Machine learning is the one of the branch in Artificial Intelligence to work automatically or give the instructions to a particular system to perform a action. The goal of machine Learning is to understand the structure of the data and fit that data into models that can be understood and utilized by the people. The proposed research work is for analysis of various machine algorithms applying on plant disease prediction. A plant shows some visible effects of disease, as a response to the pathogen. The visible features such as shape, size, dryness, wilting, are very helpful to recognize the plant condition. The research paper deals with all such features and apply various machine learning technologies to find out the output. The research work deals with decision tree, Naive Bayes theorem, artificial neural network and k-mean clustering and random forest algorithms. Disease development depends on three conditions-host plants susceptible to disease, favorable environment and viable pathogen. The presence of all three conditions is must for a disease to occur.
Assessing quality of a web site is most important especially because people world over are dependent on the quality of the content hosted on the WEB site for various purposes. Every aspect that reflects the quality of the WEB site must be considered so that the WEB site is used by many relevant users and the content is straight away used for various purposes. The content is hosted on the WEB is accepted and used by the users without any doubt. Many factors are to be considered for assessing the quality of the WEB sites. There as many as 42 factors such as look and feel, ease of navigation, structure, quality of content, quality of mullti-media etc. that needs to be considered for assessing overall quality of a WEB site. “Completeness” is one such factor. Completeness is all about total availability of the data related to a specific context that the user is pursuing. An incomplete WEB site will be unused and eventually gets out of the ambit of search engines and also the users loose interest in such web sites. Every quality factor must be assessed independently by using a model and all the models of all the factors must be combined to reflect the overall quality of the WEB site. Every quality factors can be expressed in terms of some sub-factors, the quality of which must be individually computed and combined to arrive at quality value of the Factor. This paper is primarily focused on presenting a model using which the quality assessment of a web site can be made considering the factor called “Completeness” which is one of the factors that should be considered for computing the quality of the WEB sites. There can be much of the disconnected in the content hosted on the web site such as missing href, columns in the tables and forms. The more the disconnectedness in the content that is hosted on the WEB site, the less the quality, as the information hosted on the WEB site is incomplete and less connected.
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