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Search Results (21)
  • Open Access


    Secure Dengue Epidemic Prediction System: Healthcare Perspective

    Abdulaziz Aldaej*, Tariq Ahamed Ahanger, Mohammed Yousuf Uddin, Imdad Ullah

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1723-1745, 2022, DOI:10.32604/cmc.2022.027487

    Abstract Viral diseases transmitted by mosquitoes are emerging public health problems across the globe. Dengue is considered to be the most significant mosquito-oriented disease. Conspicuously, the present study provides an effective architecture for Dengue Virus Infection surveillance. The proposed system involves a 4-level architecture for the prediction and prevention of dengue infection outspread. The architectural levels including Dengue Information Acquisition level, Dengue Information Classification level, Dengue-Mining and Extraction level, and Dengue-Prediction and Decision Modeling level enable an individual to periodically monitor his/her probabilistic dengue fever measure. The prediction process is carried out so that proactive measures are taken beforehand. For predictive… More >

  • Open Access


    Adaptive Neuro-Fuzzy Based Load Frequency Control in Presence of Energy Storage Devices

    Pankaj Jood*, Sanjeev Kumar Aggarwal, Vikram Chopra

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 785-804, 2022, DOI:10.32604/iasc.2022.025217

    Abstract Energy storage technologies are utilized for improving the primary frequency control in complex electrical systems. In this paper, the modeling and simulation of a two-area power system is done to evaluate and compare the impact of three different energy storage applications on load frequency control performance. Capacitive energy storage (CES), battery energy storage (BES), and superconducting magnetic energy storage (SMES) are considered for the study. On the basis of peak overshoot and settling time, the performance of these energy storage devices is compared. The power system consists of thermal, wind, and solar resources. All nonlinearities are incorporated in the system… More >

  • Open Access


    Self-Balancing Vehicle Based on Adaptive Neuro-Fuzzy Inference System

    M. L. Ramamoorthy1, S. Selvaperumal2,*, G. Prabhakar3

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 485-497, 2022, DOI:10.32604/iasc.2022.025824

    Abstract The scope of this research is to design and fuse the sensors used in the self-balancing vehicle through Adaptive Neuro-Fuzzy Inference systems (ANFIS) algorithm to optimize the output. The self-balancing vehicle is a wheeled inverted pendulum, which is extremely complex, nonlinear and unstable. Homogeneous and Heterogeneous sensors are involved in this sensor fusion research to identify the best feasible value among them. The data fusion algorithm present inside the controller of the self-balancing vehicle makes the inputs of the homogeneous sensors and heterogeneous sensors separately for ameliorate surrounding perception. Simulation is performed by modeling the sensors in Simulink. The outcomes… More >

  • Open Access


    Comparative Study of Machine Learning Modeling for Unsteady Aerodynamics

    Mohammad Alkhedher*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1901-1920, 2022, DOI:10.32604/cmc.2022.025334

    Abstract Modern fighters are designed to fly at high angle of attacks reaching 90 deg as part of their routine maneuvers. These maneuvers generate complex nonlinear and unsteady aerodynamic loading. In this study, different aerodynamic prediction tools are investigated to achieve a model which is highly accurate, less computational, and provides a stable prediction of associated unsteady aerodynamics that results from high angle of attack maneuvers. These prediction tools include Artificial Neural Networks (ANN) model, Adaptive Neuro Fuzzy Logic Inference System (ANFIS), Fourier model, and Polynomial Classifier Networks (PCN). The main aim of the prediction model is to estimate the pitch… More >

  • Open Access


    A Novel Hybrid MPPT Control Strategy for Isolated Solar PV Power System

    D. Sabaripandiyan1,*, H. Habeebullah Sait2, G. Aarthi3

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1055-1070, 2022, DOI:10.32604/iasc.2022.021950

