Purpose - This paper aims to predict Dogecoin price by using artificial intelligence (AI) methods and comparing the results with the econometrics models. Design/methodology/approach - An artificial neural network (ANN...
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Purpose - This paper aims to predict Dogecoin price by using artificial intelligence (AI) methods and comparing the results with the econometrics models. Design/methodology/approach - An artificial neural network (ANN) was applied as a prediction method without any optimization techniques. Additionally, the genetic algorithm (GA) is used to select the most appropriate input variables. Additionally, based on the literature review and the relationships between cryptoprice and global indices, 20 economic indicators, such as Coinbase Bitcoin, Coinbase Litecoin and US dollars, along with main global stock indices such as FTSE100 and NIFTY50, are identified as input variables for the model. Lichtenberg algorithm (LA) and aquila optimization (AO) algorithm are used to make the ANN more robust. To validate our algorithms, they have been implemented on daily data for the last three years. To demonstrate the superiority of the models over traditional methods such as econometrics, regression analysis and curve fitting techniques are used. The effectiveness of these models is then evaluated and compared using criteria such as recall, accuracy and precision. Findings - The results indicate that AI-based algorithms not only enhance the accuracy, recall and precision of calculations but also expedite the process without requiring the numerous and restrictive assumptions associated with time series and econometric models. Originality/value - The main contribution of this paper is the application of novel approaches such as AO and LA to improve the predictive capabilities of the ANN method for various cryptocurrencies' prices. It demonstrates the superiority of the proposed algorithms over traditional econometric models using real-life data.
This systematic review explores the application of Artificial Intelligence (AI) in optimizing the mix design of fly ash-based geopolymer concrete (FABGC). Analyzing studies published between 2014 and 2025, it examines...
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Fifth generation (5G) wireless networks are based on the use of spectrum blocks above 6 GHz in the millimeter wave (mmWave) range to increase throughput and reduce the overall level of interference in very busy freque...
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Fifth generation (5G) wireless networks are based on the use of spectrum blocks above 6 GHz in the millimeter wave (mmWave) range to increase throughput and reduce the overall level of interference in very busy frequency bands below 6 GHz. With the global deployment of the first commercial installations of 5G, the availability of multi-Gbps wireless connections in the mmWave frequency band becomes closer to reality and opens up some unique uses for 5G. Although, mmWave communication is expected to enable high-power radio links and broadband wireless intranet, its main challenges are inherent poor propagation conditions and high transmitter-receiver coordination requirement, which prevent it from realizing its full potential. When smart reflective surfaces are used in mmWave communication, channel state information becomes complex and imprecise. In this study, a hybrid intelligent reflecting surface consisting of a large number of passive components and a small number of RF circuits is proposed as a solution. Then, an improved deep neural network (DNN)-based technique is proposed to estimate the effective channel. The proposed technique provides better channel estimation performance according to the simulation results and improves the quality of service.
Tow-steered composites, with their curvilinear fiber paths, have the potential to significantly enhance structural performance. Nevertheless, their development is impeded by the absence of dedicated computational tool...
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ISBN:
(数字)9781624107115
ISBN:
(纸本)9781624107115
Tow-steered composites, with their curvilinear fiber paths, have the potential to significantly enhance structural performance. Nevertheless, their development is impeded by the absence of dedicated computational tools and the considerable computational effort required to probe the vast design space of aerospace structures. To address these issues, an integrated design tool is developed as a plug-in code in commercial finite element (FE) software packages. A new machine learning (ML) module is developed in the plug-in code to provide a cost-effective surrogate model to diminish computational demands. To reduce the computational cost, a mixed-fidelity model and a transfer learning model are developed, both of which utilize varying mesh densities within FE modeling to create coarse mesh, i.e., low-fidelity datasets, and fine mesh, i.e., high-fidelity datasets. The accuracy and efficiency of these ML models are evaluated against a high-fidelity FE model. The findings reveal that the mixed-fidelity and transfer learning models achieve similar accuracy to a high-fidelity FE model and markedly enhance efficiency over the single-fidelity ML model.
