Land subsidence is a morphological phenomenon, which causes negative environmental and economic consequences for human societies. Therefore, identifying the areas prone to subsidence can be one of the necessary measur...
详细信息
Land subsidence is a morphological phenomenon, which causes negative environmental and economic consequences for human societies. Therefore, identifying the areas prone to subsidence can be one of the necessary measures for reducing the potential risks. This study aims to evaluate the efficiency of the supportvectormachine (SVM) algorithm and weighted overlay index (WOI) models in zoning the rate of land subsidence hazard in Hashtgerd plain, Iran. First, the 19 criteria include groundwater depletion, groundwater extraction, aquifer thickness, alluvium thickness, aquifer recharge, well density, drainage density, groundwater depth, lithology, bedrock depth, average annual precipitation, average annual temperature, climate type, agricultural use, urban use, industrial use, distance from rivers and streams, distance from roads, distance from faults were considered. Then, the layers were weighed based on the Best-Worst Method (BWM). The results of BWM indicated that the factors of groundwater extraction (0.219), lithology (0.157), and groundwater depletion (0.079) have a greater effect on the potential for subsidence hazard. Moreover, the results of validation by performing ROC curve showed that the accuracy of RBF-SVM, LN-SVM, SIG-SVM, PL-SVM, and WOI were 95.7, 94.3, 94.9, 93.2, and 90%, respectively. Based on the ROC results, all of the models for preparing the subsidence hazard map in Hashtgerd plain exhibit excellent accuracy. Therefore, all of the models used here can predict the areas vulnerable to subsidence properly. In this study, the five land subsidence hazard maps were used as new input factors and integrated using fuzzy gamma-ensemble methods to make better hazard maps. The results of the ensemble model indicated that 19.3% of Hashtgerd plain is in the zone of high to very high sensitivity. The results of this study can help planners in managing and reducing the possible hazards of subsidence.
Information security must be maintained because the amount of data in the world today is growing exponentially. The issues related to security are growing as big data usage increases. Finding ways to identify intrusio...
详细信息
Information security must be maintained because the amount of data in the world today is growing exponentially. The issues related to security are growing as big data usage increases. Finding ways to identify intrusions into networks and information systems is one of the major issues in this subject. It is imperative and important to enhance intrusion detection skills in order to address malevolent behavior in large data. This paper presents a scalable approach to harmful data detection. Three variables have been considered in this strategy and model: scalability, user review, and temporal progress. High volumes of data can be processed using this technology. Time is split into time periods for data training in this system, and each time interval uses users' review information to train the data. Large volumes of data require sophisticated strategies to handle, and scalability in storage allows for faster processing and fewer computations. This approach is a kind of hardware-software hybrid solution for malware detection. A fresh approach to feature extraction has also been applied. In the suggested method, the bacteria algorithm in conjunction with the immune system algorithm has been utilized for the prediction operation, and the modified support vector machine algorithm and optical density have been utilized for classification. Based on the findings, the suggested combination algorithm outperforms other comparable techniques with a 21% detection rate, a 62% false alarm rate, a 15% accuracy rate, and a 73% training duration.
Effectively reducing damages and making decisions on land development policies can be achieved identifying areas that are at high risk. Thus, the aim of the current research is to explore the risk of landslides zonati...
详细信息
Effectively reducing damages and making decisions on land development policies can be achieved identifying areas that are at high risk. Thus, the aim of the current research is to explore the risk of landslides zonation in Sarvabad by use of advanced machine learning algorithms based on statistical models. At the present research, landslide hazard zonation was conducted by use of hybrid algorithms, including Frequency Ratio-Random Forest (FR-RF), Frequency Ratio-supportvectormachine (FR-SVM), Weight of Evidence-Random Forest (WoE-RF), and Weight of Evidence-supportvectormachine (Woe-SVM). First, the point shape files of 166 landslides in Sarvabad, prepared by the Natural Resource Departmens of Kurdistan State, considered as the inventory of landslides map. The data from the landslide points was split into training sets comprising 70% and validation sets comprising 30%. To achieve to conduct the landslides hazard zone, a sum of 16 items were used that are as follows: slope, aspect, elevation, distance to the stream, distance to the road, river density, distance to the fault, fault density, roadway density, precipitation, land utilization, Normalized Difference Vegetation Index (NDVI), lithology, earthquake, Stream Power Index (SPI), and topographic wetness index (TWI). Finally, the performance of the models was evaluated using the ROC curve. Among the FR-RF, WoE-RF, FR-SVM, and WoE-SVM models, the FR-RF model accounted for the most high performance. In the end, it can be concluded that obtaining an accurate and reasonable spatial prediction map can help managers and urban planners identify zones susceptible to landslide occurrence so that they can manage the potential crises of landslide-prone zones.
