The automotive industry is growing day by day and personal vehicles have become a significant transportation resource now. With the rise in private transportation vehicles, getting a free space for parking one’s car, especially in populated areas, has not only become difficult but also results in several issues, such as: (i) traffic congestion, (ii) wastage of time, (iii) environmental pollution, and most importantly (iv) unnecessary fuel consumption. On the other hand, car parking spaces in urban areas are not increasing at the same rate as the vehicles on roads. Therefore, smart car parking systems have become an essential need to address the issues mentioned above. Several researchers have attempted to automate the parking space allocation by utilizing state-of-the-art technologies. Significant work has been done in the domains of Wireless Sensor Networks (WSN), Cloud Computing, Fog Computing, and Internet of Things (IoT) to facilitate the advancements in smart parking services. Few researchers have proposed methods for smart car parking using the cloud computing infrastructures. However, latency is a significant concern in cloud-based applications, including intelligent transportation and especially in smart car parking systems. Fog computing, bringing the cloud computing resources in proximate vicinity to the network edge, overcomes not only the latency issue but also provides significant improvements, such as on-demand scaling, resource mobility, and security. The primary motivation to employ fog computing in the proposed approach is to minimize the latency as well as network usage in the overall smart car parking system. For demonstrating the effectiveness of the proposed approach for reducing the lag and network usage, simulations have been performed in iFogSim and the results have been compared with that of the cloud-based deployment of the smart car parking system. Experimental results exhibit that the proposed fog-based implementation of the efficient parking system minimizes latency significantly. It is also observed that the proposed fog-based implementation reduces the overall network usage in contrast to the cloud-based deployment of the smart car parking.

Social networking sites engage millions of users around the world. The users' interactions with these social sites, such as Twitter and Facebook have a tremendous impact and occasionally undesirable repercussions for daily life. The prominent social networking sites have turned into a target platform for the spammers to disperse a huge amount of irrelevant and deleterious information. Twitter, for example, has become one of the most extravagantly used platforms of all times and therefore allows an unreasonable amount of spam. Fake users send undesired tweets to users to promote services or websites that not only affect legitimate users but also disrupt resource consumption. Moreover, the possibility of expanding invalid information to users through fake identities has increased that results in the unrolling of harmful content. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). In this paper, we perform a review of techniques used for detecting spammers on Twitter. Moreover, a taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users. The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features. We are hopeful that the presented study will be a useful resource for researchers to find the highlights of recent developments in Twitter spam detection on a single platform.

With the increased number of data and data-generating devices in healthcare settings, the health monitoring systems have started to experience issues, such as efficient processing and latency. Several health-monitoring systems have been designed using Wireless Sensors Networks (WSN), cloud computing, fog computing, and the Internet of Things (IoT). Most of the health monitoring systems have been designed using the cloud computing architecture. However, due to the high latency introduced by the cloud-based architecture while processing massive volumes of data, large-scale deployment of latency-sensitive healthcare applications is restricted. Fog computing that places computing servers closer to the users addresses the latency problems and increases the on-demand scaling, resource accessibility, and security dramatically. In this paper, we propose a fog-based health monitoring system architecture to minimize latency and network usage. We also present a new Load Balancing Scheme (LBS) to balance the load among fog nodes when the health monitoring system is deployed on a large scale. To validate the effectiveness of the proposed approach, we conducted extensive simulations in the iFogSim toolkit and compared the results with the cloud-only implementation, Fog Node Placement Algorithm (FNPA), and LoAd Balancing (LAB) scheme, in terms of latency and network usage. The proposed implementation of the health monitoring system significantly reduces latency and network usage compared to cloud-only, FNPA, and LAB Scheme.

It is indeed a challenge for the existing machine learning approaches to segregate the hateful content from the one that is merely offensive. One prevalent reason for low accuracy of hate detection with the current methodologies is that these techniques treat hate classification as a multiclass problem. In this article, we present the hate identification on the social media as a multilabel problem. To this end, we propose a CNN-based service framework called “HateClassify” for labeling the social media contents as the hate speech, offensive, or nonoffensive. Results demonstrate that the multiclass classification accuracy for the CNN-based approaches particularly sequential CNN (SCNN) is competitive and even higher than certain state-of-the-art classifiers. Moreover, in the multilabel classification problem, sufficiently high performance is exhibited by the SCNN among other CNN-based techniques. The results have shown that using multilabel classification instead of multiclass classification, hate speech detection is increased up to 20%.

Cotton is one of the world’s most economically significant agricultural products; however, it is susceptible to numerous pest and virus attacks during the growing season. Pests (whitefly) can significantly affect a cotton crop, but timely disease detection can help pest control. Deep learning models are best suited for plant disease classification. However, data scarcity remains a critical bottleneck for rapidly growing computer vision applications. Several deep learning models have demonstrated remarkable results in disease classification. However, these models have been trained on small datasets that are not reliable due to model generalization issues. In this study, we first developed a dataset on whitefly attacked leaves containing 5135 images that are divided into two main classes, namely, (i) healthy and (ii) unhealthy. Subsequently, we proposed a Compact Convolutional Transformer (CCT)-based approach to classify the image dataset. Experimental results demonstrate the proposed CCT-based approach’s effectiveness compared to the state-of-the-art approaches. Our proposed model achieved an accuracy of 97.2%, whereas Mobile Net, ResNet152v2, and VGG-16 achieved accuracies of 95%, 92%, and 90%, respectively.