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Search Results (13)
  • Open Access

    ARTICLE

    CNN Channel Attention Intrusion Detection System Using NSL-KDD Dataset

    Fatma S. Alrayes1, Mohammed Zakariah2, Syed Umar Amin3,*, Zafar Iqbal Khan3, Jehad Saad Alqurni4

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4319-4347, 2024, DOI:10.32604/cmc.2024.050586

    Abstract Intrusion detection systems (IDS) are essential in the field of cybersecurity because they protect networks from a wide range of online threats. The goal of this research is to meet the urgent need for small-footprint, highly-adaptable Network Intrusion Detection Systems (NIDS) that can identify anomalies. The NSL-KDD dataset is used in the study; it is a sizable collection comprising 43 variables with the label’s “attack” and “level.” It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks (CNN). Furthermore, this dataset makes it easier to conduct… More >

  • Open Access

    ARTICLE

    Machine-Learning Based Packet Switching Method for Providing Stable High-Quality Video Streaming in Multi-Stream Transmission

    Yumin Jo1, Jongho Paik2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4153-4176, 2024, DOI:10.32604/cmc.2024.047046

    Abstract Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream. However, when the transmission environment is unstable, problems such as reduction in the lifespan of equipment due to frequent switching and interruption, delay, and stoppage of services may occur. Therefore, applying a machine learning (ML) method, which is possible to automatically judge and classify network-related service anomaly, and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when… More >

  • Open Access

    ARTICLE

    A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

    Arif Hussain Magsi1,*, Ali Ghulam2, Saifullah Memon1, Khalid Javeed3, Musaed Alhussein4, Imad Rida5

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1445-1465, 2023, DOI:10.32604/cmc.2023.040290

    Abstract Named Data Networking (NDN) is gaining a significant attention in Vehicular Ad-hoc Networks (VANET) due to its in-network content caching, name-based routing, and mobility-supporting characteristics. Nevertheless, existing NDN faces three significant challenges, including security, privacy, and routing. In particular, security attacks, such as Content Poisoning Attacks (CPA), can jeopardize legitimate vehicles with malicious content. For instance, attacker host vehicles can serve consumers with invalid information, which has dire consequences, including road accidents. In such a situation, trust in the content-providing vehicles brings a new challenge. On the other hand, ensuring privacy and preventing unauthorized access… More >

  • Open Access

    ARTICLE

    A Machine-Learning Approach for the Prediction of Fly-Ash Concrete Strength

    Shanqing Shao1, Aimin Gong1, Ran Wang1, Xiaoshuang Chen1, Jing Xu2, Fulai Wang1,*, Feipeng Liu2,3,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.12, pp. 3007-3019, 2023, DOI:10.32604/fdmp.2023.029545

    Abstract The composite exciter and the CaO to Na2SO4 dosing ratios are known to have a strong impact on the mechanical strength of fly-ash concrete. In the present study a hybrid approach relying on experiments and a machine-learning technique has been used to tackle this problem. The tests have shown that the optimal admixture of CaO and Na2SO4 alone is 8%. The best 3D mechanical strength of fly-ash concrete is achieved at 8% of the compound activator; If the 28-day mechanical strength is considered, then, the best performances are obtained at 4% of the compound activator. Moreover,… More >

  • Open Access

    ARTICLE

    Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

    Saeed Ali Alsareii1, Muhammad Awais2,*, Abdulrahman Manaa Alamri1, Mansour Yousef AlAsmari1, Muhammad Irfan3, Mohsin Raza2, Umer Manzoor4

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3715-3728, 2023, DOI:10.32604/csse.2023.035687

    Abstract Obesity is a critical health condition that severely affects an individual’s quality of life and well-being. The occurrence of obesity is strongly associated with extreme health conditions, such as cardiac diseases, diabetes, hypertension, and some types of cancer. Therefore, it is vital to avoid obesity and or reverse its occurrence. Incorporating healthy food habits and an active lifestyle can help to prevent obesity. In this regard, artificial intelligence (AI) can play an important role in estimating health conditions and detecting obesity and its types. This study aims to see obesity levels in adults by implementing… More >

