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Articoli di riviste sul tema "Reliability of event recognition"

Autore: Grafiati

Pubblicato: 7 giugno 2025

Ultima modifica: 2 agosto 2025

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1

Suárez-Revelo, Jazmín X., and John F. Ochoa-Gómez. "Event Related Potentials in visual recognition processing: test-retest reliability assessment." International Journal of Psychophysiology 108 (October 2016): 106. http://dx.doi.org/10.1016/j.ijpsycho.2016.07.322.

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Poroshenko, Anton, and Andriy Kovalenko. "AUDIO EVENT ANALYSIS METHOD IN NETWORK-BASED AUDIO ANALYTICS SYSTEMS." Advanced Information Systems 8, no. 4 (2024): 60–64. http://dx.doi.org/10.20998/2522-9052.2024.4.08.

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Relevance. In the rapidly evolving field of network-based audio analytics systems, the detection and analysis of audio events play a crucial role across various applications, including security, healthcare, and entertainment. Subject. This paper examines a method for recognizing audio events in network-based audio analytics systems, including preprocessing, sound separation, and the creation of machine learning models for analyzing audio signals. Objective. The objective is to develop and improve integrated methods for analyzing audio signals in network-based audio analytics systems to enhance

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Neelima Aderu. "AI-Powered Event-Driven Middleware: Revolutionizing Enterprise Integration." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 283–89. https://doi.org/10.32996/jcsts.2025.7.5.35.

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AI-powered event-driven middleware represents a transformative advancement in enterprise integration architecture, addressing the challenges of real-time data processing and system integration across distributed cloud platforms. This evolution introduces intelligent capabilities for pattern recognition, anomaly detection, and automated scaling, enabling organizations to move beyond traditional batch processing limitations. The integration of artificial intelligence enhances system performance through predictive scaling, intelligent event routing, and sophisticated event correlation mechanisms.

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4

Hirano, Kanan, Yo Fukushima, Hiroaki Maruya, Motoyuki Kido, and Motoaki Sugiura. "The Anticipated Nankai Trough Earthquake and Tsunami in Japan: Determinant Factors of Residents’ Pre-Event Evacuation Intentions." Journal of Disaster Research 18, no. 3 (2023): 233–45. http://dx.doi.org/10.20965/jdr.2023.p0233.

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As a countermeasure against M8–9 class Nankai Trough earthquakes, the Japan Meteorological Agency started a service to release “Nankai Trough Earthquake Extra Information (Megathrust Earthquake Alert).” This alert is released after an M8.0 or higher earthquake occurs and the possibility of a subsequent earthquake is evaluated to be higher than usual. This is an innovative attempt at disaster mitigation in Japan as it encourages residents in the predefined area to pre-evacuate for one week when tsunami risk is higher. However, the factors influencing the evacuation behavior of residents are unk

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Greco, Antonio, Nicolai Petkov, Alessia Saggese, and Mario Vento. "AReN: A Deep Learning Approach for Sound Event Recognition Using a Brain Inspired Representation." IEEE Transactions on Information Forensics and Security 15 (2020): 3610–24. http://dx.doi.org/10.1109/tifs.2020.2994740.

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Gu, Hongcan, Junbing Huang, Su Wu, et al. "Intrusion Monitoring Based on High Dimensional Random Matrix by Using Ultra-Weak Fiber Bragg Grating Array." Photonics 10, no. 7 (2023): 733. http://dx.doi.org/10.3390/photonics10070733.

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In order to ensure that a perimeter security system can work effectively, a convenient and effective event detection algorithm has an important engineering significance. Given the above background, in this paper, we propose a high reliability intrusion event recognition method and vibration sensing system, based on ultra-weak fiber Bragg grating array, by using high dimensional random matrix. We obtain a high sensitivity optical interference signal by constructing a patch-matched optical interference system, then compose the demodulated interference signal into a high-dimensional random matrix

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7

Levin, V. M., P. A. Petushkov, and M. A. Shvets. "Remote monitoring and control of the status of transformers in distribution electrical networks." Power engineering: research, equipment, technology 25, no. 2 (2023): 97–109. http://dx.doi.org/10.30724/1998-9903-2023-25-2-97-109.

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RELEVANCE. One of the most important expected effects of digitalization of objects of the power grid complex of the Russian Federation is to increase the level of reliability of its functioning. In this regard, research and development in this direction is undoubtedly relevant. THE PURPOSE. To propose a mathematical model for monitoring dangerous developing defects in power oil-filled transformers, this would meet the properties of predictability and adaptability. Based on the model, to develop a decision-making algorithm for long-term reliable operation oftransformers. METHODS: Methods of sta

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8

Wang, Mohan, and Pin-Chao Liao. "Personality Assessment Based on Electroencephalography Signals during Hazard Recognition." Sustainability 15, no. 11 (2023): 8906. http://dx.doi.org/10.3390/su15118906.

