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  • Open Access

    ARTICLE

    Data Analysis of Network Parameters for Secure Implementations of SDN-Based Firewall

    Rizwan Iqbal1,*, Rashid Hussain2, Sheeraz Arif3, Nadia Mustaqim Ansari4, Tayyab Ahmed Shaikh2

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1575-1598, 2023, DOI:10.32604/cmc.2023.042432

    Abstract Software-Defined Networking (SDN) is a new network technology that uses programming to complement the data plane with a control plane. To enable safe connection, however, numerous security challenges must be addressed. Flooding attacks have been one of the most prominent risks on the internet for decades, and they are now becoming challenging difficulties in SDN networks. To solve these challenges, we proposed a unique firewall application built on multiple levels of packet filtering to provide a flooding attack prevention system and a layer-based packet detection system. This study offers a systematic strategy for wrapping up the examination of SDN operations.… More >

  • Open Access

    ARTICLE

    3-D Gait Identification Utilizing Latent Canonical Covariates Consisting of Gait Features

    Ramiz Gorkem Birdal*, Ahmet Sertbas

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2727-2744, 2023, DOI:10.32604/cmc.2023.032069

    Abstract Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’ walking patterns to be recognized. Existing research in this area has primarily focused on feature analysis through the extraction of individual features, which captures most of the information but fails to capture subtle variations in gait dynamics. Therefore, a novel feature taxonomy and an approach for deriving a relationship between a function of one set of gait features with another set are introduced. The gait features extracted from body halves divided by anatomical planes on vertical, horizontal, and diagonal axes are grouped to form… More >

  • Open Access

    ARTICLE

    Stock Market Index Prediction Using Machine Learning and Deep Learning Techniques

    Abdus Saboor1,4, Arif Hussain2, Bless Lord Y. Agbley3, Amin ul Haq3,*, Jian Ping Li3, Rajesh Kumar1,*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1325-1344, 2023, DOI:10.32604/iasc.2023.038849

    Abstract Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult topic. With the objective of constructing an effective prediction model, both linear and machine learning tools have been investigated for the past couple of decades. In recent years, recurrent neural networks (RNNs) have been observed to perform well on tasks involving sequence-based data in many research domains. With this motivation, we investigated the performance of long-short term memory (LSTM) and gated recurrent units (GRU) and their combination with the attention mechanism; LSTM + Attention, GRU + Attention, and LSTM + GRU + Attention.… More >

  • Open Access

    ARTICLE

    An Ensemble Machine Learning Technique for Stroke Prognosis

    Mesfer Al Duhayyim1,*, Sidra Abbas2,*, Abdullah Al Hejaili3, Natalia Kryvinska4, Ahmad Almadhor5, Uzma Ghulam Mohammad6

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 413-429, 2023, DOI:10.32604/csse.2023.037127

    Abstract Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the brain. It has a tremendous impact on every aspect of life since it is the leading global factor of disability and morbidity. Strokes can range from minor to severe (extensive). Thus, early stroke assessment and treatment can enhance survival rates. Manual prediction is extremely time and resource intensive. Automated prediction methods such as Modern Information and Communication Technologies (ICTs), particularly those in Machine Learning (ML) area, are crucial for the early diagnosis and prognosis of stroke. Therefore, this research proposed an ensemble voting… More >

  • Open Access

    ARTICLE

    Numérique versus symbolique

    Dialogue ontologique entre deux approches

    Hélène Mathian1, Lena Sanders2

    Revue Internationale de Géomatique, Vol.31, No.1, pp. 21-45, 2022, DOI:10.3166/RIG31.21-45

    Abstract The aim of this article is to compare a statistical approach, “geometric data analysis” (GDA), and a simulation approach, the multi-agent systems (MAS), considered as representative, respectively, of a numerical and a symbolic approach of modelling. The case study concerns segregation of scholar space in the Parisian area. First the different steps leading from a thematic question to the development of an operational model to analyze this question are presented. The central and essential role of a conceptual framework at the interface of both is shown. Indeed, before operationalisation, it is necessary to verify the compatibility between the theoretical backgrounds… More >

  • Open Access

    ARTICLE

    An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction

    Meilin Wu1,2, Lianggui Tang1,2,*, Qingda Zhang1,2, Ke Yan1,2

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 179-198, 2023, DOI:10.32604/iasc.2023.036684

    Abstract As COVID-19 poses a major threat to people’s health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, and the features may then… More >

  • Open Access

    ARTICLE

    Cognitive Granular-Based Path Planning and Tracking for Intelligent Vehicle with Multi-Segment Bezier Curve Stitching

    Xudong Wang1,2, Xueshuai Qin1, Huiyan Zhang2,*, Luis Ismael Minchala3

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 385-400, 2023, DOI:10.32604/iasc.2023.036633

    Abstract Unmanned vehicles are currently facing many difficulties and challenges in improving safety performance when running in complex urban road traffic environments, such as low intelligence and poor comfort performance in the driving process. The real-time performance of vehicles and the comfort requirements of passengers in path planning and tracking control of unmanned vehicles have attracted more and more attentions. In this paper, in order to improve the real-time performance of the autonomous vehicle planning module and the comfort requirements of passengers that a local granular-based path planning method and tracking control based on multi-segment Bezier curve splicing and model predictive… More >

  • Open Access

    ARTICLE

    On a Novel Extended Lomax Distribution with Asymmetric Properties and Its Statistical Applications

    Aisha Fayomi1, Christophe Chesneau2,*, Farrukh Jamal3, Ali Algarni1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2371-2403, 2023, DOI:10.32604/cmes.2023.027000

    Abstract In this article, we highlight a new three-parameter heavy-tailed lifetime distribution that aims to extend the modeling possibilities of the Lomax distribution. It is called the extended Lomax distribution. The considered distribution naturally appears as the distribution of a transformation of a random variable following the logweighted power distribution recently introduced for percentage or proportion data analysis purposes. As a result, its cumulative distribution has the same functional basis as that of the Lomax distribution, but with a novel special logarithmic term depending on several parameters. The modulation of this logarithmic term reveals new types of asymetrical shapes, implying a… More >

  • Open Access

    ARTICLE

    Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection

    Muhammad Umair1,*, Zafar Saeed1, Faisal Saeed2, Hiba Ishtiaq1, Muhammad Zubair1, Hala Abdel Hameed3,4

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5431-5446, 2023, DOI:10.32604/cmc.2023.033884

    Abstract As big data, its technologies, and application continue to advance, the Smart Grid (SG) has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology (ICT) and cloud computing. As a result of the complicated architecture of cloud computing, the distinctive working of advanced metering infrastructures (AMI), and the use of sensitive data, it has become challenging to make the SG secure. Faults of the SG are categorized into two main categories, Technical Losses (TLs) and Non-Technical Losses (NTLs). Hardware failure, communication issues, ohmic losses, and… More >

  • Open Access

    ARTICLE

    Identifying Cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

    P. Saranya*, P. Asha

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1137-1149, 2023, DOI:10.32604/iasc.2023.031470

    Abstract In today’s growing modern world environment, as human food activities are changing, it is affecting human health, thus leading to diseases like cancer. Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death. So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observation, which has become necessary to classify the type in cancer research. The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment. This paper introduces a… More >

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