Evaluation of Pre-Trained CNN Models for Cardiovascular Disease Classification: A Benchmark Study

In this paper, we present an up-to-date benchmarking of the most commonly used pre-trained CNN models using a merged set of three available public datasets to have a large enough sample range. From the 18th century up to the present day, cardiovascular diseases, which are considered among the most significant health risks globally, have been diagnosed by the auscultation of heart sounds using a stethoscope. This method is elusive, and a highly experienced physician is required to master it. Artificial intelligence and, subsequently, machine learning are being applied to equip modern medicine with powerful tools to improve medical diagnoses. Image and audio pre-trained convolution neural network (CNN) models have been used for classifying normal and abnormal heartbeats using phonocardiogram signals. We objectively benchmark more than two dozen image-pre-trained CNN models in addition to two of the most popular audio-based pre-trained CNN models: VGGish and YAMnet, which have been developed specifically for audio classification. The experimental results have shown that audio-based models are among the best- performing models. In particular, the VGGish model had the highest average validation accuracy and average true positive rate of 87% and 85%, respectively

Evaluation of Attribute-Based Access Control (ABAC) for EHR in Fog Computing Environment

Fog computing - a connection of billions of devices nearest to the network edge- was recently proposed to support latency-sensitive and real time applications. Electronic Medical Record (EMR) systems are latency-sensitive in nature therefore fog computing considered as appropriate choice for it. This paper proposes a fog environment for E-health system that contains highly confidential information of patients Electronic Health Records (EHR). The proposed E-health system has two main goals: (1) Manage and share EHRs between multiple fog nodes and the cloud,(2) Secure access into EHR on Fog computing without effecting the performance of fog nodes. This system will serve different users based on their attributes and thus providing Attribute Based Access Control ABAC into the EHR in fog to prevent unauthorized access. We focus on reducing the storing and processes in fog nodes to support low capabilities of storage and computing of fog nodes and improve its performance. There are three major contributions in this paper first; a simulator of an E-health system is implemented using both iFogSim and our iFogSimEhealthSystem simulator. Second, the ABAC was applied at the fog to secure the access to patients EHR. Third, the performance of the proposed securing access in E-health system in fog computing was evaluated. The results showed that the performance of fog computing in the secure E-health system is higher than the performance of cloud computing

SUPER: Social-based business process management framework

© Springer International Publishing Switzerland 2015. In this demo paper, we present SUPER standing for Socialbased bUsiness Process managEment fRamework that leverages social computing principles for the design and development of social business processes (aka business processes 2.0). SUPER identifies task, person, and machine as the core components of a business process. Afterwards, SUPER establishes a set of execution and social relations to illustrate how tasks (also persons and machines) are connected together. The social relations help build configuration network of tasks, social network of persons, and support network of machines that capture the ongoing interactions during business process execution

A framework of enriching business processes life-cycle with tagging information

© Springer International Publishing Switzerland 2015. In this demonstration, we present a framework for enriching business processes with tags specialized into social, resource, location, and temporal. Using the framework, business-process engineers and end-users (i.e., executors) provide the tags with the necessary details which are then automatically propagated from one tag to another, when appropriate. At design time phase of a business process, the propagation of relations between tags reflects unidirectional-transfer-offinal-details, unidirectional-transfer-of-partial-details, and bidirectional transfer- of-partial-details while at run-time the propagation of relations reflects strong-trigger, weak-trigger, and meet-in-the-middle trigger. Our provides an elegant mechanism for monitoring business processes which is more user-driven than traditional approaches which heavily rely on log analysis mechanisms

Bentall Procedure for an Adolescent with Sickle Cell Disease, Hodgkin’s Lymphoma, and old Cerebrovascular Accident

Cardiopulmonary bypass (CPB) in patients with sickle cell anemia can trigger lethal vaso-occlusive crises, especially in cases of hypoxia, hypothermia, acidosis, or low-flow states. We described a patient with sickle cell anemia who had bicuspid aortic valve stenosis and aneurysmal dilatation of the ascending aorta complicated with infective endocarditis. The patient had a history of stroke. During routine workup, Hodgkin’s Lymphoma was diagnosed. The patient underwent exchange transfusion preoperatively and immediately before the initiation of CPB. We performed a Bentall procedure, and the patient was discharged in a stable condition.  Sickle Cell Disease can be very challenging during CPB, and special precautions are required to prevent vaso-occlusive crises

How to Make Business Processes Socialize ?

This paper presents an approach that builds upon social computing principles to make business processes; socialize . First the approach identifies the main components of a business process that are task, person, and; machine. A task is a work unit that forms with other tasks a business process and that a person and/ormachine; execute. Afterwards the approach enriches a business process with details captured from the (execution; and social) relations that connect tasks together, persons together, and machines together. While execution; relations are widely reported in the literature, there is a growing interest in studying the role of social; relations in business processes. The approach uses social relations to build configuration network of tasks,; social network of persons, and support network ofmachines. These networks capture the ongoing interactions; that arise when business processes are executed. A system illustrating how these networks are developed is; also demonstrated in the paper

Hospitality brand management by a score-based q-rung ortho pair fuzzy V.I.K.O.R. method integrated with the best worst method

Hospitality brand management is a primary concern in the hotel industry and the evaluation of brands can be considered as a decision-making problem with multiple criteria. The evaluation information of brands may be uncertain sometimes. The q-rung orthopair fuzzy set (q-R.O.F.S.), which represents the preference degree of a person from the positive and negative aspects, has turned out to be an efficient tool in depicting uncertainty and vagueness in the decision-making process. This article dedicates to presenting an integrated multiple criteria decision-making method with q-R.O.F.S.. Firstly, a score function of the q-R.O.F.S. is proposed to solve the deficiencies of two existing score functions. Then, a weight-determining method based on the additive consistency of the preference relation is developed. A decision-making method integrating the score function, the best worst method and the VIsekriterijumska optimizacija I KOmpromisno Resenje (V.I.K.O.R.) which means multiple criteria compromise optimisation in English) method is further proposed. Finally, a case study regarding the hospitality brand management is provided to show the applicability and validity of the proposed method