Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short Term Memory and Autoencoder

Currently, the wide spreading of real-time applications such as VoIP and videos-based applications require more data rates and reduced latency to ensure better quality of service (QoS). A well-designed traffic classification mechanism plays a major role for good QoS provision and network security verification. Port-based approaches and deep packet inspections (DPI) techniques have been used to classify and analyze network traffic flows. However, none of these methods can cope with the rapid growth of network traffic due to the increasing number of Internet users and the growth of real time applications. As a result, these methods lead to network congestion, resulting in packet loss, delay and inadequate QoS delivery. Recently, a deep learning approach has been explored to address the time-consumption and impracticality gaps of the above methods and maintain existing and future traffics of real-time applications. The aim of this research is then to design a dynamic traffic classifier that can detect elephant flows to prevent network congestion. Thus, we are motivated to provide efficient bandwidth and fast transmision requirements to many Internet users using SDN capability and the potential of Deep Learning. Specifically, DNN, CNN, LSTM and Deep autoencoder are used to build elephant detection models that achieve an average accuracy of 99.12%, 98.17%, and 98.78%, respectively. Deep autoencoder is also one of the promising algorithms that does not require human class labeler. It achieves an accuracy of 97.95% with a loss of 0.13 . Since the loss value is closer to zero, the performance of the model is good. Therefore, the study has a great importance to Internet service providers, Internet subscribers, as well as for future researchers in this area.Comment: 27 page

Analysis on Lung Cancer Survival from 2001 to 2007 in Qidong, China

Background and objective Lung cancer is one of the most important malignancies in China. Survival rates of lung cancer on the population-based cancer registry for the years 2001-2007 in Qidong were analysed in order to provide the basis for the prognosis assessment and the control of this cancer. Methods Total 4,451 registered lung cancer cases was followed up to December 31st, 2009. Death certificates only (DCO) cases were excluded, leaving 4,382 cases for survival analysis. Cumulative observed survival rate (OS) and relative survival rate (RS) were calculated using Hakulinen’s method performed by the SURV 3.01 software developed at the Finnish Cancer Registry. Results The 1-, 3-, and 5-year OS rates were 23.73%, 11.89%, 10.01%, and the RS rates were 24.86%, 13.69%, 12.73%, respectively. The 1-, 3-, and 5-year RS of males vs females were 23.70% vs 27.89%, 12.58% vs 16.53%, and 11.73% vs 15.21%, respectively, with statisitically significant differences (χ2=13.77, P=0.032). RS of age groups of 15-34, 35-44, 45-54, 55-64, 65-74 and 75+ were 35.46%, 17.66%, 11.97%, 13.49%, 10.61%, 15.14%, respectively. Remarkable improvement could be seen for the 5-year RS in this setting if compared with that for the years 1972-2000. Conclusion The lung cancer survival outcomes in Qidong have been improved gradually for the past decades. Further measures on the prevention, diagnosis and treatment of lung cancer should be taken

Computational Experiment Study on Selection Mechanism of Project Delivery Method Based on Complex Factors

Project delivery planning is a key stage used by the project owner (or project investor) for organizing design, construction, and other operations in a construction project. The main task in this stage is to select an appropriate project delivery method. In order to analyze different factors affecting the PDM selection, this paper establishes a multiagent model mainly to show how project complexity, governance strength, and market environment affect the project owner’s decision on PDM. Experiment results show that project owner usually choose Design-Build method when the project is very complex within a certain range. Besides, this paper points out that Design-Build method will be the prior choice when the potential contractors develop quickly. This paper provides the owners with methods and suggestions in terms of showing how the factors affect PDM selection, and it may improve the project performance

Detection of HBV Genotypes of Tumor Tissues and Serum by A Fluorescence Polarization Assay in North-Western China's Hepatocellular Carcinoma Patients

<p>Abstract</p> <p>Background</p> <p>The understanding of the distribution of hepatitis B virus genotypes and the occult hepatitis B virus infection in hepatocellular carcinoma may shed light into the prevention and treatment of hepatocellular carcinoma. The purpose of the study is to investigate hepatitis B virus genotypes distribution, the high-risk genotypes and the occult infection in north-western China's hepatocellular carcinoma patients.</p> <p>Methods</p> <p>Hepatitis B virus genotypes A-D of hepatocellular carcinoma tumor tissues and serum samples in 268 north-western China hepatocellular carcinoma patients were detected by fluorescence polarization assay. The hepatitis B virus genotypes in serum and matched primary tumor tissue samples were compared. Hepatitis B surface antigen and α-fetoprotein in serum were detected. Occult hepatitis B virus infections were analyzed. The relationship between hepatitis B virus genotypes and clinicopathologic characteristics were analyzed statistically using SPSS v.10.0.</p> <p>Results</p> <p>Intrahepatic hepatitis B virus DNA was detected in 83.6% of 268 patients, whereas serum hepatitis B virus DNA was detected in 78.7%. The hepatitis B virus genotypes in serum were consistent with the results in matched tumor tissue. Intrahepatic hepatitis B virus genotype B and C were detected respectively in 11.6% and 54.5% of the patients. Mixed intrahepatic hepatitis B virus genotypes were detected in 13.4% of 268 patients. There was not mixed hepatitis B virus infection in Edmondonson grade I. The patients with mixed HBV genotypes exhibited statistically significant different Edmondson grade than the patients with single type HBV infection (p < 0.05). Hepatitis B surface antigens were positive in 77.2% of 268 patients. Hepatitis B virus genotype C was detected in 64.7% of occult infected patients. There was no significant differences of patients' ages and α-fetoprotein level in different groups of intrahepatic hepatitis B virus genotypes (p > 0.05).</p> <p>Conclusions</p> <p>Hepatitis B virus genotype C was associated closely with the development of hepatocellular carcinoma and the occult hepatitis B virus infection in patients in north-western China. There was a relatively high prevalence of mixed hepatitis B virus infection in Edmondonson grade III-IV.</p

Simplified three-dimensional tissue clearing and incorporation of colorimetric phenotyping.

Tissue clearing methods promise to provide exquisite three-dimensional imaging information; however, there is a need for simplified methods for lower resource settings and for non-fluorescence based phenotyping to enable light microscopic imaging modalities. Here we describe the simplified CLARITY method (SCM) for tissue clearing that preserves epitopes of interest. We imaged the resulting tissues using light sheet microscopy to generate rapid 3D reconstructions of entire tissues and organs. In addition, to enable clearing and 3D tissue imaging with light microscopy methods, we developed a colorimetric, non-fluorescent method for specifically labeling cleared tissues based on horseradish peroxidase conversion of diaminobenzidine to a colored insoluble product. The methods we describe here are portable and can be accomplished at low cost, and can allow light microscopic imaging of cleared tissues, thus enabling tissue clearing and imaging in a wide variety of settings