Deep learning(DL)plays a critical role in processing and converting data into knowledge and *** technologies have been applied in a variety of applications,including image,video,and genome sequence *** deep learning t...
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Deep learning(DL)plays a critical role in processing and converting data into knowledge and *** technologies have been applied in a variety of applications,including image,video,and genome sequence *** deep learning the most widely utilized architecture is Convolutional Neural Networks(CNN)are taught discriminatory traits in a supervised *** comparison to other classic neural networks,CNN makes use of a limited number of artificial neurons,therefore it is ideal for the recognition and processing of wheat gene *** is an essential crop of cereals for people around the *** Genotypes identification has an impact on the possible development of many countries in the agricultural *** quantitative genetics prediction of genetic values is a central *** is an allohexaploid(AABBDD)with three distinct *** sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat,Therefore,genome sequence approaches based on techniques of Artificial Intelligence(AI)are *** paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources and managing genetic variation in their breeding program,as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current *** this paper,the hyperparameters of the network are optimized in the CNN to decrease the requirement for manual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks(CNN).
As the adoption of additive manufacturing continues to grow in the aerospace industry, part consolidation is an emerging design technique aimed at decreasing assembly cost. Significant research is focused on design fo...
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ISBN:
(数字)9781624105951
ISBN:
(纸本)9781624105951
As the adoption of additive manufacturing continues to grow in the aerospace industry, part consolidation is an emerging design technique aimed at decreasing assembly cost. Significant research is focused on design for additive manufacturing principles and their integration into design generation tools such as topology optimization, while part consolidation research has been limited to heuristic guidelines. This work presents the extension of topology optimization to assembly design for the simultaneous optimization of structural performance and connection layout. This methodology uses multiple domains occupying the same space along with a single joining domain to represent the assembly design. The proposed approach allows for future extensions with the calculation of additive manufacturing part costs on an individual domain level. The methodology is tested on a numerical example demonstrating the variation in part geometry and number of parts as the emphasis on joining cost is varied.
Multiple sequence alignment (MSA) is an elementary task of bioinformatics where the alignment of three or more biological sequences is produced in a way that helps to identify the homologous regions in the sequences. ...
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ISBN:
(数字)9781728166445
ISBN:
(纸本)9781728166445
Multiple sequence alignment (MSA) is an elementary task of bioinformatics where the alignment of three or more biological sequences is produced in a way that helps to identify the homologous regions in the sequences. This research paper proposes a genetic algorithm-based optimization approach that can enhance the value of multiple sequence alignment (MSA) generated by the progressive technique. Mutation operators like gap insertion mutation and gap removal mutations are applied to enhance the value of the MSA obtained by the progressive technique of alignment.
When designing an inference model, feature selection methods can be used to select a subset of relevant variables from the original set of variables to increase the performance and reduce the complexity of the model. ...
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This paper presents an actuator used for the trajectory correction fuze,which is subject to high impact loadings during launch.A simulation method is carried out to obtain the peak-peak stress value of each component,...
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This paper presents an actuator used for the trajectory correction fuze,which is subject to high impact loadings during launch.A simulation method is carried out to obtain the peak-peak stress value of each component,from which the ball bearings are possible failures according to the ***,three schemes against impact loadings,full-element deep groove ball bearing and integrated raceway,needle roller thrust bearing assembly,and gaskets are utilized for redesigning the actuator to effectively reduce the bearings’***,multi-objectives optimization still needs to be conducted for the gaskets to decrease the stress value further to the yield *** gasket’s structure parameters and three bearings’peak-peak stress are served as the four optimization variables and three objectives,*** Latin hypercube design is used for generating sample points,and Kriging model selected according to estimation result can establish the relationship between the variables and objectives,representing the simulation which is ***,two optimization algorithms work out the Pareto solutions,from which the best solutions are selected,and verified by the simulation to determine the gaskets optimized structure *** can be concluded that the simulation and optimization method based on these components is effective and efficient.
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