다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1315&context=electricalengineeringfacpub

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

Welcome to the second installment of IJBDCN. This issue contains three research papers and a comprehensive survey article on mobile ad hoc networks. We would like to publish a couple of survey or tutorial articles per year as we believe this to be of value to our readers. Many research papers are necessarily focused and do not allow for a " panoramic " view of a particular subject. This is the gap that well-written survey or tutorial papers can fill. We are also planning a guest-edited special issue for the last quarter of this year and welcome your suggestions for future special issues of the journal. This second issue starts with a paper entitled " An Approach to Solving the Surviv-able Capacitated Network Design Problem " where Sridhar and Park studied the problem of selecting links of a network to construct primary and secondary routes for transfer of commodity traffic between nodes of the network. Their technique allows the design of survivable and cost-effective networks. In the second paper, " Query Processing Strategies for Location-Dependent Information Services " , Jayaputera and Taniar propose a new approach to generate a query result for location-dependent information services (LDIS). They argue that choosing a square as the scope of a query and dividing it into four equal regions, where a user is a center point of the scope, results in a faster searching time to find targets queried and brings a bigger chance to get rare targets. In addition, by avoiding resubmitting the same queries when the query results missed, the approach reduced bandwidth use at both the server and client sides. The third paper, " Addressing SPAM E-Mail Using Hashcash " by Curran and Honan tackles a problem experienced by most of us. The authors present the Hashcash proof-of-work approach and investigate the feasibility of implementing a solution based on that mechanism along with what they called a " cocktail " of antispam measures designed to keep junk mail under control. Finally , in the last paper, entitled " MANET: Applications, Issues, and Challenges for the Future " , Dhar presents an informative survey of mobile ad hoc networks, describing the main characteristics of those networks and how they are being used, and discussing the most pressing issues and challenges associated with these temporary wireless networks. We hope that you enjoy this and the upcoming issues of IJBDCN. Our goal is to …

International Journal of Business Data Communications and Networking

Designing reliable user authentication on mobile phones is becoming an increasingly important task to protect users' private information and data. Since biometric approaches can provide many advantages over the traditional authentication methods, they have become a significant topic for both academia and industry. The major goal of biometric user authentication is to authenticate legitimate users and identify impostors based on physiological and behavioral characteristics. In this paper, we survey the development of existing biometric authentication techniques on mobile phones, particularly on touch-enabled devices, with reference to 11 biometric approaches (five physiological and six behavioral). We present a taxonomy of existing efforts regarding biometric authentication on mobile phones and analyze their feasibility of deployment on touch-enabled mobile phones. In addition, we systematically characterize a generic biometric authentication system with eight potential attack points and survey practical attacks and potential countermeasures on mobile phones. Moreover, we propose a framework for establishing a reliable authentication mechanism through implementing a multimodal biometric user authentication in an appropriate way. Experimental results are presented to validate this framework using touch dynamics, and the results show that multimodal biometrics can be deployed on touch-enabled phones to significantly reduce the false rates of a single biometric system. Finally, we identify challenges and open problems in this area and suggest that touch dynamics will become a mainstream aspect in designing future user authentication on mobile phones.

Surveying the Development of Biometric User Authentication on Mobile Phones

In recent years, the Internet of Things (IoT) has been one of the most popular technologies that facilitate new interactions among things and humans to enhance the quality of life. With the rapid development of IoT, the fog computing paradigm is emerging as an attractive solution for processing the data of IoT applications. In the fog environment, IoT applications are executed by the intermediate computing nodes in the fog, as well as the physical servers in cloud data centers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictability of fog environment, it necessitates the resource management issues as one of the challenging problems to be considered in the fog landscape. Despite the importance of resource management issues, to the best of our knowledge, there is not any systematic, comprehensive and detailed survey on the field of resource management approaches in the fog computing context. In this paper, we provide a systematic literature review (SLR) on the resource management approaches in fog environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and providing open issues as well. The presented taxonomy are classified into six main fields: application placement, resource scheduling, task offloading, load balancing, resource allocation, and resource provisioning. The resource management approaches are compared with each other according to the important factors such as the performance metrics, case studies, utilized techniques, and evaluation tools as well as their advantages and disadvantages are discussed.

