scholarly journals Applications of Extreme Gradient Boosting for Intelligent Handovers from 4G To 5G (mm Waves) Technology with Partial Radio Contact

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 545
Author(s):  
Saad Ijaz Majid ◽  
Syed Waqar Shah ◽  
Safdar Nawaz Khan Marwat
Keyword(s):  
Success Rate ◽  
Research Work ◽  
Reference Signal ◽  
Base Stations ◽  
Gradient Boosting ◽  
Received Power ◽  
Extreme Gradient Boosting ◽  
4G Lte ◽  
Radio Contact ◽  
Mm Waves

In a network topology, where 5G (mm Waves) have better coverage footprint compared to 4G (LTE or LTE-A) technology, mobile devices would generally be handed over from 4G to 5G. In this work, a supervised intelligent prediction technique for improved handover success rate (HSR) from 4G to 5G technology is proposed. The technique is applicable for base stations enabled with sub-6-GHz and mm-wave bands. This technique is novel since it can predict HSR even before switching to 5G radio circuitry or initiating its measurement gap for acquisition of mm-wave reference signal received power (RSRP) unlike conventional algorithms. Thus, preempting all handovers which are likely to fail will provide improvements in latency, delay, and handover success rate, as well as decrease call drops. Therefore, this research work answers previous research shortcomings and can unleash applications of supervised intelligent algorithms for predicting the HSR from 4G to 5G. The proposed algorithm is validated by showing improvements obtained through simulation results performed using Python-based framework. The proposed algorithm is tested for reliability with increasing parameters such as the intensity number of UEs and simulation time. Improvements in standard handover algorithm are also proposed.

4G LTE Experience: Reference Signal Received Power, Noise Ratio and Quality

2021 ◽  
Author(s):  
Gubtha Mahendra Putra ◽  
Edy Budiman ◽  
Yonatan Malewa ◽  
Dedy Cahyadi ◽  
Medi Taruk ◽  
...  
Keyword(s):  
Reference Signal ◽  
Received Power ◽  
Power Noise ◽  
Noise Ratio ◽  
4G Lte

scholarly journals Sign language dactyl recognition based on machine learning algorithms

2021 ◽  
Vol 4 (2(112)) ◽  
pp. 58-72
Author(s):  
Chingiz Kenshimov ◽  
Zholdas Buribayev ◽  
Yedilkhan Amirgaliyev ◽  
Aisulyu Ataniyazova ◽  
Askhat Aitimov
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Sign Language ◽  
Gesture Recognition ◽  
Research Work ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

In the course of our research work, the American, Russian and Turkish sign languages were analyzed. The program of recognition of the Kazakh dactylic sign language with the use of machine learning methods is implemented. A dataset of 5000 images was formed for each gesture, gesture recognition algorithms were applied, such as Random Forest, Support Vector Machine, Extreme Gradient Boosting, while two data types were combined into one database, which caused a change in the architecture of the system as a whole. The quality of the algorithms was also evaluated. The research work was carried out due to the fact that scientific work in the field of developing a system for recognizing the Kazakh language of sign dactyls is currently insufficient for a complete representation of the language. There are specific letters in the Kazakh language, because of the peculiarities of the spelling of the language, problems arise when developing recognition systems for the Kazakh sign language. The results of the work showed that the Support Vector Machine and Extreme Gradient Boosting algorithms are superior in real-time performance, but the Random Forest algorithm has high recognition accuracy. As a result, the accuracy of the classification algorithms was 98.86 % for Random Forest, 98.68 % for Support Vector Machine and 98.54 % for Extreme Gradient Boosting. Also, the evaluation of the quality of the work of classical algorithms has high indicators. The practical significance of this work lies in the fact that scientific research in the field of gesture recognition with the updated alphabet of the Kazakh language has not yet been conducted and the results of this work can be used by other researchers to conduct further research related to the recognition of the Kazakh dactyl sign language, as well as by researchers, engaged in the development of the international sign language

scholarly journals Analisis Performa Reference Signal Received Power Akibat Rugi-Rugi Propagasi Pada Frekuensi 2300 MHz Dengan Model Okumura

