Today, developments in information and communication technology (ICT) have a significant influence on education sustainability. In this study, the factors influencing students’ intentions towards using ICT in education sustainability, as well as their satisfaction from its use, were examined. This study aims to investigate student intentions to use information and communication technology, as well as their satisfaction with such use. Therefore, this study employed an extended model of the Technology Acceptance Model (TAM) as the research framework, and adopted quantitative data collection and analysis methods by surveying 502 university students who were chosen through stratified random sampling. Using structural equation modeling (SEM), student responses were sorted into eight study constructs and analyzed to explain their intentions towards technology use and satisfaction. A significant relationship was found between computer self-efficacy (CSE), subjective norms (SN), and perceived enjoyment (PE), which were significant determinants of perceived ease of use (PEU) and perceived usefulness (PU). PEU, PU, and attitudes towards computer use (ACU) influenced students’ intentions to use (SIU) ICT and students’ satisfaction (SS). The constructs succeeded in explaining usage intentions towards ICT among students and their satisfaction from this usage.

The Sixth Generation network (6G) can support autonomous driving along with various vehicular applications like Vehicular Edge Computing (VEC), a distributed computing architecture for connected autonomous vehicles. Computational offloading and resource management of Vehicular Edge Computing can help sort out some issues, such as high communication costs, privacy protection, an excessively long training process, etc., by proposing an efficient training model of the Federated Learning for computational offloading and resource management in a vehicular environment. Two research issues are highlighted in this paper. One problem is related to the current offloading system: the smart structure and operating system. Consistent access to cloud computing services, regardless of the installed operating system or used hardware, is still challenging. Another issue is related to security and privacy. Security and privacy are two important features that should be maintained in cloud data centers and data transmission during offloading and resource management. In this survey paper, a system is going to be proposed which will give a partial solution for these issues. The proposed solution, which is found while conducting this review, offers a system that can train a model and help update the edge devices’ information. The entire edge cloud system can provide updated information for edge devices and can solve the difficulties of getting some key information necessary for model-related optimization. This also can enhance the effectiveness of the frameworks of the 6G-V2X network for communication.

Regular commutes to work can cause chronic stress, which in turn can cause a physical and emotional reaction. The recognition of mental stress in its earliest stages is very necessary for effective clinical treatment. This study investigated the impact of commuting on human health based on qualitative and quantitative measures. The quantitative measures included electroencephalography (EEG) and blood pressure (BP), as well as weather temperature, while qualitative measures were established from the PANAS questionnaire, and included age, height, medication, alcohol status, weight, and smoking status. This study recruited 45 (n) healthy adults, including 18 female and 27 male participants. The modes of commute were bus (n = 8), driving (n = 6), cycling (n = 7), train (n = 9), tube (n = 13), and both bus and train (n = 2). The participants wore non-invasive wearable biosensor technology to measure EEG and blood pressure during their morning commute for 5 days in a row. A correlation analysis was applied to find the significant features associated with stress, as measured by a reduction in positive ratings in the PANAS. This study created a prediction model using random forest, support vector machine, naive Bayes, and K-nearest neighbor. The research results show that blood pressure and EEG beta waves were significantly increased, and the positive PANAS rating decreased from 34.73 to 28.60. The experiments revealed that measured systolic blood pressure was higher post commute than before the commute. For EEG waves, the model shows that the EEG beta low power exceeded alpha low power after the commute. Having a fusion of several modified decision trees within the random forest helped increase the performance of the developed model remarkably. Significant promising results were achieved using random forest with an accuracy of 91%, while K-nearest neighbor, support vector machine, and naive Bayes performed with an accuracy of 80%, 80%, and 73%, respectively.

The massive yearly population growth is causing hazards to spread swiftly around the world and have a detrimental impact on both human life and the world economy. By ensuring early prediction accuracy, remote sensing enters the scene to safeguard the globe against weather-related threats and natural disasters. Convolutional neural networks (CNNs), which are a reflection of deep learning, have been used more recently to reliably identify land use in remote sensing images. This work proposes a novel bottleneck residual and self-attention fusion-assisted architecture for land use recognition from remote sensing images. First, we proposed using the fast neural approach to generate cloud-effect satellite images. In neural style, we proposed a 5-layered residual block CNN to estimate the loss of neural-style images. After that, we proposed two novel architectures, named 3-layered bottleneck CNN architecture and 3-layered bottleneck self-attention CNN architecture, for the classification of land use images. Training has been conducted on both proposed and original neural-style generated datasets for both architectures. Subsequently, features are extracted from the deep layers and merged employing an innovative serial approach based on weighted entropy. By removing redundant and superfluous data, a novel chimp optimization technique is applied to the fused features in order to further refine them. In conclusion, selected features are classified using the help of neural network classifiers. The experimental procedure yielded respective accuracy rates of 99.0% and 99.4% when applied to both datasets. When evaluated in comparison to state-of-the-art methods, the outcomes generated by the proposed framework demonstrated enhanced precision and accuracy.