A comprehensive secure system enabling healthcare 5.0 using federated learning, intrusion detection and blockchain.

Recently, the use of the Internet of Medical Things (IoMT) has gained popularity across various sections of the health sector. The historical security risks of IoMT devices themselves and the data flowing from them are major concerns. Deploying many devices, sensors, services, and networks that connect the IoMT systems is gaining popularity. This study focuses on identifying the use of blockchain in innovative healthcare units empowered by federated learning. A collective use of blockchain with intrusion detection management (IDM) is beneficial to detect and prevent malicious activity across the storage nodes. Data accumulated at a centralized storage node is analyzed with the help of machine learning algorithms to diagnose disease and allow appropriate medication to be prescribed by a medical healthcare professional. The model proposed in this study focuses on the effective use of such models for healthcare monitoring. The amalgamation of federated learning and the proposed model makes it possible to reach 93.89 percent accuracy for disease analysis and addiction. Further, intrusion detection ensures a success rate of 97.13 percent in this study.

State-of-the-Art Research in Blockchain of Things for HealthCare.

Existing blockchain approaches exhibit a diverse set of dimensions, and on the other hand, IoT-based health care applications manifest a wide variety of requirements. The state-of-the-art analysis of blockchain concerning existing IoT-based approaches for the healthcare domain has been investigated to a limited extend. The purpose of this survey paper is to analyze current state-of-the-art blockchain work in several IoT disciplines, with a focus on the health sector. This study also attempts to demonstrate the prospective use of blockchain in healthcare, as well as the obstacles and future paths of blockchain development. Furthermore, the fundamentals of blockchain have been thoroughly explained to appeal to a diverse audience. On the contrary, we analyzed state-of-the-art studies from several IoT disciplines for eHealth, and also the study deficit but also the obstacles when considering blockchain to IoT, which are highlighted and explored in the paper with suggested alternatives.

Developing a blockchain-based digitally secured model for the educational sector in Saudi Arabia toward digital transformation.

New universities and educational organizations are increasing in Saudi Arabia with the increase in the need for high-quality education. This increased the need for a fast transformation to digitise the educational system in Saudi Arabia, which is one of the important pillars of the Saudi Vision 2030. The students who study in these organizations suffer the verification of academic records and other educational documents. Students who want to study at universities abroad also face the challenge of academic records and certificates verification. A secure, fast, and transparent model is required in the education sector in order to verify academic certificates issued by various educational organizations. Blockchain technology can be used with high data security to empower the educational sector of Saudi Arabia in the digital transformation and to help the educational organizations in verifying academic documents. In order to avoid any document fraud and forgery, along with the ease of verification of academic records and educational documents for the students. This research focuses on developing a model which will be helpful in achieving digital transformation in academic document verification by blockchain technology.

A machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets.

Social media platforms such as Twitter, YouTube, Instagram and Facebook are leading sources of large datasets nowadays. Twitter's data is one of the most reliable due to its privacy policy. Tweets have been used for sentiment analysis and to identify meaningful information within the dataset. Our study focused on the distance learning domain in Saudi Arabia by analyzing Arabic tweets about distance learning. This work proposes a model for analyzing people's feedback using a Twitter dataset in the distance learning domain. The proposed model is based on the Apache Spark product to manage the large dataset. The proposed model uses the Twitter API to get the tweets as raw data. These tweets were stored in the Apache Spark server. A regex-based technique for preprocessing removed retweets, links, hashtags, English words and numbers, usernames, and emojis from the dataset. After that, a Logistic-based Regression model was trained on the pre-processed data. This Logistic Regression model, from the field of machine learning, was used to predict the sentiment inside the tweets. Finally, a Flask application was built for sentiment analysis of the Arabic tweets. The proposed model gives better results when compared to various applied techniques. The proposed model is evaluated on test data to calculate Accuracy, F1 Score, Precision, and Recall, obtaining scores of 91%, 90%, 90%, and 89%, respectively.

Impact analysis of keyword extraction using contextual word embedding.

A document's keywords provide high-level descriptions of the content that summarize the document's central themes, concepts, ideas, or arguments. These descriptive phrases make it easier for algorithms to find relevant information quickly and efficiently. It plays a vital role in document processing, such as indexing, classification, clustering, and summarization. Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part. According to contemporary technological breakthroughs, contextual information is critical in deciding the semantics of the work at hand. Similarly, context-based features may be beneficial in the job of keyword extraction. For example, simply indicating the previous or next word of the phrase of interest might be used to describe the context of a phrase. This research presents several experiments to validate that context-based key extraction is significant compared to traditional methods. Additionally, the KeyBERT proposed methodology also results in improved results. The proposed work relies on identifying a group of important words or phrases from the document's content that can reflect the authors' main ideas, concepts, or arguments. It also uses contextual word embedding to extract keywords. Finally, the findings are compared to those obtained using older approaches such as Text Rank, Rake, Gensim, Yake, and TF-IDF. The Journals of Universal Computer (JUCS) dataset was employed in our research. Only data from abstracts were used to produce keywords for the research article, and the KeyBERT model outperformed traditional approaches in producing similar keywords to the authors' provided keywords. The average similarity of our approach with author-assigned keywords is 51%.

Assistive technology acceptance for visually impaired individuals: a case study of students in Saudi Arabia.

Assistive technology (AT) helps students who suffer from visual impairments to achieve their study goals; however, AT's adoption in Saudi universities is not yet explored. This paper adopts and then extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to incorporate factors influencing the AT's acceptance based on a designed survey. The survey data was analyzed using Structural Equational Modelling (SEM) with the Partial Least Squares (PLS) technique. The results showed that the factors influencing technology acceptance in this context differed from those previously found to influence acceptance in other contexts. The differences were further studied using post-interview, which shows that the differences are related to limited awareness of visual disability and AT and psychological sensitivity of disabled users in Saudi culture. Moreover, this study provides a list of recommendations for overcoming barriers that limit the acceptance of assistive techniques by Saudi students with visual disabilities. This work's results provide recommendations for the Saudi government and administrators concerning access to assistive technology in universities and facilitate access to other technologies and other contexts.