scholarly journals Performance of SVM technique for DoA Estimation in 5G mm-Wave band

2021 ◽  
Author(s):  
Youmni Ziade ◽  
◽  
Wissam Obeid ◽  
Keyword(s):  
Doa Estimation ◽  
Machine Learning Algorithms ◽  
Base Stations ◽  
Support Vector ◽  
Second Phase ◽  
Wave Band ◽  
Promising Technique ◽  
Training Samples ◽  
Millimeter Wave Band ◽  
Limited Frequency

Applying Machine Learning algorithms in wireless communication has shown increasing interest due to the increase of demand on capacity, the increase of the number of users, and equipment sharing the limited frequency spectrum resources. Also, the need for a reduction in power consumption at base stations and the optimization of radio coverage make ML an attractive and promising technique. In this paper, we investigate the usage of Support Vector Machine (SVM) technique for Direction of Arrival (DoA) estimation in the millimeter-wave band. The objective is to predict the location of a user in a given area by analyzing the received signals at an array of antennas, using an SVM-based model. The first phase of this technique consists of the training phase that aims to identify the characteristics of each class, and that is based on a set of training samples. The second phase consists of testing the trained model using a set of samples/users. We have carried out a set of simulations based on the developed model. The results are promising in terms of the accuracy of determining the DoA, taking into consideration a channel with noise and multipath.

scholarly journals FEASIBILITY OF MACHINE LEARNING METHODS FOR SEPARATING WOOD AND LEAF POINTS FROM TERRESTRIAL LASER SCANNING DATA

2017 ◽  
Vol IV-2/W4 ◽  
pp. 157-164 ◽  
Author(s):  
D. Wang ◽  
M. Hollaus ◽  
N. Pfeifer
Keyword(s):  
Machine Learning ◽  
Laser Scanning ◽  
Learning Algorithms ◽  
Terrestrial Laser Scanning ◽  
Gaussian Mixture ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Area Index ◽  
Training Samples

Classification of wood and leaf components of trees is an essential prerequisite for deriving vital tree attributes, such as wood mass, leaf area index (LAI) and woody-to-total area. Laser scanning emerges to be a promising solution for such a request. Intensity based approaches are widely proposed, as different components of a tree can feature discriminatory optical properties at the operating wavelengths of a sensor system. For geometry based methods, machine learning algorithms are often used to separate wood and leaf points, by providing proper training samples. However, it remains unclear how the chosen machine learning classifier and features used would influence classification results. To this purpose, we compare four popular machine learning classifiers, namely Support Vector Machine (SVM), Na¨ıve Bayes (NB), Random Forest (RF), and Gaussian Mixture Model (GMM), for separating wood and leaf points from terrestrial laser scanning (TLS) data. Two trees, an <i>Erytrophleum fordii</i> and a <i>Betula pendula</i> (silver birch) are used to test the impacts from classifier, feature set, and training samples. Our results showed that RF is the best model in terms of accuracy, and local density related features are important. Experimental results confirmed the feasibility of machine learning algorithms for the reliable classification of wood and leaf points. It is also noted that our studies are based on isolated trees. Further tests should be performed on more tree species and data from more complex environments.

scholarly journals Analysis of Machine Learning Algorithms for Opinion Mining in Different Domains

2018 ◽  
Vol 1 (1) ◽  
pp. 224-234 ◽  
Author(s):  
Donia Gamal ◽  
Marco Alfonse ◽  
El-Sayed M. El-Horbaty ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Second Phase ◽  
Adaptive Boosting ◽  
Two Phases ◽  
Textual Content

Sentiment classification (SC) is a reference to the task of sentiment analysis (SA), which is a subfield of natural language processing (NLP) and is used to decide whether textual content implies a positive or negative review. This research focuses on the various machine learning (ML) algorithms which are utilized in the analyzation of sentiments and in the mining of reviews in different datasets. Overall, an SC task consists of two phases. The first phase deals with feature extraction (FE). Three different FE algorithms are applied in this research. The second phase covers the classification of the reviews by using various ML algorithms. These are Naïve Bayes (NB), Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Passive Aggressive (PA), Maximum Entropy (ME), Adaptive Boosting (AdaBoost), Multinomial NB (MNB), Bernoulli NB (BNB), Ridge Regression (RR) and Logistic Regression (LR). The performance of PA with a unigram is the best among other algorithms for all used datasets (IMDB, Cornell Movies, Amazon and Twitter) and provides values that range from 87% to 99.96% for all evaluation metrics.

scholarly journals A systematic review of the machine learning algorithms for the computational analysis in different domains

2020 ◽  
Vol 7 (71) ◽  
pp. 147-164
Author(s):  
Ravita Chahar ◽  
Deepinder Kaur
Keyword(s):  
Machine Learning ◽  
Computational Analysis ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Second Phase ◽  
Paper Machine ◽  
K Nearest Neighbors ◽  
Systematic Analysis

In this paper machine learning algorithms have been discussed and analyzed. It has been discussed considering computational aspects in different domains. These algorithms have the capability of building mathematical and analytical model. These models may be helpful in the decision-making process. This paper elaborates the computational analysis in three different ways. The background and analytical aspect have been presented with the learning application in the first phase. In the second phase detail literature has been explored along with the pros and cons of the applied techniques in different domains. Based on the literatures, gap identification and the limitations have been discussed and highlighted in the third phase. Finally, computational analysis has been presented along with the machine learning results in terms of accuracy. The results mainly focus on the exploratory data analysis, domain applicability and the predictive problems. Our systematic analysis shows that the applicability of machine learning is wide and the results may be improved based on these algorithms. It is also inferred from the literature analysis that at the applicability of machine learning algorithm has the capability in the performance improvement. The main methods discussed here are classification and regression trees (CART), logistic regression, naïve Bayes (NB), k-nearest neighbors (KNN), support vector machine (SVM) and decision tree (DT). The domain covered mainly are disease detection, business intelligence, industry automation and sentiment analysis.

scholarly journals Hyperspectral Image Classification with Capsule Network Using Limited Training Samples

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3153 ◽  
Author(s):  
Fei Deng ◽  
Shengliang Pu ◽  
Xuehong Chen ◽  
Yusheng Shi ◽  
Ting Yuan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Network Architecture ◽  
Hyperspectral Image ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Support Vector ◽  
Complex Data ◽  
Training Samples ◽  
Limited Training Samples

Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs).