This paper presents the results of modeling the risk of forest fires in the west of Nghe An Province (north-central Vietnam) using remote sensing and GIS data. We built models for the occurrence of forest fires using ...
详细信息
This paper presents the results of modeling the risk of forest fires in the west of Nghe An Province (north-central Vietnam) using remote sensing and GIS data. We built models for the occurrence of forest fires using machine learning methods, including Random Forest (RF), Suppor vectormachine (SVM), and Classification and Regression Trees (CART). The models took into account nine factors influencing the risk of forest fires, including vegetation cover (the normalized difference vegetation index (NDVI)), surface evapotranspiration, elevation, slope, aspect, wind speed, ground surface temperature, average monthly precipitation, and population density. Various parameters are tested in the RF, SVM, and CART algorithms to select the algorithm with the highest accuracy in forest fire risk prediction. The results show that the RF algorithm with the value of the "numberOfTrees" parameter equal to 100 has the highest accuracy in predicting the risk of forest fires in the study area.
BACKGROUND Successful aging(SA)refers to the ability to maintain high levels of physical,cognitive,psychological,and social engagement in old age,with high cognitive function being the key to achieving *** To explore ...
详细信息
BACKGROUND Successful aging(SA)refers to the ability to maintain high levels of physical,cognitive,psychological,and social engagement in old age,with high cognitive function being the key to achieving *** To explore the potential characteristics of the brain network and functional connectivity(FC)of *** Twenty-six SA individuals and 47 usual aging individuals were recruited from community-dwelling elderly,which were taken the magnetic resonance imaging scan and the global cognitive function assessment by Mini Mental State Examination(MMSE).The resting state-functional magnetic resonance imaging data were preprocessed by DPABISurf,and the brain functional network was conducted by *** supportvectormachine model was constructed with altered functional connectivities to evaluate the identification value of *** The results found that the 6 inter-network FCs of 5 brain networks were significantly altered and related to MMSE *** FC of the right orbital part of the middle frontal gyrus and right angular gyrus was mostly increased and positively related to MMSE score,and the FC of the right supramarginal gyrus and right temporal pole:Middle temporal gyrus was the only one decreased and negatively related to MMSE *** 17 significantly altered FCs of SA were taken into the supportvectormachine model,and the area under the curve was *** The identification of key brain networks and FC of SA could help us better understand the brain mechanism and further explore neuroimaging biomarkers of SA.
As computer technology and intelligence progress, many fields begin to develop towards intelligence. Power system is an important component of the whole power grid operation and the work of electronic components and e...
详细信息
As computer technology and intelligence progress, many fields begin to develop towards intelligence. Power system is an important component of the whole power grid operation and the work of electronic components and electronic devices, so how to evaluate its stability is an important issue to ensure the stable operation of power system. In this study, a power system evaluation model using improved support vector machine algorithm is proposed for the transient state evaluation system of power system. Firstly, the characteristic vector is extracted from the transient steady-state data of power system, and then the traditional support vector machine algorithm is improved by adding Mahalanobis distance. Finally, the algorithm accuracy and precision are compared. It is found that the highest accuracy of the improved algorithm was 96.79%, which was 1.70% higher than that of the lowest algorithm. At the same time, the false alarm rate was lower, which was 0.42% lower than that of the highest algorithm. The accuracy of the improved support vector machine algorithm was 95.62%. Results show a higher accuracy and precision of the improved vectormachinealgorithm, and it is better in the processing ability and evaluation ability of power system data.
order to accurately predict the changes in the throughput of port petrochemical products and facilitate the formulation of relative decisions, this paper analyzes the factors affecting the throughput of port petrochem...
