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Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2013年 96卷 173-179页

作者： Romero, Jose R. Roncallo, Pablo F. Akkiraju, Pavan C. Ponzoni, Ignacio Echenique, Viviana C. Carballido, Jessica A. CCT Bahia Blanca CERZOS CONICET Buenos Aires DF Argentina UNS Dept Agron Buenos Aires DF Argentina UNS DCIC Lab Invest & Desarrollo Comp Cient Buenos Aires DF Argentina CCT Bahia Blanca Planta Piloto Ingn Quim PLAPIQUI CONICET Bahia Blanca Buenos Aires Argentina

Wheat is one of the most important cereals worldwide for human nutrition. Tetraploid wheat (Triticum turgidum L. asp. durum, 2n = 28, genomes AABB) is mainly used to produce pasta. The main objective of durum wheat breeding programs is to develop varieties with good quality and high yields. Yield is a very complex trait, and depends on different yield components that are genetically controlled and affected by environmental constraints. In this context, machine learning constitutes an excellent alternative for the analysis of a high number of traits in order to extract the most relevant ones as confident predictors of the performance of this crop, allowing a better agricultural planning. Thus, we propose the use of machine learning algorithms for the classification of yield components and for the search of new rules to infer high yields at harvest of durum wheat. The main objective of this work was to obtain rules for predicting durum wheat yield through different machine learning algorithms, and compare them to detect the one that best fits the model. In order to achieve this goal, One-R, J48, Ibk and A priori algorithms were run with data collected by our research group of a RIL (recombinant inbreed lines) population growing in six different environments from the Province of Buenos Aires in Argentina. The results indicate that the A priori method obtains the best performance for all locations, and the classificators generated using the different algorithms share a common set of selected traits. Moreover, comparing these results with the previous ones obtained using different techniques, mainly QTL mapping, the traits indicated to be the most significant ones were the same. The analysis of the resulting rules shows the soundness in the agronomic relevance of the extracted knowledge. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Machine learning Expert system classification algorithm Yield Triticum turgidum Pasta wheat

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Application of linear/non-linear classification algorithms in discrimination of pork storage time using Fourier transform near infrared (FT-NIR) spectroscopy

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LWT-FOOD SCIENCE AND TECHNOLOGY 2011年第10期44卷 2053-2058页

作者： Chen, Quansheng Cai, Jianrong Wan, Xinmin Zhao, Jiewen Jiangsu Univ Sch Food & Biol Engn Zhenjiang 212013 Jiangsu Provinc Peoples R China

To address the rapid and nondestructive determination of pork storage time associated with its freshness, Fourier transform near infrared (FT-NIR) spectroscopy technique, with the help of classification algorithm, was attempted in this work. To investigate the effects of different linear and non-linear classification algorithms on the discrimination results, linear discriminant analysis (LDA), K-nearest neighbors (KNN), and back propagation artificial neural network (BP-ANN) were used to develop the discrimination models, respectively. The number of principal components (PCs) and other parameters were optimized by cross-validation in developing discrimination models. Experimental results showed that the performance of BP-ANN model was superior to others, and the optimal BP-ANN model was achieved when 5 PCs were included. The discrimination rates of the BP-ANN model were 99.26% and 96.21% in the training and prediction sets, respectively. The overall results sufficiently demonstrate that the FT-NIR spectroscopy technique combined with BP-ANN classification algorithm has the potential to determine pork storage time associated with its freshness. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Pork Storage time Determination FT-NIR spectroscopy classification algorithm

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classification and detection using hidden Markov model-support vector machine algorithm based on optimal colour space selection for blood images

COGNITIVE COMPUTATION AND SYSTEMS

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COGNITIVE COMPUTATION AND SYSTEMS 2022年第1期4卷 68-76页

作者： Guo, Lei Wang, Yao Song, Yuan Sun, Tengyue Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Modern Post Beijing Peoples R China

Patients with cerebral haemorrhages need to drain haematomas. Fresh blood may appear during the haematoma drainage process, so this needs to be observed and detected in real time. To solve this problem, this paper studies images produced during the haematoma drainage process. A blood image feature selection recognition and classification framework is designed. First, aiming at the characteristics of the small colour differences in blood images, the general RGB colour space feature is not obvious. This study proposes an optimal colour channel selection method. By extracting the colour information from the images, it is recombined into a 3 x 3 matrix. The normalised 4-neighbourhood contrast and variance are calculated for quantitative comparison. The optimised colour channel is selected to overcome the problem of weak features caused by a single colour space. After that, the effective region in the image is intercepted, and the best colour channel of the image in the region is transformed. The first, second and third moments of the three best colour channels are extracted to form a nine-dimensional eigenvector. K-means clustering is used to obtain the image eigenvector, outliers are removed, and the results are then transferred to the hidden Markov model (HMM) and support vector machine (SVM) for classification. After selecting the best color channel, the classification accuracy of HMM-SVM is greatly improved. Compared with other classification algorithms, the proposed method offers great advantages. Experiments show that the recognition accuracy of this method reaches 98.9%.

