scholarly journals Laboratory Research on Polarized Optical Properties of Saline-Alkaline Soil Based on Semi-Empirical Models and Machine Learning Methods

2022 ◽  
Vol 14 (1) ◽  
pp. 226
Author(s):  
Qianyi Gu ◽  
Yang Han ◽  
Yaping Xu ◽  
Haiyan Yao ◽  
Haofang Niu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Empirical Models ◽  
Alkaline Soil ◽  
Alkaline Soils ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Model Based ◽  
Semi Empirical ◽  
Saline Alkaline Soils

Currently, soil salinization is a serious problem affecting agricultural production and human settlements. Remote sensing techniques have the advantages of a large monitoring range, rapid acquisition of information, implementation of dynamic monitoring, and low impact on the ground surface. Over the past two decades, many semi-empirical bidirectional polarized distribution function (BPDF) models have been proposed to accurately calculate the polarized reflectance (Rp) on the soil surface. Although there have been some studies on the BPDF model based on traditional machine learning methods, there is a lack of research on the BPDF model based on deep learning, especially using laboratory measurement spectrum data as the processing object, with limited research results. In this paper, we collected saline-alkaline soil in the field as the observation object and measured the Rp at multiple angles in the laboratory environment. We used semi-empirical models (the Nadal–Bréon model, Litvinov model, and Xie–Cheng model) and machine learning methods (support vector regression, random forest, and deep neural networks regression) to simulate and predict the surface Rp of saline-alkaline soils and compare them with experimental results. The measured values of the laboratory are compared and fitted, and the root mean squared error, R-squared, and correlation coefficient are calculated to express the prediction effect. The results show that the predictions of the BPDF model based on machine learning methods are generally better than those of the semi-empirical BPDF model, which is improved by 3.06% at 670 nm and 19.75% at 865 nm. The results of this study also provide new ideas and methods based on deep learning for the prediction of Rp on the surface of saline-alkaline soils.

scholarly journals Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1113
Author(s):  
Ming Zhong ◽  
Yajin Zhou ◽  
Gang Chen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Language Processing ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Methods ◽  
Sequential Model ◽  
Machine Learning Methods ◽  
Model Based

IoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for IoT servers. There should be a vigorous way to protect the network infrastructures from various attacks. IDS (Intrusion Detection System) is the invisible guardian for IoT servers. Many machine learning methods have been applied in IDS. However, there is a need to improve the IDS system for both accuracy and performance. Deep learning is a promising technique that has been used in many areas, including pattern recognition, natural language processing, etc. The deep learning reveals more potential than traditional machine learning methods. In this paper, sequential model is the key point, and new methods are proposed by the features of the model. The model can collect features from the network layer via tcpdump packets and application layer via system routines. Text-CNN and GRU methods are chosen because the can treat sequential data as a language model. The advantage compared with the traditional methods is that they can extract more features from the data and the experiments show that the deep learning methods have higher F1-score. We conclude that the sequential model-based intrusion detection system using deep learning method can contribute to the security of the IoT servers.

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

scholarly journals Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 601
Author(s):  
Nelson K. Dumakor-Dupey ◽  
Sampurna Arya ◽  
Ankit Jha
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Academic Research ◽  
Empirical Models ◽  
Rock Breakage ◽  
Environmental Implications ◽  
Learning Methods ◽  
Factors Affecting ◽  
Impact Prediction ◽  
Machine Learning Methods

Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.

Exploring energy-saving refrigerators through online e-commerce reviews: an augmented mining model based on machine learning methods

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuyan Luo ◽  
Zheng Yang ◽  
Yuan Liang ◽  
Xiaoxu Zhang ◽  
Hong Xiao
Keyword(s):  
Machine Learning ◽  
Energy Saving ◽  
Online Reviews ◽  
Learning Methods ◽  
Content Type ◽  
Machine Learning Methods ◽  
Model Based ◽  
Review Mining ◽  
Strategy Model ◽  
Mining Model

PurposeBased on climate issues and carbon emissions, this study aims to promote low-carbon consumption and compel consumers to actively shift to energy-saving appliances. In this big data era, online reviews in social and electronic commerce (e-commerce) websites contain valuable product information, which can facilitate firm business strategies and consumer comparison shopping. This study is designed to advance existing research on energy-saving refrigerators by incorporating machine learning models in the analysis of online reviews to provide valuable suggestions to e-commerce platform managers and manufacturers to effectively understand the psychological cognition of consumers.Design/methodology/approachThis study proposes an online e-commerce review mining and management strategy model based on “data acquisition and cleaning, data mining and analysis and strategy formation” through multiple machine learning methods, namely, Bayes networks, support vector machine (SVM), latent Dirichlet allocation (LDA) and importance–performance analysis (IPA), to help managers.FindingsBased on a case study of one of the largest e-commerce platforms in China, this study linguistically analyzes 29,216 online reviews of energy-saving refrigerators. Results indicate that the energy-saving refrigerator features that consumers are generally satisfied with are, in sequential order, logistics, function, price, outlook, after-sales service, brand, quality and space. This study also identifies ten topics with 100 keywords by analyzing 18 different refrigerator models. Finally, based on the IPA, this study allocates different priorities to the features and provides suggestions from the perspective of consumers, the government and manufacturers.Research limitations/implicationsIn terms of limitations, future research may focus on the following points. First, the topics identified in this study derive from specific points in time and reviews; thus, the topics may change with the text data. A machine learning-based online review analysis platform could be developed in the future to dynamically improve consumer satisfaction. Moreover, given that consumers' needs may change over time, e-commerce platform types and consumer characteristics, such as user profiles, can be incorporated into the model to effectively analyze trends in consumers' perceived dimensions.Originality/valueThis study fills the gap in previous research in this field, which uses small-sample data for qualitative analysis, while integrating management ideas and proposes an online e-commerce review mining and management strategy model based on machine learning methods. Moreover, this study considers how consumers' emotional and thematic preferences for products affect their purchase decision-making from the perspective of their psychological perception and linguistically analyzes online reviews of energy-saving refrigerators using the proposed mining model. Through the improved IPA model, this study provides optimizing strategies to help e-commerce platform managers and manufacturers.

