Large special-events parking involves various parking scenarios, e.g., temporary parking and on-street parking. Their occupancy detection is challenging as it is unrealistic to construct gates/stations for temporary p...
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ISBN:
(纸本)9781728162126
Large special-events parking involves various parking scenarios, e.g., temporary parking and on-street parking. Their occupancy detection is challenging as it is unrealistic to construct gates/stations for temporary parking areas or build a sensor-based detection system to cover every single street. To address this issue, this study develops a quadrotor-enabled autonomous parking occupancy detection system. A camera-equipped quadrotor is flying over the parking lot first;then the images are captured by the on-board camera of the quadrotor and transferred to the ground station;finally, the ground station will process and release the occupancy information to the driver's mobile devices. The decisiontree learning algorithm is adopted to determine the optimal flying speed for the quadrotor to balance the trade-off between the detection efficiency and accuracy. In order to tackle the complex environment in real-life parking, a convolutional neural network (CNN)-based vehicle detection model has been trained and implemented, where the realistic factors, e.g., passing pedestrians and tree blocking, are considered. Experiments are conducted to illustrate the effectiveness of the proposed system.
With increasing in the death rate due to the breast cancer is the second most cause. Breast caner prediction is one of the challenging research problem. It is essential that early prediction, diagnosis and the prevent...
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The present study aims to test relative welfare differences among regions in Europe, so as to examine whether the post-communist era has led to more socio-economic cohesion in Europe. The performance of European regio...
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The present study aims to test relative welfare differences among regions in Europe, so as to examine whether the post-communist era has led to more socio-economic cohesion in Europe. The performance of European regions is analysed, compared, and assessed by using the Regional Competitiveness Index (RCI) and stylised fixed nominal categories. The current status of regional cohesion is tested on the basis of detailed data on 268 NUTS 2 European regions by using a robust methodology oriented towards univariate comparison of location parameters, multivariate classification by the decisiontree and CHAID algorithm, and comparison of nominal variables with four values based on density plots. Multivariate classification appears to offer statistically excellent results with an overall correct prediction rate for post-socialist and capitalist regions in Europe of 99.6%. The research results from the Higher education and Innovation pillars, reveal a convergence of capitalist and post-socialist regions with capital cities and a divergence of regions with administrative capitals and other regions. Relatively, the two groups which perform best are both groups with capitals, while the group of capitalist regions with a capital city is significantly better in almost all pillars. The key message is that the transition of post-socialist regions is not yet over. Capitalist regions in Europe perform better than post-socialist regions in eight of the nine pillars of regional competitiveness. Our research results also reveal that the group of post-socialist regions without capital cities are significantly lagging behind the rest of the regions in Europe, and thus form the most vulnerable group of European regions. As there is data continuity in the official RCI classification and measurement, policy makers will be able to compare the performance of their own regions over time and to design appropriate concerted strategies accordingly. From this perspective, our study draws several inte
Data mining can carry out in-depth mining analysis of data information, and its application in college English teaching process is beneficial to explore the potential internal relationship between evaluation results a...
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Data mining can carry out in-depth mining analysis of data information, and its application in college English teaching process is beneficial to explore the potential internal relationship between evaluation results and various factors. Data mining discovers useful information from historical data and can be displayed in the form of classification rules to make better use of data accumulation, which will be beneficial to the improvement and improvement of English teaching quality. This paper proposes a new research program, which applies decisiontree technology in the English teaching evaluation system and mines valuable information behind it. This paper chooses GBDT and ID3 decision tree algorithm and draws an application model of English teaching evaluation based on decisiontree technology. It finds useful information from massive data, summarises rules and information, and enables teachers to better complete teaching work and improve students' English scores.
This paper focuses on modeling rainfall-induced massive landsliding in the Western Serbia in the 2001-2014 period. The motivation for conducting the study was the rainfall-induced flooding and landsliding that took pl...
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This paper focuses on modeling rainfall-induced massive landsliding in the Western Serbia in the 2001-2014 period. The motivation for conducting the study was the rainfall-induced flooding and landsliding that took place across most of the Serbia and Bosnia and Herzegovina in May 2014, and had devastating effects, including human casualties, and destruction of natural and urban environment. In the first part of the study, the general analysis was conducted. It includes a wide area (70,000 km(2)), wherein spatial rainfall patterns were identified using the monthly rainfall data from the 2001-2014. Areas that have higher monthly precipitation than the baseline monthly rainfall (1961-90) were outlined. One location within these zones was chosen as critical Loznica in Western Serbia. The area of Loznica was further examined: comparison between local daily rainfall and local landslide events recorded in 2001-2014;correlation between specific rainfall conditions, i.e. cumulative rainfall for different time windows, and the landsliding events in the specified period;identification of additional non-reported rainfall events that were potentially responsible for landsliding;analyses of the rainfall thresholds and temporal rainfall distribution. The decision tree algorithm was used to identify rainfall conditions that triggered landslides in the specified period. It was hypothesized that short-term rainfall has less influence on massive landsliding than the mid/long-term rainfall. Unlike other black-box techniques, decisiontree-based modeling gives a good insight into the thresholding process. Namely, it was possible to follow the decisiontree structure and reconstruct the critical cumulative rainfall distribution and thresholds that have led to landsliding. The main findings suggest that a high-yield mid-term rainfall (2 and 3-day rainfall) are the most important for massive landsliding, while long-term cumulative rainfall (30-day) has some additional influence in the case
Base on the organic matter, total nitrogen, available phosphorus and potassium content in the cultivated land of The 20 District in Dehui 20, it comes a study on using the decisiontree ID3 algorithm for the evaluatio...