关键词： classification algorithm colour space HMM-SVM image detection K-means

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Novel algorithm for RS Image classification

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2014年第2期42卷 435-437页

作者： Bharatkar, Pravada S. Patel, Rahila Durbude, Dilip G. RCERT Nagpur Univ Dept Comp Sci Chandrapur India Natl Inst Hydrol Environm Hydrol Div Roorkee Uttar Pradesh India

In the present era of modern technology, the efficacy and accuracy of output is demanding. Based on rigorous survey, Bharatkar and Patel (International Journal of Advanced Research in Computer Science 3(7):218-223, 2012) concluded that the incorporation of Block Truncation Coding (BTC) approach in the existing image classification algorithm can be used to improve the classification accuracy. Therefore, in the present study, an effort has been made to explore the Content Based Remote Sensing Image (CBRSI) classification algorithm to enhance classification accuracy with BTC approach. It is revealed from the study that BTC based maximum likelihood classifier gives better overall accuracy and kappa statistics.

关键词： Remote sensing classification algorithm Satellite image BTC etc.

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Knowledge discovery and predictive accuracy comparison of different classification algorithms for mould level fluctuation phenomenon in thin slab caster

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JOURNAL OF INTELLIGENT MANUFACTURING 2019年第1期30卷 241-254页

作者： Roy, Preetam Debasish Saha Tiwari, Prabhat Kumar Tata Steel Automat Div Jamshedpur 831005 Bihar India

Mould level fluctuation (MLF) is one of the main reasons for surface defects in continuously cast slabs. In these study first, large scale mould level fluctuations has been categorized in three different cases based on actual plant data. Moreover, theoretical formulation has been investigated to better understand the underlying physics of flow. Next, exploratory data analysis is used for preliminary investigation into the phenomenon based on actual plant data. Finally, different classification algorithms were used to classify non-mould level fluctuation cases from MLF cases for two different scenarios- one where all mould level fluctuation cases are considered and in another where only a particular case of mould level fluctuation is considered. classification algorithm such as recursive partitioning, random forest etc. has been used to identify different casting parameters affecting the phenomenon of mould level fluctuation. 70% of the dataset used as training dataset and rest 30% as the testing dataset. Prediction accuracy of these different classification algorithms along with an ensemble model has been compared on a completely unseen test set. Ladle change operation and superheat temperature has been identified as process parameters influencing the phenomenon of large scale mould level fluctuations.

关键词： Continuous casting Mould level fluctuation classification algorithm Data mining Random Forest

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Class imbalance in gradient boosting classification algorithms: Application to experimental stroke data

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STATISTICAL METHODS IN MEDICAL RESEARCH 2021年第3期30卷 916-925页

作者： Lyashevska, Olga Malone, Fiona MacCarthy, Eugene Fiehler, Jens Buhk, Jan-Hendrik Morris, Liam GMIT Enterprise Ireland Med & Engn Technol Gateway Dublin Rd Galway Ireland GMIT Marine & Freshwater Res Ctr Galway Ireland Univ Med Ctr Hamburg Eppendorf Dept Diagnost & Intervent Neuroradiol Hamburg Germany

Imbalance between positive and negative outcomes, a so-called class imbalance, is a problem generally found in medical data. Imbalanced data hinder the performance of conventional classification methods which aim to improve the overall accuracy of the model without accounting for uneven distribution of the classes. To rectify this, the data can be resampled by oversampling the positive (minority) class until the classes are approximately equally represented. After that, a prediction model such as gradient boosting algorithm can be fitted with greater confidence. This classification method allows for non-linear relationships and deep interactive effects while focusing on difficult areas by iterative shifting towards problematic observations. In this study, we demonstrate application of these methods to medical data and develop a practical framework for evaluation of features contributing into the probability of stroke.

关键词： Imbalanced data gradient boosting classification algorithm trees stroke oversampling

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Using ABC algorithm for classification and Analysis on Effects of Control Parameters

Using ABC Algorithm for Classification and Analysis on Effec...

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18th National Biomedical Engineering Meeting (BIYOMUT)

作者： Dilmac, Selim Korurek, Mehmet Istanbul Tech Univ Fen Bilimleri Enstitusu Maslak Sariyer Is Turkey

ISBN: (纸本)9781479975723

In this study, Artificial Bee Colony (ABC) algorithm based classifier is used. Also, in order to improve the effectiveness of ABC algorithm, some modifications are done. New method is called MABC algorithm. Both methods are applied on various real life data sets such as IRIS, WINE, PIMA, BUPA, ECG and results are compared. Those datasets are obtained from UCI Machine Learning Repository and MITBIH ECG database. In addition to it, validity indices and effects of some control parameters such as MCN, Limit are examined. It is observed that, selected features have significiant effect on classification success rate of classifier. If there is high overlap between the classes, success rate of classifier decreases. However observed results indicate that ABC algorithm can successfully be used for classification of multi dimensional datasets. By means of SCTR control parameter, MABC algorithm based classifier provides higher classification success rates versus ABC algorithm, independent from Limit and MCN values.

