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    Tytuł:
    Improving Crop Yield Predictions in Morocco Using Machine Learning Algorithms
    Autorzy:
    Ed-Daoudi, Rachid
    Alaoui, Altaf
    Ettaki, Badia
    Zerouaoui, Jamal
    Powiązania:
    https://bibliotekanauki.pl/articles/24202898.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Inżynierii Ekologicznej
    Tematy:
    crop yield prediction
    machine learning algorithm
    statistical model
    model evaluation
    Opis:
    In Morocco, agriculture is an important sector that contributes to the country’s economy and food security. Accurately predicting crop yields is crucial for farmers, policy makers, and other stakeholders to make informed decisions regarding resource allocation and food security. This paper investigates the potential of Machine Learning algorithms for improving the accuracy of crop yield predictions in Morocco. The study examines various factors that affect crop yields, including weather patterns, soil moisture levels, and rainfall, and how these factors can be incorporated into Machine Learning models. The performance of different algorithms, including Decision Trees, Random Forests, and Neural Networks, is evaluated and compared to traditional statistical models used for crop prediction. The study demonstrated that the Machine Learning algorithms outperformed the Statistical models in predicting crop yields. Specifically, the Machine Learning algorithms achieved mean squared error values between 0.10 and 0.23 and coefficient of determination values ranging from 0.78 to 0.90, while the Statistical models had mean squared error values ranging from 0.16 to 0.24 and coefficient of determination values ranging from 0.76 to 0.84. The Feed Forward Artificial Neural Network algorithm had the lowest mean squared error value (0.10) and the highest R² value (0.90), indicating that it performed the best among the three Machine Learning algorithms. These results suggest that Machine Learning algorithms can significantly improve the accuracy of crop yield predictions in Morocco, potentially leading to improved food security and optimized resource allocation for farmers.
    Źródło:
    Journal of Ecological Engineering; 2023, 24, 6; 392--400
    2299-8993
    Pojawia się w:
    Journal of Ecological Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Weighted accuracy algorithmic approach in counteracting fake news and disinformation
    Algorytmiczne podejście do dokładności ważonej w przeciwdziałaniu fałszywym informacjom i dezinformacji
    Autorzy:
    Bonsu, K.O.
    Powiązania:
    https://bibliotekanauki.pl/articles/2048986.pdf
    Data publikacji:
    2021
    Wydawca:
    Akademia Bialska Nauk Stosowanych im. Jana Pawła II w Białej Podlaskiej
    Tematy:
    artificial intelligence
    natural language processing
    machine learning algorithm
    disinformation
    digital revolution
    fake news
    Opis:
    Subject and purpose of work: Fake news and disinformation are polluting information environment. Hence, this paper proposes a methodology for fake news detection through the combined weighted accuracies of seven machine learning algorithms. Materials and methods: This paper uses natural language processing to analyze the text content of a list of news samples and then predicts whether they are FAKE or REAL. Results: Weighted accuracy algorithmic approach has been shown to reduce overfitting. It was revealed that the individual performance of the different algorithms improved after the data was extracted from the news outlet websites and 'quality' data was filtered by the constraint mechanism developed in the experiment. Conclusions: This model is different from the existing mechanisms in the sense that it automates the algorithm selection process and at the same time takes into account the performance of all the algorithms used, including the less performing ones, thereby increasing the mean accuracy of all the algorithm accuracies.
    Źródło:
    Economic and Regional Studies; 2021, 14, 1; 99-107
    2083-3725
    2451-182X
    Pojawia się w:
    Economic and Regional Studies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The Effect of Dual Hyperparameter Optimization on Software Vulnerability Prediction Models
    Autorzy:
    Bassi, Deepali
    Singh, Hardeep
    Powiązania:
    https://bibliotekanauki.pl/articles/2203949.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    software vulnerability
    hyperparameter optimization
    machine learning algorithm
    data balancing techniques
    data complexity measures
    Opis:
    Background: Prediction of software vulnerabilities is a major concern in the field of software security. Many researchers have worked to construct various software vulnerability prediction (SVP) models. The emerging machine learning domain aids in building effective SVP models. The employment of data balancing/resampling techniques and optimal hyperparameters can upgrade their performance. Previous research studies have shown the impact of hyperparameter optimization (HPO) on machine learning algorithms and data balancing techniques. Aim: The current study aims to analyze the impact of dual hyperparameter optimization on metrics-based SVP models. Method: This paper has proposed the methodology using the python framework Optuna that optimizes the hyperparameters for both machine learners and data balancing techniques. For the experimentation purpose, we have compared six combinations of five machine learners and five resampling techniques considering default parameters and optimized hyperparameters. Results: Additionally, the Wilcoxon signed-rank test with the Bonferroni correction method was implied, and observed that dual HPO performs better than HPO on learners and HPO on data balancers. Furthermore, the paper has assessed the impact of data complexity measures and concludes that HPO does not improve the performance of those datasets that exhibit high overlap. Conclusion: The experimental analysis unveils that dual HPO is 64% effective in enhancing the productivity of SVP models.
    Źródło:
    e-Informatica Software Engineering Journal; 2023, 17, 1; art. no. 230102
    1897-7979
    Pojawia się w:
    e-Informatica Software Engineering Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Machine learning methods applied to sea level predictions in the upper part of a tidal estuary
    Autorzy:
    Guillou, N.
