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Journal articles on the topic 'Dynamically changing detections'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Miao, Yuyang, Xihan Wang, Ning Zhang, Kai Wang, Lianhe Shao, and Quanli Gao. "Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny." Applied Sciences 14, no. 24 (2024): 11929. https://doi.org/10.3390/app142411929.

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To address the issues of missed and false detections caused by small object sizes, dense object distribution, and complex scenes in drone aerial images, this study proposes a drone-view object-detection algorithm based on YOLOv7-tiny with a Partial_C_Detect detection head. The algorithm’s performance in handling object occlusion and multi-scale detection is enhanced by introducing the VarifocalLoss loss function and improving the feature fusion network to BiFPN. Furthermore, incorporating the novel Partial_C_Detect detection head and Adaptive Kernel Convolution (AKConv) improves the detection

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Tasaki, Tsuyoshi, Fumio Ozaki, Nobuto Matsuhira, Tetsuya Ogata, and Hiroshi G. Okuno. "People Detection Based on Spatial Mapping of Friendliness and Floor Boundary Points for a Mobile Navigation Robot." Journal of Robotics 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/683975.

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Navigation robots must single out partners requiring navigation and move in the cluttered environment where people walk around. Developing such robots requires two different people detections: detecting partners and detecting all moving people around the robots. For detecting partners, we design divided spaces based on the spatial relationships and sensing ranges. Mapping the friendliness of each divided space based on the stimulus from the multiple sensors to detect people calling robots positively, robots detect partners on the highest friendliness space. For detecting moving people, we rega

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Minakov, E. I., G. A. Valikhin, A. V. Ovchinnikov, and S. S. Matveeva. "Tracking Filter for Radio Surveillance upon UAV Detection." Proceedings of Universities. Electronics 26, no. 6 (2021): 554–64. http://dx.doi.org/10.24151/1561-5405-2021-26-6-554-564.

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Unsanctioned intrusion of unmanned aerial vehicle (UAV) on the territory of the guarded object is primarily detected by specialized radio surveillance systems. The results obtained by radio surveillance systems are used for aiming of UAV visual identification and radio jamming systems. In this work, the problems of UAV detection and tracking of the target trajectory are considered. The known tracking filter systems for radio surveillance application were analyzed and a specialized matrix tracking filter system was proposed, which uses in its algorithm a dynamically changing energy potential of

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NIKITIN, DMYTRO, and OLEKSANDR RYBITSKYI. "INTEGRATION OF OBD-II VEHICLE DIAGNOSTICS WITH FINITE STATE MACHINE SOFTWARE DESIGN." Herald of Khmelnytskyi National University. Technical sciences 349, no. 2 (2025): 293–300. https://doi.org/10.31891/2307-5732-2025-349-43.

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This research presents an innovative approach to integrating On-Board Diagnostics II (OBD-II) vehicle diagnostic systems with finite state machine (FSM) software design methodologies. The study addresses the growing complexity of modern vehicle diagnostic systems and proposes a novel framework that combines real-time vehicle diagnostics with automated software control systems. By leveraging FSM-based architectures, this research seeks to enhance diagnostic accuracy, reduce false detections, and provide a scalable solution adaptable to various vehicle models and driving conditions. The integrat

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Polgarova, Kamila, Vojtech Kulvait, Karina Vargova, et al. "Clonal Architecture of MDS Somatic Mutations Dynamically Changes during Azacitidine Therapy and Has Very Limited Potential to Predict Patient Outcome." Blood 128, no. 22 (2016): 4294. http://dx.doi.org/10.1182/blood.v128.22.4294.4294.

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Abstract Introduction: Myelodysplastic syndromes (MDS) are clonal disorders of myeloid hematopoietic stem cells. Recent studies has shown that nearly 90% of patients with MDS carry somatic mutations in bone marrow (BM). These findings triggered a number of studies to identify potential uses of these mutations for diagnostics and prognostics purposes. We focused on a group of 38 patients with advanced stages of the disease that were selected for Azacitidine (AZA) therapy. We then utilized a set of 98 BM samples from the patient cohort that were collected in different stages before, during, and

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Guo, Haoxin, Jiarui Liu, Yan Li, et al. "Efficient Tomato Disease Detection Using MaxMin-Diffusion Mechanism and Lightweight Techniques." Plants 14, no. 3 (2025): 354. https://doi.org/10.3390/plants14030354.

