Volkan Kaya¹, İsmail Akgül¹, Özge Zencir Tanır^2,*

Phyton-International Journal of Experimental Botany, Vol.92, No.9, pp. 2519-2534, 2023, DOI:10.32604/phyton.2023.029811

Abstract Classification and monitoring of microalgae species in aquatic ecosystems are important for understanding population dynamics. However, manual classification of algae is a time-consuming method and requires a lot of effort with expertise due to the large number of families and genera in its classification. The recognition of microalgae species has become an increasingly important research area in image recognition in recent years. In this study, machine learning and deep learning methods were proposed to classify images of 12 different microalgae species in order to successfully classify algae cells. 8 Different novel models (MobileNetV3Small-Lr, MobileNetV3SmallRf, MobileNetV3Small-Xg, MobileNetV3Large-Lr, MobileNetV3Large-Rf, MobileNetV3Large-Xg, MobileNetV3Small-Improved and… More >

Mahdi Hosseini^1,2,*, Bingyu Jian^1,2, Jian Zhang³, Haitao Li^1,2,*, Rodolfo Lorenzo⁴, Ahmad Hosseini⁵, Pritam Ghosh⁵, Feng Shen⁶, Dong Yang^1,2, Ziang Wang^1,2

Journal of Renewable Materials, Vol.11, No.9, pp. 3517-3531, 2023, DOI:10.32604/jrm.2023.028164

Abstract The safety of civilian and military infrastructure is a concern due to an increase in explosive risks, which has led to a demand for high-strength civil infrastructure with improved energy absorption capacity. In this study, a Finite Element (FE) numerical model was developed to determine the effect of hybrid Fibre Reinforced Polymer (FRP) as a strengthening material on full-scale Reinforced Concrete (RC) slabs. The reinforcing materials under consideration were Carbon (CFRP) and Glass (GFRP) fibres, which were subjected to blast loads to determine the structural response. A laminated composite fabric material model was utilized to model the failure of composite,… More > Graphic Abstract

Augusto González Bonorino^1,*, Malick Ndiaye², Casimer DeCusatis²

Journal of Quantum Computing, Vol.4, No.3, pp. 135-146, 2022, DOI:10.32604/jqc.2022.036706

Abstract The well-known Riccati differential equations play a key role in many fields, including problems in protein folding, control and stabilization, stochastic control, and cybersecurity (risk analysis and malware propagation). Quantum computer algorithms have the potential to implement faster approximate solutions to the Riccati equations compared with strictly classical algorithms. While systems with many qubits are still under development, there is significant interest in developing algorithms for near-term quantum computers to determine their accuracy and limitations. In this paper, we propose a hybrid quantum-classical algorithm, the Matrix Riccati Solver (MRS). This approach uses a transformation of variables to turn a set… More >

Mohammed A. Tashkandi^a , Abdelkarim Aydi^b,*

Frontiers in Heat and Mass Transfer, Vol.11, pp. 1-6, 2018, DOI:10.5098/hmt.11.27

Abstract In this work, a computational study of convective heat transfer in a hybrid CNT-Al2O3/water nanofluid cavity filled. The main considered parameters are the Rayleigh number and nanoparticles volume fraction. Results are presented in terms of flow structure, temperature field, and average Nusselt number. Since CNT and Al₂O₃ have different shapes to models are used to evaluate the effective thermal conductivity. It was found that both increasing Rayleigh number and nanoparticles volume fraction increase the heat transfer intensify the flow and affect the temperature field. Adding nanoparticles enhances the heat transfer due to the enhancement of the effective thermal conductivity. The… More >

Hao Cui, Yanling Guo^*, Yaning Xiao, Yangwei Wang^*, Jian Li, Yapeng Zhang, Haoyu Zhang

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1635-1675, 2023, DOI:10.32604/cmes.2023.026019

