An unauthorized activity on the network is called network intrusion and device or software application which monitors the network parameters in order to detect such an intrusion is called network intrusion detection s...
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ISBN:
(纸本)9781538644300
An unauthorized activity on the network is called network intrusion and device or software application which monitors the network parameters in order to detect such an intrusion is called network intrusion detection system (NIDS). With high rise in malicious activities on the internet, it is extremely important for NIDS to quickly and correctly identify any kind of malicious activity on the network. Moreover, the system must refrain from raising false alarms in case of normal usage detected as malicious. This paper proposes use of machine learning classification algorithms - XGBoost and AdaBoost with and without clustering to train a model for NIDS. The models are trained and tested using NSL KDD dataset and the results are an improvement over the previous works related to intrusion detection on the same dataset.
A review of literature shows that bank branching can be influenced by economic, geographic, legal, cultural and demographic characteristics apart from institutional decision making. Identifying the branching pattern c...
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ISBN:
(纸本)9781538653142
A review of literature shows that bank branching can be influenced by economic, geographic, legal, cultural and demographic characteristics apart from institutional decision making. Identifying the branching pattern can help in answering many questions like how a particular banking group is different from other? It can reveal secrets regarding strategic decision making about diversification and growth of banking networks. Present study apply machine learning algorithms to recognize the branching pattern of banking networks in India and compare the differences across groups and types. An evaluation of the performance of the algorithms shows that it is very useful employ a narrative approach for getting important insights from the data compared to traditional approaches. The study also provides a visual map evaluation to enrich the outcomes from the pattern recognition exercise.
This paper collects data on the damage to the traffic system caused by earthquakes in China in the past two decades, and uses KNN algorithm, SVM algorithm, logistic regression algorithm, naive Bayes algorithm and deci...
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ISBN:
(纸本)9781728165790
This paper collects data on the damage to the traffic system caused by earthquakes in China in the past two decades, and uses KNN algorithm, SVM algorithm, logistic regression algorithm, naive Bayes algorithm and decision tree algorithm to train the data, then establish earthquake prediction models. The paper introduces the process of preprocessing, modelling, evaluation, and visualization of disaster data. An earthquake disaster inversion model based on traffic data has been established, which can predict the earthquake intensity based on the relevant data provided by the traffic department. The prediction accuracy is relatively accurate, which is very helpful for earthquake prediction and rescue operations.
In recent years the communication tool Wechat is used widely, however few attention to the academic research. Moreover, there are very few studies about identifying character traits from its data. Previously a method ...
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ISBN:
(纸本)9781467390262
In recent years the communication tool Wechat is used widely, however few attention to the academic research. Moreover, there are very few studies about identifying character traits from its data. Previously a method based on data features to decide character traits was proposed in our paper. Here an improve method of the feature set is given through refining new features, and building the feature subset employing feature extracting algorithm. Then it compares the contributions of the original feature set with the feature subset through several classification algorithms. Experimental results show that the classification based on data features can efficiently identify character traits.
The foundation of information extraction based on remote sensing imaging involves spectral band information. Such a method often suffers from the distinctive problem of surface features. In general, artificial orchard...
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ISBN:
(纸本)9781538650394
The foundation of information extraction based on remote sensing imaging involves spectral band information. Such a method often suffers from the distinctive problem of surface features. In general, artificial orchard planting is relatively regular;thus, it shows textural features that differ from other vegetation types in images with a specific spatial scale. This study used mango groves as research object. By introducing spectral index, texture feature parameters, and by using support vector machine classification method, based on GF-2 satellite images, mango grove information extraction was studied under different combinations of spectra band, vegetation index, and texture feature parameters. The findings show that the information extraction via single spectra band information has lower accuracy. Introduction of a combination of spectra index and spectra band information can improve extraction accuracy of mango groves;however, the overall classification accuracy still remains low. In addition, the introduction of texture information and spectra band information combination can dramatically improve extraction accuracy. Producer's accuracy and user's accuracy increased to 85.7% and 93.5%, respectively. Under different combination modes, the extracted mango grove accuracy of the combination of integrated spectra band information, textural feature, and vegetation index is optimal. Producer's accuracy and user's accuracy increased to 89.3% and 97.4%, respectively. Relative to the spectra band information, the extraction accuracy improved by 20.6% and 11.0%, respectively. As a result, the support vector machine of integrated spectra and texture can effectively extract the spatial distribution information of mango groves. This method can provide a technical reference for remote sensing extraction of artificial orchards.
