Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. When the supply of blood and other nutrients to the brain is interrupted, symptoms might develop. According to the World Health Organization (WHO), stroke is the greatest cause of death and disability globally. Early recognition of the various warning signs of a stroke can help reduce the severity of the stroke. Different machine learning (ML) models have been developed to predict the likelihood of a stroke occurring in the brain. This research uses a range of physiological parameters and machine learning algorithms, such as Logistic Regression (LR), Decision Tree (DT) Classification, Random Forest (RF) Classification, and Voting Classifier, to train four different models for reliable prediction. Random Forest was the best performing algorithm for this task with an accuracy of approximately 96 percent. The dataset used in the development of the method was the open-access Stroke Prediction dataset. The accuracy percentage of the models used in this investigation is significantly higher than that of previous studies, indicating that the models used in this investigation are more reliable. Numerous model comparisons have established their robustness, and the scheme can be deduced from the study analysis.

Financial data are a type of historical time series data that provide a large amount of information that is frequently employed in data analysis tasks. The question of how to forecast stock prices continues to be a topic of interest for both investors and financial professionals. Stock price forecasting is quite challenging because of the significant noise, non-linearity, and volatility of time series data on stock prices. The previous studies focus on a single stock parameter such as close price. A hybrid deep-learning, forecasting model is proposed. The model takes the input stock data and forecasts two stock parameters close price and high price for the next day. The experiments are conducted on the Shanghai Composite Index (000001), and the comparisons have been performed by existing methods. These existing methods are CNN, RNN, LSTM, CNN-RNN, and CNN-LSTM. The generated result shows that CNN performs worst, LSTM outperforms CNN-LSTM, CNN-RNN outperforms CNN-LSTM, CNN-RNN outperforms LSTM, and the suggested single Layer RNN model beats all other models. The proposed single Layer RNN model improves by 2.2%, 0.4%, 0.3%, 0.2%, and 0.1%. The experimental results validate the effectiveness of the proposed model, which will assist investors in increasing their profits by making good decisions.

The Industrial Internet of Things has gained more attention because of the self-governing nature of system configuration with interoperable application connectivity. In the E-Healthcare environment, it cooperates with medical sensors to capture, examine, analyze, preserve, and document day-to-day transactions of patients in real-time. The integration of E-healthcare and IIoT provides an analytical platform to handle a large amount of data with a low cost of cloud-enabling scalable storage. Throughout the transformation, patients’ personal information is at risk while exchanging records over centralized server-based systems. Thus, the rate of node connectivity, failure of parallel data sharing, and deliverance-related issues increase. In this paper, we present solutions in three folds. First, the paper proposes a novel and secure architecture for E-healthcare data security using a blockchain-distributed ledger technology named BHIIoT. Second, the transformation in the lifecycle of medical wireless sensor networks for data management and optimization with a distributed layered hierarchy is developed, which enhances the network resources and increases trust in the blockchain-enabled peer-to-peer environment. Third, the proposed BHIIoT uses the NuCypher threshold re-encryption mechanism for data encryption and protects shared resources in the form of blocks preserved in a blockchain immutable storage. For instance, chain codes are deployed to automate authentication, logging, index information deliverance, and trace transactions to resist illegal activities in the E-Healthcare distributed-application. The customized lightweight blockchain multi-proof-of-work and multi-proof-of-stake are designed with a digital signature for improving the consumption of resources and reducing the load of storage while the transaction process of E-healthcare IIoT is scheduled.

Degree attestation verification and traceability are complex one-to-one processes between the Higher Education Commission (HEC) and universities. The procedure shifted to the digitalized manner, but still, on a certain note, manual authentication is required. In the initial process, the university verified the degree and stamp seal first. Then, a physical channel of degree submission to the receiving ends is activated. After that, the degree is attested while properly examining and analyzing the tamper records related to degree credentials through e-communication with the university for verification and validation. This issue poses a serious challenge to educational information integrity and privacy. Potentially, blockchain technology could become a standardized platform to perform tasks including issuing, verifying, auditing, and tracing immutable records, which would enable the HEC, universities, and Federal Education Ministry (FEM) to quickly and easily get attested and investigate the forge proof versions of certificates. Besides, decentralized distributed data blocks in chronological order provide high security between distributed ledgers, consensus engine, digital signature, smart contracts, permissioned application, and private network node transactions that guarantee degree record validation and traceability. This paper presents an architecture (HEDU-Ledger) and detail design of blockchain-enabled hyperledger fabric applications implementation for degree attestation verification and traceable direct channel design between HEC and universities. The hyperledger fabric endorses attestation records first, and then validates (committer) the degree and maintains the secure chain of tracing between stakeholder peer nodes. Furthermore, this HEDU-Ledger architecture avoids language and administrative barriers. It also provides robustness in terms of security and privacy of records and maintains integrity with secure preservation as compared to that of the other state-of-the-art methods.

Anomaly-based intrusion detection systems (IDSs) have been deployed to monitor network activity and to protect systems and the Internet of Things (IoT) devices from attacks (or intrusions). The problem with these systems is that they generate a huge amount of inappropriate false alarms whenever abnormal activities are detected and they are not too flexible for a complex environment. The high-level rate of the generated false alarms reduces the performance of IDS against cyber-attacks and makes the tasks of the security analyst particularly difficult and the management of intrusion detection process computationally expensive. We study here one of the challenging aspects of computer and network security and we propose to build a detection model for both known and unknown intrusions (or anomaly detection) via a novel nonparametric Bayesian model. The design of our framework can be extended easily to be adequate for IoT technology and notably for intelligent smart city web-based applications. In our method, we learn the patterns of the activities (both normal and anomalous) through a Bayesian-based MCMC inference for infinite bounded generalized Gaussian mixture models. Contrary to classic clustering methods, our approach does not need to specify the number of clusters, takes into consideration the uncertainty via the introduction of prior knowledge for the parameters of the model, and permits to solve problems related to over- and under-fitting. In order to get better clustering performance, feature weights, model’s parameters, and the number of clusters are estimated simultaneously and automatically. The developed approach was evaluated using popular data sets. The obtained results demonstrate the efficiency of our approach in detecting various attacks.