scholarly journals A SVM Approach of Aircraft Conflict Detection in Free Flight

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xu-rui Jiang ◽  
Xiang-xi Wen ◽  
Ming-gong Wu ◽  
Ze-kun Wang ◽  
Xi Qiu
Keyword(s):  
False Alarm ◽  
Sampling Technique ◽  
Conflict Detection ◽  
Mapping Method ◽  
Detection Methods ◽  
Support Vector ◽  
Sigmoid Function ◽  
Free Flight ◽  
Potential Conflict ◽  
Look Ahead

Probabilistic conflict detection methods typically require high computational burden to deal with complex multiaircraft conflict detection. In this article, aircraft conflict detection is considered as a binary classification problem; therefore, it can be solved by a pattern recognition method. A potential conflict would be identified, as long as its flight data features are extracted and fed to a classifier which has been trained by a large number of flight datasets. Based on this, a new method based on support vector machine (SVM) is employed to detect multiaircraft conflict in “Free Flight” airspace and to estimate the conflict probability. For that purpose, the current positions, velocity vectors, and predicted look-ahead time are selected as detection factors, and the detection model is established by SVM to detect aircraft conflict within look-ahead time during short and medium terms. Moreover, conflict probabilities are determined by the sigmoid function mapping method. Nevertheless, false alarm rate is always a first and foremost problem that troubles air traffic controllers. For the purpose of reducing false alarm rates, Synthetic Minority Over-sampling Technique (SMOTE) method is used to handle imbalanced datasets. Extensive simulation results are presented to illustrate the rationality and accuracy of this method.

The Taxonomy of Thinking in Reading Questions in “Look Ahead an English Course for Senior High School Level 1, 2, & 3”

2017 ◽  
Vol 6 (2) ◽  
Author(s):  
Beny Septian Panjaitan And Rahmad Husein
Keyword(s):  
High School ◽  
Quantitative Research ◽  
Sampling Technique ◽  
Senior High School ◽  
School Level ◽  
High School Level ◽  
Cognitive Dimension ◽  
Look Ahead ◽  
Table Analysis ◽  
Level 1

This study aimed at analyzing the cognitive dimension based on Revised BloomTaxonomy in reading questions in Look Ahead an English Course for Senior HighSchool Level 1, 2, & 3. This study used quantitative research design. The sampleswere 141 reading questions which taken by using random sampling technique byusing Statistical Program for Social Science (SPSS) version 20.0. in Look Aheadan English Course for Senior High School Level 1, 2, & 3. The data were analyzedby using Table analysis of cognitive dimension of Revised Bloom Taxonomy. Theanalysis showed that the most dominant cognitive dimension of Revised BloomTaxonomy in remembering dimension (57.45%). The second dominant cognitivedimension is understanding dimension (26.24%). The third dominant cognitivedimension is evaluating dimension (10.64%). The fourth dominant cognitivedimension is creating dimension (3.55%). The fifth dominant cognitive dimension isanalyzing dimension (2.13%). There was no cognitive dimension of applyingdimension that applied in reading question of the textbooks.

scholarly journals A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
R. Sekhar ◽  
K. Sasirekha ◽  
P. S. Raja ◽  
K. Thangavel
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Radial Basis Function Network ◽  
Detection Methods ◽  
Support Vector ◽  
Parameter Method ◽  
List Type ◽  
Fuzzy C Means

Abstract Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax. Article Highlights A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks. Missing values have been imputed with the Fuzzy C-Means Rough Parameter method. The discriminate features are extracted using Deep Autoencoder with more hidden layers.

scholarly journals A FOD Detection Approach on Millimeter-Wave Radar Sensors Based on Optimal VMD and SVDD

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 997
Author(s):  
Jun Zhong ◽  
Xin Gou ◽  
Qin Shu ◽  
Xing Liu ◽  
Qi Zeng
Keyword(s):  
Millimeter Wave ◽  
Detection Methods ◽  
Economic Losses ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Range Resolution ◽  
Radar Sensors ◽  
Detection Approach ◽  
Wave Radar ◽  
Whale Optimization

Foreign object debris (FOD) on airport runways can cause serious accidents and huge economic losses. FOD detection systems based on millimeter-wave (MMW) radar sensors have the advantages of higher range resolution and lower power consumption. However, it is difficult for traditional FOD detection methods to detect and distinguish weak signals of targets from strong ground clutter. To solve this problem, this paper proposes a new FOD detection approach based on optimized variational mode decomposition (VMD) and support vector data description (SVDD). This approach utilizes SVDD as a classifier to distinguish FOD signals from clutter signals. More importantly, the VMD optimized by whale optimization algorithm (WOA) is used to improve the accuracy and stability of the classifier. The results from both the simulation and field case show the excellent FOD detection performance of the proposed VMD-SVDD method.

