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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Santi, Emanuele Paloscia, Simonetta Pettinato, Simone Baroni, Fabrizio Pilia, S. Colombo, Roberto Ravasio, Claudia Di Mauro, Biagio CNR Inst Appl Phys CNR IFAC Florence Italy Univ Milano Bicocca Dept Earth & Environm Sci Milan Italy ISP CNR Inst Polar Sci Venice Italy

ISBN: (纸本)9798350360332;9798350360325

In this study, the retrieval of snow Liquid Water Content (LWC, %) from C- and X-band SAR data was based on Artificial Neural Network (ANN) and Random Forest (RF). Two approaches were explored for generating a sufficient amount of data to train and test the ANN and RF algorithms: the first strategy was defined as "model-driven". The second one was defined as "data-driven". The validation results showed that RF performs better than ANN in terms of correlation coefficient R, regardless of the selected approach ("model driven" R-ANN = 0.60, R-RF = 0.68;"data-driven" R-ANN = 0.50, R-RF = 0.88 at X-band). Moreover, the RF implementation trained with the "data-driven" approach outperformed the "model-driven" approach in terms of correlation coefficient R (R-RF = 0.88 at X-band).

关键词： SAR snow machine learning algorithms Artificial Neural Network Random Forest

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machine learning algorithms to Detect Penetrations ofPVDF 9

Machine Learning Algorithms to Detect Penetrations ofPVDF

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9th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Zhai, Yayu Song, Ping Chen, Xiaoxiao Beijing Inst Technol Lab Biornimet Robots & Syst Beijing Peoples R China

ISBN: (纸本)9781538665657

Polyvinylidene fluoride (PVDF) has widely used in detecting the interplanetary dust, In the case of penetration and non-penetration, the output signals of the PVDF are quite different. Detecting whether particles penetrate PVDF is a crucial issue. We create a set of experimenral equipment for collecting the signals from the PVDF. The equipment consists of particle emitter, shield, conditioning circuits and data acquisition equipment. 600 experiments are conducted, Among 200 experiments, the particles penetrate PVDF. We successfully distinguish penetration of PVDF using four machine learning algorithms: Anomaly Detection (AD), Artificial Neural Network (ANN), K-Nearest-Neighbors (KNN), and Support Vector machines (SVM). We propose a unique evaluation criteria OP to evaluate the performance of four classifiers including their accuracy and computational time. The results show that ANN is the best machine learning algorithm for ow problem, and AD is not suitable for our problem.

关键词： machine learning algorithms PVDF classification feature extraction and reduction

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machine learning algorithms for population-specific risk score in coronary artery bypass grafting

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ASIAN CARDIOVASCULAR & THORACIC ANNALS 2023年第4期31卷 348-356页

作者： Swamy, Abhyuday Kumara Rajagopal, Vivek Krishnan, Deepak Ghorai, Paramita Auddya Palani, Santhosh Rathnam Narayan, Pradeep Medha Analyt Adv Analyt & AI Narayana Hlth Bengaluru India Narayana Hlth Dept Cardiothorac Surg Bengaluru India

Background The aim of this study was to develop a new risk prediction score (NH Score) for patients undergoing coronary artery bypass grafting (CABG) specific to the Indian population and compare it to the Society of Thoracic Surgeon (STS) Score and the EuroSCORE II. Method The baseline features of adult patients who underwent CABG between the years 2015 and 2021 (n = 6703) were taken and split into training data (2015-2020;n = 5561) and validation data (2020-2021;n = 1142). The CatBoost algorithm was trained to predict risk scores (NH score), and the performance was tested on the validation set by Precision-Recall Curve and F1 Score. Model calibration was measured by the Brier Score, Expected Calibration Error and Maximum Calibration Error. Results The NH score outperformed both the STS and EuroSCORE II for all outcomes. For mortality, the PR AUC for NH Score was (0.463 [95% confidence interval [CI], 0.28-0.64]) compared to 0.113 [95% CI, 0.04-0.22] for the STS score and 0.146 [95% CI, 0.06-0.31] for the EuroSCORE II (p MUCH LESS-THAN 0.0001). With respect to morbidity NH Score was superior to the STS score (0.43 [95% CI, 0.33-0.50]) vs. (0.229 [95% CI, 0.18-0.3, p < 0.0001). The observed to the predicted ratio for NH score was superior to the STS Score and similar to EuroSCORE II. NH Score was also more accurate at predicting the risk of prolonged ventilation compared to the STS Score. Conclusion NH score shows an excellent improvement over the performance of STS score and EuroSCORE II for modelling risk predictions for patients undergoing CABG in Indian population. It warrants further validation for larger datasets.

