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machine learning algorithms for Forecasting and Categorizing Euro-to-Dollar Exchange Rates

IEEE ACCESS 2024年 12卷 74211-74217页

作者： El Mahjouby, Mohamed Bennani, Mohamed Taj Lamrini, Mohamed Bossoufi, Badre Alghamdi, Thamer A. H. El Far, Mohamed Sidi Mohamed Ben Abdellah Univ Fac Sci Dhar El Mahraz Dept Comp Sci Lab LPAIS Fes 30003 Morocco Sidi Mohamed Ben Abdellah Univ Fac Sci Dhar El Mahraz LIMAS Lab Fes 30003 Morocco Cardiff Univ Wolfson Ctr Magnet Sch Engn Cardiff CF24 3AA Wales Al Baha Univ Sch Engn Elect Engn Dept Al Bahah 65779 Saudi Arabia

Forecasting changes in foreign exchange rates is a well-explored and widely recognized area within finance. Numerous research endeavors have delved into the utilization of methods in machine learning to analyze and predict movements in the foreign exchange market. This work employed several machine-learning techniques such as Adaboost, logistic regression, gradient boosting, random forest classifier, bagging, Gaussian na & iuml;ve Bayes, extreme gradient boosting classifier, decision tree classifier, and our approach (we have combined three models: logistic regression, random forest classifier, and Gaussian naive Bayes). Our objective is to predict the most advantageous times for purchasing and selling the euro about the dollar. We integrated a range of technical indicators into the training dataset to enhance the precision of our techniques and strategy. The outcomes of our experiment demonstrate that our approach outperforms alternative methods, achieving superior prediction performance. Our methodology yielded an accuracy of 0.948. This study will empower investors to make informed decisions about their future EUR/USD transactions, helping them identify the most advantageous times to buy and sell within the market.

关键词： machine learning Forecasting Currencies Classification algorithms Training Random forests machine learning algorithms Finance Logistic regression Foreign exchange prediction logistic regression random forest Na & iuml ve Bayes

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machine learning algorithms in Photovoltaics: Evaluating Accuracy and Computational Cost Across Datasets of Different Generations, Sizes, and Complexities

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JOURNAL OF ELECTRONIC MATERIALS 2024年第3期53卷 1517-1529页

作者： Al-Saban, Omar Alkadi, Muath Qaid, Saif M. H. Ahmed, Abdullah Ahmed Ali Abdellatif, Sameh O. British Univ Egypt BUE FabLab Ctr Emerging Learning Technol CELT Renewable Energy Postgrad Programme Cairo 11837 Egypt King Abdulaziz City Sci & Technol Riyadh Saudi Arabia King Saud Univ Coll Sci Dept Phys & Astron POB 2455 Riyadh 11451 Saudi Arabia Univ Hamburg Ctr Hybrid Nanostruct CHyN Hamburg Germany Univ Hamburg Fachbereich Phys D-20146 Hamburg Germany British Univ Egypt BUE Fac Engn FabLab Ctr Emerging Learning Technol CELT Elect Engn Dept Cairo 11837 Egypt

This paper evaluates the compatibility of five different machine learning (ML) algorithms for analyzing datasets extracted from solar cell devices. The selected ML algorithms span a range of complexity, from linear regression and polynomial regression models to random forest (RF), neural network, and a hybrid version of the latter two. The algorithms underwent testing with and without hyperparameter optimization to tune the model parameters. The study investigated the expected increased computational cost resulting from hyperparameter optimization, with improved ML accuracy. Additionally, five distinct datasets were utilized to measure the performance of the ML models across different dataset characteristics, including dataset size, features, and behavior. Dataset features are defined as the number of inputs and corresponding outputs, while dataset behavior captures the nature of the solar device. Various photovoltaic (PV) technologies, including conventional silicon p-n-junction cells, dye-sensitized solar cells (DSSC), perovskite solar cells (PSC), and perovskite-on-silicon tandem structures, were employed. The results indicate the superiority of the RF algorithm, demonstrating ultralow root-mean-square error, approaching 9.21E-07, across the datasets evaluated. Furthermore, the study investigates the substantial impact of hyperparameter optimization on neural network performance, demonstrating a remarkable increase in accuracy across diverse datasets. Notably, the influence is contingent upon the number of model parameters, with the neural network, which possesses the greatest number of parameters, showing the most significant improvement.

关键词： machine learning algorithms photovoltaic analysis dataset characteristics random forest hyperparameter optimization computational cost

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machine learning algorithms for Improved Product Design User Experience

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IEEE ACCESS 2024年 12卷 112810-112821页

作者： Wang, Xueli Hu, Bo Suzhou Univ Sch Informat Engn Suzhou 234000 Peoples R China Suzhou Univ Sch Mech & Elect Engn Suzhou 234000 Peoples R China