Improved machine learning algorithms classifier combined with a stochastic local search for Web services classification

2020 ◽  
pp. 1-29
Author(s):  
Abdelouahab Laachemi ◽  
Dalila Boughaci
Keyword(s):  
Web Services ◽  
Local Search ◽  
Web Service ◽  
Machine Learning Algorithms ◽  
Classification Method ◽  
Support Vector ◽  
Second Phase ◽  
Optimization Approach ◽  
Stochastic Local Search ◽  
The Web

The Web services classification is the process that automatically assigns a category from a list of predefined categories to the Web service described as a WSDL document and where the purpose is to improve the Web services discovery process speed. The aim of this paper is to propose an optimization approach based on the attributes selection of Web services descriptions, to automatically classify Web services found in UDDI registers in predefined categories. The proposed approach combines the meta-heuristic of Stochastic Local Search (SLS) with a supervised learning method. The purpose of this work is to optimize the classification rate of the classifier by choosing the relevant attributes that best represents the Web service. First, we propose a classification method that uses a stochastic local search (SLS) for the attributes selection, then, in a second phase, the approach calls for a supervised classification method to perform the classification task. To this end, we studied six well-known classifiers which are: Support Vector Machine (SVM), Naïve Bayes (NB), k-Nearest Neighbors (k-NN), Bayesian Network (BN), Random Tree (RT), and Random Forests (RF). The six hybrid methods which are: SVM+SLS, NB+SLS, k-NN+SLS, BN+SLS, RT+SLS, and RF+SLS are evaluated on seven real datasets. The results are interesting and demonstrate the benefits of the proposed approaches for Web service classification.

scholarly journals SVM Parameter Optimization using ALO for Object Based Land Cover Classification

2020 ◽  
Vol 8 (5) ◽  
pp. 2968-2972
Keyword(s):  
Land Cover ◽  
Parameter Optimization ◽  
Machine Learning Algorithms ◽  
Learning Ability ◽  
Misclassification Rate ◽  
Significant Feature ◽  
Support Vector ◽  
Task Support ◽  
Training Samples ◽  
Improved Performance

Machine Learning algorithms are often used to solve various kinds of data classification task. Support Vector Machine (SVM) performs better for object oriented classification of high dimensional remote sensing datasets even with minimum training samples. In order to obtain improved performance in classification, the generalization and learning ability of SVM can be enriched by proper tuning of kernel and penalizing parameters of SVM. In this methodology ALO optimizer performs the optimal searching of SVM parameter in the direction of reducing misclassification rate. The proposed approach results better SVM parameters for the significant feature sub set which characterize the Landsat image objects of the study area. Performance of ALO is compared with GA based SVM parameter optimization. Accurate thematic classification map of land cover classes of the area of study also resulted in this module.

scholarly journals Inter-Beam Co-Channel Downlink and Uplink Interference for 5G New Radio in mm-Wave Bands

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 793
Author(s):  
Kamil Bechta ◽  
Jan M. Kelner ◽  
Cezary Ziółkowski ◽  
Leszek Nowosielski
Keyword(s):  
Multipath Propagation ◽  
Propagation Model ◽  
Base Stations ◽  
Wave Band ◽  
Separation Angle ◽  
Interference Level ◽  
Fifth Generation ◽  
New Radio ◽  
Millimeter Wave Band ◽  
5G Systems

This paper presents a methodology for assessing co-channel interference that arises in multi-beam transmitting and receiving antennas used in fifth-generation (5G) systems. This evaluation is essential for minimizing spectral resources, which allows for using the same frequency bands in angularly separated antenna beams of a 5G-based station (gNodeB). In the developed methodology, a multi-ellipsoidal propagation model (MPM) provides a mapping of the multipath propagation phenomenon and considers the directivity of antenna beams. To demonstrate the designation procedure of interference level we use simulation tests. For exemplary scenarios in downlink and uplink, we showed changes in a signal-to-interference ratio versus a separation angle between the serving (useful) and interfering beams and the distance between the gNodeB and user equipment. This evaluation is the basis for determining the minimum separation angle for which an acceptable interference level is ensured. The analysis was carried out for the lower millimeter-wave band, which is planned to use in 5G micro-cells base stations.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

LTCC Partially-Filled Post-Wall Rectangular-Waveguide Slot Array Antenna in the Millimeter-Wave Band

2012 ◽  
Vol E95.C (10) ◽  
pp. 1635-1642 ◽  
Author(s):  
Yuanfeng SHE ◽  
Jiro HIROKAWA ◽  
Makoto ANDO ◽  
Daisuke HANATANI ◽  
Masahiro FUJIMOTO
Keyword(s):  
Millimeter Wave ◽  
Rectangular Waveguide ◽  
Array Antenna ◽  
Wave Band ◽  
Waveguide Slot ◽  
Millimeter Wave Band
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