Choose data Mining to study the anomaly detection in coal preparation, using ash of raw coal, rapid ash and yields of raw coal which density below 1.45, and ash and actual yields of fine coal in the database as sample...
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ISBN:
(纸本)9783037855454
Choose data Mining to study the anomaly detection in coal preparation, using ash of raw coal, rapid ash and yields of raw coal which density below 1.45, and ash and actual yields of fine coal in the database as sample attribute of coal production anomaly detection model, based on Box-plot analysis, the evaluating values range of five attribute above are determined. On this condition, by using SVM and KNN, the identification model of anomaly detection in coal preparation is established. The Receiver Operating Characteristic curves analysis result shows judging production target Abnormal Conditions using SVM will be more accurate in coal preparation.
As a key technology in the digital communication system, blind equalization algorithm based on fuzzy neural network classifier is proposed. The algorithm overcomes bad judgment error. Channel estimation algorithm and ...
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ISBN:
(纸本)1424405289
As a key technology in the digital communication system, blind equalization algorithm based on fuzzy neural network classifier is proposed. The algorithm overcomes bad judgment error. Channel estimation algorithm and fuzzy neural network classifier were combined to carry out equalization. The primary signal was attained by de-convolution. Judgment range of fuzzy neural network was adjusted dynamically by competition study algorithm, and then blind equalization judgment was realized. The algorithm reduces judgment error and bit-error ratio. The validity is approved by simulation.
This paper discusses about lips and eyebrows are used to detect driver cognitive distraction by using faceAPI toolkit. A few number of classification algorithms like Support Vector Machine (SVM), Logistic Regression (...
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ISBN:
(纸本)9781479937974
This paper discusses about lips and eyebrows are used to detect driver cognitive distraction by using faceAPI toolkit. A few number of classification algorithms like Support Vector Machine (SVM), Logistic Regression (LR) and Static Bayesian Network (SBN) and Dynamic Bayesian Network (DBN) have been used for accuracy rate comparison.
Diabetes is a lifestyle disease that affects many people all over the world, with India leading the count of diabetic *** most important organ in the human body is the eye. Any anomaly will impact the functioning of l...
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ISBN:
(纸本)9781665452519
Diabetes is a lifestyle disease that affects many people all over the world, with India leading the count of diabetic *** most important organ in the human body is the eye. Any anomaly will impact the functioning of life in its operation. The main component of the eye's internal surface, the fundus, is checked to spot any anomalies. In this study, neural networks were used to classify retinal fundus images. Methods of transfer learning are used to put the image into a category based on how bad the diabetic retinopathy is. Diabetes mellitus often evolves into diabetic retinopathy (DR), leading to lesions in the retina that impair vision. Through this paper, we propose an ensemble approach to respectively diagnose diabetes and diabetic retinopathy from blood reports and digital fundus images and accurately classify its severity. In order to do so, we first determine whether the patient has diabetes or not. This has been made possible by using machine learning classification algorithm - K Nearest Neighbors. A high-end graphics processing unit (GPU) was used to train the ensembled network on the publicly accessible APTOS-19[16] dataset, and the results are outstanding, particularly for a high-level classification task. Our proposed method worked more than 95% of the time. It has also been tested against the custom Messidor and EyePACS datasets.
Failure of rectifier circuit has the characteristics of latency and complexity, which leads to the difficulty to fault diagnosis for rectifier circuit. A new method of optimizing support vector machine (SVM) by using ...
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ISBN:
(纸本)9781424469284
Failure of rectifier circuit has the characteristics of latency and complexity, which leads to the difficulty to fault diagnosis for rectifier circuit. A new method of optimizing support vector machine (SVM) by using ant colony optimization algorithm is presented to fault diagnosis for rectifier circuit in the paper. The experimental object is provided and the six ACO-SVM classifiers are developed to identify the following seven states of the experimental object. The testing results demonstrate that the ACO-SVM classifier has higher diagnostic accuracy than normal support vector machine and BP neural network.
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