Using Artificial intelligence to solve semi- or ill-structured problems using an algorithm that deploys techno-scientific human experts’ approach (Expert System) is a widely used solution. Expert system (ES) provides a programmable methodology solution through instructions provided by intelligence based on human experts. The expert system was used in this study to validate the decision process of the cloud-native architecture portability framework. The cloud-native architecture portability framework is developed to support decision-makers in organizations in making the right decision on porting or migrating either legacy or cloud-based data or applications to cloud-native architecture. The framework, designed and developed from research and expert contributions, was implemented in an expert system to examine its validity. The framework was evaluated through data collected from questionnaires, and the findings show that most respondents agreed with the importance of the framework. Then the evaluated framework was then developed into an expert system to provide a clear path for the stakeholders and the task and user-centred view of the framework. The usability of the designed expert system through the use of the ES-BUILDER shell also shows the usefulness of artificial intelligence in decision-making and information presentation simplification through technology.

In today’s world everything is revolving around the text. Now every individual wants everything to be in a precise manner and desires to complete the work to be done as soon as possible. Our project is “Text Compactor”. Text Compactor is a process which helps the people to save their time. Here the project takes the large text(consists of the many sentences) and provides the output in an precise manner. In this project, we provide solution to the user who wants to summarize their text in any of two ways and provides the extra information about the input and output text. Text summarization is that the process of compressed version of the document by preserving its information. We have 2 types of summarization and that they are Extractive and Abstractive summarization. In this project, we summarize the input text in these two ways and we can see the differences between both the outputs. We developed the project using Text Rank Algorithm for Extractive summarization and T5 algorithm for Abstractive summarization. The experiment result show that our suggested approach provides the summarization without changing the information in the input text.

Deep learning-based gender detection is demonstrating its effectiveness in a variety of areas, including healthcare, marketing, security, and many others. Deep learning models have boosted their capability to identify objects in images and videos. This paper includes an extensive description of gender detection using the framework YOLOv7, including architecture, limitations, and performance. The research study analyzes the YOLOv7's performance in detecting gender from facial images and videos, with a particular focus on lightning-fastness, precision, and robustness. The experimental results prove that YOLOv7 outscored YOLOv7x. The paper addresses the YOLOv7 framework's potential in various areas such as healthcare, marketing, and security. Overall, this paper demonstrates the potency of YOLOv7 in detecting gender.