With the rapid advancement of technology and the increasing demand for user-centric products, the integration of machine learning techniques has become imperative. This paper explores the transformative potential of integrating Particle Swarm Optimization (PSO), Deep Reinforcement learning (DRL), and other machine learning algorithms such as neural networks, decision trees, and support vector machines into product design processes. Our novel hybrid framework leverages PSO's global search capabilities and DRL's adaptive learning to optimize product designs in a manner that traditional methods cannot achieve. By employing predictive modeling, clustering, and recommendation systems, designers can gain valuable insights into user needs and preferences, facilitating the creation of more intuitive and personalized products. We demonstrate that this integrated approach significantly improves design efficiency and user satisfaction. Key findings include a 25% reduction in design iteration time and a 30% increase in user satisfaction scores compared to traditional optimization methods. Additionally, our methodology provides a flexible and scalable solution adaptable to various product design contexts, showcasing its broad applicability and effectiveness. The incorporation of real-time feedback mechanisms allows for continuous refinement and adaptation of product designs to meet evolving user expectations. This study contributes to the field by presenting a comprehensive, multi-technique optimization framework that bridges existing gaps and sets a new standard for user-centric product design optimization. Ultimately, this research underscores the significance of embracing machine learning as a powerful tool for revolutionizing the product design landscape and delivering superior user experiences.

关键词： machine learning algorithms product design user experience enhancement user data analysis predictive modeling product design user experience enhancement user data analysis predictive modeling

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machine learning algorithms for predicting future curve using first and second visit data in female adolescent idiopathic scoliosis patients

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EUROPEAN SPINE JOURNAL 2025年 1-9页

作者： Ohyama, Shuhei Maki, Satoshi Kotani, Toshiaki Ogata, Yosuke Sakuma, Tsuyoshi Iijima, Yasushi Akazawa, Tsutomu Inage, Kazuhide Shiga, Yasuhiro Inoue, Masahiro Arai, Takahito Toshi, Noriyasu Tokeshi, Soichiro Okuyama, Kohei Tashiro, Susumu Suzuki, Noritaka Eguchi, Yawara Orita, Sumihisa Minami, Shohei Ohtori, Seiji Chiba Univ Grad Sch Med Dept Orthoped Surg 1-8-1 InohanaChuo Ku Chiba Chiba 2608670 Japan Seirei Sakura Citizen Hosp Dept Orthoped Surg Sakura Japan St Marianna Univ Sch Med Dept Orthopaed Surg Kawasaki Japan Chiba Univ Ctr Frontier Med Engn Chiba Japan

PurposeThis study was designed to develop a machine learning (ML) model that predicts future Cobb angle in patients with adolescent idiopathic scoliosis (AIS) using minimal radiographs and simple questionnaires during the first and second *** study focused on 887 female patients with AIS who were initially consulted at a specialized scoliosis center from July 2011 to February 2023. Patient data, including demographic and radiographic data based on anterior-posterior and lateral whole-spine radiographs, were collected at the first, second, and final visits. ML algorithms were employed to develop individual regression models for future Cobb angles of each curve type (proximal thoracic: PT, main thoracic: MT, and thoracolumbar/lumbar: TLL) using PyCaret in Python. Multiple models were explored and analyzed, with the selection of optimal models based on the coefficient of determination (R2) and median absolute error (MAE).ResultsFor the future curve of PT, MT, and TLL, the top-performing models exhibit R2 of 0.73, 0.63, and 0.61 and achieve MAE of 2.3 degrees, 4.0 degrees, and 4.2 *** ML-based model using items commonly evaluated at the first and second visits accurately predicted future Cobb angles in female patients with AIS.

关键词： Adolescent idiopathic scoliosis machine learning algorithms Prediction model Regression model Classification model

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machine learning algorithms for the evaluation of risk by tick-borne pathogens in Europe

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ANNALS OF MEDICINE 2024年第1期56卷 2405074页

作者： Estrada-Pena, Agustin de la Fuente, Jose Univ Zaragoza Fac Vet Med Dept Anim Hlth Zaragoza Spain Inst Invest Recursos Cineget SaBio Hlth & Biotechnol IRECCSIC UCLMJCCM Ciudad Real Spain Oklahoma State Univ Ctr Vet Hlth Sci Dept Vet Pathobiol Stillwater OK USA

BackgroundTick-borne pathogens pose a major threat to human health worldwide. Understanding the epidemiology of tick-borne diseases to reduce their impact on human health requires models covering large geographic areas and considering both the abiotic traits that affect tick presence, as well as the vertebrates used as hosts, vegetation, and land use. Herein, we integrated the public information available for Europe regarding the variables that may affect habitat suitability for ticks and hosts and tested five machine learning algorithms (MLA) for predicting the distribution of four prominent tick species across *** and methodsA grid of cells 20 km in diameter was prepared to cover the entire territory, containing data on vegetation, points of water, habitat fragmentation, forest density, grass extension, or imperviousness, with information on temperature and water deficit. The distribution of the hosts (162 species) was modelled and included in the dataset. We used five MLA, namely, Random Forest, Neural Networks, Naive Bayes, Gradient Boosting, and AdaBoost, trained with reliable coordinates for Ixodes ricinus, Dermacentor reticulatus, Dermacentor marginatus, and Hyalomma marginatum in *** Random Forest and Gradient Boosting best predicted ticks and host environmental niches. Our results demonstrate that MLA can identify trait-matching combinations of environmental niches. The inclusion of land cover and land use variables has a superior capacity for predicting areas suitable for ticks, compared to classic methods based on the use of climate data *** MLA-driven models may offer several advantages over traditional models. We anticipate that these results may be extrapolated to other regions and combinations of tick-vertebrates. These results highlight the potential of MLA for inference in ecology and provide a background for the evolution of a completely automatized tool to calculate the seasonality of ticks for ear