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Sandryones Palinggi ◽  
Aan Saputra
Keyword(s):  
Reference Signal ◽  
Received Power ◽  
4G Lte

Teknologi seluler, terutama 4G-LTE, telah berkembang ke arah yang jauh lebih canggih. Teknologi 4G-LTE merupakan kelanjutan dari teknologi sebelumnya yang disebut 3G. Dalam penelitian ini, perhitungan kekuatan sinyal kemudian digunakan untuk menghitung kerugian yang terjadi pada frekuensi 2300 MHz di sepanjang Jalan Cihampelas Bandung, yang memiliki panjang lintasan 2.7 Km. Metode penelitian yang digunakan adalah metode kuantitatif yang menggunakan data primer hasil pengukuran, kemudian membandingkannya dengan metode empiris dalam bentuk perhitungan ideal. Perhitungan di lingkungan outdoor menggunakan Model Okumura dengan mempertimbangkan topologi maupun rugi-rugi propagasi di sepanjang Jalan Cihampelas Bandung seperti Skywalk Cihampelas dan bangunan bertingkat. Hasil penelitian ini dinyatakan dalam bentuk tabel dan grafik dengan menggunakan Matlab sehingga mudah untuk menarik hasil analisis dari penelitian yang dilakukan, yang mana diketahui bahwa rugi-rugi propagasi yang terjadi mempengaruhi Reference Signal Received Power dari jaringan komunikasi seluler berbasis teknologi 4G-LTE.

scholarly journals Predictive Analytics of Chronic Kidney Disease using Machine Learning Algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 940-947
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Predictive Analytics ◽  
Research Work ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Linear Discriminant ◽  
Extreme Gradient Boosting ◽  
Data Points

According to the health statistics of India on Chronic Kidney Disease (CKD) a total of 63538 cases has been registered. Average age of men and women prone to kidney disease lies in the range of 48 to 70 years. CKD is more prevalent among male than among female. India ranks 17th position in CKD during 2015[1]. This paper focus on the predictive analytics architecture to analyse CKD dataset using feature engineering and classification algorithm. The proposed model incorporates techniques to validate the feasibility of the data points used for analysis. The main focus of this research work is to analyze the dataset of chronic kidney failure and perform the classification of CKD and Non CKD cases. The feasibility of the proposed dataset is determined through the Learning curve performance. The features which play a vital role in classification are determined using sequential forward selection algorithm. The training dataset with the selected features is fed into various classifier to determine which classifier plays a vital and accurate role in detection of CKD. The proposed dataset is classified using various Classification algorithms like Linear Regression(LR), Linear Discriminant Analysis(LDA), K-Nearest Neighbour(KNN), Classification and Regression Tree(CART), Naive Bayes(NB), Support Vector Machine(SVM), Random Forest(RF), eXtreme Gradient Boosting(XGBoost) and Ada Boost Regressor (ABR). It was found that for the given CKD dataset with 25 attributes of 11 Numeric and 14 Nominal the following classifier like LR, LDA, CART,NB,RF,XGB and ABR provides an accuracy ranging from 98% to 100% . The proposed architecture validates the dataset against the thumb rule when working with less number of data points used for classification and the classifier is validated against under fit, over fit conditions. The performance of the classifier is evaluated using accuracy and F-Score. The proposed architecture indicates that LR, RF and ABR provides a very high accuracy and F-Score

scholarly journals Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-20
Author(s):  
Nihad Brahimi ◽  
Huaping Zhang ◽  
Lin Dai ◽  
Jianzi Zhang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Research Work ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Models ◽  
Car Sharing ◽  
K Nearest Neighbors ◽  
Extreme Gradient Boosting ◽  
On The Road

