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  • Open Access

    ARTICLE

    A Hybrid Model for Improving Software Cost Estimation in Global Software Development

    Mehmood Ahmed1,3,*, Noraini B. Ibrahim1, Wasif Nisar2, Adeel Ahmed3, Muhammad Junaid3,*, Emmanuel Soriano Flores4, Divya Anand4

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1399-1422, 2024, DOI:10.32604/cmc.2023.046648

    Abstract Accurate software cost estimation in Global Software Development (GSD) remains challenging due to reliance on historical data and expert judgments. Traditional models, such as the Constructive Cost Model (COCOMO II), rely heavily on historical and accurate data. In addition, expert judgment is required to set many input parameters, which can introduce subjectivity and variability in the estimation process. Consequently, there is a need to improve the current GSD models to mitigate reliance on historical data, subjectivity in expert judgment, inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns. This study introduces a novel hybrid… More >

  • Open Access

    ARTICLE

    Influence of Hydro-Morphometric Parameters on Water Flow in the Tshopo Sub-Catchments, Democratic Republic of Congo

    Influence des paramètres hydro-morphométriques sur l’écoulement des eaux des sous-bassins versants de la Tshopo, République démocratique du Congo

    Faidance Mashauri1,2,*, Mokili Mbuluyo1,3, Nsalambi Nkongolo2,4

    Revue Internationale de Géomatique, Vol.32, pp. 79-98, 2023, DOI:10.32604/RIG.2023.044124

    Abstract The most characteristic hydro-morphometric parameters controlling water flow in the Tshopo catchment have not yet been determined. Correlation analysis, multiple linear regression and hierarchical ascending classification were applied to all the data in order to identify the most characteristic variables that significantly influence water flow velocity, and to group together physically similar sub-catchments. The results highlight the importance of topography on water flow. Three topographical variables, namely median altitude (H50), overall gradient (Dg) and specific gradient (Ds), have a significant influence (p-value ≤ 0.05) on surface water flow velocity (Ve) in the Tshopo sub-catchments. Two opposing groups (G1 and G2)… More >

  • Open Access

    ARTICLE

    IOT Based Smart Parking System Using Ensemble Learning

    Walaa H. Elashmawi1,3, Ahmad Akram2, Mohammed Yasser2, Menna Hisham2, Manar Mohammed2, Noha Ihab2, Ahmed Ali4,5,*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3637-3656, 2023, DOI:10.32604/iasc.2023.035605

    Abstract Parking space is usually very limited in major cities, especially Cairo, leading to traffic congestion, air pollution, and driver frustration. Existing car parking systems tend to tackle parking issues in a non-digitized manner. These systems require the drivers to search for an empty parking space with no guarantee of finding any wasting time, resources, and causing unnecessary congestion. To address these issues, this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of parking availability. User authentication and automated payments are handled… More >

  • Open Access

    ARTICLE

    A Three-Dimensional Real-Time Gait-Based Age Detection System Using Machine Learning

    Muhammad Azhar1,*, Sehat Ullah1, Khalil Ullah2, Habib Shah3, Abdallah Namoun4, Khaliq Ur Rahman5

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 165-182, 2023, DOI:10.32604/cmc.2023.034605

    Abstract Human biometric analysis has gotten much attention due to its widespread use in different research areas, such as security, surveillance, health, human identification, and classification. Human gait is one of the key human traits that can identify and classify humans based on their age, gender, and ethnicity. Different approaches have been proposed for the estimation of human age based on gait so far. However, challenges are there, for which an efficient, low-cost technique or algorithm is needed. In this paper, we propose a three-dimensional real-time gait-based age detection system using a machine learning approach. The proposed system consists of training… More >

  • Open Access

    ARTICLE

    Prediction of Link Failure in MANET-IoT Using Fuzzy Linear Regression

    R. Mahalakshmi1,*, V. Prasanna Srinivasan2, S. Aghalya3, D. Muthukumaran4

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1627-1637, 2023, DOI:10.32604/iasc.2023.032709

