Relevant bibliographies by topics / NODE DETECTION

Academic literature on the topic 'NODE DETECTION'

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Published: 11 September 2023

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Journal articles on the topic "NODE DETECTION"

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Lee, S., J. Yang, S. Nam, J. Lee, W. Kim, J. Choi, G. Kim, and G. Kim. "Triple detection method for sentinel lymph node detection." Journal of Clinical Oncology 27, no. 15_suppl (May 20, 2009): e11605-e11605. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.e11605.

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e11605 Background: Sentinel lymph node biopsy is widely accepted method to determine nodal stage of breast cancer. There are several reported method for detecting sentinel lymph node. The aim of this study was to show the new detection method of sentinel lymph node and show the effectiveness of this method. Methods: We did prospective study and enrolled 25 patients who underwent partial mastectomy and sentinel lymph node biopsy. We injected indigocyanine green (green dye) at peritumoral lesion, indigocarmine dye (blue dye) in subareolar area and radioisotope (Tc-99m) injection. Sentinel lymph nodes are identified by color change or radioisotope uptake, and classified by each color (blue or green) and radioisotope uptake. We compared the detection rate from our study with that from the previous studies. Results: Sentinel lymph nodes were detected in all patients (25/25). Green color stained sentinel lymph nodes were identified in 18 patients (18/25), blue color stained sentinel lymph nodes were identified in 15 patients (15/25) and radioactive lymph nodes were identified in 19 patients (19/25). Conclusions: The triple mapping method showed higher detection rate than the previous studies and this method is recommendable to detect sentinel lymph node. No significant financial relationships to disclose.
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Imam Rahmani, Mohammad Khalid, Moizuddin Mohammed, Reyazur Rashid Irshad, Sadaf Yasmin, Swati Mishra, Pooja Asopa, Asharul Islam, Sultan Ahmad, and Aleem Ali. "Design a Secure Routing and Monitoring Framework Based on Hybrid Optimization for IoT-Based Wireless Sensor Networks." Journal of Nanoelectronics and Optoelectronics 18, no. 3 (March 1, 2023): 338–46. http://dx.doi.org/10.1166/jno.2023.3397.

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Wireless Sensor Networks (WSNs) have employed in recent years for many different applications and functions. But, it has the critical task to detect the malicious node because node malicious attacks are dangerous attacks, and the concept of a malicious attack is opponents enter the network, search accidentally, and capture one or more normal nodes. A lot of research developed to overcome this problem, but no precise results are found. In this paper, design a Hybrid Vulture and African Buffalo with Node Identity Verification (HVAB-NIV) model to predict the malicious nodes in the WSN. The fitness functions of the HVAB-NIV have operated to recognize the energy level of each node and improve the performance of node detection. The developed replica includes three stages that monitor each node, calculate the energy level and detect the malicious node. More than 100 node inputs were initialized in the proposed technique and implemented in the MATLAB tool. The suggested mechanism enhances the performance of malicious node detection and gains good accuracy for detecting nodes also, it saves running time and power consumption. The experimental results of the developed model has validated with other existing replicas to running time, False Prediction Rate (FPR), detection accuracy, True Prediction Rate (TPR), and power consumption. The developed methods achieve better results by gaining a high rate of accuracy detection, less running time, and false rate detection.
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Lei, Yang, and Ying Jiang. "Anomaly Detection for Nodes Under the Cloud Computing Environment." International Journal of Distributed Systems and Technologies 12, no. 1 (January 2021): 30–48. http://dx.doi.org/10.4018/ijdst.2021010103.