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Hazard recognition assisted by human–machine collaboration (HMC) techniques can facilitate high productivity. Human–machine collaboration techniques promote safer working processes by reducing the interaction between humans and machines. Nevertheless, current HMC techniques acquire human characteristics through manual inputs to provide customized information, thereby increasing the need for an interactive interface. Herein, we propose an implicit electroencephalography (EEG)-based measurement system to automatically assess worker personalities, underpinning the development of human–machine col

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Yusoff, Nurshafitrah, and Aizreena Azaman. "Equine Gait Recognition using Wearable Technology for Endurance Monitoring: A Preliminary Study." Journal of Human Centered Technology 2, no. 1 (2023): 61–67. http://dx.doi.org/10.11113/humentech.v2n1.44.

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To overcome limitation in equine gait monitoring, a low-cost monitoring device is needed especially for long-distance event performance monitoring. Current devices capture horse gait in limited range of distance, limited duration usage and expensive. Mobile application such as Androsensor has shown a great potential in horse gait recognition. However, further study is still warranted in order to determine the reliability of this kind of mobile application for performance monitoring especially for long-distance event such as endurance. In this study, a validation test of Androsensor mobile appl

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Zhu, Shiqiang, Shizhao Zhou, Zheng Chen, Wei Song, and Lai Jin. "Gait-stride-and-frequency-based human intention recognition approach and experimental verification on lower limb exoskeleton." Transactions of the Institute of Measurement and Control 44, no. 5 (2021): 1149–60. http://dx.doi.org/10.1177/01423312211044031.

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In the research of lower extremity exoskeleton, how to achieve synchronization between human and machine is quite significant. The intention recognition, which can be divided into three categories including EMG-based, EEG-based and biomechanics-based, is one of the effective implementation methods. In this paper, a new biomechanics-based method to realize the intention recognition is proposed. Compared with the mainstream, this method identifies the characteristic value of stride and frequency during walking, which describes human intention mathematically and concretizes the intention of human

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Zhulay, Valeriy. "Phenomenon of the event in moral dimension: inner form and crucial factors." Filosofska dumka (Philosophical Thought) -, no. 1 (2022): 84–97. http://dx.doi.org/10.15407/fd2022.01.084.

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The article raises a question of what turns out to be the event, if we take human existence in its ethical dimension. The author explores the levels of actualization by a person of that, which is considered by he or she as morally significant, as well as possibilities of accepting this actualization by other members of the moral community. To the author’s opinion, this task demands a special attention to the ethic dimension of human existence, where a possibility appears to diagnose and analyze changes, which bring about an ethical shift of one’s moral set. Changes of such a level are conceptu

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12

He, Xin, Yichen Ma, Jiancang Xie, Gang Zhang, and Tuo Xie. "Enhanced Wind Power Forecasting Using Graph Convolutional Networks with Ramp Characterization and Error Correction." Energies 18, no. 11 (2025): 2763. https://doi.org/10.3390/en18112763.

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The strong volatility of wind power presents persistent challenges to the stable operation of power systems, highlighting the critical need for accurate wind power forecasting to ensure system reliability. This study proposes a wind power prediction approach based on graph convolutional networks, incorporating ramp feature recognition and error correction mechanisms. First, an improved ramp event definition is applied to detect and classify wind power ramp events more accurately, thereby reducing misjudgments caused by short-term fluctuations. Then, a GCN-based model is developed to construct

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Adu-Gyamfi, Yaw Okyere, Anuj Sharma, Skylar Knickerbocker, Neal Hawkins, and Michael Jackson. "Framework for Evaluating the Reliability of Wide-Area Probe Data." Transportation Research Record: Journal of the Transportation Research Board 2643, no. 1 (2017): 93–104. http://dx.doi.org/10.3141/2643-11.

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This paper presents a framework for evaluating the reliability of probe-sourced traffic speed data for detection of congestion and assessment of roadway performance. The methodology outlined uses pattern recognition to quantify accurately the similarities and dissimilarities of probe-sourced and benchmarked local sensor data. First, a pattern recognition algorithm called empirical mode decomposition was used to define short-, medium-, and long-term trends for the probe-sourced and infrastructure-mounted local sensor data sets. The reliability of the probe data was then estimated on the basis o

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Yang, Jian, Chen Wang, Jichao Yi, et al. "Railway Intrusion Events Classification and Location Based on Deep Learning in Distributed Vibration Sensing." Symmetry 14, no. 12 (2022): 2552. http://dx.doi.org/10.3390/sym14122552.