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This paper proposes a disease detection model based on the maxmin-diffusion mechanism, aimed at improving the accuracy and robustness of disease detection tasks in the agricultural field. With the development of smart agriculture, automated disease detection has become one of the key tasks driving agricultural modernization. Traditional disease detection models often suffer from significant accuracy loss and robustness issues when dealing with complex disease types and dynamically changing time-series data. To address these problems, this paper introduces the maxmin-diffusion mechanism, which

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Liu, Xiaoyuan, Shihong Yue, and Zeying Wang. "A New Design of Electrical Impedance Tomography Sensor System for Pulmonary Disease Diagnosis." Journal of Systems Science and Information 6, no. 5 (2018): 473–80. http://dx.doi.org/10.21078/jssi-2018-473-08.

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AbstractAs an advanced process detection technology, electrical impedance tomography (EIT) has wide application prospects and advantages in medical imaging diagnosis. However, a series of issues need to be addressed before applying EIT for bedside monitoring. Medical diagnosis and bedside monitoring are dynamic measuring process, where the positions of measuring electrodes and the shape of the detected field are changing dynamical. Due to the inability to cope with the changeable electrode positions and various dynamic fields, existing EIT systems are mainly used for industrial detection in co

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WooJang, Seok, and Siwoo Byun. "Facial region detection robust to changing backgrounds." International Journal of Engineering & Technology 7, no. 2.12 (2018): 25. http://dx.doi.org/10.14419/ijet.v7i2.12.11028.

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Background/Objectives: These days, many studies have actively been conducted on intelligent robots capable of providing human friendly service. To make natural interaction between humans and robots, it is required to develop the mobile robot-based technology of detecting human facial regions robustly in dynamically changing real backgrounds.Methods/Statistical analysis: This paper proposes a method for detecting facial regions adaptively through the mobile robot-based monitoring of backgrounds in a dynamic real environment. In the proposed method, a camera-object distance and a color change in

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Zhai, Yaning, Ling Zhang, Xin Hu, Fanghu Yang, and Yang Huang. "A Dynamic Kalman Filtering Method for Multi-Object Fruit Tracking and Counting in Complex Orchards." Sensors 25, no. 13 (2025): 4138. https://doi.org/10.3390/s25134138.

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With the rapid development of agricultural intelligence in recent years, automatic fruit detection and counting technologies have become increasingly significant for optimizing orchard management and advancing precision agriculture. However, existing deep learning-based models are primarily designed to process static and single-frame images, thereby failing to meet the large-scale detection and counting demands in the dynamically changing scenes of modern orchards. To address these challenges, this paper proposes a multi-object fruit tracking and counting method, which integrates an improved Y

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Anub, A., and S. Sreelekshmy. "Dynamic Threshold-Based Algorithm for Client-Based HTTP Proxy Attack Detection through Spatial and Temporal Behavior Pattern Analysis." Recent Trends in Androids and IOS Applications 6, no. 3 (2024): 48–53. https://doi.org/10.5281/zenodo.13626561.

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<em>This paper provides a unique approach to client- based HTTP proxy attack detection using a Dynamic Spatiotem- poral Behavior Analysis (DSTBA) algorithm. Traditional meth- ods often lack adaptability to sophisticated cyberattacks. DSTBA addresses this by dynamically adjusting detection thresholds based on real-time analysis of spatial (network node distribution and interaction) and temporal (request timing and frequency) behavior patterns. This integration with machine learning tech- niques enhances attack identification accuracy while minimizing false positives. DSTBA’s core strength

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Fernandez-Marquez, Jose Luis, Josep Lluis Arcos, and Giovanna Di Marzo Serugendo. "A DECENTRALIZED APPROACH FOR DETECTING DYNAMICALLY CHANGING DIFFUSE EVENT SOURCES IN NOISY WSN ENVIRONMENTS." Applied Artificial Intelligence 26, no. 4 (2012): 376–97. http://dx.doi.org/10.1080/08839514.2012.653659.

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Lu, Kaibin. "Network Anomaly Traffic Analysis." Academic Journal of Science and Technology 10, no. 3 (2024): 65–68. http://dx.doi.org/10.54097/8as0rg31.

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This paper rigorously analyzes two principal methodologies in network traffic anomaly detection: feature detection and anomaly detection. Each methodology exhibits distinct strengths and confronts specific challenges. The study elucidates how the integration of deep learning with artificial immune systems could potentially transform feature detection. Moreover, it illustrates the enhancement of anomaly detection through the synthesis of machine learning techniques with traditional methods. Looking ahead, the paper delineates research trajectories that concentrate on merging deep learning, arti

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Razaque, Abdul, Joon Yoo, Gulnara Bektemyssova, et al. "Efficient Internet-of-Things Cyberattack Depletion Using Blockchain-Enabled Software-Defined Networking and 6G Network Technology." Sensors 23, no. 24 (2023): 9690. http://dx.doi.org/10.3390/s23249690.