详细信息
order to accurately predict the changes in the throughput of port petrochemical products and facilitate the formulation of relative decisions, this paper analyzes the factors affecting the throughput of port petrochemical products in a city through the GRA method. After sorting and selection, PCA method is used for pretreatment. In the SVM algorithm, ICSO is used to obtain the best parameters and improve the prediction accuracy and efficiency. In view of the variability of future development, three development scenarios are set up to prepare for the throughput forecast of petrochemical products in a city's port. The results show that the optimization speed of ICSO algorithm is very fast. When the training iteration is 20, the best fitness value is obtained, which is 0.0572. The training effect of ICSO-SVM algorithm is good, the gap between it and the original data is small, and the overall trend is close to the original data. In the test prediction, ICSO-SVM algorithm has the best prediction effect, and its MAE, RMSE and MAPE are the smallest. The minimum MAE is 762.2, 477.0 smaller than CSO-SVM algorithm, and the latter's MAE is 1239.2. The minimum MAPE of the proposed algorithm is 1.05%, while that of CSO-SVM algorithm is 1.71%. In general, the prediction error of ICSO-SVM algorithm is smaller. After the prediction of different development scenarios, the throughput of petrochemical products in a port of a city shows an increasing trend in the next five years. This method can be applied to the development forecast of port petrochemical products and provide reference for decision-making.
breast cancer ranks first in female malignant tumors. Early detection and diagnosis is the key to treatment. This paper uses the open-source load_break_cancer breast cancer data set, mainly uses random forest, support...
详细信息
ISBN:
(纸本)9798400709760
breast cancer ranks first in female malignant tumors. Early detection and diagnosis is the key to treatment. This paper uses the open-source load_break_cancer breast cancer data set, mainly uses random forest, supportvectormachine, logical regression, Gauss naive Bayesian algorithm, BP neural network algorithm, k-neighborhood algorithm and XGBoost algorithm to classify and predict the breast cancer data set, conducts a lot of training and testing on the data set under a variety of machine learning algorithms, analyzes the learning curve in the training process, analyzes the training and testing results, and analyzes the performance of the algorithm processing data, which is of great significance for breast cancer diagnosis and treatment.
Kernel support vector machine algorithm and K-means clustering algorithm are used to determine the expected mortality rate for hemodialysis patients. The national nephrology database of Montenegro has been used to con...
详细信息
Kernel support vector machine algorithm and K-means clustering algorithm are used to determine the expected mortality rate for hemodialysis patients. The national nephrology database of Montenegro has been used to conduct this research. Mortality rate prediction is realized with accuracy up to 94.12% and up to 96.77%, when a complete database is observed and when a reduced database (that contains data for the three most common basic diseases) is observed, respectively. Additionally, it is shown that just a few parameters, most of which are collected during the sole patient examination, are enough for satisfying results.
At present, there are many kinds of intelligent training equipment in tennis sports, but they all need human control. If a single tennis player uses the robot to return the ball, it will save some human resources. Thi...
详细信息
At present, there are many kinds of intelligent training equipment in tennis sports, but they all need human control. If a single tennis player uses the robot to return the ball, it will save some human resources. This study aims to improve the recognition rate of tennis sports robots in the return action and the return strategy. The human-oriented motion recognition of the tennis sports robot is taken as the starting point to recognize and analyze the return action of the tennis sports robot. The OpenPose traversal dataset is used to recognize and extract human motion features of tennis sports robots under different classifications. According to the return characteristics of the tennis sports robot, the method of tennis return strategy based on the supportvectormachine (SVM) is established, and the SVM algorithm in machine learning is optimized. Finally, the return strategy of tennis sports robots under eight return actions is analyzed and studied. The results reveal that the tennis sports robot based on the SVM-Optimization (SVM-O) algorithm has the highest return recognition rate, and the average return recognition rate is 88.61%. The error rates of the backswing, forward swing, and volatilization are high in the return strategy of tennis sports robots. The preparation action, backswing, and volatilization can achieve more objective results in the analysis of the return strategy, which is more than 90%. With the increase of iteration times, the effect of the model simulation experiment based on SVM-O is the best. It suggests that the algorithm proposed has a reliable accuracy of the return strategy of tennis sports robots, which meets the research requirements. Human motion recognition is integrated with the return motion of tennis sports robots. The application of the SVM-O algorithm to the return action recognition of tennis sports robots has good practicability in the return action recognition of tennis sports robot and solves the problem that the optimizati
暂无评论