An IoT healthcare system refers to the use of Internet of Things (IoT) devices and technologies in the healthcare industry. It involves the integration of various interconnected devices, sensors, and systems to collect, monitor, and transmit health-related data for medical purposes. Blockchain-assisted intrusion detection on IoT healthcare systems is an innovative approach to enhancing the security and privacy of sensitive medical data. By combining the decentralized and immutable nature of blockchain technology with intrusion detection systems (IDS), it is possible to create a more robust and trustworthy security framework for IoT healthcare systems. With this motivation, this study presents Blockchain Assisted IoT Healthcare System using Ant Lion Optimizer with Hybrid Deep Learning (BHS-ALOHDL) technique. The presented BHS-ALOHDL technique enables IoT devices in the healthcare sector to transmit medical data securely and detects intrusions in the system. To accomplish this, the BHS-ALOHDL technique performs ALO based feature subset selection (ALO-FSS) system to produce a series of feature vectors. The HDL model integrates convolutional neural network (CNN) features and long short-term memory (LSTM) model for intrusion detection. Lastly, the flower pollination algorithm (FPA) is exploited for the optimal hyperparameter tuning of the HDL approach, which results in an enhanced detection rate. The experimental outcome of the BHS-ALOHDL system was tested on two benchmark datasets and the outcomes indicate the promising performance of the BHS-ALOHDL technique over other models.

Intelligent Cyber-Physical Transportation Systems (ICTS) rely deeply on data exchange between moving vehicles and fixed infrastructure. While exchanging data between vehicles in the network, security is a main constraint. ICTS encompasses many facilities and claims, such as managing road traffic, traveler data, public transportation systems, and autonomous vehicles. It is anticipated that ICTS will make significant contributions to future smart cities and urban design in road and traffic security, transportation and transport optimization, energy efficiency, and environmental pollution control. Flexibility, variety of quality-of-service needs, and massive volumes of data generated all present challenges when working with ITS. Previous works on cyber-physical attack detection lag in ensuring security at various levels. To monitor In-Vehicle Networks (IVN), Vehicle-to-Vehicle (V2V) Communications, and Vehicle-to-Infrastructure (V2I) Networks for malicious behavior, we introduce a deep learning-based Intrusion Detection System (IDS) for ICTS. In this research, we look into how Deep Learning (DL), a relatively new but rapidly growing subject, could help improve ICTS. This research investigates whether deep learning can enhance the security of autonomous vehicles and other forms of smart transportation. We improve the smart transport network using the deep learning method, simulate it, and statistically study its data transmission economy, accuracy projections of the future, and path-switching strategy. To identify malicious activity in the core networks of autonomous vehicles (AV), we have developed an ensemble Long-Short Term Memory (LSTM) technique based on Deep Learning architecture. The car hacking dataset (used to monitor communications within a vehicle) and the UNSWNB15 dataset (used to monitor communications with the outside world) are used to test and compare the performance of the proposed IDS. The experimental findings showed that our suggested system outperformed other intrusion detection methods by achieving 100% accuracy in detecting all sorts of assaults on the auto-hacking dataset and 99% on the UNSW-NB15 dataset.

Diabetes, in all of its types, costs countries of all income levels unacceptably enormous personal, societal, and economic expenses. To predict type-2 diabetes, this work aimed to develop an analytical predictive model based on machine learning techniques and a web-based personalized diabetes monitoring system. The history of a patient will be collected and ready for analysis purposes based on machine learning techniques by continuously monitoring the patient’s vital data. A diabetes monitoring system is proposed by utilizing the patient’s QR card that allows the patients and doctors to be connected to the Internet of Things. So, they can deliver real-time information (such as insulin records) about their health status and can visit different healthcare institutions. The proposed system can help doctors to make data-driven decisions and enhance patients’ treatment. Several machine learning algorithms that are Decision Tree, Support Vector Classifier, Random Forest, Gradient Boosting, Multi-layer Perceptron’s, Artificial Neural Network, k-Nearest Neighbors, Logistic Regression, and Naive Bayes are used. The proposed analytical model is evaluated based on two different datasets a synthetic dataset and PIMA Diabetes Dataset. The performance of the classification models was analyzed in terms of accuracy, recall, and precision based on the cross-validation strategy. The findings show that the ANN model has better prediction accuracy than other models. The evaluation findings are analyzed and compared with other existing models. The system has dashboard graphs displaying the number of patients in Saudi Arabia cities. It also contains visualized graphs that include more detailed classifications for patients’ states (Normal, Pre-Diabetes, and Diabetes).

Energy is a major driver of human activity. Demand response is of the utmost importance to maintain the efficient and reliable operation of smart grid systems. The short-term load forecasting (STLF) method is particularly significant for electric fields in the trade of energy. This model has several applications to everyday operations of electric utilities, namely load switching, energy-generation planning, contract evaluation, energy purchasing, and infrastructure maintenance. A considerable number of STLF algorithms have introduced a tradeoff between convergence rate and forecast accuracy. This study presents a new wild horse optimization method with a deep learning-based STLF scheme (WHODL-STLFS) for SGs. The presented WHODL-STLFS technique was initially used for the design of a WHO algorithm for the optimal selection of features from the electricity data. In addition, attention-based long short-term memory (ALSTM) was exploited for learning the energy consumption behaviors to forecast the load. Finally, an artificial algae optimization (AAO) algorithm was applied as the hyperparameter optimizer of the ALSTM model. The experimental validation process was carried out on an FE grid and a Dayton grid and the obtained results indicated that the WHODL-STLFS technique achieved accurate load-prediction performance in SGs.

Automated facial emotion recognition (FER) is one of the important fields of human-computer interaction (HCI). FER remains challenging due to facial accessories, non-uniform illumination, pose variation, etc. Emotion detection exploiting conventional algorithms has the demerit of mutual optimization of classification and feature extraction. Artificial intelligence (AI) techniques can be employed to identify FER automatically. Deep learning (DL) driven FER models have recently allowed for designing an end-to-end learning process. Therefore, this study designs a Henry Gas Solubility Optimization with Deep Learning Based FER (HGSO-DLFER) technique for HCI. The HGSO-DLFER technique aims to recognize and identify various kinds of facial emotions. To accomplish this, the HGSO-DLFER technique employs adaptive fuzzy filtering (AFF) for noise removal. In addition, the MobileNet model is used for feature vector generation, and the HGSO algorithm optimally chooses its hyperparameter scan. For the recognition of facial emotions, the HGSO-DLFER technique uses an autoencoder (AE) classifier with a Nadam optimizer. A widespread experimental analysis is made to facilitate a better understanding of the FER results by the HGSO-DLFER technique. The comparative analysis showed the effective performance of the HGSO-DLFER technique over other FER techniques with maximum accuracy of 98.65%.