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With the rapid development of the high-speed railway industry, the safety of railway operations is becoming increasingly important. As a symmetrical structure, traditional manual patrol and camera surveillance solutions on both sides of the railway require enormous manpower and material resources and are highly susceptible to weather and electromagnetic interference. In contrast, a distributed fiber optic vibration sensing system can be continuously monitored and is not affected by electromagnetic interference to false alarms. However, it is still a challenge to identify the type of intrusion

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15

Lal, Aleena B., Megha M, Muhammed Sufiyan M. P, David Joseph Thekkel, Aswathy M. V, and Rotney Roy Meckamalil. "Flash Flood Detection and Alert System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 45–51. http://dx.doi.org/10.22214/ijraset.2024.62971.

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Abstract: Floods can be considered as one among the top natural disasters that sometimes happens regularly in particular season thereby causing harm on human lives and also resultsin reducing economic growth. So, the news about the flooding events has to be spread to nearby localities in time so as to avoid further chaos. Therefore, it is crucial to build a warning system that informs the flooding event to reduce the casualties of flood disaster. Recognizing sensitive events in images, such as flood events is significant for the maintenance of normal public opinionand social stability. By now,

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16

Chen, Junwei. "Discussing Solutions to the Data Imbalance Problem in Emotion Recognition." Applied and Computational Engineering 174, no. 1 (2025): 23–31. https://doi.org/10.54254/2755-2721/2025.po24697.

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Emotion recognition technology has been widely used in human-computer interaction, medical health and other fields. However, in practical applications, emotion datasets often have class imbalance problems, which lead to the model being seriously biased towards the majority class, significantly reducing the recognition accuracy and reliability of minority emotion classes. This paper focuses on comparing and analyzing methods such as ESC-GAN generative data augmentation technology, DER-GCN dialogue and event relationship perception graph model, and MultiEMO multimodal fusion framework to solve t

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17

Moepi, Glen, and Topside Ehleketani Mathonsi. "Active Gait System for Real-Time Surveillance Against Cyber-Physical Attacks." International Conference on Cyber Warfare and Security 19, no. 1 (2024): 602–6. http://dx.doi.org/10.34190/iccws.19.1.2147.

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Cyberterrorism, espionage, and warfare are serious threats to national security. These attacks can harm people or destroy critical infrastructures like the data centres, computer networks, and systems. Surveillance systems currently used in monitoring critical infrastructures, national key points, and military exclusion zones (MEZ) are ineffective in detecting unauthorised intrusions. These issues compromise the stability of the countries, and the safety of the citizens and result in the loss of important assets. This experimental research study developed a Cyber Physical Security (CPS) defens

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Prentiss, Andrea S. "Early Recognition of Pediatric Venous Thromboembolism: A Risk-Assessment Tool." American Journal of Critical Care 21, no. 3 (2012): 178–84. http://dx.doi.org/10.4037/ajcc2012548.

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Background The incidence of venous thromboembolism in children has increased dramatically, with most cases occurring in children with cancer, surgery, trauma, congenital heart disease, and systemic lupus erythematosus. Early assessment of risk factors present in children would minimize morbidity and mortality from these events. Objectives To evaluate the reliability and validity of a tool for assessing risk for venous thromboembolism in children. Methods The tool was developed after a review of the literature with assessment of content validity by a multidisciplinary team of experts. Patients’

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19

Ali, Al-Ammouri, Dmytrychenko Andrew, Al-Ammori Hasan, and Kharuta Vitalii. "DEVELOPMENT OF STRUCTURES OF THE AIRCRAFT FIRE ALARM SYSTEM BY MEANS OF NESTED MODULES." Eastern-European Journal of Enterprise Technologies 2, no. 9 (98) (2019): 14–23. https://doi.org/10.15587/1729-4061.2019.163022.

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Optimization of structures of information management systems is determined by the choice of such a functional structure that would ensure high reliability of information. When creating complex systems, there is the problem of ensuring high operational reliability of connection of a large number of separate elements into a single monolithic highly efficient information system. This problem is effectively solved by combining the elements of the system of controlled information sensors by means of nested modules. The mathematical model of parallel information redundancy based on polynomial distri

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Shushpanov, Ilia, Konstantin Suslov, Pavel Ilyushin, and Denis N. Sidorov. "Towards the Flexible Distribution Networks Design Using the Reliability Performance Metric." Energies 14, no. 19 (2021): 6193. http://dx.doi.org/10.3390/en14196193.

