Purpose The purpose of this paper is to investigate building information modelling (BIM) integrated Internet of Things (IoT) architectures extensively and provide comparative evaluation of those against deciding parameters pertaining to their characteristics and subsequent applications in construction industry. Design/methodology/approach This paper identifies BIM-integrated cyber physical system frameworks, specific to project objectives, comprising of sensors working as physical assets and BIM-based virtual models acting as the cyber component , connected via wired or wireless protocols (e.g. WiFi, Zigbee, near-field communication, mobile-to-mobile, Zwave, 3 G, 4 G, long-term evolution, 5 G and low-power wide-area networks) and their potential applications in decision-making, visual management, logistics and supply chain management, smart building system management and structural performance assessment, etc. Such proposed architectures are evaluated against deciding parameters such as availability, reliability, mobility, performance, management, scalability, interoperability and security and privacy to evaluate their respective efficiencies. Findings This study finds that the underlying aim of planned IoT frameworks is to integrate systems and processes for a better information flow and to initiate shift from silo solutions to a smart ecosystem. The efficiencies of such frameworks are completely subjective to their respective project natures, objectives and requirements. Originality/value This study is unique in its nature to identify requirements of an efficient BIM-integrated IoT architecture and provide comprehensive insights about potential applications in construction industry.

E-Learning systems have unbound prospects to deliver unmatched effective learning services and feedback assistance than what it is presently offering through mediums like online tutoring, or other electronic educational management services. Different stages and application potentials of Semantic Web technology and it’s architecture can be applied at different sectors and phases of the E-Learning framework to amplify the quality and versatility of services. Features of Semantic Web have been explored in the sectors with respect to instructors to plan, analyse and execute their tasks and also in making a sustainable system that interprets the structure of distributed, self organized, and self-instructed online learning to monitor it’s influence on performance. The main objective of this work is to study how electronic and online learning frameworks can be improved and enhanced by the influence of semantic web technologies in understanding and simplifying concept clarification and description, reusable learning objects (LOs), and benefits of the applying ontology in describing the learning materials for a better and more efficient learning system.

In the constant fight with the uncertainty of life and a broken economic and mental stature, machine learning and analysis is striving to identify probable remedies and even trying to predict the future of this hostile situation. Visualisation of treatment procedures, health data and economic data yields an in-depth analysis of the scenario. Remedial measures can be practised based on the predictive outcome. We performed predictive and statistical models like the SEIR for building highly accurate and analytical data outputs and plots for better visualisation of the data. This work aims to analyse the spread of the virus across the world and different regions in India and predict the near future of this pandemic in social, health and economic sectors.

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