The concept of smart city evolved with the integration of information and communication technology (ICT) in various sub-systems and processes in urban environment. The development of the smart cities is the best possible solution to major urban issues. It contributes towards economic and social development of the residents. It aims to provide the cordial environment in the domains of healthcare, education, transportation, power generation and dissipation, security, living, industry, etc., to the inhabitants to make their lives comfortable. Sustainability of these services is another major objective in a smart city framework. Along with the true realization of the idea of a smart city, advanced computational and communication technologies are contributing hugely towards its sustainable development. Communication technologies act as backbone to ensure connectivity at the various levels in a smart city framework. Novel smart city solutions for different application domains are designed and deployed by the industry using advanced computational technologies like IoT, Artificial Intelligence, Blockchain, Big Data and Cloud Computing. In this work, authors discuss the concept of smart city, its architecture and sustainability. Different operational domains in a smart city ecosystem are elaborated. The cyber physical aspect of the smart cities is discussed in brief. The role of various computational and communication technologies in the sustainable development of smart cities is presented. Limiting factors in the deployment of various advanced technologies in different smart city domains are highlighted. Security issues associated with the technological sustainable development of different smart city services along with existing solutions are discussed. The article is concluded by highlighting the future research directions.

The Covid-19 pandemic has disrupted the world economy and significantly influenced the tourism industry. Millions of people have shared their emotions, views, facts, and circumstances on numerous social media platforms, which has resulted in a massive flow of information. The high-density social media data has drawn many researchers to extract valuable information and understand the user’s emotions during the pandemic time. The research looks at the data collected from the micro-blogging site Twitter for the tourism sector, emphasizing sub-domains hospitality and healthcare. The sentiment of approximately 20,000 tweets have been calculated using Valence Aware Dictionary for Sentiment Reasoning (VADER) model. Furthermore, topic modeling was used to reveal certain hidden themes and determine the narrative and direction of the topics related to tourism healthcare, and hospitality. Topic modeling also helped us to identify inter-cluster similar terms and analyzing the flow of information from a group of a similar opinion. Finally, a cutting-edge deep learning classification model was used with different epoch sizes of the dataset to anticipate and classify the people’s feelings. The deep learning model has been tested with multiple parameters such as training set accuracy, test set accuracy, validation loss, validation accuracy, etc., and resulted in more than a 90% in training set accuracy tourism hospitality and healthcare reported 80.9 and 78.7% respectively on test set accuracy.

One of the significant issues that the world has faced in recent decades has been the estimation of water quality and location where safe drinking water is available. Due to the unexpected nature of the mode of water contamination, it is not easy to analyze the quality and maintain it. Some machine-learning techniques are used for predicting contaminating factors but there is no technique that can predict the contamination using latitude, longitude, and elevation. The main aim of this paper is to put factors such as water body location and elevation, which are used as inputs, into the different machine-learning techniques that predict the contamination. The results are reviewed and analyzed according to groundwater contamination and the chemical composition of the groundwater location. Non-changeable factors such as latitude, longitude, and elevation are used to predict pH, temperature, turbidity, dissolved oxygen hardness, chlorides, alkalinity, and chemical oxygen demand. Such a study has not been conducted in the past where location-based factors are used to predict the water contamination of any area. This research focuses on creating a relationship between the location base factors affecting the water contamination in a given area.

Education is the cornerstone of improving people’s lives and achieving global sustainability. Intelligent systems assist sustainable education with various benefits, including recommending a personalized learning environment to learners. The classroom learning environment facilitates human tutors to interact with every learner and obtain the opportunity to understand the learner’s psychology and then provide learning material (access learner previous knowledge and well-align the learning material as per learner requirement) to them accordingly. Implementing this cognitive intelligence in Intelligent Tutoring System is quite tricky. This research focused on mimicking human tutor cognitive intelligence in the computer-aided system of offering an exclusive curriculum or quality education for sustainable learners. The prime focus of this research article was to evaluate the proposed SeisTutor using Kirkpatrick four-phase evaluation model. The experimental results depict the enhanced learning gained through intelligence incorporated SeisTutor against the intelligence absence, as demonstrated.