关键词： machine learning algorithms risk-score CABG

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machine learning algorithms for Environmental Sound Recognition: Towards Soundscape Semantics 15

Machine Learning Algorithms for Environmental Sound Recognit...

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Audio Mostly (AM) Conference on Sound, Semantics and Social Interaction

作者： Bountourakis, Vasileios Vrysis, Lazaros Papanikolaou, George Aristotle Univ Thessaloniki AUTh Univ Campus Thessaloniki 54124 Greece

ISBN: (纸本)9781450338967

This paper investigates methods aiming at the automatic recognition and classification of discrete environmental sounds, for the purpose of subsequently applying these methods to the recognition of soundscapes. Research in audio recognition has traditionally focused on the domains of speech and music. Comparatively little research has been done towards recognizing non-speech environmental sounds. For this reason, in this paper, we apply existing techniques that have been proved efficient in the other two domains. These techniques are comprehensively compared to determine the most appropriate one for addressing the problem of environmental sound recognition.

关键词： Environmental Sound Recognition audio classification semantic audio analysis computer audition feature extraction feature selection machine learning algorithms

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machine learning algorithms for Early and Accurate Diabetic Detection 2nd

Machine Learning Algorithms for Early and Accurate Diabetic ...

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3rd International Conference on Digital Technologies and Applications

作者： Chaaouan, IIanae Bouhadda, Mohamed El Alami, Rachid Essahlaoui, Abdelouahed El Ghzaoui, Mohammed Qjidaa, Hassan Jamil, Mohammed Ouazzani Sidi Mohammed Ben Abdellah Univ Fac Sci Dhar El Mehraz LESSI BP 1796 Fes Morocco Sidi Mohammed Ben Abdellah Univ Multidisciplinary Fac Engn Sci Lab LSI Taza Morocco UPF LSEED Lab Fes Morocco

ISBN: (纸本)9783031298561;9783031298578

The diagnosis of diabetes requires many physical and chemical tests, as well as many other tests, although untreated and undiagnosed diabetes causes the destruction of body organs such as the eyes, heart, kidneys, feet, and nerves and may result in loss of life. Therefore, early detection and analysis of diabetes can help reduce mortality rates. In this paper, we develop accurate machine learning models for detecting diabetes. These models are based on three algorithms: the first is Logistic Regression (LR), the second is Support Vector machine (SVM) and the third is Random Forest Classifier (RFC). They are performed on Diabetes data known as PIDD, which is obtained from the website Kaggle. Thereafter, the performance of predictive models is evaluated using accuracy, sensitivity, and F1 score measures. The model based on predictive learning of random forests emerged as one of the best performing models with 0.96 accuracy, 0.99 sensitivity, and 0.97 F1-score.

关键词： machine learning algorithms diabetes detection predictive model anomaly detection model

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machine learning algorithms in shipping: improving engine fault detection and diagnosis via ensemble methods

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WMU JOURNAL OF MARITIME AFFAIRS 2020年第1期19卷 51-72页

作者： Tsaganos, G. Nikitakos, N. Dalaklis, D. Olcer, A., I Papachristos, D. Merchant Acad Athens Dept Marine Engn Athens Greece Dept Shipping Trade & Transport Chios Greece World Maritime Univ WMU Malmo Sweden Dept Ind Engn & Prod Chios Greece

