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Multi-Channel Nonlinearity Mitigation Using machine learning algorithms

IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第4期23卷 2535-2550页

作者： Zhao, Haotian Diaz, Julian Camilo Gomez Hoyos, Sebastian Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

This paper investigates multi-channel machine learning (ML) techniques in the presence of receiver nonlinearities and noise, and compares the results with the single-channel receiver architecture. It is known that the multi-channel architecture relaxes the sampling speed requirement of analog to digital conversion and provides significant robustness to clock jitter and front-end noise due to the bandwidth-splitting property inherent in these receivers. However, when a high-voltage swing signal is used in a wireline communication link, the received signal suffers from third-order harmonic distortions and inter-modulation products caused by the nonlinearity profile of the analog front-end (AFE). To this end, this paper proposes the channel decision passing (CDP) algorithm in combination with nonlinear feedback cancellation as a low-complexity candidate for nonlinearity mitigation and compares the performance of this solution with other well-known ML algorithms. Simulation results show significant improvement in a multi-channel receiver architecture equipped with nonlinear feedback cancellation and CDP in comparison with its single-channel counterpart under practical nonlinearity profiles and noise conditions.

关键词： Receivers Clustering algorithms Classification algorithms machine learning algorithms Bandwidth Supervised learning Unsupervised learning Multi-channel receiver nonlinearities machine learning supervised learning unsupervised learning reinforcement learning

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Modeling wetland aboveground biomass in the Poyang Lake National Nature Reserve using machine learning algorithms and Landsat-8 imagery

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JOURNAL OF APPLIED REMOTE SENSING 2018年第4期12卷 046029-1-046029-16页

作者： Wan, Rongrong Wang, Peng Wang, Xiaolong Yao, Xin Dai, Xue Chinese Acad Sci Nanjing Inst Geog & Limnol Key Lab Watershed Geog Sci Nanjing Jiangsu Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing Peoples R China Nanjing Univ Informat Sci & Technol Sch Geog Sci Nanjing Jiangsu Peoples R China

Quantitative estimation of wetland aboveground biomass (AGB) is an essential aspect in evaluating the health and conservation of this valuable ecosystem. We combine AGB field measurements and remote sensing data to establish a suitable model for estimating wetland AGB in the Poyang Lake National Nature Reserve (PLNNR), which is included in the Ramsar Convention's List of Wetlands of International Importance. All field sampling points cover four dominant vegetation communities (Carex cinerascen, Phalaris arundinacea, Artemisia selengensis, and Miscanthus sacchariflorus) in the PLNNR. Wetland AGB is retrieved from the Landsat-8 OLI image. To improve the accuracy of wetland AGB estimation, we compare the performances of three machine learning algorithms, namely, random forest (RF), back-propagation neural network (BPNN), and support vector regression (SVR), with linear regression (LR) in estimating the AGB in the PLNNR. Results are as follows: (1) the RF model with a root-mean-square error of 0.25 kg m(-2) performs better than BPNN (0.29 kg m(-2)), SVR (0.27 kg m(-2)), and LR (0.31 kg m(-2)) in our testing dataset, and AGB density in the PLNNR is between 0 and 1.973 kg m(-2). (2) The four most important features for AGB modeling are near-infrared, short-wave infrared 1 band, enhanced vegetation index, and red band. Our study presents an effective and operational RF model that estimates wetland AGB from Landsat data, providing a scientific basis for floodplain wetland carbon accounting and possible future studies, such as the linkage between wetland AGB and the great water level fluctuations. (C) The Authors.

关键词： aboveground biomass machine learning algorithms random forests Landsat-8 OLI image Poyang Lake National Nature Reserve

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Analysis of Quantum machine learning algorithms in Noisy Channels for Classification Tasks in the IoT Extreme Environment

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IEEE INTERNET OF THINGS JOURNAL 2024年第3期11卷 3840-3852页

作者： Satpathy, Sritam Kumar Vibhu, Vallabh Behera, Bikash K. Al-Kuwari, Saif Mumtaz, Shahid Farouk, Ahmed Indian Inst Sci Educ & Res Dept Phys Sci Berhampur 760010 India Bikashs Quantum Private Ltd Quantum Comp Mohanpur 741246 India Hamad Bin Khalifa Univ Qatar Fdn Coll Sci & Engn Doha Qatar Silesian Tech Univ Dept Appl Informat PL-44100 Gliwice Poland Nottingham Trent Univ Engn Dept Nottingham NG1 4FQ England South Valley Univ Fac Comp & Artificial Intelligence Dept Comp Sci Hurghada 83523 Egypt