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ISBN:
(纸本)9781467329644;9781467329637
Base on the organic matter, total nitrogen, available phosphorus and potassium content in the cultivated land of The 20 District in Dehui 20, it comes a study on using the decisiontree ID3 algorithm for the evaluation of farmland fertility levels. Through experimental analysis, it gets the number of grade 6, grade sub- region soil nutrient content is similar or smaller differences, and significant differences among grades. Fertility Evaluation based on the ID3 algorithm farmland can be used for variable rate fertilization in the guidance of precision agriculture, to provide an effective division for farmland fertility level partition.
Intrusion Detection System (IDS) is the most powerful system that can handle the intrusions of the computer environments by triggering alerts to make the analysts take actions to stop this intrusion. IDS's are bas...
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ISBN:
(纸本)9781467321013;9781467321006
Intrusion Detection System (IDS) is the most powerful system that can handle the intrusions of the computer environments by triggering alerts to make the analysts take actions to stop this intrusion. IDS's are based on the belief that an intruder's behavior will be noticeably different from that of a legitimate user. A variety of intrusion detection systems (IDS) have been employed for protecting computers and networks from malicious attacks by using traditional statistical methods to new data mining approaches in last decades. However, today's commercially available intrusion detection systems are signature-based that are not capable of detecting unknown attacks. In this paper we analyze a classification model for misuse and anomaly attack detection using decision tree algorithm.
The decision tree algorithm is a kind of approximate discrete function value method with high precision, construction model of classification of noise data is simple and has good robustness etc, it is currently the mo...
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ISBN:
(纸本)9783037853689
The decision tree algorithm is a kind of approximate discrete function value method with high precision, construction model of classification of noise data is simple and has good robustness etc, it is currently the most widely used in one of the inductive reasoning algorithms in data mining, extensive attention by researchers. This paper selects the decisiontree ID3 algorithm to realize the standardization of lumber level division, to ensure the accuracy of the lumber division, while improving the partition of speed.
The purpose of this paper was to effectively apply data mining technology to scientifically analyze the students' physical education (PE) performance so as to serve the physical teaching. The methodology adopted i...
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The purpose of this paper was to effectively apply data mining technology to scientifically analyze the students' physical education (PE) performance so as to serve the physical teaching. The methodology adopted in this paper was to apply *** 3-layer architecture and design and implement college PE performance management and analysis system under the premise of fully analyzing the system requirements based on Visual Studio2008 software development platform and using SQL Server 2005 database platform. Based on data mining technology, students' PE performances were analyzed, and decision tree algorithm was used to make valuable judgments on student performance. The results indicated that applying computer technology to the management and analysis of college PE performance can effectively reduce the teaching and managing workload of PE teachers so that the teachers concentrate more on the quality of physical education.
Currently, the commonly used fault diagnosis methods of power transformers are often difficult to deal with the ambiguity problems encountered in the troubleshooting process. Even with some artificial intelligent tech...
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ISBN:
(纸本)9781728117188
Currently, the commonly used fault diagnosis methods of power transformers are often difficult to deal with the ambiguity problems encountered in the troubleshooting process. Even with some artificial intelligent techniques already experimented, the results have not been not systematic compared and are far from real application. Therefore, this paper tries to make a thorough use of machine learning tools towards result data from dissolved gas analysis. This paper establishes a machine learning model based on dissolved gas analysis for internal fault diagnosis of power transformers, and makes a comparison between multiple machine learning methods. Firstly, an overall neural network model of transformer diagnosis based on the dissolved gas analysis is formed. Next, the performance of optimized BP neural network, probabilistic neural network (PNN), and decision tree algorithm is compared from the aspects of speed and accuracy. Furthermore, the case-based reasoning method based on the Euclidean distance and normalized energy intensity algorithm is employed to get the closest matching similar case to realize the transformer defect prediction and assistant decision-making. With actual examples verified, the case-based reasoning method can help detect the most likely abnormal causes of faulty transformers through providing the most similar matching case.
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