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Journal articles on the topic 'Feature behavior analysis'
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KARA, LEVENT BURAK, and THOMAS F. STAHOVICH. "Causal reasoning using geometric analysis." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 5 (November 2002): 363–84. http://dx.doi.org/10.1017/s0890060402165036.
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We describe an approach that uses causal and geometric reasoning to construct explanations for the purposes of the geometric features on the parts of a mechanical device. To identify the purpose of a feature, the device is simulated with and without the feature. The simulations are then translated into a “causal-process” representation, which allows qualitatively important differences to be identified. These differences reveal the behaviors caused and prevented by the feature and thus provide useful cues about the feature's purpose. A clear understanding of the feature's purpose, however, requires a detailed analysis of the causal connections between the caused and prevented behaviors. This presents a significant challenge because one has to understand how a behavior that normally takes place affects (or is affected by) another behavior that is normally absent. This article describes techniques for identifying such elusive relationships. These techniques employ a set of rules that can determine if one behavior enables or disables another that is spatially and temporally far away. They do so by geometrically examining the traces of the causal processes in the device's configuration space. Using the results of this analysis, our program can automatically generate text output describing how the feature performs its function.
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T.Sajana, Monali Gulhane,. "Human Behavior Prediction and Analysis Using Machine Learning-A Review." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 870–76. http://dx.doi.org/10.17762/turcomat.v12i5.1499.
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Nowadays many trends are being in the area of medicine to predict the human behaviour and analysis of patient behaviour is being studied but the technical difficulty of cost efficient method to predict the behaviour of user is overcome in the proposed researched methodology .The mental health of the used can lead to good immunity system to be healthy in this pandemic of COVID-19. Hence After a detailed study on different human health disease classification techniques it is found that machine learning techniques are reliable for the feature extraction and analysis of the different human parameters. CNN is the most optimum choice of classification of diseases. Feature extraction and feature selection is automatically managed by the CNN layers, which reduces the training speed. Techniques like sensor-based feature extraction like EEG, ECG, etc. will be further explored using machine learning algorithms for detection of early detections of diseases from human behavior on different platforms in this research. Social behavior and eating habits play a vital role in disease detection. A system that combines such a wide variety of features with effective classification techniques at each stage is needed. The research in this paper contributes the review of the human behavior analysis through different body parameters, food habits and social media influences with social behavior of the person. The main objective of research is to analysis theses different area parameters to predict the early signs of the diseases.
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Lu, Jia, Jun Shen, Wei Qi Yan, and Boris Bačić. "An Empirical Study for Human Behavior Analysis." International Journal of Digital Crime and Forensics 9, no. 3 (July 2017): 11–27. http://dx.doi.org/10.4018/ijdcf.2017070102.
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This paper presents an empirical study for human behavior analysis based on three distinct feature extraction techniques: Histograms of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Scale Invariant Local Ternary Pattern (SILTP). The utilised public videos representing spatio-temporal problem area of investigation include INRIA person detection and Weizmann pedestrian activity datasets. For INRIA dataset, both LBP and HOG were able to eliminate redundant video data and show human-intelligible feature visualisation of extracted features required for classification tasks. However, for Weizmann dataset only HOG feature extraction was found to work well with classifying five selected activities/exercises (walking, running, skipping, jumping and jacking).
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Dang, Zijun, Shunshun Liu, Tong Li, and Liang Gao. "Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm." Computational Intelligence and Neuroscience 2021 (July 5, 2021): 1–10. http://dx.doi.org/10.1155/2021/3715116.
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In this paper, a deep confidence neural network algorithm is used to design and deeply analyze the risk warning model for stadium operation. Many factors, such as video shooting angle, background brightness, diversity of features, and the relationship between human behaviors, make feature attribute-based behavior detection a focus of researchers’ attention. To address these factors, researchers have proposed a method to extract human behavior skeleton and optical flow feature information from videos. The key of the deep confidence neural network-based recognition method is the extraction of the human skeleton, which extracts the skeleton sequence of human behavior from a surveillance video, where each frame of the skeleton contains 18 joints of the human skeleton and the confidence value estimated for each frame of the skeleton, and builds a deep confidence neural network model to classify the dangerous behavior based on the obtained skeleton feature information combined with the time vector in the skeleton sequence and determine the danger level of the behavior by setting the corresponding threshold value. The deep confidence neural network uses different feature information compared with the spatiotemporal graph convolutional network. The deep confidence neural network establishes the deep confidence neural network model based on the human optical flow information, combined with the temporal relational inference of video frames. The key of the temporal relationship network-based recognition method is to extract some frames from the video in an orderly or random way into the temporal relationship network. In this paper, we use several methods for comparison experiments, and the results show that the recognition method based on skeleton and optical flow features is significantly better than the algorithm of manual feature extraction.
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OU, YONGSHENG, HUIHUAN QIAN, XINYU WU, and YANGSHENG XU. "REAL-TIME SURVEILLANCE BASED ON HUMAN BEHAVIOR ANALYSIS." International Journal of Information Acquisition 02, no. 04 (December 2005): 353–65. http://dx.doi.org/10.1142/s0219878905000714.
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This paper introduces a real-time video surveillance system which can track people and detect human abnormal behaviors. In the blob detection part, an optical flow algorithm for crowd environment is studied experimentally and a comparison study with respect to traditional subtraction approach is carried out. The different approaches in segmentation and tracking enable the system to track persons when they change movement unpredictably in occlusion. We developed two methods for the human abnormal behavior analysis. The first one employs Principal Component Analysis for feature selection and Support Vector Machine for classification of human behaviors. The proposed feature selection method is based on the border information of four consecutive blobs. The second approach computes optical flow to obtain the velocity of each pixel for determining whether a human behavior is normal or not. Both algorithms are successfully developed in crowded environments to detect the following human abnormal behaviors: (1) Running people in a crowded environment; (2) falling down movement while most are walking or standing; (3) a person carrying an abnormal bar in a square; (4) a person waving hand in the crowd. Experimental results demonstrate these two methods are robust in detecting human abnormal behaviors.
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Zhao, Guangyong. "Feature Recognition of Human Motion Behavior Based on Depth Sequence Analysis." Complexity 2021 (July 5, 2021): 1–10. http://dx.doi.org/10.1155/2021/4104716.
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The current research on still image recognition has been very successful, but the study of action recognition for video classes is still a challenging topic. In this work, we propose a random projection-based human action recognition algorithm to address the lack of depth information in color information (RGB video frames) that is not easily affected by environmental factors such as illumination and the lack of ability to recognize actions along the direction of view. A network structure is designed to take the obvious advantage of long- and short-term memory networks for controlling and remembering long sequences of historical information. The network structure in this paper is constituted by multiple memory units. At the same time, this paper constructs the spatial features, temporal features, and depth features of the three recognition stream outputs into a feature matrix, whose feature matrix is divided into multiple temporal segments according to the temporal dimension, then inputs them into the network layer in order, and achieves the fusion of the feature matrix in this paper according to their correlation characteristics on the temporal axis. Here, we proposed the concept of random batch projection operators. This basically uses as much sublimitation information as possible to improve projection accuracy by randomly selecting several subdependencies as projections defined during projection. A compressed sensing design of human motion acceleration data for low-power body area networks is proposed, and the basic idea and implementation process of compressed sensing theory for human motion data compression and reconstruction in wireless body area networks are introduced in detail.