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Due to the services diversity and dynamic deployment, the anomalies will occur on nodes under cloud computing environment. If a single node generates an anomaly, the associated nodes are affected by the abnormal node, which will result in anomaly propagation and nodes failure. In this paper, a method of anomaly detection for nodes under the cloud computing environment is proposed. Firstly, the node monitoring model is established by the agents deployed on each node. Secondly, the comprehensive score is used to identify abnormal data. The anomaly of the single node is judged by the time window-based method. Then, the status of directly associated nodes is detected through normalized mutual information and the status of indirectly associated nodes is detected through the node attributes in the case of a single node anomaly. Finally, other associated nodes affected by the abnormal node are detected. The experimental results showed that the method in this paper can detect the anomalies of single node and associated node under the cloud computing environment effectively.
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Masoud, Mohammad Z., Yousef Jaradat, Ismael Jannoud, and Mustafa A. Al Sibahee. "A hybrid clustering routing protocol based on machine learning and graph theory for energy conservation and hole detection in wireless sensor network." International Journal of Distributed Sensor Networks 15, no. 6 (June 2019): 155014771985823. http://dx.doi.org/10.1177/1550147719858231.

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In this work, a new hybrid clustering routing protocol is proposed to prolong network life time through detecting holes and edges nodes. The detection process attempts to generate a connected graph without any isolated nodes or clusters that have no connection with the sink node. To this end, soft clustering/estimation maximization with graph metrics, PageRank, node degree, and local cluster coefficient, has been utilized. Holes and edges detection process is performed by the sink node to reduce energy consumption of wireless sensor network nodes. The clustering process is dynamic among sensor nodes. Hybrid clustering routing protocol–hole detection converts the network into a number of rings to overcome transmission distances. We compared hybrid clustering routing protocol–hole detection with four different protocols. The accuracy of detection reached 98%. Moreover, network life time has prolonged 10%. Finally, hybrid clustering routing protocol–hole detection has eliminated the disconnectivity in the network for more than 80% of network life time.
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Ezeh, Chinenye, Ren Tao, Li Zhe, Wang Yiqun, and Qu Ying. "Multi-Type Node Detection in Network Communities." Entropy 21, no. 12 (December 17, 2019): 1237. http://dx.doi.org/10.3390/e21121237.

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Patterns of connectivity among nodes on networks can be revealed by community detection algorithms. The great significance of communities in the study of clustering patterns of nodes in different systems has led to the development of various methods for identifying different node types on diverse complex systems. However, most of the existing methods identify only either disjoint nodes or overlapping nodes. Many of these methods rarely identify disjunct nodes, even though they could play significant roles on networks. In this paper, a new method, which distinctly identifies disjoint nodes (node clusters), disjunct nodes (single node partitions) and overlapping nodes (nodes binding overlapping communities), is proposed. The approach, which differs from existing methods, involves iterative computation of bridging centrality to determine nodes with the highest bridging centrality value. Additionally, node similarity is computed between the bridge-node and its neighbours, and the neighbours with the least node similarity values are disconnected. This process is sustained until a stoppage criterion condition is met. Bridging centrality metric and Jaccard similarity coefficient are employed to identify bridge-nodes (nodes at cut points) and the level of similarity between the bridge-nodes and their direct neighbours respectively. Properties that characterise disjunct nodes are equally highlighted. Extensive experiments are conducted with artificial networks and real-world datasets and the results obtained demonstrate efficiency of the proposed method in distinctly detecting and classifying multi-type nodes in network communities. This method can be applied to vast areas such as examination of cell interactions and drug designs, disease control in epidemics, dislodging organised crime gangs and drug courier networks, etc.
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Feng, You Bing, and Rong Biao Zhang. "Fault Detection of WSN Based on Spatial Correlation." Applied Mechanics and Materials 58-60 (June 2011): 1504–10. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1504.

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Regarding application with smooth variation of detection, spatial correlation of sensors’ data within a small field was applied to sensor nodes’ fault diagnosis. The data were sorted into several continuous sequences by sink node. Sequence with minimum variance was regarded as normal data to determine normal nodes. For undetermined nodes, it can be determined via calculation on deviation to normal nodes’ data of vicinity area. If deviation does not exceed the threshold, the node is normal; otherwise, it is regarded as a fault node. The research on WSN in a greenhouse shows that fault node can be effectively detected in time by this method.
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Li, Feng, Yali Si, Ning Lu, Zhen Chen, and Limin Shen. "A Security and Efficient Routing Scheme with Misbehavior Detection in Delay-Tolerant Networks." Security and Communication Networks 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/2761486.

