Cost and Income Structure of Organic and Conventional French Bean (Phaseolus vulgaris) Cultivation: A Case Study of Himachal Pradesh, India

The objective of this study was to investigate the cost and income structure of organic and conventional French bean cultivation in Solan district, Himachal Pradesh, in the 2018/ 2019 crop year. The sample included 80 farmers selected using a purposive sampling method, consisting of 40 organic and 40 conventional French bean growers. Survey questionnaires were used as the main instrument for data collection. Descriptive statistics and cost and income analysis were used for data analysis. The results indicate that the cost of production was higher, and output was lower under organic bean cultivation. Despite this, organic bean cultivation was more profitable than conventional farming, which was attributed to the higher prevailing market price for organic beans. Organic growers encounter numerous challenges and issues when cultivating and marketing of vegetables. Farmers seek a variety of aid from the government, business sector, and co-operative organizations to solve all of these challenges

Combination of data mining and artificial intelligence algorithms for efficient web page recommendation

Due to the obvious unstable increase in information, the web is saturated with data, which makes the data search a complicated task. Existing web-based recommendation systems include shortcomings such as a lack of capability as well as scalability when dealing with online data, and blockages created by traffic while utilising the website during peak hours. Web recommendation systems help consumers find the right content and make the information search process easier. Web usage mining is regarded as the primary source for web recommendation, and it is used in conjunction with association rule mining and the C4.5 algorithm to recommend online pages to the user. The Google search engine has been widely enhanced the likelihood on the system's suggested structure. A web log is created when a user enters a search query into a search engine. This query would be compared to the web logs by the proposed system. The associate rule mining technique helps in matching the user's search query to the online log. The C4.5 algorithm is linked to a priority based on reviews, which obviously ranks the search based on priority for greater validation result.&nbsp

Hybrid artificial neural network algorithm for air pollution estimation

In recent years, airborne broadcasting has grown more prevalent in cities. Air quality degradation is a severe air pollution issue that exists daily. To forecast the amount of pollutants, Artificial Neural Network (ANN) and Linear Vector Quantization (LVQ) techniques were utilized. The data set dimensions are defined by the pre-processing procedure and the feature extraction mechanism. The ANN model predicts categorization concentration, allowing the LVQ model to classify direct situations with greater accuracy using explanatory factors. The ANN+LVQ model outperformed other technologies in terms of classification accuracy. The raw data was cleaned to improve the accuracy of the prediction algorithms. The pollutants discovered in the collection are NO2, NOx, O3, Benzene, Xylene, NH3, CO, SO2, PM10, NO, and Toluene. The performance of the recommendation and forecast models were tested in this study using two datasets in two distinct experiments. In urban, rural, and industrial settings, the proposed ANN model is successful in detecting air quality and predicting pollution levels. The ANN-LVQ model obtained 90% percent sensitivity, 97.59% accuracy, and 99.46% specificity with 2.43% error rate. The suggested model's accuracy is much greater than that of other current research models