A secured platform is a critical component of digital governance, as it helps to ensure the privacy, security, and reliability of the electronic platforms and systems used to manage and deliver public services. Interoperability and data exchange are essential for digital governance, as they enable different government agencies and departments to share data, information, and resources seamlessly, regardless of the platforms and technologies they use. In this paper, we build a secure platform to enhance the trustworthiness of digital governance interoperability and data exchange using blockchain and deep learning-based frameworks. Initially, an optimal blockchain leveraging approach is designed using the bonobo optimization algorithm to authenticate data generated from smart city environments. Furthermore, we introduce the integration of a lightweight Feistel structure with optimal operations to enhance privacy preservation. This integration provides two levels of security and ensures interoperability and double-secured data exchange in digital governance systems. In addition, we utilize a deep reinforcement learning (DRL) model to detect and prevent intrusions such as fraud/corruption in the smart city data. This approach enhances transparency and accountability in accessing the data and shows its predominance over other cutting-edge techniques on two benchmark datasets, BoT-IoT and ToN-IoT. Furthermore, the effectiveness of the framework in real-time scenarios has been demonstrated through two case studies. Overall, our proposed framework provides a trustworthy platform for digital governance, interoperability, and data exchange, addressing the challenges of privacy, security, and reliability in managing and delivering public services.

A vital component of maintaining the world’s expanding populace is farming. In agriculture field, factors such as soil quality, weather patterns, and crop yields are essential components of usual possessions that affect farming manufacture. Despite advancements, prevailing smart systems quiet struggle with handling big amounts in prediction claims, often facing difficulties in balancing prediction accuracy and learning efficiency. To ensure sustainable food production, integrating advanced machineries such as machine learning and artificial intelligence in agriculture is essential. This study proposes an explainable AI (XAI)-based smart agriculture system to provide holistic recommendation for precision farming aimed at improving productivity while reducing environmental impact. We compiled a comprehensive weather, soil, and crop dataset from official and verified sources in India. From this dataset, we extracted and optimized features using pre-trained architectures and enhanced barnacles mating optimization (EBMO) algorithm, addressing the high-measure mentality and computational complexity issues often encountered in agricultural data analysis. The selected features were analyzed to provide holistic recommendations for precision farming using baseline ML models such as support vector machine, random forest, neural network, and decision tree. Additionally, we integrate the XAI framework to offer interpretable recommendation for optimizes the agricultural practices. The study developed an XAI-based smart agriculture system that provides holistic recommendations for precision farming to boost productivity. By using a comprehensive dataset and optimizing features with the EBMO algorithm, the research achieved high accuracy in crop yield predictions, particularly with the XLNet+SVM model, which outperformed existing models across various crops. The integration of SHAP and LIME further enabled interpretable AI-driven insights, enhancing transparency in decision-making. The results demonstrated significant improvements in prediction accuracy, resource management, and sustainability, offering valuable contributions to global food security through smart agriculture. The ability to explain these AI decisions further supports the adoption of AI technologies in agriculture, fostering resilient agricultural systems that are essential for feeding the world’s growing population.

Stock market forecasting involves predicting fluctuations and trends in the value of financial assets, utilizing statistical and machine learning models to analyze historical market data for insights into future behavior. This practice aids investors, traders, financial institutions, and governments in making informed decisions, managing risks, and assessing economic conditions. Forecasting financial markets is difficult due to the intricate interplay of global economics, politics, and investor sentiment, making it inherently unpredictable. This study introduces a Deep Learning based Expert Framework for Stock Market forecasting (Portfolio prediction) called DLEF-SM. The methodology begins with an improved jellyfish-induced filtering (IJF-F) technique for preprocessing, effectively analyzing raw data and eliminating artifacts. To address imbalanced data and enhance data quality, pre-trained convolutional neural network (CNN) architectures, VGGFace2 and ResNet-50, are used for feature extraction. Additionally, an improved black widow optimization (IBWO) algorithm is designed for feature selection, reducing data dimensionality and preventing under-fitting. For precise stock market predictions, integrate deep reinforcement learning with artificial neural network (DRL-ANN) is proposed. Simulation outcomes reveal that the proposed framework achieves maximum forecasting accuracy, reaching 99.562%, 98.235%, and 98.825% for S&P500-S, S&P500-L, and DAX markets, respectively.

Skin disorders encompass a wide range of conditions that affect the skin, a vital organ of the human body. Early detection of skin diseases is difficult due to a lack of awareness, subtle symptoms, similarities, inter-individual heterogeneity in symptoms, limited access to dermatologists, and difficulties in imaging techniques. In this article, we propose a clinical decision support model for detection and classification of skin diseases (DermCDSM) through productive improvement of division capabilities and a cross-breed profound learning procedure. The division cycle is expected to be further developed using an improved chameleon swarm optimization (ICSO) method that takes into account a more accurate and proficient identification of the main cause of the disease. By employing the ICSO algorithm, we aim to enhance the overall accuracy and reliability of disease detection methods. Multi-strategy seeking optimization (MSSO), which is used to optimize feature selection by identifying the most significant features for the task at hand, has been introduced to handle the tests connected to data dimensionality. Convolutional deep spiking neural networks (CD-SNN), a deep learning method, have been implemented to improve the precision of skin cancer diagnosis and multi-class classification. The benchmark ISIC 2017 dataset is utilized to validate the efficacy of our proposed framework, DermCDSM, and its superiority over existing approaches in terms of accuracy, dependability, and efficiency is demonstrated.

The Internet of Medical Things (IoMT) has become a pivotal aspect of IoT applications, playing a crucial role in cutting down healthcare expenses, enhancing access to clinical services, and refining operational efficiency within the healthcare domain. An early detection of neurological brain disorders continues to present a formidable challenge. In response, our research endeavors are directed towards the IoMT-based system used for the real-time diagnosis of neurological disorders. In this paper, IoMT-based real-time diagnosis model is developed for neurological brain disorders using systematic deep learning and electroencephalogram (EEG) signal (RT-NeuroDDSM). We first introduce the time domain, channel and spatial attention network (TCSNet) for feature extraction which extracts the high-level time series, channel and spatial features, respectively. TCSNet aims to learn more valuable features from the input data to achieve good classification results. Furthermore, in order to maximize features, we create the modified normative fish swarm (MNFS) feature selection algorithm. Next, the detection of various neurological brain problems, including neuro-typical, epilepsy, and autism spectrum disorder (ASD), is accomplished by applying the hinging hyperplane neural network (HHNN). To verify the performance, we used the publicly accessible EEG datasets from University of Bonn-Germany, CHB-MIT EEG repository, and King Abdul-Aziz University. The RT-NeuroDDSM has an overall classification accuracy of 99.956%, making it 5.471% more in efficiency compared to the existing state-of-the-art model.