Network intrusion detection based on a general regression neural network optimized by an improved artificial immune algorithm.

To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accuracy. In this paper, the mean square errors (MSEs) were considered the affinity function. The AIAE was used to optimize the smooth factors of the GRNN; then, the optimal smooth factor was solved and substituted into the trained GRNN. Thus, the intrusive data were classified. The paper selected a GRNN that was separately optimized using a genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy C-mean clustering (FCM) to enable a comparison of these approaches. As shown in the results, the AIAE-GRNN achieves a higher classification accuracy than PSO-GRNN, but the running time of AIAE-GRNN is long, which was proved first. FCM and GA-GRNN were eliminated because of their deficiencies in terms of accuracy and convergence. To improve the running speed, the paper adopted principal component analysis (PCA) to reduce the dimensions of the intrusive data. With the reduction in dimensionality, the PCA-AIAE-GRNN decreases in accuracy less and has better convergence than the PCA-PSO-GRNN, and the running speed of the PCA-AIAE-GRNN was relatively improved. The experimental results show that the AIAE-GRNN has a higher robustness and accuracy than the other algorithms considered and can thus be used to classify the intrusive data

Research on the Relationship between Water Diversion and Water Quality of Xuanwu Lake, China

Water diversion is often used to improve water quality to reach the standard of China in the short term. However, this large amount of water diversion can not only improve the water quality, but also lead to a decline in the water quality (total phosphorus, total nitrogen) of Xuanwu Lake. Through theoretical analysis, the relationship between water quality and water diversion is established. We also found that the multiplication of the pollutant degradation coefficient (K) and the water residence time (T) is a constant (N), K⋅T=N. The water quality changed better at first, with the increase of inflow discharge, and then became worse, and the optimal water quality inflow discharge is 180,000 m3/day. By constructing two-dimensional hydrodynamic and water quality models, the optimal diversion water plan is calculated. Through model calculations, it can be seen that reducing the inflow discharge makes the water residence time longer (15.3 days changed to 23.8 days). Thereby, increasing the degradation of pollutants, and thus improving water quality. Compared with other wind directions, the southwest wind makes the water quality of Xuanwu Lake the most uniform. The concentration of water quality first became smaller and then became larger, as the wind speed increased, and eventually became constant. Implementing these results for water quality improvement in small and medium lakes will significantly reduce the cost of water diversion

Main Coronary Vessel Segmentation Using Deep Learning in Smart Medical

The automatic segmentation of main vessels on X-ray angiography (XRA) images is of great importance in the smart coronary artery disease diagnosis system. However, existing methods have been developed to this task, but these methods have difficulty in recognizing the coronary artery structure in XRA images. Main vessel segmentation is still a challenging task due to the diversity and small-size region of the vessel in the XRA images. In this study, we propose a robust method for main vessel segmentation by using deep learning architectures with fully convolutional networks. Four deep learning models based on the UNet architecture are evaluated on a clinical dataset, which consists of 3200 X-ray angiography images collected from 1118 patients. Using the precision (Pre), recall (Re), and F1 score (F1) as evaluation metrics, the average Pre, Re, and F1 for main vessel segmentation in the entire experimental dataset is 0.901, 0.898, and 0.900, respectively. 89.8% of the images exhibited a high F1 score >0.8. For the main vessel segmentation in XRA images, our deep learning methods demonstrated that vessels could be segmented in real time with a more optimized implementation, to further facilitate the online diagnosis in smart medical

Research on Water Environment Regulation of Artificial Playground Lake Interconnected Yangtze River