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Zhu, Xiaoliang, Yuanxin Ye, Liang Zhao, and Chen Shen. "MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy." Sensors 21, no. 19 (October 5, 2021): 6629. http://dx.doi.org/10.3390/s21196629.
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In recent years, massive open online courses (MOOCs) have received widespread attention owing to their flexibility and free access, which has attracted millions of online learners to participate in courses. With the wide application of MOOCs in educational institutions, a large amount of learners’ log data exist in the MOOCs platform, and this lays a solid data foundation for exploring learners’ online learning behaviors. Using data mining techniques to process these log data and then analyze the relationship between learner behavior and academic performance has become a hot topic of research. Firstly, this paper summarizes the commonly used predictive models in the relevant research fields. Based on the behavior log data of learners participating in 12 courses in MOOCs, an entropy-based indicator quantifying behavior change trends is proposed, which explores the relationships between behavior change trends and learners’ academic performance. Next, we build a set of behavioral features, which further analyze the relationships between behaviors and academic performance. The results demonstrate that entropy has a certain correlation with the corresponding behavior, which can effectively represent the change trends of behavior. Finally, to verify the effectiveness and importance of the predictive features, we choose four benchmark models to predict learners’ academic performance and compare them with the previous relevant research results. The results show that the proposed feature selection-based model can effectively identify the key features and obtain good prediction performance. Furthermore, our prediction results are better than the related studies in the performance prediction based on the same Xuetang MOOC platform, which demonstrates that the combination of the selected learner-related features (behavioral features + behavior entropy) can lead to a much better prediction performance.
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Li, Bin, and Fan Zhang. "Analysis of Interaction Grouping Modeling Fusion Group Behavior Recognition Algorithm." Academic Journal of Science and Technology 4, no. 1 (December 13, 2022): 149–53. http://dx.doi.org/10.54097/ajst.v4i1.3607.
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In order to make full use of the effective information in the video, this paper proposes a multi-model interactive video behavior recognition method. In order to solve the problems of incomplete human target detection and redundant feature extraction, YOLO_V4 is used to detect the human body and remove the redundant background information. Then, it is proposed to introduce the channel attention model SE-NET into the Inception_V3 network, so as to strengthen the extraction of key features and make the network pay more attention to the details of key features. Finally, the feature information is sent to LSTM network with memory function for action recognition and classification. The multi-model mutual fusion algorithm proposed in this paper is tested and verified on an internationally published UT-Interaction data set. The experimental results show that the accuracy of interactive behavior recognition is improved, and the improved accuracy is 85.1%, which indicates that the multi-model fusion method has higher accuracy.
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Zhou, Aijun, Nurbol Luktarhan, and Zhuang Ai. "Research on WebShell Detection Method Based on Regularized Neighborhood Component Analysis (RNCA)." Symmetry 13, no. 7 (July 4, 2021): 1202. http://dx.doi.org/10.3390/sym13071202.
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The variant, encryption, and confusion of WebShell results in problems in the detection method based on feature selection, such as poor detection effect and weak generalization ability. In order to solve this problem, a method of WebShell detection based on regularized neighborhood component analysis (RNCA) is proposed. The RNCA algorithm can effectively reduce the dimension of data while ensuring the accuracy of classification. In this paper, it is innovatively applied to a WebShell detection neighborhood, taking opcode behavior sequence features as the main research object, constructing vocabulary by using opcode sequence features with variable length, and effectively reducing the dimension of WebShell features from the perspective of feature selection. The opcode sequence selected by the algorithm is symmetrical with the source code file, which has great reference value for WebShell classification. On the issue of the single feature, this paper uses the fusion of behavior sequence features and text static features to construct a feature combination with stronger representation ability, which effectively improves the recognition rate of WebShell to a certain extent.
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Ye, Mingtao, Xin Sheng, Yanjie Lu, Guodao Zhang, Huiling Chen, Bo Jiang, Senhao Zou, and Liting Dai. "SA-FEM: Combined Feature Selection and Feature Fusion for Students’ Performance Prediction." Sensors 22, no. 22 (November 15, 2022): 8838. http://dx.doi.org/10.3390/s22228838.
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Around the world, the COVID-19 pandemic has created significant obstacles for education, driving people to discover workarounds to maintain education. Because of the excellent benefit of cheap-cost information distribution brought about by the advent of the Internet, some offline instructional activity started to go online in an effort to stop the spread of the disease. How to guarantee the quality of teaching and promote the steady progress of education has become more and more important. Currently, one of the ways to guarantee the quality of online learning is to use independent online learning behavior data to build learning performance predictors, which can provide real-time monitoring and feedback during the learning process. This method, however, ignores the internal correlation between e-learning behaviors. In contrast, the e-learning behavior classification model (EBC model) can reflect the internal correlation between learning behaviors. Therefore, this study proposes an online learning performance prediction model, SA-FEM, based on adaptive feature fusion and feature selection. The proposed method utilizes the relationship among features and fuses features according to the category that achieved better performance. Through the analysis of experimental results, the feature space mined by the fine-grained differential evolution algorithm and the adaptive fusion of features combined with the differential evolution algorithm can better support online learning performance prediction, and it is also verified that the adaptive feature fusion strategy based on the EBC model proposed in this paper outperforms the benchmark method.
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Sravani, Thatiparthi, Srinivasa Rao Madala, and Sk HeenaKauser. "College students’ Network behavior Using data mining and feature analysis." Journal of Physics: Conference Series 2089, no. 1 (November 1, 2021): 012075. http://dx.doi.org/10.1088/1742-6596/2089/1/012075.
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Abstract Teachers may use advanced analytics to rapidly and correctly understand undergraduate behavior trends, especially when it comes to identifying undergraduate groupings that need to be focused on at a later time. This study uses data mining cluster analysis to analyze the constituent behavior of 3,245 undergraduates in a specific level ‘B’ institution’s college network. According to the data, there are four different undergraduate groups with different Web access features, with 350 participants using the accomplishments and other variables of their success have an influence on these students. As a result of this research, we were able to collect data on undergraduate college network activity, which may be used to aid in the development of academic advising management.
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Gaines, D. M., F. Castan˜o, and C. C. Hayes. "MEDIATOR: A Resource Adaptive Feature Recognizer that Intertwines Feature Extraction and Manufacturing Analysis." Journal of Mechanical Design 121, no. 1 (March 1, 1999): 145–58. http://dx.doi.org/10.1115/1.2829415.
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A deterrent to practical use of many feature extraction systems is that they are difficult to maintain, either because they depend on the use of a library of feature-types which must be updated when the underlying manufacturing resources change (e.g. tools and fixtures), or they rely on the use of task-specific post processors, which must also be updated. For such systems to become practical, it must be easy for a user to update the system to match the current resources. This paper presents MEDIATOR (Maintainable, Extensible Design and manufacturing Integration Architecture and TranslatOR). MEDIATOR is a resource adaptive feature extraction and early process planning system for 3-axis milling. A resource adaptive system is one that changes its behavior as the manufacturing resources in a shop change. MEDIATOR allows users to select from a standard set of tools and fixtures, and automatically identifies any changes in the features that result. It attains its resource adaptive behavior by blurring the line between feature extraction and process planning; descriptions of the manufacturing resources are used to directly identify manufacturable areas of the part. A non-programmer can easily update MEDIATOR by selecting different shop resources.
