scholarly journals Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4208
Author(s):  
Janez Trontelj ml. ◽  
Olga Chambers
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Soil Properties ◽  
Principal Components ◽  
Learning Strategy ◽  
Spectroscopic Method ◽  
Principal Component ◽  
Least Square ◽  
Support Vector ◽  
Local Farm

The research presented in this paper is based on the hypothesis that the machine learning approach improves the accuracy of soil properties prediction. The correlations obtained in this research are important for understanding the overall strategy for soil properties prediction using optical spectroscopy sensors. Several research results have been stated and investigated. A comparison is made between six commonly used techniques: Random Forest, Decision Tree, Naïve Bayes, Support Vector Machine, Least-Square Support Vector Machine and Artificial Neural Network, showing that the best prediction accuracy cannot always be achieved by the most common and complicated method. The influence of the chosen category for nutrient characterization was investigated, indicating better prediction when a multi-component strategy was used. In contrast, the prediction of single-component soil properties was less accurate. In addition, the influence of category levels was not as significant as expected when choosing between 3-level, 5-level or 13-level nutrient characterization for some nutrients, which can be used for a more precise nutrient characterization strategy. A comparative analysis was performed between soil from a local farm with similar texture and soils collected from different locations in Slovenia, which gave a better prediction for a local farm. Finally, the influence of principal component analysis was validated using 5, 10, 20 and 50 first principal components, indicating the better performance of machine learning when using the 50 principal components.

Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine

2011 ◽  
Vol 130-134 ◽  
pp. 2047-2050 ◽  
Author(s):  
Hong Chun Qu ◽  
Xie Bin Ding
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Coefficient Matrix ◽  
Least Square ◽  
Support Vector ◽  
Influence Coefficient ◽  
Structural Risk ◽  
Better Than

SVM(Support Vector Machine) is a new artificial intelligence methodolgy, basing on structural risk mininization principle, which has better generalization than the traditional machine learning and SVM shows powerfulability in learning with limited samples. To solve the problem of lack of engine fault samples, FLS-SVM theory, an improved SVM, which is a method is applied. 10 common engine faults are trained and recognized in the paper.The simulated datas are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of FLS-SVM is better than LS-SVM.

A hybrid combination of LS-SVM and KPCA with bat algorithm for intrusion detection

2019 ◽  
Vol 12 ◽  
Author(s):  
Thiruppathy Kesavan. V ◽  
Loheswaran K
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Principal Component ◽  
Bat Algorithm ◽  
Least Square ◽  
Kernel Principal Component Analysis ◽  
Support Vector

: Intrusion Detection System is one of the prominent ways to identify the attacks by effectively monitoring the network. Designing an intrusion detection system that utilizes the resources efficiently by improving the precision is a challenging factor. This paper proposes a Least Square Support Vector Machine (LS-SVM) based on bat algorithm (BA) for efficient intrusion detection. The proposed technique is divided into two phases. In the first phase, the Kernel principal component analysis (KPCA) is utilized as a pre-processing of LS-SVM to decrease the dimension of feature vectors and abbreviates the preparing time with a specific end goal to decrease the noise caused by feature contrasts and enhance the implementation of LS-SVM. In the second phase, the LS-SVM with bat algorithm is applied for the classification of detection. BA utilizes programmed zooming to adjust investigation and abuse among the hunting procedure. Finally, as per the ideal feature subset, the feature weights and the parameters of LS-SVM are optimized at the same time. The proposed algorithm is named as Kernel principal component analysis based least square support vector machine with bat algorithm (KPCA-BA-LS-SVM). To show the adequacy of proposed method, the tests are completed on KDD 99 dataset which is viewed as an accepted benchmark for assessing the execution of intrusions detection. Furthermore, our proposed hybridization method gets a sensible execution regarding precision and efficiency.

scholarly journals A Survey and Analysis of Extreme Machine Learning Models and Its Techniques

2021 ◽  
Author(s):  
Shubhangi Pande ◽  
Neeraj Kumar Rathore ◽  
Anuradha Purohit
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Global Optimum ◽  
Least Square ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Feed Forward

Abstract Machine learning applications employ FFNN (Feed Forward Neural Network) in their discipline enormously. But, it has been observed that the FFNN requisite speed is not up the mark. The fundamental causes of this problem are: 1) for training neural networks, slow gradient descent methods are broadly used and 2) for such methods, there is a need for iteratively tuning hidden layer parameters including biases and weights. To resolve these problems, a new emanant machine learning algorithm, which is a substitution of the feed-forward neural network, entitled as Extreme Learning Machine (ELM) introduced in this paper. ELM also come up with a general learning scheme for the immense diversity of different networks (SLFNs and multilayer networks). According to ELM originators, the learning capacity of networks trained using backpropagation is a thousand times slower than the networks trained using ELM, along with this, ELM models exhibit good generalization performance. ELM is more efficient in contradiction of Least Square Support Vector Machine (LS-SVM), Support Vector Machine (SVM), and rest of the precocious approaches. ELM’s eccentric outline has three main targets: 1) high learning accuracy 2) less human intervention 3) fast learning speed. ELM consider as a greater capacity to achieve global optimum. The distribution of application of ELM incorporates: feature learning, clustering, regression, compression, and classification. With this paper, our goal is to familiarize various ELM variants, their applications, ELM strengths, ELM researches and comparison with other learning algorithms, and many more concepts related to ELM.