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Low-speed internet can negatively impact incident response by causing delayed detection, ineffective response, poor collaboration, inaccurate analysis, and increased risk. Slow internet speeds can delay the receipt and analysis of data, making it difficult for security teams to access the relevant information and take action, leading to a fragmented and inadequate response. All of these factors can increase the risk of data breaches and other security incidents and their impact on IoT-enabled communication. This study combines virtual network function (VNF) technology with software -defined ne

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Sacks, E., and L. Joskowicz. "Dynamical Simulation of Planar Systems With Changing Contacts Using Configuration Spaces." Journal of Mechanical Design 120, no. 2 (1998): 181–87. http://dx.doi.org/10.1115/1.2826957.

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This paper presents a contact analysis algorithm for pairs of rigid, curved, planar parts based on configuration space computation. The algorithm is part of a dynamical simulator for planar systems with changing contact topologies. The configuration space of a pair of parts is a data structure that encodes the contact configurations for all pairs of part features. The configuration spaces of the interacting pairs in the mechanical system are constructed before the simulation. At each time step, the simulator queries the configuration spaces for contact changes instead of performing collision d

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Azita Laily Yusof, Nur Haidah Mohd Hanapiah, and Norsuzila Ya’acob. "Performance Evaluation of Changing Energy Detection Threshold on Wi-Fi and LTE-LAA Coexistence Networks." Journal of Advanced Research in Applied Sciences and Engineering Technology 34, no. 2 (2023): 342–51. http://dx.doi.org/10.37934/araset.34.2.342351.

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The demand on cellular and Wi-Fi infrastructure has increased exponentially along with the number of mobile devices in use today, thus requiring that both licensed (cellular) and unlicensed (Wi-Fi) spectrum be utilized as efficiently as possible. Licensed-Assisted Access (LAA) based unlicensed spectrum LTE service is seen as an alternative to improve the capability of 4G/5G wireless networks. This approach helps eNodeB to compete with other nodes by using the shared medium and using both licensed and unlicensed bands via carrier aggregation to provide best-effort services. Nevertheless, in ord

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Yang, Zhengqiang, Junwei Tian, and Ning Li. "Flow Graph Anomaly Detection Based on Unsupervised Learning." Mobile Information Systems 2022 (March 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/4194714.

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In this paper, a flow graph anomaly detection framework based on unsupervised learning is proposed. Compared with traditional anomaly detection, graph anomaly detection faces some problems. Firstly, the training of a reasonable network embedding is challenging. Secondly, the information data in the real world is often dynamically changing. Thirdly, due to the lack of sufficient training labeled data in most cases, anomaly detection models can only use unsupervised learning methods. In order to resolve these problems, three modules in the framework are proposed in this paper: preprocessor, cont

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Muhl-Richardson, Alex, Hayward Godwin, Matthew Garner, Julie Hadwin, Simon Liversedge, and Nick Donnelly. "Target detection in dynamically changing visual displays: Predictive search, working memory capacity and intolerance of uncertainty." Journal of Vision 16, no. 12 (2016): 1158. http://dx.doi.org/10.1167/16.12.1158.

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Fritzen, Claus Peter, Peter Kraemer, and Inka Buethe. "Vibration-Based Damage Detection under Changing Environmental and Operational Conditions." Advances in Science and Technology 83 (September 2012): 95–104. http://dx.doi.org/10.4028/www.scientific.net/ast.83.95.

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Structural Health Monitoring (SHM) allows to perform a diagnosis on demand which assists the operator to plan his future maintenance or repair activities. Using structural vibrations to extract damage sensitive features, problems can arise due to variations of the dynamical properties with changing environmental and operational conditions (EOC). The dynamic changes due to changing EOCs like variations in temperature, rotational speed, wind speed, etc. may be of the same order of magnitude as the variations due to damage making a reliable damage detection impossible. In this paper, we show a me

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Klouček, Tomáš, Petr Klápště, Jana Marešová, and Jan Komárek. "UAV-Borne Imagery Can Supplement Airborne Lidar in the Precise Description of Dynamically Changing Shrubland Woody Vegetation." Remote Sensing 14, no. 9 (2022): 2287. http://dx.doi.org/10.3390/rs14092287.