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Cho, Hyunsung, DaEun Choi, Donghwi Kim, Wan Ju Kang, Eun Kyoung Choe, and Sung-Ju Lee. "Reflect, not Regret: Understanding Regretful Smartphone Use with App Feature-Level Analysis." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (October 13, 2021): 1–36. http://dx.doi.org/10.1145/3479600.
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Digital intervention tools against problematic smartphone usage help users control their consumption on smartphones, for example, by setting a time limit on an app. However, today's social media apps offer a mix of quasiessential and addictive features in an app (e.g., Instagram has following feeds, recommended feeds, stories, and direct messaging features), which makes it hard to apply a uniform logic for all uses of an app without a nuanced understanding of feature-level usage behaviors. We study when and why people regret using different features of social media apps on smartphones. We examine regretful feature uses in four smartphone social media apps (Facebook, Instagram, YouTube, and KakaoTalk) by utilizing feature usage logs, ESM surveys on regretful use collected for a week, and retrospective interviews from 29 Android users. In determining whether a feature use is regretful, users considered different types of rewards they obtained from using a certain feature (i.e., social, informational, personal interests, and entertainment) as well as alternative rewards they could have gained had they not used the smartphone (e.g., productivity). Depending on the types of rewards and the way rewards are presented to users, probabilities to regret vary across features of the same app. We highlight three patterns of features with different characteristics that lead to regretful use. First, "following"-based features (e.g., Facebook's News Feed and Instagram's Following Posts and Stories) induce habitual checking and quickly deplete rewards from app use. Second, recommendation-based features situated close to actively used features (e.g., Instagram's Suggested Posts adjacent to Search) cause habitual feature tour and sidetracking from the original intention of app use. Third, recommendation-based features with bite-sized contents (e.g., Facebook's Watch Videos) induce using "just a bit more," making people fall into prolonged use. We discuss implications of our findings for how social media apps and intervention tools can be designed to reduce regretful use and how feature-level usage information can strengthen self-reflection and behavior changes.
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Zhao, Zhijun, Chen Xu, and Bo Li. "A LSTM-Based Anomaly Detection Model for Log Analysis." Journal of Signal Processing Systems 93, no. 7 (February 5, 2021): 745–51. http://dx.doi.org/10.1007/s11265-021-01644-4.
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AbstractSecurity devices produce huge number of logs which are far beyond the processing speed of human beings. This paper introduces an unsupervised approach to detecting anomalous behavior in large scale security logs. We propose a novel feature extracting mechanism and could precisely characterize the features of malicious behaviors. We design a LSTM-based anomaly detection approach and could successfully identify attacks on two widely-used datasets. Our approach outperforms three popular anomaly detection algorithms, one-class SVM, GMM and Principal Components Analysis, in terms of accuracy and efficiency.
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G M, Basavaraj, and Ashok Kusagur. "Crowd Anomaly Detection Using Motion Based Spatio-Temporal Feature Analysis." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 3 (September 1, 2017): 737. http://dx.doi.org/10.11591/ijeecs.v7.i3.pp737-747.
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<p>Recently, the demand for surveillance system is increasing in real time application to enhance the security system. These surveillance systems are mainly used in crowded places such as shopping malls, sports stadium etc. In order to support enhance the security system, crowd behavior analysis has been proven a significant technique which is used for crowd monitoring, visual surveillance etc. For crowd behavior analysis, motion analysis is a crucial task which can be achieved with the help of trajectories and tracking of objects. Various approaches have been proposed for crowd behavior analysis which has limitation for densely crowded scenarios, a new object entering the scene etc. In this work, we propose a new approach for abnormal crowd behavior detection. Proposed approach is a motion based spatio-temporal feature analysis technique which is capable of obtaining trajectories of each detected object. We also present a technique to carry out the evaluation of individual object and group of objects by considering relational descriptors based on their environmental context. Finally, a classification is carried out for detection of abnormal or normal crowd behavior by following patch based process. In the results, we have reported that proposed model is able to achieve better performance when compared to existing techniques in terms of classification accuracy, true positive rate, and false positive rate.</p>
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Qiu, Feiyue, Lijia Zhu, Guodao Zhang, Xin Sheng, Mingtao Ye, Qifeng Xiang, and Ping-Kuo Chen. "E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior." Entropy 24, no. 5 (May 19, 2022): 722. http://dx.doi.org/10.3390/e24050722.
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Learning analysis provides a new opportunity for the development of online education, and has received extensive attention from scholars at home and abroad. How to use data and models to predict learners’ academic success or failure and give teaching feedback in a timely manner is a core problem in the field of learning analytics. At present, many scholars use key learning behaviors to improve the prediction effect by exploring the implicit relationship between learning behavior data and grades. At the same time, it is very important to explore the association between categories and prediction effects in learning behavior classification. This paper proposes a self-adaptive feature fusion strategy based on learning behavior classification, aiming to mine the effective E-learning behavior feature space and further improve the performance of the learning performance prediction model. First, a behavior classification model (E-learning Behavior Classification Model, EBC Model) based on interaction objects and learning process is constructed; second, the feature space is preliminarily reduced by entropy weight method and variance filtering method; finally, combined with EBC Model and a self-adaptive feature fusion strategy to build a learning performance predictor. The experiment uses the British Open University Learning Analysis Dataset (OULAD). Through the experimental analysis, an effective feature space is obtained, that is, the basic interactive behavior (BI) and knowledge interaction behavior (KI) of learning behavior category has the strongest correlation with learning performance.And it is proved that the self-adaptive feature fusion strategy proposed in this paper can effectively improve the performance of the learning performance predictor, and the performance index of accuracy(ACC), F1-score(F1) and kappa(K) reach 98.44%, 0.9893, 0.9600. This study constructs E-learning performance predictors and mines the effective feature space from a new perspective, and provides some auxiliary references for online learners and managers.
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Wang, Min, Shuguang Li, Lei Zhu, and Jin Yao. "Analysis of drivers’ characteristic driving operations based on combined features." Journal of Intelligent and Connected Vehicles 1, no. 3 (October 1, 2018): 114–19. http://dx.doi.org/10.1108/jicv-09-2018-0009.
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Purpose Analysis of characteristic driving operations can help develop supports for drivers with different driving skills. However, the existing knowledge on analysis of driving skills only focuses on single driving operation and cannot reflect the differences on proficiency of coordination of driving operations. Thus, the purpose of this paper is to analyze driving skills from driving coordinating operations. There are two main contributions: the first involves a method for feature extraction based on AdaBoost, which selects features critical for coordinating operations of experienced drivers and inexperienced drivers, and the second involves a generating method for candidate features, called the combined features method, through which two or more different driving operations at the same location are combined into a candidate combined feature. A series of experiments based on driving simulator and specific course with several different curves were carried out, and the result indicated the feasibility of analyzing driving behavior through AdaBoost and the combined features method. Design/methodology/approach AdaBoost was used to extract features and the combined features method was used to combine two or more different driving operations at the same location. Findings A series of experiments based on driving simulator and specific course with several different curves were carried out, and the result indicated the feasibility of analyzing driving behavior through AdaBoost and the combined features method. Originality/value There are two main contributions: the first involves a method for feature extraction based on AdaBoost, which selects features critical for coordinating operations of experienced drivers and inexperienced drivers, and the second involves a generating method for candidate features, called the combined features method, through which two or more different driving operations at the same location are combined into a candidate combined feature.
