Detection Method of Abnormal Behavior of Network Public Opinion Data Based on Artificial Intelligence

As society develops, the demand for security is rapidly increasing. Accordingly, there is growing interest in research on methods to detect and prevent abnormal behavior using surveillance cameras in public places and private spaces such as shopping malls and airports for human safety. Many detection techniques based on deep learning models have been researched in the field of abnormal behavior detection. However, due to the lack of labeled abnormal behavior data, there are significant difficulties in developing an effective detection system. This paper surveys methods for deep learning methods to detect abnormal human behavior in surveillance video and presents recent techniques. First, I will introduce popular datasets that have often been used in previous research. After that, we categorized the existing methods for detecting abnormal behavior using deep learning into three types: supervised learning, unsupervised learning, and partially-supervised learning. We then explained the basic concepts and advantages of each method and summarized their shortcomings. We also briefly describe future research directions based on the advantages and disadvantages of each method. Based on this, it is expected that the technology of video surveillance systems that apply abnormal behavior detection will further develop.

Internal user threat detection is an important research problem in the field of system security. Recently, the analysis of abnormal behaviors of users is divided into supervised learning method (SID) and unsupervised learning method (AD). However, supervised learning method relies on domain knowledge and user background, which means it cannot detect previously unknown attacks and is not suitable for multi-detection domain scenarios. Most existing AD methods use the clustering algorithm directly. But for threat detection on internal users’ behavior, mostly for high-dimensional cross-domain log files, as far as we know, there are few methods of multi-domain audit log data with effective feature extraction. An effective feature extraction method which can not only reduce testing cost greatly, but also detect the abnormal behavior of users more accurately. We propose a new unsupervised log abnormal behavior detection method which is based on the denoising autoencoders to encode the user log file, and adopts the integrated method to detect the abnormal data after encoding. Compared with the traditional detection method, it can analyze the abnormal information in the user behavior more effectively, thus playing a preventive role against internal threats. In addition, the method is completely data driven and does not rely on relevant domain knowledge and user’s background attributes. Experimental results verify the effectiveness of the integrated anomaly detection method under the multi-domain detection scenario of user log files.

We propose a method to detect abnormal crowd behavior using integrated multiple behavior models. Traditional abnormal detection methods are based only on personal behavior models. However, a single behavior model cannot accurately reflect complex crowd behavior. To solve this problem, we introduce an integrated multiple behavior model for accurate abnormal behavior detection in a complex crowd scene. We use not only the personal behavior model, but also multiple social behavior models. The experimental results show that our proposed method efficiently detects the abnormal behavior in a crowded scene.

Wind farm power data is an essential data source for characteristics analysis of wind farms, and its precision highly influences the operation and control of wind power. Existing methods for abnormal wind power data detection are mainly based on supervised or unsupervised algorithms, which may suffer from huge effort consumption on artificial label-setting or improvable detection precision respectively. In this paper, a semi-supervised anomaly detection method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed. Supervision from a limited number of labelled data is utilized to guide the anomaly detection process. Simulation based on realistic wind farm power data from China is performed to verify the method validity. The simulation results illustrate that the proposed method can obtain a satisfactory performance on avoiding false dismissals and false alarms among irregular-shaped wind power clusters, as well as an improved distinguishing ability around the confusing boundaries between normal and abnormal data.

In order to solve the problem of insufficient use of sequence information and low detection efficiency of traditional anomaly detection methods, this paper introduces Markov chain into user behaviour sequence detection, and proposes a description based on Markov chain and support vector data field ( SVDD) User Behaviour Sequence Detection Method (ASDMS), which first uses the Markov chain to accurately quantify the user behaviour sequence, then constructs the user’s normal behaviour sequence model based on the support vector data field description model, and identifies the user anomaly behaviour. The experimental results show that the ASDMS method has better performance and timeliness than the traditional abnormal behaviour detection method.

Abnormal signals are inevitable in big data acquired from harsh industrial environments. Abnormal data detection is a crucial component of condition monitoring for rotating parts and is also the premise of data cleaning, compensation, and mining. To detect abnormal data segments of rolling bearings, this paper proposes a dynamic adaptive local outlier factor (DALOF) anomaly detection method. First, a data dynamic segmentation method based on sliding windows is designed to determine samples with variable lengths. Then, a time-domain feature extraction and fusion method based on principal component analysis is exploited to reduce the feature space discrepancy. To improve the accuracy of abnormal data detection, a data quality evaluation model is established to assess each data segment using DALOF. The validity of the proposed method is also verified by analyzing signals including missing data, random interference data, and drift data. Several other methods are respectively applied to identify these abnormal data to further demonstrate the benefits of the developed methodology.

