Muhammad Shoaib Arif^1,2,*, Kamaleldin Abodayeh¹, Yasir Nawaz²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3417-3434, 2024, DOI:10.32604/cmes.2024.046944

Abstract This study focuses on the urgent requirement for improved accuracy in disease modeling by introducing a new computational framework called the Hybrid SIR-Fuzzy Model. By integrating the traditional Susceptible-Infectious-Recovered (SIR) model with fuzzy logic, our method effectively addresses the complex nature of epidemic dynamics by accurately accounting for uncertainties and imprecisions in both data and model parameters. The main aim of this research is to provide a model for disease transmission using fuzzy theory, which can successfully address uncertainty in mathematical modeling. Our main emphasis is on the imprecise transmission rate parameter, utilizing a three-part description of its membership level.… More >

Emmanuel Lule^1,2,*, Chomora Mikeka³, Alexander Ngenzi⁴, Didacienne Mukanyiligira⁵

Intelligent Automation & Soft Computing, Vol.38, No.1, pp. 57-81, 2023, DOI:10.32604/iasc.2023.037988

Abstract Local markets in East Africa have been destroyed by raging fires, leading to the loss of life and property in the nearby communities. Electrical circuits, arson, and neglected charcoal stoves are the major causes of these fires. Previous methods, i.e., satellites, are expensive to maintain and cause unnecessary delays. Also, unit-smoke detectors are highly prone to false alerts. In this paper, an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach. A free open–source MATLAB/Simulink fuzzy toolbox integrated into MATLAB 2018a is used… More >

Aoqi Xu¹, Khalid A. Alattas², Nasreen Kausar³, Ardashir Mohammadzadeh⁴, Ebru Ozbilge^5,*, Tonguc Cagin⁵

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 17-32, 2023, DOI:10.32604/iasc.2023.036623

Abstract In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs. noisy environments. The primary objective of this study is to present a more robust method against uncertainties. This paper proposes a new deep learning scheme for modeling and identification applications. The suggested approach is based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support measurement errors and high-level uncertainties. Besides the rule optimization, the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalman filter (SCKF).… More >

M. M. Alqarni¹, Muhammad Rafiq², Fazal Dayan^3,*, Jan Awrejcewicz⁴, Nauman Ahmed⁵, Ali Raza⁶, Muhammad Ozair Ahmad⁵, Witold Pawłowski⁷, Emad E. Mahmoud⁸

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6371-6388, 2023, DOI:10.32604/cmc.2023.033553

Abstract Amoebiasis is a parasitic intestinal infection caused by the highly pathogenic amoeba Entamoeba histolytica. It is spread through person-to-person contact or by eating or drinking food or water contaminated with feces. Its transmission rate depends on the number of cysts present in the environment. The traditional models assumed a homogeneous and contradictory transmission with reality. The heterogeneity of its transmission rate is a significant factor when modeling disease dynamics. The heterogeneity of disease transmission can be described mathematically by introducing fuzzy theory. In this context, a fuzzy SEIR Amoebiasis disease model is considered in this study. The equilibrium analysis and… More >

V. A. Vijayakumar^*, J. Shanthini, S. Karthik, K. Srihari

Computer Systems Science and Engineering, Vol.45, No.1, pp. 659-669, 2023, DOI:10.32604/csse.2023.028400

Abstract The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans. Hence, there exist a prediction of the background scenes and that leads to discontinuity between the predicted and planned outputs. An optimal prediction engine is required that suitably reads the background objects and make optimal decisions. In this paper, the author(s) develop an autonomous model for vehicle driving using ensemble model for large Sport Utility Vehicles (SUVs) that uses three different modules involving (a) recognition model, (b) planning model and (c) prediction model. The… More >

Gang Lei^1,*, Chunxiang Xu²

Energy Engineering, Vol.119, No.5, pp. 2005-2029, 2022, DOI:10.32604/ee.2022.021154

Abstract In the present paper, the distribution feeder reconfiguration in the presence of distributed generation resources (DGR) and energy storage systems (ESS) is solved in the dynamic form. Since studies on the reconfiguration problem have ignored the grid security and reliability, the non-distributed energy index along with the energy loss and voltage stability indices has been assumed as the objective functions of the given problem. To achieve the mentioned benefits, there are several practical plans in the distribution network. One of these applications is the network rearrangement plan, which is the simplest and least expensive way to add equipment to the… More >

Chia-Nan Wang¹, Hoang Tuyet Nhi Thai^1,*, Nguyen Van Thanh²

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2291-2304, 2022, DOI:10.32604/cmc.2022.025477

Abstract Supplier selection is a vital part of the supply chain and is also a current issue that concerns businesses today as supplier quality directly affects the operations of the organization. Choosing the right supplier can help businesses increase productivity, competitiveness in the market, and profits without having to lower the quality of the products. However, choosing a supplier is not a simple matter, it requires businesses to consider many aspects about their suppliers. Therefore, the goal of this study is to propose an integrated model consisting of two models: Fuzzy Analytics Network Process (Fuzzy-ANP) model and Weighted Aggregated Sum Product… More >

P. Perumal^*

Computer Systems Science and Engineering, Vol.43, No.1, pp. 203-218, 2022, DOI:10.32604/csse.2022.020459

Abstract With the wider growth of web-based documents, the necessity of automatic document clustering and text summarization is increased. Here, document summarization that is extracting the essential task with appropriate information, removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting task. In this research, a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation (gFAR). Initially, the graph model is used to map the relationship among the data (multi-source) followed by the establishment of document clustering with the generation of association… More >

J. V. Anchitaalagammai^1,*, Rajesh Verma², M. Kavitha³, A. R. Revathi⁴, S. R. Preethi⁵, Kiranmai Bellam⁶

Computer Systems Science and Engineering, Vol.41, No.3, pp. 861-874, 2022, DOI:10.32604/csse.2022.021049

Abstract Energy management and packet delivery rate are the important factors in ad hoc networks. It is the major network where nodes share the information without administration. Due to the mobility of nodes, maximum energy is spent on transmission of packets. Mostly energy is wasted on packet dropping and false route discovery. In this research work, Fuzzy Based Reliable Load Balanced Routing Approach (RLRA) is proposed to provide high energy efficiency and more network lifetime using optimal multicast route discovery mechanism. It contains three phases. In first phase, optimal multicast route discovery is initiated to resolve the link failures. In second… More >

Parul Gandhi¹, Mohammad Zubair Khan², Ravi Kumar Sharma³, Omar H. Alhazmi², Surbhi Bhatia^4,*, Chinmay Chakraborty⁵

Computer Systems Science and Engineering, Vol.41, No.3, pp. 891-902, 2022, DOI:10.32604/csse.2022.019943

Abstract Software reliability is the primary concern of software development organizations, and the exponentially increasing demand for reliable software requires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of these errors helps the organization improve and enhance the software’s reliability and save money, time, and effort. Many soft computing techniques are available to get solutions for critical problems but selecting the appropriate technique is a big challenge. This paper proposed an efficient algorithm that can be used for the prediction of software reliability. The proposed algorithm is… More >