By 2050, there will be a 50% rise in energy demand,and existing natural and renewable resources will be under extreme scrutiny. Optimizing current power generation and transmission to reduce energy consumption, cost, and other factors is equally vital to upgrading methods for effectively har-vesting renewable energy. However, it gets more challenging for conventional computers to perform optimization as the number of factors affecting power generation and transmission *** environmental cases will consequently lead to the imper-fect functioning of Internet of Things (IoT) systems. By utilizing quantum-mechanical properties, such as superposition and entan-glement, quantum computers can computationally out perform classical computers while consuming much less energy. In thisarticle, we investigate various quantum machine learning algo-rithms on two data sets (TWTDUS and SDWTT18) related toIoT extreme environment and study the effect of a noisy quan-tum environment. We observe that for the TWTDUS data set, thevariational UU dagger with analytical clustering methods achieves thehighest accuracy of 98.10%. Similarly, for the SDWTT18 data set,theUU dagger method withk-means clustering achieves an accuracyof 94.43%. The results show that the accuracy of the proposedquantum algorithms outperforms the existing classical methodsand can be utilized to forecast output power generation daily bymeasuring the metrics required in energy sector decision-makingsituations. This will be useful to save energy and costs in an IoT-extreme environment, where energy organizations must decideinstantly whether to start or stop generating units.

关键词： Quantum computing Internet of Things Computers Optimization Costs Power generation machine learning algorithms Internet of Things (IoT) extreme environment quantum energy utilization quantum neural network (QNN) UU dagger method variational UU dagger method

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Selective Detection of VOCs With WO₃ Nanoplates-Based Single Chemiresistive Sensor Device Using machine learning algorithms

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IEEE SENSORS JOURNAL 2021年第5期21卷 5771-5778页

作者： Acharyya, Snehanjan Nag, Sudip Guha, Prasanta Kumar Indian Inst Technol Kharagpur Adv Technol Dev Ctr Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

The paper presents the integration of single metal-oxide based chemiresistive sensor device and machine learning tools for selective discrimination of different volatile organic compounds (VOCs) for indoor air quality monitoring applications. Tungsten oxide (WO3) nanoplates has been employed as the gas sensing material which were obtained by acidification followed by low temperature hydrothermal process. Synthesized WO3 nanoplate structure was confirmed by different characterization tools explaining surface morphology and structural properties. The sensor device was fabricated by using a simple drop coating technique on top of aluminum based interdigitated electrodes. An extensive gas sensing study was carried out where adequate sensor response was observed for each target VOC. The sensing mechanism has been discussed to realize the behavior of the sensor towards the introduction of target VOCs. Collective data obtained from the sensor device were engaged with machine learning algorithms (best results shown by multilayer perceptron) to discriminate the target VOCs accurately. Furthermore, concentrations of tested VOCs were predicted in a quantitative manner using a regression model with fair accuracy.

关键词： Sensors Gas detectors Prediction algorithms Temperature sensors machine learning algorithms Classification algorithms Chemical sensors Indoor air quality volatile organic compound WO₃ nanoplates hydrothermal method selectivity machine learning algorithm

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Comparative Analysis of machine learning algorithms for CKD Risk Prediction

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IEEE ACCESS 2024年 12卷 171205-171220页

作者： Yang, Weilin Ahmed, Nasim Barczak, Andre L. C. Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia Bond Univ Ctr Data Analyt Gold Coast Qld 4226 Australia

Chronic Kidney Disease (CKD) remains a significant global health challenge, with increasing prevalence and a substantial impact on patient quality of life. Early and accurate prediction of CKD risk is crucial for timely intervention and management. This study presents a comprehensive comparative analysis of both machine learning and deep learning algorithms applied to predict CKD risk. The research involved the application of eight traditional machine learning algorithms: Naive Bayes, K-nearest Neighbors, Decision Tree, Random Forest, Support Vector machine, Logistic Regression, AdaBoost, and XGBoost, each implemented on a CKD dataset retrieved from the UCI data repository. Furthermore, three neural network-based algorithms, Artificial Neural Network (ANN), Simple Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) were used to compare to the traditional algorithms. This comparative study not only assessed each algorithm's performance in terms of accuracy, precision, recall, and F1 score but also examined their computational efficiency and applicability in real-world clinical settings. All eleven algorithms were trained with three versions of the dataset. The first version kept the original unbalance between classes and used KNN imputation to fill up missing values (unbalanced). The second dataset used SMOTENC to create new samples to balance the dataset (balanced). The third dataset used feature selection to choose 14 features from the original 24. The results showed that there is almost no performance difference among the classifiers produced with the balanced, unbalanced and feature selection datasets. This means that the best algorithms for this task are the ones with short training and testing runtime, namely RF, SVM, AdaBoost and XGBoost. The experiments also showed that the neural network-based algorithms had no performance advantage and were slower to train due to the small size of samples available in the original dataset.

