Machine Learning Methods to Classify Mushrooms for Edibility-A Review

There are thousands of species of Mushrooms in the world; they are edible and non-edible being poisonous. It is difficult for non-expertise person to Identify poisonous and edible mushroom of all the species manually. So a computer aided system with software or algorithm is required to classify poisonous and nonpoisonous mushrooms. In this paper a literature review is presented on classification of poisonous and nonpoisonous mushrooms. Most of the research works to classify the type of mushroom have applied, machine learning techniques like Naïve Bayes, K-Neural Network, Support vector Machine(SVM), Artificial Neural Network(ANN), Decision Tree techniques. In this literature review, a summary and comparisons of all different techniques of mushroom classification in terms of its performance parameters, merits and demerits faced during the classification of mushrooms using machine learning techniques.

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Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

Journal of Environmental Geography ◽

10.2478/jengeo-2020-0005 ◽

2020 ◽

Vol 13 (1-2) ◽

pp. 43-52

Author(s):

Boudewijn van Leeuwen ◽

Zalán Tobak ◽

Ferenc Kovács

Keyword(s):

Neural Network ◽

Machine Learning ◽

Land Use ◽

Land Cover ◽

Classification Model ◽

Machine Learning Techniques ◽

Support Vector ◽

Land Use Land Cover ◽

Learning Techniques

AbstractClassification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

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Stock Prediction Using Machine Learning

WSEAS TRANSACTIONS ON COMPUTER RESEARCH ◽

10.37394/232018.2021.9.17 ◽

2021 ◽

Vol 9 ◽

pp. 152-158

Author(s):

Shubha Singh ◽

Sreedevi Gutta ◽

Ahmad Hadaegh

Keyword(s):

Neural Network ◽

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Support Vector ◽

Stock Price Prediction ◽

Price Prediction ◽

Learning Techniques ◽

Artificial Neural Network Ann

The Trend of stock price prediction is becoming more popular than ever. Share market is difficult to predict due to its volatile nature. There are no rules to follow to predict what will happen with the stock in the future. To predict accurately is a huge challenge since the market trend always keep changing depending on many factors. The objective is to apply machine learning techniques to predict stocks and maximize the profit. In this work, we have shown that with the help of artificial intelligence and machine learning, the process of prediction can be improved. While doing the literature review, we realized that the most effective machine learning tool for this research include: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Genetic Algorithms (GA). All categories have common and unique findings and limitations. We collected data for about 10 years and used Long Short-Term Memory (LSTM) Neural Network-based machine learning models to analyze and predict the stock price. The Recurrent Neural Network (RNN) is useful to preserve the time-series features for improving profits. The financial data High and Close are used as input for the model.

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Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

Sustainability ◽

10.3390/su10010203 ◽

2018 ◽

Vol 10 (1) ◽

pp. 203 ◽

Cited By ~ 8

Author(s):

Xianming Dou ◽

Yongguo Yang ◽

Jinhui Luo

Keyword(s):

Neural Network ◽

Machine Learning ◽

Eddy Covariance ◽

Carbon Flux ◽

Carbon Fluxes ◽

Forest Carbon ◽

Machine Learning Techniques ◽

Support Vector ◽

Inference System ◽

Learning Techniques

Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the scientific community aiming to gain new insights into the temporal and spatial variation of different carbon fluxes in terrestrial ecosystems. In this study, adaptive neuro-fuzzy inference system (ANFIS) and generalized regression neural network (GRNN) models were developed to predict the daily carbon fluxes in three boreal forest ecosystems based on eddy covariance (EC) measurements. Moreover, a comparison was made between the modeled values derived from these models and those of traditional artificial neural network (ANN) and support vector machine (SVM) models. These models were also compared with multiple linear regression (MLR). Several statistical indicators, including coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), bias error (Bias) and root mean square error (RMSE) were utilized to evaluate the performance of the applied models. The results showed that the developed machine learning models were able to account for the most variance in the carbon fluxes at both daily and hourly time scales in the three stands and they consistently and substantially outperformed the MLR model for both daily and hourly carbon flux estimates. It was demonstrated that the ANFIS and ANN models provided similar estimates in the testing period with an approximate value of R2 = 0.93, NSE = 0.91, Bias = 0.11 g C m−2 day−1 and RMSE = 1.04 g C m−2 day−1 for daily gross primary productivity, 0.94, 0.82, 0.24 g C m−2 day−1 and 0.72 g C m−2 day−1 for daily ecosystem respiration, and 0.79, 0.75, 0.14 g C m−2 day−1 and 0.89 g C m−2 day−1 for daily net ecosystem exchange, and slightly outperformed the GRNN and SVM models. In practical terms, however, the newly developed models (ANFIS and GRNN) are more robust and flexible, and have less parameters needed for selection and optimization in comparison with traditional ANN and SVM models. Consequently, they can be used as valuable tools to estimate forest carbon fluxes and fill the missing carbon flux data during the long-term EC measurements.