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Due to the unique network characteristics, the security and efficient routing in DTNs are considered as two great challenges. In this paper, we design a security and efficient routing scheme, called SER, which integrates the routing decision and the attacks detection mechanisms. In SER scheme, each DTNs node locally maintains a one-dimensional vector table to record the summary information about the contact with other nodes and the trust degree of other nodes. To obtain the global status and the contact relationship among all nodes, the trusted routing table consisting of vectors of all nodes is built in each DTNs node. The method for detecting malicious nodes and selfish nodes is proposed, which exploits the global summary information to analyze the history forwarding behavior of node and judge whether it is a malicious node or selfish node. The routing decision method is proposed based on trust degree of forwarding messages between nodes, which adopts trust degree as relay node selection strategy. Simulation results show that compared with existing schemes SER scheme could detect the attacks behavior of malicious nodes and selfish nodes, at the same time, with higher delivery rate and lower average delivery delay.
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M, Sajitha, D. Kavitha, and P. Chenna Reddy. "Clone Node Detection in Heterogeneous WSN with Low Memory Overhead." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 3 (September 30, 2022): 21–26. http://dx.doi.org/10.35940/ijrte.c7206.0911322.

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In unattended areas, the wireless sensor network is deployed and the nodes are always open to attacks. An adversary can capture a node and can deploy many nodes, which are clone of the captured, in the network called clone node or replicated node by using the credential information retrieved from captured node. These clone nodes can damage the network directly or indirectly. This attack is called node replication o attack or clone node attack. In this area, so many works are introduced and all of these methods use a random key or code, or location information, to detect clone nodes. This paper presents a method that does not use any of this information. The simulation results show that it performs better than previous methods.
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Ganesh, P., G. B. S. R. Naidu, Korla Swaroopa, R. Rahul, Ahmad Almadhor, C. Senthilkumar, Durgaprasad Gangodkar, A. Rajaram, and Alazar Yeshitla. "Implementation of Hidden Node Detection Scheme for Self-Organization of Data Packet." Wireless Communications and Mobile Computing 2022 (March 26, 2022): 1–9. http://dx.doi.org/10.1155/2022/1332373.

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The mobile nodes are infrequent movement in nature; therefore, its packet transmission is also infrequent. Packet overload occurred for routing process, and data are lossed by receiver node, since hackers hide the normal routing node. Basically, the hidden node problem is created based on the malicious nodes that are planned to hide the vital relay node in the specific routing path. The packet transmission loss occurred for routing; so, it minimizes the packet delivery ratio and network lifetime. Then, proposed enhanced self-organization of data packet (EAOD) mechanism is planned to aggregate the data packet sequencially from network structure. The hacker node present in routing path is easy to separate from network with trusty nodes. In order to secure the regular characteristics of organizer node from being confirmed as misbehaving node, the hidden node detection technique is designed for abnormal routing node identification. This algorithm checks the neighboring nodes that are hacker node, which hide the trust node in the routing path. And that trust nodes are initially found based on strength value of every node and assign path immediately. It increases network lifetime and minimizes the packet loss rate.
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Sindhuja, L. S., and G. Padmavathi. "Replica Node Detection Using Enhanced Single Hop Detection with Clonal Selection Algorithm in Mobile Wireless Sensor Networks." Journal of Computer Networks and Communications 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/1620343.

