In recent years, neurological diseases have become a standout amongst all the other diseases and are the most important reasons for mortality and morbidity all over the world. The current study’s aim is to conduct a pilot study for testing the prototype of the designed glove-wearable technology that could detect and analyze the heart rate and EEG for better management and avoiding stroke consequences. The qualitative, clinical experimental method of assessment was explored by incorporating use of an IoT-based real-time assessing medical glove that was designed using heart rate-based and EEG-based sensors. We conducted structured interviews with 90 patients, and the results of the interviews were analyzed by using the Barthel index and were grouped accordingly. Overall, the proportion of patients who followed proper daily heart rate recording behavior went from 46.9% in the first month of the trial to 78.2% after 3–10 months of the interventions. Meanwhile, the percentage of individuals having an irregular heart rate fell from 19.5% in the first month of the trial to 9.1% after 3–10 months of intervention research. In T5, we found that delta relative power decreased by 12.1% and 5.8% compared with baseline at 3 and at 6 months and an average increase was 24.3 ± 0.08. Beta-1 remained relatively steady, while theta relative power grew by 7% and alpha relative power increased by 31%. The T1 hemisphere had greater mean values of delta and theta relative power than the T5 hemisphere. For alpha ( < 0.05) and beta relative power, the opposite pattern was seen. The distinction was statistically significant for delta ( < 0.001), alpha ( < 0.01), and beta-1 ( < 0.05) among T1 and T5 patient groups. In conclusion, our single center-based study found that such IoT-based real-time medical monitoring devices significantly reduce the complexity of real-time monitoring and data acquisition processes for a healthcare provider and thus provide better healthcare management. The emergence of significant risks and controlling mechanisms can be improved by boosting the awareness. Furthermore, it identifies the high-risk factors besides facilitating the prevention of strokes. The EEG-based brain-computer interface has a promising future in upcoming years to avert DALY.

The quality of service (QoS) in 5G/6G communication enormously depends upon the mobility and agility of the network architecture. An increase in the possible uses of 5G vehicular network simultaneously expands the scope of the network’s quality of service (QoS). To this end, a safety-critical real-time system has become one of the most demanding criteria for the vehicular network. Although different mathematical and computation methods have traditionally been used to optimize the allocation of resources, but the nonconvexity of optimization issues creates unique type of challenges. In recent years, machine learning (ML) has emerged as a valuable tool for dealing with computational complexity that involves large amounts of data in heterogeneous vehicular networks. By using optimization and cutting-edge machine learning techniques, this article gives readers an insight about how 5G vehicular network resources can be allocated to reinforce network communication. Furthermore, a new federated deep reinforcement learning- (FDRL-) based vehicle communication method is presented as a new insight. Finally, a UAV-aided vehicular communication system based on FDRL-based UAVs is proposed as a novel resource management technique to optimize 5G and 6G quality of services.

Wireless sensor networks (WSNs) based on the Internet of Things (IoT) are now one of the most prominent wireless sensor communication technologies. WSNs are often developed for particular applications such as monitoring or tracking in either indoor or outdoor environments, where battery power is a critical consideration. To overcome this issue, several routing approaches have been presented in recent years. Nonetheless, the extension of the network lifetime in light of the sensor capabilities remains an open subject. In this research, a CUCKOO-ANN based optimization technique is applied to obtain a more efficient and dependable energy efficient solution in IoT-WSN. The proposed method uses time constraints to minimize the distance between sources and sink with the objective of a low-cost path. Using the property of CUCKOO method for solving nonlinear problem and utilizing the ANN parallel handling capability, this method is formulated. The resented model holds significant promise since it reduces average execution time, has a high potential for enhancing data centre energy efficiency, and can effectively meet customer service level agreements. By considering the mobility of the nodes, the technique outperformed with an efficiency of 98% compared with other methods. The MATLAB software is used to simulate the proposed model.

(1) Background: Nowadays, technological advancement plays a key role in the economic uplift of developing countries, and it is paramount that the youth of these countries are well-equipped with both vocational and technical skills to bring about sustainability in their careers. (2) Methods: The present study aims to investigate the perception gap between vocational students and industrial employers with regard to employability skills. In this regard, data were collected from 683 vocational students from urban/rural areas studying in the public/private vocational institutions of a developing country. Similarly, 82 vocational employers were contacted about their perception of employability skills. A questionnaire comprising 20 questions was administered online to both students and employers for registering their responses. Data were analyzed with the help of descriptive statistics, interval plots, and an Ishikawa diagram. (3) Results: The findings reveal that vocational employers give maximum importance to interpersonal, technical, and entrepreneurial skills, while students perceive that their technical and computing skills are vital for sustainable careers. Similarly, the rural/public students showed minimum levels of competency in these skills as compared with their counterparts living in rural areas and studying in the private institutions. Furthermore, structured interviews were conducted with the concerned stakeholders to develop a remedial framework for the sustainable careers of these vocational students. (4) Conclusions: The results of the study not only bring sustainability to the vocational sector of developing countries, but to all those countries enjoying similar socioeconomic backgrounds.

This research aims to assess the impact of campus culture on the satisfaction level of engineering students. Five latent variables, namely, respect, creativity, support, extracurricular activity, and individual responsibility, were used to examine the impact of campus culture. The data were collected with the help of an online questionnaire, which was distributed among male and female students. Subsequently, 304 responses were collected, which were analyzed using descriptive statistics, multicollinearity, and structural equation modeling (SEM) via AMOS 23 software. Internal consistency and reliability of each construct were done through Cronbach’s alpha, while confirmatory factor analysis was used for the validation of results. The findings of the metaheuristics-based research indicate that the campus culture has a positive impact on the satisfaction level of students (r = 0.774, ). Improving these factors reciprocally increases the satisfaction level, besides raising the education quality. This research can provide a gateway for educational leaders in focusing their resources for the best satisfaction of their students.