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Roundness prediction in centreless grinding using physics-enhanced machine learning techniques

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-020-06407-2 ◽

2020 ◽

Author(s):

Hossein Safarzadeh ◽

Marco Leonesio ◽

Giacomo Bianchi ◽

Michele Monno

Keyword(s):

Neural Network ◽

Machine Learning ◽

Regression Model ◽

Hybrid Models ◽

Physical Models ◽

Machine Learning Techniques ◽

Support Vector ◽

Grinding Wheel ◽

Wheel Wear ◽

Learning Techniques

AbstractThis work proposes a model for suggesting optimal process configuration in plunge centreless grinding operations. Seven different approaches were implemented and compared: first principles model, neural network model with one hidden layer, support vector regression model with polynomial kernel function, Gaussian process regression model and hybrid versions of those three models. The first approach is based on an enhancement of the well-known numerical process simulation of geometrical instability. The model takes into account raw workpiece profile and possible wheel-workpiece loss of contact, which introduces an inherent limitation on the resulting profile waviness. Physical models, because of epistemic errors due to neglected or oversimplified functional relationships, can be too approximated for being considered in industrial applications. Moreover, in deterministic models, uncertainties affecting the various parameters are not explicitly considered. Complexity in centreless grinding models arises from phenomena like contact length dependency on local compliance, contact force and grinding wheel roughness, unpredicted material properties of the grinding wheel and workpiece, precision of the manual setup done by the operator, wheel wear and nature of wheel wear. In order to improve the overall model prediction accuracy and allow automated continuous learning, several machine learning techniques have been investigated: a Bayesian regularized neural network, an SVR model and a GPR model. To exploit the a priori knowledge embedded in physical models, hybrid models are proposed, where neural network, SVR and GPR models are fed by the nominal process parameters enriched with the roundness predicted by the first principle model. Those hybrid models result in an improved prediction capability.

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A Machine Learning View on Momentum and Reversal Trading

Algorithms ◽

10.3390/a11110170 ◽

2018 ◽

Vol 11 (11) ◽

pp. 170 ◽

Cited By ~ 2

Author(s):

Zhixi Li ◽

Vincent Tam

Keyword(s):

Neural Network ◽

Machine Learning ◽

Stock Market ◽

Short Term Memory ◽

Predictive Ability ◽

Trading Strategies ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Learning Techniques

Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns.

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A Study on Brain Tumor Detection and Segmentation Using Deep Learning Techniques

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8468 ◽

2020 ◽

Vol 17 (4) ◽

pp. 1925-1930

Author(s):

Ambeshwar Kumar ◽

R. Manikandan ◽

Robbi Rahim

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Brain Tumor ◽

Tumor Detection ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Techniques ◽

Convolution Algorithm ◽

The Brain

It’s a new era technology in the field of medical engineering giving awareness about the various healthcare features. Deep learning is a part of machine learning, it is capable of handling high dimensional data and is efficient in concentrating on the right features. Tumor is an unbelievably complex disease: a multifaceted cell has more than hundred billion cells; each cell acquires mutation exclusively. Detection of tumor particles in experiment is easily done by MRI or CT. Brain tumors can also be detected by MRI, however, deep learning techniques give a better approach to segment the brain tumor images. Deep Learning models are imprecisely encouraged by information handling and communication designs in biological nervous system. Classification plays an significant role in brain tumor detection. Neural network is creating a well-organized rule for classification. To accomplish medical image data, neural network is trained to use the Convolution algorithm. Multilayer perceptron is intended for identification of a image. In this study article, the brain images are categorized into two types: normal and abnormal. This article emphasize the importance of classification and feature selection approach for predicting the brain tumor. This classification is done by machine learning techniques like Artificial Neural Networks, Support Vector Machine and Deep Neural Network. It could be noted that more than one technique can be applied for the segmentation of tumor. The several samples of brain tumor images are classified using deep learning algorithms, convolution neural network and multi-layer perceptron.

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Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

10.7287/peerj.preprints.27020v1 ◽

2018 ◽

Author(s):

Behrouz Alizadeh Savareh ◽

Azadeh Bashiri ◽

Ali Behmanesh ◽

Gholam Hossein Meftahi ◽

Boshra Hatef

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Dimension Reduction ◽

Sleep Disorders ◽

Component Analysis ◽

Machine Learning Techniques ◽

Support Vector ◽

Sleep Scoring ◽

Learning Techniques

Introduction: Sleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques. methods: Sleep-edf polysomnography was used in this study as a dataset. Support Vector Machines and Artificial Neural Network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis. Results: Neighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy respectively. Discussion and Conclusion: Similar to the state of the art performance, introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.

