Augmented reality (AR) has gained enormous popularity and acceptance in the past few years. AR is indeed a combination of different immersive experiences and solutions that serve as integrated components to assemble and accelerate the augmented reality phenomena as a workable and marvelous adaptive solution for many realms. These solutions of AR include tracking as a means for keeping track of the point of reference to make virtual objects visible in a real scene. Similarly, display technologies combine the virtual and real world with the user’s eye. Authoring tools provide platforms to develop AR applications by providing access to low-level libraries. The libraries can thereafter interact with the hardware of tracking sensors, cameras, and other technologies. In addition to this, advances in distributed computing and collaborative augmented reality also need stable solutions. The various participants can collaborate in an AR setting. The authors of this research have explored many solutions in this regard and present a comprehensive review to aid in doing research and improving different business transformations. However, during the course of this study, we identified that there is a lack of security solutions in various areas of collaborative AR (CAR), specifically in the area of distributed trust management in CAR. This research study also proposed a trusted CAR architecture with a use-case of tourism that can be used as a model for researchers with an interest in making secure AR-based remote communication sessions.

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

During the time of the pandemic, travel restrictions have impacted the tourism industry with an estimated loss of more than a trillion USD; however, at the same time, we have seen a significant increase in profits for the industries which empower remote connectivity. Various studies have identified the positive impact of virtual tourism, in which tourists can be attracted by providing a VR/AR-based experience of the destination. Similarly, virtual, mixed, and augmented realities are being used to enhance user experience in digital heritage and its preservation. With emerging technologies and increasing demand for e-tourism (due to travel restrictions), there is a need to review the technological changes and analyze user requirements with respect to virtual tourism. This paper provides a literature review of the latest technologies and applications that can potentially benefit the virtual tourism and digital heritage industry. We also provide an analysis of its impact on user experience, awareness, and interest, as well as the pros and cons of virtual experiences, which may benefit the research community about the current spectrum of virtual tourism and digital heritage.

Wireless mesh network (WMN) is a new wireless networking paradigm. Unlike traditional wireless networks, WMNs do not rely on any fixed infrastructure. Instead, hosts rely on each other to keep the network connected. Wireless Internet service providers are choosing WMNs to offer Internet connectivity, as it allows a fast, easy and inexpensive network deployment. One main challenge in design of these networks is their vulnerability to security attacks. In this paper, we investigate the principal security issues for WMNs. We study the threats a WMN faces and the security goals to be achieved. We identify the new challenges and opportunities posed by this new networking environment and explore approaches to secure its communication.

Dictionaries not only are the source of getting meanings of the word but also serve the purpose of comprehending the context in which the words are used. For such purpose, we see a small sentence as an example for the very word in comprehensive book‐dictionaries and more recently in online dictionaries. The lexicographers perform a very meticulous activity for the elicitation of Good Dictionary EXamples (GDEX)—a sentence that is best fit in a dictionary for the word’s definition. The rules for the elicitation of GDEX are very strenuous and require a lot of time for committing the manual process. In this regard, this paper focuses on two major tasks, i.e., the development of labelled corpora for top 3K English words through the usage of distant supervision approach and devising a state‐of‐the‐art artificial intelligence‐based automated procedure for discriminating Good Dictionary EXamples from the bad ones. The proposed methodology involves a suite of five machine learning (ML) and five word embedding‐based deep learning (DL) architectures. A thorough analysis of the results shows that GDEX elicitation can be done by both ML and DL models; however, DL‐based models show a trivial improvement of 3.5% over the conventional ML models. We find that the random forests with parts‐of‐speech information and word2vec‐based bidirectional LSTM are the most optimal ML and DL combinations for automated GDEX elicitation; on the test set, these models, respectively, secured a balanced accuracy of 73% and 77%.