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Yousukkee, Sawita, and Nawaporn Wisitpongphan. "Analysis of spammers’ behavior on a live streaming chat." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (March 1, 2021): 139. http://dx.doi.org/10.11591/ijai.v10.i1.pp139-150.
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<span id="docs-internal-guid-f908fd2e-7fff-1849-4fda-c2cf9baed97e"><span>Live streaming is becoming a popular channel for advertising and marketing. An advertising company can use this feature to broadcast and reach a large number of customers. YouTube is one of the streaming media with an extreme growth rate and a large number of viewers. Thus, it has become a primary target of spammers and attackers. Understanding the behavior of users on live chat may reduce the moderator’s time in identifying and preventing spammers from disturbing other users. In this paper, we analyzed YouTube live streaming comments in order to understand spammers’ behavior. Seven user’s behavior features and message characteristic features were comprehensively analyzed. According to our findings, features that performed best in terms of run time and classification efficiency is the relevant score together with the time spent in live chat and the number of messages per user. The accuracy is as high as 66.22 percent. In addition, the most suitable technique for real-time classification is a decision tree.</span></span>
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Malik, Meenakshi, Rainu Nandal, Yudhvir Singh, Dheer Dhwaj Barak, and Yekula Prasanna Kumar. "A Metaheuristic Approach to Map Driving Pattern for Analyzing Driver Behavior Using Big Data Analysis." Mathematical Problems in Engineering 2022 (May 14, 2022): 1–13. http://dx.doi.org/10.1155/2022/1971436.
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The modern-day influx of vehicular traffic along with rapid expansion of roadways has made the selection of the best driver based on driving best practices an imperative, thus optimizing cost and ensuring safe arrival at the destination. A key factor in this is the analysis of driver behavior based on driver activities by monitoring adherence to the features comprising the established driving principles. In general, indiscriminate use of features to predict driver performance can increase process complexity due to inclusion of redundant features. An effective knowledge-based approach with a reduced set of features can help attune the driver behavior and improve driving patterns. Hence, a Deep Mutual Invariance Feature Classification (DMIFC) model has been proposed in this study for predicting driver performance to recommend the best driver. To achieve this, first, the driver behavior is broken down into various features corresponding to a simulated driving dataset and subjected to preprocessing to reduce the noise and form a redundant dataset. Thereafter, a Mutual Invariance Scale Feature Selection (MISFS) filter is used to select the relational features by calculating the spectral variance weight between mutual features. The observed mutual features are promoted to create a dominant pattern to estimate the Max feature-pattern generation using Driver Activity Intense Rate (DAIR). The features are then selected for classification based on the DAIR weightage. Additionally, the Interclass-ReLU (Rectified Linear Unit) is used to generate activation functions to produce logical neurons. The logical neurons are further optimized with Multiperceptron Radial Basis Function Networks (MP-RBFNs) to enable better classification of driver features for best prediction results. The proposed system was found to improve the driver pattern prediction accuracy and enable optimal recommendations of driving principles to the driver.
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Su, Tao, Haiyu Sun, and Zhijia Guo. "Transformer Behavior Feature Identification and Analysis Technology Based on Acoustic Interference." Journal of Physics: Conference Series 2310, no. 1 (October 1, 2022): 012051. http://dx.doi.org/10.1088/1742-6596/2310/1/012051.
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Abstract At present, most of the researches are based on linear systems, whose convergence speed and steady-state error are irreconcilable, and in actual operation, nonlinear factors greatly reduce the control performance of linear systems. If the nonlinear problem in the system can be solved, the active noise reduction system can choose low-cost electroacoustic devices with nonlinear distortion, which can not only improve the noise reduction performance, but also has special significance for reducing the cost of the system. Therefore, the main purpose of this paper is to study the feature identification and analysis of transformers based on sound wave interference. This paper explores the propagation, absorption and radiation process of the vibration generated by the transformer core and winding in the surrounding space, and finally obtains the vibration and noise signals of the transformer body, as well as the air sound field distribution at a specific spatial Experiments show that, compared with before the noise reduction experiment, the mean value of the noise in each main frequency band is reduced by about 7dB after the noise reduction experiment.
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Fradi, Hajer, and Jean-Luc Dugelay. "Spatial and temporal variations of feature tracks for crowd behavior analysis." Journal on Multimodal User Interfaces 10, no. 4 (May 30, 2015): 307–17. http://dx.doi.org/10.1007/s12193-015-0179-2.
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Xia, Huosong, Yuting Meng, Wuyue An, Zixuan Chen, and Zuopeng Zhang. "Feature mining and analysis of gray privacy products." Information Discovery and Delivery 48, no. 2 (January 10, 2020): 67–78. http://dx.doi.org/10.1108/idd-09-2019-0063.
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Purpose Excavating valuable outlier information of gray privacy products, the purpose of this study takes the online reviews of women’s underwear as an example, explores the outlier characteristics of online commentary data, and analyzes the online consumer behavior of consumers’ gray privacy products. Design/methodology/approach This research adopts the social network analysis method to analyze online reviews. Based on the online reviews collected from women’s underwear flagship store Victoria’s Secret at Tmall, this study performs word segmentation and word frequency analysis. Using the fuzzy query method, the research builds the corresponding co-word matrix and conducts co-occurrence analysis to summarize the factors affecting consumers’ purchase behavior of female underwear. Findings Establishing a formal framework of gray privacy products, this paper confirms the commonalities among consumers with respect to their perceptions of gray privacy products, shows that consumers have high privacy concerns about the disclosure or secondary use of personal private information when shopping gray privacy products, and demonstrates the big difference between online reviews of gray privacy products and their consumer descriptions. Originality/value The research lays a solid foundation for future research in gray privacy products. The factors identified in this study provide a practical reference for the continuous improvement of gray privacy products and services.
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Vezey, Edward L., and John J. Skvarla. "Computerized feature analysis of exine sculpture patterns." Review of Palaeobotany and Palynology 64, no. 1-4 (October 1990): 187–96. http://dx.doi.org/10.1016/0034-6667(90)90132-3.
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Belyaev, Sergey A., N. V. Martyushev, and Irina V. Belyaeva. "Production Tribological Behavior Feature of Metallic Nanoparticle Additives." Applied Mechanics and Materials 756 (April 2015): 275–80. http://dx.doi.org/10.4028/www.scientific.net/amm.756.275.
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Today an application of metal nanoparticles as additives to base oils is widely studied in tribological centers in many countries. The additives containing nanoparticles essentially raise the wear resistance ability of lubricants and reduce the friction coefficient. However, such lubricants are still not widely used. This paper gives a brief analysis of the problem.
