Event Recognition Research Articles

Transfer learning model for anomalous event recognition in big video data.

Video surveillance faces challenges due to the need for improved anomalous event recognition techniques for human activity recognition. Growing security concerns make standard CCTV systems insufficient because of high monitoring costs and operator exhaustion. Therefore, automated security systems with real-time event recognition are essential. This research introduces a semantic key frame extraction algorithm based on action recognition to minimize frame volume big video data. This approach has not been previously applied with ResNet50, VGG19, EfficientNetB7, and ViT_b16 models for recognizing anomalous events in surveillance videos. The findings demonstrate the effectiveness of this method in achieving high accuracy rates. The proposed method addresses the challenges posed by large volumes of frames generated by surveillance videos, requiring effective processing techniques. A large number of videos from the UCF-Crime dataset were used for proposed model evaluation, including both abnormal and normal videos during the training and testing phase. EfficientNetB7 achieved 86.34% accuracy, VGG19 reached 87.90%, ResNet50 attained 90.46%, and ViT_b16 excelled with 95.87% accuracy. Compared to state-of-the-art models from other studies, the transformer model (ViT_b16) outperformed these algorithms, demonstrating significant improvements in recognizing anomalous events.

Intrusion event recognition method for distributed fibre optic sensing systems based on Siamese networks

ABSTRACT Due to its high sensitivity and wide measurement range, the distributed optical fibre sensing system has been widely used in long-distance infrastructure monitoring, where it detects vibration signals caused by external events and provides effective early warnings. Given the complexity and diversity of external intrusion events, traditional closed-set classification methods cannot effectively exclude unknown signals. Open-set recognition methods, on the other hand, involve complex model designs, requiring boundary Algorithms and often the inclusion of information related to unknown classes. In this paper, we combine a Siamese network with a residual network to recognise events in the distributed optical fibre sensing system. Through experiments, by comparing the similarity with data in the database, we not only ensure very high accuracy in closed-set classification but also effectively exclude unknown class signals. Experimental results show that our method successfully rejects unknown class data with a 92% success rate, while maintaining closed-set classification accuracy at an average of 99%. Our approach demonstrates excellent performance.

Impact of gut microbiome on immunotherapy resistance in hepatocellular carcinoma: a review article

This article reviews the interactions between gut microbiota and cancer immunotherapies and discusses microbiota-based patient stratification strategies such as the prediction of response and the early recognition of toxic events. Additionally, it explores the emerging potential of microbiotherapy to enhance anti-neoplastic efficacy and alleviate toxicity, with the ultimate goal of developing personalized treatment regimens for patients with hepatocellular carcinoma (HCC).

Implementation of a novel TRIZ-based model to increase the reporting of adverse events in the healthcare center.

Underreporting of adverse events in healthcare systems is a global concern. This study aims to address the underreporting of adverse events (AE) by implementing a TRIZ-based model to identify and overcome barriers to reporting, thus filling gaps in current reporting practices and improving incident recognition. A TRIZ (Theory of Inventive Problem Solving) approach was adopted, integrating with SERVQUAL methodologies to design interventions. Preintervention and postintervention surveys were conducted to evaluate changes in the recognition of adverse events and barriers to reporting. Statistical analyses were performed to assess the effectiveness of the interventions. Recognition improved and barriers to reporting AEs significantly decreased. Monthly reported cases rose from 33.7 to 50.3 (p = 0.000), demonstrating the effectiveness of the TRIZ-based interventions. Implementing a TRIZ-based model significantly improved adverse event reporting by enhancing the recognition of reportable events and overcoming identified barriers. Future research should explore the long-term sustainability of these interventions and their broader applicability in diverse healthcare settings.

Local and global context cooperation for temporal action detection

Local and global context cooperation for temporal action detection

Complex Event Recognition meets Hierarchical Conjunctive Queries

Complex Event Recognition meets Hierarchical Conjunctive Queries

Cash Recognition of Events in Accounting in the Polish Territories under the Partitions

Cash Recognition of Events in Accounting in the Polish Territories under the Partitions

