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A novel approach for Parkinson's disease detection using Vold-Kalman order filtering and machine learning algorithms

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NEURAL COMPUTING & APPLICATIONS 2024年第16期36卷 9297-9311页

作者： Latifoglu, Fatma Penekli, Sultan Orhanbulucu, Firat Chowdhury, Muhammad E. H. Erciyes Univ Dept Biomed Engn Kayseri Turkiye Inonu Univ Dept Biomed Engn Malatya Turkiye Qatar Univ Dept Elect Engn Doha Qatar

Parkinson's disease (PD) is the second most common neurological disorder caused by damage to dopaminergic neurons. Therefore, it is important to develop systems for early and automatic diagnosis of PD. For this purpose, a study that will contribute to the development of systems for the automatic diagnosis of PD is presented. The Electroencephalography (EEG) signals were decomposed into sub-bands using adaptive decomposition methods, such as empirical mode decomposition, variational mode decomposition, and Vold-Kalman order filtering (VKF). Various features were extracted from the sub-band decomposed signals, and the significant ones were determined by Chi-squared test. These important features were applied as input to support vector machine (SVM), fitch neural network (FNN), k-nearest neighbours (KNN), and decision trees (DT), machine learning (ML) models and classification was performed. We analysed the performance of ML models by obtaining accuracy, sensitivity, specificity, positive predictive value, negative predictive values, F1-score, false-positive rate, kappa statistics, and area under the curve. The classification process was performed for two cases: PD ON-HC and PD OFF-HC groups. The most successful method in this study was the VKF method, which was applied for the first time in this field with the approach specified for both cases. In both instances, the SVM algorithm was employed as the ML model, with classifier performance criterion values close to 100%. The results obtained in this study seem to be successful compared to the results of recent research on the diagnosis of PD.

关键词： Parkinson's disease classification Electroencephalography signals Vold-Kalman order filtering machine learning algorithms

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Detailed analysis of Türkiye's agricultural biomass-based energy potential with machine learning algorithms based on environmental and climatic conditions

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CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2024年第12期26卷 4177-4195页

作者： Pence, I. Kumas, K. Cesmeli, M. Siseci Akyuz, A. Burdur Mehmet Akif Ersoy Univ Bucak Technol Fac TR-15300 Burdur Turkiye Burdur Mehmet Akif Ersoy Univ Bucak Emin Gulmez Vocat Sch Tech Sci TR-15300 Burdur Turkiye

In the study, the biomass and energy potential of each province of Turkiye was calculated for the years 2010-2021, using data from 15 different fields and 16 different horticultural crops. The total theoretical energy potential obtained from field and garden products was calculated as 222,620 Terajoule (TJ) and 61,737 TJ for 2010 and 308,888 TJ and 77,002 TJ for 2021, respectively. The agricultural biomass potential for 2021 was estimated using machine learning algorithms, depending on the environmental and climate data covering 2010-2020, which has not been studied in the literature. In this study, agricultural biomass potential for Turkiye was tried to be modeled by using Random Forest, K-Nearest Neighbors (KNN), Gradient Boosting, and eXtreme Gradient Boosting Regressor (XGBR) from machine learning methods. Agricultural biomass potential was tested in a tenfold cross-validation analysis and prediction for 2021 using only climatic and agricultural area data. In addition, by applying feature selection, it has been tried to reduce the features to be used and increase the success rate. Accordingly, when the results of the Random Forest algorithm were generalized, it achieved an R2 value of 0.9328 using all features for the tenfold cross-validation analysis. At the same time, it reached an R2 value of 0.9434 using four features in the prediction of 2021 and was found to be successful. Considering only the 2021 forecast, the KNN algorithm reached the highest result with an R2 value of 0.9560 using only four features. Also, the Wilcoxon rank-sum test result at p = 0.05 shows no significant difference between the predictions and the actual values.

关键词： Biomass Energy potential Climate data machine learning algorithms

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Quantification of the concrete freeze–thaw environment across the Qinghai–Tibet Plateau based on machine learning algorithms

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Journal of Mountain Science 2024年第1期21卷 322-334页

作者： QIN Yanhui MA Haoyuan ZHANG Lele YIN Jinshuai ZHENG Xionghui LI Shuo Department of Civil Engineering Qingdao University of TechnologyQingdao 266520China Department of Geography Qinghai Normal UniversityXining 810008China

The reasonable quantification of the concrete freezing environment on the Qinghai–Tibet Plateau(QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering managers should take into account. In this paper, we propose a more realistic method to calculate the number of concrete freeze–thaw cycles(NFTCs) on the QTP. The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7. Four machine learning methods, i.e., the random forest(RF) model, generalized boosting method(GBM), generalized linear model(GLM), and generalized additive model(GAM), are used to fit the NFTCs. The root mean square error(RMSE) values of the RF, GBM, GLM, and GAM are 32.3, 4.3, 247.9, and 161.3, respectively. The R^(2) values of the RF, GBM, GLM, and GAM are 0.93, 0.99, 0.48, and 0.66, respectively. The GBM method performs the best compared to the other three methods, which was shown by the results of RMSE and R^(2) values. The quantitative results from the GBM method indicate that the lowest, medium, and highest NFTC values are distributed in the northern, central, and southern parts of the QTP, respectively. The annual NFTCs in the QTP region are mainly concentrated at 160 and above, and the average NFTCs is 200 across the QTP. Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP.

关键词： Freeze–thaw cycles Quantification machine learning algorithms Qinghai–Tibet Plateau Concrete

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Comparison of diverse machine learning algorithms for forest fire susceptibility mapping in Antalya, Turkiye

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ADVANCES IN SPACE RESEARCH 2024年第2期74卷 647-667页

作者： Akinci, Hazan Alkan Akinci, Halil Zeybek, Mustafa Artvin Coruh Univ Fac Forestry Dept Forestry Engn TR-08100 Artvin Turkiye Artvin Coruh Univ Fac Engn Dept Geomatics Engn TR-08100 Artvin Turkiye Selcuk Univ Guneysinir Vocat Sch Konya Turkiye

Antalya is one of the provinces with the highest number of forest fires in Turkiye. In 2021, 278 forest fires occurred within the administrative boundaries of Antalya Regional Directorate of Forestry. The main objective of this study is to produce forest fire susceptibility (FFS) maps of Antalya province using machine learning (ML) models. In addition to forest fire inventory data, 16 factors, including topographic, environmental, meteorological, and human-driven, were used in the study. Inventory data included 2166 fire ignition points from the General Directorate of Forestry. 70 % of the inventory dataset was used to train the ML models and 30 % to validate the models. Overall accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) approaches were considered as validation metrics. FFS maps of Antalya were produced using stand-alone ML algorithms, K-Nearest Neighbors, and Support Vector machines, as well as tree-based Conditional Inference Trees (CTREE), Random Forest (RF), Gradient Boosting machines (GBM), and Extreme Gradient Boosting (XGBoost) algorithms. To the best of our knowledge, this is the first study using the CTREE algorithm for forest fire susceptibility mapping. Therefore, this study is important for the related literature. The validation results revealed that the XGBoost model outperformed other models. It is thought that the FFS map produced using the XGBoost model will guide forest engineers, wildland firefighting teams, and firefighters to minimize damage and control forest fires. (c) 2024 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Forest fire Susceptibility mapping machine learning algorithms GIS Antalya

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Comprehensive evaluation of machine learning algorithms for flood susceptibility mapping in Wardha River sub-basin, India

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ACTA GEOPHYSICA 2024年第3期73卷 2725-2748页

作者： Sharma, Asheesh Nerkar, Sudhanshu Banyal, Rishit Poonia, Mandeep Kadaverugu, Rakesh Damahe, Lalit Tuegel, Franziska Holzbecher, Ekkehard Hinkelmann, Reinhard CSIR Natl Environm Engn Res Inst CSIR NEERI Nehru Marg Nagpur 440020 Maharashtra India Tech Univ Berlin TU Berlin Fac Water Resources Management & Modeling Hydrosys Gustav Meyer Alee 25Sec TIB1-B14 D-13355 Berlin Germany CSIR NEERI Nagpur India Yashwantrao Chavan Coll Engn Head Dept Comp Sci & Engn Nagpur India Univ Twente Fac Geoinformat Sci & Earth Observat ITC Dept Water Resources Drienerlolaan 5 NL-7522 NB Enschede Netherlands Tech Univ Berlin TU Berlin Fac Engn Technol Dept Water Engn & Management Gustav Meyer Allee 25Sec TIB1 B14 D-13355 Berlin Germany German Univ Technol Oman GUtech Dept Appl Geosci POB 1816 Muscat 130 Oman Tech Univ Berlin TU Berlin Chair Water Resources Management & Modeling Hydros Gustav Meyer Allee 25Sec TIB1 B14 D-13355 Berlin Germany