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Qian, Huihuan, Yongsheng Ou, Xinyu Wu, Xiaoning Meng, and Yangsheng Xu. "Support Vector Machine for Behavior-Based Driver Identification System." Journal of Robotics 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/397865.
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We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described.
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Chen, Zhiwen, Guihua Wang, Weiyan Zhang, and Dali Zhou. "Anomaly Analysis Technology Based on Deterministic Characteristics of Intranet." MATEC Web of Conferences 232 (2018): 01030. http://dx.doi.org/10.1051/matecconf/201823201030.
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An enterprise intranet has the characteristics of service determination, limited network components, descriptive and observable characteristics, and the state of network components and network interaction behaviors need to strictly comply with security policies. Therefore, a variety of descriptive certainty can be used to describe the subject, object, and action of the network access. According to this important feature, the anomaly analysis method is simplified, and the abnormal discovery of the intranet is transformed into the problem of network dynamic feature collection and deterministic feature characterization. Based on the network state and behavior collection and analysis network dynamic characteristics, combined with the deterministic feature priori knowledge of the network, an anomaly analysis model which is especially suitable for deterministic intranet is proposed. Based on the model design, a traffic-based anomaly analysis system is implemented. The system can effectively find a variety of high-risk anomalies in the intranet.
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Fung, Wai-keung, and Yun-hui Liu. "Feature Extraction of Robot Sensor Data Using Factor Analysis for Behavior Learning." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 3 (May 20, 2004): 284–94. http://dx.doi.org/10.20965/jaciii.2004.p0284.
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The paper addresses feature extraction of sensor data for robot behavior learning using factor analysis. Redundancies in sensor types and quantities are common in sensing competence of robots. The redundancies cause the high dimensionality of the perceptual space. It is impractical to incorporate all available sensor information in decision-making and learning of robots due to the huge memory and computational requirements. This paper proposes a new approach to extract important knowledge from sensor data based on the inter-correlation of sensor data using factor analysis and construct logical perceptual space for robot behavior learning. The logical perceptual space is constructed by hypothetical latent factors extracted using factor analysis. Since the latent factors extracted have fewer dimensions than raw sensor data, using the logical perceptual space in behavior learning would significantly simplify the learning process and architecture. Experiments have been conducted to demonstrate the process of logical perceptual space extraction from ultrasonic range data for robot behavior learning.
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Zhu, Jinnuo, S. B. Goyal, Chaman Verma, Maria Simona Raboaca, and Traian Candin Mihaltan. "Machine Learning Human Behavior Detection Mechanism Based on Python Architecture." Mathematics 10, no. 17 (September 2, 2022): 3159. http://dx.doi.org/10.3390/math10173159.
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Human behavior is stimulated by the outside world, and the emotional response caused by it is a subjective response expressed by the body. Humans generally behave in common ways, such as lying, sitting, standing, walking, and running. In real life of human beings, there are more and more dangerous behaviors in human beings due to negative emotions in family and work. With the transformation of the information age, human beings can use Industry 4.0 smart devices to realize intelligent behavior monitoring, remote operation, and other means to effectively understand and identify human behavior characteristics. According to the literature survey, researchers at this stage analyze the characteristics of human behavior and cannot achieve the classification learning algorithm of single characteristics and composite characteristics in the process of identifying and judging human behavior. For example, the characteristic analysis of changes in the sitting and sitting process cannot be for classification and identification, and the overall detection rate also needs to be improved. In order to solve this situation, this paper develops an improved machine learning method to identify single and compound features. In this paper, the HATP algorithm is first used for sample collection and learning, which is divided into 12 categories by single and composite features; secondly, the CNN convolutional neural network algorithm dimension, recurrent neural network RNN algorithm, long- and short-term extreme value network LSTM algorithm, and gate control is used. The ring unit GRU algorithm uses the existing algorithm to design the model graph and the existing algorithm for the whole process; thirdly, the machine learning algorithm and the main control algorithm using the proposed fusion feature are used for HATP and human beings under the action of wearable sensors. The output features of each stage of behavior are fused; finally, by using SPSS data analysis and re-optimization of the fusion feature algorithm, the detection mechanism achieves an overall target sample recognition rate of about 83.6%. Finally, the research on the algorithm mechanism of machine learning for human behavior feature classification under the new algorithm is realized.
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Li, Xiaohan, Wenshuo Wang, Zhang Zhang, and Matthias Rötting. "Effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data." Journal of Intelligent and Connected Vehicles 1, no. 3 (October 1, 2018): 85–98. http://dx.doi.org/10.1108/jicv-09-2018-0010.
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PurposeFeature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition. This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data.Design/methodology/approachIn total, 1,375 LC cases are analyzed. To comprehensively select features, the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid. Then the effect size (Cohen’s d) andp-value of every feature are computed to assess their contribution for each scenario.FindingsIt has been found that the common lateral features, e.g. yaw rate, lateral acceleration and time-to-lane crossing, are not strong features for recognition of LC maneuver as empirical knowledge. Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification.Originality/valueIn this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.
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Aboaoja, Faitouri A., Anazida Zainal, Abdullah Marish Ali, Fuad A. Ghaleb, Fawaz Jaber Alsolami, and Murad A. Rassam. "Dynamic Extraction of Initial Behavior for Evasive Malware Detection." Mathematics 11, no. 2 (January 12, 2023): 416. http://dx.doi.org/10.3390/math11020416.
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Recently, malware has become more abundant and complex as the Internet has become more widely used in daily services. Achieving satisfactory accuracy in malware detection is a challenging task since malicious software exhibit non-relevant features when they change the performed behaviors as a result of their awareness of the analysis environments. However, the existing solutions extract features from the entire collected data offered by malware during the run time. Accordingly, the actual malicious behaviors are hidden during the training, leading to a model trained using unrepresentative features. To this end, this study presents a feature extraction scheme based on the proposed dynamic initial evasion behaviors determination (DIEBD) technique to improve the performance of evasive malware detection. To effectively represent evasion behaviors, the collected behaviors are tracked by examining the entropy distributions of APIs-gram features using the box-whisker plot algorithm. A feature set suggested by the DIEBD-based feature extraction scheme is used to train machine learning algorithms to evaluate the proposed scheme. Our experiments’ outcomes on a dataset of benign and evasive malware samples show that the proposed scheme achieved an accuracy of 0.967, false positive rate of 0.040, and F1 of 0.975.
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PUDIMAT, RAINER, ROLF BACKOFEN, and ERNST G. SCHUKAT-TALAMAZZINI. "FAST FEATURE SUBSET SELECTION IN BIOLOGICAL SEQUENCE ANALYSIS." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 02 (March 2009): 191–207. http://dx.doi.org/10.1142/s0218001409007107.
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Biological research produces a wealth of measured data. Neither it is easy for biologists to postulate hypotheses about the behavior or structure of the observed entity because the relevant properties measured are not seen in the ocean of measurements. Nor is it easy to design machine learning algorithms to classify or cluster the data items for the same reason. Algorithms for automatically selecting a highly predictive subset of the measured features can help to overcome these difficulties. We present an efficient feature selection strategy which can be applied to arbitrary feature selection problems. The core technique is a new method for estimating the quality of subsets from previously calculated qualities for smaller subsets by minimizing the mean standard error of estimated values with an approach common to support vector machines. This method can be integrated in many feature subset search algorithms. We have applied it with sequential search algorithms and have been able to reduce the number of quality calculations for finding accurate feature subsets by about 70%. We show these improvements by applying our approach to the problem of finding highly predictive feature subsets for transcription factor binding sites.