    Chapalain, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/2078822.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Instytut Oceanologii PAN
    Tematy:
    multiple regression model
    artificial neural network
    multilayer perceptron
    regression function
    machine learning algorithm
    sea level
    Opis:
    Sea levels variations in the upper part of estuary are traditionally approached by relying on refined numerical simulations with high computational cost. As an alternative efficient and rapid solution, we assessed here the performances of two types of machine learning algorithms: (i) multiple regression methods based on linear and polynomial regression functions, and (ii) an artificial neural network, the multilayer perceptron. These algorithms were applied to three-year observations of sea levels maxima during high tides in the city of Landerneau, in the upper part of the Elorn estuary (western Brittany, France). Four input variables were considered in relation to tidal and coastal surge effects on sea level: the French tidal coefficient, the atmospheric pressure, the wind velocity and the river discharge. Whereas a part of these input variables derived from large-scale models with coarse spatial resolutions, the different algorithms showed good performances in this local environment, thus being able to capture sea level temporal variations at semi-diurnal and spring-neap time scales. Predictions improved furthermore the assessment of inundation events based so far on the exploitation of observations or numerical simulations in the downstream part of the estuary. Results obtained exhibited finally the weak influences of wind and river discharges on inundation events.
    Źródło:
    Oceanologia; 2021, 63, 4; 531-544
    0078-3234
    Pojawia się w:
    Oceanologia
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    CellProfiler and WEKA Tools: Image Analysis for Fish Erythrocytes Shape and Machine Learning Model Algorithm Accuracy Prediction of Dataset
    Autorzy:
    Talapatra, Soumendra Nath
    Chaudhuri, Rupa
    Ghosh, Subhasis
    Powiązania:
    https://bibliotekanauki.pl/articles/1193348.pdf
    Data publikacji:
    2021
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    Automatic image analysis
    CellProfiler tool
    Fish erythrocytes quantification
    Machine learning algorithm
    Model classifier accuracy
    Shapes measurement
    WEKA tool
    Opis:
    The first part of the study was detected the number of cells and measurement of shape of cells, cytoplasm, and nuclei in an image of Giemsa-stained of fish peripheral erythrocytes by using CellProfiler (CP, version 2.1.0) tool, an image analysis tool. In the second part, it was evaluated machine learning (ML) algorithm models viz. BayesNet (BN), NaiveBayes (NB), logistic regression (LR), Lazy.KStar (K*), decision tree (DT) J48, Random forest (RF) and Random tree (RT) in the WEKA tool (version 3.8.5) for the prediction of the accuracy of the dataset generated from an image. The CP predicts the numbers and individual cellular area shape (arbitrary unit) of cells, cytoplasm, and nuclei as primary, secondary, and tertiary object data in an image. The performance of model accuracy of studied ML algorithm classifications as per correctly and incorrectly classified instances, the highest values were observed in RF and RT followed by K*, LR, BN and DTJ48 and lowest in NB as per training and testing set of correctly classified instances. In case of performance accuracy of class for K value, the highest values were observed in RF and RT followed by K*, LR, BN and DTJ48 and lowest in NB while lowest values were obtained for mean absolute error (MAE) and root mean squared error (RMSE) in case of RT followed by RF, K*, LR, BN and DTJ48 and comparatively highest value in case of NB as per training and testing set. In conclusion, both tools performed well as an image to the dataset and obtained dataset to rich information through ML modelling and future study in WEKA tool can easily be analysed many biological big data to predict classifier accuracy.
    Źródło:
    World Scientific News; 2021, 154; 101-116
    2392-2192
    Pojawia się w:
    World Scientific News
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A comprehensive study on the application of firefly algorithm in prediction of energy dissipation on block ramps
    Autorzy:
    Mahdavi-Meymand, Amin
    Sulisz, Wojciech
    Zounemat-Kermani, Mohammad
    Powiązania:
    https://bibliotekanauki.pl/articles/2087026.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    firefly algorithm
    machine learning
    energy dissipation
    block ramp
    Opis:
    In this study novel integrative machine learning models embedded with the firefly algorithm (FA) were developed and employed to predict energy dissipation on block ramps. The used models include multi-layer perceptron neural network (MLPNN), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), support vector regression (SVR), linear equation (LE), and nonlinear regression equation (NE). The investigation focused on the evaluation of the performance of standard and integrative models in different runs. The performances of machine learning models and the nonlinear equation are higher than the linear equation. The results also show that FA increases the performance of all applied models. Moreover, the results indicate that the ANFIS-FA is the most stable integrative model in comparison to the other embedded methods and reveal that GMDH and SVR are the most stable technique among all applied models. The results also show that the accuracy of the LE-FA technique is relatively low, RMSE=0.091. The most accurate results provide SVR-FA, RMSE=0.034.
    Źródło:
    Eksploatacja i Niezawodność; 2022, 24, 2; 200--210
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Man-algorithm Cooperation Intelligent Design of Clothing Products in Multi Links
    Autorzy:
    Han, Chen
    Lei, Shen
    Shaogeng, Zhang
    Mingming, Wang
    Ying, Tang
    Powiązania:
    https://bibliotekanauki.pl/articles/2056307.pdf
    Data publikacji:
    2022
    Wydawca:
    Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
    Tematy:
    intelligent algorithm
    cooperation
    clothing design
    machine learning
    efficiency
    Opis:
    The changes in technology have led to a synchronous change in the clothing design method, as well as media and artistic aesthetics in the same period. The intelligence algorithm is constantly increasing its participation in development and production in the clothing industry. In this study, a variety of intelligent algorithms, including the parameterised numer state algorithm, Generative Adversarial Networks, and style transfer were introduced into the multi-links of clothing product design and development, such as clothing shape, print pattern, texture craft, product vision, and so on. Then, an innovative clothing design method based on the cooperation of the intelligent algorithm and various human functional roles was constructed. The method improves the efficiency of the multiple links of clothing design, such as generating 10000 printing patterns every 72.12 seconds, and completing the migration of 92.7 frames of the garment process style every second. To a certain extent, this study realizes the scale economy of clothing design and reduces its marginal cost through the unlimited computing power brought about by Moore’s law of digital technology, which provides a reference for the exploration of clothing design in the era of industry 4.0.
