Invading Testing System Based on Clustering and HMM Distributed

2013 ◽  
Vol 443 ◽  
pp. 456-461
Author(s):  
Ru Dan Lin ◽  
Ling Jian Wang
Keyword(s):  
Clustering Analysis ◽  
Experimental Results ◽  
Training Data ◽  
Testing System ◽  
Testing Methods ◽  
Data Set ◽  
Real Network ◽  
Testing Method ◽  
Normal Behavior ◽  
Different Types

In the invading testing, the testing of unknown is mainly accomplished by the abnormal testing. Traditional abnormal testing methods need to construct a normal behavior feature outline reference mode. When establish this mode, it is needed to have large amount of pure normal data set, and this data set usually is not easy to gain from the real network. Whats worse, the problem of too much error reports and leaking reports in the abnormal testing is pervasive. In order to overcome this shortage, this paper rises a abnormal testing method which is combine clustering analysis and HMM. This method doesnt need any training data set of manual marking; it can explore many different types of invading behaviors. The experimental results indicate that this method has better effect on the testing, which is of a higher testing rate and lower error report rate.

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

Adaptive K-Means Algorithm with Dynamically Changing Cluster Centers and K-Value

2012 ◽  
Vol 532-533 ◽  
pp. 1373-1377 ◽  
Author(s):  
Ai Ping Deng ◽  
Ben Xiao ◽  
Hui Yong Yuan
Keyword(s):  
Nearest Neighbor ◽  
Experimental Results ◽  
Data Set ◽  
Number Of Clusters ◽  
K Value ◽  
Testing Data ◽  
Different Types ◽  
Data Points ◽  
Shared Nearest Neighbor

In allusion to the disadvantage of having to obtain the number of clusters in advance and the sensitivity to selecting initial clustering centers in the K-means algorithm, an improved K-means algorithm is proposed, that the cluster centers and the number of clusters are dynamically changing. The new algorithm determines the cluster centers by calculating the density of data points and shared nearest neighbor similarity, and controls the clustering categories by using the average shared nearest neighbor self-similarity.The experimental results of IRIS testing data set show that the algorithm can select the cluster cennters and can distinguish between different types of cluster efficiently.

Multi-Rhythm Capsule Network Recognition Structure for Motor Imagery Classification

2021 ◽  
Author(s):  
Meiyan Xu ◽  
Junfeng Yao ◽  
Yifeng Zheng ◽  
Yaojin Lin
Keyword(s):  
Machine Learning ◽  
Motor Imagery ◽  
Feature Learning ◽  
Experimental Results ◽  
Training Data ◽  
Limited Data ◽  
Data Set ◽  
Recognition Model ◽  
Machine Learning Methods ◽  
Key Features

Existing machine learning methods for classification and recognition of EEG motor imagery usually suffer from reduced accuracy for limited training data. To address this problem, this paper proposes a multi-rhythm capsule network (FBCapsNet) that uses as little EEG information as possible with key features to classify motor imagery and further improves the classification efficiency. The network conforms to a small recognition model with only 3 acquisition channels but it can effectively use the limited data for feature learning. Based on the BCI Competition IV 2b data set, experimental results show that the proposed network can achieve 2.41% better performance than existing cutting-edge methods.

scholarly journals Autonomous Interpretation of the Microstructure of Steels and Special Alloys

2019 ◽  
Vol 949 ◽  
pp. 24-31 ◽  
Author(s):  
Bartłomiej Mulewicz ◽  
Grzegorz Korpala ◽  
Jan Kusiak ◽  
Ulrich Prahl
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Classification Systems ◽  
Training Data ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Steel Microstructure ◽  
Different Types ◽  
Steel Grades

The main objective of presented research is an attempt of application of techniques taken from a dynamically developing field of image analysis based on Artificial Intelligence, particularly on Deep Learning, in classification of steel microstructures. Our research focused on developing and implementation of Deep Convolutional Neural Networks (DCNN) for classification of different types of steel microstructure photographs received from the light microscopy at the TU Bergakademie, Freiberg. First, brief presentation of the idea of the system based on DCNN is given. Next, the results of tests of developed classification system on 8 different types (classes) of microstructure of the following different steel grades: C15, C45, C60, C80, V33, X70 and carbide free steel. The DCNN based classification systems require numerous training data and the system accuracy strongly depend on the size of these data. Therefore, created data set of numerous micrograph images of different types of microstructure (33283 photographs) gave the opportunity to develop high precision classification systems and segmentation routines, reaching the accuracy of 99.8%. Presented results confirm, that DCNN can be a useful tool in microstructure classification.