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McDonald, Anthony D., Thomas K. Ferris, and Tyler A. Wiener. "Classification of Driver Distraction: A Comprehensive Analysis of Feature Generation, Machine Learning, and Input Measures." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 6 (June 25, 2019): 1019–35. http://dx.doi.org/10.1177/0018720819856454.
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Objective The objective of this study was to analyze a set of driver performance and physiological data using advanced machine learning approaches, including feature generation, to determine the best-performing algorithms for detecting driver distraction and predicting the source of distraction. Background Distracted driving is a causal factor in many vehicle crashes, often resulting in injuries and deaths. As mobile devices and in-vehicle information systems become more prevalent, the ability to detect and mitigate driver distraction becomes more important. Method This study trained 21 algorithms to identify when drivers were distracted by secondary cognitive and texting tasks. The algorithms included physiological and driving behavioral input processed with a comprehensive feature generation package, Time Series Feature Extraction based on Scalable Hypothesis tests. Results Results showed that a Random Forest algorithm, trained using only driving behavior measures and excluding driver physiological data, was the highest-performing algorithm for accurately classifying driver distraction. The most important input measures identified were lane offset, speed, and steering, whereas the most important feature types were standard deviation, quantiles, and nonlinear transforms. Conclusion This work suggests that distraction detection algorithms may be improved by considering ensemble machine learning algorithms that are trained with driving behavior measures and nonstandard features. In addition, the study presents several new indicators of distraction derived from speed and steering measures. Application Future development of distraction mitigation systems should focus on driver behavior–based algorithms that use complex feature generation techniques.
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Naseri, Hamed, E. Owen D. Waygood, Bobin Wang, Zachary Patterson, and Ricardo A. Daziano. "A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change." Sustainability 14, no. 1 (December 21, 2021): 40. http://dx.doi.org/10.3390/su14010040.
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Indications of people’s environmental concern are linked to transport decisions and can provide great support for policymaking on climate change. This study aims to better predict individual climate change stage of change (CC-SoC) based on different features of transport-related behavior, General Ecological Behavior, New Environmental Paradigm, and socio-demographic characteristics. Together these sources result in over 100 possible features that indicate someone’s level of environmental concern. Such a large number of features may create several analytical problems, such as overfitting, accuracy reduction, and high computational costs. To this end, a new feature selection technique, named the Coyote Optimization Algorithm-Quadratic Discriminant Analysis (COA-QDA), is first proposed to find the optimal features to predict CC-SoC with the highest accuracy. Different conventional feature selection methods (Lasso, Elastic Net, Random Forest Feature Selection, Extra Trees, and Principal Component Analysis Feature Selection) are employed to compare with the COA-QDA. Afterward, eight classification techniques are applied to solve the prediction problem. Finally, a sensitivity analysis is performed to determine the most important features affecting the prediction of CC-SoC. The results indicate that COA-QDA outperforms conventional feature selection methods by increasing average testing data accuracy from 0.7% to 5.6%. Logistic Regression surpasses other classifiers with the highest prediction accuracy.
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Li, Sheliang, and Huaqi Chai. "Recognition of Teaching Features and Behaviors in Online Open Courses Based on Image Processing." Traitement du Signal 38, no. 1 (February 28, 2021): 155–64. http://dx.doi.org/10.18280/ts.380116.
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High-quality online open courses have a wide audience. To further improve the quality of these courses, it is critical to analyze the teaching behaviors in class, which are the manifestation of the overall quality of the teacher. Considering the popularity of image processing-based behavior recognition in many disciplines, this paper explores deep into the teaching features and behaviors in online open courses based on image processing. Firstly, a coding scale was designed for teaching behaviors in online open courses. Next, the principle of optical flow solving was explained for teaching video images. Then, a teaching behavior feature extraction model was established based on dual-flow deep CNN, and used to extract the key points of teacher body and the behavior features of the teacher. After that, a teaching behavior recognition method was developed combining histogram of oriented gradients (HOG) and support vector machine (SVM) to accurately allocate the teaching features and behaviors to the corresponding teaching links. Finally, the proposed model was proved effective through experiments. Based on the recognized teaching behaviors, the frequency and duration of such behaviors were subject to comparative analysis, revealing the teaching features in high-quality online open courses.
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Li, Mi, Lei Cao, Qian Zhai, Peng Li, Sa Liu, Richeng Li, Lei Feng, Gang Wang, Bin Hu, and Shengfu Lu. "Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement." Complexity 2020 (January 14, 2020): 1–9. http://dx.doi.org/10.1155/2020/4174857.
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This paper presents a method of depression recognition based on direct measurement of affective disorder. Firstly, visual emotional stimuli are used to obtain eye movement behavior signals and physiological signals directly related to mood. Then, in order to eliminate noise and redundant information and obtain better classification features, statistical methods (FDR corrected t-test) and principal component analysis (PCA) are used to select features of eye movement behavior and physiological signals. Finally, based on feature extraction, we use kernel extreme learning machine (KELM) to recognize depression based on PCA features. The results show that, on the one hand, the classification performance based on the fusion features of eye movement behavior and physiological signals is better than using a single behavior feature and a single physiological feature; on the other hand, compared with previous methods, the proposed method for depression recognition achieves better classification results. This study is of great value for the establishment of an automatic depression diagnosis system for clinical use.
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ETO, Masashi, Kotaro SONODA, Daisuke INOUE, Katsunari YOSHIOKA, and Koji NAKAO. "Fine-Grain Feature Extraction from Malware's Scan Behavior Based on Spectrum Analysis." IEICE Transactions on Information and Systems E93-D, no. 5 (2010): 1106–16. http://dx.doi.org/10.1587/transinf.e93.d.1106.
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Taki, Tsuyoshi, and Junichi Hasegawa. "Analysis and Simulation of Group Behavior Using a Dynamic Sphere of Influence." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 2 (March 20, 2005): 159–65. http://dx.doi.org/10.20965/jaciii.2005.p0159.
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To evaluate or predict human behavior, extraction of characteristic patterns from actual crowd scenes and crowd simulation in different situations based on general human behavior model are needed. We use changes in a spatial feature formed by individual movement called a "dominant region", a type of dynamic personal space. In the paper, a basic concept and calculation of the dominant region and its applications are presented. Experiments show that the proposed feature is useful in evaluating and simulating human behavior.
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Wang, Baosen, Bobo Zong, Hongwei Wang, and Bo Han. "Analysis of Digital Long Jump Take-off Wearable Sensor Monitoring System." Journal of Sensors 2021 (December 22, 2021): 1–10. http://dx.doi.org/10.1155/2021/4857624.
