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  • Open Access

    ARTICLE

    The Lambert-G Family: Properties, Inference, and Applications

    Jamal N. Al Abbasi1, Ahmed Z. Afify2,*, Badr Alnssyan3,*, Mustafa S. Shama4,5

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 513-536, 2024, DOI:10.32604/cmes.2024.046533

    Abstract This study proposes a new flexible family of distributions called the Lambert-G family. The Lambert family is very flexible and exhibits desirable properties. Its three-parameter special sub-models provide all significant monotonic and non-monotonic failure rates. A special sub-model of the Lambert family called the Lambert-Lomax (LL) distribution is investigated. General expressions for the LL statistical properties are established. Characterizations of the LL distribution are addressed mathematically based on its hazard function. The estimation of the LL parameters is discussed using six estimation methods. The performance of this estimation method is explored through simulation experiments. The usefulness and flexibility of the… More >

  • Open Access

    ARTICLE

    Women Entrepreneurship Index Prediction Model with Automated Statistical Analysis

    V. Saikumari*, V. Sunitha

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1797-1810, 2023, DOI:10.32604/iasc.2023.034038

    Abstract Recently, gender equality and women’s entrepreneurship have gained considerable attention in global economic development. Prior to the design of any policy interventions to increase women’s entrepreneurship, it is significant to comprehend the factors motivating women to become entrepreneurs. The non-understanding of the factors can result in the endurance of low living standards and the design of expensive and ineffectual policies. But female involvement in entrepreneurship becomes higher in developing economies compared to developed economies. Women Entrepreneurship Index (WEI) plays a vital role in determining the factors that enable the flourishment of high potential female entrepreneurs which enhances economic welfare and… More >

  • Open Access

    ARTICLE

    Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution Monitoring Framework

    Manar Ahmed Hamza1,*, Hadil Shaiba2, Radwa Marzouk3, Ahmad Alhindi4, Mashael M. Asiri5, Ishfaq Yaseen1, Abdelwahed Motwakel1, Mohammed Rizwanullah1

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3235-3250, 2022, DOI:10.32604/cmc.2022.029604

    Abstract Environmental sustainability is the rate of renewable resource harvesting, pollution control, and non-renewable resource exhaustion. Air pollution is a significant issue confronted by the environment particularly by highly populated countries like India. Due to increased population, the number of vehicles also continues to increase. Each vehicle has its individual emission rate; however, the issue arises when the emission rate crosses the standard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop prediction approaches to monitor and control pollution using real time data. With the development of… More >

  • Open Access

    ARTICLE

    Remote Sensing Image Retrieval Based on 3D-Local Ternary Pattern (LTP) Features and Non-subsampled Shearlet Transform (NSST) Domain Statistical Features

    Hilly Gohain Baruah*, Vijay Kumar Nath, Deepika Hazarika

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 137-164, 2022, DOI:10.32604/cmes.2022.018339

    Abstract With the increasing popularity of high-resolution remote sensing images, the remote sensing image retrieval (RSIR) has always been a topic of major issue. A combined, global non-subsampled shearlet transform (NSST)-domain statistical features (NSSTds) and local three dimensional local ternary pattern (3D-LTP) features, is proposed for high-resolution remote sensing images. We model the NSST image coefficients of detail subbands using 2-state laplacian mixture (LM) distribution and its three parameters are estimated using Expectation-Maximization (EM) algorithm. We also calculate the statistical parameters such as subband kurtosis and skewness from detail subbands along with mean and standard deviation calculated from approximation subband, and… More >

  • Open Access

    ARTICLE

    Securing Privacy Using Optimization and Statistical Models in Cognitive Radio Networks

    R. Neelaveni1,*, B. Sridevi2, J. Sivasankari3

    Computer Systems Science and Engineering, Vol.42, No.2, pp. 523-533, 2022, DOI:10.32604/csse.2022.021433

    Abstract Cognitive Radio Networks (CRN) are the possible and ideal solution for meeting the spectrum needs of next-generation communication systems. CRN is a promising alternative approach that allows spectrum sharing in many applications. The licensed users considered Primary Users (PU) and unlicensed users as Secondary Users (SU). Time and power consumption on security issues are considered degrading factors in performance for improving the Quality of Service (QoS). Irrespective of using different optimization techniques, the same methodology is to be updated for the task. So that, learning and optimization go hand in hand. It ensures the security in CRN, risk factors in… More >

