Javaria Amin¹, Muhammad Almas Anjum², Muhammad Sharif³, Seifedine Kadry⁴, Yunyoung Nam^5,*, ShuiHua Wang⁶

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2693-2709, 2021, DOI:10.32604/cmc.2021.016871

Abstract Recognition of human gait is a difficult assignment, particularly for unobtrusive surveillance in a video and human identification from a large distance. Therefore, a method is proposed for the classification and recognition of different types of human gait. The proposed approach is consisting of two phases. In phase I, the new model is proposed named convolutional bidirectional long short-term memory (Conv-BiLSTM) to classify the video frames of human gait. In this model, features are derived through convolutional neural network (CNN) named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal information. In phase II,… More >

Jinxin Zuo^1,2, Yueming Lu^1,2,*, Hui Gao^2,3, Tong Peng^1,2, Ziyv Guo^2,3, Tong An^1,2, Enjie Liu⁴

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2579-2595, 2021, DOI:10.32604/cmc.2021.016623

Abstract With the skyrocketing development of technologies, there are many issues in information security quantitative evaluation (ISQE) of complex heterogeneous information systems (CHISs). The development of CHIS calls for an ISQE model based on security-critical components to improve the efficiency of system security evaluation urgently. In this paper, we summarize the implication of critical components in different filed and propose a recognition algorithm of security-critical components based on threat attack tree to support the ISQE process. The evaluation model establishes a framework for ISQE of CHISs that are updated iteratively. Firstly, with the support of asset identification and topology data, we… More >

Amany M. Sarhan¹, Nada M. Elshennawy¹, Dina M. Ibrahim^1,2,*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1531-1549, 2021, DOI:10.32604/cmc.2021.016509

Abstract

Lip reading is typically regarded as visually interpreting the speaker’s lip movements during the speaking. This is a task of decoding the text from the speaker’s mouth movement. This paper proposes a lip-reading model that helps deaf people and persons with hearing problems to understand a speaker by capturing a video of the speaker and inputting it into the proposed model to obtain the corresponding subtitles. Using deep learning technologies makes it easier for users to extract a large number of different features, which can then be converted to probabilities of letters to obtain accurate results. Recently proposed methods for… More >

Masoud Muhammed Hassan^1,*, Haval Ismael Hussein¹, Adel Sabry Eesa¹, Ramadhan J. Mstafa^1,2

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1637-1659, 2021, DOI:10.32604/cmc.2021.016467

Abstract Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis… More >

Zainab Nayyar¹, Muhammad Attique Khan¹, Musaed Alhussein², Muhammad Nazir¹, Khursheed Aurangzeb², Yunyoung Nam^3,*, Seifedine Kadry⁴, Syed Irtaza Haider²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2041-2056, 2021, DOI:10.32604/cmc.2021.015916

Abstract Artificial intelligence aids for healthcare have received a great deal of attention. Approximately one million patients with gastrointestinal diseases have been diagnosed via wireless capsule endoscopy (WCE). Early diagnosis facilitates appropriate treatment and saves lives. Deep learning-based techniques have been used to identify gastrointestinal ulcers, bleeding sites, and polyps. However, small lesions may be misclassified. We developed a deep learning-based best-feature method to classify various stomach diseases evident in WCE images. Initially, we use hybrid contrast enhancement to distinguish diseased from normal regions. Then, a pretrained model is fine-tuned, and further training is done via transfer learning. Deep features are… More >

Mahmoud Badry^1,*, Mohammed Hassanin^1,2, Asghar Chandio^2,3, Nour Moustafa²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1847-1858, 2021, DOI:10.32604/cmc.2021.015489

Abstract Since the worldwide spread of internet-connected devices and rapid advances made in Internet of Things (IoT) systems, much research has been done in using machine learning methods to recognize IoT sensors data. This is particularly the case for optical character recognition of handwritten scripts. Recognizing text in images has several useful applications, including content-based image retrieval, searching and document archiving. The Arabic language is one of the mostly used tongues in the world. However, Arabic text recognition in imagery is still very much in the nascent stage, especially handwritten text. This is mainly due to the language complexities, different writing… More >

Yuetian Wang, Chuanjing Zhang, Xuxin Liao, Xingang Wang, Zhaoquan Gu^*

Journal on Artificial Intelligence, Vol.3, No.1, pp. 1-8, 2021, DOI:10.32604/jai.2021.014175

Abstract Deep neural networks (DNNs) are widely adopted in daily life and the security problems of DNNs have drawn attention from both scientific researchers and industrial engineers. Many related works show that DNNs are vulnerable to adversarial examples that are generated with subtle perturbation to original images in both digital domain and physical domain. As a most common application of DNNs, face recognition systems are likely to cause serious consequences if they are attacked by the adversarial examples. In this paper, we implement an adversarial attack system for face recognition in both digital domain that generates adversarial face images to fool… More >

Ying Cui¹, Shi Qiu^2,*, Tong Li³

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 417-428, 2021, DOI:10.32604/iasc.2021.016802

Abstract It is difficult to recognize a target object from foggy images. Aiming at solving this problem, a new algorithm is thereby proposed. Fog concentration estimation is the prerequisite for image defogging. Due to the uncertainty of fog distribution, a fog concentration estimation model is accordingly proposed. Establish the brightness and gradient model in the spatial domain, and establish the FFT model in the frequency domain. Also, a multiple branch network framework is established to realize the qualitative estimation of the fog concentration in images based on comprehensive analysis from spatial and frequency domain levels. In the aspect of foggy image… More >

Mohammed Ibrahim Ahmed Al-mashhadani¹, Theyazn H. H. Aldhyani^2,*, Mosleh Hmoud Al-Adhaileh³, Alwi M. Bamhdi⁴, Mohammed Y. Alzahrani⁵, Fawaz Waselallah Alsaade⁶, Hasan Alkahtani^1,6

Computer Systems Science and Engineering, Vol.38, No.1, pp. 25-37, 2021, DOI:10.32604/csse.2021.014992

Abstract Touch gesture recognition is an important aspect in human–robot interaction, as it makes such interaction effective and realistic. The novelty of this study is the development of a system that recognizes human–animal affective robot touch (HAART) using a deep learning algorithm. The proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge 2015. The dataset was tested with numerous subjects performing different HAART gestures; each touch was performed on a robotic animal covered by a pressure sensor skin. A convolutional neural network algorithm is proposed to implement the touch… More >

Shan Zhang¹, Bin Cao¹, Yueshen Xu^2,*, Jing Fan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.1, pp. 309-323, 2021, DOI:10.32604/cmes.2021.014802

Abstract As a representative technique in natural language processing (NLP), named entity recognition is used in many tasks, such as dialogue systems, machine translation and information extraction. In dialogue systems, there is a common case for named entity recognition, where a lot of entities are composed of numbers, and are segmented to be located in different places. For example, in multiple rounds of dialogue systems, a phone number is likely to be divided into several parts, because the phone number is usually long and is emphasized. In this paper, the entity consisting of numbers is named as number entity. The discontinuous… More >