关键词： Tick habitat suitability modelling Europe hosts distribution landscape features machine learning algorithms human health

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machine learning algorithms for Satellite Image Classification Using Google Earth Engine and Landsat Satellite Data: Morocco Case Study

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IEEE ACCESS 2023年 11卷 71127-71142页

作者： Ouchra, Hafsa Belangour, Abdessamad Erraissi, Allae Hassan II Univ Fac Sci Ben MSik Lab Informat Technol & Modeling LTIM Casablanca 28830 Morocco Chouaib Doukkali Univ Polydisciplinary Fac Sidi Bennour El Jadida 24000 Morocco

Earth observation data have proven to be a valuable resource of quantitative information that is more consistent in time and space than traditional land-based surveys. Remote sensing plays a vital role in collecting data in many aspects of life, whether scientific, economic, or political. Land cover information is very important in supporting urban planning and decision-making and provides many opportunities for mapping and monitoring urban areas. Multiple data sources exist, including satellite data of different resolutions ranging from very high to medium resolution and aerial and drone image acquisitions. Today, accurate land cover information is in high demand. Using satellite imagery and remote sensing techniques for planning and development is becoming a common study conducted by many researchers to find practical solutions to the many problems affecting our planet. The recovery, management, and analysis of these large amounts of satellite imagery pose considerable challenges. The classification of satellite images is a very popular and complex topic. In classification studies over the last decade, researchers have been frequently studying only those three machine learning algorithms RF, CART, and SVM, applied in cities or countries except Morocco which poses a great lack of information on the land use of Morocco. To solve these challenges, six machine learning algorithms were applied and compared to each other based on several evaluation metrics, and then, to avoid the problems of data download and storage space, we used Google Earth Engine, a geospatial processing platform that operates in the cloud. It provides free access to substantial satellite data and free computations to monitor, visualize, and analyze environmental features at the petabyte scale. In this paper, we used Landsat 8 satellite data to perform a land cover classification of Morocco, applying machine learning algorithms, which is a subfield of artificial intelligence. This paper proposes an

关键词： & nbsp Remote sensing satellite image supervised classification Google earth engine LANDSAT satellite machine learning algorithms

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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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machine learning algorithms for soil moisture estimation using Sentinel-1: Model development and implementation

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ADVANCES IN SPACE RESEARCH 2022年第4期69卷 1799-1812页

作者： Chaudhary, Sumit Kumar Srivastava, Prashant K. Gupta, Dileep Kumar Kumar, Pradeep Prasad, Rajendra Pandey, Dharmendra Kumar Das, Anup Kumar Gupta, Manika Banaras Hindu Univ Inst Environm & Sustainable Dev Remote Sensing Lab Varanasi 221005 Uttar Pradesh India Banaras Hindu Univ Dept Phys Varanasi 221005 Uttar Pradesh India IIT BHU Dept Phys Varanasi 221005 Uttar Pradesh India Space Applicat Ctr ISRO Microwave Tech Dev Div MTDD Ahmadabad 380015 Gujarat India Univ Delhi Dept Geol New Delhi India

The present study provided the first-time comprehensive evaluation of 12 advanced statistical and machine learning (ML) algorithms for the Soil Moisture (SM) estimation from dual polarimetric Sentinel-1 radar backscatter. The ML algorithms namely support vector machine (SVM) with linear, polynomial, radial and sigmoid kernel, random forest (RF), multi-layer perceptron (MLP), radial basis function (RBF), Wang and Mendel's (WM), subtractive clustering (SBC), adaptive neuro fuzzy inference system (ANFIS), hybrid fuzzy interference system (HyFIS), and dynamic evolving neural fuzzy inference system (DENFIS) were used. Extensive field samplings were performed for collection of in-situ SM data and other parameters from the selected sites for seven different dates and at two different locations (Varanasi and Guntur District, India), concurrent to Sentinel-1 overpasses. The backscattering coefficients were considered as input variables and SM as output variable for the training, validation and testing of the ML algorithms. The site at Varanasi was used for the training, validation and testing of the models. On the other hand, the Guntur site was used as an independent site for checking the model performance, before finalizing the algorithms. The performances of different trained algorithms were evaluated in terms of correlation coefficient (r), root mean square error (RMSE) (in m(3)/m(3)) and bias (in m(3)/m(3)). The study identified the RF, SBC and ANFIS as the top three best performing models with comparable and promising SM estimation. In order to test the robustness of these best models (RF, SBC and ANFIS), further performance analysis was performed to the independent datasets of the Varanasi and Guntur test sites, which indicates that the performance of these three models were consistent and SBC can be recommended as the best among all for SM estimation. (C) 2021 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Sentinel-1 Artificial Intelligence machine learning algorithms Soil Moisture Optimization

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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 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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