    Abstract The main aspiration of this paper is to improve the efficiency of Solar Photovoltaic (SPV) power system with a new Hybrid controller for standalone/isolated Solar PV applications is proposed. This controller uses the merits of both Adapted Neuro-Fuzzy Inference System (ANFIS) and Perturbation & Observation (P&O) control techniques to concede rapid recovery at dynamic change of environment conditions such as solar irradiation and temperature. The ANFIS strategy itself has the merits over Fuzzy Logic and ANN methods. Conversely, P&O has its simplicity in implementation. Hence a case study for rapid recovery with the proposed controller and conventional P&O control strategy… More >

  • Open Access


    Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

    Parul Gandhi1, Mohammad Zubair Khan2, Ravi Kumar Sharma3, Omar H. Alhazmi2, Surbhi Bhatia4,*, 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 >

  • Open Access


    Enhanced Neuro-Fuzzy-Based Crop Ontology for Effective Information Retrieval

    K. Ezhilarasi1,*, G. Maria Kalavathy2

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 569-582, 2022, DOI:10.32604/csse.2022.020280

    Abstract Ontology is the progression of interpreting the conceptions of the information domain for an assembly of handlers. Familiarizing ontology as information retrieval (IR) aids in augmenting the searching effects of user-required relevant information. The crux of conventional keyword matching-related IR utilizes advanced algorithms for recovering facts from the Internet, mapping the connection between keywords and information, and categorizing the retrieval outcomes. The prevailing procedures for IR consume considerable time, and they could not recover information proficiently. In this study, through applying a modified neuro-fuzzy algorithm (MNFA), the IR time is mitigated, and the retrieval accuracy is enhanced for trouncing the… More >

  • Open Access


    QoS Based Cloud Security Evaluation Using Neuro Fuzzy Model

    Nadia Tabassum1, Tahir Alyas2, Muhammad Hamid3,*, Muhammad Saleem4, Saadia Malik5, Syeda Binish Zahra2

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1127-1140, 2022, DOI:10.32604/cmc.2022.019760

    Abstract Cloud systems are tools and software for cloud computing that are deployed on the Internet or a cloud computing network, and users can use them at any time. After assessing and choosing cloud providers, however, customers confront the variety and difficulty of quality of service (QoS). To increase customer retention and engagement success rates, it is critical to research and develops an accurate and objective evaluation model. Cloud is the emerging environment for distributed services at various layers. Due to the benefits of this environment, globally cloud is being taken as a standard environment for individuals as well as for… More >

  • Open Access


    Diagnosis of Neem Leaf Diseases Using Fuzzy-HOBINM and ANFIS Algorithms

    K. K. Thyagharajan, I. Kiruba Raji*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2061-2076, 2021, DOI:10.32604/cmc.2021.017591

    Abstract This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model (F-HOBINM) and adaptive neuro classifier (ANFIS). India exports USD 0.28-million worth of neem leaf to the UK, USA, UAE, and Europe in the form of dried leaves and powder, both of which help reduce diabetes-related issues, cardiovascular problems, and eye disorders. Diagnosing neem leaf disease is difficult through visual interpretation, owing to similarity in their color and texture patterns. The most common diseases include bacterial blight, Colletotrichum and Alternaria leaf spot, blight, damping-off, powdery mildew, Pseudocercospora leaf spot, leaf web… More >

  • Open Access


    Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms

    Mavra Mehmood1, Ember Ayub1, Fahad Ahmad1,6,*, Madallah Alruwaili2, Ziyad A. Alrowaili3, Saad Alanazi2, Mamoona Humayun2, Muhammad Rizwan1, Shahid Naseem4, Tahir Alyas5

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 641-657, 2021, DOI:10.32604/cmc.2021.013774

    Abstract Clinical image processing plays a significant role in healthcare systems and is currently a widely used methodology. In carcinogenic diseases, time is crucial; thus, an image’s accurate analysis can help treat disease at an early stage. Ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) are common types of malignancies that affect both women and men. The number of cases of DCIS and LCIS has increased every year since 2002, while it still takes a considerable amount of time to recommend a controlling technique. Image processing is a powerful technique to analyze preprocessed images to retrieve useful information… More >

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