The amount of road accidents caused by driver drowsiness is one of the world’s major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver’s body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection methods using only single measures that failed to produce good outcomes. Furthermore, these measures were only functional in certain conditions. This paper proposes a model that combines the two approaches, non-intrusive and intrusive, to detect driver drowsiness. Behavioral measures as a non-intrusive approach and sensor-based physiological measures as an intrusive approach are combined to detect driver drowsiness. The proposed hybrid model uses AI-based Multi-Task Cascaded Convolutional Neural Networks (MTCNN) as a behavioral measure to recognize the driver’s facial features, and the Galvanic Skin Response (GSR) sensor as a physiological measure to collect the skin conductance of the driver that helps to increase the overall accuracy. Furthermore, the model’s efficacy has been computed in a simulated environment. The outcome shows that the proposed hybrid model is capable of identifying the transition from awake to a drowsy state in the driver in all conditions with the efficacy of 91%.

Mobile computing and technology are becoming more common in many parts of private life and public services, and they are playing an increasingly important role in healthcare, not just for sensory devices but also for communication, recording, and display. They are used for more than only sensory devices but also for communications, recording, and display. Numerous medical indications and postoperative days must be monitored carefully. As a result, the most recent development in Internet of Things‐ (IoT‐) based healthcare communication has been embraced. The Internet of Things (IoT), which is employed in a wide range of applications, is a catalyst for the healthcare industry. Healthcare data is complicated, making it difficult to handle and evaluate in order to derive useful information for decision‐making. On the other hand, data security is a vital requirement in a healthcare data systems. Determining the need for a smart and secure IoT platform for healthcare applications, we create one in this study. Here, a cutting‐edge encryption algorithm is used to protect the health data. Normalization is first used to preprocess the data and remove any irrelevant information. Using principal component analysis and logistic regression, the data’s features are extracted (LR‐PCA). To choose the pertinent features, a feature selection process based on genetic algorithms is used. We have put out a brand‐new kernel homomorphism. To increase the security of the IoT network, use the two‐fish Encryption algorithm (KHTEA). EBSMO (exponential Boolean spider monkey optimization) is used to further boost the encryption process’ effectiveness. Utilizing the MATLAB simulation tool, the proposed system is assessed, and the metrics are contrasted with the accepted practices. Our suggested solution has been shown to be effective in protecting medical healthcare data. The effectiveness of the proposed and existing approaches is assessed using metrics for encryption time, execution time, and security level. The security precautions we suggested for healthcare data worked well.

Blockchain is a technology that operates on distributed peer-to-peer network and is widely adopted by large number of organizations due to its attractive features such as highly secured, transparent, traceable, low transaction fee, no need of central authority and confidentiality. There are lot of confusions and myths regarding the blockchain technology as it is emerging technology and too complex in nature. This paper is aimed at revealing blockchain technology in depth along with its applications and open research challenges. The past published literature was reviewed to achieve the objectives. The study found that apart from cryptocurrency, blockchain is also suitable for other applications such as smart cities, supply chain, healthcare, smart transportation and authenticating IoT (Internet of Things) devices. This study is also disclosing various open research challenges such as scalability issues, huge power consumption and delay in completing transactions. This paper is also sharing the mitigation approaches and the future of blockchain with smart contracts.

BACKGROUND: In recent years, a lot of research work has been done to transmit vital parameters of the patient using IOT (Internet of Things). The information related to Patient is sent by means of WBAN (Wireless Body Area Network) and sensors to the healthcare database. This has been shown a great potential to transmit vital parameter of the patient at a remote location. Remote monitoring of patient(s), including vital signs, sound and video is to be transmitted, particularly when a patient is in transit. By transmitting the imperative parameters of the patient to a specialist diminishes, the ideal opportunity for starting treatment and permits the emergency crew to be better prepared. This will facilitate a doctor to make a diagnostic conclusion.OBJECTIVES: This study aims to develop an advance system to transmit the vital parameters of the patient to the smart phone of the doctor at a remote location. After seeing essential parameters of the patient, the specialist can convey/endorse the medication from the remote area.METHODS: To monitor patient’s data in real-time an android application is developed which helps the medical staff to monitor the patient from remote location. All the patient related information is stored in Real Time Clinical Database.RESULTS: The proposed model will allow the doctors to monitor physiological parameters (HR, NIBP, Temp, ECG, SPO2) of the patient, as well as endorse medication from the remote location. This system will be helpful to provide virtual presence of the doctor inside the ambulance and to provide lifesaving drugs to the patient while the patient is in transit.CONCLUSION: The present invention provides a fully integrated system for the doctor to view the vital signs of the patient from the remote location in real-time.