    Źródło:
    Fibres & Textiles in Eastern Europe; 2022, 1 (151); 59--66
    1230-3666
    2300-7354
    Pojawia się w:
    Fibres & Textiles in Eastern Europe
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Automatic identification of malfunctions of large turbomachinery during transient states with genetic algorithm optimization
    Autorzy:
    Barszcz, Tomasz
    Zabaryłło, Mateusz
    Powiązania:
    https://bibliotekanauki.pl/articles/2052104.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    machine learning
    fault detection
    transient
    turbine generator
    genetic algorithm
    Opis:
    Turbines and generators operating in the power generation industry are a major source of electrical energy worldwide. These are critical machines and their malfunctions should be detected in advance in order to avoid catastrophic failures and unplanned shutdowns. A maintenance strategy which enables to detect malfunctions at early stages of their existence plays a crucial role in facilities using such types of machinery. The best source of data applied for assessment of the technical condition are the transient data measured during start-ups and coast-downs. Most of the proposed methods using signal decomposition are applied to small machines with a rolling element bearing in steady-state operation with a shaft considered as a rigid body. The machines examined in the authors’ research operate above their first critical rotational speed interval and thus their shafts are considered to be flexible and are equipped with a hydrodynamic sliding bearing. Such an arrangement introduces significant complexity to the analysis of the machine behavior, and consequently, analyzing such data requires a highly skilled human expert. The main novelty proposed in the paper is the decomposition of transient vibration data into components responsible for particular failure modes. The method is automated and can be used for identification of turbogenerator malfunctions. Each parameter of a particular decomposed function has its physical representation and can help the maintenance staff to operate the machine properly. The parameters can also be used by the managing personnel to plan overhauls more precisely. The method has been validated on real-life data originating from a 200 MW class turbine. The real-life field data, along with the data generated by means of the commercial software utilized in GE’s engineering department for this particular class of machines, was used as the reference data set for an unbalanced response during the transients in question.
    Źródło:
    Metrology and Measurement Systems; 2022, 29, 1; 175-190
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A real-valued genetic algorithm to optimize the parameters of support vector machine for classification of multiple faults in NPP
    Autorzy:
    Amer, F. Z.
    El-Garhy, A. M.
    Awadalla, M. H.
    Rashad, S. M.
    Abdien, A. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/147652.pdf
    Data publikacji:
    2011
    Wydawca:
    Instytut Chemii i Techniki Jądrowej
    Tematy:
    support vector machine (SVM)
    fault classification
    multi fault classification
    genetic algorithm (GA)
    machine learning
    Opis:
    Two parameters, regularization parameter c, which determines the trade off cost between minimizing the training error and minimizing the complexity of the model and parameter sigma (σ) of the kernel function which defines the non-linear mapping from the input space to some high-dimensional feature space, which constructs a non-linear decision hyper surface in an input space, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GASVM) model that can automatically determine the optimal parameters, c and sigma, of SVM with the highest predictive accuracy and generalization ability simultaneously. The GASVM scheme is applied on observed monitored data of a pressurized water reactor nuclear power plant (PWRNPP) to classify its associated faults. Compared to the standard SVM model, simulation of GASVM indicates its superiority when applied on the dataset with unbalanced classes. GASVM scheme can gain higher classification with accurate and faster learning speed.
    Źródło:
    Nukleonika; 2011, 56, 4; 323-332
    0029-5922
    1508-5791
    Pojawia się w:
    Nukleonika
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Designing Smart Antennas Using Machine Learning Algorithms
    Autorzy:
    Samantaray, Barsa
    Das, Kunal Kumar
    Roy, Jibendu Sekhar
    Powiązania:
    https://bibliotekanauki.pl/articles/27312957.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    artificial neural network
    decision tree
    ensemble algorithm
    machine learning
    smart antenna
    support vector machine
    Opis:
    Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm.
    Źródło:
    Journal of Telecommunications and Information Technology; 2023, 4; 46--52
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of ship's speed through ground using the previous voyage's drift speed
    Autorzy:
    Yamane, D.
    Kano, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/24201461.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    machine learning
    weather routing
    ship's speed estimation
    drift speed
    algorithm
    route planning
    tidal current
    Opis:
    In recent years, 'weather routing' has been attracting increasing attention as a means of reducing costs and environmental impact. In order to achieve high-quality weather routing, it is important to accurately predict the ship's speed through ground during a voyage from ship control variables and predicted data on weather and sea conditions. Because sea condition forecasts are difficult to produce in-house, external data is often used, but there is a problem that the accuracy of sea condition forecasts is not sufficient and it is impossible to improve the accuracy of the forecasts because the data is external. In this study, we propose a machine learning method for predicting speed through ground by considering the actual values of the previous voyage’s drift speed for ships that regularly operate on the same route, such as ferries. Experimental results showed that this method improves the prediction performance of ship’s speed through ground.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2023, 17, 1; 129--137
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A survey on prediction of diabetes using classification algorithms
    Autorzy:
    Khanwalkar, A.
