Rapid discrimination of Salvia miltiorrhiza according to their geographical regions by laser induced breakdown spectroscopy (LIBS) and particle swarm optimization-kernel extreme learning machine (PSO-KELM)
تبعیض سریع miltiorrhiza مریم گلی با توجه به مناطق جغرافیایی خود را با طیف سنجی شکست ناشی از لیزر (LIBS) و یادگیری ماشین افراطی بهینه سازی ازدحام ذرات (PSO-KELM)-2020
Laser-induced breakdown spectroscopy (LIBS) coupled with particle swarm optimization-kernel extreme learning machine (PSO-KELM) method was developed for classification and identification of six types Salvia miltiorrhiza samples in different regions. The spectral data of 15 Salvia miltiorrhiza samples were collected by LIBS spectrometer. An unsupervised classification model based on principal components analysis (PCA) was employed first for the classification of Salvia miltiorrhiza in different regions. The results showed that only Salvia miltiorrhiza samples from Gansu and Sichuan Province can be easily distinguished, and the samples in other regions present a bigger challenge in classification based on PCA. A supervised classification model based on KELM was then developed for the classification of Salvia miltiorrhiza, and two methods of random forest (RF) and PSO were used as the variable selection method to eliminate useless information and improve classification ability of the KELM model. The results showed that PSO-KELM model has a better classification result with a classification accuracy of 94.87%. Comparing the results with that obtained by particle swarm optimization-least squares support vector machines (PSO-LSSVM) and PSO-RF model, the PSO-KELM model possess the best classification performance. The overall results demonstrate that LIBS technique combined with PSO-KELM method would be a promising method for classification and identification of Salvia miltiorrhiza samples in different regions.
Keywords: Laser-induced breakdown spectroscopy | Particle swarm optimization | Kernel extreme learning machine | Salvia miltiorrhiza | Classification
Multiple AI model integration strategy : Application to saturated hydraulic conductivity prediction from easily available soil properties
استراتژی یکپارچه سازی مدل هوش مصنوعی چندگانه: کاربرد در پیش بینی هدایت هیدرولیکی اشباع شده از خصوصیات خاک که به راحتی در دسترس است-2020
A multiple model integration scheme driven by artificial neural network (ANN) (MM-ANN) was developed and tested to improve the prediction accuracy of soil hydraulic conductivity (Ks) in Tabriz plain, an arid region of Iran. The soil parameters such as silt, clay, organic matter (OM), bulk density (BD), pH and electrical conductivity (EC) were used as model inputs to predict soil Ks. Standalone models including multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), support vector machine (SVM) and extreme learning machine (ELM) were also implemented for comparative evaluation with MM-ANN model predictions. Based on several performance indicators such as Nash Sutcliffe Efficiency (NSE), results showed that the calibrated MMANN model involving the predictions of MARS, M5Tree, SVM and ELM models by considering all the soil parameters used in this study as inputs provided superior soil Ks estimates. The proposed hybrid model (MMANN) emerged as a reliable intelligence model for the assessment of soil hydraulic conductivity with an NSE=0.939 & 0.917 during training and testing, respectively. Accurate prediction of field-scale soil hydraulic conductivity is crucial from the view point of agricultural sustainability and management prospects.
Keywords: Saturated hydraulic conductivity | Extreme learning machine | Multiple model strategy | Multivariate adaptive regression splines | M5Tree | Support | vector machine | Prediction
Extreme learning machine for a new hybrid morphological/linear perceptron
دستگاه یادگیری شدید برای مورفولوژی جدید ترکیبی / پرسپترون خطی-2020
Morphological neural networks (MNNs) can be characterized as a class of artificial neural networks that perform an operation of mathematical morphology at every node, possibly followed by the application of an activation function. Morphological perceptrons (MPs) and (gray-scale) morphological associative memories are among the most widely known MNN models. Since their neuronal aggregation functions are not differentiable, classical methods of non-linear optimization can in principle not be directly applied in order to train these networks. The same observation holds true for hybrid morphological/ linear perceptrons and other related models. Circumventing these problems of non-differentiability, this paper introduces an extreme learning machine approach for training a hybrid morphological/linear perceptron, whose morphological components were drawn from previous MP models. We apply the resulting model to a number of well-known classification problems from the literature and compare the performance of our model with the ones of several related models, including some recent MNNs and hybrid morphological/linear neural networks.
