A Method of Evaluating Trust and Reputation for Online Transaction

The widespread use of the Internet and evaluater-based technologies has transformed the way business is conducted. Traditional offline businesses have increasingly become online, and there are new kinds of businesses that solely exist online. Unlike offline business environments, interpersonal trust is generally lacking in online business settings. Trading partners might feel insecure about the exchange of products and services over the net as they have limited information about each other's reliability or about the product quality. Considering that enough trust needs to be created to get the online buyer and seller to take actions, trust is a precious asset in online transactions. In order to address the issue of evaluating trust and reputation in online transaction environments, this paper makes use of a social network that graphically represents interpersonal relationships. This paper proposes computational models that systematically evaluate the quantitative level of trust and reputation based on the social network. A method that combines the evaluated trust and reputation levels is also proposed to increase the reliability of online transactions

Sustainable Situation-Aware Recommendation Services with Collective Intelligence

With the recent advances of information and communication technology, people communicate with each other through online communities or social networking services, such as PatientsLikeMe and Facebook. One of the key challenges in aspects of providing sustainable situation-aware services is how to utilize peoples’ experiences shared as reusable social-intelligence. If domain-specific collective intelligence is well constructed, the knowledge usages can be extended to situation-awareness-based personal situation understanding, and sustainable recommendation services with user intent. In this paper, we introduce a sustainable situation-awareness supporting framework based on text-mining techniques and a domain-specific knowledge model, the so-called Service Quality Model for Hospitals (SQM-H). Different from obtaining sustainable contexts from heterogeneous sensors surrounding users, it aggregates SQM-H based service-specific knowledge from online health communities. Our framework includes a set of components: data aggregation, text-mining, service quality analysis, and open Application Programming Interface (APIs) for recommendation services. Those components have been designed to deal with users’ immediate request, providing service quality related information reflected in collective intelligence and analyzed information based on that along with the SQM-H. As a proof of concept, we implemented a prototype system which interacts with users through smartphone user interface. Our framework supports qualitative and quantitative information based on SQM-H and statistical analyses for the given user queries. Through the implementation and user tests, we confirmed an increased knowledge support for decision-making and an easy mashup with provided Open APIs. We believe that the suggested situation-awareness supporting framework can be applied to numerous sustainable applications related to healthcare and wellness domain areas if domain-specific knowledge models are redesigned

CAPNet: An Enhanced Load Balancing Clustering Algorithm for Prolonging Network Lifetime in WSNs

A wireless sensor network consisting of resources, size, and cost-limited sensors is used in many military and civil applications. This paper proposes an energy-efficient clustering algorithm that extends the lifetime of sensor networks. The proposed clustering algorithm is an extended hierarchical clustering protocol that minimizes the overall amount of consumed energy in the network. The proposed approach dynamically updates clusters and distributes the load on the heavily loaded cluster heads among different nodes. It also balances the remaining energy on nodes in the network, which leads to prolonged network lifetime. The performance is evaluated in terms of network lifetime, average energy consumption, and standard deviation of residual energy

Deep learning-assisted IoMT framework for cerebral microbleed detection

The Internet of Things (IoT), big data, and artificial intelligence (AI) are all key technologies that influence the formation and implementation of digital medical services. Building Internet of Medical Things (IoMT) systems that combine advanced sensors with AI-powered insights is critical for intelligent medical systems. This paper presents an IoMT framework for brain magnetic resonance imaging (MRI) analysis to lessen the unavoidable diagnosis and therapy faults that occur in human clinical settings for the accurate detection of cerebral microbleeds (CMBs). The problems in accurate CMB detection include that CMBs are tiny dots 5–10 mm in diameter; they are similar to healthy tissues and are exceedingly difficult to identify, necessitating specialist guidance in remote and underdeveloped medical centers. Secondly, in the existing studies, computer-aided diagnostic (CAD) systems are designed for accurate CMB detection, however, their proposed approaches consist of two stages. Potential candidate CMBs from the complete MRI image are selected in the first stage and then passed to the phase of false-positive reduction. These pre-and post-processing steps make it difficult to build a completely automated CAD system for CMB that can produce results without human intervention. Hence, as a key goal of this work, an end-to-end enhanced UNet-based model for effective CMB detection and segmentation for IoMT devices is proposed. The proposed system requires no pre-processing or post-processing steps for CMB segmentation, and no existing research localizes each CMB pixel from the complete MRI image input. The findings indicate that the suggested method outperforms in detecting CMBs in the presence of contrast variations and similarities with other normal tissues and yields a good dice score of 0.70, an accuracy of 99 %, as well as a false-positive rate of 0.002 %.© 2017 Elsevier Inc. All rights reserved

Image Denoising Based on Improved Wavelet Threshold Function for Wireless Camera Networks and Transmissions

A new wavelet threshold denoising function and an improved threshold are proposed. It not only retains the advantages of hard and soft denoising functions but also overcomes the disadvantages of the continuity problem of hard denoising function and the constant deviation of the soft denoising function in the new method. In the case of the improved threshold conditions, the new threshold function has a better performance in outstanding image details. It can adapt to different images by joining an adjusting factor. Simulation results show that the new threshold function has a better ability of performing image details and a higher peak signal to noise ratio (PSNR)

An Efficient DA-Net Architecture for Lung Nodule Segmentation

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score

Impact of Dynamic Path Loss Models in an Urban Obstacle Aware Ad Hoc Network Environment

This study highlights the importance of the physical layer and its impact on network performance in Mobile Ad Hoc Networks (MANETs). This was demonstrated by simulating various MANET scenarios using Network Simulator-2 (NS-2) with enhanced capability by adding propagation loss models (e.g., modified Two-Ray Ground model, ITU Line of Sight and Nonline of Sight (ITU-LoS and NLoS) model into street canyons and combined path loss and shadowing model (C-Shadowing)). The simulation results were then compared with the original Two-Ray Ground (TRG) model already available into NS-2. The scenario primarily simulated was that of a mobile environment using Random Way Point (RWP) mobility model with a variable number of obstacles in the simulation field (such as buildings, etc., causing variable attenuation) in order to analyze the extent of communication losses in various propagation loss models. Performance of the Ad Hoc On-demand Distance Vector (AODV) routing protocol was also analyzed in an ad hoc environment with 20 nodes