    Soni, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/1818807.pdf
    Data publikacji:
    2021
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    diabetes
    diabetes prediction
    algorithm
    data mining
    machine learning
    cukrzyca
    algorytm
    eksploracja danych
    uczenie maszynowe
    Opis:
    Purpose: Diabetes is a chronic disease that pays for a large proportion of the nation's healthcare expenses when people with diabetes want medical care continuously. Several complications will occur if the polymer disorder is not treated and unrecognizable. The prescribed condition leads to a diagnostic center and a doctor's intention. One of the real-world subjects essential is to find the first phase of the polytechnic. In this work, basically a survey that has been analyzed in several parameters within the poly-infected disorder diagnosis. It resembles the classification algorithms of data collection that plays an important role in the data collection method. Automation of polygenic disorder analysis, as well as another machine learning algorithm. Design/methodology/approach: This paper provides extensive surveys of different analogies which have been used for the analysis of medical data, For the purpose of early detection of polygenic disorder. This paper takes into consideration methods such as J48, CART, SVMs and KNN square, this paper also conducts a formal surveying of all the studies, and provides a conclusion at the end. Findings: This surveying has been analyzed on several parameters within the poly-infected disorder diagnosis. It resembles that the classification algorithms of data collection plays an important role in the data collection method in Automation of polygenic disorder analysis, as well as another machine learning algorithm. Practical implications: This paper will help future researchers in the field of Healthcare, specifically in the domain of diabetes, to understand differences between classification algorithms. Originality/value: This paper will help in comparing machine learning algorithms by going through results and selecting the appropriate approach based on requirements.
    Źródło:
    Journal of Achievements in Materials and Manufacturing Engineering; 2021, 104, 2; 77--84
    1734-8412
    Pojawia się w:
    Journal of Achievements in Materials and Manufacturing Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Building computer vision systems using machine learning algorithms
    Autorzy:
    Boyko, N.
    Sokil, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/410768.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Oddział w Lublinie PAN
    Tematy:
    algorithm
    information system
    neural network
    machine learning
    client-server architecture
    script
    artificial system
    machine learning algorithms
    algorytm
    systemy informacyjne
    sieci neuronowe
    systemy uczące
    architektura klient-serwer
    skrypt
    Opis:
    In this paper theoretic aspects of machine learning system in the field of computer vision is considered. There are presented methods of behavior analysis. There are offered tasks and problems associated with building systems using machine learning algorithm. The paper provides signs of problems that can be solved by using machine learning algorithms There is demonstrated step by step construction of computer vision system. The paper provides the algorithm of solving the problem of binary (two classes) classification for demonstration the machine learning algorithm possibilities in image recognition field, which can recognize the gender of the person on the photo. Aspects related to the search of data processing are also considered. There is analyzed the search of optimal parameters for algorithms. An interpretation of results in machine learning algorithm is provided. Binarization methods in machine learning algorithm are offered. There is analyzed the technology for improving the accuracy of machine learning algorithm. There are proposed ways to improve computer vision system in neural systems. Also there are analyzed large software modules that work using machine learning systems. The article provides prospects of powerful information technologies, which are necessary for the proper data selection in learning and configuration of feature extraction algorithm to create a computer vision system.
    Źródło:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2017, 6, 2; 15-20
    2084-5715
    Pojawia się w:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Innovative stress analysis and machine learning forecasting for semi-trailer truck body durability
    Autorzy:
    Lyashuk, Oleh
    Levkovych, Mykhailo
    Stashkiv, Mykola
    Pastukh, Oleh
    Martyniuk, Volodymyr
    Mironov, Dmytro
    Rabe, Marcin
    Vovk, Yuriy
    Powiązania:
    https://bibliotekanauki.pl/articles/27324297.pdf
    Data publikacji:
    2023
    Wydawca:
    Fundacja Centrum Badań Socjologicznych
    Tematy:
    transport
    energy resources
    semi-trailer truck body
    static stress
    static displacement
    CAD model
    algorithm
    machine learning
    Opis:
    This article presents an in-depth analysis of the stress-deformation state (SDS) in the bottom structure of a semi-trailer truck body. Engineering analysis was conducted utilizing the SolidWorks software, focusing on a comprehensive CAD model of the semi-trailer truck body. The study explored variations in SDS parameters resulting from alterations in the geometric parameters of the body bottom elements. The research investigated alterations in static stress and displacement relative to changes in the proportions of the cross-section of the channel while maintaining fixed geometric dimensions of the workpiece, thickness of the workpiece, and the material of the body bottom. Graphical representations were generated to illustrate the variations in static stress, displacement, and safety margin concerning the thickness of the shelf and channel. Additionally, dependencies were derived that correlate static stresses in the channel with the thickness of the channel wall and the thickness of the body bottom sheet. The study results were compiled and summarized, offering valuable insights into the stress-deformation state of the semi-trailer truck body's bottom. Furthermore, machine learning techniques, specifically the RandomForest algorithm, were implemented in a Python environment to predict changes in static stress based on various factors. The model's predictions were validated by comparing predicted static stress values with actual values on a test sample. These findings facilitate efficient selection of appropriately sized elements by predicting static stress values, employing the RandomForest machine learning algorithm.