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At present, the entire world is moving towards digitalization, including in the electric power industry. Digitalization is in its heyday and a lot of articles and reports are devoted to this topic. At the same time, the least digitalized of the electrical networks are distribution networks that account for a very large share in electric power systems. The article proposes a methodology for creating a flexible distribution network based on the use of digital technology. Additionally, we elaborate a methodology with the identification and collection of the necessary information to create digital

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Yan, Jingjing, and Zhenbin Gao. "Quantifying the Impact of Single-Event Upsets on Neural Networks with Uncertainty Analysis." Journal of Physics: Conference Series 2759, no. 1 (2024): 012007. http://dx.doi.org/10.1088/1742-6596/2759/1/012007.

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Abstract The Convolutional neural networks have gradually penetrated into various fields of social life, in addition to aerospace devices, neural network chips are also indispensable for target recognition and detection, etc. Due to the complex environment in space, neural network chips are extremely easy to be damaged by cosmic rays, so it is urgent to evaluate and enhance the robustness of neural network chips. However, most of the existing researches are monolithic error injection of neural networks and do not build relevant models for quantitative analysis. The goal of this paper is to ana

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Nowell, Branda, and Joseph Stutler. "Public Management in an Era of the Unprecedented: Dominant Institutional Logics as a Barrier to Organizational Sensemaking." Perspectives on Public Management and Governance 3, no. 2 (2020): 125–39. http://dx.doi.org/10.1093/ppmgov/gvz028.

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Abstract In an era when unprecedented events are occurring with increasing frequency, public management theory is challenged to consider whether it is possible to better prepare agencies to respond to situations previously neither expected, nor even seriously imagined. In this paper, we consider the case of the 2016 Chimney Tops 2 wildfire that contributed to the destruction of neighborhoods in and around Gatlinburg, TN. We argue this case illuminates a critical gap in extant organizational theory concerning the factors that impede sensemaking processes, which are fundamental to models of high

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Lee, Min-Hye, and Soon-Ja Lim. "Deep-Learning Based Recognition of Mask Wearers for a Droplet Infection Prevention and Monitoring System." Journal of Computational and Theoretical Nanoscience 18, no. 5 (2021): 1525–29. http://dx.doi.org/10.1166/jctn.2021.9595.

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The primary method to prevent virus infection through droplets is to check if individuals wear a mask. However, inspecting and controlling each individual involves challenges. This study suggests a method to distinguish those who wear a mask from those who do not and to issue a warning alarm when detecting a person whose access is restricted. With data sets generated in the preprocessing steps, those who do not wear a mask are distinguished from mask wearers in application of the deep-learning model. Individuals are counted for image analysis, and alarms are issued upon an event. In order to s

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Rao, Suman, Oluwateniola Olatunde, Akhila Sunkara, Vrinda Vyas, and Andrew Weinberg. "A Significant Pericardial Effusion Leading to Bronchial Compression: A Case Report." Journal of Investigative Medicine High Impact Case Reports 9 (January 2021): 232470962110050. http://dx.doi.org/10.1177/23247096211005064.

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Commonly, pericardial effusions can cause suboptimal heart contractility. Larger pericardial effusions can lead to compression of structures that surround in the heart in the mediastinum. Our patient presented with dyspnea that required mechanical ventilation. Bronchoscopy revealed compression of the bronchus from an external source. Echocardiogram showed a large circumferential pericardial effusion, which compressed the left main stem bronchus causing left lung atelectasis and persistent respiratory failure. A subxiphoid pericardial window was performed, which led to an improvement in her oxy

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Panpaliya, Mayur, Nihar Ranjan, and Arun Algude. "CEP-DTHP : A Complex Event Processing using the Dual-Tier Hybrid Paradigm Over the Stream Mining Process." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10s (2023): 52–63. http://dx.doi.org/10.17762/ijritcc.v11i10s.7595.

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CEP is a widely used technique for the reliability and recognition of arbitrarily complex patterns in enormous data streams with great performance in real time. Real-time detection of crucial events and rapid response to them are the key goals of sophisticated event processing. The performance of event processing systems can be improved by parallelizing CEP evaluation procedures. Utilizing CEP in parallel while deploying a multi-core or distributed environment is one of the most popular and widely recognized tackles to accomplish the goal. This paper demonstrates the ability to use an unusual

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Doğan, Muzaffer Berna, and Sevda Karakaş. "The Role of Electronic Fetal Monitoring (EFM) in Obstetric Malpractice: An Evaluation from the Perspectives of Midwives, Nurses and Forensic Medicine." Turkish Journal of Forensic Medicine 39, no. 1 (2025): 93–101. https://doi.org/10.61970/adlitip.1597681.

