The current SMAC (Social, Mobile, Analytic, Cloud) technology trend paves the way to a future in which intelligent machines, networked processes and big data are brought together. This virtual world has generated vast amount of data which is accelerating the adoption of machine learning solutions& practices. Machine Learning enables computers to imitate and adapt human-like behaviour. Using machine learning, each interaction, each action performed, becomes something the system can learn and use as experience for the next time. This work is an overview of this data analytics method which enables computers to learn and do what comes naturally to humans, i.e. learn from experience. It includes the preliminaries of machine learning, the definition, nomenclature and applications' describing it's what, how and why. The technology roadmap of machine learning is discussed to understand and verify its potential as a market & industry practice. The primary intent of this work is to give insight into why machine learning is the future.

Industrial cloud computing and Internet of Things have transformed the healthcare industry with the rapid growth of distributed healthcare data. Security and privacy of healthcare data are crucial challenges in the healthcare industry. This article proposes a novel technique using deep learning and blockchain techniques for electronic health record privacy-preservation. The processed dataset classified normal and abnormal users using the convolutional neural network approach. Then, by using blockchain integrated with a cryptography-based federated learning module, the abnormal users have been processed and removed from the database along with the accessibility for the health records. The simulation has been done in the Python tool and experimental results show that the model’s classification results and performance are better than other existing techniques.

The demand for medical sensors in the Smart Healthcare System (SHS) creates an intelligent Internet of Medical Things (IoMT) system. This system plays an important role in detecting the vital parameters of the human body. However, security and privacy issues in terms of network vulnerability have arisen due to the transmission of data and lack of control over the data. The Intrusion Detection System (IDS) is one of the security solutions to identify various threats and vulnerabilities in the consumable edge-centric IoMT industry. Several IDS techniques have been developed in previous years. However, a real-time and highly accurate attack detection system in the edge-centric IoMT industry is needed. This paper proposes a blended deep learning framework that leverages the strengths and capabilities of different deep learning architectures. The proposed model combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to recognize the latest intruders accurately and defend the healthcare data. The major outcome of the proposed framework is to detect different attacks during data transmission at the edge of the network with high accuracy and efficiency. The proposed model was analyzed on the CSE-CIC-IDS 2018 systematic dataset containing two distinct classes of profiles. The experimental results demonstrate that the proposed framework’s accuracy is higher than the existing approach.