Adel Hafeezallah¹, Ahlam Al-Dhamari^2,3,*, Syed Abd Rahman Abu-Bakar²

Computer Systems Science and Engineering, Vol.47, No.1, pp. 593-611, 2023, DOI:10.32604/csse.2023.039479 - 26 May 2023

Abstract Visual motion segmentation (VMS) is an important and key part of many intelligent crowd systems. It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes, which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades. Trajectory clustering has become one of the most popular methods in VMS. However, complex data, such as a large number of samples and parameters, makes it difficult for trajectory clustering to work well with accurate motion segmentation… More >

Neelam Mughees^1,2, Mujtaba Hussain Jaffery¹, Abdullah Mughees³, Anam Mughees⁴, Krzysztof Ejsmont^5,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6375-6393, 2023, DOI:10.32604/cmc.2023.038564 - 29 April 2023

Abstract Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050. However, they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions. In microgrids, smart energy management systems, such as integrated demand response programs, are permanently established on a step-ahead basis, which means that accurate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids. With this in mind, a novel “bidirectional long short-term memory network” (Bi-LSTM)-based, deep stacked,… More >

Lelisa Adeba Jilcha¹, Deuk-Hun Kim², Julian Jang-Jaccard³, Jin Kwak^4,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3261-3284, 2023, DOI:10.32604/csse.2023.037615 - 03 April 2023

Abstract Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces… More >

Manar Ahmed Hamza^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2879-2895, 2023, DOI:10.32604/iasc.2023.034719 - 15 March 2023

Abstract Breast Cancer (BC) is considered the most commonly scrutinized cancer in women worldwide, affecting one in eight women in a lifetime. Mammography screening becomes one such standard method that is helpful in identifying suspicious masses’ malignancy of BC at an initial level. However, the prior identification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis. Deep learning (DL) techniques were broadly utilized for medical imaging applications, particularly breast mass classification. The advancements in the DL field paved the… More >

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 - 15 March 2023

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… 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 - 15 March 2023

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… 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 - 09 February 2023

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… 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 - 20 January 2023

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… 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 - 05 January 2023

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 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 - 28 December 2022

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… More >