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Airborne laser scanning (ALS) is increasingly used for detailed vegetation structure mapping; however, there are many local-scale applications where it is economically ineffective or unfeasible from the temporal perspective. Unmanned aerial vehicles (UAVs) or airborne imagery (AImg) appear to be promising alternatives, but only a few studies have examined this assumption outside economically exploited areas (forests, orchards, etc.). The main aim of this study was to compare the usability of normalized digital surface models (nDSMs) photogrammetrically derived from UAV-borne and airborne image

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Olakunle Abayomi Ajala and Olusegun Abiodun Balogun. "Leveraging AI/ML for anomaly detection, threat prediction, and automated response." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 2584–98. http://dx.doi.org/10.30574/wjarr.2024.21.1.0287.

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The rapid evolution of information and communication technologies, notably the Internet, has yielded substantial benefits while posing challenges to information system security. With an increasing frequency of cyber threats—from unauthorized access to data breaches—the digital landscape's vulnerability is evident. Addressing the financial impact of cybercrime, this study delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cybersecurity. Analyzing advancements and outcomes, the research explores practical techniques for anomaly detection, threat predic

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Olakunle, Abayomi Ajala, and Abiodun Balogun Olusegun. "Leveraging AI/ML for anomaly detection, threat prediction, and automated response." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 2584–98. https://doi.org/10.5281/zenodo.13377680.

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The rapid evolution of information and communication technologies, notably the Internet, has yielded substantial benefits while posing challenges to information system security. With an increasing frequency of cyber threats—from unauthorized access to data breaches—the digital landscape's vulnerability is evident. Addressing the financial impact of cybercrime, this study delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cybersecurity. Analyzing advancements and outcomes, the research explores practical techniques for anomaly detection, t

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Fujita, Kazuhisa. "An efficient and straightforward online vector quantization method for a data stream through remove-birth updating." PeerJ Computer Science 10 (January 8, 2024): e1789. http://dx.doi.org/10.7717/peerj-cs.1789.

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The growth of network-connected devices has led to an exponential increase in data generation, creating significant challenges for efficient data analysis. This data is generated continuously, creating a dynamic flow known as a data stream. The characteristics of a data stream may change dynamically, and this change is known as concept drift. Consequently, a method for handling data streams must efficiently reduce their volume while dynamically adapting to these changing characteristics. This article proposes a simple online vector quantization method for concept drift. The proposed method ide

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Muhl-Richardson, Alex, Hayward Godwin, Matthew Garner, Julie Hadwin, Simon Liversedge, and Donnelly Nick. "Eye movements reveal two search modes for the detection of targets in novel dynamically changing visual displays." Journal of Vision 15, no. 12 (2015): 59. http://dx.doi.org/10.1167/15.12.59.

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Abd-Ellatif, Laila, Mohammad Abrar, and Alaa A. K. Ismaeel. "ATAD-Net: An Adaptive Deep Learning Framework for Real-Time Financial Fraud Detection." Advances in Artificial Intelligence and Machine Learning 05, no. 02 (2025): 3988–4003. https://doi.org/10.54364/aaiml.2025.52225.

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With the fast growth of financial transaction fraud, there is a need for advanced detection systems capable of real-time analysis. Rule-based and machine-learning approaches to fraud traditionally suffer from being unable to adapt to changing fraud patterns, returning very high back result rates and much inefficiency in the security of financial operations. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) methods are suitable, but they lack adaptability and interpretability. This paper proposes an Adaptive Transactional Anomaly Detection Network (ATAD-Net), a

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Soproniuk, I., and O. Komar. "Adaptive approach to spectrum monitoring in cognitive radio networks through signal detection optimization." COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, no. 56 (September 28, 2024): 392–400. http://dx.doi.org/10.36910/6775-2524-0560-2024-56-47.

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The article considers the improvement of the adaptive algorithm of the spectral monitoring method for cognitive radio networks by introducing adaptive wavelet transforms and filters. The use of adaptive Morle and Dobechy wavelet transforms, as well as adaptive Kalman, LMS, and RLS filters is proposed, which allows dynamically changing parameters depending on the conditions of the radio environment. The comparative analysis with traditional methods showed that adaptive methods significantly increase the efficiency of signal detection in conditions of low SNR values, reducing the noise level, im

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Qiu, Xiaoying, Xuan Sun, and Monson Hayes. "Enhanced Security Authentication Based on Convolutional-LSTM Networks." Sensors 21, no. 16 (2021): 5379. http://dx.doi.org/10.3390/s21165379.