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Ecg Heartbeat Classification: Conceptual Understanding through Cnn & Rnn – A Machine Learning Approach

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b8285.1210220 ◽

2020 ◽

Vol 10 (2) ◽

pp. 143-147

Author(s):

P. Rama Santosh Naidu ◽

K.Venkata Ramana ◽

G. Lavanya Devi

Keyword(s):

Neural Network ◽

Machine Learning ◽

Dominant Role ◽

Machine Learning Techniques ◽

Heartbeat Classification ◽

Ecg Signals ◽

The Past ◽

Learning Techniques ◽

Machine Learning Approach

In recent days Machine Learning has become major study aspect in various applications that includes medical care where convenient discovery of anomalies in ECG signals plays an important role in monitoring patient's condition regularly. This study concentrates on various MachineLearning techniques applied for classification of ECG signals which include CNN and RNN. In the past few years, it is being observed that CNN is playing a dominant role in feature extraction from which we can infer that machine learning techniques have been showing accuracy and progress in classification of ECG signals. Therefore, this paper includes Convolutional Neural Network and Recurrent Neural Network which is being classified into two types for better results from considerably increased depth.

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A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images

Remote Sensing ◽

10.3390/rs11131600 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1600 ◽

Cited By ~ 9

Author(s):

Flávio F. Camargo ◽

Edson E. Sano ◽

Cláudia M. Almeida ◽

José C. Mura ◽

Tati Almeida

Keyword(s):

Machine Learning ◽

Land Use ◽

Land Cover ◽

Urban Areas ◽

Machine Learning Techniques ◽

Support Vector ◽

Tropical Savanna ◽

Learning Techniques ◽

Bare Soils

This study proposes a workflow for land use and land cover (LULC) classification of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) images of the Brazilian tropical savanna (Cerrado) biome. The following LULC classes were considered: forestlands; shrublands; grasslands; reforestations; croplands; pasturelands; bare soils/straws; urban areas; and water reservoirs. The proposed approach combines polarimetric attributes, image segmentation, and machine-learning procedures. A set of 125 attributes was generated using polarimetric ALOS-2/PALSAR-2 images, including the van Zyl, Freeman–Durden, Yamaguchi, and Cloude–Pottier target decomposition components, incoherent polarimetric parameters (biomass indices and polarization ratios), and HH-, HV-, VH-, and VV-polarized amplitude images. These attributes were classified using the Naive Bayes (NB), DT J48 (DT = decision tree), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) algorithms. The RF, MLP, and SVM classifiers presented the most accurate performances. NB and DT J48 classifiers showed a lower performance in relation to the RF, MLP, and SVM. The DT J48 classifier was the most suitable algorithm for discriminating urban areas and natural vegetation cover. The proposed workflow can be replicated for other SAR images with different acquisition modes or for other types of vegetation domains.

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Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things

Symmetry ◽

10.3390/sym13030403 ◽

2021 ◽

Vol 13 (3) ◽

pp. 403

Author(s):

Muhammad Waleed ◽

Tai-Won Um ◽

Tariq Kamal ◽

Syed Muhammad Usman

Keyword(s):

Machine Learning ◽

Random Forest ◽

Decision Tree ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Support Vector ◽

Farm Machinery ◽

Learning Techniques

In this paper, we apply the multi-class supervised machine learning techniques for classifying the agriculture farm machinery. The classification of farm machinery is important when performing the automatic authentication of field activity in a remote setup. In the absence of a sound machine recognition system, there is every possibility of a fraudulent activity taking place. To address this need, we classify the machinery using five machine learning techniques—K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB). For training of the model, we use the vibration and tilt of machinery. The vibration and tilt of machinery are recorded using the accelerometer and gyroscope sensors, respectively. The machinery included the leveler, rotavator and cultivator. The preliminary analysis on the collected data revealed that the farm machinery (when in operation) showed big variations in vibration and tilt, but observed similar means. Additionally, the accuracies of vibration-based and tilt-based classifications of farm machinery show good accuracy when used alone (with vibration showing slightly better numbers than the tilt). However, the accuracies improve further when both (the tilt and vibration) are used together. Furthermore, all five machine learning algorithms used for classification have an accuracy of more than 82%, but random forest was the best performing. The gradient boosting and random forest show slight over-fitting (about 9%), but both algorithms produce high testing accuracy. In terms of execution time, the decision tree takes the least time to train, while the gradient boosting takes the most time.

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