关键词： classification data analysis electrocardiography feature extraction feature selection learning (artificial intelligence) medical information systems medical signal processing signal classification swarm intelligence ABC algorithm effectiveness BUPA IRIS MABC algorithm based classifier MCN parameter MITBIH ECG database PIMA SCTR control parameter UCI Machine Learning Repository WINE analysis algorithm artificial bee colony algorithm based classifier class overlap classification algorithm classification success rate control parameter effect feature effect limit parameter multidimensional dataset classification validity index classification algorithms Educational institutions Electrocardiography Indexes Iris Machine learning algorithms Particle swarm optimization Feature extraction Sorting algorithm Electrocardiography Particle swarm optimization Wine medical information systems control parameter INTEGRATED RISK INFORMATION SYSTEM Machine learning algorithms medical signal processing signal classification Educational institutions Classifiers activity based costing Iris

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Deriving the Relationship Between User Satisfaction on Engine Sounds and Affective Variable Sets Based on classification algorithms

Deriving the Relationship Between User Satisfaction on Engin...

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IEEE International Conference on Industrial Engineering and Engineering Management

作者： Kim, W. J. Kim, G. W. Lee, Y. S. Yun, M. H. Seoul Natl Univ Dept Ind Engn Seoul South Korea

ISBN: (纸本)9781479964109

This study aims to extract the most relevant set consisted of affective variables to the level of user satisfaction on engine sounds using classification algorithm. The affective variables for engine sounds were defined by three axes, and two classification algorithms were used to determine the prediction accuracy for those affective axes. The study was consisted of three phases: 1) extracting sets of affective variables and the level of satisfaction on engine sounds, 2) preprocessing of engine sounds and experiment design, and 3) analysis of the most relevant sets of affective variables to user satisfaction. As a result, PA (Powerful-Affective) variable set showed the highest prediction accuracy of user satisfaction compared to other sets. Predicting the level of satisfaction based on classification algorithm could help to generalize the relationship between user satisfaction and affective variables more easily, beyond the limitation with a small size of subjects.

关键词： Emotion prediction product sounds classification algorithm user satisfaction engine sounds

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Digital Modulation classification Based on Characteristic Features and GentleBoost algorithm

Digital Modulation Classification Based on Characteristic Fe...

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34th International Conference on Telecommunications and Signal Processing (TSP)

作者： Kubankova, Anna Kubanek, David Prinosil, Jiri Brno Univ Technol Dept Telecommun Fac Elect Engn & Commun Brno Czech Republic

ISBN: (纸本)9781457714115

The paper describes algorithm for the classification of digital modulations and its testing with disturbed signals. 2ASK, 2FSK, 4FSK, MSK, BPSK, QPSK, 8PSK and 16QAM were chosen for recognition as the best-known digital modulations used in modern communication technologies. The method designed uses ten features computed from parameters of recognized signal such as instantaneous amplitude, instantaneous phase, instantaneous frequency and spectrum characteristic. The GentleBoost algorithm was used to analyze the features and classify the modulations. We used multipath fading channel to model signal propagation and disturbed the signal by white Gaussian noise for the purpose of testing the algorithm.

关键词： Recognition of modulations Digital modulation classification algorithm Features extraction

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A Geometric algorithm for Fault-Tolerant classification of COVID-19 Infected People 26

A Geometric Algorithm for Fault-Tolerant Classification of C...

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26th International Computer Conference of the Computer-Society-of-Iran

作者： Sheikhi, Farnaz Alipour, Sharareh KN Toosi Univ Technol Fac Comp Engn Tehran Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran Iran

ISBN: (纸本)9781665412414

As the world is struggling against COVID-19 pandemic, and unfortunately no certain treatments are discovered yet, prevention of further transmission by isolating infected people has become an effective strategy to overcome this outbreak. That is why scaling up COVID-19 testing is strongly recommended. However, depending on the time tests are performed, they may have a high rate of false-negative results. This inaccuracy of COVID-19 testing is a challenge against controlling the pandemic. Therefore, in this paper we propose a geometric classification algorithm that is fault -tolerant to handle the inaccuracy of tests. So, in a metropolis of n people, let w r be the number of cases that are tested, where r is the number of positive, while w is the number of negative COVID-19 cases, and k is an upper bound on the number of false -negative COVID-19 cases. The proposed algorithm takes 0(r " (log r log w) w3 w logfitrO) time for isolating all positive cases together with at most k (according to the rate of error of testing) possibly positive (false -negative) cases from the rest of the people. The term hR in the time complexity is the size of convex hull of the set of positive cases, and obviously k E 0(w). For simplicity of this isolation, we consider a simple convex shape (a triangle) for this classification algorithm.

关键词： COVID-19 Transmissibility Diagnostic Tests False-Negative Reactions classification algorithm Computational geometry

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