Higher education is the face of innovation for any country. The quality and dedication of professors help to maintain quality in this process. With time, parameters were raised to check the quality of professor attributes. In this paper, we discuss all possible parameters taken by universities to evaluate faculty performance. Gradually it grew overhead pressure on professors and impacted the teaching-learning process. Our paper focused on stress parameters with possible solutions for the same issue. The process consists of several parameters to evaluate an employee's performance, such as no publications in conferences and journals, no patents filed, additional responsibilities performed, other qualifications achieved, result in the analysis of courses taught, etc. Still, it also puts a lot of pressure on both of them because they have to balance all this extra work and teaching. This paper focuses on different faculty assessment parameters and their impact on the faculty teaching-learning process. We also propose possible solutions on how this stress can be alleviated, and the existing strategy can be simplified.
AI is today's wave of technology, and it caters to various problems. One of them is Nanotechnology. Nanotechnology is another thread of technology. The use of nanotechnology is well known, including in the health sector, cosmetic industry, and agriculture. The role of AI in healthcare is broad, from detecting lung disease to skin analysis. This study aims to explore healthcare possibilities by integrating AI and nanotechnology. The method of applying nanotechnology in the health sector uses a Nanomedicine Microscope. Nanomedicine is the medical application of nanotechnology used to diagnose, monitor, and control biological systems. Therefore, the author uses the Nanomedicine Microscope as the object in this study because the wonders of nanotechnology in various operations are also well established. The novelty in this paper focuses on AI and Nanotechnology together as possibilities for healthcare. AI and nanotechnology are two critical technologies. The ultimate goal is to integrate the uses and possibilities of these two technologies and do wonders in the healthcare domain. This study will benefit those working in AI and medicine. Nanotechnology integrating AI technology into the medical industry enables many conveniences, including task automation and analyzing large amounts of patient data for better, faster, and more affordable healthcare.
The concept of a smart city has attracted the attention of all corners of the world. It updated with new technologies like AI, Blockchain, IoT, Drones, and many other things. Security in big cities is one of the main concerns, and everyone wants to feel safe 24/7 in every activity they do. In this paper, our research aims to highlight the use and importance of drones for intelligent city management, especially in the context of security. The drone security management flow is explained using the methods and technical details. This is followed by discussion on security concern in cities and how it is dealt with AI based drones. In the conclusion section, it is discussed how emerging technology such as blockchain can help improve the management of smart cities. The use of drones supports the intelligent city concept with all its advantages in regional monitoring. Concept of smart cities is catching attention across the globe and it’s important as per context of emerging economies. In this paper, we discussed about the use of emerging technologies to make it possible. AI, Blockchain and Drone technologies are playing important role. Their significance and attention by researchers in exploring possibilities is shown in this paper. This paper will be very useful for researchers and engineers working in the same domain.
With the emergence of the web, traditional learning has changed significantly. Hence, a huge number of ‘e-learning systems' with the advantages of time and space have been created. Currently, many e-learning systems are being used by a large number of academic institutions worldwide which allow different users of the system to perform various tasks based on their goals. However, most of these systems follow a ‘one size fits all' approach where same resources are offered to learners irrespective of their unique learning requirements. Therefore, personalization is required as a part of e-learning systems which offers resources to learners based on their profile. This research aims to perform cluster analyses in order to validate clusters created through a k-means algorithm. The clusters will be used to classify a new learner into its appropriate class and recommend relevant courses. Finally, the accuracy of the recommendation is evaluated using various evaluation metrics. The proposed recommendation system helps learners to improve their academic performance and hence overall learning process as well.
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