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Academic literature on the topic 'GA. Information industry'
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Journal articles on the topic "GA. Information industry"
1
Rao, Xiaoyang, and Xuesong Yan. "Particle Swarm Optimization Algorithm Based on Information Sharing in Industry 4.0." Wireless Communications and Mobile Computing 2022 (March 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/4328185.
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Intelligent manufacturing is an important part of Industry 4.0; artificial intelligence technology is a necessary means to realize intelligent manufacturing. This requires the exploration of pattern recognition, computer vision, intelligent optimization, and other related technologies. Particle swarm optimization (PSO) algorithm is an optimization algorithm inspired by the foraging behavior of birds. PSO was an intelligent technology and an efficient optimization algorithm verified by a lot of research and experiments. In this paper, the traditional PSO algorithm is compared with genetic algorithms (GA) to illustrate the performance of the traditional PSO algorithm. By analyzing the advantages and disadvantages of the traditional PSO algorithm, the traditional PSO algorithm is improved through introducing into the sharing information mechanism and the competition strategy, called information sharing based PSO (IPSO). The novel algorithm IPSO was the rapid convergence speed similar to the traditional PSO and enhanced the global search capability. Our experimental results show that IPSO has better performance than the traditional PSO and the GA algorithm on benchmark functions, especially for difficult functions.
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Kim, Seonhyeon, Ephraim Kwashie Thompson, and Changki Kim. "Insurance Transactions through Blockchain Applications: Current Status and Implications for the Domestic Insurance Industry." Crisis and Emergency Management: Theory and Praxis 17, no. 11 (2021): 137–56. http://dx.doi.org/10.14251/crisisonomy.2021.17.11.137.
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This paper discusses the necessity for introducing blockchain technology into insurance sales channels. We focus on two factors that have hindered the sustainable development of the domestic insurance industry. The first problem is that insurance companies’ profitability is on the decline and the second issue relates to information asymmetry problems besetting insurance sales channels. The channels are heavily concentrated on general agencies (GA) but the GA channel has been characterized by problems such as incomplete sales and high termination rates. Due to the advantages of blockchain technology such as the transparency of information and reduction in transaction and security costs, insurance sales channels such as GA can help reduce insurance companies’ business costs and alleviate information asymmetry by adopting blockchain technology. Thus, in this paper, we discuss ways to utilize applications using blockchain platforms in insurance sales channels. This paper also introduces the web-based blockchain application of International Business Machines (IBM) and suggests that various stakeholders of insurance can interact through blockchain applications.
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Jendrzejczyk-Handzlik, D. "Phase equilibria in the ternary Ag-Au-Ga system: Isothermal sections at 250°C and 450°C." Journal of Mining and Metallurgy, Section B: Metallurgy 53, no. 3 (2017): 215–22. http://dx.doi.org/10.2298/jmmb170531043j.
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The ternary Ag-Au-Ga system seems to be interesting in jeweller?s craft as a joint. Moreover, the ternary systems based on gold and silver have found applications in the dental industry. A literature overview of the Ag-Au-Ga system shows that the information about phase equilibria of this system does not exist. In the present work, phase equilibria in the Ag-Au-Ga ternary system have been studied by using scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS) analysis and X-ray diffraction analysis (XRD). Twenty two annealed alloys in the 10-70 at.% Ga region have been investigated. Obtained experimental results were compared with the predicted isothermal sections at two temperatures (250?C and 450?C). No ternary compounds are found.
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Liu, Chao, Yixin Fan, and Xiangyu Zhu. "Fintech Index Prediction Based on RF-GA-DNN Algorithm." Wireless Communications and Mobile Computing 2021 (June 7, 2021): 1–9. http://dx.doi.org/10.1155/2021/3950981.
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The Fintech index has been more active in the stock market with the Fintech industry expanding. The prediction of the Fintech index is significant as it is capable of instructing investors to avoid risks and provide guidance for financial regulators. Traditional prediction methods adopt the deep neural network (DNN) or the combination of genetic algorithm (GA) and DNN mostly. However, heavy computational load is required by these algorithms. In this paper, we propose an integrated artificial intelligence-based algorithm, consisting of the random frog algorithm (RF), GA, and DNN, to predict the Fintech index. The proposed RF-GA-DNN prediction algorithm filters the key input variables and optimizes the hyperparameters of DNN. We compare the proposed RF-GA-DNN with the traditional GA-DNN in terms of convergence time and prediction accuracy. Results show that the convergence time of GA-DNN is up to 20 hours and its prediction accuracy is 97.4%. In comparison, the convergence time of our RF-GA-DNN is only about 1.5 hours and the prediction accuracy reaches 97.0%. These results demonstrate that the proposed RF-GA-DNN prediction algorithm significantly reduces the convergence time with the promise of competitive prediction accuracy. Thus, the proposed algorithm deserves to be widely recommended for predicting the Fintech index.
5
Kandiri, Amirreza, Farid Sartipi, and Mahdi Kioumarsi. "Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms." Applied Sciences 11, no. 2 (2021): 485. http://dx.doi.org/10.3390/app11020485.
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Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other.
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Wang, Di, Lin Xie, Simon Yang, and Fengchun Tian. "Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors." Sensors 18, no. 10 (2018): 3222. http://dx.doi.org/10.3390/s18103222.