Abstract Harris Hawks Optimization (HHO) is a novel meta-heuristic algorithm that imitates the predation characteristics of Harris Hawk and combines Lévy flight to solve complex multidimensional problems. Nevertheless, the basic HHO algorithm still has certain limitations, including the tendency to fall into the local optima and poor convergence accuracy. Coot Bird Optimization (CBO) is another new swarm-based optimization algorithm. CBO originates from the regular and irregular motion of a bird called Coot on the water’s surface. Although the framework of CBO is slightly complicated, it has outstanding exploration potential and excellent capability to avoid falling into local optimal solutions. This paper… More > Graphic Abstract

Zhiwei Ye¹, Ziqian Fang¹, Zhina Song^1,*, Haigang Sui², Chunyan Yan¹, Wen Zhou¹, Mingwei Wang¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2019-2035, 2023, DOI:10.32604/iasc.2023.039949

Abstract Hyperparameters play a vital impact in the performance of most machine learning algorithms. It is a challenge for traditional methods to configure hyperparameters of the capsule network to obtain high-performance manually. Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a combinatorial optimization problem. However, these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted. The inspiration for the hybrid rice optimization (HRO) algorithm is from the breeding technology of three-line hybrid rice in China, which has the advantages of easy implementation, less parameters and… More >

Deepika Saravagi¹, Shweta Agrawal^2,*, Manisha Saravagi³, Sanjiv K. Jain⁴, Bhisham Sharma⁵, Abolfazl Mehbodniya^6,*, Subrata Chowdhury⁷, Julian L. Webber⁶

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2133-2151, 2023, DOI:10.32604/iasc.2023.039836

Abstract Spondylolisthesis is a chronic disease, and a timely diagnosis of it may help in avoiding surgery. Disease identification in x-ray radiographs is very challenging. Strengthening the feature extraction tool in VGG16 has improved the classification rate. But the fully connected layers of VGG16 are not efficient at capturing the positional structure of an object in images. Capsule network (CapsNet) works with capsules (neuron clusters) rather than a single neuron to grasp the properties of the provided image to match the pattern. In this study, an integrated model that is a combination of VGG16 and CapsNet (S-VCNet) is proposed. In the… More >

Hang Yang^1,2,*, Xunbo Li¹, Witold Pedrycz²

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1531-1551, 2023, DOI:10.32604/iasc.2023.039256

Abstract Energy supply is one of the most critical challenges of wireless sensor networks (WSNs) and industrial wireless sensor networks (IWSNs). While research on coverage optimization problem (COP) centers on the network’s monitoring coverage, this research focuses on the power banks’ energy supply coverage. The study of 2-D and 3-D spaces is typical in IWSN, with the realistic environment being more complex with obstacles (i.e., machines). A 3-D surface is the field of interest (FOI) in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN. The hybrid power bank deployment model is… More >

Anuj Kumar, Shakti Kumar^*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2175-2198, 2023, DOI:10.32604/iasc.2023.039093

Abstract Melanoma is a perfidious form of skin cancer. The study offers a hybrid framework for the automatic classification of melanoma. An Automatic Melanoma Detection System (AMDS) is used for identifying melanoma from the infected area of the skin image using image processing techniques. A larger number of pre-existing automatic melanoma detection systems are either commercial or their accuracy can be further improved. The research problem is to identify the best preprocessing technique, feature extractor, and classifier for melanoma detection using publically available MED-NODE data set. AMDS goes through four stages. The preprocessing stage is for noise removal; the segmentation stage… More >

Zhizhong Liu^*, Bin Zhou, Lingqiang Meng, Guangyu Huang

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1963-1981, 2023, DOI:10.32604/iasc.2023.038835

Abstract Recently, multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams, which has great potential to surpass unimodal sentiment analysis. One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy. Unfortunately, existing work always considers feature-level fusion or decision-level fusion, and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion. To improve the performance of multimodal sentiment analysis, we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy (BAHFS). Firstly, we apply BiGRU to learn the unimodal features of… More >