At Present there are lots of open source content management systems (CMS) available in the market. As open source CMS has its own advantages but out of so many only few like Joomla, Drupal, WordPress are popular in the market because of good functionality and support. It is the obvious question for everyone that, what are the basic reasons that only these CMS are mostly acceptable? Lots of surveys have been done to know which is the best and why? But still it is unanswered. By certain comparison on data we want to show that why mentioned CMS are best in their areas? We also want discover ranking for these three CMS that out of these which one is preferable in which condition. In this paper we are comparing above mentioned CMS by Goggle page rank, their documentation support, their popularity, installation survey and many more.

No abstract

Each primary, secondary, tertiary, quaternary, and the quinary sector has huge or very huge incremental data from large-scale, small-scale industries, medium industries, or cottage industries. The data associated with each of them are very crucial from every point of view. The complex problems are increasing day by day in real-time execution which can be addressed using current trends of technology like machine learning and deep learning. Machine learning is a subset of artificial intelligence. ML is functioning for image & speech recognition, mail filtering, Facebook tagging mechanism, and many others. Deep learning is an advanced technology that is a subset of machine learning with the capacity to learn more intelligently on a large set of data. Deep learning works with multiple hidden layers to produce the predicted outcomes. Deep learning algorithms include convolutional neural networks, recurrent neural networks, long short-term memory networks, stacked auto-encoders, deep boltzmann machines &, etc.

Advancements of the last decade in edge computing, edge IoT, and edge artificial intelligence now allow for autonomous, efficient, and intelligent systems to be proposed for various industrial applications. Intelligence agricultural solutions allow farmers to achieve more with less while improving quality and providing a rapid go-to-market approach for produce. Using AI is an effective technique to detect any crop health concerns or nutrient inadequacies in the field. Plant diseases affect the food system, economy, and environment. This chapter covers intelligent agriculture & challenges in front of technology. It focuses AI application using machine learning, artificial neural network (ANN), and deep learning. The various AI applications in agriculture for land monitoring, crop and varietal selection, smart irrigation or automation of irrigation, monitoring of crop health, crop disease detection, predictive analytics, weed control, precision agriculture, harvesting, yield estimation and phenotyping, supply chain management, and food quality.

No abstract