A Hybrid Algorithm for Improving the Quality of Service in MANET

A mobile ad-hoc network (MANET) exhibits a dynamic topology with flexible infrastructure. The MANET nodes may serve as both host and router functionalities. The routing feature of the MANET is a stand-alone multi-hop mobile network that can be utilized in many real-time applications. Therefore, identifying paths that ensure high Quality of Service (QoS), such as their topology and applications is a vital issue in MANET. A QoS-aware protocol in MANETs aims to find more efficient paths between the source and destination nodes of the network and, hence, the requirements of the QoS. This paper proposes a different hybrid algorithm that combines Cellular Automata (CA) with the African Buffalo Optimization (ABO), CAABO, to improve the QoS of MANETs. The CAABO optimizes the path selection in the ad-hoc on-demand distance vector (AODV) routing protocol. The test results show that with the aid of the CAABO, the AODV manifests energy and delay-aware routing protocol

A conceptual privacy framework for privacy-aware IoT health applications

Internet of things (IoT) is intensely gaining reputation due to its necessity and efficiency in the computer realm.The support of wireless connectivity as well as the emergence of gadgets alleviates its usage essentially in governing systems in various fields.Though these systems are ubiquitous, pervasive and seamless, an issue concerning consumers’ privacy remains debatable. This is most evident in the health sector, as there is an immaculate rise in terms of awareness amongst patients where data privacy is concerned. In this paper, we propose a framework modelling the privacy requirements for IoT-based health applications.We have reviewed several privacy frameworks to derive at the essential principles required to develop privacy-aware IoT health applications.The proposed framework presents important privacy requirements to be addressed in the development of novel IoT health applications

A conceptual privacy framework for privacy-aware IoT health applications

Internet of things (IoT) is intensely gaining reputation due to its necessity and efficiency in the computer realm.The support of wireless connectivity as well as the emergence of gadgets alleviates its usage essentially in governing systems in various fields.Though these systems are ubiquitous, pervasive and seamless, an issue concerning consumers’ privacy remains debatable. This is most evident in the health sector, as there is an immaculate rise in terms of awareness amongst patients where data privacy is concerned. In this paper, we propose a framework modelling the privacy requirements for IoT-based health applications.We have reviewed several privacy frameworks to derive at the essential principles required to develop privacy-aware IoT health applications.The proposed framework presents important privacy requirements to be addressed in the development of novel IoT health applications

A Fuzzy Case-Based Reasoning Model for Software Requirements Specifications Quality Assessment

Different software Quality Assurance (SQA) audit techniques are applied in the literature to determine whether the required standards and procedures within the Software Requirements Specification (SRS) phase are adhered to. The inspection of the Software Requirements Specification (iSRS) system is an analytical assurance tool which is proposed to strengthen the ability to scrutinize how to optimally create high-quality SRSs. The iSRS utilizes a Case-Based Reasoning (CBR) model in carrying out the SRS quality analysis based on the experience of the previously analyzed cases. This paper presents the contribution of integrating fuzzy Logic technique in the CBR steps to form a Fuzzy Case-Based Reasoning (FCBR) model for improving the reasoning and accuracy of the iSRS system. Additionally, for efficient cases retrieval in the CBR, relevant cases selection and nearest cases selection heuristic search algorithms are used in the system. Basically, the input to the relevant cases algorithm is the available cases in the system case base and the output is the relevant cases. The input to the nearest cases algorithm is the relevant cases and the output is the nearest cases. The fuzzy Logic technique works on the selected nearest cases and it utilizes similarity measurement methods to classify the cases into no-match, partial-match and complete-match cases. The features matching results assist the revised step of the CBR to generate a new solution. The implementation of the new FCBR model shows that converting numerical representation to qualitative terms simplifies the matching process and improves the decision-making of the system

A systematic review of mobile health adoption factors for Iraqi Healthcare Institutions

The advancement of new technologies, particularly information technology (IT), has a significant influence on healthcare as well as the quality of life. Mobile health is becoming increasingly important in healthcare. However, previous research on this area has been primarily anecdotal, scattered, and speculative. This study includes a comprehensive evaluation of mobile health implementations worldwide, as well as reporting on results such as difficulties, variables and advantages connected with mobile health adoption. However, as described in the literature, the adoption of this sophisticated innovation is a challenging undertaking; hence, careful thought and preparation to all critical elements that impact the adoption process through stakeholders is essential. The purpose of this research is to assess the factors that impact the adoption of mobile health frameworks in healthcare organizations. The study employed a non-experimental research exploratory research design. This exploratory study includes an important secondary data inquiry. The creation of an investigation and the modeling using secondary data in order to emphasize the research's ultimate conclusions. Through a review of the literature on existing frameworks for mobile health adoption, it was discovered that healthcare institutions in Iraq require ongoing attention in order to obtain government support

Performance Evaluation of Ad-Hoc On-Demand Distance Vector and Optimized Link State Routing Protocols in Mobile Ad-Hoc Networks