Detection and diagnosis of marine engines faults are extremely important functions for the optimized voyage of any sea-going vessel, as well as the safe conduct of navigation. Early detection of these faults is a prerequisite for reliability: incidents of engine breakdowns can be avoided, since the timely resolving of these faults can ensure the non-interrupted tempo of the sail. Avoiding malfunctions could also improve the ship's overall environmental "footprint" and even ensure reduced fuel consumption. Initial results of the analysis at hand were presented during the 3rd International Symposium on Naval Architecture and Maritime (INT-NAM 2018), in Istanbul-Turkey. Further exploring the use of machine learning algorithms in shipping and by elaborating more on that effort, an evaluation of intelligent diagnostic methods applicable for a two-stroke slow-speed marine diesel engine is taking place, with the aim to facilitate effective detection and classification of occurring faults. This research was carried out via the cost-free Weka data mining tool, which was used to analyze the data of the engine's operating parameters that were found outside of the prescribed boundaries. The proposed method is based on the construction of an ensemble classification model "AdaBoost", which further improves the performance of a basic Simple Cart classifier. During the related experimental activities, the overall recorded performance was 96.5%, clearly establishing this method as a very appropriate choice.

关键词： Marine diesel engine Fault detection machine learning algorithms Ensemble methods Weka Cross-validation Confusion matrix

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machine learning algorithms for Unbalanced Dataset Promotion Prediction for Employees 2

Machine Learning Algorithms for Unbalanced Dataset Promotion...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Kediya, Shailesh Somnathe, Tushar Surya, Amrin Garg, Neha Ali, Athar Javed Gudadhe, Amit A. School of Logistics and Supply Chain Management Symbiosis Skills and Professional University Pune India Placements and Corporate Connect Imt Nagpur India Renaissance Institute of Management Studies Chandrapur India Central Institute of Business Management Research and Development Nagpur India Faculty of Engineering and Technology Wardha India

ISBN: (纸本)9798350369960

The practice of giving and accepting promotions is one of the most important in human resources. Management can keep morale and productivity high with the use of a merit-based, open promotion system inside the company. The prospect of a promotion is one of several extraneous reasons that drives many employees. maintaining the worker engaged and committed to the organization is crucial to maintaining his current productivity levels intact. It's a vital tool for motivating workers and monitoring their output. The promotion process takes into account a wide range of factors, including an employee's seniority, performance, competencies, age, awards, training score, and organizational devotion. Workers' promotion needs will be evaluated using machine learning methods such Support Vector machine, Artificial Neural Network, and Random Forest, and a prediction strategy will be explored based on the findings. Accuracy, precision, and recall all improved by 1%, and the f1-score reached a perfect 98% when the ROS technique was combined with the Random Forest algorithm. Human resource professionals and business leaders might use the findings of this study to make more accurate predictions about who and when promotions will occur. © 2023 IEEE.

关键词： machine learning algorithms Principal Component Analysis Support Vector machine

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Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study

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Scientific reports 2025年第1期15卷 16124页

作者： Izhar Hussain Kok Boon Ching Chessda Uttraphan Kim Gaik Tay Adil Noor Departement of Electrical Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johar 86400 Malaysia. izharhussain@bbsutsd.edu.pk. Department of Electronics Engineering Benazir Bhutto Shaheed University of Technology and Skill Development Khairpure mirs Khairpur 66020 Pakistan. izharhussain@bbsutsd.edu.pk. Departement of Electrical Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johar 86400 Malaysia. bckok@uthm.edu.my. Department of Computer Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia 86400 Parit Raja Batu Pahat Johor Malaysia. Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johar 86400 Malaysia. Departement of Electrical Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johar 86400 Malaysia.

An accurate energy consumption prediction becomes crucial with increasing electric vehicle usage for effective power grid management. This research examined the performance of eleven machine learning models for this purpose: Ridge Regression, Lasso Regression, K-Nearest Neighbors, Gradient Boosting, Support Vector Regression, Multi-Layer Perceptron, XGBoost, CatBoost, LightGBM, Gaussian Processes for Regression(GPR) and Extra Trees Regressor, considering real historical data from Colorado. The models were evaluated using different metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), R², Root Mean Squared Error(RMSE) and Normalized Root Mean Squared Error(NRMSE), with visual analyses through scatter plots and time series plots. The best model observed was the Extra Trees Regressor, which had an MAE of 0.5888, an MSE of 3.2683, R² value of 0.9592, RMSE of 1.8078 and NRMSE of 0.020. Gradient Boosting and KNN also returned good results, although they were slightly more dispersed. Nevertheless, while non-linear models like MLP, XGBoost, CatBoost, LightGBM and linear models such as Ridge and Lasso Regression offer valuable insights, they exhibit shortcomings in estimating energy, especially at extreme levels, highlighting limitations in capturing complex non-linear interactions. This study focuses on their applicability to energy projections to demonstrate how well ensemble and non-linear models may capture intricate patterns in time series. These cutting-edge machine learning techniques might greatly enhance energy demand predictions.