machine learning offers a powerful and versatile approach to flood susceptibility mapping, enabling us to leverage complex data and improve prediction accuracy. Given the plethora of available techniques and the challenges in selecting the optimal approach, this study investigates prominent ML algorithms for flood susceptibility mapping (FSM) in the Wardha River sub-basin, India. Seven machine learning algorithms, viz. support vector machine (SVM), extreme gradient boosting (XGB), artificial neural network (ANN), generalized linear model (GLM), gradient boosting machine (GBM), random forest (RF), and linear discriminant analysis (LDA), were evaluated at varying spatial resolutions (30 m, 50 m, 100 m, and 200 m). Seven flood-inducing factors (elevation, flow accumulation, topographic wetness index, slope, rainfall, land use, and drain density) were considered. Model performance was assessed using sensitivity, specificity, area under the curve (AUC), overall correlation, overall standard deviation ratio, and overall root mean square difference (RMSD). The impact of spatial resolution on models' accuracy was analysed. SVM, GBM, and RF were significantly affected, while ANN, GLM, and XGB were less sensitive. LDA excelled in execution time and spatial resolution resilience. The overall ranking of models was executed based on their accuracy, AUC, and execution time. XGB outperformed GBM and RF, securing first place, while SVM ranked last. GLM, ANN, and LDA ranked third to fifth. The results highlighted the importance of algorithm selection in accurately mapping flood susceptibility, particularly when working with varying spatial resolution data. The study findings can inform the decision-making process for implementing FSM using these machine learning algorithms.

关键词： Flood susceptibility mapping machine learning algorithms Flood-contributing factor Spatial resolution Execution time

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Utilizing machine learning algorithms for prediction of the rheological behavior of ZnO (50%)-MWCNTs (50%)/ Ethylene glycol (20%)-water (80%) nano-refrigerant

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2024年 156卷

作者： Song, Xiedong Baghoolizadeh, Mohammadreza Alizadeh, As'ad Basem, Ali Jasim, Dheyaa J. Sultan, Abbas J. Salahshour, Soheil Piromradian, Mostafa JiNing Univ Sch Comp Sci & Engn JiNing 273155 Peoples R China Inner Mongolia Univ Finance & Econ Sch Comp Informat Management Hohhot 010000 Peoples R China Shahrekord Univ Dept Mech Engn Shahrekord *** Iran Cihan Univ Erbil Coll Engn Dept Civil Engn Erbil Iraq Al Amarah Univ Coll Dept Petr Engn Maysan Iraq Warith Al Anbiyaa Univ Fac Engn Karbala 56001 Iraq Univ Technol Iraq Dept Chem Engn Baghdad Iraq Missouri Univ Sci & Technol Dept Chem & Biochem Engn Rolla MO 65409 USA Istanbul Okan Univ Fac Engn & Nat Sci Istanbul Turkiye Bahcesehir Univ Fac Engn & Nat Sci Istanbul Turkiye Lebanese Amer Univ Dept Comp Sci & Math Beirut Lebanon Islamic Azad Univ Dept Mech Engn Khomeinishahr Branch Khomeinishahr Iran

This paper aims to explore the utilization of machine learning techniques for the accurate prediction of rheological properties in a specific nanofluid system, ZnO(50 %)-MWCNTs (50 %)/Ethylene glycol (20 %)-water (80 %), designed for nano-refrigeration applications. The effective manipulation of the rheological behavior of nanofluids is pivotal for enhancing their heat transfer efficiency and overall performance. By harnessing the predictive power of machine learning, this study endeavors to unravel the intricate relationships governing the rheological characteristics of the nano-refrigerant, ultimately contributing to the development of advanced cooling solutions. The obtained results show that pnf of ZnO(50%)-MWCNTs (50%)/ Ethylene glycol(20%)-water (80%) nano-refrigerant is little affected by T, and even when T varies, this result does not alter much. Also, the lowest pnf occurs when it has the highest temperature and the lowest gamma and m. Finally, it was concluded that the best algorithm in terms of the Taylor diagram for pnf output is the MPR algorithm and the worst is the ECR algorithm and the pattern of gamma changes shows that the ideal value of gamma is the biggest when pnf levels fall in tandem with their growth.

关键词： machine learning algorithms Rheological behavior Nano -refrigerant

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Hospital Length-of-Stay Prediction Using machine learning algorithms-A Literature Review

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10523页

作者： Almeida, Guilherme Correia, Fernanda Brito Borges, Ana Rosa Bernardino, Jorge Polytech Univ Coimbra Coimbra Inst Engn ISEC Rua Misericordia P-3045093 Coimbra Portugal Univ Coimbra Ctr Informat & Syst Polo 2 P-3030290 Coimbra Portugal

Predicting hospital length of stay is critical for efficient hospital management, enabling proactive resource allocation, the optimization of bed availability, and optimal patient care. This paper explores the potential of machine learning algorithms to revolutionize hospital length-of-stay predictions, contributing to healthcare efficiency and patient care. The main objective is to identify the most effective machine learning algorithm for building a predictive model capable of predicting hospital length of stay. The bibliographic search of the existing literature on machine learning algorithms applied to hospital length of stay predictions highlighted the most relevant papers within this area of research. The papers were analyzed in terms of model types and metrics that contributed to the considerable impact on healthcare decision making. We also discuss the challenges and limitations of machine learning algorithms for predicting length of stay, and the importance of data quality and ethical considerations.

