Covid-19 is an acute respiratory infection and presents various clinical features ranging from no symptoms to severe pneumonia and death. Medical expert systems, especially in diagnosis and monitoring stages, can give positive consequences in the struggle against Covid-19. In this study, a rule-based expert system is designed as a predictive tool in self-pre-diagnosis of Covid-19. The potential users are smartphone users, healthcare experts and government health authorities. The system does not only share the data gathered from the users with experts, but also analyzes the symptom data as a diagnostic assistant to predict possible Covid-19 risk. To do this, a user needs to fill out a patient examination card that conducts an online Covid-19 diagnostic test, to receive an unconfirmed online test prediction result and a set of precautionary and supportive action suggestions. The system was tested for 169 positive cases. The results produced by the system were compared with the real PCR test results for the same cases. For patients with certain symptomatic findings, there was no significant difference found between the results of the system and the confirmed test results with PCR test. Furthermore, a set of suitable suggestions produced by the system were compared with the written suggestions of a collaborated health expert. The suggestions deduced and the written suggestions of the health expert were similar and the system suggestions in line with suggestions of the expert. The system can be suitable for diagnosing and monitoring of positive cases in the areas other than clinics and hospitals during the Covid-19 pandemic. The results of the case studies are promising, and it demonstrates the applicability, effectiveness, and efficiency of the proposed approach in all communities.

This study investigates reverse image search performance of Google, in terms of Average Precisions (APs) at various cut-off points, on finding out similar images by using fresh Image Queries (IQs) from the five categories "Fashion", "Computer", "Home", "Sports", and "Toys", in order to have an insight about reverse image search performance of Google and then, motivate the researchers and inform the users. Five fresh IQs with different main concepts were created for each of the five categories. These 25 IQs were run on the search engine and for each, the first 100 images retrieved were evaluated with binary relevance judgment. APs at the cut-off points 20, 40, 60, 80, and 100 were calculated for each category and for all 25 IQs. The performance range is from ~42% for Toys category at the cut-off point 100 to 71% for Home category at the cut-off point 20. When the categories are ignored, Google's performance range is from ~52% at the cut-off point 100 to ~57% at the cut-off point 20. It seems that reverse image search performance of Google needs to be improved.

Ontology-

[Abstract Not Available]

Abstract Health 3.0 is a health‐related extension of the Web 3.0 concept. It is based on the semantic Web which provides for semantically organizing electronic health records of individuals. Health 3.0 is rapidly gaining ground as a new research topic in many academic and industrial disciplines. Due to the recent rapid spread of wearable sensors and smart devices with access to social media, migrating health services from the traditional centre‐based health system to personal health care is inevitable. In this current era of greater personalization, treating patients' health problems according to their profile and medical data gathered is possible using the latest information technologies. Consequently, personalized health recommender systems have gained importance. Empowering the utility of advanced Web technology in personalized health systems is still challenging due to pressing issues, such as lack of low cost and accurate smart medical sensors and wearable devices, existing investment in legacy Web system architecture in health sector, heterogeneity of medical data gathered by myriad health care institutions and isolated health services, and interoperability issues as well as multi‐dimensionality of medical data. By tracing recent developments, this paper offers a systematic review through recent research on semantic Web‐enabled personalized health systems, namely, semanticized personalized health recommender systems with the key enabling technologies, major applications, and successful case studies. Critical questions derived from the research studies were discussed, and main directions of open issues were identified leading to recommendations for future study in the field of personalized health recommender systems.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Obesity is a public health problem that has become widespread worldwide. Obesity may increase the risk for many health problems such as early puberty, Type 2 diabetes, certain types of cancer, high blood pressure, heart diseases, and sleep apnea. The detection of obesity in children and adolescents at early stages is important and it is crucial to start individual based treatments. Due to the widespread use of smart mobile devices among family members and increasing number of m-Health applications to fight against obesity as well as other diseases, m-Health applications can be a good choice for early detection of obesity in childhood and adolescence stages. Recently, many studies about mobile-based obesity tracking system that mostly for adults have been done. This research study especially emphases obesity management in childhood and adolescence stages with the contribution of Semantic Web technology. Therefore, the system is an ontology-based obesity tracking system for children and adolescents which has its own Obesity Tracking Ontology and medical semantic rule knowledge base with an inference engine.