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The wearable sensor monitoring system builds a long jump take-off recognition network model based on different digital feature extraction methods (one-dimensional digital feature extraction method, two-dimensional digital feature extraction method, and feature extraction method combining one-dimensional digitization and recursion). Experimental verification and analysis are performed on the processed sample data, and the identification effects, advantages, and disadvantages of the four methods are obtained. First, the sensor behavior movement collection software is designed based on the Android system, and the collection time and frequency are specified at the same time. In addition, for the problem of multisensor behavior recognition, an effective result fusion method is proposed. In a multisensor behavior recognition system, constructing a parallel processing architecture is conducive to improving the rate of behavior recognition. To maintain or increase the rate of behavior recognition, the result fusion method plays a vital role. Finally, this paper analyzes the process of multitask behavior recognition and constructs a residual model that can effectively integrate multitask results and fully mine data information. The experimental results show that, for the monitoring of exercise volume, we use step count statistics to extract feature values that can distinguish activity types based on human motion characteristics. This paper proposes a sample autonomous learning method to find the optimal sample training set and avoid occurrence of overfitting problems. In the recognition of 11 types of long jump take-offs, the average accuracy rate reached 98.7%. The average replacement method is used to count the number of steps, which provides a data reference for the user’s daily exercise volume.
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Chen, Ju-Chin, Chien-Yi Lee, Peng-Yu Huang, and Cheng-Rong Lin. "Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network." Applied Sciences 10, no. 6 (March 11, 2020): 1908. http://dx.doi.org/10.3390/app10061908.
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According to the World Health Organization global status report on road safety, traffic accidents are the eighth leading cause of death in the world, and nearly one-fifth of the traffic accidents were cause by driver distractions. Inspired by the famous two-stream convolutional neural network (CNN) model, we propose a driver behavior analysis system using one spatial stream ConvNet to extract the spatial features and one temporal stream ConvNet to capture the driver’s motion information. Instead of using three-dimensional (3D) ConvNet, which would suffer from large parameters and the lack of a pre-trained model, two-dimensional (2D) ConvNet is used to construct the spatial and temporal ConvNet streams, and they were pre-trained by the large-scale ImageNet. In addition, in order to integrate different modalities, the feature-level fusion methodology was applied, and a fusion network was designed to integrate the spatial and temporal features for further classification. Moreover, a self-compiled dataset of 10 actions in the vehicle was established. According to the experimental results, the proposed system can increase the accuracy rate by nearly 30% compared to the two-stream CNN model with a score-level fusion.
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Hui, Ruan. "Video Content Analysis of Human Sports under Engineering Management Incorporating High-Level Semantic Recognition Models." Computational Intelligence and Neuroscience 2022 (January 12, 2022): 1–12. http://dx.doi.org/10.1155/2022/6761857.
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In this paper, a high-level semantic recognition model is used to parse the video content of human sports under engineering management, and the stream shape of the previous layer is embedded in the convolutional operation of the next layer, so that each layer of the convolutional neural network can effectively maintain the stream structure of the previous layer, thus obtaining a video image feature representation that can reflect the image nearest neighbor relationship and association features. The method is applied to image classification, and the experimental results show that the method can extract image features more effectively, thus improving the accuracy of feature classification. Since fine-grained actions usually share a very high similarity in phenotypes and motion patterns, with only minor differences in local regions, inspired by the human visual system, this paper proposes integrating visual attention mechanisms into the fine-grained action feature extraction process to extract features for cues. Taking the problem as the guide, we formulate the athlete’s tacit knowledge management strategy and select the distinctive freestyle aerial skills national team as the object of empirical analysis, compose a more scientific and organization-specific tacit knowledge management program, exert influence on the members in the implementation, and revise to form a tacit knowledge management implementation program with certain promotion value. Group behavior can be identified by analyzing the behavior of individuals and the interaction information between individuals. Individual interactions in a group can be represented by individual representations, and the relationship between individual behaviors can be analyzed by modeling the relationship between individual representations. The performance improvement of the method on mismatched datasets is comparable between the long-short time network based on temporal information and the language recognition method with high-level semantic embedding vectors, with the two methods improving about 12.6% and 23.0%, respectively, compared with the method using the original model and with the i-vector baseline system based on the support vector machine classification method with radial basis functions, with performance improvements about 10.10% and 10.88%, respectively.
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Wang, Lu, Ya Ping Peng, Xiang Lin Gu, Wei Cui, and Wei Ping Zhang. "System Based Seismic Strengthening Design Analysis for a Historic Building." Advanced Materials Research 133-134 (October 2010): 1265–70. http://dx.doi.org/10.4028/www.scientific.net/amr.133-134.1265.
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In system based seismic strengthening design, the redistribution of inner forces brought by the change of structural members can be considered, which is very important to guarantee the safety of a strengthened structure. Using SAP2000, the seismic behavior of a historic building was analyzed. After that, a system based seismic strengthening plan and a member based seismic strengthening plan were proposed. And the seismic behaviors of the building before and after strengthening were compared. The results show that the seismic behavior of the building can be improved with the system based seismic strengthening and the style and the feature of the building can be protected well.
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Li, Zongmin, Qi Zhang, Yuhong Wang, and Shihang Wang. "Social Media Rumor Refuter Feature Analysis and Crowd Identification Based on XGBoost and NLP." Applied Sciences 10, no. 14 (July 8, 2020): 4711. http://dx.doi.org/10.3390/app10144711.
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One prominent dark side of online information behavior is the spreading of rumors. The feature analysis and crowd identification of social media rumor refuters based on machine learning methods can shed light on the rumor refutation process. This paper analyzed the association between user features and rumor refuting behavior in five main rumor categories: economics, society, disaster, politics, and military. Natural language processing (NLP) techniques are applied to quantify the user’s sentiment tendency and recent interests. Then, those results were combined with other personalized features to train an XGBoost classification model, and potential refuters can be identified. Information from 58,807 Sina Weibo users (including their 646,877 microblogs) for the five anti-rumor microblog categories was collected for model training and feature analysis. The results revealed that there were significant differences between rumor stiflers and refuters, as well as between refuters for different categories. Refuters tended to be more active on social media and a large proportion of them gathered in more developed regions. Tweeting history was a vital reference as well, and refuters showed higher interest in topics related with the rumor refuting message. Meanwhile, features such as gender, age, user labels and sentiment tendency also varied between refuters considering categories.
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Qin, Na, Wei Dong Jin, Jin Huang, Peng Jiang, and Zhi Min Li. "High Speed Train Bogie Fault Signal Analysis Based on Wavelet Entropy Feature." Advanced Materials Research 753-755 (August 2013): 2286–89. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2286.
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Mechanical behavior of high speed trains bogie seriously impact the reliability of the train system. Performance monitoring and fault diagnosis for the critical component on bogie are very important. Simulation data of high speed train bogie fault signal is selected in data experiment. Based on multiresolution analysis, wavelet entropy features are extracted to reflect the uncertainty level of the vibration signal on scales. In the high dimension space composed by several wavelet entropy features, the dates from four fault patterns are classified and the result is satisfactory. Result show that wavelet entropy feature is effective for fault signal analysis of high speed train bogie.
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Meghdouri, Fares, Tanja Zseby, and Félix Iglesias. "Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic." Applied Sciences 8, no. 11 (November 9, 2018): 2196. http://dx.doi.org/10.3390/app8112196.