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Security of Mobile Wireless Sensor Networks is a vital challenge as the sensor nodes are deployed in unattended environment and they are prone to various attacks. One among them is the node replication attack. In this, the physically insecure nodes are acquired by the adversary to clone them by having the same identity of the captured node, and the adversary deploys an unpredictable number of replicas throughout the network. Hence replica node detection is an important challenge in Mobile Wireless Sensor Networks. Various replica node detection techniques have been proposed to detect these replica nodes. These methods incur control overheads and the detection accuracy is low when the replica is selected as a witness node. This paper proposes to solve these issues by enhancing the Single Hop Detection (SHD) method using the Clonal Selection algorithm to detect the clones by selecting the appropriate witness nodes. The advantages of the proposed method include (i) increase in the detection ratio, (ii) decrease in the control overhead, and (iii) increase in throughput. The performance of the proposed work is measured using detection ratio, false detection ratio, packet delivery ratio, average delay, control overheads, and throughput. The implementation is done using ns-2 to exhibit the actuality of the proposed work.
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Dissertations / Theses on the topic "NODE DETECTION"

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Resul, Hulya. "Node replication detection in sensor networks." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/3253.

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Thesis (M.S.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Dept. of Electrical and Computer Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Chen, Wan Qing. "Predictors of Auxillary Lymph Node Involvement in Screen Detected Breast Cancer." Thesis, The University of Sydney, 2004. http://hdl.handle.net/2123/676.

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Background: Axillary lymph node dissection as routine part of breast cancer treatment has been questioned in relation to the balance between benefits and morbidity. The purpose of this study is to determine the association of tumor size, age and histological grade with axillary lymph node metastasis, to determine if some patients could be exempted from axillary dissection. Methods: The data are derived from BreastScreen NSW, the government sponsored population-based breast screening program. In New South Wales (NSW) Australia between 1995 and 2002, 7,221 patients with invasive breast carcinoma were diagnosed and 5,290 patients were eligible for this study. The relationship between incidence of positive axillary lymph nodes and three study factors (tumor size, age and histological grade) was investigated by univariate and multivariate analysis. Logistic regression models were used to predict probability of axillary metastases. Results: The incidence of axillary lymph node metastases was 28.6% (95% CI: 27.4%- 29.8%). Univariate analysis showed that age, tumor size and histological grade were significant predictors of axillary lymph node metastases (p<0.0001). Multivariate analysis identified age, tumor size and histological grade remained as independent predictors (p<0.0001). From multivariate analysis, patients with T1a (Less than or equal to 5mm) and grade I tumors regardless of age had 5.2% (95% CI: 1.2%- 9.3%) frequency of node metastases. Patients 70 years or older with grade I, T1a and T1b (6-10mm) tumors had 4.9% (95% CI: 3.2%- 7.5%) and 6.6% (95% CI: 5.3%-8.3%) predicted frequency of node metastases. Conclusions: Tumor size, age and histological grade are predictors of axillary lymph node metastases. Routine axillary lymph node dissection could be avoided in some patient groups with a low frequency of involved lymph nodes if the benefits are considered to exceed the risks.
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Chen, Wan Qing. "Predictors of Auxillary Lymph Node Involvement in Screen Detected Breast Cancer." University of Sydney. Public Health, 2004. http://hdl.handle.net/2123/676.

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Background: Axillary lymph node dissection as routine part of breast cancer treatment has been questioned in relation to the balance between benefits and morbidity. The purpose of this study is to determine the association of tumor size, age and histological grade with axillary lymph node metastasis, to determine if some patients could be exempted from axillary dissection. Methods: The data are derived from BreastScreen NSW, the government sponsored population-based breast screening program. In New South Wales (NSW) Australia between 1995 and 2002, 7,221 patients with invasive breast carcinoma were diagnosed and 5,290 patients were eligible for this study. The relationship between incidence of positive axillary lymph nodes and three study factors (tumor size, age and histological grade) was investigated by univariate and multivariate analysis. Logistic regression models were used to predict probability of axillary metastases. Results: The incidence of axillary lymph node metastases was 28.6% (95% CI: 27.4%- 29.8%). Univariate analysis showed that age, tumor size and histological grade were significant predictors of axillary lymph node metastases (p<0.0001). Multivariate analysis identified age, tumor size and histological grade remained as independent predictors (p<0.0001). From multivariate analysis, patients with T1a (Less than or equal to 5mm) and grade I tumors regardless of age had 5.2% (95% CI: 1.2%- 9.3%) frequency of node metastases. Patients 70 years or older with grade I, T1a and T1b (6-10mm) tumors had 4.9% (95% CI: 3.2%- 7.5%) and 6.6% (95% CI: 5.3%-8.3%) predicted frequency of node metastases. Conclusions: Tumor size, age and histological grade are predictors of axillary lymph node metastases. Routine axillary lymph node dissection could be avoided in some patient groups with a low frequency of involved lymph nodes if the benefits are considered to exceed the risks.
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Schleip, Christoph. "Climate change detection in natural systems by Bayesian methods." kostenfrei, 2009. http://mediatum2.ub.tum.de/node?id=805580.