Detecting a keystone species European aspen in boreal forests with airborne hyperspectral, LiDAR and UAV data with machine learning methods

2021 ◽  
Author(s):  
Timo Kumpula ◽  
Janne Mäyrä ◽  
Anton Kuzmin ◽  
Arto Viinikka ◽  
Sonja Kivinen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Boreal Forests ◽  
Tree Level ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
European Aspen

<p>Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. Different proxy variables indicating species richness and quality of the sites are essential for efficient detecting and monitoring forest biodiversity. European aspen (Populus tremula L.) is a minor deciduous tree species with a high importance in maintaining biodiversity in boreal forests. Large aspen trees host hundreds of species, many of them classified as threatened. However, accurate fine-scale spatial data on aspen occurrence remains scarce and incomprehensive.</p><p> </p><p>We studied detection of aspen using different remote sensing techniques in Evo, southern Finland. Our study area of 83 km<sup>2</sup> contains both managed and protected southern boreal forests characterized by Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst), and birch (Betula pendula and pubescens L.), whereas European aspen has a relatively sparse and scattered occurrence in the area. We collected high-resolution airborne hyperspectral and airborne laser scanning data covering the whole study area and ultra-high resolution unmanned aerial vehicle (UAV) data with RGB and multispectral sensors from selected parts of the area. We tested the discrimination of aspen from other species at tree level using different machine learning methods (Support Vector Machines, Random Forest, Gradient Boosting Machine) and deep learning methods (3D convolutional neural networks).</p><p> </p><p>Airborne hyperspectral and lidar data gave excellent results with machine learning and deep learning classification methods The highest classification accuracies for aspen varied between 91-92% (F1-score). The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724–727 nm) and shortwave infrared (1520–1564 nm and 1684–1706 nm) (Viinikka et al. 2020; Mäyrä et al 2021). Aspen detection using RGB and multispectral data also gave good results (highest F1-score of aspen = 87%) (Kuzmin et al 2021). Different remote sensing data enabled production of a spatially explicit map of aspen occurrence in the study area. Information on aspen occurrence and abundance can significantly contribute to biodiversity management and conservation efforts in boreal forests. Our results can be further utilized in upscaling efforts aiming at aspen detection over larger geographical areas using satellite images.</p>

scholarly journals Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

2019 ◽  
Vol 11 (2) ◽  
pp. 196 ◽  
Author(s):  
Omid Ghorbanzadeh ◽  
Thomas Blaschke ◽  
Khalil Gholamnia ◽  
Sansar Meena ◽  
Dirk Tiede ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Expert Knowledge ◽  
Accuracy Assessment ◽  
Window Size ◽  
Optical Data ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

There is a growing demand for detailed and accurate landslide maps and inventories around the globe, but particularly in hazard-prone regions such as the Himalayas. Most standard mapping methods require expert knowledge, supervision and fieldwork. In this study, we use optical data from the Rapid Eye satellite and topographic factors to analyze the potential of machine learning methods, i.e., artificial neural network (ANN), support vector machines (SVM) and random forest (RF), and different deep-learning convolution neural networks (CNNs) for landslide detection. We use two training zones and one test zone to independently evaluate the performance of different methods in the highly landslide-prone Rasuwa district in Nepal. Twenty different maps are created using ANN, SVM and RF and different CNN instantiations and are compared against the results of extensive fieldwork through a mean intersection-over-union (mIOU) and other common metrics. This accuracy assessment yields the best result of 78.26% mIOU for a small window size CNN, which uses spectral information only. The additional information from a 5 m digital elevation model helps to discriminate between human settlements and landslides but does not improve the overall classification accuracy. CNNs do not automatically outperform ANN, SVM and RF, although this is sometimes claimed. Rather, the performance of CNNs strongly depends on their design, i.e., layer depth, input window sizes and training strategies. Here, we conclude that the CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner. Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the effects of augmentation strategies to artificially increase the number of existing samples are better understood.

scholarly journals Modulation Classification of Underwater Communication with Deep Learning Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yan Wang ◽  
Hao Zhang ◽  
Zhanliang Sang ◽  
Lingwei Xu ◽  
Conghui Cao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Language Processing ◽  
Communication Process ◽  
Underwater Acoustic Communication ◽  
Learning Method ◽  
Modulation Recognition ◽  
Learning Methods ◽  
Machine Learning Methods

Automatic modulation recognition has successfully used various machine learning methods and achieved certain results. As a subarea of machine learning, deep learning has made great progress in recent years and has made remarkable progress in the field of image and language processing. Deep learning requires a large amount of data support. As a communication field with a large amount of data, there is an inherent advantage of applying deep learning. However, the extensive application of deep learning in the field of communication has not yet been fully developed, especially in underwater acoustic communication. In this paper, we mainly discuss the modulation recognition process which is an important part of communication process by using the deep learning method. Different from the common machine learning methods that require feature extraction, the deep learning method does not require feature extraction and obtains more effects than common machine learning.

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