Badriyya B. Al-onazi¹, Mohamed K. Nour², Hassan Alshamrani³, Mesfer Al Duhayyim^4,*, Heba Mohsen⁵, Amgad Atta Abdelmageed⁶, Gouse Pasha Mohammed⁶, Abu Sarwar Zamani⁶

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2529-2544, 2023, DOI:10.32604/iasc.2023.034623

Abstract Gender analysis of Twitter could reveal significant socio-cultural differences between female and male users. Efforts had been made to analyze and automatically infer gender formerly for more commonly spoken languages’ content, but, as we now know that limited work is being undertaken for Arabic. Most of the research works are done mainly for English and least amount of effort for non-English language. The study for Arabic demographic inference like gender is relatively uncommon for social networking users, especially for Twitter. Therefore, this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter (OMSDAE-GIAT) model.… More >

Bowen Zhang^1,2,3, Yanbo Shi⁴, Jianming Zhao^1,2,3,*, Tianyu Wang^1,2,3, Kaidi Wang⁵

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2703-2717, 2023, DOI:10.32604/iasc.2023.033762

Abstract Feature extraction plays an important role in constructing artificial intelligence (AI) models of industrial control systems (ICSs). Three challenges in this field are learning effective representation from high-dimensional features, data heterogeneity, and data noise due to the diversity of data dimensions, formats and noise of sensors, controllers and actuators. Hence, a novel unsupervised learning autoencoder model is proposed for ICS data in this paper. Although traditional methods only capture the linear correlations of ICS features, our deep industrial representation learning model (DIRL) based on a convolutional neural network can mine high-order features, thus solving the problem of high-dimensional and heterogeneous… More >

Jiali Wang¹, Xiangmei Chen¹, Xiaozhen Huang¹, Yichen Zhao^1,*, Degang Zhao^1,2,*

Phyton-International Journal of Experimental Botany, Vol.92, No.5, pp. 1377-1389, 2023, DOI:10.32604/phyton.2023.026830

Abstract The phytoene desaturase (PDS) encodes a crucial enzyme in the carotenoid biosynthesis pathway. Silencing or inhibiting PDS expression leads to the appearance of mottled, chlorosis, or albino leaves. In this study, the CDS sequence of EuPDS (Eucommia ulmoides Phytoene Desaturase) was first cloned and then PDS was silenced in Nicotiana benthamiana. Result showed the expression level of EuPDS in leaves was higher than that in the roots and stems. In N. benthamiana leaves, which were treated by Agrobacterium for 24 h, photo-bleaching was shown on the fresh leaves one week after injection and the transcript level of PDS was down-regulated… More >

Younghoon Ban, Jeong Hyun Yi, Haehyun Cho^*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2215-2230, 2023, DOI:10.32604/csse.2023.036555

Abstract Android malware has evolved in various forms such as adware that continuously exposes advertisements, banking malware designed to access users’ online banking accounts, and Short Message Service (SMS) malware that uses a Command & Control (C&C) server to send malicious SMS, intercept SMS, and steal data. By using many malicious strategies, the number of malware is steadily increasing. Increasing Android malware threats numerous users, and thus, it is necessary to detect malware quickly and accurately. Each malware has distinguishable characteristics based on its actions. Therefore, security researchers have tried to categorize malware based on their behaviors by conducting the familial… More >

Maha Sabir¹, Jawad Ahmad^2,*, Daniyal Alghazzawi¹

Computer Systems Science and Engineering, Vol.46, No.1, pp. 57-72, 2023, DOI:10.32604/csse.2023.034277

Abstract The Internet of things (IoT) is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision. Despite several advantages, the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals. A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries. To overcome the security challenges of IoT networks, this article proposes a lightweight deep autoencoder (DAE) based cyberattack detection framework. The proposed… More >

Chen Chen^1,2, Xingqiu Li^2,3,*, Kai Huang⁴, Zhongwei Xu¹, Meng Mei¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 471-485, 2023, DOI:10.32604/cmes.2023.024033