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This review aims to assess the significance of electronic fetal monitoring (EFM) in medical error claims in obstetrics from the perspectives of midwifery, nursing, and forensic medicine. One fundamental approach to preventing adverse events during labor is the early recognition of fetal distress. Midwives and nurses are responsible for applying, evaluating, and recording EFM tracings during labor, which are crucial evidence in forensic medical evaluations. In the absence of medical records, the follow-up, treatment, and medical practices become contentious during trials, leading to delays in t

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Kozłowski, Maciej, Andrzej Czerepicki, Piotr Jaskowski, and Kamil Aniszewski. "Analysis of the System of Controlling Paid Parking Zones." Sustainability 13, no. 8 (2021): 4211. http://dx.doi.org/10.3390/su13084211.

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Urban traffic can be curbed in various ways, for instance, by introducing paid unguarded parking zones (PUPZ). The operational functionality of this system depends on whether or not the various system features used to document parking cases function properly, including those which enable positioning of vehicles parked in the PUPZ, recognition of plate numbers, event time recording, and the correct anonymisation of persons and other vehicles. The most fundamental problem of this system is its reliability, understood as the conformity of control results with the actual state of matters. This cha

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Ali, Al-Ammouri, Lebid Iryna, Dekhtiar Marina, Lebid Ievgenii, and Al-Ammori Hasan. "Development of a mathematical model of reliable structures of information-control systems." Eastern-European Journal of Enterprise Technologies 5, no. 9(119) (2022): 68–78. https://doi.org/10.15587/1729-4061.2022.265953.

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A mathematical model of the information system (IS) for monitoring the state of objects that may be exposed to extreme influences has been built. The system consists of <em>n</em> devices that work independently of each other. To construct the specified information system, which has the minimum permissible reliability of event recognition, a class of structures of the type «<em>k</em> with <em>n</em>» is considered. Formulas for determining the probabilistic characteristics of <em>n</em> parallel reserved sensors for structures of the «<em>k</em>

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Romeiro, Joana, Paulo Nogueira, Jenny Hall, and Sílvia Caldeira. "The Resilience Scale in Portuguese Adults under Assisted Reproductive Techniques." International Journal of Environmental Research and Public Health 19, no. 10 (2022): 5932. http://dx.doi.org/10.3390/ijerph19105932.

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Assessing resilience response to an adverse event such as infertility requires measuring the same construct adequately and accurately by nurses. The objective of this study was to explore the validity and reliability of the Portuguese version of the Resilience Scale for adults. A cross-sectional and methodological design was used, and factor analyses were performed. The sample comprised 140 adult Portuguese individuals under fertility treatment recruited from health-related websites. The findings yielded a bad fit of the hypothesized Resilience Scale structure with the observed data. Instead,

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Emel, E., and E. Kannatey-Asibu. "Tool Failure Monitoring in Turning by Pattern Recognition Analysis of AE Signals." Journal of Engineering for Industry 110, no. 2 (1988): 137–45. http://dx.doi.org/10.1115/1.3187862.

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Sensing of both gradual and catastrophic tool failure is a key aspect in producing high quality parts on fully automated machine tool systems. Acoustic emission provides a means of sensing tool failure, since it is generated from the processes that cause tool failure (e.g., tool wear, tool fracture). A linear discriminant function-based technique for detection of tool wear, tool fracture, or chip disturbance events is developed using the spectra of signals generated by these sources. In addition, a methodology for determining the feature dimensionality, the selection of best features, and the

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Aman, Kumar, Dr Shivani Dubey, Dr Ajay Kumar Sahu, and Prof Vikas Singhal. "Monitoring the workflow of Earthquake by using Machine Learning." International Journal of Innovative Research in Advanced Engineering 11, no. 01 (2024): 22–26. http://dx.doi.org/10.26562/ijirae.2024.v1101.04.

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The main goal is to improve the accuracy and efficiency of earthquake detection, prediction and response through automation and, over time, to improve machine learning techniques for earthquake monitoring. However, the application of machine learning to earthquake location problems faces challenges in areas with limited data. This article presents an engine-based approach to monitor earthquake performance. We use a variety of Machine Learning algorithms, including deep learning neural networks and decision trees, for processing and interpret large amounts of seismic data. These algorithms are

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Asing, Md Eaqub Ali, and Sharifah Bee Abd Hamid. "SERS-Modeling in Molecular Sensing." Advanced Materials Research 1109 (June 2015): 223–26. http://dx.doi.org/10.4028/www.scientific.net/amr.1109.223.

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Surface enhanced Raman spectroscopy (SERS) is an ultrasensitive vibrational spectroscopic technique that useful tools in detecting biomolecules at near or on the surface of plasmonic nanostructures. Unique physicochemical and optical properties of noble metal nanostructures allow the assimilation of biomolecular probes and exhibit distinctive spectra, prompting the development of a plethora of biosensing platforms in molecular diagnostics. In SERS biosensor, signal to noise ration such as recognition and transducer elements that provide fingerprint spectrum at the lower limit of detection with

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Peruzzi, Giacomo, Alessandro Pozzebon, and Mattia Van Der Meer. "Fight Fire with Fire: Detecting Forest Fires with Embedded Machine Learning Models Dealing with Audio and Images on Low Power IoT Devices." Sensors 23, no. 2 (2023): 783. http://dx.doi.org/10.3390/s23020783.