Keywords: Mathematical morphology | Lattice computing | Morphological neural networks | Hybrid morphological/linear perceptron | Extreme learning machine | Classification
Stochastic parallel extreme artificial hydrocarbon networks: An implementation for fast and robust supervised machine learning in high-dimensional data
شبکه های هیدروکربنی مصنوعی موازی تصادفی تصادفی: پیاده سازی برای یادگیری ماشین تحت نظارت سریع و قوی در داده های با ابعاد بالا-2020
Artificial hydrocarbon networks (AHN) – a supervised learning method inspired on organic chemical structures and mechanisms – have shown improvements in predictive power and interpretability in comparison with other well-known machine learning models. However, AHN are very time-consuming that are not able to deal with large data until now. In this paper, we introduce the stochastic parallel extreme artificial hydrocarbon networks (SPE-AHN), an algorithm for fast and robust training of supervised AHN models in high-dimensional data. This training method comprises a population-based meta-heuristic optimization with defined individual encoding and objective function related to the AHN-model, an implementation in parallel-computing, and a stochastic learning approach for consuming large data. We conducted three experiments with synthetic and real data sets to validate the training execution time and performance of the proposed algorithm. Experimental results demonstrated that the proposed SPE-AHN outperforms the original-AHN method, increasing the speed of training more than 10, 000???? times in the worst case scenario. Additionally, we present two case studies in real data sets for solar-panel deployment prediction (regression problem), and human falls and daily activities classification in healthcare monitoring systems (classification problem). These case studies showed that SPEAHN improves the state-of-the-art machine learning models in both engineering problems. We anticipate our new training algorithm to be useful in many applications of AHN like robotics, finance, medical engineering, aerospace, and others, in which large amounts of data (e.g. big data) is essential.
Keywords: Machine learning | Parallel computing | Extreme learning machines | Stochastic learning | Regression | Classification | Big data
Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses
بهینه ساز مبتنی بر یادگیری تقویتی برای بهبود پیش بینی پاسخ های ناشی از tunneling-2020
Prediction of ground responses is important for improving performance of tunneling. This study proposes a novel reinforcement learning (RL) based optimizer with the integration of deep-Q network (DQN) and particle swarm optimization (PSO). Such optimizer is used to improve the extreme learning machine (ELM) based tunnelinginduced settlement prediction model. Herein, DQN-PSO optimizer is used to optimize the weights and biases of ELM. Based on the prescribed states, actions, rewards, rules and objective functions, DQN-PSO optimizer evaluates the rewards of actions at each step, thereby guides particles which action should be conducted and when should take this action. Such hybrid model is applied in a practical tunnel project. Regarding the search of global best weights and biases of ELM, the results indicate the DQN-PSO optimizer obviously outperforms conventional metaheuristic optimization algorithms with higher accuracy and lower computational cost. Meanwhile, this model can identify relationships among influential factors and ground responses through selfpracticing. The ultimate model can be expressed with an explicit formulation and used to predict tunnelinginduced ground response in real time, facilitating its application in engineering practice.
Keywords: Tunnel | Ground response | Reinforcement learning | Extreme learning machine | Optimization
Comparing of deep neural networks and extreme learning machines based on growing and pruning approach
مقایسه شبکه های عصبی عمیق و دستگاههای یادگیری افراطی بر اساس رویکرد در حال رشد و هرس-2020
Recently, the studies based on Deep Neural Networks and Extreme Learning Machines have become prominent. The models of parameters designed in these studies have been chosen randomly and the models have been designed in this direction. The main focus of this study is to determine the ideal pa- rameters i.e. optimum hidden layer number, optimum hidden neuron number and activation function for Deep Neural Networks and Extreme Learning Machines architectures based on growing and pruning ap- proach and to compare the performances of the models designed. The performances of the models are evaluated on two datasets; Parkinson and Self-Care Activities Dataset. Multi experiments have verified that the Deep Neural Networks architectures present a good prediction performance and this architec- ture outperforms the Extreme Learning Machines.
Keywords: Deep Neural Networks | Extreme Learning Machines | Growing and pruning | Parkinson | Self-care activities
On initial population generation in feature subset selection
تولید جمعیت اولیه در انتخاب زیر مجموعه ویژگی-2019
Performance of evolutionary algorithms depends on many factors such as population size, number of generations, crossover or mutation probability, etc. Generating the initial population is one of the impor- tant steps in evolutionary algorithms. A poor initial population may unnecessarily increase the number of searches or it may cause the algorithm to converge at local optima. In this study, we aim to find a promis- ing method for generating the initial population, in the Feature Subset Selection (FSS) domain. FSS is not considered as an expert system by itself, yet it constitutes a significant step in many expert systems. It eliminates redundancy in data, which decreases training time and improves solution quality. To achieve our goal, we compare a total of five different initial population generation methods; Information Gain Ranking (IGR), greedy approach and three types of random approaches. We evaluate these methods using a specialized Teaching Learning Based Optimization searching algorithm (MTLBO-MD), and three super- vised learning classifiers: Logistic Regression, Support Vector Machines, and Extreme Learning Machine. In our experiments, we employ 12 publicly available datasets, mostly obtained from the well-known UCI Machine Learning Repository. According to their feature sizes and instance counts, we manually classify these datasets as small, medium, or large-sized. Experimental results indicate that all tested methods achieve similar solutions on small-sized datasets. For medium-sized and large-sized datasets, however, the IGR method provides a better starting point in terms of execution time and learning performance. Finally, when compared with other studies in literature, the IGR method proves to be a viable option for initial population generation.