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Kotari, Ravi Teja. "Node failure detection and data retrieval in wireless sensor networks." Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10108190.

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This project presents a method for detecting node failure in a wireless sensor network. The defective node is identified using round-trip delay measurements. Data transfer from the transmitter section to the receiver section is accomplished via the ZigBee protocol. As soon as a node has been identified as defective, the node is removed from the sensor network. Information about the failed node is provided to users with registered mobile device through the Global System for Mobile (GSM) module. The proposed method has been successfully implemented and tested experimentally on a small sensor network using the LPC2148 ARM7 microcontroller.

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Pettersson, Christopher. "Automatic fault detection and localization in IPnetworks : Active probing from a single node perspective." Thesis, Linköpings universitet, Programvara och system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-120414.

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Fault management is a continuously demanded function in any kind of network management. Commonly it is carried out by a centralized entity on the network which correlates collected information into likely diagnoses of the current system states. We survey the use of active-on-demand-measurement, often called active probes, together with passive readings from the perspective of one single node. The solution is confined to the node and is isolated from the surrounding environment. The utility for this approach, to fault diagnosis, was found to depend on the environment in which the specific node was located within. Conclusively, the less environment knowledge, the more useful this solution presents. Consequently this approach to fault diagnosis offers limited opportunities in the test environment. However, greater prospects was found for this approach while located in a heterogeneous customer environment.
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Al-Riyami, Ahmed. "Towards an adaptive solution to data privacy protection in hierarchical wireless sensor networks." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/towards-an-adaptive-solution-to-data-privacy-protection-in-hierarchical-wireless-sensor-networks(a096db2a-251c-4e9e-a4ff-8bfe4c6f1bf4).html.

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Hierarchical Wireless Sensor networks (WSNs) are becoming attractive to many applications due to their energy efficiency and scalability. However, if such networks are deployed in a privacy sensitive application context such as home utility consumption, protecting data privacy becomes an essential requirement. Our threat analysis in such networks has revealed that PPDA (Privacy Preserving Data Aggregation), NIDA (Node ID Anonymity) and ENCD (Early Node Compromise Detection) are three essential properties for protecting data privacy. The scope of this thesis is on protecting data privacy in hierarchical WSNs byaddressing issues in relation to two of the three properties identified, i.e., NIDA and ENCD, effectively and efficiently. The effectiveness property is achieved by considering NIDA and ENCD in an integrated manner, and the efficiency property is achieved by using an adaptive approach to security provisioning. To this end, the thesis has made the following four novel contributions. Firstly, this thesis presents a comprehensive analysis of the threats to data privacy and literature review of the countermeasures proposed to address these threats. The analysis and literature review have led to the identification of two main areas for improvements: (1) to reduce the resources consumed as the result of protecting data privacy, and (2) to address the compatibility issue between NIDA and ENCD.Secondly, a novel Adaptive Pseudonym Length Estimation (AdaptPLE) method has been proposed. The method allows the determination of a minimum acceptable length of the pseudonyms used in NIDA based on a given set of security and application related requirements and constraints. In this way, we can balance the trade-off between an ID anonymity protection level and the costs (i.e., transmission and energy) incurred in achieving the protection level. To demonstrate its effectiveness, we have evaluated the method by applying it to two existing NIDA schemes, the Efficient Anonymous Communication (EAC) scheme and theCryptographic Anonymous Scheme (CAS).Thirdly, a novel Adaptive Early Node Compromise Detection (AdaptENCD) scheme for cluster-based WSNs has been proposed. This scheme allows early detections of compromised nodes more effectively and efficiently than existing proposals. This is achieved by adjusting, at run-time, the transmission rate of heartbeat messages, used to detect nodes' aliveness, in response to the average message loss ratio in a cluster. This adaptive approach allows us to significantly reduce detection errors while keeping the number of transmitted heartbeat messages as low as possible, thus reducing transmission costs. Fourthly, a novel Node ID Anonymity Preserving Scheme (ID-APS) for clusterbased WSNs has been proposed. ID-APS protects nodes ID anonymity while, at the same time, also allowing the global identification of nodes. This later property supports the identification and removal of compromised nodes in the network, which is a significant improvement over the state-of-the-art solution, the CAS scheme. ID-APS supports both NIDA and ENCD by making a hybrid use of dynamic and global identification pseudonyms. More importantly, ID-APS achieves these properties with less overhead costs than CAS. All proposed solutions have been analysed and evaluated comprehensively to prove their effectiveness and efficiency.
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Barrus, Joseph D. "Intrusion detection in real-time in a multi-node, multi-host environment." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA341406.