    Abstract A Mobile Ad-hoc NETwork (MANET) contains numerous mobile nodes, and it forms a structure-less network associated with wireless links. But, the node movement is the key feature of MANETs; hence, the quick action of the nodes guides a link failure. This link failure creates more data packet drops that can cause a long time delay. As a result, measuring accurate link failure time is the key factor in the MANET. This paper presents a Fuzzy Linear Regression Method to measure Link Failure (FLRLF) and provide an optimal route in the MANET-Internet of Things (IoT). This work aims to predict link… More >

  • Open Access

    ARTICLE

    Reconfigurable Sensing Time in Cooperative Cognitive Network Using Machine Learning

    Noor Gul1,2, Saeed Ahmed1,3, Su Min Kim1, Muhammad Sajjad Khan4, Junsu Kim1,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5209-5227, 2023, DOI:10.32604/cmc.2023.026945

    Abstract A cognitive radio network (CRN) intelligently utilizes the available spectral resources by sensing and learning from the radio environment to maximize spectrum utilization. In CRNs, the secondary users (SUs) opportunistically access the primary users (PUs) spectrum. Therefore, unambiguous detection of the PU channel occupancy is the most critical aspect of the operations of CRNs. Cooperative spectrum sensing (CSS) is rated as the best choice for making reliable sensing decisions. This paper employs machine-learning tools to sense the PU channels reliably in CSS. The sensing parameters are reconfigured to maximize the spectrum utilization while reducing sensing error and cost with improved… More >

  • Open Access

    ARTICLE

    Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization

    Neema George1,*, B. K. Anoop1, Vinodh P. Vijayan2

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2281-2297, 2023, DOI:10.32604/csse.2023.028117

    Abstract Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or application. When workload execution, accuracy, and cost are accurately stabilized in opposition to the best possible framework in real-time, efficiency is attained. In addition, every workload or application required for the framework is characteristic and these essentials change over time. But, the existing method was failed to ensure the high Quality of Service (QoS). In order to address this issue, a Tricube Weighted Linear Regression-based Inter Quartile (TWLR-IQ) for Cloud Infrastructural Resource Optimization is introduced. A Tricube Weighted Linear Regression… More >

  • Open Access

    ARTICLE

    Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

    Mehdi Jamei1, Nadjem Bailek2,*, Kada Bouchouicha3, Muhammed A. Hassan4, Ahmed Elbeltagi5, Alban Kuriqi6, Nadhir Al-Ansar7, Javier Almorox8, El-Sayed M. El-kenawy9,10

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1625-1640, 2023, DOI:10.32604/cmc.2023.031406

    Abstract Solar energy represents one of the most important renewable energy sources contributing to the energy transition process. Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it. Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory. Here, four ensemble-based hybrid models were developed by hybridizing Additive Regression (AR) with Random Forest (RF), Locally Weighted Linear Regression (LWLR),… More >

  • Open Access

    ARTICLE

    Prediction of Residential Building’s Solar Installation Energy Demand in Morocco Using Multiple Linear Regression Analysis

    Nada Yamoul1,*, Latifa Dlimi1, Baraka Achraf Chakir2

    Energy Engineering, Vol.119, No.5, pp. 2135-2148, 2022, DOI:10.32604/ee.2022.020005

    Abstract The building sector is one of the main energy-consuming sectors in Morocco. In fact, it accounts for 33% of the final consumption of energy and records a high increase in the annual consumption of energy caused by further planned large-scale projects. Indeed, the energy consumption of the building sector is experiencing a significant acceleration justified by the rapid need for the development of housing stock, wich is estimated at an average increase of 1,5% per year; furthermore, tant is an estimated increase of about 6,4%. In this sense, building constitutes an important potential source for rationalizing both energy consumption and… More >

  • Open Access

    ARTICLE

    Detection of DDoS Attack in IoT Networks Using Sample Selected RNN-ELM

    S. Hariprasad1,*, T. Deepa1, N. Bharathiraja2

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1425-1440, 2022, DOI:10.32604/iasc.2022.022856

    Abstract The Internet of Things (IoT) is a global information and communication technology which aims to connect any type of device to the internet at any time and in any location. Nowadays billions of IoT devices are connected to the world, this leads to easily cause vulnerability to IoT devices. The increasing of users in different IoT-related applications leads to more data attacks is happening in the IoT networks after the fog layer. To detect and reduce the attacks the deep learning model is used. In this article, a hybrid sample selected recurrent neural network-extreme learning machine (hybrid SSRNN-ELM) algorithm that… More >

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