  • Open Access

    ARTICLE

    Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

    Hanadi AlZaabi1, Khaled Shaalan1, Taher M. Ghazal2,3,*, Muhammad A. Khan4,5, Sagheer Abbas6, Beenu Mago7, Mohsen A. A. Tomh6, Munir Ahmad6

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2261-2278, 2023, DOI:10.32604/cmc.2023.031834

    Abstract Energy is essential to practically all exercises and is imperative for the development of personal satisfaction. So, valuable energy has been in great demand for many years, especially for using smart homes and structures, as individuals quickly improve their way of life depending on current innovations. However, there is a shortage of energy, as the energy required is higher than that produced. Many new plans are being designed to meet the consumer’s energy requirements. In many regions, energy utilization in the housing area is 30%–40%. The growth of smart homes has raised the requirement for… More >

  • Open Access

    ARTICLE

    An Effective Machine-Learning Based Feature Extraction/Recognition Model for Fetal Heart Defect Detection from 2D Ultrasonic Imageries

    Bingzheng Wu1, Peizhong Liu1, Huiling Wu2, Shunlan Liu2, Shaozheng He2, Guorong Lv2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1069-1089, 2023, DOI:10.32604/cmes.2022.020870

    Abstract Congenital heart defect, accounting for about 30% of congenital defects, is the most common one. Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns. In Fetal and Neonatal Cardiology, medical imaging technology (2D ultrasonic, MRI) has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis. It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane (FHUSP) manually. Compared with manual identification, automatic identification through artificial intelligence can save a lot of time, ensure the efficiency… More >

  • Open Access

    ARTICLE

    Compared Insights on Machine-Learning Anomaly Detection for Process Control Feature

    Ming Wan1, Quanliang Li1, Jiangyuan Yao2,*, Yan Song3, Yang Liu4, Yuxin Wan5

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4033-4049, 2022, DOI:10.32604/cmc.2022.030895

    Abstract Anomaly detection is becoming increasingly significant in industrial cyber security, and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber attacks. However, different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature samples. As a sequence, after developing one feature generation approach, the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning algorithm. Based on process control features generated by directed function transition diagrams, this paper introduces five different machine-learning algorithms as alternative… More >

  • Open Access

    ARTICLE

    A Machine-Learning Framework to Improve Wi-Fi Based Indoorpositioning

    Venkateswari Pichaimani1, K. R. Manjula2,*

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 383-397, 2022, DOI:10.32604/iasc.2022.023105

    Abstract The indoor positioning system comprises portable wireless devices that aid in finding the location of people or objects within the buildings. Identification of the items is through the capacity level of the signal received from various access points (i.e., Wi-Fi routers). The positioning of the devices utilizing some algorithms has drawn more attention from the researchers. Yet, the designed algorithm still has problems for accurate floor planning. So, the accuracy of position estimation with minimum error is made possible by introducing Gaussian Distributive Feature Embedding based Deep Recurrent Perceptive Neural Learning (GDFE-DRPNL), a novel framework.… More >

  • Open Access

    ARTICLE

    Developing a Recognition System for Classifying COVID-19 Using a Convolutional Neural Network Algorithm

    Fawaz Waselallah Alsaade1, Theyazn H. H. Aldhyani2,*, Mosleh Hmoud Al-Adhaileh3

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 805-819, 2021, DOI:10.32604/cmc.2021.016264

    Abstract The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity, and developing a system to identify COVID-19 in its early stages will save millions of lives. This study applied support vector machine (SVM), k-nearest neighbor (K-NN) and deep learning convolutional neural network (CNN) algorithms to classify and detect COVID-19 using chest X-ray radiographs. To test the proposed system, chest X-ray radiographs and CT images were collected from different standard databases, which contained 95 normal images, 140 COVID-19 images and 10 SARS images. Two scenarios were considered to… More >

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