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Today, most users need search engines to facilitate search and information retrieval processes. Unfortunately, traditional search engines have a significant challenge that they should retrieve high-precision results for a specific unclear query at a minimum response time. Also, a traditional search engine cannot expand a small, ambiguous query based on the meaning of each keyword and their semantic relationship. Therefore, this paper proposes a comprehensive search engine framework that combines the benefits of both a keyword-based and a semantic ontology-based search engine. The main contributions of this work are developing an algorithm for ranking results based on fuzzy membership value and a mathematical model of exploring a semantic relationship between different keywords. In the conducting experiments, eight different test cases were implemented to evaluate the proposed system. Executed test cases have achieved a precision rate of 97% with appropriate response time compared to the relevant systems.

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In developed countries, age-related macular degeneration (AMD), a retinal disease, is the main cause of vision loss in the elderly. Optical Coherence Tomography (OCT) is currently the gold standard for assessing individuals for initial AMD diagnosis. In this paper, we look at how OCT imaging can be used to diagnose AMD. Our main aim is to examine and compare automated computer-aided diagnostic (CAD) systems for diagnosing and grading of AMD. We provide a brief summary, outlining the main aspects of performance assessment and providing a basis for current research in AMD diagnosis. As a result, the only viable alternative is to prevent AMD and stop both this devastating eye condition and unwanted visual impairment. On the other hand, the grading of AMD is very important in order to detect early AMD and prevent patients from reaching advanced AMD disease. In light of this, we explore the remaining issues with automated systems for AMD detection based on OCT imaging, as well as potential directions for diagnosis and monitoring systems based on OCT imaging and telemedicine applications.

Wireless Body Area Networks (WBANs) are increasingly employed in different medical applications, such as remote health monitoring, early detection of medical conditions, and computer-assisted rehabilitation. A WBAN connects a number of sensor nodes implanted in and/or fixed on the human body for monitoring his/her physiological characteristics. Although medical healthcare systems could significantly benefit from the advancement of WBAN technology, collecting and transmitting private physiological data in such an open environment raises serious security and privacy concerns. In this paper, we propose a novel key-agreement protocol to secure communications among sensor nodes of WBANs. The proposed protocol is based on measuring and verifying common physiological features at both sender and recipient sensors prior to communicating. Unlike existing protocols, the proposed protocol enables communicating sensors to use their previous session pre-knowledge for secure communication within a specific period of time. This will reduce the time required for establishing the shared key as well as avoid retransmitting extracted features in the medium and hence thwarting eavesdropping attacks while maintaining randomness of the key. Experimental results illustrate the superiority of the proposed key agreement protocol in terms of both feature extraction and key agreement phases with an accuracy of 99.50% and an error rate of 0.005%. The efficacy of the proposed protocol with respect to energy and memory utilization is demonstrated compared with existing key agreement protocols.