Artificial Neural Network Military Impulse Noise Classifier

2006 ◽  
Author(s):  
Brian Bucci ◽  
Jeffrey Vipperman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Impulse Noise ◽  
Training Data ◽  
Noise Sources ◽  
Wind Noise ◽  
Validation Data ◽  
Data Set ◽  
Different Types ◽  
Artificial Neural

Civilian noise complaints and damage claims have created the need for stations to monitor the production of military impulse noise. However, these stations suffer from numerous false positive detections (due to wind noise) of impulse events and often miss many events of interest. There is also interest in identifying specific noise sources, such different types of ordinance or different types of aircraft. To improve the accuracy of military impulse noise monitoring and make and initial effort to specifically classify noise source, an algorithm based upon an artificial neural network with inputs of conventional and custom acoustic metrics was proposed. To train and evaluate the noise classifier approximately 1,000 waveforms were field collected (110 military aircraft noise, 330 military impulse noise, and 560 non-impulse noise). The final noise classifier used kurtosis and crest factor and the custom metrics spectral slope and weighted square error as inputs. The classifier was able to achieve 99.7% accuracy on the training data set and 99.4% accuracy on the validation data set.

Research on Dynamic Characteristic Testing Method for Large-Scale Rolling Mill Servo Cylinder

2010 ◽  
Vol 44-47 ◽  
pp. 275-279 ◽  
Author(s):  
Yuan Yuan Liang ◽  
Fu Xuan Huang ◽  
Kui Sheng Chen ◽  
Xin Yuan Chen ◽  
Shu Guang Fu ◽  
...  
Keyword(s):  
Dynamic Characteristic ◽  
Rolling Mill ◽  
Hydraulic System ◽  
Large Scale ◽  
Test Bench ◽  
Testing System ◽  
Testing Methods ◽  
Testing Method ◽  
Computer Aided ◽  
A Company

Through the analysis of the current researches on servo cylinder dynamic characteristic testing methods, this paper puts forward a new testing method of dynamic characteristic on large scale rolling mill servo cylinder, and elucidates the composition and testing method of the hydraulic system. It introduces the process of computer aided testing system and analyzes the dynamic characteristic of the frame. The testing system has been to realization in a test bench developed for a company.

A Computational Method for the Identification of Endolysins and Autolysins

2020 ◽  
Vol 27 (4) ◽  
pp. 329-336 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Baowen Chen ◽  
Xu Tan ◽  
Huaikun Xiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cell Wall ◽  
Experimental Results ◽  
Computational Method ◽  
Lytic Enzyme ◽  
Support Vector ◽  
Lytic Enzymes ◽  
Data Set ◽  
Optimal Feature ◽  
Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

scholarly journals Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector

2019 ◽  
Vol 9 (6) ◽  
pp. 1128 ◽  
Author(s):  
Yundong Li ◽  
Wei Hu ◽  
Han Dong ◽  
Xueyan Zhang
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Hurricane Sandy ◽  
Training Data ◽  
Aerial Images ◽  
Detection Methods ◽  
Single Shot ◽  
Data Set ◽  
Augmentation Strategies ◽  
Post Disaster

Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be replaced by machine learning-based methods combined with image processing techniques. Given the development of machine learning, researchers find that convolutional neural networks can effectively extract features from images. Some target detection methods based on deep learning, such as the single-shot multibox detector (SSD) algorithm, can achieve better results than traditional methods. However, the impressive performance of machine learning-based methods results from the numerous labeled samples. Given the complexity of post-disaster scenarios, obtaining many samples in the aftermath of disasters is difficult. To address this issue, a damaged building assessment method using SSD with pretraining and data augmentation is proposed in the current study and highlights the following aspects. (1) Objects can be detected and classified into undamaged buildings, damaged buildings, and ruins. (2) A convolution auto-encoder (CAE) that consists of VGG16 is constructed and trained using unlabeled post-disaster images. As a transfer learning strategy, the weights of the SSD model are initialized using the weights of the CAE counterpart. (3) Data augmentation strategies, such as image mirroring, rotation, Gaussian blur, and Gaussian noise processing, are utilized to augment the training data set. As a case study, aerial images of Hurricane Sandy in 2012 were maximized to validate the proposed method’s effectiveness. Experiments show that the pretraining strategy can improve of 10% in terms of overall accuracy compared with the SSD trained from scratch. These experiments also demonstrate that using data augmentation strategies can improve mAP and mF1 by 72% and 20%, respectively. Finally, the experiment is further verified by another dataset of Hurricane Irma, and it is concluded that the paper method is feasible.

scholarly journals How the Multiplicity of Suggested Information Affects the Behavior of a User in a Recommender System

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 741
Author(s):  
Yuseok Ban ◽  
Kyungjae Lee
Keyword(s):  
High Precision ◽  
Recommender System ◽  
Visual Information ◽  
Experimental Results ◽  
Supplementary Information ◽  
Digital Platform ◽  
Different Types ◽  
Types Of Information

Many researchers have suggested improving the retention of a user in the digital platform using a recommender system. Recent studies show that there are many potential ways to assist users to find interesting items, other than high-precision rating predictions. In this paper, we study how the diverse types of information suggested to a user can influence their behavior. The types have been divided into visual information, evaluative information, categorial information, and narrational information. Based on our experimental results, we analyze how different types of supplementary information affect the performance of a recommender in terms of encouraging users to click more items or spend more time in the digital platform.

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