In the current agricultural scenario, availability of suitable land for cultivation is less and profitable allocation of the land for cultivating crops seems to be a cumbersome task. Crop planning optimization is a major research field in agriculture, in which land optimization is a significant challenge, which falls under the category of combinatorial optimization problems. The main objective of the present research is to maximize the net income from agriculture through optimal land allocation. Bio-inspired algorithms are quite popular in solving combinatorial optimization problems. Social Spider Algorithm (SSA), a new bio-inspired algorithm, is used to solve land optimization problem in this research based on the simulation of cooperative behaviour of social spiders. The agricultural area chosen for case study is the Coimbatore region, located in Tamilnadu state, India and the relevant data for the crops are collected from Tamilnadu Agricultural University Coimbatore, India. The optimal planting area, crop productivity for various land holdings and the water requirements are computed by SSA and the results have shown better directions for agricultural planning to improve the profit with constrained land area and water limitations.

This paper investigates ensemble feature selection using the q-rung orthopair hesitant fuzzy multi-criteria decision-making (MCDM) process. A novel algorithm is proposed for the study of ensemble feature selection and it is called as q-rung orthopair hesitant fuzzy MCDM extended to the Visekriterijumska optimizacija Ikompromisno Resenje (VIKOR) (q-ROHFS VIKOR). This is the first time in the literature an ensemble feature selection problem is modelled as a q-rung orthopair hesitant fuzzy MCDM extended to VIKOR technique. In the proposed method, every feature is ranked based on different available rankers and a preference matrix is obtained. In the sigmoidal transformation, the tuning parameter $a$ plays a vital role in the fuzzification process. This tuning parameter reduces the computation run-time in the fuzzification process for different high-dimensional datasets that are considered in this paper. By using q-ROHFS VIKOR method, a score is assigned to each feature based on the values of the preference matrix. At last, an output rank vector is produced for all features from which the user can select the desired number of features. To prove the efficiency and optimality of the proposed method, the comparison with basic filter-based feature selections and ensemble feature selection using feature ranking strategy is obtained. The proposed method in this paper is superior and efficient than the ensemble methods based on the accuracy and F-score levels upto 0.96.

Online business and marketing are becoming popular now a day due to the wide variety of products available from multiple vendors in online. One of the major challenges of e-business merchants is predicting the buying and selling patterns of online customers. Global level competition is another challenge faced by online merchants due to the lowest prices and offers provided by multiple sellers for the same or similar product. Hence, the development of an efficient web mining framework to analyze and predict buyer’s interest based on the browsing history will be a great support to the online sellers by providing exact or relevant product details to the buyers in online. Association rule mining plays an essential role in Web Mining for finding the most frequent and predictive patterns of the user. The major challenge in this approach is the generation of many rules for a huge volume of datasets. Decision making based on association rule mining is critical because knowledge is not directly present in frequent patterns. This research work focuses on the analysis of standard web mining approaches such as k-means clustering, fuzzy c-means clustering, fuzzy k-medoids clustering and fuzzy clustering with weighted session page matrix approach. In this work, MSNBC dataset from UCI Machine Learning Repository has been taken for analysis. Dimensionality reduction plays an important role in the accurate classification of users with respect to their interests. This research work proposed fuzzy C-Means using Kernel Principal Component Analysis (k-PCA) as a dimensionality reduction method based association rule mining classification, grouping and pattern prediction with 100% “confidence” along with a “lift” value greater than 1. The “support” value also shows higher compare with other existing methods and features are effectively reduced in the proposed architecture.