    Źródło:
    Journal of Sustainable Development of Transport and Logistics; 2023, 8, 2; 43--57
    2520-2979
    Pojawia się w:
    Journal of Sustainable Development of Transport and Logistics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight
    Autorzy:
    Chen, Xuan
    Przystupa, Krzysztof
    Ye, Zhiwei
    Chen, Feng
    Wang, Chunzhi
    Liu, Jinhang
    Gao, Rong
    Wei, Ming
    Kochan, Orest
    Powiązania:
    https://bibliotekanauki.pl/articles/2087016.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    short-term electric load forecast
    extreme learning machine
    Lévy flight
    tree-seed algorithm
    Kernel principal component analysis
    Opis:
    In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. In this paper, a hybrid method, composed of kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed for short-term load forecasting. Specifically, the randomly generated weights and biases of ELM have a significant impact on the stability of prediction results. Therefore, in order to solve this problem, LTSA is utilized to obtain the optimal parameters before the prediction process is executed by ELM, which is called LTSA-ELM. Meanwhile, the input data is extracted by KPCA considering the sparseness of the electric load data and used as the input of LTSA-ELM model. The proposed method is tested on the data from European network on intelligent technologies (EUNITE) and experimental results demonstrate the superiority of the proposed approaches compared to the other methods involved in the paper.
    Źródło:
    Eksploatacja i Niezawodność; 2022, 24, 2; 153--162
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analiza wybranych metod tworzenia sztucznej inteligencji na przykładzie popularnej gry w karty
    Analysis of selected methods of creating artificial intelligence on the example of a popular card game
    Autorzy:
    Gałka, Łukasz
    Dzieńkowski, Mariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/98306.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Lubelska. Instytut Informatyki
    Tematy:
    artificial intelligence
    machine learning
    algorithm efficiency evaluation
    computer games
    sztuczna inteligencja
    uczenie maszynowe
    ocena skuteczności algorytmów
    gry komputerowe
    Opis:
    The aim of the article was to analyze selected methods of creating artificial intelligence in a popular card game. Two experiments were conducted: with a human and with a computer. The following algorithms were analyzed: random, min-max, based on a neural network, statistical and statistical with the use of “cheating” technique. The examined parameters were as follows: efficiency, execution time, number of implementation code lines, implementation time and training duration. The indicator with the greatest impact on the selection of the most optimal method was efficiency. The research has shown no difference in efficiency for the neural network-based algorithm and the statistical algorithm. In other cases, the differences in this feature were significant. The use of the “cheating” technique has increased the efficiency.
    Celem artykułu była analiza wybranych metod tworzenia sztucznej inteligencji w popularnej grze w karty. Zostały przeprowadzone dwa eksperymenty: z człowiekiem oraz z komputerem. Analizie poddano algorytmy: losowy, minmax, bazujący na sieci neuronowej, statystyczny oraz statystyczny z użyciem techniki „oszukiwania”. Zbadano takie parametry jak: skuteczność, czas wykonania, liczbę linii kodu implementacji, czas implementacji oraz czas trwania treningu. Wskaźnikiem mającym największy wpływ na wybór najbardziej optymalnej metody była skuteczność. Badania wykazały brak różnic w skuteczności dla algorytmu bazującego na sieci neuronowej i algorytmu statystycznego. W pozostałych przypadkach różnice tej cechy były istotne. Użycie techniki „oszukiwania” zwiększyło skuteczność.
    Źródło:
    Journal of Computer Sciences Institute; 2020, 16; 233-240
    2544-0764
    Pojawia się w:
    Journal of Computer Sciences Institute
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analiza istotności cech znamion skórnych dla celów diagnostyki czerniaka złośliwego
    Skin lesion features analysis for malignant melanoma classification
    Autorzy:
    Mikołajczyk, A.
    Grochowski, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/268540.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Gdańska. Wydział Elektrotechniki i Automatyki
    Tematy:
    algorytm ewolucyjny
    uczenie maszynowe
    sieci neuronowe
    systemy wspomagania decyzji
    evolutionary algorithm
    neural networks
    decision support system
    machine learning
    Opis:
    Pomimo dynamicznego rozwoju metod uczenia maszynowego i ich wdrażania do praktyki lekarskiej, automatyczna analiza znamion skórnych wciąż jest nierozwiązanym problemem. Poniższy artykuł proponuje zastosowanie algorytmu ewolucyjnego do zaprojektowania, wytrenowania i przetestowania całych populacji klasyfikatorów (sztucznych sieci neuronowych) oraz ich iteracyjnego udoskonalania w każdej kolejnej populacji, w celu osiągnięcia jak najlepszej dokładności klasyfikacji znamion skórnych. Algorytm zwraca optymalny zestaw cech opisujących obraz dermatoskopowy wraz z proponowaną architekturą sieci neuronowej. Uzyskano dokładność równą 85,83%, swoistość równą 79,07% oraz czułość równą 92,60%.
    Despite the dynamic development of machine learning methods, automatic analysis of skin lesions is still open issue. The following article proposes the use of an evolutionary algorithm to design, train, and to test a whole population of classifiers (artificial neural networks) and to iteratively improve them in each subsequent population, in order to achieve the best possible accuracy in the classification of skin lesions task. The algorithm returns an optimal set of features describing the dermatoscopic image together with the proposed architecture of the neural network. High classification results were obtained, in particular: accuracy equal to 85.83%, specificity 79.07% and sensitivity 92.60%.
