b) 2023-Scopus Open Access (Pdf)

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/5193

Browse

Subject: search.filters.subject.rough set

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    A STUDY ON NEW CLASSES OF BINARY SOFT SETS IN TOPOLOGICAL ROUGH APPROXIMATION SPACE (Article)
    (E.A. Buketov Karaganda University Publish house, 2023-12) Parvathy, C R; Sofia, A
    Soft binary relation is used to define new classes of soft sets, namely BR-soft simply open set and BR-soft simply* alpha open set, in topological rough approximation space over two different universes. The defined set provides information on the missing elements of a BR-soft set and can help in simplifying decision making. Approximation operators are defined and the characteristics of the proposed sets are studied with examples. The relationship between the defined sets and other soft sets is brought out. An accuracy check was done to compare the proposed method with other methods. It is identified that the proposed method is more accurate.
  • Thumbnail Image
    Item
    PESTICIDE RECOMMENDATION FOR DIFFERENT LEAF DISEASES AND RELATED PESTS USING MULTI-DIMENSIONAL FEATURE LEARNING DEEP CLASSIFIER (Article)
    (International Information and Engineering Technology Association, 2023-02) Saleem, Jaithoon Bibi Mohammed; Shanmugam, Karpagavalli
    In agricultural applications, the most essential task is to classify leaf diseases and their associated pests from various aspects. To achieve this, a Deep Convolutional Neural Network (DCNN) model was developed to classify the leaf diseases based on the soil and climatic features. But it needs a recommendation system to control the pesticide use for controlling the leaf diseases caused by specific pests. Hence, this paper hybridizes the Multi-dimensional Feature Learning-based DCNN (MFL-DCNN) with the Rough Set (RS) on an intuitionistic Fuzzy approximation space (RSF)-based decision support system to suggest the proper pesticides for a certain crop to be planted in a particular region. First, the leaf images are augmented by the Positional-aware Dual-Attention and Topology-Fusion with Evolutionary Generative Adversarial Network (PDATFEGAN) model. Then, the multi-dimensional data such as the created leaf images, pest, soil, weather, and pesticide data are fed to the DCNN with a softmax classifier for classifying leaf diseases and related pests. Then, the RSF-based decision model is applied, which determines the correlation between leaf disease and pests to recommend suitable pesticides. Finally, the experimental results reveal that the MFL-DCNN-RSF accomplishes a maximum efficiency than all other models for recommending pesticides to control leaf diseases and pests.