This article proposes an Intelligent Tracking System for Acute Respiratory Tract Infection (TrackARTI) via a smart mobile for monitoring disease term of 0-6 age group child patients remotely (e.g. home, clinics). It is possible to maximize the quality of life of the child patients and decrease parental anxiety by keeping the child under control during monitoring stage and achieve a proper distant diagnose by the patient's clinician. This is possible with the designs of intelligent M-Health systems that can be used for diagnosing and monitoring the child patients away from hospitals by presenting the instant medical data to their registered doctors. Intelligent M-Health systems require strong knowledge management technology and ease of extension to provide information from additional medical tools. With the contribution of intelligent M-Health systems, it is possible to infer new facts from the certain gathered medical data during examination from child patients. This article mentions an intelligent and easy medical data gathering system that can be used by pediatricians or parents any time. In addition, the system has its own inferencing mechanism that involves two main steps, inferencing on image processing and Semantic Web rule knowledge base.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Children with pervasive developmental disorders have special needs so they should be supported with special education programs. These programs are planned according to the type and degree of the disorder, age, characteristics, and needs of the children. Families prefer Internet to search resources about related educational methods (and associated activity/game) and materials. However, syntactic search in today's Internet is insufficient because of its static base and the search results are not sufficient enough to offer desired relevant results corresponding to a child's needs. With the help of Semantic Web-based agents an intelligent system able to identify the suitable educational methods and material, will contribute to the development of children with disorders, and support education specialists in this process. In addition it will be extremely useful for the families of these children in need of assisting and monitoring their child's development. In this article, an Educational Activity Finder (EAF) is being proposed. The architecture of EAF is structured after semantic Web technology so that it can find and propose semantically relevant suitable educational methods (i.e., activities to carry out) and material for use by children with disorders. The knowledge base of EAF keeps educational methods using the OWL language. All related concepts, attributes, and relations between these concepts and features about pervasive developmental disorders are defined through ontology. EAF is structured to recognize a pervasive developmental disorder, sort out symptoms and anomalies, suggest activities, compile performance scores, and decide on new activity based on user preferences by utilizing the semantic descriptions of available methods and activities. Strength of EAF is demonstrated through sample scenarios. EAF may be used by parents, pre-school educators, primary schools, special educational institutions and experts working for these institutions, university students studying in related fields, and individuals interested in pervasive developmental disorders.

This article presents semantic-based composition of processes of Semantic Web Services using predetermined semantic descriptions of the services. Currently most proposed techniques are syntactically, rather than semantically, oriented. Our proposed method involves a semantic-based composition agent which is called Semantic Composition Agent (SCA). The novel design of SCA applies two different well-known approaches, namely Process Algebra and Armstrong Axioms, in its two major components (planner and inference engine respectively) of the process composition framework. In order to demonstrate its applicability, a prototype of SCA was implemented and tested against a number of Web services composition cases.

UDDI and WSDL do not provide sufficient grounds to find appropriate web services at the required accuracy and quality of service (QoS) level by the consumer. This article discusses a Semantic Search Agent (SSA) approach that discovers appropriate web services according to semantic description. The descriptions comprise QoS requirements and input/output information of fetched Web Services according to consumer requirements. This study considers a broker or dispatcher model which consists of a QoS broker-based scheme by a multi-class queuing model. We depicted a new system that is an extension of the SSA to find appropriate web services according to consumer request based on matching of input/output and QoS information of web services; then compose them according to consumer requirements through semantic web and ontology.

This paper shows a searching mechanism to discover Semantic Web Services satisfying client requirements. The increase in web services and lack of semantic base in search mechanisms of UDDI make it difficult for clients to find a required web service. We developed a system which uses a Semantic Search Agent (SSA) to discover required web services from web and selects them according to the client requirements then presents them as a result page. The system uses Ontology Web Language for services (OWL-S), which allows semantic description of web services and hence, the Semantic Search Agent is able to understand predefined concepts of Semantic Web Services, extract necessary information and decide on the requirement of a service for a client. The system combines complimentary aspects of two research topics (Smart Web Query Engine and Matchmaking Algorithm of OWL-S/UDDI Matchmaker) to facilitate the provision of web services to a client.