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  • Open Access

    ARTICLE

    A New Speed Limit Recognition Methodology Based on Ensemble Learning: Hardware Validation

    Mohamed Karray1,*, Nesrine Triki2,*, Mohamed Ksantini2

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 119-138, 2024, DOI:10.32604/cmc.2024.051562

    Abstract Advanced Driver Assistance Systems (ADAS) technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road. Traffic Sign Recognition System (TSRS) is one of the most important components of ADAS. Among the challenges with TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time. Accordingly, this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules. Firstly, the Speed Limit Detection (SLD) module uses… More >

  • Open Access

    ARTICLE

    5G Resource Allocation Using Feature Selection and Greylag Goose Optimization Algorithm

    Amel Ali Alhussan1, S. K. Towfek2,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1179-1201, 2024, DOI:10.32604/cmc.2024.049874

    Abstract In the contemporary world of highly efficient technological development, fifth-generation technology (5G) is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second (Gbps). As far as the current implementations are concerned, they are at the level of slightly below 1 Gbps, but this allowed a great leap forward from fourth generation technology (4G), as well as enabling significantly reduced latency, making 5G an absolute necessity for applications such as gaming, virtual conferencing, and other interactive electronic processes. Prospects of this change are not limited to connectivity… More >

  • Open Access

    ARTICLE

    A Machine Learning Approach to Cyberbullying Detection in Arabic Tweets

    Dhiaa Musleh1, Atta Rahman1,*, Mohammed Abbas Alkherallah1, Menhal Kamel Al-Bohassan1, Mustafa Mohammed Alawami1, Hayder Ali Alsebaa1, Jawad Ali Alnemer1, Ghazi Fayez Al-Mutairi1, May Issa Aldossary2, Dalal A. Aldowaihi1, Fahd Alhaidari3

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1033-1054, 2024, DOI:10.32604/cmc.2024.048003

    Abstract With the rapid growth of internet usage, a new situation has been created that enables practicing bullying. Cyberbullying has increased over the past decade, and it has the same adverse effects as face-to-face bullying, like anger, sadness, anxiety, and fear. With the anonymity people get on the internet, they tend to be more aggressive and express their emotions freely without considering the effects, which can be a reason for the increase in cyberbullying and it is the main motive behind the current study. This study presents a thorough background of cyberbullying and the techniques used… More >

  • Open Access

    ARTICLE

    Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

    Asad Raza1,*, Shahzad Memon1, Muhammad Ali Nizamani1, Mahmood Hussain Shah2

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 545-566, 2024, DOI:10.32604/iasc.2024.051779

    Abstract Smart Industrial environments use the Industrial Internet of Things (IIoT) for their routine operations and transform their industrial operations with intelligent and driven approaches. However, IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet. Traditional signature-based IDS are effective in detecting known attacks, but they are unable to detect unknown emerging attacks. Therefore, there is the need for an IDS which can learn from data and detect new threats. Ensemble Machine Learning (ML) and individual Deep Learning (DL) based IDS have been developed, and these individual models achieved… More >

  • Open Access

    ARTICLE

    Improving Channel Estimation in a NOMA Modulation Environment Based on Ensemble Learning

    Lassaad K. Smirani1, Leila Jamel2,*, Latifah Almuqren2

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1315-1337, 2024, DOI:10.32604/cmes.2024.047551

    Abstract This study presents a layered generalization ensemble model for next generation radio mobiles, focusing on supervised channel estimation approaches. Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout. The model, called Stacked Generalization for Channel Estimation (SGCE), aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput. The SGCE model incorporates six machine learning methods: random forest (RF), gradient boosting machine (GB), light gradient boosting machine (LGBM), support vector regression (SVR), extremely randomized tree (ERT), and extreme gradient boosting (XGB). By generating meta-data from five… More >

  • Open Access

    ARTICLE

    Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

    Maha Farouk Sabir1, Mahmoud Ragab2,3,*, Adil O. Khadidos2, Khaled H. Alyoubi1, Alaa O. Khadidos1,4

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 627-643, 2024, DOI:10.32604/csse.2023.041551

    Abstract Big data and information and communication technologies can be important to the effectiveness of smart cities. Based on the maximal attention on smart city sustainability, developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems. Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions. Relating to air pollution occurs a main environmental problem in smart city environments. The effect of the deep learning (DL) approach quickly increased and penetrated almost every domain, comprising air pollution forecast. Therefore, this article develops… More >

  • Open Access

    ARTICLE

    ABMRF: An Ensemble Model for Author Profiling Based on Stylistic Features Using Roman Urdu

    Aiman1, Muhammad Arshad1, Bilal Khan1, Khalil Khan2, Ali Mustafa Qamar3,*, Rehan Ullah Khan4

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 301-317, 2024, DOI:10.32604/iasc.2024.045402

    Abstract This study explores the area of Author Profiling (AP) and its importance in several industries, including forensics, security, marketing, and education. A key component of AP is the extraction of useful information from text, with an emphasis on the writers’ ages and genders. To improve the accuracy of AP tasks, the study develops an ensemble model dubbed ABMRF that combines AdaBoostM1 (ABM1) and Random Forest (RF). The work uses an extensive technique that involves text message dataset pretreatment, model training, and assessment. To evaluate the effectiveness of several machine learning (ML) algorithms in classifying age… More >

  • Open Access

    ARTICLE

    Optimizing Optical Fiber Faults Detection: A Comparative Analysis of Advanced Machine Learning Approaches

    Kamlesh Kumar Soothar1,2, Yuanxiang Chen1,2,*, Arif Hussain Magsi3, Cong Hu1, Hussain Shah1

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2697-2721, 2024, DOI:10.32604/cmc.2024.049607

    Abstract Efficient optical network management poses significant importance in backhaul and access network communication for preventing service disruptions and ensuring Quality of Service (QoS) satisfaction. The emerging faults in optical networks introduce challenges that can jeopardize the network with a variety of faults. The existing literature witnessed various partial or inadequate solutions. On the other hand, Machine Learning (ML) has revolutionized as a promising technique for fault detection and prevention. Unlike traditional fault management systems, this research has three-fold contributions. First, this research leverages the ML and Deep Learning (DL) multi-classification system and evaluates their accuracy… More >

  • Open Access

    ARTICLE

    Predicting Age and Gender in Author Profiling: A Multi-Feature Exploration

    Aiman1, Muhammad Arshad1,*, Bilal Khan1, Sadique Ahmad2,*, Muhammad Asim2,3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3333-3353, 2024, DOI:10.32604/cmc.2024.049254

    Abstract Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author’s personal information, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic, semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, and marketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French, etc. However, the research on Roman Urdu is not up to the mark. Hence, this study focuses on detecting the author’s age and gender based on Roman Urdu text messages.… More >

  • Open Access

    ARTICLE

    A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

    Farhan Ullah1, Xuexia Zhang1,*, Mansoor Khan2, Muhammad Abid3,*, Abdullah Mohamed4

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3373-3395, 2024, DOI:10.32604/cmc.2024.048656

    Abstract Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows. Traditional approaches frequently struggle with complex data and non-linear connections. This article presents a novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts. The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-Era Retrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms using in-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model, while a temporal convolutional network handles time-series complexities and data… More >

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