Smart cities provide citizens with smart and advanced services to improve their quality of life. However, it has been observed that the collection, storage, processing, and analysis of heterogeneous data that are usually borne by citizens will bear certain difficulties. The development of the Internet of Things, cloud computing, social media, and other Industry 4.0 influencers pushed technology into a smart society’s framework, bringing potential vulnerabilities to sensor data, services, and smart city applications. These vulnerabilities lead to data security problems. We propose a decentralized data management system for smart and secure transportation that uses blockchain and the Internet of Things in a sustainable smart city environment to solve the data vulnerability problem. A smart transportation mobility system demands creating an interconnected transit system to ensure flexibility and efficiency. This article introduces prior knowledge and then provides a Hyperledger Fabric-based data architecture that supports a secure, trusted, smart transportation system. The simulation results show the balance between the blockchain mining time and the number of blocks created. We also use the average transaction delay evaluation model to evaluate the model and to test the proposed system’s performance. The system will address residents’ and authorities’ security challenges of the transportation system in smart, sustainable cities and lead to better governance.

Epilepsy is a common neurological condition. The effects of epilepsy are not restricted to seizures alone. They comprise a wide spectrum of problems that might impair and reduce quality of life. Even with medication, 30% of epilepsy patients still have recurring seizures. An epileptic seizure is caused by significant neuronal electrical activity, which affects brain activity. EEG shows these changes as high-amplitude spiky and sluggish waves. Recognizing seizures on an electroencephalogram (EEG) manually by a professional neurologist is a time-consuming and labor-intensive process, hence an efficient automated approach is necessary for the identification of epileptic seizure. One technique to increase the speed and accuracy with which a diagnosis of epileptic seizures could be made is by utilizing computer-aided diagnosis systems that are built on deep neural networks, or DNN. This study introduces a fusion of recurrent neural networks (RNNs) and bi-directional long short-term memories (BiLSTMs) for automatic epileptic seizure identification via EEG signal processing in order to tackle the aforementioned informational challenges. An electroencephalogram’s (EEG) raw data were first normalized after undergoing pre-processing. A RNN model was fed the normalized EEG sequence data and trained to accurately extract features from the data. Afterwards, the features were passed to the BiLSTM layers for processing so that further temporal information could be retrieved. In addition, the proposed RNN-BiLSTM model was tested in an experimental setting using the freely accessible UCI epileptic seizure dataset. Experimental findings of the suggested model have achieved avg values of 98.90%, 98.50%, 98. 20%, and 98.60%, respectively, for accuracy, sensitivity, precision, and specificity. To further verify the new model’s efficacy, it is compared to other models, such as the RNN-LSTM and the RNN-GRU learning models, and is shown to have improved the same metrics by 1.8%, 1.69%, 1.95%, and 2.2% on using 5-fold. Additionally, the proposed method was compared to state-of-the-art approaches and proved to be a more accurate categorization of such techniques.

Breast cancer is responsible for the deaths of thousands of women each year. The diagnosis of breast cancer (BC) frequently makes the use of several imaging techniques. On the other hand, incorrect identification might occasionally result in unnecessary therapy and diagnosis. Therefore, the accurate identification of breast cancer can save a significant number of patients from undergoing unnecessary surgery and biopsy procedures. As a result of recent developments in the field, the performance of deep learning systems used for medical image processing has showed significant benefits. Deep learning (DL) models have found widespread use for the aim of extracting important features from histopathologic BC images. This has helped to improve the classification performance and has assisted in the automation of the process. In recent times, both convolutional neural networks (CNNs) and hybrid models of deep learning-based approaches have demonstrated impressive performance. In this research, three different types of CNN models are proposed: a straightforward CNN model (1-CNN), a fusion CNN model (2-CNN), and a three CNN model (3-CNN). The findings of the experiment demonstrate that the techniques based on the 3-CNN algorithm performed the best in terms of accuracy (90.10%), recall (89.90%), precision (89.80%), and f1-Score (89.90%). In conclusion, the CNN-based approaches that have been developed are contrasted with more modern machine learning and deep learning models. The application of CNN-based methods has resulted in a significant increase in the accuracy of the BC classification.

The smart industry is a significant realization of the internet of things. IPv6 over time-slotted channel hopping (6TiSCH) is the new standard that has emerged as a promising technology for industrial communications. The demanding issues of smart industries include scalability, latency, and energy efficiency. Existing studies have presented scheduling schemes for 6TiSCH-based industries. However, these works are not suitable for practical scenarios where emergencies frequently occur. Hence, this study aims to improve scalability, latency, and energy efficiency in 6TiSCH-based smart industries for practical scenarios. A new fog assisted 6TiSCH Tri-Layer architecture is designed with a network layer, smart communication layer, and cloud layer. Scalability and latency are realized by fog computing in the smart communication layer. The rough set-based root selection (R2S) algorithm improves the network layer’s energy efficiency by constructing an R2S and destination-oriented direct acyclic graph. Emergency cognizant distributed adaptive scheduling minimizes the latency for data transmission through fuzzy Bayesian learning-based parent selection and multiobjective gravitational search algorithm-based channel selection. In the fog layer, the rank-based Q-learning algorithm performs offloading to manage the energy consumption among fog nodes. Industrial data are stored in the cloud layer where they can be accessed by end users. The performance is evaluated by modeling the tri-layer 6TiSCH network in network simulator-3.26. Experiments show that the proposed work achieves satisfactory results in terms of energy consumption, latency, end-to-end delay, response time, and transmission efficiency.