N. Jayanthi^1,*, D. Manohari², Mohamed Yacin Sikkandar³, Mohamed Abdelkader Aboamer³, Mohamed Ibrahim Waly³, C. Bharatiraja⁴

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1317-1329, 2022, DOI:10.32604/iasc.2022.018974 - 22 September 2021

Abstract Lung cancer is a curable disease if detected early, and its mortality rate decreases with forwarding treatment measures. At first, an easy and accurate way to detect is by using image processing techniques on the cancer-affected images captured from the patients. This paper proposes a novel lung cancer detection method. Firstly, an adaptive median filter algorithm (AMF) is applied to preprocess those images for improving the quality of the affected area. Then, a supervised image edge detection algorithm (SIED) is presented to segment those images. Then, feature extraction is employed to extract the mean, standard More >

Guanghao Jin¹, Na Zhao¹, Chunmei Pei¹, Hengguang Li², Qingzeng Song³, Jing Yu^1,*

Journal on Artificial Intelligence, Vol.3, No.4, pp. 135-143, 2021, DOI:10.32604/jai.2021.027154 - 07 February 2022

Abstract Generally, the performance of deep learning models is related to the captured features of training samples. When the training samples belong to different domains, the diverse features may increase the difficulty of training high performance models. In this paper, we built a new framework that generates multiple models on the organized samples to increase the accuracy of classification. Firstly, our framework selects some existing models and trains each of them on organized training sets to get multiple trained models. Secondly, we select some of them based on a validation set. Finally, we use some fusion More >

Bin Lin¹, Jixin Chen², Zhihong Zhang³, Yanlin Lai², Xinlong Wu², Lulu Tian⁴, Wangchi Cheng^5,*

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 641-649, 2021, DOI:10.32604/iasc.2021.016818 - 11 August 2021

Abstract Researches have shown that Deep neural networks (DNNs) are vulnerable to adversarial examples. In this paper, we propose a generative model to explore how to produce adversarial examples that can deceive multiple deep learning models simultaneously. Unlike most of popular adversarial attack algorithms, the one proposed in this paper is based on the Generative Adversarial Networks (GAN). It can quickly produce adversarial examples and perform black-box attacks on multi-model. To enhance the transferability of the samples generated by our approach, we use multiple neural networks in the training process. Experimental results on MNIST showed that More >

Abdul-Wahid A. Saif¹, Mohammad Ataur-Rahman¹, Sami Elferik¹, Muhammad F. Mysorewala¹, Mujahed Al-Dhaifallah^1,*, Fouad Yacef²

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 817-834, 2021, DOI:10.32604/iasc.2021.015932 - 01 March 2021

Abstract In this paper, the formation control problem of a group of unmanned air vehicle (UAV) quadrotors is solved using the Takagi–Sugeno (T–S) multi-model approach to linearize the nonlinear model of UAVs. The nonlinear model sof the quadrotor is linearized first around a set of operating points using Taylor series to get a set of local models. Our approach’s novelty is in considering the difference between the nonlinear model and the linearized ones as disturbance. Then, these linear models are interpolated using the fuzzy T–S approach to approximate the entire nonlinear model. Comparison of the nonlinear… More >

Lihua Zhu¹, Zhiqiang Wu¹, Lei Wang², Yu Wang^{1, *}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 405-418, 2020, DOI:10.32604/cmc.2020.010124 - 23 July 2020

Abstract The wind as a natural phenomenon would cause the derivation of the pesticide drops during the operation of agricultural unmanned aerial vehicles (UAV). In particular, the changeable wind makes it difficult for the precision agriculture. For accurate spraying of pesticide, it is necessary to estimate the real-time wind parameters to provide the correction reference for the UAV path. Most estimation algorithms are model based, and as such, serious errors can arise when the models fail to properly fit the physical wind motions. To address this problem, a robust estimation model is proposed in this paper. More >