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    Wyświetlanie 1-3 z 3
    Tytuł:
    A hybrid geometallurgical study using coupled Historical Data (HD) and Deep Learning (DL) techniques on a copper ore mine
    Autorzy:
    Gholami, Alireza
    Asgari, Kaveh
    Khoshdast, Hamid
    Hassanzadeh, Ahmad
    Powiązania:
    https://bibliotekanauki.pl/articles/2146884.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    hybrid geometallurgy
    historical data
    deep learning
    copper ore
    flotation
    Opis:
    This research work introduces a novel hybrid geometallurgical approach to develop a deep and comprehensive relationship between geological and mining characteristics with metallurgical parameters in a mineral processing plant. This technique involves statistically screening mineralogical and operational parameters using the Historical Data (HD) method. Further, it creates an intelligent bridge between effective parameters and metallurgical responses by the Deep Learning (DL) simulation method. In the HD method, the time and cost of common approaches in geometallurgical studies were minimized through the use of available archived data. Then, the generated DL-based predictive model was enabled to accurately forecast the process behavior in the mineral processing units. The efficiency of the proposed method for a copper ore sample was practically evaluated. For this purpose, six representative samples from different active mining zone were collected and used for flotation tests organized using a randomizing code. The experimental results were then statistically analyzed using HD method to assess the significance of mineralogical and operational parameters, including the proportions of effective minerals, particle size, collector and frother concentration, solid content and pH. Based on the HD analysis, the metallurgical responses including the copper grade and recovery, copper kinetics constant and iron grade in concentrate were modeled with an accuracy of about 90%. Next, the geometallurgical model of the process was developed using the long short-term memory neural network (LSTM) algorithm. The results showed that the studied metallurgical responses could be predicted with more than 95% accuracy. The results of this study showed that the hybrid geometallurgy approach can be used as a promising tool to achieve a reliable relationship between the mining and mineral processing sectors, and sustainable and predictable production.
    Źródło:
    Physicochemical Problems of Mineral Processing; 2022, 58, 3; art. no. 147841
    1643-1049
    2084-4735
    Pojawia się w:
    Physicochemical Problems of Mineral Processing
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Enhancing the performance of deep learning technique by combining with gradient boosting in rainfall-runoff simulation
    Autorzy:
    Abdullaeva, Barno S.
    Powiązania:
    https://bibliotekanauki.pl/articles/28411647.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Technologiczno-Przyrodniczy
    Tematy:
    deep learning
    gradient boosting
    hybrid model
    multi-step ahead forecasting
    rainfall-runoff simulation
    Opis:
    Artificial neural networks are widely employed as data mining methods by researchers across various fields, including rainfall-runoff (R-R) statistical modelling. To enhance the performance of these networks, deep learning (DL) neural networks have been developed to improve modelling accuracy. The present study aims to improve the effectiveness of DL networks in enhancing the performance of artificial neural networks via merging with the gradient boosting (GB) technique for daily runoff data forecasting in the river Amu Darya, Uzbekistan. The obtained results showed that the new hybrid proposed model performed exceptionally well, achieving a 16.67% improvement in determination coefficient (R2) and a 23.18% reduction in root mean square error (RMSE) during the training phase compared to the single DL model. Moreover, during the verification phase, the hybrid model displayed remarkable performance, demonstrating a 66.67% increase in R2 and a 50% reduction in RMSE. Furthermore, the hybrid model outperformed the single GB model by a significant margin. During the training phase, the new model showed an 18.18% increase in R2 and a 25% reduction in RMSE. In the verification phase, it improved by an impressive 75% in R2 and a 33.33% reduction in RMSE compared to the single GB model. These findings highlight the potential of the hybrid DL-GB model in improving daily runoff data forecasting in the challenging hydrological context of the Amu Darya River basin in Uzbekistan.
    Źródło:
    Journal of Water and Land Development; 2023, 59; 216--223
    1429-7426
    2083-4535
    Pojawia się w:
    Journal of Water and Land Development
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Hybrid deep learning method for detection of liver cancer
    Autorzy:
    Deshmukh, Sunita P.
    Choudhari, Dharmaveer
    Amalraj, Shankar
    Matte, Pravin N.
    Powiązania:
    https://bibliotekanauki.pl/articles/38701864.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Podstawowych Problemów Techniki PAN
    Tematy:
    liver cancer detection
    deep learning
    fully convolutional neural network
    hybrid approach
    discrete wavelet transform
    wykrywanie raka wątroby
    uczenie głębokie
    neuronowa sieć konwulcyjna
    podejście hybrydowe
    dyskretna transformata falkowa
    Opis:
    Liver disease refers to any liver irregularity causing its damage. There are several kinds of liver ailments. Benign growths are rarely life threatening and can be removed by specialists. Liver malignant tumor is leading causes of cancer death. Identifying malignant growth tissue is a troublesome and tedious task. There is significantly less information and statistical analysis presented related to cholangiocarcinoma and hepatoblastoma. This research focuses on the image analysis of these two types of cancer. The framework’s performance is evaluated using 2871 images, and a dual hybrid model is used to accomplish superb exactness. The aftereffects of both neural networks are sent into the result prioritizer that decides the most ideal choice for image arrangement. The relevance of elements appears to address the appropriate imaging rules for each class, and feature maps matching the original picture voxel features. The significance of features represents the most important imaging criteria for each class. This deep learning system demonstrates the concept of illuminating elements of a pre-trained deep neural network’s decision-making process by an examination of inner layers and the description of attributes that contribute to predictions.
    Źródło:
    Computer Assisted Methods in Engineering and Science; 2023, 30, 2; 151-165
    2299-3649
    Pojawia się w:
    Computer Assisted Methods in Engineering and Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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