    Źródło:
    Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej; 2018, 60; 67-70
    1425-5766
    2353-1290
    Pojawia się w:
    Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Cost-sensitive feature selection
    Selekcja cech z uwzględnieniem kosztu ich pozyskania
    Autorzy:
    Ciupke, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/327830.pdf
    Data publikacji:
    2006
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    selekcja cech
    algorytm mrówkowy
    uczenie maszynowe
    sztuczna inteligencja
    diagnostyka techniczna
    feature selection
    ant algorithm
    machine learning
    artificial intelligence
    technical diagnostics
    Opis:
    The paper concerns the selection of features in the technical diagnostics domain. The author focused his attention on a wrapper approach. In this approach an application of the ant algorithm as a search engine is proposed. The proposed method of so-called ant wrapper approach is presented. The method takes advantage of cost of features, where the cost is connected with the cost of sensors. The algorithm as a pseudo-code and some results of a verification experiment are shown. The verification was carried out on data derived from an active diagnostic experiment concerning a rotating machine. The obtained results show, that the proposed method could allow to reduce the number of used sensors.
    W artykule opisano metodę selekcji cech z zastosowaniem algorytmu mrówkowego. Metoda pozwala także na uwzględnienie kosztu atrybutu, przy czym jego koszt związany jest z kosztem pozyskanie sygnału diagnostycznego. W przypadku gdy sygnał ten jest już wykorzystywany uznaje się, że koszt wyznaczenia danej cechy jest pomijalnie mały. Metodę przedstawiono w postaci pseudo-kodu i zweryfikowano dla danych pochodzących z czynnego eksperymentu diagnostycznego. Uzyskane wyniki pokazują, że istnieje możliwość ograniczenia liczby stosowanych czujników.
    Źródło:
    Diagnostyka; 2006, 2(38); 45-48
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Applicability of artificial intelligence in smart healthcare systems for automatic detection of Parkinson’s Disease
    Autorzy:
    Pallathadka, Harikumar
    Padminivalli V., S.J.R.K.
    Vasavi, M.
    Nancy, P.
    Naved, Mohd
    Kumar, Harish
    Ray, Samrat
    Powiązania:
    https://bibliotekanauki.pl/articles/38709253.pdf
    Data publikacji:
    2024
    Wydawca:
    Instytut Podstawowych Problemów Techniki PAN
    Tematy:
    Parkinson’s disease
    detection
    machine learning
    relief algorithm
    LDA algorithm
    SVM-RBF
    accuracy
    sensitivity
    specificity
    choroba Parkinsona
    wykrywanie
    nauczanie maszynowe
    algorytm ulgi
    Algorytm LDA
    dokładność
    wrażliwość
    specyficzność
    Opis:
    Parkinson’s disease is associated with memory loss, anxiety, and depression in the brain. Problems such as poor balance and difficulty during walking can be observed in addition to symptoms of impaired posture and rigidity. The field dedicated to making computers capable of learning autonomously, without having to be explicitly programmed, is known as machine learning. An approach to the diagnosis of Parkinson’s disease, which is based on artificial intelligence, is discussed in this article. The input for this system is provided through photographic examples of Parkinson’s disease patient handwriting. Received photos are preprocessed using the relief feature option to begin the process. This is helpful in the process of selecting characteristics for the identification of Parkinson’s disease. After that, the linear discriminant analysis (LDA) algorithm is employed to reduce the dimensions, bringing down the total number of dimensions that are present in the input data. The photos are then classified via radial basis function-support vector machine (SVM-RBF), k-nearest neighbors (KNN), and naive Bayes algorithms, respectively.
    Źródło:
    Computer Assisted Methods in Engineering and Science; 2024, 31, 2; 175-185
    2299-3649
    Pojawia się w:
    Computer Assisted Methods in Engineering and Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market
    Autorzy:
    Ślepaczuk, Robert
    Zenkova, Maryna
    Powiązania:
    https://bibliotekanauki.pl/articles/1356913.pdf
    Data publikacji:
    2019-08-07
    Wydawca:
    Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
    Tematy:
    Machine learning
    support vector machines
    investment algorithm
    algorithmic trading
    strategy
    optimization
    cross-validation
    overfitting
    cryptocurrency market
    technical analysis
    meta parameters
    Opis:
    This study investigates the profitability of an algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns are in the highest or the lowest quintile. Each cryptocurrency is represented by a set of six technical features. SVM is trained on historical tail sets and tested on the current data. The classifier is chosen to be a nonlinear support vector machine. The portfolio is formed by ranking coins using the SVM output. The highest ranked coins are used for long positions to be included in the portfolio for one reallocation period. The following metrics were used to estimate the portfolio profitability: %ARC (the annualized rate of change), %ASD (the annualized standard deviation of daily returns), MDD (the maximum drawdown coefficient), IR1, IR2 (the information ratio coefficients). The performance of the SVM portfolio is compared to the performance of the four benchmark strategies based on the values of the information ratio coefficient IR1, which quantifies the risk-weighted gain. The question of how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.
    Źródło:
    Central European Economic Journal; 2018, 5, 52; 186 - 205
    2543-6821
    Pojawia się w:
    Central European Economic Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A rule based machine learning approach to the nonlinear multifingered robot gripper problem
    Autorzy:
    Abu-Zitar, R.