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The performance of classical security authentication models can be severely affected by imperfect channel estimation as well as time-varying communication links. The commonly used approach of statistical decisions for the physical layer authenticator faces significant challenges in a dynamically changing, non-stationary environment. To address this problem, this paper introduces a deep learning-based authentication approach to learn and track the variations of channel characteristics, and thus improving the adaptability and convergence of the physical layer authentication. Specifically, an int

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Ishiya, Koji, and Sachiyo Aburatani. "Multivariate statistical monitoring system for microbial population dynamics." Physical Biology 19, no. 1 (2021): 016003. http://dx.doi.org/10.1088/1478-3975/ac3ad6.

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Abstract Microbiomes in their natural environments vary dynamically with changing environmental conditions. The detection of these dynamic changes in microbial populations is critical for understanding the impact of environmental changes on the microbial community. Here, we propose a novel method to detect time-series changes in the microbiome, based on multivariate statistical process control. By focusing on the interspecies structures, this approach enables the robust detection of time-series changes in a microbiome composed of a large number of microbial species. Applying this approach to e

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Cohen, A., D. Hegg, M. de Michele, Q. Song, and N. Kasabov. "An intelligent controller for automated operation of sequencing batch reactors." Water Science and Technology 47, no. 12 (2003): 57–63. http://dx.doi.org/10.2166/wst.2003.0628.

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In this paper the results are presented of original research into the automatic and “intelligent” detection of breakpoints in Dissolved Oxygen (DO) profiles. The research has been based on a large body of data collected from laboratory SBRs operating on synthetic wastewater. Two different approaches were followed to identify the endpoints. The paper analyses and evaluates the results of automatic breakpoint detection on the basis of geometric features in the DO profiles. This was followed by classification of the detected breakpoints using different soft computing techniques based on Neural Ne

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Amit Dogra and Taqdir. "Enhancing DDoS Attack Detection and Network Resilience Through Ensemble-Based Packet Processing and Bandwidth Optimization." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 04 (2024): 930–37. http://dx.doi.org/10.47392/irjaeh.2024.0130.

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It is critical to identify Distributed Denial of Service (DDoS) attacks to preserve network integrity and guarantee continuous service delivery. Our research suggests a novel way to lower the network's packet drop ratio and improve the accuracy of DDoS attack detection. Conventional techniques occasionally just use anomaly detection or signature-based detection, which might not be sufficient to protect against DDoS assault schemes that are always changing. To increase the precision and resilience of DDoS detection, our system incorporates several detection strategies, such as signature-based,

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G, Murali,, and Amit Kumar Tamrakar. "Intelligent Dental Materials." EAS Journal of Dentistry and Oral Medicine 6, no. 06 (2024): 103–7. http://dx.doi.org/10.36349/easjdom.2024.v06i06.002.

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In modern dentistry, the development of intelligent dental materials marks a significant innovation aimed at enhancing patient care and treatment outcomes. These materials, which dynamically respond to changes in the oral environment, include self-healing dental fillings, antibacterial properties, color-changing indicators for early decay detection, biocompatible implants, and temperature-sensitive prosthetics. This paper explores various categories of intelligent dental materials, emphasizing their potential to improve restoration longevity, promote oral health, enhance patient comfort, and f

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Phursule, Rajesh, Dhirajkumar Lal, Sandhya Waghere, Mohammad Abdul Mughni, Sarvesh Ransubhe, and Chinmay Shiralkar. "Enhancing Traffic Flow Using Computer Vision Based - Dynamic Traffic Light Control and Lane Management." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7s (2023): 386–91. http://dx.doi.org/10.17762/ijritcc.v11i7s.7014.

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Traffic congestion is a persistent problem in many metropolises worldwide. Despite the existence of traffic control systems, they are not always efficient enough to manage the ever-changing traffic density environment. The traditional approach of allocating specific times to each lane with the green light, regardless of the traffic situation, has not been very effective. In fact, it often can make the traffic congestion worse. Thus, the need for a more sophisticated system has emerged to simulate and optimize traffic control. This paper proposes the use of computer vision technology to develop

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Ma, Hongtao, Zhisheng Zhang, and Junai Zhao. "A Novel ST-YOLO Network for Steel-Surface-Defect Detection." Sensors 23, no. 22 (2023): 9152. http://dx.doi.org/10.3390/s23229152.

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Recent progress has been made in defect detection using methods based on deep learning, but there are still formidable obstacles. Defect images have rich semantic levels and diverse morphological features, and the model is dynamically changing due to ongoing learning. In response to these issues, this article proposes a shunt feature fusion model (ST-YOLO) for steel-defect detection, which uses a split feature network structure and a self-correcting transmission allocation method for training. The network structure is designed to specialize the process of classification and localization tasks

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Prolay Ghosh. "SMS Spam Detection with Machine Learning Model & Classification." Journal of Information Systems Engineering and Management 10, no. 2 (2025): 444–52. https://doi.org/10.52783/jisem.v10i2.2172.