In the context of the new era, teachers and students in colleges and universities, as well as the general public, rely more on the Internet and social media to obtain news, express their opinions, and share information, and the dissemination of public opinion events in colleges and universities is not only related to the physical and mental health of teachers and students but also to the reform and development of colleges and universities. In this study, we took campus public opinion events as the main research object in which we selected three recent campus public opinion events to be analyzed. The public opinion data used in the research was collected from Weibo social media platforms. Firstly, we analyzed the dissemination cycle and regional dissemination patterns of a college food safety public opinion hot event through the popularity and regional distribution of public opinion data, thus revealing its formation and evolution patterns. Secondly, the LDA topic mining method is used to mine the themes of the three hot public opinion events, and then analyze the hot factors of the dissemination of each public opinion event from the massive public opinion data. This is crucial for the management department to grasp the dynamics of public opinion. Then, we used the SKEP sentiment classification method to analyze the emotional factors of the public opinion data of the three events to obtain the overall public opinion sentiment situation of the events. Finally, based on the characteristics of time, region, and gender, the evolution and diffusion rules of public topics and emotional distribution under different types of events are analyzed. The precision of the analyses associated with this paper may be limited to the effects of current mainstream as well as state-of-the-art analytical models. The analysis methods and conclusions in this paper provide a scientific theoretical basis and improvement measures for campus public opinion management, which helps to enhance the level of campus public opinion management and safeguard campus stability and public order.

In this paper; we propose a novel crowd behavior representation method to detect abnormal behaviors in videos. An adaptive optical flow filtering method is proposed to utilize low-level optical flow informations. Furthermore, a simple framework is developed to detect and to localize abnormal crowd behavior using adaptive optical flow filtering result. The proposed method is more robust than other modeling methods in representing different behaviors. In this model, a normal behavior is presented by the general value. Some outliers in the temporal domain or spatial domain are presented by a higher value. Spatio-temporal cuboids are extracted from the filtering result to present the likelihood of anomaly in the frame. Experimental evaluations are performed on two public datasets with comparison to the provisos abnormal behavior detection methods in the literature. Experimental results show that the proposed methods outperform previous abnormal behavior detection techniques in the literature.

The aim of this review is to highlight novel artificial intelligence-based methods for the detection of optic disc abnormalities, with particular focus on neurology and neuro-ophthalmology. Methods for detection of optic disc abnormalities on retinal fundus images have evolved considerably over the last few years, from classical ophthalmoscopy to artificial intelligence-based identification methods being applied to retinal imaging with the aim of predicting sight and life-threatening complications of underlying brain or optic nerve conditions. Artificial intelligence and in particular newly developed deep-learning systems are playing an increasingly important role for the detection and classification of acquired neuro-ophthalmic optic disc abnormalities on ocular fundus images. The implementation of automatic deep-learning methods for detection of abnormal optic discs, coupled with innovative hardware solutions for fundus imaging, could revolutionize the practice of neurologists and other non-ophthalmic healthcare providers.

Artificial features are adopted in most of the existing abnormal behavior detection. However, it is difficult to choose and design an effective behavior feature for the reason of highly computational complexity and complex scenarios. To solve this problem, temporal consistency based weak supervised abnormal behavior detection method is proposed in this paper. First, temporal gram matrices are constructed for a given video sequence. Then, a pair of behavior units (Candidate action fragment) are formed by exploiting the temporal consistency and the smoothness of human behavior, which helps to locate the start frame and end frame of the related class of abnormal behavior in the video sequence to train the corresponding classifier. Finally, sparse reconstruction is here utilized to detect abnormal behavior. Experiments conducted on common database including CAVIAR and Crossing demonstrate the effectiveness of the proposed method.

Abnormal crowd behavior detection has recently attracted increasing attention due to its wide applications in computer vision research areas. However, it is still an extremely challenging task due to the great variability of abnormal behavior coupled with huge ambiguity and uncertainty of video contents. To tackle these challenges, we propose a new probabilistic framework named variational abnormal behavior detection (VABD), which can detect abnormal crowd behavior in video sequences. We make three major contributions: (1) We develop a new probabilistic latent variable model that combines the strengths of the U-Net and conditional variational auto-encoder, which also are the backbone of our model; (2) We propose a motion loss based on an optical flow network to impose the motion consistency of generated video frames and input video frames; (3) We embed a Wasserstein generative adversarial network at the end of the backbone network to enhance the framework performance. VABD can accurately discriminate abnormal video frames from video sequences. Experimental results on UCSD, CUHK Avenue, IITB-Corridor, and ShanghaiTech datasets show that VABD outperforms the state-of-the-art algorithms on abnormal crowd behavior detection. Without data augmentation, our VABD achieves 72.24% in terms of AUC on IITB-Corridor, which surpasses the state-of-the-art methods by nearly 5%.