关键词： Chronic kidney disease Predictive models Accuracy Random forests machine learning algorithms machine learning Prediction algorithms Nearest neighbor methods Support vector machines Classification algorithms machine learning deep learning risk prediction healthcare analytics

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Plastic properties determination using virtual dynamic spherical indentation test and machine learning algorithms

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2022年第1期36卷 325-331页

作者： Kashfi, Mohammad Goodarzi, Sepehr Rastgou, Mostafa Ayatollah Boroujerdi Univ Engn Fac Mech Engn Dept Boroujerd Iran Bu Ali Sina Univ Fac Agr Dept Soil Sci Hamadan Hamadan Iran

Nondestructive experiments are widely employed for determining mechanical properties. In this work, a spherical impact test is virtually performed to determine the plastic properties of a thick aluminum plate. The finite element model is validated experimentally, and several simulations are then performed in accordance with the design of the experiment program. Several algorithms, including support vector machine, Gaussian process regression (GPR), and nonlinear regressions (second- and third-order polynomials) as machine learning techniques, are employed to estimate the material plastic properties. The indentation depth and indentation radius are considered as input variables to predict the tangent modulus (TM) and yield stress (YS). Results reveal that the second-order polynomial and GPR methods realize better performance in terms of determination coefficient and root mean square error criteria in assessing YS.

关键词： Finite element analysis Spherical indentation test machine learning algorithms Mechanical properties

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Remote Sensing of Turbidity for Lakes in Northeast China Using Sentinel-2 Images With machine learning algorithms

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 9132-9146页

作者： Ma, Yue Song, Kaishan Wen, Zhidan Liu, Ge Shang, Yingxin Lyu, Lili Du, Jia Yang, Qian Li, Sijia Tao, Hui Hou, Junbin Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130102 Jilin Peoples R China Jilin Jianzhu Univ Sch Geomat & Prospecting Engn Changchun 130118 Jilin Peoples R China

Monitoring water quality of inland lakes and reservoirs is a great concern for the public and government in China. Water turbidity is a reliable and direct indicator that can reflect the water quality. Remote sensing has become an efficient technology for monitoring large-scale water turbidity. This study aims to search an optimal regression model to accurately predict water turbidity using remote sensing data. To achieve this goal, 187 water samples were collected from field campaigns across Northeast China, in 2018, of which the samples were gathered within +/- 6 days of Sentinel-2 overpasses. The spectral reflectance data was used as independent variables for modeling. The simple regression, partial least squares regression, support vector regression, extreme learning machine, back-propagation neural network, classification and regression tree, gradient boosting decision tree (GBDT), random forest (RF), and K-nearest neighbor were used to compare. From model validation, we identified GBDT as the best regression model (R-2 = 0.88, RMSE = 9.90 NTU, MAE = 6.71 NTU). We applied GBDT to retrieve the water turbidity and obtained a satisfactory result. Feature selection technique from tree-based ensemble method was also tested. We selected B2, B3, B4, and B5 as the important variables because of their high ability to explain the variation of turbidity. These results demonstrated the significance of using a promising method to retrieve water turbidity using Sentinel-2 imagery at the regional scale. It is beneficial to monitor the spatial-temporal distribution of water turbidity;support water quality management and inland water environment protection.

关键词： Lakes Reservoirs Water quality Remote sensing Reflectivity Monitoring machine learning machine learning algorithms remote estimation Sentinel-2 water turbidity

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PREDICTION OF DRIVER'S DROWSINESS USING machine learning algorithms FOR MINIMAL RISK CONDITION

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2022年第4期23卷 917-926页

作者： Nam, Deok Ho Kim, Gyeong Pil Baek, Keon Hee Lee, Da Som Lee, Ho Yong Suh, Myung Won Sungkyunkwan Univ Grad Sch Mech Engn Gyeonggi 16419 South Korea Korea Railrd Res Inst Urban Railrd Res Dept 176 Railrd Museum Ro Uiwang Si 16105 Gyeonggi South Korea Sungkyunkwan Univ Dept Mech Engn Gyeonggi 16419 South Korea