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Forest fires are the main cause of desertification, and they have a disastrous impact on agricultural and forest ecosystems. Modern fire detection and warning systems rely on several techniques: satellite monitoring, sensor networks, image processing, data fusion, etc. Recently, Artificial Intelligence (AI) algorithms have been applied to fire recognition systems, enhancing their efficiency and reliability. However, these devices usually need constant data transmission along with a proper amount of computing power, entailing high costs and energy consumption. This paper presents the prototype

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Aman, Kumar, Dr Shivani Dubey, Dr Ajay kumar Sahu, and Prof Vikas Singhal. "Monitoring the Workflow of Earthquake by Using Machine Learning." International Research Journal of Computer Science 11, no. 02 (2024): 84–87. http://dx.doi.org/10.26562/irjcs.2024.v1102.02.

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This study explores the innovative application of machine learning techniques in monitoring and analyzing the workflow associated with earthquakes. The primary objective is to enhance the accuracy and efficiency of earthquake detection, prediction, and response mechanisms the development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. This paper proposes a machine learning-based appro

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Mirheidari, Bahman, André Bittar, Nicholas Cummins, Johnny Downs, Helen L. Fisher, and Heidi Christensen. "Automatic detection of expressed emotion from Five-Minute Speech Samples: Challenges and opportunities." PLOS ONE 19, no. 3 (2024): e0300518. http://dx.doi.org/10.1371/journal.pone.0300518.

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Research into clinical applications of speech-based emotion recognition (SER) technologies has been steadily increasing over the past few years. One such potential application is the automatic recognition of expressed emotion (EE) components within family environments. The identification of EE is highly important as they have been linked with a range of adverse life events. Manual coding of these events requires time-consuming specialist training, amplifying the need for automated approaches. Herein we describe an automated machine learning approach for determining the degree of warmth, a key

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Ali, Mohammed Hasan, Mustafa Musa Jaber, Sura Khalil Abd, et al. "Threat Analysis and Distributed Denial of Service (DDoS) Attack Recognition in the Internet of Things (IoT)." Electronics 11, no. 3 (2022): 494. http://dx.doi.org/10.3390/electronics11030494.

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The Internet of Things (IoT) plays a crucial role in various sectors such as automobiles and the logistic tracking medical field because it consists of distributed nodes, servers, and software for effective communication. Although this IoT paradigm has suffered from intrusion threats and attacks that cause security and privacy issues, existing intrusion detection techniques fail to maintain reliability against the attacks. Therefore, the IoT intrusion threat has been analyzed using the sparse convolute network to contest the threats and attacks. The web is trained using sets of intrusion data,

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Al-Ammouri, Ali, Iryna Lebid, Marina Dekhtiar, Ievgenii Lebid, and Hasan Al-Ammori. "Development of a mathematical model of reliable structures of information-control systems." Eastern-European Journal of Enterprise Technologies 5, no. 9(119) (2022): 68–78. http://dx.doi.org/10.15587/1729-4061.2022.265953.

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A mathematical model of the information system (IS) for monitoring the state of objects that may be exposed to extreme influences has been built. The system consists of n devices that work independently of each other. To construct the specified information system, which has the minimum permissible reliability of event recognition, a class of structures of the type «k with n» is considered. Formulas for determining the probabilistic characteristics of n parallel reserved sensors for structures of the «k with n» type were derived; the probability of these events was calculated; and plots of thei

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Davlatova, Nafosat. "THE USE OF ARTIFICIAL INTELLIGENCE IN JOURNALISM." Oriental Renaissance: Innovative, educational, natural and social sciences 2, no. 27 (2022): 305–8. https://doi.org/10.5281/zenodo.7353730.

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<em>The article is devoted to the use of technologies based on artificial intelligence algorithms by the media. The principles of operation of machine learning, deep learning based on neural networks, and recommender systems are disclosed. An analytical review of the use of these technologies in such areas as data processing and analysis is made; automatic production of information about current events and facts (messages about emergencies, results of sports competitions, the course of election campaigns, etc.); interactive communication with the audience; tracking informational occasions; ver

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R., R., and K. Selvakumar. "Efficient Sink Node Position Estimation using Harris Hawks Optimization Algorithm in Wireless Sensor Networks." Fusion: Practice and Applications 16, no. 1 (2024): 233–43. http://dx.doi.org/10.54216/fpa.160116.

