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Journal articles on the topic 'Feature behavior analysis'

Author: Grafiati

Published: 10 January 2023

Last updated: 31 July 2025

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1

KARA, LEVENT BURAK, and THOMAS F. STAHOVICH. "Causal reasoning using geometric analysis." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 5 (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, requ

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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 (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 paramet

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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 (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

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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 th

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Qi, Yu, Siyu Xiong, and Bo Wu. "Analysis, Evaluation, and Prediction of Machine Learning-Based Animal Behavior Imitation." Electronics 14, no. 14 (2025): 2816. https://doi.org/10.3390/electronics14142816.

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Expressive imitation in the performing arts is typically trained through animal behavior imitation, aiming not only to reproduce action trajectories but also to recreate rhythm, style and emotional states. However, evaluation of such animal imitation behaviors relies heavily on teachers’ subjective judgments, lacking structured criteria, exhibiting low inter-rater consistency and being difficult to quantify. To enhance the objectivity and interpretability of the scoring process, this study develops a machine learning and structured pose data-based auxiliary evaluation framework for imitation q

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Donia, Manar M. F., Wessam H. El-Behaidy, and Aliaa A. A. Youssif. "Impulsive Aggression Break, Based on Early Recognition Using Spatiotemporal Features." Big Data and Cognitive Computing 7, no. 3 (2023): 150. http://dx.doi.org/10.3390/bdcc7030150.

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The study of human behaviors aims to gain a deeper perception of stimuli that control decision making. To describe, explain, predict, and control behavior, human behavior can be classified as either non-aggressive or anomalous behavior. Anomalous behavior is any unusual activity; impulsive aggressive, or violent behaviors are the most harmful. The detection of such behaviors at the initial spark is critical for guiding public safety decisions and a key to its security. This paper proposes an automatic aggressive-event recognition method based on effective feature representation and analysis. T

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Hsiang-Hua Hung, Hsiang-Hua Hung, Jiann-Liang Chen Hsiang-Hua Hung, and Yi-Wei Ma Jiann-Liang Chen. "Machine Learning Approaches to Malicious PowerShell Scripts Detection and Feature Combination Analysis." 網際網路技術學刊 25, no. 1 (2024): 167–73. http://dx.doi.org/10.53106/160792642024012501014.

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<p>With advances in communication technology, modern society relies more than ever on the Internet and various user-friendly digital tools. It provides access to and enables the manipulation of files, trips, and the Windows API. Attackers frequently use various obfuscation techniques PowerShell scripts to avoid detection by anti-virus software. Their doing so can significantly reduce the readability of the script. This work statically analyzes PowerShell scripts. Thirty-three features that were based on the script&rsquo;s keywords, format, and string combinations were used herein to

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Maitham, Ali Naji, Ahmed Salman Ghalib, and Jasim Fadhil Muthna. "Face recognition using selected topographical features." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 4695–700. https://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 t

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Bhavya, Rajeev*1 &. Mohammed Malik C. K2. "PERSONALITY AND SOCIAL NETWORK MENTAL DISORDER PREDICTION VIA TWITTER ANALYSIS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY Metanoia 19 (April 4, 2019): 6–11. https://doi.org/10.5281/zenodo.2629143.

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With the rise in popularity of social networking, various types of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, has shown up on the surface. Symptoms of these mental disorders are usually observed passively, resulting in delayed clinical intervention. From the studies it was observed that the core reason for some of this addictive behavior was depression. In this paper we propose a machine learning framework, enhanced with sentimental analysis that exploits features extracted from social network data, to accurately ide

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Karaaslan, Mahmut, Bahaeddin Turkoglu, Ersin Kaya, and Tunc Asuroglu. "Voice Analysis in Dogs with Deep Learning: Development of a Fully Automatic Voice Analysis System for Bioacoustics Studies." Sensors 24, no. 24 (2024): 7978. https://doi.org/10.3390/s24247978.

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Extracting behavioral information from animal sounds has long been a focus of research in bioacoustics, as sound-derived data are crucial for understanding animal behavior and environmental interactions. Traditional methods, which involve manual review of extensive recordings, pose significant challenges. This study proposes an automated system for detecting and classifying animal vocalizations, enhancing efficiency in behavior analysis. The system uses a preprocessing step to segment relevant sound regions from audio recordings, followed by feature extraction using Short-Time Fourier Transfor

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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 (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 Sup

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Wang, Tianjiao, and Xiaona Xia. "The Study of Hierarchical Learning Behaviors and Interactive Cooperation Based on Feature Clusters." SAGE Open 13, no. 2 (2023): 215824402311665. http://dx.doi.org/10.1177/21582440231166593.