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Tool wear monitoring and predictive maintenance are critical in manufacturing, where traditional methods often struggle to adapt to changing conditions. This research presents an Adaptive Reinforcement Learning Framework for Real-Time Tool Wear Optimization and Predictive Maintenance (ARTOM). The framework integrates reinforcement learning with real-time sensor feedback to optimize machining parameters and maintenance schedules dynamically. Proximal Policy Optimization (PPO) is used to guide decision-making by balancing tool life, product quality, and operational costs. Multi-agent reinforceme

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Wang, Di, Jin Liu, Haima Yang, Bo Huang, and Guohui Zeng. "Research on Tunable SPR Sensors Based on WS2 and Graphene Hybrid Nanosheets." Photonics 9, no. 7 (2022): 490. http://dx.doi.org/10.3390/photonics9070490.

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A prismatic excitation-based affinity biosensor consisting of the prism (BK7), WS2/graphene hybrid nanosheets, and silver (Ag) as the active metal for the surface plasmon resonance is proposed in this present research. The introduction of the transition metal WS2/graphene layer protected the silver substrate and enhanced the adsorption of biomolecules, which facilitated the quality and performance of detection. Here, we improved the detection structure by focusing on the metallic materials, graphene and WS2 film layers, and the thickness of the measured medium on the sensing effect. The result

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Srinivasa Rao Kongarana. "Nonlinear Reinforcement Learning-Based Dynamic Test Case Prioritization with Anomaly Detection for Continuous Integration." Communications on Applied Nonlinear Analysis 32, no. 1s (2024): 122–42. http://dx.doi.org/10.52783/cana.v32.2114.

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This research article introduces DynamicR-TCP, a nonlinear approach to dynamic test case prioritization (TCP) leveraging reinforcement learning and anomaly detection. Designed to enhance the efficiency of software testing in continuous integration (CI) environments, the proposed model dynamically adapts to changing conditions by learning complex patterns from historical test data. A reinforcement learning agent, employing policy and value networks, guides nonlinear prioritization by optimizing the sequence of test case executions. The model integrates a sliding window strategy for adaptive foc

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Priya Kumari. "Adaptive QoS-aware Congestion Window Adaptation through Contention Detection in Wireless Networks." Advances in Nonlinear Variational Inequalities 28, no. 2 (2024): 203–9. http://dx.doi.org/10.52783/anvi.v28.1939.

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In wireless networks, maintaining Quality of Service (QoS) while managing congestion effectively is a critical challenge due to changing network conditions. Quality of service (QoS) technique operates on a network to run high-priority applications and traffic reliably despite limited network capacity. This paper proposes an adaptive QoS-aware congestion window adaptation mechanism that leverages contention detection to optimize performance in wireless networks. A cooperative relay node has also been introduced to relay safety messages that failed to reach the destination. According to the prop

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Adiuku, Ndidiamaka, Nicolas P. Avdelidis, Gilbert Tang, and Angelos Plastropoulos. "Improved Hybrid Model for Obstacle Detection and Avoidance in Robot Operating System Framework (Rapidly Exploring Random Tree and Dynamic Windows Approach)." Sensors 24, no. 7 (2024): 2262. http://dx.doi.org/10.3390/s24072262.

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The integration of machine learning and robotics brings promising potential to tackle the application challenges of mobile robot navigation in industries. The real-world environment is highly dynamic and unpredictable, with increasing necessities for efficiency and safety. This demands a multi-faceted approach that combines advanced sensing, robust obstacle detection, and avoidance mechanisms for an effective robot navigation experience. While hybrid methods with default robot operating system (ROS) navigation stack have demonstrated significant results, their performance in real time and high

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Zhu, Guanglang, Huiying Sun, Jiannan Wang, et al. "In Vivo Detection and Measurement of Aortic Aneurysm and Dissection in Mouse Models Using Microcomputed Tomography with Contrast Agent." Contrast Media & Molecular Imaging 2019 (March 6, 2019): 1–9. http://dx.doi.org/10.1155/2019/5940301.

