Background: In a variety of medical specialty, Telemedicine is extensively applied. Nevertheless, it is not evident whether this approach will be critical once COVID-19 is endemic. In this research, we have discussed the opinions of physicians and patients regarding the use of telemedicine. Methods: The anonymous electronic questionnaire was sent to forwarded to physicians working in the colleges of Medicine at the University of Mosul and the University of Nineveh plus to physicians working in the public and private hospitals and also in the private clinics in Mosul randomly without regard to their specialty. The survey contained 18 questions of the feasibility and acceptability of video consultations, ease of use, quality, and other issues. The evaluation of most of the items was on five-point Likert scale, but the perceived benefits and future expectations were open-ended. The survey of patients was also carried out through an electronic connection that was distributed randomly and included 14 questions on the willingness to undergo a video consultation, ease of use, benefits, difficulties, the mode of choice of consultation and the overall satisfaction measured on the same scale. Results: 114 physicians and 142 patients were involved. Video consultations were positively received by patients, with the reportedly acceptable communication and patient satisfaction, although there were some fears about technical difficulties and privacy. physicians were more reserved, indicating that they did not feel comfortable with virtual communication, decreased trust with remote clinical evaluation, and significant doubts about the lack of physical examination and laboratory results. The questionnaire had a satisfactory degree of reliability (Cronbach alpha = 0.736). There were considerable differences in a number of physician perceptions, but the perception of patients in different groups did not exhibit significant differences. Conclusion: The physicians and patients found telemedicine to be an appropriate and viable mode of care provision and reception. Technical problem was the key to the successful use.
With the rapid growth of the Internet of Things (IoT), ever greater quantities of complex data are created. To analyze this data and take better decisions, Artificial Intelligence (AI) especially in the form of Machine Learning (ML) and Deep Learning (DL) has been widely adopted, which has led to the rise of Artificial Intelligence of Things (AIoT). This study is a systematic literature review consisting of 79 peer-reviewed articles published in the last five years (from 2019 to 2024), following the PRISMA guidelines. We thoroughly searched major databases, and used strict quality assessment and inclusion/exclusion criteria for assessing the influence of the AI in five fields, namely, Smart Home, Smart Transportation, Smart Education, Smart Grid, and Robotics. Based on our quantitative results, we notice that the most studied domain is the Smart Transportation, and the most used technologies are recurrent models such as LSTM and convolutional networks (CNN). The review compares the performance of the models critically, by considering the evaluation metrics including accuracy, latency, and computational cost. Moreover, the paper discusses key challenges and limitations in the existing AIoT deployments such as data privacy, cybersecurity threats, power consumption and explainability of the models. This review aims to fill the gap of literature by providing a structured reference towards future research on scalable, secure and energy-efficient AIoT systems.
The offline voice control systems are becoming more prominent in the smart home environment because of their ability to deliver quick feedback, preserve the privacy of the user, and be able to function even without network connectivity. However, their usage has not been mainstreamed, due to restrictions on vocabulary, resistance to noise, and hardware performance. In this paper, the authors attempt to provide a critical assessment of the recent developments in offline voice control technology in smart homes. On devices with limited resources, it evaluates a variety of datasets, frameworks, embedded platforms, and algorithmic enhancements. Moreover, the paper examines the issues linked to the growth of vocabulary, audio variability, and privacy, as well as interoperability and compares offline and online paradigms in terms of a more detailed analytical framework. Among the emerging trends highlighted include lightweight keyword spotting, embedded spoken language understanding, neural processing units, and on-device personalization, as being crucial in the evolution of future systems. This paper summarizes the current situation and outlines the future trends of the offline voice control technologies as it is applicable to smart home usage by synthesizing recent achievements and pinpointing the current limitations of the technology.
As we know, cloud computing has become an increasingly popular solution to deliver scalable and effective solutions to the needs of today's modern applications. One of the greatest problems in this context is the efficient allocation of resources to the different needs of numerous users and applications. Reinforcement learning (RL) is an approach that can adapt dynamically to changing environments and is a promising solution. In this paper, the SARSA (state action reward state action) RL algorithm is combined with the whale optimization algorithm (WOA) to propose a new resource allocation approach for cloud computing based on a multi objective optimization. We are going to refer to this method as MO SAWOA. It assists the SARSA algorithm to faster converge and prevents it from being trapped in the local optima. The proposed system handles complex multi objective resource allocation issues by improving load balancing, reducing makespan, saving costs, and enhancing user experience in dynamic cloud environments. It can also be used for real time systems (RTS) like public clouds like Amazon Web Services (AWS).
Social media has modernized advocacy by enabling individuals to mobilize, spread opinions, and champion justice globally. Accurate prediction of social media advocacy campaign success can play a significant role in promoting social and political change. Motivated by the recent #JusticeForNavalAbbas advocacy campaign in Nigeria, this study aims to predict the success rate of posts related to the campaign. By examining how social media platforms including Facebook, TikTok, Instagram, and X (formerly known as Twitter) influence public opinion, this paper investigates how these platforms attract engagement, amplify the voices of the marginalized, and encourage collective action. The present research leverages five machine learning techniques, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost (XGB). The dataset was preprocessed and split into training and testing subsets comprising 75% and 25% of the data, respectively. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and Area Under the ROC Curve (AUC). RF and XGB demonstrated exceptional performance in predicting social media post success, achieving accuracies of 98% and 97%, respectively. The findings highlight the significance of social media platforms in amplifying advocacy campaigns at both local and global levels.