关键词： Electric vehicles Energy consumption prediction machine learning algorithms

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Applications of machine learning algorithms on the compressive strength of laterite blocks made with metakaolin-based geopolymer and sugarcane molasses

Waste Management Bulletin

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Waste Management Bulletin 2025年第3期3卷

作者： David Sinkhonde Derrick Mirindi Ismael Dabakuyo Tajebe Bezabih Destine Mashava Frederic Mirindi Department of Civil and Construction Engineering Pan African University Institute for Basic Sciences Technology and Innovation Nairobi Kenya Department of Mechanical Engineering Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Department of Economics African Economic Research Consortium (AERC) Nairobi Kenya

To refine the process of anticipating the structural integrity of laterite block components, the use of machine learning (ML) algorithms is required. This study initiates an exploration into forecasting the compressive strength of laterite blocks infused with metakaolin-based geopolymer (MKG) and sugarcane molasses (SM), utilizing machine learning techniques such as artificial neural networks (ANN), random forests (RF), decision trees (DT), and support vector machines (SVM). The models were developed using four input values, including the MKG, SM, laterite soil, and water, with compressive strength as the output. Results show that for all the models, the majority of the data points lie within the error lines range of −20 % and +20 %. Using the Taylor diagram model, the results demonstrate that the SVM (train) model achieves the highest performance in predicting the compressive strength of laterite blocks, with a correlation coefficient of 0.99 and the lowest root mean square error (RMSE) of 0.139. The correlation coefficient values (R) for training and testing algorithm models ranged between 0.65 and 0.99, implying that all models fairly predict the compressive strength of laterite blocks containing MKG and SM. The RF model emerges as an important model for generalization across training and testing phases, with R values of 0.9828 and 0.789, respectively. SHapley Additive exPlanations (SHAP) analysis assesses the model’s explainability behavior. According to a SHAP-based feature importance study, age (85.33 %) and water content (17.87 %) are critical components that may improve compressive strength compared to MKG (8.60 %) and SM (6.74 %), respectively. This study not only assists in comprehending the essential parameters necessary for making well-informed decisions but also opens exciting possibilities for the application of ML in fostering sustainable construction practices.

关键词： machine learning algorithms Sugarcane molasses Laterite blocks Metakaolin-based geopolymer Sustainable construction

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Flood Inundation Mapping of a River Stretch Using machine learning algorithms in the Google Earth Engine Environment

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Journal of Flood Risk Management 2025年第2期18卷

作者： Maaz Ashhar Venkata Reddy Keesara Venkataramana Sridhar Department of Civil Engineering National Institute of Technology Warangal India Department of Biological Systems Engineering Virginia Polytechnic Institute and State University Blacksburg Virginia USA

Floods are among the most common natural disasters in India, causing significant socio-economic and environmental impacts. This study focuses on a frequently flooded stretch of the Godavari River in Telangana, India, to analyze the flood event that occurred between 14th July 2022 and 20th July 2022. Sentinel-1 SAR data from 6th July 2022 to 20th July 2022 were used to perform flood inundation mapping. Various machine learning algorithms, including Support Vector machine (SVM), Random Forest (RF), Gradient Boosting Tree (GBT), and Classification and Regression Tree (CART), were employed. The analysis revealed that out of the total study area of 1,556,544 ha, SVM classified 59,823 ha, RF classified 60,088 ha, GBT classified 57,497 ha, and CART classified 58,374 ha as flooded areas. In contrast, Otsu's Thresholding technique identified a significantly larger flooded area of 359,253 ha. For validation, 70 flooded and 30 non-flooded points were randomly selected from the flood map provided by the National Remote Sensing Center (NRSC). The RF algorithm achieved the best performance, correctly classifying 58 flooded points and 26 non-flooded points, resulting in an overall accuracy of 84%. The findings highlight the effectiveness of machine learning algorithms, particularly Random Forest, in flood inundation mapping.

关键词： flood inundation map Google earth engine machine learning algorithms SAR data

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