H. M. Shahzad^1,3, Sohail Masood Bhatti^1,3,*, Arfan Jaffar^1,3, Muhammad Rashid²

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1561-1570, 2023, DOI:10.32604/iasc.2023.032525

Abstract In recent years, research on facial expression recognition (FER) under mask is trending. Wearing a mask for protection from Covid 19 has become a compulsion and it hides the facial expressions that is why FER under the mask is a difficult task. The prevailing unimodal techniques for facial recognition are not up to the mark in terms of good results for the masked face, however, a multimodal technique can be employed to generate better results. We proposed a multimodal methodology based on deep learning for facial recognition under a masked face using facial and vocal expressions. The multimodal has been… More >

Fatma Harby¹, Adel Thaljaoui¹, Durre Nayab², Suliman Aladhadh^3,*, Salim EL Khediri^3,4, Rehan Ullah Khan³

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5191-5207, 2023, DOI:10.32604/cmc.2022.029420

Abstract In the machine learning (ML) paradigm, data augmentation serves as a regularization approach for creating ML models. The increase in the diversification of training samples increases the generalization capabilities, which enhances the prediction performance of classifiers when tested on unseen examples. Deep learning (DL) models have a lot of parameters, and they frequently overfit. Effectively, to avoid overfitting, data plays a major role to augment the latest improvements in DL. Nevertheless, reliable data collection is a major limiting factor. Frequently, this problem is undertaken by combining augmentation of data, transfer learning, dropout, and methods of normalization in batches. In this… More >

S. Kalavathi^1,*, R. Nedunchelian²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 39-56, 2023, DOI:10.32604/iasc.2023.031522

Abstract Network analysis is a promising field in the area of network applications as different types of traffic grow enormously and exponentially. Reliable route prediction is a challenging task in the Large Scale Networks (LSN). Various non-self-learning and self-learning approaches have been adopted to predict reliable routing. Routing protocols decide how to send all the packets from source to the destination addresses across the network through their IP. In the current era, dynamic protocols are preferred as they network self-learning internally using an algorithm and may not entail being updated physically more than the static protocols. A novel method named Reliable… More >

Amit Ramesh Khaparde^1,*, Fawaz Alassery², Arvind Kumar³, Youseef Alotaibi⁴, Osamah Ibrahim Khalaf⁵, Sofia Pillai⁶, Saleh Alghamdi⁷

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1045-1062, 2022, DOI:10.32604/iasc.2022.023270

Abstract This paper presents the study of differential evolution algorithm with hierarchical fair competition model (HFC-DE). HFC model is based on the fair competition of societal system found in natural world. In this model, the population is split into hierarchy and the competition is allowed between the hierarchical members. During evolution, the population members are allowed to move within the hierarchy levels. The standard differential evolution algorithm is used for population evolution. Experimentation has carried out to define the parameter for proposed model on test suit having unimodal problems and multi-model problems. After analyzing the results, the two variants of HFC-DE… 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

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 method on the outputs of… More >

Harmandeep Singh Gill^1,*, Osamah Ibrahim Khalaf², Youseef Alotaibi³, Saleh Alghamdi⁴, Fawaz Alassery⁵

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 637-650, 2022, DOI:10.32604/iasc.2022.022589

Abstract Contemporary vision and pattern recognition issues such as image, face, fingerprint identification, and recognition, DNA sequencing, often have a large number of properties and classes. To handle such types of complex problems, one type of feature descriptor is not enough. To overcome these issues, this paper proposed a multi-model recognition and classification strategy using multi-feature fusion approaches. One of the growing topics in computer and machine vision is fruit and vegetable identification and categorization. A fruit identification system may be employed to assist customers and purchasers in identifying the species and quality of fruit. Using Convolution Neural Network (CNN), Recurrent… More >

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

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 deviation, energy, contrast, etc., of… 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

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 our method can efficiently generate… 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

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 and the T–S model shows… 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

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. Considering the diversity of the… More >