Keywords: Feature subset selection | Initial population | Multiobjective optimization
Neural trees with peer-to-peer and server-to-client knowledge transferring models for high-dimensional data classification
درختان عصبی با دانش همتا به همتا و سرور به مشتری انتقال مدل برای طبقه بندی داده های بعدی-2019
Classification of the high-dimensional data by a new expert system is followed in the current paper. The proposed system defines some non-disjoint clusters of highly relevant features with the least inner- redundancy. For each cluster, a neural tree is implemented exploiting an Extreme Learning Machine (ELM) together an inference engine in any node. The derived classification rules from ELM are stored in the rule- base of the inference engine to recognize the classes. A majority voting is used to unify the results of the different neural trees. This structure is refereed as the Forest of Extreme Learning Machines with Rule- base Transferring (FELM-RT). The contribution of FELM-RT is to decrease the duplicated computations by using two novel interaction models between the neural trees. In the first interaction model, namely Peer- to-Peer (P2P) model, each node can share its rule-base with the other nodes of the various neural trees. In the second that is referred as Server-to-Client (S2C) model, a neural tree that works on a cluster with the best relevancy and redundancy, shares the rules with the other neural trees. In both of the models, a fuzzy aggregation technique is used to adjust the certainty of the rules. The processing time of FELM-RT decreases essentially and it improves the classification accuracy. The high results of F-measure and G- mean, show that FELM-RT classifies the high-dimensional datasets without over-fitting. The comparison between FELM-RT and some state-of-the-art classifiers reveals that FELM-RT overcomes them specially on the datasets with more than 3 million features.
Keywords: Neural tree | Rule-base transferring | Feature clustering | Extreme learning machine | Communication models
پیش بینی ورود گردشگران از طریق یادگیری ماشین و شاخص جستجوی اینترنتی
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 38
مطالعات قبلی نشان داده است که داده های آنلاین، مانند پرس وجوهای انجام شده در موتورهای جستجو، یک منبع اطلاعاتی جدید محسوب می شوند که می توانند برای پیش بینی تقاضای گردشگری مورد استفاده قرار گیرند. در این مطالعه، ما چارچوبی را برای این پیش بینی ارائه می دهیم که با استفاده از یادگیری ماشین و شاخص های جستجوی اینترنتی، ورود گردشگران به مکان های محبوب چین را پیش بینی می کند و عملکرد این پیش بینی، را به ترتیب با نتایج جستجوی تولید شده توسط گوگل و بایدو مقایسه می کنیم. این تحقیق، علیت گرانجر و همبستگیِ میانِ شاخص جستجوی اینترنتی و ورود گردشگران به پکن را تایید می کند. نتایج تجربی ما نشان می دهد که عملکردِ پیش-بینیِ مدل های پیشنهادیِ هسته ی ماشین یادگیری افراطی (KELM )، که مجموعه هایی از گردشگران را با شاخص بایدو و شاخص گوگل ادغام می کنند، در مقایسه با مدل های معیار، به میزان قابل توجهی از نظر دقت پیش بینی و قدرت تحلیل ، بهتر بوده اند.
کلمه های کلیدی: پیش بینی تقاضای گردشگری | هسته ی ماشین یادگیری افراطی | جستجوی داده-های پرس وجو | تحلیل داده های بزرگ | شاخص جستجوی ترکیبی.
|مقاله ترجمه شده|
Extreme learning machine-based prediction of uptake of pharmaceuticals in reclaimed water irrigation lettuces in the Region of Murcia, Spain
پیش بینی مبتنی بر یادگیری ماشین افراطی از جذب داروهای دارویی در کاهو های آبیاری قابل احیا در منطقه مورسیا ، اسپانیا-2019
The availability of water resources is limited, and rising consumption has increased pressure on natural resources. Therefore, reclaimed water represents an alternative option for use in urban areas, industry and, in particular, agriculture. Recent research has shown that some pharmaceutical compounds are not fully removed by wastewater treatment plants (WWTPs) and may eventually be released into agricultural systems through the application of wastewater-based resources (sludge and effluent). The present study develops an intelligent expert system (based on a feedforward neural network trained via an extreme learning machine algorithm) for predicting the carbamazepine (CBZ) and diclofenac (DCF) content in lettuce tissues irrigated with reclaimed water from WWTPs. This reduces laboratory costs, mitigates the negative impacts on the environment and leads to more effective, safer decisions on the use of reclaimed water in agriculture. The results obtained, which were validated through statistical testing, demonstrate that the intelligent expert system is well calibrated and reliable. Finally, this system was used to predict maps of CBZ and DCF accumulation in lettuce crops if they were watered with effluent from 10 WWTPs located in the Region of Murcia (Spain). In conclusion, our system provides highly accurate predictions of the amount of CBZ and DCF contained in different lettuce tissues (roots and leaves) and the predicted concentrations do not present any health risk.
Keywords: WWTP effluent | Intelligent expert system | ELM | Carbamazepine | Diclofenac | Lettuces