Artificial intelligence is an emerging technology that revolutionizes human lives. Despite the fact that this technology is used in higher education, many professors are unaware of it. In this current scenario, there is a huge need to arise, implement information bridge technology, and enhance communication in the classroom. Through this paper, the authors try to predict the future of higher education with the help of artificial intelligence. This research article throws light on the current education system the problems faced by the subject faculties, students, changing government rules, and regulations in the educational sector. Various arguments and challenges on the implementation of artificial intelligence are prevailing in the educational sector. In this concern, we have built a use case model by using a student assessment data of our students and then built a synthesized using generative adversarial network (GAN). The dataset analyzed, visualized, and fed to different machine learning algorithms such as logistic Regression (LR), linear discriminant analysis (LDA), K-nearest neighbors (KNN), classification and regression trees (CART), naive Bayes (NB), support vector machines (SVM), and finally random forest (RF) algorithm and achieved a maximum accuracy of 58%. This article aims to bridge the gap between human lecturers and the machine. We are also concerned about the psychological emotions of the faculty and the students when artificial intelligence takes control.

Improper neck postures and movements are the major causes of human neck-related musculoskeletal disorders. To monitor, quantify, analyze, and detect the movements, remote and non-invasive based methods are being developed for prevention and rehabilitation. The purpose of this research is to provide a digital platform for analyzing the impact of human neck movements on the neck musculoskeletal system. The secondary objective is to design a rehabilitation monitoring system that brings accountability in the treatment prescribed, which is shown in the use-case model. To record neck movements effectively, a Smart Neckband integrated with the Inertial Measurement Unit (IMU) was designed. The initial task was to find a suitable position to locate the sensors embedded in the Smart Neckband. IMU-based real-world kinematic data were captured from eight research subjects and were used to extract kinetic data from the OpenSim simulation platform. A Random Forest algorithm was trained using the kinetic data to predict the neck movements. The results obtained correlated with the novel idea proposed in this paper of using the hyoid muscles to accurately detect neck postures and movements. The innovative approach of integrating kinematic data and kinetic data for analyzing neck postures and movements has been successfully demonstrated through the efficient application in a rehabilitation use case with about 95% accuracy. This research study presents a robust digital platform for the integration of kinematic and kinetic data that has enabled the design of a context-aware neckband for the support in the treatment of neck musculoskeletal disorders.

Self-driving vehicles such as autonomous cars are manufactured mostly with smart sensors and IoT devices with artificial intelligence (AI) techniques. In most of the cases, smart sensors are networked with IoT devices to transmit the data in real-time. IoT devices transmit the sensor data to the processing unit to do necessary actions based on sensor output data. The processing unit executes the tasks based on pre-defined instructions given to the processor with embedded and AI coding techniques. Continuous streaming of sensors raw data to the processing unit and for cloud storage are creating a huge load on cloud devices or on servers. In order to reduce the amount of stream data load on the cloud, fog computing, or fogging technology, helps a lot. Fogging is nothing but the pre-processing of the data before deploying it into the cloud. In fog environment, data optimization and analytical techniques take place as a part of data processing in a data hub on IoT devices or in a gateway.

India is laying its steps rapidly towards Smart India, adopting technological advancements and implementations to make India more sustainable and citizen-friendly. Smart City's mission launched in the year 2015 to make cities smarter it ultimately makes India smarter and reliable. Infrastructure when adopts advance technologies enlighten the way of living, in this context, we designed a vision for smart roads. Though India is developing at full pace, the graph of road accidents per year due to bad road conditions has a very positive slope from the past two decades. The number of accidents due to potholes increased in the past few years, Integrating advanced technologies to the current road's infrastructure helps us to cut the number of accidents. Artificial Intelligent, Internet of Things, Machine Learning, Deep Learning, Cloud, Fog and Edge computing technologies help to design an accident prevention system further improves the safety and security of citizens.

Musculoskeletal disorders (MSD) are the second leading cause of disability and one-fifth of adults worldwide have been reporting about neck pain annually. Accidents, sudden jolts, poor posture, prolonged immobilization, and other chronic diseases are among the major causes of MSD. The main objective of this research is to support therapists in the rehabilitation of neck musculoskeletal disorders. We present a Smart Neck Band embedded with Inertia Measurement Unit (IMU), and associated applications that analyze the sensor data to provide valuable insights into the condition or progress of the MSD. Patients can use the neck band to monitor the improvement during the period of the therapy and during regular exercises recommended post-therapy. The flexibility and range of motion of the neck region can be self-assessed and the entire process of rehabilitation can be digitally recorded for further analysis. For the research presented in this paper, eight subjects participated voluntarily. Among them, one subject was diagnosed with cervical spondylosis. During the experimental study, the Smart Neck-Band was used to track and record the movements of the neck using inbuilt accelerometer and gyroscope sensors. The data generated by the sensors represent the kinematics of the neck movement. Using this kinematic data, the Neck Range of Motion (NROM) can be calculated for movement assessment during therapy. The kinematic data can be analyzed further using the OpenSim simulation tool to get more insights into the musculoskeletal aspects of cervical dysfunction. The OpenSim tool generates kinetic data for the corresponding kinematic data captured by the Smart Neck-Band. Kinetic data generated by OpenSim includes muscle action, force, power, velocity, etc. The Smart Neck-Band enables the integrated analysis of kinematic and kinetic data, providing innovative technology for effective rehabilitation of neck MSD. The experimental results of this research show that the Smart Neck-Band with OpenSim analysis have great potential in the efficient treatment and management of musculoskeletal disorders of the Neck.

The recent evolution of the fourth industrial revolution is Industry 4.0, projecting the enhancement of the technology, development, and trends towards the smart processing of the automation in industries. The advancements in communication and connectivity are the major source for the Industrial IoT (IIoT). It collaborates all the industrial functional units to work under a single control channel, digital quantification analytic methods deployment for the prediction of machinery, sensors, monitoring systems, control systems, products, workers, managers, locations, suppliers, and customers. In addition to IIoT, AI methods are also playing a vital role in predictive modeling and analytic methods for the assessment, control, and development of rapid production, from the industries. Other side security issues are challenging the development, concerning all the factors digitalization processes of the industries need to move forward. This chapter focuses on IIoT core concepts, applications, and key challenges to enhance the industrial automation process.