In order to reduce the interference of abnormal energy big data and improve the accuracy of anomaly cleaning and detection, this paper proposes a method for cleaning abnormal energy big database based on sparse self-coding. Firstly, the abnormal data detection method based on multi-criteria evaluation is used to analyze the spectral feature distribution of abnormal energy big data. By using chaotic time series reconstruction model, robust local weighted regression analysis and sparse self-coding method, the feature decomposition of time series of energy data is realized. Then, according to the periodicity of the original sequence, the original sequence is segmitated adaptively to represent the morphological characteristics of the abnormal energy data sub-sequences driven by dynamic carbon emissions, and the anomaly index of each sub-sequence is obtained by AFCM algorithm. Secondly, an energy anomaly evaluation model based on LOF value is established. Finally, the output decision function of data cleaning is constructed to realize the abnormal energy big data cleaning. The test results show that the error detection rate of this method is 0.24%, the missing detection rate is 0.27%, the cleaning rate can reach 99.49%, and the cleaning time is less than 2s.

Amid rapid advancements in artificial intelligence, the detection of abnormal human behaviors in complex traffic environments has garnered significant attention. However, detection errors frequently occur due to interference from complex backgrounds, small targets, and other factors. Therefore, this paper proposes a research methodology that integrates the anomaly detection YOLO-SGCF algorithm with the tracking BoT-SORT-ReID algorithm. The detection module uses YOLOv8 as the baseline model, incorporating Swin Transformer to enhance global feature modeling capabilities in complex scenes. CBAM and CA attention are embedded into the Neck and backbone, respectively: CBAM enables dual-dimensional channel-spatial weighting, while CA precisely captures object location features by encoding coordinate information. The Neck layer incorporates GSConv convolutional modules to reduce computational load while expanding feature receptive fields. The loss function is replaced with Focal-EIoU to address sample imbalance issues and precisely optimize bounding box regression. For tracking, to enhance long-term tracking stability, ReID feature distances are incorporated during the BoT-SORT data association phase. This integrates behavioral category information from YOLO-SGCF, enabling the identification and tracking of abnormal pedestrian behaviors in complex environments. Evaluations on our self-built dataset (covering four abnormal behaviors: Climb, Fall, Fight, Phone) show mAP@50%, precision, and recall reaching 92.2%, 90.75%, and 86.57% respectively-improvements of 3.4%, 4.4%, and 6% over the original model-while maintaining an inference speed of 328.49 FPS. Additionally, generalization testing on the UCSD Ped1 dataset (covering six abnormal behaviors: Biker, Skater, Car, Wheelchair, Lawn, Runner) yielded an mAP score of 92.7%, representing a 1.5% improvement over the original model and outperforming existing mainstream models. Furthermore, the tracking algorithm achieved an MOTA of 90.8% and an MOTP of 92.6%, with a 47.6% reduction in IDS, demonstrating superior tracking performance compared to existing mainstream algorithms.

The high proportion abnormal data in photovoltaic power generation output has the characteristics of overlapping and multi Gaussian distribution. How to accurately remove the high proportion abnormal data in photovoltaic power generation output is a key issue in achieving accurate prediction of photovoltaic power. The article analyzes the characteristics of high proportion abnormal data in photovoltaic power generation output, and studies a detection method for high proportion abnormal data in photovoltaic power generation output based on Gaussian mixture model. We have established an algorithm model for the Gaussian mixture model, using the Expectation Maximization (EM) algorithm to estimate the parameters of the Gaussian mixture model, and using the algorithm model to detect and eliminate high proportion abnormal data in photovoltaic power generation output. Actual case analysis and comparative experiments show that the method proposed in the article can accurately detect high proportion abnormal data from multiple classifications, making it more suitable for detecting high proportion abnormal data in photovoltaic power generation output compared to traditional abnormal data detection methods.

The accuracy of the pitch angle deviation directly affects the guidance accuracy of the laser seeker. During the guidance process, the abnormal pitch angle deviation data will be produced when the seeker is affected by interference sources. In this paper, a new abnormal data detection method based on Smooth Multi-Kernel Polarization Support Vector Data Description (SMP-SVDD) is proposed. In the proposed method, the polarization value is used to determine the weight of the multi-kernel combination coefficient to obtain the multi-kernel polarization function, in which the particle swarm optimization is used to find the optimal kernels for higher detection accuracy. Besides, by using smoothing mechanism, the constrained quadratic programming problem is translated to be smooth and differentiable. Then, this problem can be solved by the conjugate gradient method, which could reduce the computational complexity. In experimental section, abundant simulation experiments were designed and the experimental results verify that the proposed SMP-SVDD method could achieve higher detection accuracy and low computational cost compared with different detection methods in different guidance stages.