    Al-Fahed Nuseirat, A. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/970099.pdf
    Data publikacji:
    2005
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    zacisk robota
    programowanie ewolucyjne
    komputerowe uczenie się
    robot gripper
    nonlinear complementarity problem (NCP)
    Evolutionary Programming (EP)
    machine learning
    nearest-classifier-algorithm
    Opis:
    In this paper, we present a novel method that utilizes the accumulation of knowledge in a rule base for solving the nonlinear frictional gripper problem for both the isotropic and orthotropic cases. The knowledge is discovered and accumulated in a rule base with the aid of a genetic based machine learning mechanism. This machine learning mechanism extracts rules for solving the problem with the help of the Evolutionary Programming [EP) algorithm. The retrievals are done using the nearest-classifier-algorithm. This approach provides online solutions for the problem, and establishes a dynamic and evolving environment that adapts with new and sudden changes on the grip specifications or on the external forces. The resulting grasping forces using the presented method are compared with grasping forces obtained using other methods, such as the Complementarity Problems. The proposed online method could update the needed grasping forces to keep firm grip if the configuration of the forces externally applied to the object is changed. Numerical examples that illustrate the proposed method are presented.
    Źródło:
    Control and Cybernetics; 2005, 34, 2; 553-573
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Zastosowanie algorytmów przeszukiwania grafów do analizy obrazów medycznych
    Analysis of medical images based on graph search algorithms
    Autorzy:
    Dimitrova-Grekow, T.
    Dąbkowski, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/156629.pdf
    Data publikacji:
    2012
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    analiza obrazów medycznych
    algorytmy przeszukiwania grafów
    uczenie maszynowe
    eksploracja danych
    rozpoznawanie choroby
    image analysis
    graph search algorithm
    machine learning
    data mining
    disease recognition
    Opis:
    W artykule przedstawiono wyniki testów niekonwencjonalnego zastosowania metod do przeszukiwania grafów w celu analizy obrazów powstałych z rezonansu magnetycznego głowy. Zaprezentowano GUI do automatycznej obróbki serii obrazów. Zbudowane klasyfikatory wykazały, że metoda BFS analizy plików DICOM, po odpowiednej selekcji cech, pozwala na 100% rozpoznawanie chorych na wodogłowie i ponad 90% zdrowych, co zachęca do dalszych badań i obserwacji, np. czy osoby sklasyfikowane błędnie jako chorzy, po czasie rzeczywiście nie rozwinęli tej choroby.
    There are many methods for image segmentation [1, 2]: threshold, area, edge and hybrid methods. Area methods indicate groups of similar pixels form local regions [3, 4]. Edge methods detect boundaries between homogeneous segments [5, 6, 7]. In this paper we present the results of tests of unconventional implementation of graph search methods for the analysis of images generated from magnetic resonance imaging [8]. We explored the effectiveness of different approaches for dividing areas within a similar gray scale, using adapted graph search algorithms (DFS, BFS) after appropriate modification (Fig. 1). For this purpose, the Weka package (a tool for pre-processing, classification, regression, clustering and data visualization) was used [9]. A training set was generated after analyzing all the series of images from the database. First, we evaluated models created using certain algorithms and compared their efficacy (Tab. 1). This was followed by a selection of attributes (Tab. 2) and a re-evaluation of the models (Tab. 3). Comparison of the results of both evaluations showed that after selection of the relevant product attributes, you can achieve up to 100% detection of patients with hydrocephalus and over 90% proper recognition of healthy persons. This encourages further research and observation, such as whether persons wrongly classified as sick actually developed the disease in time. We designed a web application for the study, written in Windows Azure, as well as a GUI for automatic processing of a series of images (Fig. 2).
    Źródło:
    Pomiary Automatyka Kontrola; 2012, R. 58, nr 7, 7; 578-580
    0032-4140
    Pojawia się w:
    Pomiary Automatyka Kontrola
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Clothing Image Classification with a Dragonfly Algorithm Optimised Online Sequential Extreme Learning Machine
    Klasyfikacja obrazu odzieży za pomocą zoptymalizowanego algorytmu Dragonfly sekwencyjnej maszyny uczącej się
    Autorzy:
    Li, Jianqiang
    Shi, Weimin
    Yang, Donghe
    Powiązania:
    https://bibliotekanauki.pl/articles/1419412.pdf
    Data publikacji:
    2021
    Wydawca:
    Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
    Tematy:
    Dragonfly algorithm
    Online Sequential Extreme Learning Machine
    clothing image classification
    optimised parameter
    algorytm Dragonfly
    OSELM
    maszyna ucząca się
    klasyfikacja obrazu odzieży
    parametr zoptymalizowany
    Opis:
    This study proposes a solution for the issue of the low classification accuracy of clothing images. Using Fashion-MNIST as the clothing image dataset, we propose a clothing image classification technology based on an online sequential extreme learning machine (OSELM) optimised by the dragonfly algorithm (DA). First, we transform the Fashion-MNIST dataset into a data set that we extract from the corresponding grey image. Then, considering that the input weight and hidden layer bias of an OSELM are generated randomly, a DA is proposed to optimise the input weight and hidden layer bias of the OSELM to reduce the influence of random generation on the classification results. Finally, the optimised OSELM is applied to the clothing image classification. Compared to the other seven types of classification algorithms, the proposed clothing image classification model with the DA-optimised OSELM reached 93.98% accuracy when it contained 350 hidden nodes. Its performance was superior to other algorithms that were configured with the same number of hidden nodes. From a stability analysis of the box-plot, it was found that there were no outliers exhibited by the DA-OSELM model, whereas some other models had outliers or had lower stability compared to the model proposed, thereby validating the efficacy of the solution proposed.