A hybrid predictive framework for evaluating P2P credit risks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Liang He ◽  
Haiyan Xu ◽  
Ginger Y. Ke
Keyword(s):  
Imbalanced Data ◽  
Sampling Technique ◽  
Critical Value ◽  
Quantitative Model ◽  
Support Vector ◽  
Content Type ◽  
Dynamic Selection ◽  
Credit Risks ◽  
Classification Feature ◽  
Selection Approach

PurposeDespite better accessibility and flexibility, peer-to-peer (P2P) lending has suffered from excessive credit risks, which may cause significant losses to the lenders and even lead to the collapse of P2P platforms. The purpose of this research is to construct a hybrid predictive framework that integrates classification, feature selection, and data balance algorithms to cope with the high-dimensional and imbalanced nature of P2P credit data.Design/methodology/approachAn improved synthetic minority over-sampling technique (IMSMOTE) is developed to incorporate the randomness and probability into the traditional synthetic minority over-sampling technique (SMOTE) to enhance the quality of synthetic samples and the controllability of synthetic processes. IMSMOTE is then implemented along with the grey relational clustering (GRC) and the support vector machine (SVM) to facilitate a comprehensive assessment of the P2P credit risks. To enhance the associativity and functionality of the algorithm, a dynamic selection approach is integrated with GRC and then fed in the SVM's process of parameter adaptive adjustment to select the optimal critical value. A quantitative model is constructed to recognize key criteria via multidimensional representativeness.FindingsA series of experiments based on real-world P2P data from Prosper Funding LLC demonstrates that our proposed model outperforms other existing approaches. It is also confirmed that the grey-based GRC approach with dynamic selection succeeds in reducing data dimensions, selecting a critical value, identifying key criteria, and IMSMOTE can efficiently handle the imbalanced data.Originality/valueThe grey-based machine-learning framework proposed in this work can be practically implemented by P2P platforms in predicting the borrowers' credit risks. The dynamic selection approach makes the first attempt in the literature to select a critical value and indicate key criteria in a dynamic, visual and quantitative manner.

scholarly journals UAV-BASED STRUCTURAL DAMAGE MAPPING – RESULTS FROM 6 YEARS OF RESEARCH IN TWO EUROPEAN PROJECTS

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 187-194 ◽  
Author(s):  
N. Kerle ◽  
F. Nex ◽  
D. Duarte ◽  
A. Vetrivel
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Point Cloud ◽  
Feature Fusion ◽  
Texture Features ◽  
Mobile App ◽  
Detection Methods ◽  
Support Vector ◽  
3D Point Cloud ◽  
Damage Mapping

<p><strong>Abstract.</strong> Structural disaster damage detection and characterisation is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of UAV in recent years has opened up many new opportunities for damage mapping, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. We have addressed the problem in the context of two European research projects, RECONASS and INACHUS. In this paper we synthesize and evaluate the progress of 6 years of research focused on advanced image analysis that was driven by progress in computer vision, photogrammetry and machine learning, but also by constraints imposed by the needs of first responder and other civil protection end users. The projects focused on damage to individual buildings caused by seismic activity but also explosions, and our work centred on the processing of 3D point cloud information acquired from stereo imagery. Initially focusing on the development of both supervised and unsupervised damage detection methods built on advanced texture features and basic classifiers such as Support Vector Machine and Random Forest, the work moved on to the use of deep learning. In particular the coupling of image-derived features and 3D point cloud information in a Convolutional Neural Network (CNN) proved successful in detecting also subtle damage features. In addition to the detection of standard rubble and debris, CNN-based methods were developed to detect typical façade damage indicators, such as cracks and spalling, including with a focus on multi-temporal and multi-scale feature fusion. We further developed a processing pipeline and mobile app to facilitate near-real time damage mapping. The solutions were tested in a number of pilot experiments and evaluated by a variety of stakeholders.</p>

scholarly journals Measuring driver cognitive distraction through lips and eyebrows

