Purpose: The purpose of this paper is to develop a conceptual framework to check if an organization is ready to adopt an AI-integrated CRM system. The study also analyzes different situations which can provide a comprehensive check list in the form of indicators that could provide a signal indicating whether the organization is ready to adopt an AI-integrated CRM system by capturing actionable and appropriate data. Design/methodology/approach: The paper is a general review, and appropriate literature has been used to support the conceptual framework. Findings: The key findings of this study are the different indicators that make up the conceptual framework. This framework can help organizations to check at a glance whether they are ready to adopt AI-integrated CRM system in their organizations. Specifically, it has been identified that different approaches are needed to tackle various types of customer data so that those may be made fit and actionable for appropriate utilization of AI algorithms to facilitate business success of an organization. Practical implications: The paper has elaborately discussed the different approaches to be undertaken to calibrate and reorient the various kinds of actionable data and the contemplated challenges one would face in doing so. This would help the practitioners that how the data so captured can be made fit for action and utilization toward application of AI technologies integrated with existing CRM system in an organization. Originality/value: This study is claimed to be a unique study to provide a conceptual framework which could help arranging and rearranging of captured data by an organization for making the data fit and ready for use with the help of AI technologies. This successful integration of AI with CRM system can help organizations toward taking quick and automated decision-making without much intervention of human beings.

The design of mobility-aware framework for edge/fog computing for IoT systems with back-end cloud is gaining research interest. In this paper, a mobility-driven cloud-fog-edge collaborative real-time framework, Mobi-IoST, has been proposed, which has IoT, Edge, Fog and Cloud layers and exploits the mobility dynamics of the moving agent. The IoT and edge devices are considered to be the moving agents in a 2-D space, typically over the road-network. The framework analyses the spatio-temporal mobility data (GPS logs) along with the other contextual information and employs machine learning algorithm to predict the location of the moving agents (IoT and Edge devices) in real-time. The accumulated spatio-temporal traces from the moving agents are modelled using probabilistic graphical model. The major features of the proposed framework are: (i) hierarchical processing of the information using IoT-Edge-Fog-Cloud architecture to provide better QoS in real-time applications, (ii) uses mobility information for predicting next location of the agents to deliver processed information, and (iii) efficiently handles delay and power consumption. The performance evaluations yield that the proposed Mobi-IoST framework has approximately 93% accuracy and reduced the delay and power by approximately 23-26% and 37-41% respectively than the existing mobility-aware task delegation system.

With the advent of advanced remote sensing technologies in past few decades, acquiring higher resolution satellite images has become easier and cheaper in recent days. However, on the other hand, it has offered a big challenge to the remote sensing community in smart image interpretation from such huge volume of data. Deep learning, which offers efficient algorithms for extracting multiple levels of feature abstractions, may be suitable to serve the purpose. This letter presents a deep learning approach (Deep-STEP) for spatiotemporal prediction of satellite remote sensing data. The proposed learning architecture is derived from a deep stacking network, consisting of a stack of multilayer perceptron, each of which models the spatial feature of the associated region at a particular time instant. The proposed method has been demonstrated on normalized difference vegetation index (NDVI) data sets, derived from satellite remote sensing imagery, containing several thousands to millions of pixels/records. The experimental results (related to NDVI prediction) reveal that the proposed architecture exhibits fairly satisfactory performance with promising learning capabilities.

Recent advancements in artificial intelligence (AI) and IoT technology have revolutionized the healthcare industry by providing effective remote healthcare. Furthermore, with the aging of the world’s population, remote health monitoring and recommendations are becoming imperative to provide cost-effective healthcare solutions for improving the quality of life of our senior citizens. The explosive growth of wearable sensors (IoT sensors) and health bands has facilitated the interconnection among patients and caregivers to enable assisted living by leveraging AI techniques. This work proposes an end-to-end connected smart home healthcare system (FEEL) for elderly people. Our proposed framework addresses the main challenges of the Internet of Medical Things (IoMT) system namely, the scarcity of labeled data and user’s diverse needs. The major contributions of the work are: 1) few-shot learning-enabled novel federated learning (FL) framework for health data and context information analysis and recommendation; 2) user and context-based knowledge graph (UKG) to represent and model health parameters and environmental impacts on recommendations; 3) deep learning architecture for activity monitoring and location estimation of the users; and 4) edge-fog-IoMT collaborative framework to collect, store, and share medical recommendations while protecting the privacy of the users. FEEL is specifically beneficial for elderly homes where several aged people stay together and require constant care. We aim to develop a novel AI module where along with the health parameters, the social context of the home can be augmented to provide an accurate and improved healthcare service. FEEL has been evaluated for three tasks, namely: 1) activity monitoring and location estimation; 2) fall detection; and 3) medical recommendations for unusual health conditions. A customized wearable device has been used to collect, store, and send health-related parameters. The experimental evaluation demonstrates promising accuracy (F1 score 0.86–0.94 range) for the tasks and outperforms the baselines by a significant margin ( $\approx 10$ %–16%).