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Thesis (M.S. in Software Engineering) Naval Postgraduate School, September 1997.
"September 1997." Thesis advisor(s): Neil C. Rowe, Ronald Broersma. Includes bibliography references (p. 77). Also available online.
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Khanapure, Vishal. "Memory efficient distributed detection of node replication attacks in wireless sensor networks." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0025072.

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Thekedar, Bhushan. "Investigations on the use of breath gas analysis with Proton Transfer Reaction Mass Spectrometry (PTR-MS) for a non-invasive method of early lung cancer detection." kostenfrei, 2009. https://mediatum2.ub.tum.de/node?id=821780.

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Books on the topic "NODE DETECTION"

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Barrus, Joseph D. Intrusion detection in real-time in a multi-node, multi-host environment. Monterey, Calif: Naval Postgraduate School, 1997.

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K, kokula Krishna Hari, ed. Detection of Replica Node Attack Based on Hybrid Artificial Immune System Technique: ICCS 2014. Bangkok, Thailand: Association of Scientists, Developers and Faculties, 2014.

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Heritage, English, and Cathedrals Fabric Commission for England., eds. Smoke detection systems for cathedrals: An English Heritage advisory note. London: English Heritage, 1997.

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Loecker, Jan de. A note on detecting learning by exporting. Cambridge, MA: National Bureau of Economic Research, 2010.

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Annabel the detective: The case of the mystery note. Toronto: Harper Collins Publishers, 1992.

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Andrew, Taylor. The private nose. Cambridge, Mass: Candlewick Press, 1993.

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Funeral note. London: Headline, 2013.

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Copyright Paperback Collection (Library of Congress), ed. Treasure under Finny's Nose: A Finny's Nose mystery. Uhrichsville, OH: Heartsong Presents, 2008.

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Mentink, Dana. Trouble up Finny's Nose: A Finny's Nose mystery. Uhrichsville, OH: Barbour Pub., 2008.

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Copyright Paperback Collection (Library of Congress), ed. Fog over Finny's Nose: A Finny's Nose mystery. Uhrichsville, OH: Barbour Pub., 2008.

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Book chapters on the topic "NODE DETECTION"

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Dumka, Ankur, Alaknanda Ashok, Parag Verma, Anuj Bhardwaj, and Navneet Kaur. "Malicious Node Detection Mechanisms." In Security Issues for Wireless Sensor Networks, 213–40. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003257608-8.

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Keshtgar, Mohammad R. S., Wendy A. Waddington, Sunil R. Lakhani, and Peter J. Ell. "Sentinel Node Detection and Imaging." In The Sentinel Node in Surgical Oncology, 1–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60038-8_1.