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The consolidation of encryption and big data in network communications have made deep packet inspection no longer feasible in large networks. Early attack detection requires feature vectors which are easy to extract, process, and analyze, allowing their generation also from encrypted traffic. So far, experts have selected features based on their intuition, previous research, or acritically assuming standards, but there is no general agreement about the features to use for attack detection in a broad scope. We compared five lightweight feature sets that have been proposed in the scientific literature for the last few years, and evaluated them with supervised machine learning. For our experiments, we use the UNSW-NB15 dataset, recently published as a new benchmark for network security. Results showed three remarkable findings: (1) Analysis based on source behavior instead of classic flow profiles is more effective for attack detection; (2) meta-studies on past research can be used to establish satisfactory benchmarks; and (3) features based on packet length are clearly determinant for capturing malicious activity. Our research showed that vectors currently used for attack detection are oversized, their accuracy and speed can be improved, and are to be adapted for dealing with encrypted traffic.
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Naji, Maitham Ali, Ghalib Ahmed Salman, and Muthna Jasim Fadhil. "Face recognition using selected topographical features." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (October 1, 2020): 4695. http://dx.doi.org/10.11591/ijece.v10i5.pp4695-4700.
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This paper represents a new features selection method to improve an existed feature type. Topographical (TGH) features provide large set of features by assigning each image pixel to the related feature depending on image gradient and Hessian matrix. Such type of features was handled by a proposed features selection method. A face recognition feature selector (FRFS) method is presented to inspect TGH features. FRFS depends in its main concept on linear discriminant analysis (LDA) technique, which is used in evaluating features efficiency. FRFS studies feature behavior over a dataset of images to determine the level of its performance. At the end, each feature is assigned to its related level of performance with different levels of performance over the whole image. Depending on a chosen threshold, the highest set of features is selected to be classified by SVM classifier
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Mantri, Prof Shamla, Dr Pankaj Agrawal, Prof Dipti Patil, and Dr V. M. Wadhai. "Depression Analysis using ECG Signal." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 7 (November 17, 2013): 2746–51. http://dx.doi.org/10.24297/ijct.v11i7.3470.
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ECG is a bio-medical signal which records the electrical activity of the heart versus time. They are important for diagnostic and research purposes of the human heart. In this paper we discuss a method of feature extraction which is an inevitable step in most approaches in diagnosing abnormalities in the heart. A web application is developed which extracts features of ECG signal like ST segment, QRS wave, etc. and use these features for identifying whether a person suffers from any of the four levels of stress, that is, Hyper Acute stress (Myocardial Infarction), Acute stress (Type A), Hyper Chronic stress (Ischemia) or Chronic Stress (Type B). The application is built using a decision support system formed by extensive learning of behavior of the signals of various persons.Â
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Wilson, Paul N., and John M. Pearce. "A configural analysis for feature-negative discrimination learning." Journal of Experimental Psychology: Animal Behavior Processes 18, no. 3 (1992): 265–72. http://dx.doi.org/10.1037/0097-7403.18.3.265.
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Ren, Panhong, and Mengjian Nie. "Design and Analysis of a Smart Sensor-Based Early Warning Intervention Network for School Sports Bullying among Left-Behind Children." Journal of Sensors 2022 (July 18, 2022): 1–12. http://dx.doi.org/10.1155/2022/2929254.
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This paper constructs a sports bullying early warning intervention system using smart sensors to conduct in-depth research and analysis on early warning intervention of school sports bullying behaviors among left-behind children. Unlike daily behavior recognition based on motion sensors, school sports bullying actions are very random and difficult to be described by a specific motion trajectory. For the characteristics of violent actions and daily actions, action features in the time and frequency domains are extracted and action categories are recognized by BP neural networks; for complex actions, it is proposed to decompose complex actions into basic actions to improve the recognition rate. The algorithm of combining action features and speech features to achieve violence recognition is proposed. For the complexity of audio data features, this paper firstly preprocesses the audio data with preweighting, framing, and windowing and secondly extracts the MFCC feature parameters from the audio data and then builds a deep convolutional neural network to design the violence emotion recognition algorithm. The simulation results show that the algorithm effectively improves the accuracy rate of violent action recognition to 91.25% and the recall rate of violent action recognition to 92.13%. Finally, the LDA dimensionality reduction algorithm is introduced to address the problem of the high complexity of the algorithm due to the high number of feature dimensions. The LDA dimensionality reduction algorithm reduces the number of feature dimensions to 7 dimensions, which reduces the system running time by about 52% and improves the recognition rate of specific complex actions by about 12.1% while ensuring the overall system performance.
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Siswanto, Siswanto, Zakiyah Mar'ah, Alfiyah Salsa Dila Sabir, Taufik Hidayat, Fadilah Amirul Adhel, and Waode Sitti Amni. "The Sentiment Analysis Using Naïve Bayes with Lexicon-Based Feature on TikTok Application." Jurnal Varian 6, no. 1 (November 13, 2022): 89–96. http://dx.doi.org/10.30812/varian.v6i1.2205.
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On TikTok application, there are several types of content in the form of education, cooking recipes, comedy, various tips, beauty, business, etc. However, some non-educational contents sometimes appear on TikTok homepage even though minors can access the app. As a result, TikTok application can influence the behavior of minors to be disgraceful, therefore, an assessment of the application can be one of the objects for conducting sentiment analysis. The purpose of this study is to compare the results of sentiment analysis on TikTok application using Naïve Bayes with Lexicon-Based and without Lexicon-Based features. We used the TikTok reviews on Google Play Store as our data. According to the analysis, without Lexicon-Based feature, we obtained the accuracy rate, precision rate, and recall rate of 83%, 78%, and 69%, respectively. Meanwhile, the accuracy, precision, and recall rates using the Lexicon-Based feature were 85%, 91%, and 93%, respectively. Therefore, we concluded that sentiment analysis using Naïve Bayes with Lexicon-Based feature was better than without Lexicon-Based feature on TikTok reviews.
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Kim, Gap-Yong, Muammer Koç, Rhet Mayor, and Jun Ni. "Modeling of the Semi-Solid Material Behavior and Analysis of Micro-/Mesoscale Feature Forming." Journal of Manufacturing Science and Engineering 129, no. 2 (October 21, 2006): 237–45. http://dx.doi.org/10.1115/1.2673300.
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One of the major challenges in simulation of semi-solid forming is characterizing the complex behavior of a material that consists of both solid and liquid phases. In this study, a material model for an A356 alloy in a semi-solid state has been developed for high solid fractions (>0.6) and implemented into a finite element simulation tool to investigate the micro-/mesoscale feature formation during the forming process. Compared to previous stress models, which are limited to expressing the stress dependency on only the strain rate and the temperature (or the solid fraction), the proposed stress model adds the capability of describing the semi-solid material behavior in terms of strain and structural evolution. The proposed stress model was able to explain the strain-softening behavior of the semi-solid material. Furthermore, a simulation model that includes the yield function, the flow rule, and the stress model has been developed and utilized to investigate the effects of various process parameters, including analysis type (isothermal vs nonisothermal), punch velocity, initial solid fraction, and workpiece shape (“flat” versus “tall”) on the micro-/mesofeature formation process.