Management of Anesthesia in Mandibular Tumors with Difficult Airway

A 39-year-old female patient with a diagnosis of mandibular tumor underwent mandibleectomy surgery and Free Fibular Flap reconstruction. Preoperative evaluation is carried out on the patient, namely by conducting an anamnesis, physical examination, and supporting examination to determine the problem and conclusion of the patient's physical status. The physical status of the patient is ASA III. During the surgical durante, a tracheostomy is performed with local anesthesia. Next, the patient is positioned head down to undergo the surgical procedure. The operation lasted for 10 hours with stable hemodynamics. Postoperatively, patients are treated in the ICU treatment room, vital signs observation, and postoperative pain management. A closed study by the American Society of Anesthesiologists (ASA) Professional Committee revealed that the risks and severe complications of anesthesia are most often closely related to airway management problems. Success in overcoming complications depends on early detection of symptoms and corrective actions to prevent the situation from worsening. In this patient, airway management based on the Difficult Airway Algorithm other options, local anesthesia with tracheostomy and watershed algorithm was successful with plan A, namely facemask ventilation and tracheal intubation.

Proposal Semantic Relationship Graph Network for Temporal Action Detection

Temporal action detection, a critical task in video activity understanding, is typically divided into two stages: proposal generation and classification. However, most existing methods overlook the importance of information transfer among proposals during classification, often treating each proposal in isolation, which hampers accurate label prediction. In this paper, we propose a novel method for inferring semantic relationships both within and between action proposals, guiding the fusion of action proposal features accordingly. Building on this approach, we introduce the Proposal Semantic Relationship Graph Network (PSRGN), an end-to-end model that leverages intra-proposal semantic relationship graphs to extract cross-scale temporal context and an inter-proposal semantic relationship graph to incorporate complementary neighboring information, significantly improving proposal feature quality and overall detection performance. This is the first method to apply graph structure learning in temporal action detection, adaptively constructing the inter-proposal semantic graph. Extensive experiments on two datasets demonstrate the effectiveness of our approach, achieving state-of-the-art results. Code and results are available at http://github.com/Riiick2011/PSRGN .

DSTANet: learning a dual-stream model for anomaly driving action detection using spatio-temporal and appearance features

DSTANet: learning a dual-stream model for anomaly driving action detection using spatio-temporal and appearance features

A Review on Robust Credit Card Fraud Detection System Leveraging Big Data and Machine Learning

This review offers a detailed strategy to address the growing threat of credit card fraud in today's digital landscape. By utilizing Big Data analytics alongside machine learning methods, the system aims to transform fraud detection processes. It tackles the challenges arising from the increasing volume and complexity of credit card transactions, enabling the real-time detection and prevention of fraudulent actions. The system employs sophisticated machine learning algorithms to identify patterns and anomalies linked to fraudulent activities, allowing for proactive responses to emerging fraud tactics. Additionally, the system is optimized to handle and analyze large datasets efficiently, ensuring timely and precise detection of fraud. It also incorporates strong security protocols to protect sensitive customer data while adhering to privacy regulations. This review ultimately seeks to enhance the safety and reliability of electronic payments, protecting financial institutions and consumers from the harmful effects of credit card fraud.

Efficient Credit Card Fraud Detection System Using Big Data and Machine Learning

This review offers a detailed strategy to address the growing threat of credit card fraud in today's digital landscape. By utilizing Big Data analytics alongside machine learning methods, the system aims to transform fraud detection processes. It tackles the challenges arising from the increasing volume and complexity of credit card transactions, enabling the real-time detection and prevention of fraudulent actions. The system employs sophisticated machine learning algorithms to identify patterns and anomalies linked to fraudulent activities, allowing for proactive responses to emerging fraud tactics. Additionally, the system is optimized to handle and analyze large datasets efficiently, ensuring timely and precise detection of fraud. It also incorporates strong security protocols to protect sensitive customer data while adhering to privacy regulations. This review ultimately seeks to enhance the safety and reliability of electronic payments, protecting financial institutions and consumers from the harmful effects of credit card fraud.

Multisource Threatening Event Recognition Scheme Targeting Drone Intrusion in the Fiber Optic DAS System

Multisource Threatening Event Recognition Scheme Targeting Drone Intrusion in the Fiber Optic DAS System

Research on fiber OTDR signal recognition and classification based on the ViT model

In the field of event recognition for phase-sensitive optical time-domain reflectometry (-OTDR), convolutional neural networks (CNNs) have been the mainstream tool. However, Transformer models, with their self-attention mechanism, have provided a new perspective for image recognition tasks. This paper proposes a -OTDR event recognition method based on the Transformer model and experimentally demonstrates its significant advantages over CNNs. The method utilizes an attention-based feature extraction approach to recognize distributed fiber optic sensing signals in a phase-sensitive optical time-domain reflectometer (-OTDR). In perimeter defense applications, various interferences and noise severely affect the recognition of -OTDR sensing signals, leading to false alarms, making accurate signal recognition both challenging and necessary. Extracting signal features and using machine learning classification models to recognize signals has been a research hotspot in recent years. The effectiveness of feature extraction is crucial to classification performance. This project introduces a temporal-spatial dual attention mechanism into the feature extraction of fiber optic sensing signals to improve the accuracy and robustness of signal recognition.