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Objectives. The aim of this study was to evaluate the potential of microcomputed tomography (micro-CT) using the intravascular contrast agent ExiTron nano 12000 for aorta imaging and monitoring the dynamic changing process of the aorta in mouse models with aortic aneurysm and dissection. Materials and Methods. Experiments were performed on healthy mice and mice with aortic dissection. Mice that were developing aortic dissection and healthy mice underwent micro-CT imaging after injection of ExiTron nano 12000. Time-dependent signal enhancement (at 1, 2, 3, 6, and 12 hours after intravenous inje

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Benn, William, and Stanislao Lauria. "Robot Navigation Control Based on Monocular Images: An Image Processing Algorithm for Obstacle Avoidance Decisions." Mathematical Problems in Engineering 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/240476.

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This paper covers the use of monocular vision to control autonomous navigation for a robot in a dynamically changing environment. The solution focused on using colour segmentation against a selected floor plane to distinctly separate obstacles from traversable space: this is then supplemented with canny edge detection to separate similarly coloured boundaries to the floor plane. The resulting binary map (where white identifies an obstacle-free area and black identifies an obstacle) could then be processed by fuzzy logic or neural networks to control the robot’s next movements. Findings show th

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Zeng, Xi, Guangchun Luo, and Ke Qin. "Joint Event Detection with Dynamic Adaptation and Semantic Relevance." Electronics 14, no. 2 (2025): 234. https://doi.org/10.3390/electronics14020234.

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Event detection is a crucial task in natural language processing, and it plays a significant role in numerous applications, such as information retrieval, question answering, and situational awareness. Real-world tasks typically require robust models that can dynamically adapt to changing data distributions and seamlessly accommodate emerging event types while maintaining high accuracy and efficiency. However, existing methods often face catastrophic forgetting, a significant challenge where models lose previously acquired knowledge when learning new information. This phenomenon hinders models

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KROON, MICHEL, and IAN STEWART. "DETECTING THE SYMMETRY OF ATTRACTORS FOR SIX OSCILLATORS COUPLED IN A RING." International Journal of Bifurcation and Chaos 05, no. 01 (1995): 209–29. http://dx.doi.org/10.1142/s0218127495000168.

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We study a dynamical system modelling six coupled identical oscillators, introduced by Collins and Stewart in connection with hexapod gaits. The system has dihedral group symmetry D6, and they suggest that symmetric chaos may be present at some parameter values. We confirm this by employing the method of “detectives” introduced by Barany, Dellnitz, and Golubitsky. The paper is mainly intended as a case study in the use of detectives to analyse the symmetries of attractors, employing a system with a rich and varied range of bifurcations—both changing the symmetry and changing the dynamics—with

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Kaur, Rajveer, Dr Shaveta Rani, and Dr Paramjeet Singh. "Review of Acknowledgment Based Techniques for Detection of Black Hole/Gray Hole Attacks in MANETs." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 5, no. 3 (2013): 214–19. http://dx.doi.org/10.24297/ijct.v5i3.3522.

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In Mobile Ad hoc NETworks (MANETs) nodes communicate via wireless links, without any fixed infrastructure like base stations, central servers or mobile switching. Each node in MANET can act as a host or as a router. Due inherent characteristics like decentralization, self configuring, self -organizing networks, they can be deployed easily without need of expensive infrastructure andÂ have wide range of military to civilian and commercial applications. But wireless medium, dynamically changing topology, limited battery and lack of centralized control in MANETs, make them vulnerable to various t

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Nurusheva, Assel, Nurzipa Medelbayeva, Dina Satybaldina, and Nikolaj Goranin. "Machine learning algorithms in SIEM systems for enhanced detection and management of security events." Bulletin of L.N. Gumilyov Eurasian National University. Mathematics, computer science, mechanics series 148, no. 3 (2024): 6–17. http://dx.doi.org/10.32523/bulmathenu.2024/3.1.

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As cyber threats become increasingly sophisticated, traditional Security Information and Event Management (SIEM) systems face challenges in effectively identifying and responding to these dangers. This research presents the development of a SIEM system integrated with machine learning (ML) to enhance threat detection, anomaly identification, and automated incident response. The integration of ML allows the SIEM system to go beyond conventional rule-based approaches, enabling the detection of previously unknown threats by learning from historical data. The system employs advanced algorithms to

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Xi, Hailong, Le Ru, Jiwei Tian, et al. "URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection." Mathematics 13, no. 4 (2025): 591. https://doi.org/10.3390/math13040591.

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In recent years, deep learning has been extensively deployed on unmanned aerial vehicles (UAVs), particularly for object detection. As the cornerstone of UAV-based object detection, deep neural networks are susceptible to adversarial attacks, with adversarial patches being a relatively straightforward method to implement. However, current research on adversarial patches, especially those targeting UAV object detection, is limited. This scarcity is notable given the complex and dynamically changing environment inherent in UAV image acquisition, which necessitates the development of more robust

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Khalil, Ayman, and Besma Zeddini. "Cross-Layer Optimization for Enhanced IoT Connectivity: A Novel Routing Protocol for Opportunistic Networks." Future Internet 16, no. 6 (2024): 183. http://dx.doi.org/10.3390/fi16060183.

