The cumulative amount of greenhouse gases that are shaped by our actions is a carbon footmark. In the US, the total carbon footmark of a humanoid is 16 tonnes, one of the largest amounts in the world. The average is closer to 4 tonnes worldwide. The average universal carbon footmark per year requirements is to drop below 3 tonnes by 2050 to have the utmost chance of stopping a 2°C point rise in worldwide temperature. Rahul et al. already predicted that the carbon footprint reduced by 17% with the use of IoT-enabled services. In this research study a novel approach to reduce carbon footprint using IoT with reinforcement AI learning is presented, which further reduced carbon footprint by 5% when using and nearly 7% when it is done using Q-Learning. The detailed findings are included to demonstrate the result.
Learning is an ongoing process irrespective of age, gender and geographical location of acquiring new understanding, knowledge, behaviours, skills, values, attitudes, and preferences. Formative assessment methods have emerged and evolved to integrate learning, evaluation and education models. Not only is it critical to understand a learner's skills and how to improve and enhance them, but we also need to consider what the learner is doing; we need to consider navigational patterns. The extended learning and assessment system, a paradigm for doing research, captures this entire view of learning and evaluation systems. The function of computational psychometrics is to facilitating the translation from raw data to meaningful concepts. In this research study, several factors are considered for psychometric analysis of different kinds of learners, and based on a motivational level, many interesting conclusions have been drawn and presented in the result section at the end of the paper. Deep learning model Ludwig Classifier used to calculate, motivational Level is obtained for 100 number of epochs and it is found that the loss is decreasing and in other words, the accuracy of the machine goes on increasing. Each of the categories discussed here has new capabilities, or at the very least expansions of current ones.
The Industrial Revolution 4.0 has produced a wide range of innovative possibilities for the employment of artificial intelligence and computer-guided devices in rehabilitation. In recent times, medical field has shown a keen interest in Human-Robot Interventions (HRI) due to their application in treatment and rehabilitation of various diseases. Social robots are designed to interact with people in a manner that is consistent with human social psychology because of their capacity to elicit social and emotional responses from users. They work in a human-centric environment by speaking, moving, making gestures, or utilizing facial expressions to communicate with their users while adhering to a set of social norms. We outline some intriguing possibilities for social robots in healthcare-related applications in this chapter. Some of these include treatment of social anxiety, dementia, autism spectrum disorder, depression, alzheimer's disease, anorexia nervosa, neurodevelopmental issues, cancer, diabetes and upper limb rehabilitation after stroke.
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