Tsg101 UEV Interaction with Nedd4 HECT Relieves E3 Ligase Auto-Inhibition, Promoting HIV-1 Assembly and CA-SP1 Maturation Cleavage.

Tsg101, a component of the endosomal sorting complex required for transport (ESCRT), is responsible for recognition of events requiring the machinery, as signaled by cargo tagging with ubiquitin (Ub), and for recruitment of downstream acting subunits to the site. Although much is known about the latter function, little is known about its role in the earlier event. The N-terminal domain of Tsg101 is a structural homologue of Ub conjugases (E2 enzymes) and the protein associates with Ub ligases (E3 enzymes) that regulate several cellular processes including virus budding. A pocket in the domain recognizes a motif, PT/SAP, that permits its recruitment. PT/SAP disruption makes budding dependent on Nedd4L E3 ligases. Using HIV-1 encoding a PT/SAP mutation that makes budding Nedd4L-dependent, we identified as critical for rescue the residues in the catalytic (HECT) domain of the E3 enzyme that lie in proximity to sites in Tsg101 that bind Ub non-covalently. Mutation of these residues impaired rescue by Nedd4L but the same mutations had no apparent effect in the context of a Nedd4 isomer, Nedd4-2s, whose N-terminal (C2) domain is naturally truncated, precluding C2-HECT auto-inhibition. Surprisingly, like small molecules that disrupt Tsg101 Ub-binding, small molecules that interfered with Nedd4 substrate recognition arrested budding at an early stage, supporting the conclusion that Tsg101-Ub-Nedd4 interaction promotes enzyme activation and regulates Nedd4 signaling for viral egress. Tsg101 regulation of E3 ligases may underlie its broad ability to function as an effector in various cellular activities, including viral particle assembly and budding.

STMixer: A One-Stage Sparse Action Detector.

Traditional video action detectors typically adopt the two-stage pipeline, where a person detector is first employed to generate actor boxes and then 3D RoIAlign is used to extract actor-specific features for action recognition. This detection paradigm requires multi-stage training and inference, and the feature sampling is only constrained inside the box, failing to effectively leverage richer context information outside. Recently, several query-based action detectors have been proposed to predict action instances in an end-to-end manner. However, they still lack adaptability in feature sampling and decoding, thus suffering from the issues of inferior performance or slower convergence. In this paper, we propose two core designs for a more flexible one-stage sparse action detector. First, we present a query-based adaptive feature sampling module, which endows the detector with the flexibility of mining a group of discriminative features from the entire spatio-temporal domain. Second, we devise a decoupled feature mixing module, which dynamically attends to and mixes video features along the spatial and temporal dimensions respectively for better feature decoding. Based on these designs, we instantiate two detection pipelines, that is, STMixer-K for keyframe action detection and STMixer-T for action tubelet detection. Without bells and whistles, our STMixer detectors obtain the state-of-the-art results on five challenging spatio-temporal action detection benchmarks for keyframe action detection or action tube detection.

Pharmacovigilance: An Ethical Issue for Clinical Psychology, Psychiatry, and Pharmacy?

Pharmacovigilance helps determine when a treatment might cause novel or unintended effects. Patients, clinicians, regulators, and pharmaceutical companies have a stake in determining how the causality of treatment-induced effects should be established and when effects should be noted in settings from the academic literature to a medicine’s label. There have been different approaches to this task in different jurisdictions, with reporting to companies more common in the United States and reporting to regulators more common in Europe. Over time, pharmacovigilance has switched from being primarily a clinical enterprise to being a regulatory one, which has led to the elimination of the names of those suffering from an adverse event and the outsourcing of the job of recording the details of events to private companies, which appears likely to reduce the recognition of adverse events. This article calls for a restoration of clinical input on adverse events aimed at increasing reporting and recognition rates for important behavioral events.

