Summary Parking vehicles in densely populated areas are often challenging, stressful, and sometimes it becomes a monotonous job for the drivers in jam‐packed areas. There are several reasons for the delay in finding parking spaces such as scarcity of parking slots, disordered or unmanaged parking of vehicles, lacking or unaware of parking information at the destination, which further leads to the wastage of time, fuel, energy and increase in environmental pollution. Literature has revealed a significant number of smart parking solutions based on the Internet of Things (IoT) and context‐awareness with the incorporation of routing strategies and vehicle detection techniques in a pervasive computing environment. With the rapid escalation of the smart and intelligent devices along with their applicability in a highly decentralized environment, real‐time traffic monitoring, and finding parking spaces have become quite trivial. Smart parking sensors and technologies assist drivers in finding vacant parking slots while they are on the way to their destination. Considering the needs, wants, and demands of metropolitan cities, in this article, we have reviewed the recently published articles, mostly from the last 5 years, on smart parking systems augmented with sensors, embedded systems, context‐awareness capability, and IoT which yields in saving time, fuel, energy, and reduces the stress of the drivers. To accomplish this, we have reviewed different models on smart parking solutions based on algorithmic formalisms, theoretical frameworks, formal models, smart device‐based prototypes as well as real‐time applications, and verifying the correctness properties of the system. The results shown may provide a base for the state of the art future research directions.
Modern means of communication, economic crises, and political decisions play imperative roles in reshaping political and administrative systems throughout the world. Twitter, a micro-blogging website, has gained paramount importance in terms of public opinion-sharing. Manual intelligence of law enforcement agencies (i.e., in changing situations) cannot cope in real time. Thus, to address this problem, we built an alert system for government authorities in the province of Punjab, Pakistan. The alert system gathers real-time data from Twitter in English and Roman Urdu about forthcoming gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.). To determine public sentiment regarding upcoming anti-government gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.), the alert system determines the polarity of tweets. Using keywords, the system provides information for future gatherings by extracting the entities like date, time, and location from Twitter data obtained in real time. Our system was trained and tested with different machine learning (ML) algorithms, such as random forest (RF), decision tree (DT), support vector machine (SVM), multinomial naïve Bayes (MNB), and Gaussian naïve Bayes (GNB), along with two vectorization techniques, i.e., term frequency–inverse document frequency (TFIDF) and count vectorization. Moreover, this paper compares the accuracy results of sentiment analysis (SA) of Twitter data by applying supervised machine learning (ML) algorithms. In our research experiment, we used two data sets, i.e., a small data set of 1000 tweets and a large data set of 4000 tweets. Results showed that RF along with count vectorization performed best for the small data set with an accuracy of 82%; with the large data set, MNB along with count vectorization outperformed all other classifiers with an accuracy of 75%. Additionally, language models, e.g., bigram and trigram, were used to generate the word clouds of positive and negative words to visualize the most frequently used words.
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