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Opportunistic networks, an evolution of mobile Ad Hoc networks (MANETs), offer decentralized communication without relying on preinstalled infrastructure, enabling nodes to route packets through different mobile nodes dynamically. However, due to the absence of complete paths and rapidly changing connectivity, routing in opportunistic networks presents unique challenges. This paper proposes a novel probabilistic routing model for opportunistic networks, leveraging nodes’ meeting probabilities to route packets towards their destinations. Thismodel dynamically builds routes based on the likeliho

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Ren, Yitong, Zhaojun Gu, Zhi Wang, et al. "System Log Detection Model Based on Conformal Prediction." Electronics 9, no. 2 (2020): 232. http://dx.doi.org/10.3390/electronics9020232.

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With the rapid development of the Internet of Things, the combination of the Internet of Things with machine learning, Hadoop and other fields are current development trends. Hadoop Distributed File System (HDFS) is one of the core components of Hadoop, which is used to process files that are divided into data blocks distributed in the cluster. Once the distributed log data are abnormal, it will cause serious losses. When using machine learning algorithms for system log anomaly detection, the output of threshold-based classification models are only normal or abnormal simple predictions. This p

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Gâlmeanu, Honorius, and Răzvan Andonie. "Concept Drift Adaptation with Incremental–Decremental SVM." Applied Sciences 11, no. 20 (2021): 9644. http://dx.doi.org/10.3390/app11209644.

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Data classification in streams where the underlying distribution changes over time is known to be difficult. This problem—known as concept drift detection—involves two aspects: (i) detecting the concept drift and (ii) adapting the classifier. Online training only considers the most recent samples; they form the so-called shifting window. Dynamic adaptation to concept drift is performed by varying the width of the window. Defining an online Support Vector Machine (SVM) classifier able to cope with concept drift by dynamically changing the window size and avoiding retraining from scratch is curr

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S., Ashwin, Nafila B., Sajana S., Sutharsan S., and Arthi K. "Enhancing Safe and Precise Maneuvering in Autonomous Electric Vehicle." Journal of Electrical Engineering and Automation 7, no. 1 (2025): 13–26. https://doi.org/10.36548/jeea.2025.1.002.

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This research deals with the design of an autonomous vehicle navigation system based on a master-slave computational paradigm, combining Raspberry Pi 4 and Arduino UNO for visual perception in real-time, decision-making, and actuation. The Raspberry Pi 4, supported by a Pi Camera module, performs lane detection and near-object identification through image processing techniques, and then sends over extracted data through serial communication to Arduino UNO. Contrary to traditional autonomous driving systems that are based largely on monolithic processing architectures, this two-part control par

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Kacheru, Goutham, Rohit Bajjuru, and Nagaraju Arthan. "Artificial Intelligence in Finance: Predictive Analytics, Fraud Detection, and Risk Management in 2024." Formosa Journal of Science and Technology 4, no. 1 (2025): 141–54. https://doi.org/10.55927/fjst.v4i1.13398.

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AI is poised to be transformative across virtually all industries, and the financial sector has already experienced major impacts from AI in predictive analytics, fraud detection and risk management among others. This paper also describes the innovation of AI, machine learning and natural language processing (NLP) technologies and their availability in financial services in 2024. Its scope covers richer credit scoring models which harness predictive analytics to assess borrower performance, more sophisticated fraudulent activity detection frameworks that can identify suspicious transactions in

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BOURBAKIS, N., P. KAKUMANU, S. MAKROGIANNIS, R. BRYLL, and S. PANCHANATHAN. "NEURAL NETWORK APPROACH FOR IMAGE CHROMATIC ADAPTATION FOR SKIN COLOR DETECTION." International Journal of Neural Systems 17, no. 01 (2007): 1–12. http://dx.doi.org/10.1142/s0129065707000920.

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The goal of image chromatic adaptation is to remove the effect of illumination and to obtain color data that reflects precisely the physical contents of the scene. We present in this paper an approach to image chromatic adaptation using Neural Networks (NN) with application for detecting — adapting human skin color. The NN is trained on randomly chosen color images containing human subject under various illuminating conditions, thereby enabling the model to dynamically adapt to the changing illumination conditions. The proposed network predicts directly the illuminant estimate in the image so

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