Smart cities are a current worldwide topic requiring much scientific investigation. This research instigates the necessity of an organized review to a heedful insight of the research trends and patterns prevailing in this domain. The string is formulated to extract the corpus from Scopus largest database of publications. The corpus of 8320 articles published from 2010 to 2022 is processed using Latent Dirichlet Allocation. Two, five, and ten topics have been extracted to provide the recent trends for IoT in smart cities. There has been an increased recognition that more attention needs to be paid to the area of smart cities so a complete overview of the topic of smart cities research, including the most prominent nations (institutions, sources, and authors) and noteworthy research directions has been presented in this paper. The scientific collaboration across countries (regions), organizations, and authors has also been widely discussed. A detailed and comprehensive overview and visualization of the trends and research patterns used to integrate the Internet of Things in Smart Cities. This data based experimental study signifies a roadmap of the research trends in Smart Cities by implementing topic modeling technique that has never been used in this domain. Based upon the topic modeling using LDA, authors have formulated three research questions and answered those question based on the in-depth research. At the end this study concludes the areas suggested are at the growing phase and need more insight for their growth.
Cloud computing paradigm is growing rapidly, and it allows users to get services via the Internet as pay-per-use and it is convenient for developing, deploying, and accessing mobile applications. Currently, security is a requisite concern owning to the open and distributed nature of the cloud. Copious amounts of data are responsible for alluring hackers. Thus, developing efficacious IDS is an imperative task. This article analyzed four intrusion detection systems for the detection of attacks. Two standard benchmark datasets, namely, NSL-KDD and UNSW-NB15, were used for the simulations. Additionally, this study highlights the proliferating challenges for the security of sensitive user data and gives useful recommendations to address the identified issues. Finally, the projected results show that the hybridization method with support vector machine classifier outperforms the existing techniques in the case of the datasets investigated.
For Facial Expression Recognition, occlusion and position change that may drastically alter facial expressions are two important challenges (FER). Due to advances in automated FER over the last several decades, it has received less attention in the real world, where occlusion‐ and pose‐invariant aspects of FER are critical. Online education learners' cognitive states may be assessed using this paper's focus on real‐world stance and occlusion robust FER. The human visual system's attention mechanism inspired us to develop a new kind of spatial attention network (SAN‐CNN). Saliency characteristics and spatial importance between adjacent pixels are emphasized in the SAN‐CNN model. Preprocessing an input picture using a median contour filter is used here initially, followed by mask‐based ROI for segmentation. Using CNNs for facial recognition, landmark localization, and head position estimation based on spatial attention networks, it is possible to do emotional categorization. Using the Kaggle public video‐based facial expression datasets, we were able to demonstrate that our proposed approach is more accurate and faster than the usual techniques. In addition, we evaluated the suggested method's performance metrics with those of the already used approaches. FER with occlusion and variant posture performs better than standard approaches using our suggested method, as shown by the results of the experiments.
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