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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 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 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 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 and Quantitative Electroencephalography Predictors for Outcome Prediction in Traumatic Brain Injury: A Systematic Review

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IEEE ACCESS 2020年 8卷 102075-102092页

作者： Noor, Nor Safira Elaina Mohd Ibrahim, Haidi Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Malaysia Univ Sains Malaysia Sch Med Sci Dept Neurosci Hlth Campus Kota Baharu 16150 Kelantan Malaysia

Recent developments in the field of machine learning (ML) have led to a renewed interest in the use of electroencephalography (EEG) to predict the outcome after traumatic brain injury (TBI). This systematic review aims to determine how previous studies have taken into consideration the important modeling issues for quantitative EEG (qEEG) predictors in developing prognostic models. A systematic search in the PubMed and Google Scholar databases was performed to identify all predictive models for the extended Glasgow outcome scale (GOSE) and Glasgow outcome scale (GOS) based on EEG data. Fourteen studies were identified that evaluated ML algorithms using qEEG predictors to predict outcome in patients with moderate to severe TBI. In each model, a maximum of five qEEG predictors were selected to determine the association between these parameters, and favorable or unfavorable predicted outcomes. The most common ML technique used was logistic regression, but the algorithms varied depending on the types and numbers of qEEG predictors selected in each model. The qEEG variability for the relative and absolute band powers were the most common qEEG predictors included in the models (46%) followed by total EEG power of all frequency bands (31%), EEG-reactivity (31%) and coherence (15%). Model performance was often quantified by the area under the receiving-operating characteristic curve (AUROC) rather than by accuracy rate. Various ML models have demonstrated great potential, especially using qEEG predictors, to predict outcome in patients with moderate to severe TBI.

关键词： Electroencephalography machine learning algorithms prediction methods reviews

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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 improving security on touch screen devices: a survey, challenges and new perspectives

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NEURAL COMPUTING & APPLICATIONS 2020年第17期32卷 13651-13678页

作者： Bello, Auwal Ahmed Chiroma, Haruna Gital, Abdulsalam Ya'u Gabralla, Lubna A. Abdulhamid, Shafi'i M. Shuib, Liyana Abubakar Tafawa Balewa Univ Dept Math Sci Bauchi Nigeria Fed Coll Educ Tech Dept Comp Sci Gombe Gombe Nigeria Princess Nourah Bint Abdulrahman Univ Community Coll Dept Comp Sci & Informat Technol Riyadh Saudi Arabia Fed Univ Technol Dept Cyber Secur Sci Minna Minna Nigeria Univ Malaya Fac Comp Sci & Informat Technol Dept Informat Syst Kuala Lumpur Malaysia

Mobile phone touch screen devices are equipped with high processing power and high memory. This led to users not only storing photos or videos but stored sensitive application such as banking applications. As a result of that the security system of the mobile phone touch screen devices becomes sacrosanct. The application of machine learning algorithms in enhancing security on mobile phone touch screen devices is gaining a tremendous popularity in both academia and the industry. However, notwithstanding the growing popularity, up to date no comprehensive survey has been conducted on machine learning algorithms solutions to improve the security of mobile phone touch screen devices. This survey aims to connect this gap by conducting a comprehensive survey on the solutions of machine learning algorithms to improve the security of mobile phone touch screen devices including the analysis and synthesis of the algorithms and methodologies provided for those solutions. This article presents a comprehensive survey and a new taxonomy of the state-of-the-art literature on machine learning algorithms in improving the security of mobile phone touch screen devices. The limitation of the methodology in each article reviewed is pointed out. Challenges of the existing approaches and new perspective of future research directions for developing more accurate and robust solutions to mobile phone touch screen security are discussed. In particular, the survey found that exploring of different aspects of deep learning solutions to improve the security of mobile phone touch screen devices is under-explored.

关键词： machine learning algorithms Deep learning Mobile phone touch screen Android Support vector machine Command attention Security

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machine learning algorithms for event detection

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machine learning 2010年第3期79卷 257-259页

作者： Margineantu, Dragos Wong, Weng-Keen Dash, Denver Boeing Res & Technol Seattle WA USA Oregon State Univ Corvallis OR 97331 USA Intel Labs Pittsburgh PA USA

A common task in many machine learning application domains involves monitoring routinely collected data for \'interesting\' events. This task is prevalent in surveillance, but also in tasks ranging from the analysis of scientific data to the monitoring of naturally occurring events, and from supervising industrial processes to observing human behavior. We will refer to this monitoring process with the purpose of identifying interesting occurrences, as event detection. We put together this special issue of the machine learning journal with the belief that principled machine learning approaches can and will be a differentiator in addressing event detection tasks, and that theoretical and practical advances of machine learning in this area have the potential to impact a wide range of important real-world applications such as security, public health and medicine, biology, environmental sciences, manufacturing, astrophysics, business, and economics. In the recent past, domain experts in these areas have had the laborious job of manually examining the collected data for events of interest. With the emergence of computers, many efforts have been made to replace manual inspection with an automated process. Data, however, have become increasingly complex, and the quantities of collected data have become extremely large in recent years. Multivariate records, images, video footage, audio recordings, spatial and spatio-temporal data, text documents, and even relational data are now routinely collected.

关键词： Event detection machine learning Process monitoring Data Collection Methods Religious Missions machine learning algorithms Artificial Intelligence DIFFERENTIATORS economics

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machine learning algorithms for Automated NIF Capsule Mandrel Selection

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FUSION SCIENCE AND TECHNOLOGY 2020年第6期76卷 749-757页

作者： Boehm, K. -J. Ayzman, Y. Blake, R. Garcia, A. Sequoia, K. Sundram, S. Sweet, W. Gen Atom POB 85608 San Diego CA 92186 USA Lawrence Livermore Natl Lab POB 808 Livermore CA 94550 USA

Small shells, approximately 2 mm in diameter, made from Poly(alpha-methylstyrene) (PAMS) are used as mandrels in the production of glow discharge polymer capsules located at the center of inertial confinement fusion experiments. The visual inspection process of microscope images of these shell mandrels, including detection of micron-sized defects on the shell surface as well as the handling and sorting, is a very labor-intensive, repetitive, and highly subjective process that stands to benefit greatly from automation. As part of an effort to decrease the number of labor hours spent in capsule handling, inspection, and metrology, the development of robotic systems was presented in a paper by Carlson et al., "Automation in Target Fabrication" [Fusion Sci. Technol., Vol. 70, p. 274 (2016)]. The current work expands the automated image acquisition systems developed previously and adds the use of convolutional neural networks to select capsules best suited for use in the downstream production process. Through the use of these machine learning algorithms, the selection process becomes robust, repeatable, and operator independent. As an added benefit the system developed as part of this work is able to provide defect statistics on entire shell batches and feed this information upstream to the production team.

关键词： machine learning algorithms image acquisition capsule mandrels convolutional neural networks

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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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