Efficient Local Cloud-Based Solution for Liver Cancer detection using Deep Learning

Liver cancer is one the most common forms of cancer. As per statistics in 2018 published by World Health Organization, a quarter of all cancer cases are caused by infections, particularly prevalent in developing countries, including hepatitis B, which is linked to liver cancer. The mortality rate is higher in liver cancer as compared to other types of cancer. Quick and reliable diagnosis tools are of paramount importance for detecting and treating liver cancer in early stage, thus improving the likely course of a medical condition of patient. We have developed a cloud-based solution for liver tumour Segmentation, Classification and Detection in CT images based on GoogleNet architecture of Convolutional Neural Network. Experiment is carried out with training and test sets derived from TCIA repository. The results yield 96.7% accuracy for classification of tumour cells. GoogleNet architecture is used for implementation. The GoogleNet has 70,000 images in diagnosis of malignant tumor in liver cancer, providing a rich database for testing. Our algorithm has been deployed in Azure cloud.

Short-Term Traffic State Prediction Based on the Critical Road Selection Optimization in Transportation Networks

Short-term traffic prediction under corrupted or missing data for large-scale transportation networks has become an important and challenging topic in recent decades. Since the critical roads have predictive power on their adjacent roads, this paper proposes a novel hybrid short-term traffic state prediction method based on critical road selection optimization. First, the utility function of the quality of service (QoS) for the critical roads in a large-scale road network is proposed based on the coverage and the data score. Then, the critical road selection optimization model in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS. Also, an innovative critical road selection method is introduced, which is considering the topological structure and the mobility of the urban road network. Subsequently, the traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network. Experiment results on the Beijing traffic network indicate that the proposed method outperforms prevailing DL approaches in the case of considering critical road sections.

Online Blended Learning in Small Private Online Course

In this work, we studied the online blended learning model of computer network experimentation, focusing mainly on the problem of traditional network experiments being limited by location and time, and explore the applicability of the small private online course (SPOC) advanced teaching concepts to computer network online experiment teaching. Based on the structure of a combination of virtual and real, real and not virtual, an online network experiment platform and management system has been designed and constructed, enabling students to carry out remote online computer network hardware experiments anytime and anywhere, without being restricted by time, space, or content. Using the online network experiment platform, we can organize the experimental modules and knowledge points via the SPOC course concept, by developing online network experimental content, modularizing and fragmenting of the experiments, creating the pre-experimental explanation and experiment preview videos, and evaluating the assignments via peer grading to analyze students’ learning behavior. By exploring online network experimental teaching methods and management models, offering experimental guidance in an interactive manner, and highlighting the openness and sharing characteristics of online experimental teaching platforms, we can improve the utilization rate for teaching resources, and provide ideas for applied scientific research methods.

Perceptions of Personalization in Company-consumer Interactions on Social Network: Experiment Conducted in the Czech Republic

Social media has revolutionized the way in which organizations communicate with their customers and stakeholders. Therefore, understanding the characteristics that influence the interactivity and the relationship between an organization and its page visitors is essential for the capability of an organization ability to respond to the current and potential customers on social networking sites in an appropriate manner. The research investigates how companies using brand communities on Facebook influence consumers’ intention to purchase through online interactivity, responsiveness, and personalization. In an online experiment, participants viewed selected records of communication with customers from companies on Facebook pages that differed as to whether and how specifically the companies responded to their consumers. The results were obtained using mediation analyses. These indicate that perceptions of company responsiveness and personalization, based on existing consumer interactions on a Facebook page, influence how participants rated the company, the quality of their own potential company interactions, and the likelihood of them purchasing products from the company in future. The findings of this research also support the assertion that social media has an impact on brand communities as a platform on which consumers interact. Additionally, eye tracking was used to collect data about customers’ activities that assist in understanding how people acquire information from web pages. The research provides a better understanding of which distinct components of a message meaningfully influence perceptions of interactivity. The findings show that marketers should focus on customer complaints as this component of the messages on social media influences the viewer’s visual search. To test other cultural contexts and for further validation of identified relationships, future research could be conducted globally.

REVIEW OF TWENTY YEARS OF ECOSYSTEM SERVICES RESEARCH AT HENFAES SILVOPASTORAL NATIONAL NETWORK EXPERIMENT

Purpose: This study systematically reviewed and synthesised all the scientific literature that has so far been conducted on the ecosystem services of the UK’s Silvopastoral National Network Experiment, the Henfaes Silvopastoral Systems Experimental Farm of Bangor University, Wales, and other studies in temperate Europe from 1988 to 2012 to establish what has been done to date, the benefits and contributions to our knowledge base, and potential knowledge gaps and priorities for future research.Methodology: All available papers and grey literatures, since the inception of the UK’s Silvopastoral National Network Experiment in 1988, were extracted and reviewed primarily by accessing various electronic databases and existing library collections. The research papers were split into peer-reviewed (published) and non-peer-reviewed (unpublished) papers. The ecosystem services framework was used to relate the four major categories of ecosystem services (provisioning, regulating, cultural and supporting) to the scientific domain of the research studies. The scientific domains addressed include timber or wood-fuel potential, pasture/livestock management, carbon sequestration, soil improvement, water management, and biodiversity enhancement.Findings: Results show that 66 research studies were conducted over the 20-year study period on ecosystem services of which 45% were produced based on studies at Henfaes Silvopastoral Systems Experimental Farm, 32% at UK’s Silvopastoral National Network Experiment, 12% were from other silvopastoral systems trials in the UK, and 11% were from European-wide silvopastoral systems studies. The trendline indicated that the number of annual studies on ecosystem services were greatest in the mid and late 1990s than in any other time over the 20-year study period. The studied ecosystem services dealt with provisioning services (40%), regulating services (13%), and supporting services (47%). The scientific domains addressed include timber or wood-fuel potential (20%), pasture/livestock management (20%), biodiversity (20%), carbon sequestration (13%), water management (15%), and soils (12%).Unique contribution to theory, practice and policy: It is hoped that the results of this study will lead to better understanding of the economic and environmental implications of silvopastoral system, and hence generate more attention towards accelerating its adoption and institutionalization in national rural development policies.