With the rapid development of China, water pollution is still a serious problem despite implementation of control measures. Reasonable water environment management measures are very important for improving water quality and controlling eutrophication. In this study, the coupled models of hydrodynamics, water quality, and eutrophication were used to predict artificial Playground Lake water quality in the Zhenjiang, China. Recommended “unilateral” and “bilateral” river numerical models were constructed to simulate the water quality in the Playground Lake without or with water diversion by pump, sluice and push pump. Under “unilateral” and “bilateral” river layouts, total nitrogen and total phosphorus meet the landscape water requirement through water diversion. Tourist season in spring and summer and its suitable temperature result in heavier eutrophication, while winter is lighter. Under pumping condition, water quality and eutrophication of “unilateral” river is better than “bilateral” rivers. Under sluice diversion, the central landscape lake of “unilateral river” is not smooth, and water quality and eutrophication is inferior to the “bilateral”. When the water level exceeds the flood control level (4.1 m), priority 1 is launched to discharge water from the Playground Lake. During operation of playground, when water level is less than the minimum level (3.3 m), priority 2 is turned on for pumping diversion or sluice diversion to Playground Lake. After opening the Yangtze river diversion channel sluice, priority 3 is launched for sluice diversion to the Playground Lake. When the temperature is less than 15 °C, from 15 °C to 25 °C and higher than 25 °C, the water quality can be maintained for 15 days, 10 days and 7 days, respectively. Corresponding to the conditions of different priority levels, reasonable choices of scheduling measures under different conditions to improve the water quality and control eutrophication of the Playground Lake. This article is relevant for the environmental management of the artificial Playground Lake, and similar lakes elsewhere

The output performances of PCA-AIAE-GRNN and PCA-PSO-GRNN.

<p>(A) The relationship between the optimized GRNN smooth factors and the iterations of PCA-AIAE-GRNN and PCA-PSO-GRNN. (B) The relationship between MSE and the iterations of PCA-AIAE-GRNN and PCA-PSO-GRNN.</p

The evaluation indexes for PCA-PSO-GRNN and PCA-AIAE-GRNN.

<p>By reducing dimensions in PCA, compared with <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120976#pone.0120976.t002" target="_blank">Table 2</a>, the convergence and relative running time were improved. This result showed that the robustness of AIAE-GRNN was better than PSO-GRNN.</p><p>The evaluation indexes for PCA-PSO-GRNN and PCA-AIAE-GRNN.</p

Selection of Atmospheric Environmental Monitoring Sites based on Geographic Parameters Extraction of GIS and Fuzzy Matter-Element Analysis

<div><p>To effectively monitor the atmospheric quality of small-scale areas, it is necessary to optimize the locations of the monitoring sites. This study combined geographic parameters extraction by GIS with fuzzy matter-element analysis. Geographic coordinates were extracted by GIS and transformed into rectangular coordinates. These coordinates were input into the Gaussian plume model to calculate the pollutant concentration at each site. Fuzzy matter-element analysis, which is used to solve incompatible problems, was used to select the locations of sites. The matter element matrices were established according to the concentration parameters. The comprehensive correlation functions <i>K_A (x_j)</i> and <i>K_B (x_j)</i>, which reflect the degree of correlation among monitoring indices, were solved for each site, and a scatter diagram of the sites was drawn to determine the final positions of the sites based on the functions. The sites could be classified and ultimately selected by the scatter diagram. An actual case was tested, and the results showed that 5 positions can be used for monitoring, and the locations conformed to the technical standard. In the results of this paper, the hierarchical clustering method was used to improve the methods. The sites were classified into 5 types, and 7 locations were selected. Five of the 7 locations were completely identical to the sites determined by fuzzy matter-element analysis. The selections according to these two methods are similar, and these methods can be used in combination. In contrast to traditional methods, this study monitors the isolated point pollutant source within a small range, which can reduce the cost of monitoring.</p></div

The algorithm flow chart of AIAE-GRNN.

<p>The algorithm flow chart of AIAE-GRNN.</p

DR & FPR of each intrusion category by PCA-PSO-GRNN and PCA-AIAE-GRNN.

<p>By reducing dimensions in PCA, compared with <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120976#pone.0120976.t001" target="_blank">Table 1</a>, the DR and FPR of PSO-GRNN and AIAE-GRNN declined to a certain extent, but the DR and FPR of AIAE-GRNN was still higher than PSO-GRNN.</p><p>DR & FPR of each intrusion category by PCA-PSO-GRNN and PCA-AIAE-GRNN.</p

The evaluation indexes for the different algorithms.

<p>The result showed that the GA-GRNN had the premature convergence problem. In contrast, the PSO-GRNN and AIAE-GRNN overcame this problem. The running time of PSO-GRNN was shorter than AIAE-GRNN.</p><p>The evaluation indexes for the different algorithms.</p