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Wireless sensor network (WSN) was utilized widely in numerous areas owing to their accessibility in data collection, processing, and transmission, and the strength and reliability of data processing and transmission are based on the accuracy of the positions of sensor nodes (SNs) in the WSN. Sink node location estimation in WSN is a vital task intended to define the geographical position of the sink node in the network area of coverage. This procedure normally includes using numerous localization techniques that trust data like received signal strength, arrival time, time variance of arrival,

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JunGuo, Xin Liu,. "A Systematic Study on the Application of Computer Big Data Analysis to Improve the Accuracy of Referee Decision in Sports Events." Journal of Electrical Systems 20, no. 2 (2024): 1170–80. http://dx.doi.org/10.52783/jes.1313.

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Due to the complex working environment, long communication distance and high reliability requirements of referees in weightlifting events, this project uses TI's MSP430F5299 as its core processing chip. RS485 for long-distance communication. This system realizes a variety of judgments, arbitration supervision, system function setting, LED large screen display, PC data real-time transmission and other functions. Then, Fourier pyramid filtering method is used to extract human skeleton information, and SVM is used to classify human behavior to complete the construction of human behavior recogniti

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G. Sh. Asanova and U. M. Abdyldaeva. "ROLE OF FINANCIAL REPORTING IN DECISION-MAKING IN ORGANIZATIONS." Herald of KSUCTA n a N Isanov, no. 4 (December 16, 2019): 680–85. http://dx.doi.org/10.35803/1694-5298.2019.4.680-685.

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This article discusses the role and analytical capabilities of financial statements in making management decisions, since financial statements must faithfully reflect the financial position, financial results and cash flow of an enterprise, which requires truthful reflection of completed operations, other events and conditions in accordance with the definitions and criteria recognition of assets, liabilities, income and expenses. The value of financial statements is that it is the information base of financial analysis and audit. Therefore, the reliability of all objects reflected in the accou

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Magar, Hend S., Rabeay Y. A. Hassan, and Ashok Mulchandani. "Electrochemical Impedance Spectroscopy (EIS): Principles, Construction, and Biosensing Applications." Sensors 21, no. 19 (2021): 6578. http://dx.doi.org/10.3390/s21196578.

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Electrochemical impedance spectroscopy (EIS) is a powerful technique used for the analysis of interfacial properties related to bio-recognition events occurring at the electrode surface, such as antibody–antigen recognition, substrate–enzyme interaction, or whole cell capturing. Thus, EIS could be exploited in several important biomedical diagnosis and environmental applications. However, the EIS is one of the most complex electrochemical methods, therefore, this review introduced the basic concepts and the theoretical background of the impedimetric technique along with the state of the art of

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Caron-Laramée, Amélie, Roua Walha, Patrick Boissy, Nathaly Gaudreault, Nikola Zelovic, and Karina Lebel. "Comparison of Three Motion Capture-Based Algorithms for Spatiotemporal Gait Characteristics: How Do Algorithms Affect Accuracy and Precision of Clinical Outcomes?" Sensors 23, no. 4 (2023): 2209. http://dx.doi.org/10.3390/s23042209.

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Abstract (sommario):

Gait assessment is of interest to clinicians and researchers because it provides information about patients’ functional mobility. Optoelectronic camera-based systems with gait event detection algorithms are considered the gold standard for gait assessment. Yet, the choice of the algorithm used to process data and extract the desired parameters from those detected gait events has an impact on the validity and reliability of the gait parameters computed. There are multiple techniques documented in the literature for computing gait events, including the analysis of the minimal position of the hee

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Chien, Cheng-Yu, Wei-Che Chien, Li-Heng Tsai, et al. "Impact of the caller’s emotional state and cooperation on out-of-hospital cardiac arrest recognition and dispatcher-assisted cardiopulmonary resuscitation." Emergency Medicine Journal 36, no. 10 (2019): 595–600. http://dx.doi.org/10.1136/emermed-2018-208353.

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ObjectiveThis study determined the impact of the caller’s emotional state and cooperation on out-of-hospital cardiac arrest (OHCA) recognition and dispatcher-assisted cardiopulmonary resuscitation (DA-CPR) performance metrics.MethodsThis was a retrospective study using data from November 2015 to October 2016 from the emergency medical service dispatching centre in northern Taiwan. Audio recordings of callers contacting the centre regarding adult patients with non-traumatic OHCA were reviewed. The reviewers assigned an emotional content and cooperation score (ECCS) to the callers. ECCS 1–3 call

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Ozduran, Erkan, Volkan Hancı, Yüksel Erkin, İlhan Celil Özbek, and Vugar Abdulkerimov. "Assessing the readability, quality and reliability of responses produced by ChatGPT, Gemini, and Perplexity regarding most frequently asked keywords about low back pain." PeerJ 13 (January 22, 2025): e18847. https://doi.org/10.7717/peerj.18847.

