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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 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 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 ASSESSMENT FOR SNOW LWC RETRIEVAL FROM SAR DATA

MACHINE LEARNING ALGORITHMS ASSESSMENT FOR SNOW LWC RETRIEVA...

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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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Assessing land cover change detection in a developing country with machine and deep learning algorithms: a case study

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Environment, Development and Sustainability 2025年 1-21页

作者： Hmamou, Mohamed Chafiq, Tarik Ouhammou, Imrane Lmoudden, Othmane Ait Raji, Mohammed Ouaskit, Said LTIM Faculty of Sciences Ben M’Sik University Hassan II of Casablanca Casablanca B.P. 7955 Morocco LCMP Faculty of Sciences Ben M’Sik University Hassan II of Casablanca Casablanca B.P. 7955 Morocco LGAGE Faculty of Sciences Ben M’Sik University Hassan II of Casablanca Casablanca B.P. 7955 Morocco

This study examines the evolution of land cover in the Fez province of Morocco through a detailed analysis of Land Cover Change Detection (LCCD). Using satellite imagery from Landsat-8 and Landsat-9, we generated high-resolution land cover maps by applying a range of machine learning and deep learning techniques. The analysis spans the period from 2014 to 2022, aiming to identify the most suitable algorithm for accurate LCCD. The methods tested in this research include Support Vector machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Upon thorough comparison, SVM emerged as the top-performing model, yielding the most reliable classification results, while MLP demonstrated comparatively limited accuracy in this context. Our findings also underscore significant land use transitions, particularly an expansion of urban and industrial zones accompanied by a decline in agricultural lands trends largely attributed to anthropogenic pressures. These patterns are intricately linked to broader environmental shifts, including those related to climate. By analyzing spatial and temporal variations in land cover, this research contributes to a better grasp of the environmental transformations occurring in Fez, offering valuable knowledge to support evidence-based planning and the promotion of sustainable land use strategies. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Deep learning models GIS-based analysis Land cover classification machine learning algorithms Satellite image processing

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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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Laser induced-breakdown spectroscopy (LIBS) combined with machine learning algorithms: An accurate tool to identify primed seeds

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Microchemical Journal 2025年 214卷

作者： Córdoba Figueroa, Mónica Eliana Ferreira, Dennis Silva Rezende, Brunna Rithielly Batista, Thiago Barbosa Pereira-Filho, Edenir Rodrigues Pereira Sartori, Maria Márcia Amaral da Silva, Edvaldo Aparecido Department of Plant Production São Paulo State University (UNESP) São Paulo Botucatu Brazil Group of Applied Instrumental Analysis (GAIA) Department of Chemistry Federal University of São Carlos (UFSCar) P.O. Box 676 São Paulo State São Carlos 13565-905 Brazil

The priming technique is commonly used to enhance and standardize seed germination. Its effectiveness is assessed through germination uniformity and vigor tests. These tests require trained analysts and are time-consuming, highlighting the need for faster, more accurate methods. This study analyzed an innovative technique capable of distinguishing between primed and unprimed tomato seeds based on their elemental composition using Laser-Induced Breakdown Spectroscopy (LIBS), principal component analysis (PCA) and machine learning algorithms: linear, quadratic and mixture discriminant analyzes (LDA, QDA, MDA), and nearest neighbor methods (KNN). Germination and vigor tested were to compare the effects of priming. The seed analyzed using LIBS predominantly displayed chemical emission lines corresponding to potassium, calcium and magnesium. PCA reduced the variables to two components, revealing two distinct groups and explaining 94.4 % of the variability. The LDA model, with a sensitivity (92.98 %), specificity (93.75 %), and error rate (0.1554), demonstrated superior performance in classifying primed and unprimed seeds using the LIBS technique. The biplot revealed that the seeds subjected to priming exhibited the intended effects. Consequently, the LIBS technique, combined with PCA and machine learning algorithms, demonstrated significant potential for distinguishing and selecting primed seeds. © 2025 Elsevier B.V.

关键词： Elemental composition machine learning algorithms PCA Solanum lycopersicum L. Vigor

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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 1-20页

作者： Hussain, Izhar Ching, Kok Boon Uttraphan, Chessda Tay, Kim Gaik Noor, Adil Departement of Electrical Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johar 86400 Malaysia Department of Electronics Engineering Benazir Bhutto Shaheed University of Technology and Skill Development Khairpure mirs Khairpur 66020 Pakistan Department of Computer Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Parit Raja Batu Pahat 86400 Malaysia 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. © The Author(s) 2025.

关键词： Electric vehicles Energy consumption prediction machine learning algorithms

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NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat

Applied Food Research

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Applied Food Research 2025年第1期5卷

作者： Kamrunnahar Khan Bristy Dip Ghosh Md. Abul Hashem Department of Animal Science Faculty of Animal Husbandry Bangladesh Agricultural University Mymensigh 2202 Bangladesh

This study investigated the utilization of near-infrared spectroscopy (NIRS) and machine learning methodologies to differentiate cooked broiler and duck meat. Nearinfrared spectral data were acquired using a portable spectrometer (700–1100 nm) from 40 samples (20 broilers and 20 duck breasts). The data were preprocessed and analyzed using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). PCA revealed 87.97 % variance, demonstrating distinct groupings. Various machine learning algorithms, including linear discriminant analysis (LDA), support vector machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, Decision Tree, Naive Bayes, Neural Network, and XGB, were evaluated. Hyperparameter tuning and 5-fold cross-validation were implemented to mitigate overfitting and enhance the generalization. Logistic Regression, SVM, and LDA achieved the highest accuracy (84.13 %, 84 %, and 83.50 %, respectively) and ROC AUC (92.99 %, 92.63 %, and 91.71 %, respectively), indicating robust discriminative power. Random Forest and Gradient Boosting also exhibited satisfactory performance, with consistent cross-validation scores (∼0.824). XGB demonstrated marginally superior recall compared to K-Nearest Neighbors, while Neural Network excelled in recall (90.74 %), a critical factor for detecting true positives. Decision Tree and Naive Bayes, although interpretable, underperformed relative to other algorithms. Logistic Regression and SVM have emerged as the most efficacious methods, attaining high accuracy and balanced precision and recall. This study underscores the potential of integrating spectroscopic data with advanced machinelearning techniques for meat classification and fraud detection.

关键词： Nir spectroscopy machine learning algorithms Principal component analysis Hyperparameter Optimization Ensemble Model

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