Abstract Railway turnout is one of the critical equipment of Switch & Crossing (S&C) Systems in railway, related to the train’s safety and operation efficiency. With the advancement of intelligent sensors, data-driven fault detection technology for railway turnout has become an important research topic. However, little research in the literature has investigated the capability of data-driven fault detection technology for metro railway turnout. This paper presents a convolutional autoencoder-based fault detection method for the metro railway turnout considering human field inspection scenarios. First, the one-dimensional original time-series signal is converted into a two-dimensional image by data pre-processing and 2D representation. Next,… More >

Mohammed Maray¹, Haya Mesfer Alshahrani², Khalid A. Alissa³, Najm Alotaibi⁴, Abdulbaset Gaddah⁵, Ali Meree^1,6, Mahmoud Othman⁷, Manar Ahmed Hamza^8,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6587-6604, 2023, DOI:10.32604/cmc.2023.034176

Abstract In recent years, wireless networks are widely used in different domains. This phenomenon has increased the number of Internet of Things (IoT) devices and their applications. Though IoT has numerous advantages, the commonly-used IoT devices are exposed to cyber-attacks periodically. This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks. The Software-Defined Networking (SDN) technique and the Machine Learning (ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks. The Intrusion Detection System (IDS) models can be employed to secure the SDN-enabled IoT… More >

Yongzhong Li^1,2,*, Cong Li¹, Yuheng Li³, Shipeng Zhang²

Journal of New Media, Vol.4, No.4, pp. 205-217, 2022, DOI:10.32604/jnm.2022.034778

Abstract In this paper, we propose a intrusion detection algorithm based on auto-encoder and three-way decisions (AE-3WD) for industrial control networks, aiming at the security problem of industrial control network. The ideology of deep learning is similar to the idea of intrusion detection. Deep learning is a kind of intelligent algorithm and has the ability of automatically learning. It uses self-learning to enhance the experience and dynamic classification capabilities. We use deep learning to improve the intrusion detection rate and reduce the false alarm rate through learning, a denoising AutoEncoder and three-way decisions intrusion detection method AE-3WD is proposed to improve… More >

Muhammad Ahmad¹, Fahd Jarad^2,3,*, Zohaib Zahid¹, Imran Siddique¹

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2681-2696, 2023, DOI:10.32604/cmes.2023.022819

Abstract The doubly resolving sets are a natural tool to identify where diffusion occurs in a complicated network. Many real-world phenomena, such as rumour spreading on social networks, the spread of infectious diseases, and the spread of the virus on the internet, may be modelled using information diffusion in networks. It is obviously impractical to monitor every node due to cost and overhead limits because there are too many nodes in the network, some of which may be unable or unwilling to send information about their state. As a result, the source localization problem is to find the number of nodes… More >

Haneen D. Jabbar^1,*, Makram A. Fakhri^1,*, Mohammed Jalal Abdul Razzaq¹, Omar S. Dahham^2,3, Evan T. Salim⁴, Forat H. Alsultany⁵, U. Hashim⁶

Journal of Renewable Materials, Vol.11, No.3, pp. 1101-1122, 2023, DOI:10.32604/jrm.2023.023698

Abstract Gallium nitride (GaN)/porous silicon (PSi) film was prepared using a pulsed laser deposition method and 1064 nm Nd: YAG laser for optoelectronic applications and a series of Psi substrates were fabricated using a photoelectrochemical etching method assisted by laser at different etching times for 2.5–15 min at 2.5 min intervals. X-ray diffraction, room-temperature photoluminescence, atomic force microscopy and field emission scanning electron microscopy images, and electrical characteristics in the prepared GaN on the Psi film were investigated. The optimum Psi substrate was obtained under the following conditions: 10 min, 10 mA/cm², and 24% hydrofluoric acid. The substrate exhibited two highly cubic crystalline structures at (200)… More >