Mobile Ad-hoc Networks (MANETs) are self-sufficient networks that can work without the need for centralized controls, pre-configuration to the routes or advance infrastructures. The nodes of a MANET are autonomously controlled, which allow them to act freely in a random manner within the MANET. The nodes can leave their MANET and join other MANETs at any time. These characteristics, however, might negatively affect the performance of the routing protocols and the overall topology of the networks. Subsequently, MANETs comprise specially designed routing protocols that reactively and/or proactively perform the routing. This paper evaluates and compares the performance of two routing protocols which are Ad-Hoc On-Demand Distance Vector (AODV) and Optimized Link State Routing (OLSR) in MANET environment. The study includes implementing a simulation to examine the performance of the routing protocols based on the variables of the nodes’ number and network size. The evaluation results show that the AODV outperforms the OLSR in most of the simulated cases. The results further show that the number of nodes and network size have a great impact on the Throughput (TH), Packet Delivery Ratio (PDR), and End-to-End delay (E2E) of the network

A hybrid algorithm for improving the quality of service in MANET

A mobile ad-hoc network (MANET) exhibits a dynamic topology with flexible infrastructure. The MANET nodes may serve as both host and router functionalities. The routing feature of the MANET is a stand-alone multi-hop mobile network that can be utilized in many real-time applications. Therefore, identifying paths that ensure high Quality of Service (QoS), such as their topology and applications is a vital issue in MANET. A QoS-aware protocol in MANETs aims to find more efficient paths between the source and destination nodes of the network and, hence, the requirements of the QoS. This paper proposes a different hybrid algorithm that combines Cellular Automata (CA) with the African Buffalo Optimization (ABO), CAABO, to improve the QoS of MANETs. The CAABO optimizes the path selection in the ad-hoc on-demand distance vector (AODV) routing protocol. The test results show that with the aid of the CAABO, the AODV manifests energy and delay-aware routing protocol

An Automated Image Segmentation and Useful Feature Extraction Algorithm for Retinal Blood Vessels in Fundus Images

The manual segmentation of the blood vessels in retinal images has numerous limitations. It is very time consuming and prone to human error, particularly with a very twisted structure of the blood vessel and a vast number of retinal images that needs to be analysed. Therefore, an automatic algorithm for segmenting and extracting useful clinical features from the retinal blood vessels is critical to help ophthalmologists and eye specialists to diagnose different retinal diseases and to assess early treatment. An accurate, rapid, and fully automatic blood vessel segmentation and clinical features measurement algorithm for retinal fundus images is proposed to improve the diagnosis precision and decrease the workload of the ophthalmologists. The main pipeline of the proposed algorithm is composed of two essential stages: image segmentation and clinical features extraction stage. Several comprehensive experiments were carried out to assess the performance of the developed fully automated segmentation algorithm in detecting the retinal blood vessels using two extremely challenging fundus images datasets, named the DRIVE and HRF. Initially, the accuracy of the proposed algorithm was evaluated in terms of adequately detecting the retinal blood vessels. In these experiments, five quantitative performances were measured and calculated to validate the efficiency of the proposed algorithm, which consist of the Acc., Sen., Spe., PPV, and NPV measures compared with current state-of-the-art vessel segmentation approaches on the DRIVE dataset. The results obtained showed a significantly improvement by achieving an Acc., Sen., Spe., PPV, and NPV of 99.55%, 99.93%, 99.09%, 93.45%, and 98.89, respectively

An Automated Image Segmentation and Useful Feature Extraction Algorithm for Retinal Blood Vessels in Fundus Images

The manual segmentation of the blood vessels in retinal images has numerous limitations. It is very time consuming and prone to human error, particularly with a very twisted structure of the blood vessel and a vast number of retinal images that needs to be analysed. Therefore, an automatic algorithm for segmenting and extracting useful clinical features from the retinal blood vessels is critical to help ophthalmologists and eye specialists to diagnose different retinal diseases and to assess early treatment. An accurate, rapid, and fully automatic blood vessel segmentation and clinical features measurement algorithm for retinal fundus images is proposed to improve the diagnosis precision and decrease the workload of the ophthalmologists. The main pipeline of the proposed algorithm is composed of two essential stages: image segmentation and clinical features extraction stage. Several comprehensive experiments were carried out to assess the performance of the developed fully automated segmentation algorithm in detecting the retinal blood vessels using two extremely challenging fundus images datasets, named the DRIVE and HRF. Initially, the accuracy of the proposed algorithm was evaluated in terms of adequately detecting the retinal blood vessels. In these experiments, five quantitative performances were measured and calculated to validate the efficiency of the proposed algorithm, which consist of the Acc., Sen., Spe., PPV, and NPV measures compared with current state-of-the-art vessel segmentation approaches on the DRIVE dataset. The results obtained showed a significantly improvement by achieving an Acc., Sen., Spe., PPV, and NPV of 99.55%, 99.93%, 99.09%, 93.45%, and 98.89, respectively