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Introduction: Governance is the management of various actions to improve human capacities and boost the efficiency with which services are delivered to the general public. The study analyses the relationship between two variables: geographic location and desire to switch to an E-governance system. The study also aims to explore the relevance of artificial intelligence (AI) in supporting E-Governance.

Objectives of study: (1) To investigate the notion of e-governance and the many approaches available. (2) To research various government E-Governance initiatives and raise awareness about the difficulties and opportunities facing India’s e-government system. (3) To study the acceptance of E-governance by the public from rural and urban districts.

Methodology: This study will use a descriptive research approach as its research strategy. Primary data was collected to check for the preference for an acceptance rate of E-Governance based on geographic location (URBAN and RURAL). The current investigation is conducted on 200 respondents from Northern India’s selected urban and rural districts.

Finding and implications: The report summarises the significance of e-governing system adoption in India and offers ways to improve the operation of these systems in the future. The results of the test show that both factors are highly significant. The study recommends that future research integrate our search for scientific studies with a search for non-scientific publications, as journal and conference publications may lag behind the most recent breakthroughs in the implications of AI use in public administration.

Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.

Introduction: With many new technologies requiring real-time data processing, cloud computing has become challenging to implement due to high bandwidth and high latency requirements.

Purpose: To overcome this issue, edge computing is used to process data at the network’s edge. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed. It is used to process time-sensitive data.

Methodology: The authors implemented the model using Linux Foundation’s open-source platform EdgeX Foundry to create an edge-computing device. The model involved getting data from an on-board sensor (on-board diagnostics (OBD-II)) and the GPS sensor of a car. The data are then observed and computed to the EdgeX server. The single server will send data to serve three real-life internet of things (IoT) use cases: auto insurance, supporting a smart city, and building a personal driving record.

Findings: The main aim of this model is to illustrate how edge computing can improve both latency and bandwidth usage needed for real-world IoT applications.

Introduction: Banking institutions are instrumental for lending support and steering the economy in the planned direction to achieve long-term goals. Sustainable development has become the focal point of new policies so that the economies attain inclusive growth. This needs substantial funding to accelerate industrial activity; hence, banks have to play a dominant role in helping such plans succeed. Banks need to look beyond their current framework and play a proactive role in promoting sectors focussed on sustainable development. Banks can prioritise lending to green initiatives to reduce carbon footprint, which will provide impetus to the goals laid out in the COP 26 United Nations (UN) Climate Change Conference. The chapter aims to identify the gap in investment for sustainable development and the funding support required from banks to help India achieve the desired sustainable goals. The chapter recommends that banks increase their green financing to provide the impetus for creating sustainable infrastructure.

Purpose: The present study aims to understand the banking sector’s importance in developing sustainable economic growth through lending practices. The study recommends certain practices for increasing focussed lending towards sustainable projects.

Methodology: In this study, the authors developed prepositions based on a literature review. Significant issues were raised based on the lending policies per the guidelines of Reserve Bank of India (RBI), and a solution was proposed by preparing a conceptual model.

Findings: The study offers a lending technique that can assist the financial sector in supporting sustainable economic growth.

Background: Human resource management (HRM) is the tactical method for a business enterprise’s optimistic and systemic administration. This study aims to identify the common and major triggering attributes and the knowledge gap between HRM and an organisation’s employee attrition rate.

Method: The employee Attrition Case Study Dataset used is an anecdotal data set that tries to figure out relevant variables that determine employee behavioural aspects towards attrition. This study investigates why attrition occurs, the major triggering attributes for employee turnover, and how it might be anticipated to employ artificial intelligence (AI) to avert corporate losses.

Results: Employees’ monthly income, age, average monthly hours, distance from home, total working years, years at the company, per cent of salary hike, number of companies worked, stock options level, job role and other factors are taken into consideration. A feature importance extraction framework was devised to investigate the various dormant factors. The findings also show feasible hypotheses that help enhance employee engagement, reinvent the worker dynamic, and higher levels of risk decrease attrition rate.

Implications: Employees’ monthly income, age, average monthly hours, distance from home, etc., are all major variables in employee attrition in the Indian IT business. This research adds to the theory development of behavioural elements in people analytics based on AI.

Purpose: Can we predict employee attrition through employee behavioural patterns advancement using AI tools.

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.