data mining tasks discrete-valued cost function optimisation distribution algorithm estimation feature selection algorithms formally specified composition model friendly-graphical board open-source software prefabricated self-contained software module assembling relevance estimation software tools visual components Benchmark testing Cost function Genetic algorithms Sociology Software Visualization

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Machine Learning Approach to Segment Saccharomyces cerevisiae Yeast Cells

Machine Learning Approach to Segment <i>Saccharomyces cerevi...

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International Conference on Advances in Biomedical Engineering (ICABME)

作者： Tleis, Mohamed Verbeek, Fons Leiden Univ LIACS Sect Imaging & BioInformat Leiden Netherlands

ISBN: (纸本)9781467365161

In biological studies, Saccharomyces cerevisiae yeast cells are used to study the behaviour of proteins. This is a time consuming and not completely objective process. Hence, image analysis platforms are developed to address these problems and to offer analysis per cell as well. The segmentation algorithms implemented in such platforms can segment the healthy cells, along with artefacts such as debris and dead cells that exist in the cultured medium. The novel idea in this work is to apply a machine learning approach to train the segmentation system in order to classify the healthy cell objects from the other objects. Such approach is based on the analysis of a set of relevant individual cell features extracted from the microscope images of yeast cells. These features include texture measurements and wavelet-based texture measurements, as well as moment invariant features. Those features were introduced to describe the intensity and morphology characteristics in a more sophisticated way. A set of classification systems, data sampling techniques, data normalization schemes and feature selection algorithms were tested and evaluated to build a classification model in order to be used within the segmentation module. The study picks the simple logistic classification model as the best approach to classify our dataset of 1380 cells. This system increases the performance level in our image and data analysis modules, improve the segmentation and consequently the analysis of the measurement results. This leads to a better pattern recognition system as well.

关键词： biological techniques biology computing cellular biophysics feature extraction image classification image segmentation image texture learning (artificial intelligence) microorganisms optical microscopy wavelet transforms S. cerevisiae cell segmentation Saccharomyces cerevisiae data analysis module data normalization schemes data sampling techniques feature selection algorithms image analysis module logistic classification model machine learning approach moment invariant features pattern recognition system segmentation system training wavelet based texture measurements yeast cell microscope images yeast cells Accuracy Biomedical measurement Image segmentation Logistics Machine learning algorithms Vegetation Saccharomyces cerevisiae image segmentation Pattern Recognition System biology computing cellular biophysics Machine learning algorithms Biological techniques image texture Image classification wavelet transforms feature extraction Logistics optical microscopy biomedical measurement selection algorithm

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Assessing optimization techniques for improving water quality model

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JOURNAL OF CLEANER PRODUCTION 2023年 385卷

作者： Uddin, Md Galal Nash, Stephen Rahman, Azizur Olbert, Agnieszka I. Univ Galway Sch Engn Galway Ireland Univ Galway Ryan Inst Galway Ireland Univ Galway MaREI Res Ctr Galway Ireland Charles Sturt Univ Sch Comp Math & Engn Wagga Wagga NSW Australia Charles Sturt Univ Gulbali Inst Agr Water & Environm Wagga Wagga NSW Australia Univ Galway Coll Sci & Engn Civil Engn Galway Ireland

In order to keep the "good" status of coastal water quality, it is essential to monitor and assess frequently. The Water quality index (WQI) model is one of the most widely used techniques for the assessment of water quality. It consists of five components, with the indicator selection technique being one of the more crucial components. Several studies conducted recently have shown that the use of the existing techniques results in a significant amount of uncertainty being produced in the final assessment due to the inappropriate indicator selection. The present study carried out a comprehensive assessment of various features selection (FS) techniques for selecting crucial coastal water quality indicators in order to develop an efficient WQI model. This study aims to analyse the effects of eighteen different FS techniques, including (i) nine filter methods, (ii) two wrapper methods, and (iii) seven embedded methods for the comparison of model performance of the WQI. In total, fifteen combinations (subsets) of water quality indicators were constructed, and WQI values were calculated for each combination using the improvement methodology for coastal water quality. The WQI model's performance was tested using nine machine-learning algorithms, which validated the model's performance using various metrics. The results indicated that the tree-based random forest algorithm could be effective for selecting crucial water quality indicators in terms of assessing coastal water. Deep neural network algorithm showed better performance for predicting coastal water quality more accurately incorporating the subset of the random forest.

关键词： Coastal water quality feature selection algorithms Artificial intelligence Machine-learning techniques Water quality index model

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