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  • Open Access

    ARTICLE

    Multi Criteria Decision Making for Evaluation and Ranking of Cancer Information

    Shahid Mahmood1,*, Muhammad Amin2, Mubashir Baig Mirza1, Salem Abu-Ghumsan1, Muhammad Akram3, Zahid Mahmood Janjua4, Arslan Shahid5, Usman Shahid6

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4469-4481, 2023, DOI:10.32604/cmc.2023.030728 - 31 October 2022

    Abstract Cancer is a disease that is rapidly expanding in prevalence all over the world. Cancer cells can metastasize, or spread, across the body and impact several different cell types. Additionally, the incidence rates of several subtypes of cancer have been on the rise in India. The countermeasures for the cancer disease can be taken by determining the specific expansion rate of each type. To rank the various forms of cancer’s rate of progression, we used some of the available data. Numerous studies are available in the literature which show the growth rate of cancer by… More >

  • Open Access

    ARTICLE

    Novel Distance Measures on Hesitant Fuzzy Sets Based on Equal-Probability Transformation and Their Application in Decision Making on Intersection Traffic Control

    Fangwei Zhang1,2, Yi Zhao3,*, Jun Ye4, Shuhong Wang5, Jingyi Hu6

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1589-1602, 2023, DOI:10.32604/cmes.2022.022431 - 27 October 2022

    Abstract The purpose of this study is to reduce the uncertainty in the calculation process on hesitant fuzzy sets (HFSs). The innovation of this study is to unify the cardinal numbers of hesitant fuzzy elements (HFEs) in a special way. Firstly, a probability density function is assigned for any given HFE. Thereafter, equal-probability transformation is introduced to transform HFEs with different cardinal numbers on the condition into the same probability density function. The characteristic of this transformation is that the higher the consistency of the membership degrees in HFEs, the higher the credibility of the mentioned More >

  • Open Access

    ARTICLE

    A Boosted Tree-Based Predictive Model for Business Analytics

    Mohammad Al-Omari1, Fadi Qutaishat1, Majdi Rawashdeh1, Samah H. Alajmani2, Mehedi Masud3,*

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 515-527, 2023, DOI:10.32604/iasc.2023.030374 - 29 September 2022

    Abstract Business Analytics is one of the vital processes that must be incorporated into any business. It supports decision-makers in analyzing and predicting future trends based on facts (Data-driven decisions), especially when dealing with a massive amount of business data. Decision Trees are essential for business analytics to predict business opportunities and future trends that can retain corporations’ competitive advantage and survival and improve their business value. This research proposes a tree-based predictive model for business analytics. The model is developed based on ranking business features and gradient-boosted trees. For validation purposes, the model is tested More >

  • Open Access

    ARTICLE

    Empirical Analysis of Software Success Rate Forecasting During Requirement Engineering Processes

    Muhammad Hasnain1, Imran Ghani2, Seung Ryul Jeong3,*, Muhammad Fermi Pasha4, Sardar Usman5, Anjum Abbas6

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 783-799, 2023, DOI:10.32604/cmc.2023.030162 - 22 September 2022

    Abstract Forecasting on success or failure of software has become an interesting and, in fact, an essential task in the software development industry. In order to explore the latest data on successes and failures, this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework? What human factors contribute to success or failure of a software? What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work? In order to… More >

  • Open Access

    ARTICLE

    Arithmetic Optimization with Deep Learning Enabled Churn Prediction Model for Telecommunication Industries

    Vani Haridasan*, Kavitha Muthukumaran, K. Hariharanath

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3531-3544, 2023, DOI:10.32604/iasc.2023.030628 - 17 August 2022

    Abstract Customer retention is one of the challenging issues in different business sectors, and various firms utilize customer churn prediction (CCP) process to retain existing customers. Because of the direct impact on the company revenues, particularly in the telecommunication sector, firms are needed to design effective CCP models. The recent advances in machine learning (ML) and deep learning (DL) models enable researchers to introduce accurate CCP models in the telecommunication sector. CCP can be considered as a classification problem, which aims to classify the customer into churners and non-churners. With this motivation, this article focuses on… More >

