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Artificial Intelligence (AI) techniques getting at any point present in modern business and regular day to day existence is additionally consistently being applied to Healthcare. In various pieces of regulatory cycles and patient thoughts by making use of Artificial Intelligence in Healthcare services can possibly help Healthcare services suppliers. Most of the AI and medical services innovations have solid importance to the Healthcare field, yet the strategies they backing can shift essentially and keeping in mind that a few review papers on Artificial Intelligence in Healthcare recommend that by making use of Artificial Intelligence in Healthcare can perform similarly too or better than people at specific techniques, like diagnosing disease, it will be a significant number of years before AI in Healthcare services replaces people for an expansive scope of medical tasks. By using Artificial Intelligence it is expecting to mirror human mental capacities and limits. It is acquiring a change point of view to constrain by extending accessibility of Healthcare, services information, and quick progression of investigation methods. Significant illness areas by making use of AI techniques incorporate cardiology, Cancer, neurology, and then we review the applications, Ethical issues in the application of AI to Healthcare and conclude about the different AI frameworks.

Requirements elicitation (RE) phase is very critical and crucial to the success of IS projects in telecommunication sector. Unfortunately, this phase of IS projects development is susceptible to a large degree of errors, affected by key factors embedded in the applied communication techniques, which results in a requirement conflicts that widen the gap of what is being build and what is being desired by the stakeholders. The aim of this paper is to present a Requirements conflict resolution and communication model to address the lack of a systematic mechanism to quantify the communication obstacles and to classify the requirements conflicts in the RE phase. The proposed model will introduce the conflict detection and resolution mechanism based on the normalized cross correlations function (NCCF), standard deviation (SD) and the standard error (SE) functions to detect and quantify the conflicted requirements based on the calculation of the requirements correlations and accuracy. The proposed model will overcome the reoccurring issues of the requirement elicitation phase.

Technique selection is one of the frequent issues in the requirement elicitation process; this issue has a major impact on the final requirement report output. The inappropriate selection of the techniques could lead to improper requirements and thus increase the risk of failure for the intended project. This paper addresses the technique selection issue encountered during the requirements elicitation stage, through a proposed a machine learning model to transfer the experts’ knowledge of elicitation technique selection of the less experienced. Based on the system analysts, stakeholders and technique properties as such systems and automate the technique selection process to provide the best optimization technique nomination, for the elicitation case complexity characteristics.

The job of this research is to filter the diseased part of the leaf from the leaf images. The authors proposed and implemented image processing technique using OpenCV for separating the diseased part of the leaf from the image of the leaf. Foreground Extraction, Edge Detection, Color filtering and Combination of Edge Detection with Color Filtering is done for wheat images.This novel technique can facilitate the process of the detection of diseases in wheat plants. The system allows to follow a particular pattern of capturing images of plants so that threats will be analysed quickly. Apart from helping humans to detect the diseases in plants, this research work can automate the process of detection without the need of human resources for feeding dataset to model, which is a much reliable process. This research will ultimately contribute in automation of agriculture processes faster and will make farmers to cultivate more in less amount of time.