The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.

Internet of Things (IoT) is one of the recent innovations in Information Technology, which intends to interconnect the physical and digital worlds. It introduces a vision of smartness by enabling communication between objects and humans through the Internet. IoT has diverse applications in almost all sectors like Smart Health, Smart Transportation, and Smart Cities, etc. In healthcare applications, IoT eases communication between doctors and patients as the latter can be diagnosed remotely in emergency scenarios through body sensor networks and wearable sensors. However, using IoT in healthcare systems can lead to violation of the privacy of patients. Thus, security should be taken into consideration. Blockchain is one of the trending research topics nowadays and can be applied to the majority of IoT scenarios. Few major reasons for using the Blockchain in healthcare systems are its prominent features, i.e., Decentralization, Immutability, Security and Privacy, and Transparency. This paper’s main objective was to enhance the functionality of healthcare systems using emerging and innovative computer technologies like IoT and Blockchain. So, initially, a brief introduction to the basic concepts of IoT and Blockchain is provided. After this, the applicability of IoT and Blockchain in the medical sector is explored in three major areas—drug traceability, remote patient-monitoring, and medical record management. At last, the challenges of deploying IoT and Blockchain in healthcare systems are discussed.

Predicting traffic is the main duty of an intelligent transportation system (ITS). Precise traffic forecasts can significantly enhance the use of public funds. However, the dynamic and complex nature of spatio-temporal relationships presents significant challenges. Most current methods utilize static adjacency matrices, leading to reduced forecasting accuracy and precision. This approach fails to account for the complex spatio-temporal correlations that interact simultaneously. In order to show how different spatio-temporal correlations change over time in the traffic flow network, this study suggests a unified simultaneous Multi Fusion Graph Network (DMFGNet) model. The goal of the suggested DMFGNet model is to identify dynamic spatio-temporal linkages between various regions. Meanwhile, we propose a model, the Spatio-Temporal Attention Unit (STAU), to control the weights of neighbor aggregation. It is capable of meticulously combining spatio-temporal characteristics from different neighbors. We tested the model for both real-time and pre-processed predictions using a combination of edge and cloud infrastructure. This setup performs prediction tasks at the edge layer and conducts training in the remote cloud. This approach guarantees the use of only relevant data for model training and prediction-making, thereby boosting the system’s overall effectiveness. This approach not only optimizes resource allocation but also aids in reducing latency and enhancing the overall performance of cloud-based prediction models, potentially enhancing the capabilities of consumer technology and electronics solutions. We carefully tested and evaluated two large real-world traffic flow datasets to show that the proposed method works and is useful. The results of the tests show that the suggested model is better than the current best baseline methods. Additionally, the results demonstrate the effectiveness and usefulness of the recommended strategy.

Recently, researchers are investing more fervently in fault diagnosis area of electrical machines. The users and manufacturers of these various efforts are strong to contain diagnostic features in software for improving reliability and scalability. Internet of Things (IoT) has grown immensely and contributing for the development of recent technological advancements in industries, medical and various environmental applications. It provides efficient processing power through cloud, and presents various new opportunities for industrial automation by implementing IoT and industrial wireless sensor networks. The process of regular monitoring enables early detection of machine faults and hence beneficial for Industrial automation by providing efficient process control. The performance of fault detection and its classification by implementing machine-learning algorithms highly dependent on the amount of features involved. The accuracy of classification will adversely affect by the dimensionality features increment. To address these problems, the proposed work presents the extraction of relevant features based on oriented sport vector machine (FO-SVM). The proposed algorithm is capable for extracting the most relevant feature set and hence presenting the accurate classification of faults accordingly. The extraction of most relevant features before the process of classification results in higher classification accuracy. Moreover it is observed that the lesser dimensionality of propose process consumes less time and more suitable for cloud. The experimental analysis based on the implementation of proposed approach provides and solution for the monitoring of machine condition and prediction of fault accurately based on cloud platform using industrial wireless sensor networks and IoT service.

The Industrial Internet of Things has grown significantly in recent years. While implementing industrial digitalization, automation, and intelligence introduced a slew of cyber risks, the complex and varied industrial Internet of Things environment provided a new attack surface for network attackers. As a result, conventional intrusion detection technology cannot satisfy the network threat discovery requirements in today’s Industrial Internet of Things environment. In this research, the authors have used reinforcement learning rather than supervised and unsupervised learning, because it could very well improve the decision‐making ability of the learning process by integrating abstract thinking of complete understanding, using deep knowledge to perform simple and nonlinear transformations of large‐scale original input data into higher‐level abstract expressions, and using learning algorithm or learning based on feedback signals, in the lack of guiding knowledge, which is based on the trial‐and‐error learning model, from the interaction with the environment to find the best good solution. In this respect, this article presents a near‐end strategy optimization method for the Industrial Internet of Things intrusion detection system based on a deep reinforcement learning algorithm. This method combines deep learning’s observation capability with reinforcement learning’s decision‐making capability to enable efficient detection of different kinds of cyberassaults on the Industrial Internet of Things. In this manuscript, the DRL‐IDS intrusion detection system is built on a feature selection method based on LightGBM, which efficiently selects the most attractive feature set from industrial Internet of Things data; when paired with deep learning algorithms, it effectively detects intrusions. To begin, the application is based on GBM’s feature selection algorithm, which extracts the most compelling feature set from Industrial Internet of Things data; then, in conjunction with the deep learning algorithm, the hidden layer of the multilayer perception network is used as the shared network structure for the value network and strategic network in the PPO2 algorithm; and finally, the intrusion detection model is constructed using the PPO2 algorithm and ReLU (R). Numerous tests conducted on a publicly available data set of the Industrial Internet of Things demonstrate that the suggested intrusion detection system detects 99 percent of different kinds of network assaults on the Industrial Internet of Things. Additionally, the accuracy rate is 0.9%. The accuracy, precision, recall rate, F1 score, and other performance indicators are superior to those of the existing intrusion detection system, which is based on deep learning models such as LSTM, CNN, and RNN, as well as deep reinforcement learning models such as DDQN and DQN.