Ontology-based approach for retrieving knowledge in Al-Quran

Information retrieval relies on obtaining relevant data from a set of knowledge resources, such as Al-Quran. Searching can be based on metadata, indexing, or other content-based. Al-Quran is the most widely read book in the world and automating knowledge retrieval from this of religious literature is very challenging. This has led to the development of a number of search applications, which can retrieve knowledge based on keywords. Retrieving the knowledge of Al-Quran ontology includes several fundamental problems, one of which is the lack of accuracy. In most cases, the searching cannot retrieve the relevant concept of knowledge and verses. Current approaches use conventional methods such as taxonomy, hierarchy, or tree structure, which only provide the definition of the concept of themes without linking to the correct knowledge concept of Al-Quran. The main aim of this study is to design a method that uses the ontology approach to search and retrieve relevant verses in Al-Quran. The new approach consists of two stages. The first stage: involves the Al-Quran ontology development based on thematic classification which was implemented using Protégé-OWL. The second stage: involves the development of a search method by using the Jena framework which is based on Java programming languages. The search method allows ontology processing, and performed the searching using the given keywords and retrieve the knowledge pertaining to the keyword. The search approach was evaluated using the Recall and Precision measurements, which shows a high accuracy in retrieving the knowledge of Al-Quran. Furthermore, the ontology classification was evaluated by two experts in Islamic Studies field. This study contributes to the ease of learning and understanding Al-Quran by people of all ages

Machine learning algorithms for distributed operations in internet of things IoT

Generally, the things that have the great role in facilitating the emergence of internet-connected sensory devices can be embodied in the developments that happen in the sphere of software, hardware, and communication technologies. The internet-connected sensory devices present perceptions and measurements of data from the real world. It is suggested that nearly through 2020, the total use of internet-connected devices may reach to 25 to 50 billion. Actually, the relation between technologies and the volume of data being published is kept in one line. That is, if there is growth in the technologies, the volume of the data will be increased. Such technology, i.e. internet-connected devices, can be called as Internet of Things (IoT). Its role is to connect the real world with the cyber one. Furthermore, generating great data with velocity as its main characteristic will help in increasing the volume of IoT. To develop smart IoT applications, one can use such intelligent processing and analyzing such big data. In this paper, we tend to study the impact of implementing machine learning (ML) algorithms and methods and their efficiency in the IoT domain. As well as explore how these algorithms help in founding efficient backbone solutions to analyze and estimate the huge amounts of data that are expected to arise in the coming few years due to the rapid growth on demands for IoT based applications

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population