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The study of learning behaviors with multi features is of great significance for interactive cooperation. The data prediction and decision are to realize the comprehensive analysis and value mining. In this study, hierarchical learning behavior based on feature cluster is proposed. Based on the massive data in interactive learning environment, the descriptive model and learning algorithm suitable for feature clustering are designed, and sufficient experiments obtain the optimal performance indexes. The data analysis results are reliable. On this basis, the hierarchical learning behaviors based

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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 (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 voca

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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 (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.

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黄, 廷禾. "Feature Point-Driven Vision Transformer for Driving Behavior Analysis." Modeling and Simulation 14, no. 05 (2025): 211–22. https://doi.org/10.12677/mos.2025.145387.

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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 (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

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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 remem

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Marr, M. Jackson. "TWEEDLEDUM AND TWEEDLEDEE: SYMMETRY IN BEHAVIOR ANALYSIS." CONDUCTUAL 1, no. 1 (2013): 16–25. http://dx.doi.org/10.59792/uemg3633.

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Symmetry is revealed when some non-trivial transformation of a system leaves the system unchanged or invariant. Symmetry is a pervasive feature in many sciences from physics to embryology. In physics, for example, symmetry is reflected in fundamental laws. Can this be said of behavioral findings and principles? I explore this question by discussing several examples from behavior analysis including the operational and functional aspects of reinforcement, stimulus and schedule control, the three-term contingency, and putative scale-invariance of behavioral principles—a feature conferring unity t

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Fan, Qile, Penghang Yu, Zhiyi Tan, Bing-Kun Bao, and Guanming Lu. "BeFA: A General Behavior-driven Feature Adapter for Multimedia Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11634–44. https://doi.org/10.1609/aaai.v39i11.33266.

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Multimedia recommender systems focus on utilizing behavioral information and content information to model user preferences. Typically, it employs pre-trained feature encoders to extract content features, then fuses them with behavioral features. However, pre-trained feature encoders often extract features from the entire content simultaneously, including excessive preference-irrelevant details.We speculate that it may result in the extracted features not containing sufficient features to accurately reflect user preferences. To verify our hypothesis, we introduce an attribution analysis method

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Ye, Mingtao, Xin Sheng, Yanjie Lu, et al. "SA-FEM: Combined Feature Selection and Feature Fusion for Students’ Performance Prediction." Sensors 22, no. 22 (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 learn

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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 (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 accurac

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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 (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 exam

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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 (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). ME

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Cahigas, Maela Madel L., Ferani E. Zulvia, Ardvin Kester S. Ong, and Yogi Tri Prasetyo. "A Comprehensive Analysis of Clustering Public Utility Bus Passenger’s Behavior during the COVID-19 Pandemic: Utilization of Machine Learning with Metaheuristic Algorithm." Sustainability 15, no. 9 (2023): 7410. http://dx.doi.org/10.3390/su15097410.

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Public utility bus (PUB) systems and passenger behaviors drastically changed during the COVID-19 pandemic. This study assessed the clustered behavior of 505 PUB passengers using feature selection, K-means clustering, and particle swarm optimization (PSO). The wrapper method was seen to be the best among the six feature selection techniques through recursive feature selection with a 90% training set and a 10% testing set. It was revealed that this technique produced 26 optimal feature subsets. These features were then fed into K-means clustering and PSO to find PUB passengers’ clusters. The alg

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Qiu, Feiyue, Lijia Zhu, Guodao Zhang, et al. "E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior." Entropy 24, no. 5 (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

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Yusfrizal, Yusfrizal, Andrian Syahputra, Yahya Tanjung, and Safrizal. "Analysis of Markov Blanket Based Feature Ranking for Android Malware Detection." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 3, no. 3 (2024): 740–45. http://dx.doi.org/10.59934/jaiea.v3i3.506.

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The ubiquity of Android applications in our daily lives has brought forth an indispensable need for robust app security mechanisms. Malware-infested applications not only jeopardize user privacy but also compromise data integrity and overall device security. Detecting and mitigating malicious behavior within Android applications is becoming increasingly challenging due to the high-dimensional nature of the data. Moving forward, employing Machine Learning (ML) techniques to detect malware in Android apps has become the norm. High dimensional feature space poses several formidable challenges, in

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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 (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 stu

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Chen, Jingying, Chang Chen, Ruyi Xu, and Leyuan Liu. "Autism Identification Based on the Intelligent Analysis of Facial Behaviors: An Approach Combining Coarse- and Fine-Grained Analysis." Children 11, no. 11 (2024): 1306. http://dx.doi.org/10.3390/children11111306.

