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JournalISSN: 2590-1885

Expert systems with applications 

Elsevier BV
About: Expert systems with applications is an academic journal published by Elsevier BV. The journal publishes majorly in the area(s): Computer science & Artificial intelligence. It has an ISSN identifier of 2590-1885. It is also open access. Over the lifetime, 2039 publications have been published receiving 9421 citations.

Papers published on a yearly basis

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Journal ArticleDOI
TL;DR: In this article , the authors proposed a novel nature-inspired meta-heuristic optimizer, called Reptile Search Algorithm (RSA), motivated by the hunting behaviour of Crocodiles.
Abstract: This paper proposes a novel nature-inspired meta-heuristic optimizer, called Reptile Search Algorithm (RSA), motivated by the hunting behaviour of Crocodiles. Two main steps of Crocodile behaviour are implemented, such as encircling, which is performed by high walking or belly walking, and hunting, which is performed by hunting coordination or hunting cooperation. The mentioned search methods of the proposed RSA are unique compared to other existing algorithms. The performance of the proposed RSA is evaluated using twenty-three classical test functions, thirty CEC2017 test functions, ten CEC2019 test functions, and seven real-world engineering problems. The obtained results of the proposed RSA are compared to various existing optimization algorithms in the literature. The results of the tested three benchmark functions revealed that the proposed RSA achieved better results than the other competitive optimization algorithms. The results of the Friedman ranking test proved that the RSA is a significantly superior method than other comparative methods. Finally, the results of the examined engineering problems showed that the RSA obtained better results compared to other various methods. Source codes of RSA are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/101385-reptile-search-algorithm-rsa-a-nature-inspired-optimizer • Developed a novel optimization algorithm inspired by hunting behaviour of Reptiles (RSA). • Tested RSA against classical, CEC2017, CEC2019 test functions and engineering problems. • Compared the RSA to other well-known optimization algorithms. • Demonstrated effectiveness and superiority of the proposed RSA.

457 citations

Journal ArticleDOI
TL;DR: In this paper , a new nature-inspired optimization method, named the Golden Jackal Optimization (GJO) algorithm is proposed, which aims to provide an alternative optimization method for solving real-world engineering problems.
Abstract: • Developed Golden Jackal Optimization (GJO) Algorithm as an optimization method. • Tested the performance of proposed algorithm against mathematical and engineering benchmarks. • Compared proposed algorithm with other well-known optimization algorithms. • Conducted statistical analyses. • Demonstrated superiority of proposed algorithm in various conditions. A new nature-inspired optimization method, named the Golden Jackal Optimization (GJO) algorithm is proposed, which aims to provide an alternative optimization method for solving real-world engineering problems. GJO is inspired by the collaborative hunting behaviour of the golden jackals (Canis aureus). The three elementary steps of algorithm are prey searching, enclosing, and pouncing, which are mathematically modelled and applied. The ability of proposed algorithm is assessed, by comparing with different state of the art metaheuristics, on benchmark functions. The proposed algorithm is further tested for solving seven different engineering design problems and introduces a real implementation of the proposed method in the field of electrical engineering. The results of the classical engineering design problems and real implementation verify that the proposed algorithm is appropriate for tackling challenging problems with unidentified search spaces.

114 citations

Journal ArticleDOI
TL;DR: In this paper , a review of machine learning techniques applied for stock market prediction is presented, focusing on the stock markets investigated in the literature as well as the types of variables used as input in the machine learning methods used for predicting these markets.
Abstract: In this literature review, we investigate machine learning techniques that are applied for stock market prediction. A focus area in this literature review is the stock markets investigated in the literature as well as the types of variables used as input in the machine learning techniques used for predicting these markets. We examined 138 journal articles published between 2000 and 2019. The main contributions of this review are: (1) an extensive examination of the data, in particular, the markets and stock indices covered in the predictions, as well as the 2173 unique variables used for stock market predictions, including technical indicators, macro-economic variables, and fundamental indicators, and (2) an in-depth review of the machine learning techniques and their variants deployed for the predictions. In addition, we provide a bibliometric analysis of these journal articles, highlighting the most influential works and articles.