  • Open Access

    ARTICLE

    Selection of Wind Turbine Systems for the Sultanate of Oman

    M. A. A. Younis1,*, Anas Quteishat1,2

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 343-359, 2023, DOI:10.32604/csse.2023.029510 - 16 August 2022

    Abstract The Sultanate of Oman has been dealing with a severe renewable energy issue for the past few decades, and the government has struggled to find a solution. In addition, Oman’s strategy for converting power generation to sources of renewable energy includes a goal of 60 percent of national energy demands being met by renewables by 2040, including solar and wind turbines. Furthermore, the use of small-scale energy from wind devices has been on the rise in recent years. This upward trend is attributed to advancements in wind turbine technology, which have lowered the cost of… More >

  • Open Access

    ARTICLE

    Strategic Renewable Energy Resource Selection Using a Fuzzy Decision-Making Method

    Anas Quteishat1,2,*, M. A. A. Younis2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2117-2134, 2023, DOI:10.32604/iasc.2023.029419 - 19 July 2022

    Abstract Renewable energy is created by renewable natural resources such as geothermal heat, sunlight, tides, rain, and wind. Energy resources are vital for all countries in terms of their economies and politics. As a result, selecting the optimal option for any country is critical in terms of energy investments. Every country is nowadays planning to increase the share of renewable energy in their universal energy sources as a result of global warming. In the present work, the authors suggest fuzzy multi-characteristic decision-making approaches for renewable energy source selection, and fuzzy set theory is a valuable methodology More >

  • Open Access

    ARTICLE

    Application of Intuitionistic Z-Numbers in Supplier Selection

    Nik Muhammad Farhan Hakim Nik Badrul Alam1,2, Ku Muhammad Naim Ku Khalif1,*, Nor Izzati Jaini1

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 47-61, 2023, DOI:10.32604/iasc.2023.024660 - 06 June 2022

    Abstract Intuitionistic fuzzy numbers incorporate the membership and non-membership degrees. In contrast, Z-numbers consist of restriction components, with the existence of a reliability component describing the degree of certainty for the restriction. The combination of intuitionistic fuzzy numbers and Z-numbers produce a new type of fuzzy numbers, namely intuitionistic Z-numbers (IZN). The strength of IZN is their capability of better handling the uncertainty compared to Zadeh's Z-numbers since both components of Z-numbers are characterized by the membership and non-membership functions, exhibiting the degree of the hesitancy of decision-makers. This paper presents the application of such numbers… More >

  • Open Access

    ARTICLE

    On Soft Pre-Rough Approximation Space with Applications in Decision Making

    M. El Sayed1,*, Wadia Faid Hassan Al-shameri1, M. A. El Safty2

    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 865-879, 2022, DOI:10.32604/cmes.2022.020066 - 27 June 2022

    Abstract A soft, rough set model is a distinctive mathematical model that can be used to relate a variety of real-life data. In the present work, we introduce new concepts of rough set based on soft pre-lower and soft pre-upper approximation space. These concepts are soft pre-rough equality, soft pre-rough inclusion, soft pre-rough belonging, soft predefinability, soft pre-internal lower, and soft pre-external lower. We study the properties of these concepts. Finally, we use the soft pre-rough approximation to illustrate the importance of our method in decision-making for Chikungunya medical illnesses. In reality, the impact factors of More >

  • Open Access

    ARTICLE

    Fuzzy Multi-criteria Decision Making for Decision Support in Port Capacity Upgrade

    Chia-Nan Wang, Tien–Lin Chao*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2485-2494, 2022, DOI:10.32604/cmc.2022.026682 - 16 June 2022

    Abstract In many port capacity upgrade projects, choosing a supplier of equipment is a complicated decision, project managers must consider many criteria to choose a supplier to ensure the project is completed on time, optimal in terms of benefit and cost. Therefore, selecting the equipment supplier in this project is a multi-criteria decision-making process. The multi-criteria decision-making (MCDM) model is applied in many fields to select the optimal solution, but there are very few studies using the MCDM model to support project managers in evaluating and selecting optimal solutions in port capacity upgrade project. In this More >

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