COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus. The foremost and most prime sector among those affected were schools, colleges, and universities. The education system of entire nations had shifted to online education during this time. Many shortcomings of Learning Management Systems (LMSs) were detected to support education in an online mode that spawned the research in Artificial Intelligence (AI) based tools that are being developed by the research community to improve the effectiveness of LMSs. This paper presents a detailed survey of the different enhancements to LMSs, which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience. The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources, including AI-based chatbots. Session layer enhancements are also required, such as AI-based online proctoring and user authentication using Biometrics. These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms. It also needs the support of the Networking layer for IP-based geolocation features, the Virtual Private Network (VPN) feature, and the support of Software-Defined Networks (SDN) for optimum Quality of Service (QoS). Finally, in addition to these, non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

Recently, there has been a notable surge of interest in scientific research regarding spectral images. The potential of these images to revolutionize the digital photography industry, like aerial photography through Unmanned Aerial Vehicles (UAVs), has captured considerable attention. One encouraging aspect is their combination with machine learning and deep learning algorithms, which have demonstrated remarkable outcomes in image classification. As a result of this powerful amalgamation, the adoption of spectral images has experienced exponential growth across various domains, with agriculture being one of the prominent beneficiaries. This paper presents an extensive survey encompassing multispectral and hyperspectral images, focusing on their applications for classification challenges in diverse agricultural areas, including plants, grains, fruits, and vegetables. By meticulously examining primary studies, we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use. Additionally, our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context. The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture. Nevertheless, we also shed light on the various issues and limitations of working with spectral images. This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

This research focuses on intelligent unmanned aerial vehicle (UAV)-based, real-time video surveillance to ensure better monitoring and security of remote locations over 4G-LTE cellular networks by maximizing end-user quality of experience (QoE). We propose a novel server-based crosslayer rate-adaptive scheme (SCRAS) for real-time video surveillance over 4G-LTE networks using UAVs. Our key contributions are: (1) In SCRAS, mobile UAVs having preprogrammed flight co-ordinates act as servers, streaming real-time video towards a remote client; (2) server-side video rate adaptation occurs in 4G-LTE based on the physical characteristics of the received signal conditions due to variations in the wireless channel and handovers; (3) SCRAS is fully automated and independent of client assistance for rate adaptation, as it is intended for real-time, mission-critical surveillance applications; (4) SCRAS ensures that during rate adaptation, the current video frame should not be damaged by completing the current group of packet (GoP) before adaptation. Our simulations in NS3 provide credible evidence that SCRAS outperforms recently proposed schemes in providing better QoE for real-time, rate-adaptive, video surveillance over 4G-LTE under varying channel quality and frequent handovers that occur during flight by UAVs.