Human Intelligence Analysis through Perception of AI in Teaching and Learning.

Instructional practices have undergone a drastic change as a result of the development of new educational technology. Artificial intelligence (AI) as a teaching and learning technology will be examined in this theoretical review study. To enhance the quality of teaching and learning, the use of artificial intelligence approaches is being studied. Artificial intelligence integration in educational institutions has been addressed, though. Students' assistance, teaching, learning, and administration are also addressed in the discussion of students' adoption of artificial intelligence. Artificial intelligence has the potential to revolutionize our social interactions and generate new teaching and learning methods that may be evaluated in a variety of contexts. New educational technology can help students and teachers better accomplish and manage their educational objectives. Artificial intelligence algorithms are used in a hybrid teaching mode in this work to examine students' attributes and introduce predictions of future learning success. The teaching process may be carried out in a more efficient manner using the hybrid mode. Educators and scientists alike will benefit from artificial intelligence algorithms that may be used to extract useful information from the vast amounts of data collected on human behavior.

Artificial Intelligence-Based Robotic Technique for Reusable Waste Materials.

Waste management is a critical problem for every country, whether it is developed or developing. Selecting and managing waste are a critical part of preserving the environment and maximizing resource efficiency. In addition to reducing trash and disposal, reusable items are predicted to be of great benefit since they lessen our dependence on raw materials. The usage of compostable trash may be expanded outside fertilizers and dung after the metallic, chemicals, and glass items have been recycled. After a good scrubbing, the glass may be broken down and remelted to create new items. Reusing waste items via garbage recovery is one of the best methods to do so. This document outlines the steps that must be taken to maximize the use of garbage. This work describes a reusable industrial robot arm for grasping and sorting things depending on the resources they contain. Gripping, motion control, and object material categorization are all integrated into a full-automation, reusable system architecture in this study. LeNet also was adjusted to classify garbage into cartons and plastics using an artificial intelligent technique, with the use of a customized LeNet model. Movement in terms of moving the robot in the most efficient way possible, the robot's grabbing, and categorization were incorporated into the movement design process. The system's grabbing and object categorization success rates and computation time are calculated as metrics for evaluation.