Resource Management Approaches in Fog Computing: a Comprehensive Review

COVID-19 infection is growing in a rapid rate. Due to unavailability of specific drugs, early detection of (COVID-19) patients is essential for disease cure and control. There is a vital need to detect the disease at early stage and instantly quarantine the infected people. Many research have been going on, however, none of them introduces satisfactory results yet. In spite of its simplicity, K-Nearest Neighbor (KNN) classifier has proven high flexibility in complex classification problems. However, it can be easily trapped. In this paper, a new COVID-19 diagnose strategy is introduced, which is called COVID-19 Patients Detection Strategy (CPDS). The novelty of CPDS is concentrated in two contributions. The first is a new hybrid feature selection Methodology (HFSM), which elects the most informative features from those extracted from chest Computed Tomography (CT) images for COVID-19 patients and non COVID-19 peoples. HFSM is a hybrid methodology as it combines evidence from both wrapper and filter feature selection methods. It consists of two stages, namely; Fast Selection Stage (FS2) and Accurate Selection Stage (AS2). FS2relies on filter, while AS2 uses Genetic Algorithm (GA) as a wrapper method. As a hybrid methodology, HFSM elects the significant features for the next detection phase. The second contribution is an enhanced K-Nearest Neighbor (EKNN) classifier, which avoids the trapping problem of the traditional KNN by adding solid heuristics in choosing the neighbors of the tested item. EKNN depends on measuring the degree of both closeness and strength of each neighbor of the tested item, then elects only the qualified neighbors for classification. Accordingly, EKNN can accurately detect infected patients with the minimum time penalty based on those significant features selected by HFSM technique. Extensive experiments have been done considering the proposed detection strategy as well as recent competitive techniques on the chest CT images. Experimental results have shown that the proposed detection strategy outperforms recent techniques as it introduces the maximum accuracy rate.

A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

Gene expression microarray classification is a crucial research field as it has been employed in cancer prediction and diagnosis systems. Gene expression data are composed of dozens of samples characterized by thousands of genes. Hence, an accurate and effective classification of such samples is a challenge. Machine learning techniques have been broadly utilized to build substantial and precise classification models. This paper proposes a new classification technique for gene expression data, which is called Modified k-nearest neighbor (MKNN). MKNN is applied in two scenarios namely; smallest modified KNN (SMKNN) and largest modified KNN (LMKNN). Both implementations are undertaken to enhance the performance of KNN. The key idea is to employ robust neighbors from training data by using a new weighting strategy. Several experiments have been performed on six different gene expression datasets. Experiments have shown that MKNN in its both scenarios outperforms traditional as well as recent ones. MKNN has been compared against (i) KNN, (ii) weighted KNN, (iii) support vector machine (SVM), (iv) fuzzy support vector machine, (v) brain emotional learning (BEL) in terms of classification accuracy, precision, and recall. On the other hand, results show that MKNN introduces smaller testing time than both KNN and weighted KNN.

Gene expression cancer classification using modified K-Nearest Neighbors technique

Wireless sensor network (WSN) is a group of small power-constrained nodes that sense data and communicate it to the base station (BS). These nodes cover a vast region of interest (ROI) for several purposes according to the application need. The first challenge encountered in WSNs is how to cover the ROI perfectly and send the monitored data to the BS. Although the energy introduced during setup phase and the violation of energy fairness constraint of dynamic routing topologies, they achieve high network performance in terms of coverage and connectivity. In this paper, we categorize the applications of WSN based on different aspects to show the major protocol design issues. Thus, the energy efficiency of the recent proactive routing protocols is studied from different angles. The energy overhead and energy fairness of each protocol were carefully analyzed. The most energy efficient routing protocols for homogeneous proactive networks were studied and compared to highlight the research challenges and existing problems in this area. The results proved that energy overhead and route selection are the most effective aspects of network lifetime and network efficiency.

Survey on Wireless Sensor Network Applications and Energy Efficient Routing Protocols

Energy-efficient routing protocols for solving energy hole problem in wireless sensor networks

Fog computing (FC) can be considered as a computing paradigm which performs Internet of Things (IoT) applications at the edge of the network. Recently, there is a great growth of data requests and FC which lead to enhance data accessibility and adaptability. However, FC has been exposed to many challenges as load balancing (LB) and adaptation to failure. Many LB strategies have been proposed in cloud computing, but they are still not applied effectively in fog. LB is an important issue to achieve high resource utilization, avoid bottlenecks, avoid overload and low load, and reduce response time. In this paper, a LB and optimization strategy (LBOS) using dynamic resource allocation method based on Reinforcement learning and genetic algorithm is proposed. LBOS monitors the traffic in the network continuously, collects the information about each server load, handles the incoming requests, and distributes them between the available servers equally using dynamic resource allocation method. Hence, it enhances the performance even when it’s the peak time. Accordingly, LBOS is simple and efficient in real-time systems in fog computing such as in the case of healthcare system. LBOS is concerned with designing an IoT-Fog based healthcare system. The proposed IoT-Fog system consists of three layers, namely: (1) IoT layer, (2) fog layer, and (3) cloud layer. Finally, the experiments are carried out and the results show that the proposed solution improves the quality-of-service in the cloud/fog computing environment in terms of the allocation cost and reduce the response time. Comparing the LBOS with the state-of-the-art algorithms, it achieved the best load balancing Level (85.71%). Hence, LBOS is an efficient way to establish the resource utilization and ensure the continuous service.

Ahmed I. Saleh

Papers

A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

Gene expression cancer classification using modified K-Nearest Neighbors technique

Survey on Wireless Sensor Network Applications and Energy Efficient Routing Protocols

Energy-efficient routing protocols for solving energy hole problem in wireless sensor networks

A load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment