The emergence of the latest technologies gives rise to the usage of noninvasive techniques for assisting health-care systems. Amongst the four major cardiovascular diseases, stroke is one of the most dangerous and life-threatening disease, but the life of a patient can be saved if the stroke is detected during early stage. The literature reveals that the patients always experience ministrokes which are also known as transient ischemic attacks (TIA) before experiencing the actual attack of the stroke. Most of the literature work is based on the MRI and CT scan images for classifying the cardiovascular diseases including a stroke which is an expensive approach for diagnosis of early strokes. In India where cases of strokes are rising, there is a need to explore noninvasive cheap methods for the diagnosis of early strokes. Hence, this problem has motivated us to conduct the study presented in this paper. A noninvasive approach for the early diagnosis of the strokes is proposed. The cascaded prediction algorithms are time-consuming in producing the results and cannot work on the raw data and without making use of the properties of EEG. Therefore, the objective of this paper is to devise mechanisms to forecast strokes on the basis of processed EEG data. This paper is proposing time series-based approaches such as LSTM, biLSTM, GRU, and FFNN that can handle time series-based predictions to make useful decisions. The experimental research outcome reveals that all the algorithms taken up for the research study perform well on the prediction problem of early stroke detection, but GRU performs the best with 95.6% accuracy, whereas biLSTM gives 91% accuracy and LSTM gives 87% accuracy and FFNN gives 83% accuracy. The experimental outcome is able to measure the brain waves to predict the signs of strokes. The findings can certainly assist the physicians to detect the stroke at early stages to save the lives of the patients.

Industrial Internet of Things (IIoT) is attempting to integrate the real world into the digital world through smart devices, information technology, and Internet. IIoT is connecting enormous devices in industry 4.0/5.0 which may be heterogeneous in nature. With the evolution of IoT, diverse technologies have been employed to deliver the quality of services to the end users in a seamless manner. Cloud computing has considerably boosted the growth of IIoT by serving the computational and data storage needs of IIoT- and blockchain-based applications in industry 4.0/5.0. However, cloud is providing services to IIoT users, but still, there is a need to improve the latency rate of delivery of services, the transmission rate of end-to-end delivery, and overall throughput of the network channels in industry 4.0 and blockchain-based distributed systems. The cloud servers those are located at remote locations are not capable to offer the quality of services to users who require real-time responses, minimum network latency, and optimum throughput. The advancements in Edge computing are making Edge-Cloud more suitable for end users in blockchain-based transactions and industry 4.0 to serve the requirements of IIoT-based applications. This paper aims at providing the resources of Edge-Cloud to the end users by proposing a soft-computing technique for selecting the most suitable resources from the pool of available resources at Edge-Cloud. This paper is proposing a multicriteria statistical approach for resource selection to exploit the benefits of Edge-Cloud and to suffice the needs of IIoT and blockchain-based applications in industry 4.0. The results obtained from the proposed research assist in enhancing the service providing rate, minimizing the delay in transmission, and optimizing the throughput of Edge-Cloud servers.

Information extraction and data mining are the important aspects in blockchain technology. Big- data mining tools can perform pattern recognition assignments from thousands to billions of blockchain communications to recognize evil users and fraudulent transactions. The current information extraction algorithms have the limitations of large memory occupation, long running time, high dimensionality, low extraction accuracy, poor convergence precision and slow convergence speed. There is a need to devise newer techniques with the potential of fast and accurate information extraction features in blockchain based systems to support high risk international financial transactions. Thus, in this paper, the automatic extraction algorithm of data mining as a case study of blockchain communications based on QACS (quantum adaption cuckoo search) method is proposed. Principal component analysis (PCA) is used to map the original features of blockchain communications into low-dimensional feature space through linear transformation, and then to replace the original features with fewer absolutely needed features. The features of coding information in information management system are reduced, and the optimal feature subset is obtained. K-means is used to extract the key information in the optimal feature subset. An improved and adaptive Cuckoo search is proposed in this paper where quantum operation is introduced into the original K-means algorithm for automatic extraction of coded information in blockchain communications. The results prove that the proposed QACS method has the potential of automatic extraction of the encoded information with lower execution time, higher extraction accuracy, and faster convergence speed, and is significant for blockchain based systems.