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Abstract (sommario):

Background Patients who are informed about the causes, pathophysiology, treatment and prevention of a disease are better able to participate in treatment procedures in the event of illness. Artificial intelligence (AI), which has gained popularity in recent years, is defined as the study of algorithms that provide machines with the ability to reason and perform cognitive functions, including object and word recognition, problem solving and decision making. This study aimed to examine the readability, reliability and quality of responses to frequently asked keywords about low back pain (LBP) gi

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Tatia Gurtskaia, Tatia Gurtskaia, Maka Gogoladze Maka Gogoladze, and Karlo Gurtskaia Karlo Gurtskaia. "Japan's Industrial Policy as a Factor of the Country's Success." Economics 106, no. 11-12 (2024): 75–78. https://doi.org/10.36962/ecs106/11-12/2024-75.

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Abstract (sommario):

Industrial policy occupies a special place in the Japanese economy, and thanks to it, favorable conditions were created for the development of certain industries, which became the basis of Japanese industry and formed a favorable investment climate. The country has gained international recognition. After World War II, Japan became one of the leaders of the world economy and the second country in terms of labor productivity, in which the economic and industrial policy of the state played an important role. In 1964, the country successfully hosted the Olympic Games, demonstrating organizational

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Yildirim, Mustafa, Christian Salbach, Matthias Mueller-Hennessen, Norbert Frey, and Evangelos Giannitsis. "Diagnostic and Prognostic Evaluation of Novel Biomarkers Compared to ESC 0/1 h and 0/3 h Algorithms in Patients with Suspected Non-ST-Elevation Myocardial Infarction." Journal of Clinical Medicine 14, no. 9 (2025): 2957. https://doi.org/10.3390/jcm14092957.

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Abstract (sommario):

(1) Background: Prompt acute coronary syndrome (ACS) recognition remains challenging. This study evaluated the diagnostic and prognostic performance of novel biomarkers for non-ST-elevation myocardial infarction (NSTEMI). (2) Methods: Patients with suspected ACS presenting to Heidelberg University Hospital’s Emergency Department between August 2014 and February 2023 were analyzed. The biomarker panel included high-sensitivity cardiac troponin T (hs-cTnT), cardiac myosin-binding protein C (cMyBP-C), pro-B-type natriuretic peptide (proBNP), total N-terminal pro-B-type natriuretic peptide (t-Ntpr

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Huang, Hainan, Rongjie Zhang, Chengguang Xie, and Xiaofeng Li. "Identifying Subway Passenger Flow under Large-Scale Events Using Symbolic Aggregate Approximation Algorithm." Transportation Research Record: Journal of the Transportation Research Board 2676, no. 2 (2021): 800–810. http://dx.doi.org/10.1177/03611981211047835.

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Abstract (sommario):

Various social events, such as holidays, important sporting events, and major celebrations, may result in sudden large-scale passenger flows in certain sections and stations of urban rail transit systems. The sudden inbound passenger flows caused by these events can easily lead to continuous congestion of the subway network, which has a profound impact on the safety, reliability, and stability of a subway system. Because of the large magnitude of swipe data and the high dimensionality of time series, it is difficult to identify the emergence of such large passenger flows. Additionally, the rec

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Mohammadi-Fard, Massoud, and Rahim Ostovar. "The relationship between mental health crisis management and employees grain and Business Services tri-state area (Fars and Kohgiluyeh&Boyrahmad)." Journal of Management and Accounting Studies 5, no. 01 (2019): 66–71. http://dx.doi.org/10.24200/jmas.vol5iss01pp66-71.

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Abstract (sommario):

Crisis management is the application of systematic observation crises and analyzed by seeking the means by which they can be prevented or in the event of crises concerning mitigation, preparedness and rapid recovery Vamdadrsany be taken. This study aimed to investigate the relationship between mental health crisis management and employees was conducted cereal and Business Services tristate area. Methodology: This study is based on applied research, the nature and methods, correlation, time-sectional. Given that the study population size is 338 persons Morgan table was used to determine the sam

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Shuang, Qing, Yisheng Liu, Jing Liu, and Qigang Chen. "Serviceability Assessment for Cascading Failures in Water Distribution Network under Seismic Scenario." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/1431457.

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Abstract (sommario):

The stability of water service is a hot point in industrial production, public safety, and academic research. The paper establishes a service evaluation model for the water distribution network (WDN). The serviceability is measured in three aspects: (1) the functionality of structural components under disaster environment; (2) the recognition of cascading failure process; and (3) the calculation of system reliability. The node and edge failures in WDN are interrelated under seismic excitations. The cascading failure process is provided with the balance of water supply and demand. The matrix-ba

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