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  • Open Access

    ARTICLE

    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 - 25 October 2021

    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… More >

  • Open Access

    ARTICLE

    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 - 07 September 2021

    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… More >

  • Open Access

    ARTICLE

    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 - 21 July 2021

    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,… More >

  • Open Access

    ARTICLE

    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 - 12 January 2021

    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… More >

  • Open Access

    ARTICLE

    Applying ANN, ANFIS and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2

    Amin Bemani1, Alireza Baghban2, Shahaboddin Shamshirband3, 4, *, Amir Mosavi5, 6, 7, Peter Csiba7, Annamaria R. Varkonyi-Koczy5, 7

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1175-1204, 2020, DOI:10.32604/cmc.2020.07723 - 30 April 2020

    Abstract In the present work, a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide. Four different machine learning algorithms of radial basis function, multi-layer perceptron (MLP), artificial neural networks (ANN), least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are used to model the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and the dissociation constant of acid. To evaluate the proposed models, different graphical and statistical analyses, along with novel More >

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