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Background: Facial behavior has emerged as a crucial biomarker for autism identification. However, heterogeneity among individuals with autism poses a significant obstacle to traditional feature extraction methods, which often lack the necessary discriminative power. While deep-learning methods hold promise, they are often criticized for their lack of interpretability. Methods: To address these challenges, we developed an innovative facial behavior characterization model that integrates coarse- and fine-grained analyses for intelligent autism identification. The coarse-grained analysis provide

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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 (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

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Basavaraj, G. M., and Kusagur Ashok. "Crowd Anomaly Detection Using Motion Based Spatio-Temporal Feature Analysis." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 1 (2017): 737–47. https://doi.org/10.11591/ijeecs.v7.i3.pp737-747.

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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

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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 (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

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Sawita, Yousukkee, and Wisitpongphan Nawaporn. "Analysis of spammers' behavior on a live streaming chat." International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 139–50. https://doi.org/10.11591/ijai.v10.i1.pp139-150.

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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. Seve

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Ding, Damin, Yueyang Zhao, Jingru Zhang, et al. "A New Method of Classroom Behavior Recognition Based on WS-FC SLOWFAST." International Journal of Gaming and Computer-Mediated Simulations 17, no. 1 (2025): 1–28. https://doi.org/10.4018/ijgcms.371423.

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With the growing integration of deep learning and educational informatization, applying artificial intelligence to classroom behavior analysis has garnered significant attention. This article specifies 14 types of classroom behaviors and their classification criteria. By clipping and frame extraction from surveillance videos, target detection, manual annotation, temporal association, and other operations, a multi-label behavior dataset was created. This article also proposes a Weakly supervised fine-grained classification SlowFast SlowFast behavior recognition algorithm, which improves the acc

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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 re

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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 (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 YouTub

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Terada, Kazunori, Taku Imaizumi, Kazuhiro Ueda, et al. "Visual feature analysis on selective appetite in individuals with autism spectrum disorders." PLOS One 20, no. 6 (2025): e0325416. https://doi.org/10.1371/journal.pone.0325416.

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Background Individuals with autism spectrum disorders (ASD) experience more severe selective eating problems than their neurotypical peers. Identifying the causes of selective eating behavior poses a considerable challenge, even for caregivers. Accurate identification of the underlying causes of this behavior is essential for developing interventions aimed at overcoming dysfunctional, unbalanced diets. However, studies that meticulously identify the causes of selective eating behaviors are scarce. This investigation aims to explore the differences in preferences for sunny-side-up eggs between

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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 (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 vide

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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 beh

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Liu, Zhanbo, and Tieshi Song. "Big Data Analysis and User Behavior Prediction of Social Networks Based on Artificial Neural Network." Volume 31, Issue 3 31, no. 3 (2024): 185–201. http://dx.doi.org/10.20532/cit.2023.1005756.

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The prediction of user behavior in social networks is of great significance for understanding user dynamics, personalized recommendation, and information dissemination. With the development of artificial intelligence (AI) technology, especially the application of artificial neural networks in big data analysis, new solutions and technical means have been provided for the analysis of user behavior in social networks. This study constructs a social network user behavior prediction model based on artificial neural networks. The article first reviews related research, establishes a research framew

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Li, Mi, Lei Cao, Qian Zhai, et al. "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 recogni

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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 (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 fuzz

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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 (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, accur

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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 f

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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 (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 ch

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AKM, Bahalul Haque. "ANALYSIS OF ATTACK TECHNIQUES ON CLOUD BASED DATA DEDUPLICATION TECHNIQUES." International Journal on Cloud Computing: Services and Architecture (IJCCSA) 9, no. 1 (2022): 1. https://doi.org/10.5281/zenodo.7491792.

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Data in the cloud is increasing rapidly. This huge amount of data is stored in various data centers around the world. Data deduplication allows lossless compression by removing the duplicate data. So, these data centers are able to utilize the storage efficiently by removing the redundant data. Attacks in the cloud computing infrastructure are not new, but attacks based on the deduplication feature in the cloud computing is relatively new and has made its urge nowadays. Attacks on deduplication features in the cloud environment can happen in several ways and can give away sensitive information

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AKM, Bahalul Haque. "Analysis of Attack Techniques on Cloud Based Data Deduplication Techniques." International Journal on Cloud Computing: Services and Architecture (IJCCSA) 9, no. 1 (2019): 1–14. https://doi.org/10.5281/zenodo.3524211.

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Data in the cloud is increasing rapidly. This huge amount of data is stored in various data centers around the world. Data deduplication allows lossless compression by removing the duplicate data. So, these data centers are able to utilize the storage efficiently by removing the redundant data. Attacks in the cloud computing infrastructure are not new, but attacks based on the deduplication feature in the cloud computing is relatively new and has made its urge nowadays. Attacks on deduplication features in the cloud environment can happen in several ways and can give away sensitive information

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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 (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 ide

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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 (2015): 307–17. http://dx.doi.org/10.1007/s12193-015-0179-2.

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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 (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 feat

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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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