The car-sharing system is a popular rental model for cars in shared use. It has become particularly attractive due to its flexibility; that is, the car can be rented and returned anywhere within one of the authorized parking slots. The main objective of this research work is to predict the car usage in parking stations and to investigate the factors that help to improve the prediction. Thus, new strategies can be designed to make more cars on the road and fewer in the parking stations. To achieve that, various machine learning models, namely vector autoregression (VAR), support vector regression (SVR), eXtreme gradient boosting (XGBoost), k-nearest neighbors (kNN), and deep learning models specifically long short-time memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), CNN-LSTM, and multilayer perceptron (MLP), were performed on different kinds of features. These features include the past usage levels, Chongqing’s environmental conditions, and temporal information. After comparing the obtained results using different metrics, we found that CNN-LSTM outperformed other methods to predict the future car usage. Meanwhile, the model using all the different feature categories results in the most precise prediction than any of the models using one feature category at a time

scholarly journals Machine-Learning-Based Prediction of Corrosion Behavior in Additively Manufactured Inconel 718

Data ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 80
Author(s):  
O. V. Mythreyi ◽  
M. Rohith Srinivaas ◽  
Tigga Amit Kumar ◽  
R. Jayaganthan
Keyword(s):  
Machine Learning ◽  
Corrosion Behavior ◽  
Inconel 718 ◽  
Polynomial Regression ◽  
Research Work ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

This research work focuses on machine-learning-assisted prediction of the corrosion behavior of laser-powder-bed-fused (LPBF) and postprocessed Inconel 718. Corrosion testing data of these specimens were collected and fit into the following machine learning algorithms: polynomial regression, support vector regression, decision tree, and extreme gradient boosting. The model performance, after hyperparameter optimization, was evaluated using a set of established metrics: R2, mean absolute error, and root mean square error. Among the algorithms, the extreme gradient boosting algorithm performed best in predicting the corrosion behavior, closely followed by other algorithms. Feature importance analysis was executed in order to determine the postprocessing parameters that influenced the most the corrosion behavior in Inconel 718 manufactured by LPBF.

scholarly journals Optimization of a clamping concept based on machine learning

2021 ◽  
Author(s):  
Qi Feng ◽  
Walther Maier ◽  
Thomas Stehle ◽  
Hans-Christian Möhring
Keyword(s):  
Machine Learning ◽  
Dynamic Stiffness ◽  
Research Work ◽  
Gradient Boosting ◽  
Machining Accuracy ◽  
Fixture Design ◽  
Regression Methods ◽  
Extreme Gradient Boosting ◽  
Novel Method ◽  
Systematic Selection

AbstractFixtures are an important element of the manufacturing system, as they ensure productive and accurate machining of differently shaped workpieces. Regarding the fixture design or the layout of fixture elements, a high static and dynamic stiffness of fixtures is therefore required to ensure the defined position and orientation of workpieces under process loads, e.g. cutting forces. Nowadays, with the increase in computing performance and the development of new algorithms, machine learning (ML) offers an appropriate possibility to use regression methods for creating realistic, rapid and reliable equivalent ML models instead of simulations based on the finite element method (FEM). This research work introduces a novel method that allows an optimization of clamping concepts and fixture design by means of ML, in order to reduce manufacturing errors and to obtain an increased stiffness of fixtures and machining accuracy. This paper describes the preparation of a dataset for training ML models, the systematic selection of the most promising regression algorithm based on relevant criteria, the implementation of the chosen algorithm Extreme Gradient Boosting (XGBoost) and other comparable algorithms, the analysis of their regression results, and the validation of the optimization for a selected clamping concept.