Use of an Automated Driving System is expected to improve traffic safety by protecting drivers from drowsy driving. Previous studies on the use of Automated Driving Systems mainly focused on detecting a driver's level of drowsiness and protecting drivers from accidents by performing fallback maneuvers. However, maneuvers conducted in drowsy states are limited in their ability to achieve Minimal Risk Conditions because human drivers show a gradual degradation in their driving ability as they fall asleep and the probability of an accident increases greatly after a driver becomes drowsy. Thus, current Automated Driving Systems require algorithms to predict drowsiness and perform maneuvers before the driver becomes too drowsy. This paper suggests an algorithm that not only detects but also predicts driver drowsiness using 6 vehicle data points. Driver condition is classified into 4 states and Driver drowsiness can be predicted by detecting the severe fatigue state, which tends to occur one minute before the drowsy state. The vehicle driving data are collected using a simulator and features that can be used to distinguish between the 4 states are investigated through data analysis. Ultimately, an optimum machine learning algorithm that can predict driver drowsiness is developed using the investigated factors.

关键词： Driver drowsiness Minimal risk condition Light fatigue Severe fatigue Vehicle driving data machine learning algorithms

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Survival Analysis of Thyroid Cancer Patients Using machine learning algorithms

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IEEE ACCESS 2024年 12卷 61978-61990页

作者： Alhashmi, Saadat M. Polash, M. D. Shohidul Islam Haque, Aminul Rabbe, Fazley Hossen, Shazzad Faruqui, Nuruzzaman Hashem, Ibrahim Abaker Targio Fathima Abubacker, Nirase Univ Sharjah Dept Informat Syst Sharjah U Arab Emirates Daffodil Int Univ Dept Comp Sci & Engn Dhaka 1216 Bangladesh Frankfurt Univ Appl Sci Dept Informat Technol D-60318 Frankfurt am Main Germany Daffodil Int Univ Dept Software Engn Dhaka 1216 Bangladesh Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates Asia Pacific Univ Sch Comp Kuala Lumpur 57000 Malaysia

The medical community strives continually to improve the quality of care patients receive. Predictions of prognosis are essential for doctors and patients to choose a course of treatment. Recent years have witnessed the development of numerous new cancer survival prediction models. Most attempts to predict the prognosis of people with malignant growth rely on classification techniques. We could experiment with significantly different results using only a subset of SEER (Surveillance, Epidemiology, and End Results) data. These models were created using machine learning techniques by selecting univariate features and calculating correlations. We illustrated the variation in results and discrepancy of impurity that can result from varying data quantities and critical factors. Seventeen crucial factors were identified, and a group of classification algorithms were trained to evaluate the effectiveness of an estimation technique. In the display mode, the accuracy of these computations ranges from 97% to 99% Along with accuracy, the models are further evaluated regarding the F1 score, precision, recall, and the AUC score. Compared to earlier studies, a more accurate model has been developed, and, to the best of our knowledge, our prediction model is superior to the models studied in the previous works.

关键词： Thyroid cancer Predictive models Encoding Random forests machine learning algorithms Medical treatment Patient monitoring Logistic regression Life estimation machine learning random forest thyroid survivability

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Annotating mobile phone location data with activity purposes using machine learning algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第8期40卷 3299-3311页

作者： Liu, Feng Janssens, Davy Wets, Geert Cools, Mario Hasselt Univ Transportat Res Inst IMOB B-3590 Diepenbeek Belgium TLU C B-4000 Liege Belgium

Individual human travel patterns captured by mobile phone data have been quantitatively characterized by mathematical models, but the underlying activities which initiate the movement are still in a less-explored stage. As a result of the nature of how activity and related travel decisions are made in daily life, human activity-travel behavior exhibits a high degree of spatial and temporal regularities as well as sequential ordering. In this study, we investigate to what extent the behavioral routines could reveal the activities being performed at mobile phone call locations that are captured when users initiate or receive a voice call or message. Our exploration consists of four steps. First, we define a set of comprehensive temporal variables characterizing each call location. Feature selection techniques are then applied to choose the most effective variables in the second step. Next, a set of state-of-the-art machine learning algorithms including Support Vector machines, Logistic Regression, Decision Trees and Random Forests are employed to build classification models. Alongside, an ensemble of the results of the above models is also tested. Finally, the inference performance is further enhanced by a post-processing algorithm. Using data collected from natural mobile phone communication patterns of 80 users over a period of more than one year, we evaluated our approach via a set of extensive experiments. Based on the ensemble of the models, we achieved prediction accuracy of 69.7%. Furthermore, using the post processing algorithm, the performance obtained a 7.6% improvement. The experiment results demonstrate the potential to annotate mobile phone locations based on the integration of data mining techniques with the characteristics of underlying activity-travel behavior, contributing towards the semantic comprehension and further application of the massive data. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Activity-travel behavior Sequential information machine learning algorithms Feature selection techniques Mobile phone location annotation

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