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Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco industry. In this paper, a novel method based on the support vector machine (SVM) is proposed to discriminate the tobacco cultivation region using the near-infrared (NIR) sensors, where the genetic algorithm (GA) is employed for input subset selection to identify the effective principal components (PCs) for the SVM model. With the same number of PCs as the inputs to the SVM model, a number of comparative experiments were conducted between the effective PCs selected by GA and the PCs orderly starting from the first one. The model performance was evaluated in terms of prediction accuracy and four parameters of assessment criteria (true positive rate, true negative rate, positive predictive value and F1 score). From the results, it is interesting to find that some PCs with less information may contribute more to the cultivation regions and are considered as more effective PCs, and the SVM model with the effective PCs selected by GA has a superior discrimination capacity. The proposed GA-SVM model can effectively learn the relationship between tobacco cultivation regions and tobacco NIR sensor data.
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Praveen, S. Phani, Hesam Ghasempoor, Negar Shahabi, and Fatemeh Izanloo. "A Hybrid Gravitational Emulation Local Search-Based Algorithm for Task Scheduling in Cloud Computing." Mathematical Problems in Engineering 2023 (February 4, 2023): 1–9. http://dx.doi.org/10.1155/2023/6516482.
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The flexibility of cloud computing to provide a dynamic and adaptable infrastructure in the context of information technology and service quality has made it one of the most challenging issues in the computer industry. Task scheduling is a major challenge in cloud computing. Scheduling tasks so that they may be processed by the most effective cloud network resources has been identified as a critical challenge for maximizing cloud computing’s performance. Due to the complexity of the issue and the size of the search space, random search techniques are often used to find a solution. Several algorithms have been offered as possible solutions to this issue. In this study, we employ a combination of the genetic algorithm (GA) and the gravitational emulation local search (GELS) algorithm to overcome the task scheduling issue in cloud computing. GA and the particle swarm optimization (PSO) algorithms are compared to the suggested algorithm to demonstrate its efficacy. The suggested algorithm outperforms the GA and PSO, as shown by the experiments.
8
Chen, Chen, Thomas Phang, and Lee Kong Tiong. "Planning semi-automated precast production using GA." International Journal of Industrialized Construction 1, no. 1 (2020): 48–63. http://dx.doi.org/10.29173/ijic215.
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Although fully automated production systems have been developed and used in some industry leaders, most of the precast factories have yet to be developed to that stage. Semi-automated production lines are still popularly used. As production productivity can be maximally improved within the physical constraints by applying a sound production plan, this paper tends to propose a production planning method for the semi-automated precast production line using genetic algorithm (GA). The production planning problem is formulated into a flexible job shop scheduling problem (FJSSP) model and solved using an integrated approach. Thanks to the development of new technologies such as building information modeling (BIM) platform and radio frequency identification (RFID), implementation of a just-in-time (JIT) schedule in the semi-automated precast production line becomes practicable on the grounds of risk mitigation and enhanced demand forecast capability. In this regard, the optimization objectives are minimum makespan, station idle time, and earliness and tardiness penalty. An example was applied to validate the integrated GA approach. The experimental results show that the developed GA approach is a useful and effective method for solving the problem that it can return high-quality solutions. This paper thus contributes to the body of knowledge new precast production planning method for practical usage.
9
Kourousis, Kyriakos I. "A HOLISTIC APPROACH TO GENERAL AVIATION AIRCRAFT STRUCTURAL FAILURE PREVENTION IN AUSTRALIA." Aviation 17, no. 3 (2013): 98–103. http://dx.doi.org/10.3846/16487788.2013.840055.
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Ageing aircraft are becoming a major issue in the general aviation (GA) industry, both in terms of safety and maintenance and support cost. Ensuring a sound structure is considered one of the primary challenges in this area, it is, therefore, attracting the attention of the regulating authorities. The Civil Aviation Safety Agency (CASA) has taken a mixture of actions to tackle the various issues related to the diverse Australian GA ageing aircraft fleet. Further efforts focus on increasing the awareness of the different parties engaged in aircraft operations, maintenance and design, as well as quantification of the associated risk. In this frame a holistic approach is proposed to cover the various aspects, emphasizing the use of cost-effective structural health monitoring (SHM) systems and web-based education and information dissemination on ageing aircraft issues.
10
Yang, Zeyin. "Application and Development of Digital Enhancement of Traditional Sculpture Art." Scientific Programming 2022 (February 3, 2022): 1–8. http://dx.doi.org/10.1155/2022/9095577.
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Sculpture art, as an important carrier of spiritual civilization, also portrays a prosperous scene as an industry with urban and cultural development. Three-dimensional technology offers a new platform for sculpture creation, allowing for the digitization of sculpture works via electronic information technology, and the display of sculpture works in front of people via displays, facilitating the exchange and dissemination of information and promoting the growth and progress of the entire sculpture creation industry. We plan to use digital enhancement technology to conduct small-scale creation experiments on traditional sculpture works, discuss the method of GA (Genetic Algorithm) in image restoration processing, investigate the method of image segmentation processing based on the genetic algorithm, and propose the method of image segmentation processing based on the fuzzy membership surface genetic algorithm, in order to verify and solve the creation difficulties of traditional sculpture works.