2022 ◽  
Vol 12 (1) ◽  
pp. 756
Author(s):  
Afizan Azman ◽  
Mohd. Fikri Azli Abdullah ◽  
Sumendra Yogarayan ◽  
Siti Fatimah Abdul Razak ◽  
Hartini Azman ◽  
...  
Keyword(s):  
Facial Expression ◽  
Bayesian Network ◽  
Dynamic Bayesian Network ◽  
Detection Methods ◽  
Support Vector ◽  
Head Orientation ◽  
Strongly Correlated ◽  
Physiological Measurement ◽  
Cognitive Distraction ◽  
Study Results

<span>Cognitive distraction is one of the several contributory factors in road accidents. A number of cognitive distraction detection methods have been developed. One of the most popular methods is based on physiological measurement. Head orientation, gaze rotation, blinking and pupil diameter are among popular physiological parameters that are measured for driver cognitive distraction. In this paper, lips and eyebrows are studied. These new features on human facial expression are obvious and can be easily measured when a person is in cognitive distraction. There are several types of movement on lips and eyebrows that can be captured to indicate cognitive distraction. Correlation and classification techniques are used in this paper for performance measurement and comparison. Real time driving experiment was setup and faceAPI was installed in the car to capture driver’s facial expression. Linear regression, support vector machine (SVM), static Bayesian network (SBN) and logistic regression (LR) are used in this study. Results showed that lips and eyebrows are strongly correlated and have a significant role in improving cognitive distraction detection. Dynamic Bayesian network (DBN) with different confidence of levels was also used in this study to classify whether a driver is distracted or not.</span>

scholarly journals Recognition of Position Group Relationship in Dynamic Social Networks

2019 ◽  
Vol 8 (12) ◽  
pp. 118-121
Keyword(s):  
News Media ◽  
Online Social Network ◽  
Research Area ◽  
Detection Methods ◽  
Support Vector ◽  
Vector Method ◽  
Detection Algorithms ◽  
Network Intrusion ◽  
Dynamic Social Networks ◽  
Network Anomaly Detection

Mental stress is turning into a threat to people's health currently days. With the last step of life, a lot of and a lot of folks are feeling stressed. A novel hybrid model combined with Convolution Neural Network (CNN) to control tweet content and social interaction information for stress detection effectively. Network anomaly detection is an important and dynamic research area. Many network intrusion detection methods and systems (NIDS) have been proposed in the literature. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ineffective or not applicable. Based on the information that is provided by the online social network, the conditions are limited. This method can opinion investigation of Facebook post after Formation of point utilizing Support Vector Method (SVM). After grouping client is in pressure or not k-closest neighbor calculation (KNN) is utilized for proposal emergency clinic on a guide just as Admin can send letters of precautionary measure list for the client for end up solid and upbeat throughout everyday life

scholarly journals Arabic English Cross-Lingual Plagiarism Detection Based on Keyphrases Extraction, Monolingual and Machine Learning Approach

2019 ◽  
pp. 1-12
Author(s):  
Mokhtar Al-Suhaiqi ◽  
Muneer A. S. Hazaa ◽  
Mohammed Albared
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Approach ◽  
Plagiarism Detection ◽  
Machine Learning Approach ◽  
Cross Lingual ◽  
Cross Language

Due to rapid growth of research articles in various languages, cross-lingual plagiarism detection problem has received increasing interest in recent years. Cross-lingual plagiarism detection is more challenging task than monolingual plagiarism detection. This paper addresses the problem of cross-lingual plagiarism detection (CLPD) by proposing a method that combines keyphrases extraction, monolingual detection methods and machine learning approach. The research methodology used in this study has facilitated to accomplish the objectives in terms of designing, developing, and implementing an efficient Arabic – English cross lingual plagiarism detection. This paper empirically evaluates five different monolingual plagiarism detection methods namely i)N-Grams Similarity, ii)Longest Common Subsequence, iii)Dice Coefficient, iv)Fingerprint based Jaccard Similarity  and v) Fingerprint based Containment Similarity. In addition, three machine learning approaches namely i) naïve Bayes, ii) Support Vector Machine, and iii) linear logistic regression classifiers are used for Arabic-English Cross-language plagiarism detection. Several experiments are conducted to evaluate the performance of the key phrases extraction methods. In addition, Several experiments to investigate the performance of machine learning techniques to find the best method for Arabic-English Cross-language plagiarism detection. According to the experiments of Arabic-English Cross-language plagiarism detection, the highest result was obtained using SVM   classifier with 92% f-measure. In addition, the highest results were obtained by all classifiers are achieved, when most of the monolingual plagiarism detection methods are used. 

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