72 citations

Journal ArticleDOI
TL;DR: In this article , an improved YOLO-Oleifera fruit detection model was proposed to detect and locate oil-seed camellia fruit in a complex environment of an orchard.
Abstract: • Object detection based on deep learning is applied to binocular location. • An improved Camellia oleifera fruit detection model is proposed. • The calculation amount of stereo matching is reduced. • It provides visual technical reference for field picking robots. In the complex environment of an orchard, changes in illumination, leaf occlusion, and fruit overlap make it challenging for mobile picking robots to detect and locate oil-seed camellia fruit. To address this problem, YOLO-Oleifera was developed as a fruit detection model method based on a YOLOv4-tiny model, To obtain clustering results appropriate to the size of the Camellia oleifera fruit, the k-means++ clustering algorithm was used instead of the k-means clustering algorithm used by the YOLOv4-tiny model to determine bounding box priors. Two convolutional kernels of 1×1 and 3×3 were respectively added after the second and third CSPBlock modules of the YOLOv4-tiny model. This model allows the learning of Camellia oleifera fruit feature information and reduces overall computational complexity. Compared with the classic stereo matching method based on binocular camera images, this method innovatively used the bounding box generated by the YOLO-Oleifera model to extract the region of interest of the fruit, and then adaptively performs stereo matching according to the generation mechanism of the bounding box. This allows the determination of disparity and facilitates the subsequent use of the triangulation principle to determine the picking position of the fruit. An ablation experiment demonstrated the effective improvement of the YOLOv4-tiny model. Camellia oleifera fruit images obtained under sunlight and shading conditions were used to test the YOLO-Oleifera model, and the model robustly detected the fruit under different illumination conditions. Occluded Camellia oleifera fruit decreased precision and recall due to the loss of semantic information. Comparison of this model with deep learning models YOLOv5-s,YOLOv3-tiny, and YOLOv4-tiny, the YOLO-Oleifera model achieved the highest AP of 0.9207 with the smallest data weight of 29 MB. The YOLO-Oleifera model took an average of 31 ms to detect each fruit image, fast enough to meet the demand for real-time detection. The algorithm exhibited high positioning stability and robust function despite changes in illumination. The results of this study can provide a technical reference for the robust detection and positioning of Camellia oleifera fruit by a mobile picking robot in a complex orchard environment.

68 citations

Journal ArticleDOI
TL;DR: In this paper , the authors proposed a sustainable supply chain model under environment friendly approach, which mainly focuses on the flexibility of production rate under the multi-retailer based supply chain to satisfy customer's demand.
Abstract: Environmental protection and the availability of natural resources help to maintain sustainability for manufacturing system. Reusing/remanufacturing economically viable used products, collected from end customers, has become an integral part of many supply chain operations. Remanufacturing with green investment plays an essential role in sustainable supply chain management by utilizing an environment-friendly approach. Considering this environmental, social, and economic development of society, this model mainly focuses on the flexibility of production rate under the multi-retailer based supply chain to satisfy customer’s demand. In this study, the manufacturer produces final products from new raw materials and used collected products. Subsequently, the products are transported to retailers and sold along with their service facilities. A mathematical model of this flexible manufacturing–remanufacturing system is developed to improve the service and to maintain sustainability always. The global optimization is established theoretically and a proposition is developed. Through numerical experiments, the global optimality is also verified. Some special cases, along with a comparison graph, are also provided for the validation of this results. The obtained results indicate that the concerned idea of service facility helps the customer to choose products without hesitation and the supply chain management achieves the maximum profit under green investment. It is found that the proposed study converges over the fixed production rate and without service studies. • We propose a sustainable supply chain model under environment friendly approach. • Multi-retailer based SCM is developed with a flexible production and a variable demand. • Service facility under manufacturing–remanufacturing maximizes the SCM total profit. • Separate holding costs for fresh and recovered items are helpful for smooth business. • The SCM is developed through a green investment and a waste reduction initiative.

62 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
2023985
20221,677