International Journal of Engineering and Manufacturing @ijem
Статьи журнала - International Journal of Engineering and Manufacturing
Все статьи: 544
Статья научная
In the modern era agriculture development is the highly contribute field of food security. Data Science is one of the top analysis experimental methods for forecasting and mapping synchronize. In our study, we experiment with three major parameters (Rainfall, Relative Humidity and Temperature) that can be affected crop production rate as well as area-based mapping. To complete the procedure, the cluster groping and prediction system has created a machine learning BOT combined analysis system. Bangladesh and its 13 areas with 46 years of data have visualized with proper analysis and build up a 2D map of each separate production area. Multi Linear Regression (MLR) and KMean Clustering is the main key point algorithm for the production analysis. Experiment analyzing, we can see that some elements of our environment are closely associated with the productivity of the crop. An untactful environmental change on parameters (Rainfall, Humidity, and Temperature) reduces agricultural productivity by 32-38%. Developed model accuracy 91.25% forecasting methodological analysis for production mapping and prediction. Extreme population food security has ensured ICT and Agriculture combine BOT & EVPM method is essential for the scientific world. This study will allow farmers to choose the proper crop in the right environmental condition, which will play a key role in strengthening the economy of the country.
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Cystic Region Detection Using Hybrid Fuzzy-based Multi-Region Normalization
Статья научная
One of the main purposes of this approach is to automatically extract the cystic border. Several of the semi-automatic segmentation strategies that have already been used may result in incomplete categorization, which is likely to fail as well as causes solitary pixel in noise dentistry x-rays images due to sampling artifact. As such, cyst boundaries are not removed appropriately. It focuses on the elimination of solitary pixels caused by artefacts. This suggested technique uses both the fuzzy memberships function of every pixel as well as localized spatially information of the neighbor pixels to accomplish the maximum feasible levels of automated processes for computers-aided diagnostics or identification of illnesses. That fuzzy-based multi-region normalization is implemented in five phases. To begin, FCM techniques are used to determine the numbers of centroids. This fuzzified function is constructed as well as provides memberships degree numbers to any and every pixel within every class based on the number of cluster centers as well as the shape of the histogram. It generates an intermediary segmentation output by fuzzy memberships degrees at about this step. This fuzzy localized aggregating of the neighborhood pixels will be the fourth phase, with the greatest responsiveness of the memberships degree generating pixels being kept in mind only for ultimate cystic area retrieved outputs.
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