  • Open Access

    ARTICLE

    Fast Intra Mode Selection in HEVC Using Statistical Model

    Junaid Tariq1,*, Ayman Alfalou2, Amir Ijaz1, Hashim Ali3, Imran Ashraf1, Hameedur Rahman1, Ammar Armghan4, Inzamam Mashood1, Saad Rehman1

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3903-3918, 2022, DOI:10.32604/cmc.2022.019541

    Abstract Comprehension algorithms like High Efficiency Video Coding (HEVC) facilitates fast and efficient handling of multimedia contents. Such algorithms involve various computation modules that help to reduce the size of content but preserve the same subjective viewing quality. However, the brute-force behavior of HEVC is the biggest hurdle in the communication of multimedia content. Therefore, a novel method will be presented here to accelerate the encoding process of HEVC by making early intra mode decisions for the block. Normally, the HEVC applies 35 intra modes to every block of the frame and selects the best among them based on the RD-cost… More >

  • Open Access

    ARTICLE

    Statistical Model for Impact and Energy Absorption of 3D Printed Coconut Wood-PLA

    J. Kananathan1,2, M. Samykano2,*, K. Kadirgama3, D. Ramasamy2, M. M. Rahman2

    Energy Engineering, Vol.118, No.5, pp. 1305-1315, 2021, DOI:10.32604/EE.2021.016131

    Abstract Fused deposition modeling (FDM)-3D printing has been the favored technology to build functional components in various industries. The present study investigates infill percentage and infill pattern effects on the printed parts’ impact properties through the 3D printing technique using coconut wood-filled PLA composites. Mathematical models are also proposed in the present study with the aim for future property prediction. According to the ASTM standard, fifteen specimens with different parameter combinations were printed using a low-cost FDM 3D printer to evaluate their impact properties. Statistical analysis was performed using MINITAB to validate the experimental data and model development. The experimental outcomes… More >

  • Open Access

    ARTICLE

    A New Generalized Weibull Model: Classical and Bayesian Estimation

    Mi Zichuan1, Saddam Hussain1, Zubair Ahmad2,*, Omid Kharazmi3, Zahra Almaspoor2

    Computer Systems Science and Engineering, Vol.38, No.1, pp. 79-92, 2021, DOI:10.32604/csse.2021.015146

    Abstract Statistical distributions play a prominent role in applied sciences, particularly in biomedical sciences. The medical data sets are generally skewed to the right, and skewed distributions can be used quite effectively to model such kind of data sets. In the present study, therefore, we propose a new family of distributions suitable for modeling right-skewed medical data sets. The proposed family may be called a new generalized-X family. A special sub-model of the proposed family called a new generalized-Weibull distribution is discussed in detail. The maximum likelihood estimators of the model parameters are obtained. A brief Monte Carlo simulation study is… More >

  • Open Access

    ARTICLE

    On the Genesis of the Marshall-Olkin Family of Distributions via the T-X Family Approach: Statistical Modeling

    Yang Zhenwu1, Zubair Ahmad2,*, Zahra Almaspoor2, Saima K. Khosa3

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 753-760, 2021, DOI:10.32604/cmc.2021.012393

    Abstract In the last couple of years, there Has been an increased interest among the statisticians to define new families of distributions by adding one or more additional parameter(s) to the baseline distribution. In this regard, a number of families have been introduced and studied. One such example is the Marshall-Olkin family of distributions that is one of the most prominent approaches used to generalize the existing distributions. Whenever, we see a new method, the natural questions come in to mind are (i) what are the genesis of the newly proposed method and (ii) how did the proposed method is obtained.… More >

  • Open Access

    ARTICLE

    On Modeling the Medical Care Insurance Data via a New Statistical Model

    Yen Liang Tung1, Zubair Ahmad2,*, G. G. Hamedani3

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 113-126, 2021, DOI:10.32604/cmc.2020.012780

    Abstract Proposing new statistical distributions which are more flexible than the existing distributions have become a recent trend in the practice of distribution theory. Actuaries often search for new and appropriate statistical models to address data related to financial and risk management problems. In the present study, an extension of the Lomax distribution is proposed via using the approach of the weighted T-X family of distributions. The mathematical properties along with the characterization of the new model via truncated moments are derived. The model parameters are estimated via a prominent approach called the maximum likelihood estimation method. A brief Monte Carlo… More >

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