关键词： hospital length of stay prediction models machine learning algorithms

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Comparative Analysis of machine learning algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

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IEEE ACCESS 2024年 12卷 18939-18956页

作者： Yuliana, Hajiar Iskandar Hendrawan Inst Teknol Bandung Sch Elect Engn & Informat Bandung 40132 Indonesia

5G technology is a key factor in delivering faster and more reliable wireless connectivity. One crucial aspect in 5G network planning is coverage prediction, which enables network providers to optimize infrastructure deployment and deliver high-quality services to customers. This study conducts a comprehensive analysis of machine learning algorithms for 5G coverage prediction, focusing on dominant feature parameters and accuracy. Notably, the Random Forest algorithm demonstrates superior performance with an RMSE of 1.14 dB, MAE of 0.12, and R2 of 0.97. The CNN model, the standout among deep learning algorithms, achieves an RMSE of 0.289, MAE of 0.289, and R2 of 0.78, showcasing high accuracy in 5G coverage prediction. Random Forest models exhibit near-perfect metrics with 98.4% accuracy, precision, recall, and F1-score. Although CNN outperforms other deep learning models, it slightly trails Random Forest in performance. The research highlights that the final Random Forest and CNN models outperform other models and surpass those developed in previous studies. Notably, 2D Distance Tx Rx emerges as the most dominant feature parameter across all algorithms, significantly influencing 5G coverage prediction. The inclusion of horizontal and vertical distances further improves prediction results, surpassing previous studies. The study underscores the relevance of machine learning and deep learning algorithms in predicting 5G coverage and recommends their use in network development and optimization. In conclusion, while the Random Forest algorithm stands out as the optimal choice for 5G coverage prediction, deep learning algorithms, particularly CNN, offer viable alternatives, especially for spatial data derived from satellite images. These accurate predictions facilitate efficient resource allocation by network providers, ensuring high-quality services in the rapidly evolving landscape of 5G technology. A profound understanding of coverage prediction remains pivotal for suc

关键词： machine learning algorithms Prediction algorithms 5G mobile communication Deep learning machine learning Predictive models Classification algorithms Performance evaluation 5G coverage prediction classifier algorithm deep learning performance evaluation feature importance

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Real-time tool condition monitoring with the internet of things and machine learning algorithms

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2024年

作者： Mohanraj, T. Bharath, R. Sai Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Mech Engn Coimbatore India

machine learning and the Internet of Things (IoT) has an essential role in machine health monitoring and maintenance. An intelligent system that can predict the tool condition is created by collecting data from various sensors. With the rich insights derived from the collected data and integrated through the IoT system, predictive analytics is gaining prominence in Tool Condition Monitoring systems (TCMs). The research reported in this paper uses an accelerometer sensor and a microphone positioned on the spindle head to collect vibration and sound signals associated with eight different tool conditions. Then, the statistical and wavelet features are extracted from the collected signals. The T-test is employed to identify the dominant features. These dominant features are used to train the various machine learning classifiers to predict the tool condition. Finally, the statistical features from sound and vibration are combined and trained to explore sensor fusion techniques. The Ensemble Trees is selected as the best-performing algorithm for online IoT-based TCMs. This model predicts tool conditions with an accuracy of 98% and provides alert messages and indications through Thingspeak. By integrating multiple sensors and employing advanced analytical techniques, this approach aims to improve the accuracy and effectiveness of TCMs, enabling proactive maintenance and optimization of tool usage.

关键词： CNC milling TCMs IoT machine learning algorithms sensor fusion

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Experimental Evaluation of Spectrum Handoff Management with machine learning algorithms Using Software Defined Radio

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WIRELESS PERSONAL COMMUNICATIONS 2024年第1期138卷 149-170页

作者： Babjan, Patan Rajendran, V. Vels Inst Sci Technol & Adv Studies VISTAS Elect & Commun Engn Chennai India

Although the design of spectrum switching has been studied, little is known about how random user movement affects the handoff. This issue can occur when a user moves to a new location. In this paper, the authors present a framework that verifies the necessity of spectrum handoff to improve the performance of the system by employing machine learning (ML) techniques. Some of these include the Logistic Regression, KNN Algorithm, SVM Algorithm, Na & iuml;ve Bayes Classifier, Decision Tree Classification and Random Forest Algorithm. The system is implemented on a real-time dataset where all the users are separated in power domain using the concept of non-orthogonal multiple access (NOMA) technique. The dataset values are prepared using a software-defined radio experimental setup, which is used to analyse the performance of various ML techniques in terms of confusion matrix, specificity, precision, F1_score, sensitivity and accuracy. The performance of proposed system is compared with the literature and shown a significant improvement that proves the evidence of our findings.

关键词： Spectrum handoff Spectrum sensing Cognitive radio machine learning algorithms Software defined radio NOMA

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