scholarly journals PEMODELAN PENGARUH SENTIMEN PUBLIK DI TWITTER TERKAIT POLITIK TERHADAP KURS RUPIAH DAN INDEKS HARGA SAHAM DENGAN PENDEKATAN MACHINE LEARNING

2021 ◽  
Vol 2020 (1) ◽  
pp. 989-999
Author(s):  
Epan Mareza Primahendra ◽  
Budi Yuniarto
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Least Square ◽  
Support Vector ◽  
Hidden Layer ◽  
Hidden Neurons

Kurs Rupiah dan indeks harga saham (IHS) berpengaruh terhadap perekonomian Indonesia. Pergerakan kurs Rupiah dan IHS dipengaruhi oleh, informasi publik, kondisi sosial, dan politik. Kejadian politik banyak menimbulkan sentimen dari masyarakat. Sentimen tersebut banyak disampaikan melalui media sosial terutama Twitter. Twitter merupakan sumber big data yang jika datanya tidak dimanfaatkan akan menjadi sampah. Pengumpulan data dilakukan pada periode 26 September 2019 - 27 Oktober 2019. Pola jumlah tweets harian yang sesuai dengan pergerakan kurs Rupiah dan IHS mengindikasikan bahwa terdapat hubungan antara sentimen di Twitter terkait situasi politik terhadap kurs Rupiah dan IHS. Penelitian ini menggunakan pendekatan machine learning dengan algoritma Neural Network dan Least Square Support Vector Machine. Penelitian ini bertujuan untuk mengetahui pengaruh sentimen terhadap kurs Rupiah dan IHS sekaligus mengkaji kedua algoritmanya. Hasilnya menjelaskan bahwa model terbaik untuk estimasi IHS yaitu NN dengan 1 hidden layer dan 2 hidden neurons. Modelnya menunjukan bahwa terdapat pengaruh antara sentimen tersebut terhadap IHS karena volatilitas estimasi IHS sudah cukup mengikuti pola pergerakan IHS aktual. Model terbaik untuk estimasi kurs Rupiah yaitu LSSVM. Pola pergerakan estimasi kurs Rupiah cenderung stagnan di atas nilai aktual. Ini mengindikasikan bahwa modelnya masih belum memuaskan dalam mengestimasi pengaruh sentimen publik terhadap kurs Rupiah.

scholarly journals An Analysis of Extreme Machine Learning Models

2021 ◽  
Author(s):  
Shubhangi Pande ◽  
Neeraj Rathore ◽  
Anuradha Purohit
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Global Optimum ◽  
Least Square ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Feed Forward

Abstract Machine learning applications employ FFNN (Feed Forward Neural Network) in their discipline enormously. But, it has been observed that the FFNN requisite speed is not up the mark. The fundamental causes of this problem are: 1) for training neural networks, slow gradient descent methods are broadly used and 2) for such methods, there is a need for iteratively tuning hidden layer parameters including biases and weights. To resolve these problems, a new emanant machine learning algorithm, which is a substitution of the feed-forward neural network, entitled as Extreme Learning Machine (ELM) introduced in this paper. ELM also come up with a general learning scheme for the immense diversity of different networks (SLFNs and multilayer networks). According to ELM originators, the learning capacity of networks trained using backpropagation is a thousand times slower than the networks trained using ELM, along with this, ELM models exhibit good generalization performance. ELM is more efficient in contradiction of Least Square Support Vector Machine (LS-SVM), Support Vector Machine (SVM), and rest of the precocious approaches. ELM’s eccentric outline has three main targets: 1) high learning accuracy 2) less human intervention 3) fast learning speed. ELM consider as a greater capacity to achieve global optimum. The distribution of application of ELM incorporates: feature learning, clustering, regression, compression, and classification. With this paper, our goal is to familiarize various ELM variants, their applications, ELM strengths, ELM researches and comparison with other learning algorithms, and many more concepts related to ELM.

Distinguishing the Representative Marshes in China Based on Artificial Intelligence

2017 ◽  
Vol 33 (4) ◽  
pp. 471-476
Author(s):  
Yongming Chen ◽  
Ping Lin
Keyword(s):  
Artificial Intelligence ◽  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Marsh Habitat ◽  
Principal Component Analysis Algorithm ◽  
True Color

Abstract. A new means to distinguish the habitat spectacles of the representative marshes in China based on artificial intelligence is presented in this article. Three typical instances including Yancheng mudflat marsh, Zoige plateau marsh, and Dongzhai Harbor mangrove forest marsh were investigated. Firstly, the RGB true-color pictures of the marsh habitat spectacles were resized to the appropriate sizes and switched to gray intensity pictures. Secondly, the GIST descriptors were evaluated for encoding the marsh habitat spectacles at both a basic level and a superordinate level. Thirdly, the principal component analysis algorithm was performed to extract the principal components from the encoded features. Finally, the multi-class support vector machine (MSVM) algorithm was used to discriminate the marsh habitat spectacles using the principal components. The recognition percisions for the training and test set reached 72.5% and 70.6%, respectively. It was accounted that the proposed methods could be applied to distinguishing the representative marsh habitat spectacles in China. Keywords: Classification, GIST descriptiors, Marsh Habitat spectacle, Multi-class support vector machine, Principal component analysis.

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