Improved RNN Model for Real-Time Human Activity Recognition

This study focuses on the automatic detection of human actions in video streams. The requirement to detect what human activities happen in videos is recognition of human action due to significant differences in people's visual and motion appearance and actions, camera perspective shifts, moving background, occlusions, noise, and a massive amount of video data. The human activity recognition challenge involves identifying physical activities carried out by individuals or groups based on traces of movements, including gestures, actions, interactions, and group activities. The detection of concepts usually requires additional annotations for the training dataset. In this paper, useful methods for categorizing human action recognition are discussed. The current models are an accurate deep learning method that is based on models that have been changed to be more useful. The large disparities that result from the backdrop and the size of the objects have prevented the identification of activities in videos from being fully and effectively addressed. The main objective is to achieve better accuracy for the Long Short-Term Memory (LSTM) method, which was used to improve the Recurrent Neural Networks (RNN) model. In this paper, LSTM is used to come up with models for different action recognition tasks. The model was made better by making the LSTM have four layers and putting 128 units, 64 units, 32 units, and 16 units in each layer, respectively. In addition, the performance evaluation of deep learning-based approaches has been compared to other related works. Therefore, an improved approach to RNN is proposed to recognize human actions. To classify the videos, a multilayer RNN with a specific type of LSTM is used to extract features from video sequences. The UCF-101 and UCF Sports human action recognition datasets are utilized in this study for both training and assessment. Test findings demonstrate that the suggested strategy achieved increased accuracy. Finally, the enhanced RNN model's total model accuracy in the UCF-101 dataset is 93.78% and 95.70% for the UCF Sport dataset.

A benchmark dataset and semantics-guided detection network for spatial–temporal human actions in urban driving scenes

In real urban driving scenes, human actions are very complex and have the characteristic of multiple concurrent actions. It has a great significance to detect human actions in urban traffic scenes for auxiliary or autonomous driving systems. In this view, we introduce the TITAN-Human Action dataset for the task of multi-person spatial–temporal action detection in urban driving scenes. TITAN-Human Action provides the fine-grained action labels and location coordinates for 17,574 persons in the processed frames from the TITAN dataset. Furthermore, we propose a semantics-guided detection network (SGDNet) based on a semantic inference module (SIM) for spatial–temporal human action detection in urban driving scenes. SIM encodes the category labels into sentence vectors at the semantic level with prompting and embedding, utilizes graphs to represent the directed co-occurrence relations between categories, and adopts the graph convolutional network for semantic inference. SGDNet exploits the inference results of SIM to guide the visual branch in better performing human action detection, thereby achieving the integration of visual and linguistic information. We conducted experiments to evaluate SGDNet and several baseline methods on the TITAN-Human Action dataset, and reveal the generalizability of SIM in spatial–temporal human action detection. The source code and annotation files will be available at https://github.com/yyhbswyn/SGDNet.

Annealing Temporal–Spatial Contrastive Learning for multi-view Online Action Detection

In open-world scenarios, the analysis of action events from multiple viewpoints is crucial for achieving a holistic understanding, a concept that resonates with human perception. However, achieving a synthesis of information from multiple viewpoints presents a challenge, as the data can induce inter-view regularization, complicating the learning process. This paper is the first to delve into Online Action Detection (OAD) through a multi-view lens, underscoring the value of cross-observation in enriching view-level information. By harnessing the spatiotemporal dynamics inherent in multi-view video sequences, an Annealing Temporal–Spatial Contrastive Learning (ATSCL) consisting of Annealing Temporal Contrastive Learning (ATCL) and Spatial Contrastive Learning (SCL) is proposed, optimized for compatibility with RNN-based models. ATCL employs an annealing temporal loss to uncover the intrinsic video structures via a temporal annealing sampling mechanism. Concurrently, SCL utilizes a spatial loss to draw representations from various viewpoints closer together, mitigating the regularization effects. The ATSCL liberates training multi-view OAD from the stringent requirements of synchronized training videos, enabling the execution of OAD tasks asynchronously. Experiments demonstrate that the RNN-based models realize an average improvement of 5.92% on the DAHLIA dataset, 3.36% on the IKEA ASM dataset, 2.92% on the BREAKFAST dataset and 0.6% (mAP) on the THUMOS’14 dataset following the integration of the ATSCL framework, underscoring the ATSCL’s efficacy across different RNN structures.