Predicting Undesired Treatment Outcome in Mental Healthcare: Machine Learning Study (Preprint)

2019 ◽  
Author(s):  
Kasper Van Mens ◽  
Joran Lokkerbol ◽  
Richard Janssen ◽  
Robert de Lange ◽  
Bea Tiemens
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Mental Health Treatment ◽  
Mental Healthcare ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Trade Off ◽  
Trade Offs ◽  
Outcome Monitoring ◽  
Extreme Gradient Boosting

BACKGROUND It remains a challenge to predict which treatment will work for which patient in mental healthcare. OBJECTIVE In this study we compare machine algorithms to predict during treatment which patients will not benefit from brief mental health treatment and present trade-offs that must be considered before an algorithm can be used in clinical practice. METHODS Using an anonymized dataset containing routine outcome monitoring data from a mental healthcare organization in the Netherlands (n = 2,655), we applied three machine learning algorithms to predict treatment outcome. The algorithms were internally validated with cross-validation on a training sample (n = 1,860) and externally validated on an unseen test sample (n = 795). RESULTS The performance of the three algorithms did not significantly differ on the test set. With a default classification cut-off at 0.5 predicted probability, the extreme gradient boosting algorithm showed the highest positive predictive value (ppv) of 0.71(0.61 – 0.77) with a sensitivity of 0.35 (0.29 – 0.41) and area under the curve of 0.78. A trade-off can be made between ppv and sensitivity by choosing different cut-off probabilities. With a cut-off at 0.63, the ppv increased to 0.87 and the sensitivity dropped to 0.17. With a cut-off of at 0.38, the ppv decreased to 0.61 and the sensitivity increased to 0.57. CONCLUSIONS Machine learning can be used to predict treatment outcomes based on routine monitoring data.This allows practitioners to choose their own trade-off between being selective and more certain versus inclusive and less certain.

XGBoost and Network Analysis for Prediction of Proteins Affecting Insulin based on Protein Protein Interactions

2020 ◽  
pp. 253-262
Author(s):  
Mohammad Hamim Zajuli Al Faroby ◽  
Mohammad Isa Irawan ◽  
Ni Nyoman Tri Puspaningsih
Keyword(s):  
Protein Interactions ◽  
Interaction Analysis ◽  
Synthesis Process ◽  
Gradient Boosting ◽  
Protein Protein Interactions ◽  
Central Function ◽  
Extreme Gradient Boosting ◽  
Main Protein ◽  
The Right ◽  
Roc Score

Protein Interaction Analysis (PPI) can be used to identify proteins that have a supporting function on the main protein, especially in the synthesis process. Insulin is synthesized by proteins that have the same molecular function covering different but mutually supportive roles. To identify this function, the translation of Gene Ontology (GO) gives certain characteristics to each protein. This study purpose to predict proteins that interact with insulin using the centrality method as a feature extractor and extreme gradient boosting as a classification algorithm. Characteristics using the centralized method produces  features as a central function of protein. Classification results are measured using measurements, precision, recall and ROC scores. Optimizing the model by finding the right parameters produces an accuracy of  and a ROC score of . The prediction model produced by XGBoost has capabilities above the average of other machine learning methods.

scholarly journals Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China

2021 ◽  
Vol 13 (5) ◽  
pp. 1021
Author(s):  
Hu Ding ◽  
Jiaming Na ◽  
Shangjing Jiang ◽  
Jie Zhu ◽  
Kai Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Loess Plateau ◽  
Water Conservation ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
The Loess Plateau ◽  
Object Based ◽  
Extreme Gradient Boosting

Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. As a result of the importance of the contextual information for terrace mapping, object-based image analysis (OBIA) combined with machine learning (ML) technologies are widely used. However, the selection of an appropriate classifier is of great importance for the terrace mapping task. In this study, the performance of an integrated framework using OBIA and ML for terrace mapping was tested. A catchment, Zhifanggou, in the Loess Plateau, China, was used as the study area. First, optimized image segmentation was conducted. Then, features from the DEMs and imagery were extracted, and the correlations between the features were analyzed and ranked for classification. Finally, three different commonly-used ML classifiers, namely, extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN), were used for terrace mapping. The comparison with the ground truth, as delineated by field survey, indicated that random forest performed best, with a 95.60% overall accuracy (followed by 94.16% and 92.33% for XGBoost and KNN, respectively). The influence of class imbalance and feature selection is discussed. This work provides a credible framework for mapping artificial terraces.

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