    W pracy zaproponowano rozwiązanie problemu niskiej dokładności klasyfikacyjnej obrazów odzieży. Wykorzystując Fashion-MNIST jako zbiór danych obrazu odzieży, zaproponowano technologię klasyfikacji obrazów odzieży w oparciu o sekwencyjną maszynę uczącą się (OSELM) zoptymalizowaną przez algorytm Dragonfly (DA). Najpierw przekształcono zbiór danych Fashion-MNIST w zestaw danych, który wyodrębniono z obrazu. Następnie, biorąc pod uwagę, że waga wejściowa i odchylenie warstwy ukrytej OSELM były generowane losowo, w celu zmniejszenia wpływu generowania losowego na wyniki klasyfikacji zaproponowano DA w celu optymalizacji wagi wejściowej i obciążenia warstwy ukrytej OSELM. Następnie, zoptymalizowany OSELM zastosowano do klasyfikacji obrazu odzieży. W porównaniu z pozostałymi siedmioma typami algorytmów klasyfikacji, proponowany model klasyfikacji obrazu odzieży ze zoptymalizowanym przez DA OSELM osiągnął dokładność 93,98%. Jego wydajność przewyższyła inne algorytmy. Na podstawie analizy stabilności wykresu stwierdzono, że nie było wartości odstających wykazywanych przez model DA-OSELM, podczas gdy niektóre inne modele miały wartości odstające lub miały niższą stabilność w porównaniu z proponowanym modelem, potwierdzono w ten sposób skuteczność proponowanego rozwiązania.
    Źródło:
    Fibres & Textiles in Eastern Europe; 2021, 3 (147); 91-96
    1230-3666
    2300-7354
    Pojawia się w:
    Fibres & Textiles in Eastern Europe
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Machine Learning-Aided Architectural Design for Carbon Footprint Reduction
    Wspomagane uczeniem maszynowym projektowanie architektury w celu zmniejszenia śladu węglowego
    Autorzy:
    Płoszaj-Mazurek, Mateusz
    Powiązania:
    https://bibliotekanauki.pl/articles/129265.pdf
    Data publikacji:
    2020
    Wydawca:
    PWB MEDIA Zdziebłowski
    Tematy:
    ocena cyklu życia
    optymalizacja parametryczna
    ślad węglowy
    uczenie maszynowe
    sztuczna inteligencja
    algorytm
    emisja ghg
    architektura zrównoważona
    zbiór danych duży
    life cycle assessment
    parametric optimization
    carbon footprint
    artificial intelligence
    algorithm
    ghg emissions
    sustainable architecture
    big data
    machine learning
    Opis:
    The built environment is considered responsible for at least 20-40% of greenhouse gases emission. The way we design may exert an impact on this percentage. A new paradigm, namely artificial intelligence, is arriving. More and more tasks are becoming automated via algorithms. How could this power be applied in order to strengthen our knowledge about the ways we design buildings? The author of the following paper presents a study in which carbon footprint yielded by a multifamily building is analysed. ML has been used to generate an extensive overview of the possible design solutions. This, in turn, made it possible to observe correlations between various parameters that resulted in a reduced carbon footprint.
    Środowisko zabudowane odpowiada za co najmniej 20 do 40% emisji gazów cieplarnianych, a sposób, w jaki projektujemy, może wpłynąć na tę wartość. Coraz więcej zadań zostaje zautomatyzowanych za pomocą algorytmów. Jak możemy wykorzystać to narzędzie, aby wspomóc naszą wiedzę na temat sposobów projektowania budynków? Autor przedstawia badanie analizujące ślad węglowy budynku wielorodzinnego. Algorytm uczenia maszynowego został wykorzystany do wygenerowania obszernego przeglądu możliwych rozwiązań projektowych. Umożliwiło to zaobserwowanie korelacji między różnymi parametrami, co pozwoliło na wybór kombinacji parametrów o najniższym śladzie węglowym.
    Źródło:
    Builder; 2020, 24, 7; 35-39
    1896-0642
    Pojawia się w:
    Builder
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Artificial intelligence-powered pulse sequences in nuclear magnetic resonance and magnetic resonance imaging: historical trends, current innovations and perspectives
    Autorzy:
    Tokarz, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/35508129.pdf
    Data publikacji:
    2024
    Wydawca:
    Radomskie Towarzystwo Naukowe
    Tematy:
    artificial intelligence
    machine learning
    evolutionary algorithm
    artificial neural network
    nuclear magnetic resonance
    magnetic resonance imaging
    pulse sequence
    shaped pulse
    sztuczna inteligencja
    uczenie maszynowe
    algorytm ewolucyjny
    sztuczna sieć neuronowa
    magnetyczny rezonans jądrowy
    rezonans magnetyczny
    sekwencja impulsów
    impuls ukształtowany
    Opis:
    This review article explores the historical background and recent advances in the application of artificial intelligence (AI) in the development of radiofrequency pulses and pulse sequences in nuclear magnetic resonance spectroscopy (NMR) and imaging (MRI). The introduction of AI into this field, which traces back to the late 1970s, has recently witnessed remarkable progress, leading to the design of specialized frameworks and software solutions such as DeepRF, MRzero, and GENETICS-AI. Through an analysis of literature and case studies, this review tracks the transformation of AI-driven pulse design from initial proof-of-concept studies to comprehensive scientific programs, shedding light on the potential implications for the broader NMR and MRI communities. The fusion of artificial intelligence and magnetic resonance pulse design stands as a promising frontier in spectroscopy and imaging, offering innovative enhancements in data acquisition, analysis, and interpretation across diverse scientific domains.
    Źródło:
    Scientiae Radices; 2024, 3, 1; 30-52
    2956-4808
    Pojawia się w:
    Scientiae Radices
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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