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Zhou, Yi. "Detection of Minimal Residual Disease." In Practical Lymph Node and Bone Marrow Pathology, 701–11. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-32189-5_31.

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Zhang, Lingxiao, and Xuefeng Yan. "A Critical Node Detection Algorithm Based Node Interplay Model." In Security, Privacy, and Anonymity in Computation, Communication, and Storage, 467–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68884-4_39.

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Chan, A. D., and D. L. Morton. "Sentinel Node Detection in Malignant Melanoma." In Recent Results in Cancer Research, 161–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57151-0_14.

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Arulselvan, Ashwin, Clayton W. Commander, Oleg Shylo, and Panos M. Pardalos. "Cardinality-Constrained Critical Node Detection Problem." In Performance Models and Risk Management in Communications Systems, 79–91. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-0534-5_4.

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Ma, Huan, and Wei Wang. "A Label Propagation Based User Locations Prediction Algorithm in Social Network." In Communications in Computer and Information Science, 165–74. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4922-3_12.

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AbstractNetwork community detection is an important service provided by social networks, and social network user location can greatly improve the quality of community detection. Label propagation is one of the main methods to realize the user location prediction. The traditional label propagation algorithm has the problems including “location label countercurrent” and the update randomness of node location label, which seriously affects the accuracy of user location prediction. In this paper, a new location prediction algorithm for social networks based on improved label propagation algorithm is proposed. By computing the K-hop public neighbor of any two point in the social network graph, the nodes with the maximal similarity and their K-hopping neighbors are merged to constitute the initial label propagation set. The degree of nodes not in the initial set are calculated. The node location labels are updated asynchronously is adopted during the iterative process, and the node with the largest degree is selected to update the location label. The improvement proposed solves the “location label countercurrent” and reduces location label updating randomness. The experimental results show that the proposed algorithm improves the accuracy of position prediction and reduces the time cost compared with the traditional algorithms.
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Pan, Hanxiao, Teng Guo, Hayat Dino Bedru, Qing Qing, Dongyu Zhang, and Feng Xia. "DEFINE: Friendship Detection Based on Node Enhancement." In Lecture Notes in Computer Science, 81–92. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39469-1_7.

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Yang, Huai. "Illegal Node Detection of Wireless Mesh Network Based on Node Reputation Value." In Advances in Intelligent and Soft Computing, 545–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25986-9_85.

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Yu, Xinmin, Xingyun Chen, Xin Ye, Jingfeng Guo, and Bijian Chen. "Optimization of node structure of transmission tower." In Advances in Civil Engineering: Structural Seismic Resistance, Monitoring and Detection, 355–60. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003310884-46.

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Conference papers on the topic "NODE DETECTION"

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Huang, Tiancheng, Donglin Wang, Yuan Fang, and Zhengyu Chen. "End-to-End Open-Set Semi-Supervised Node Classification with Out-of-Distribution Detection." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/290.

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Out-Of-Distribution (OOD) samples are prevalent in real-world applications. The OOD issue becomes even more severe on graph data, as the effect of OOD nodes can be potentially amplified by propagation through the graph topology. Recent works have considered the OOD detection problem, which is critical for reducing the uncertainty in learning and improving the robustness. However, no prior work considers simultaneously OOD detection and node classification on graphs in an end-to-end manner. In this paper, we study a novel problem of end-to-end open-set semi-supervised node classification (OSSNC) on graphs, which deals with node classification in the presence of OOD nodes. Given the lack of supervision on OOD nodes, we introduce a latent variable to indicate in-distribution or OOD nodes in a variational inference framework, and further propose a novel algorithm named as Learning to Mix Neighbors (LMN) which learns to dampen the influence of OOD nodes through the messaging-passing in typical graph neural networks. Extensive experiments on various datasets show that the proposed method outperforms state-of-the-art baselines in terms of both node classification and OOD detection.
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Song, Hui, Liang Xie, Sencun Zhu, and Guohong Cao. "Sensor node compromise detection." In the 2007 international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1280940.1280993.

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Rounds, Cody C., Veronica C. Torres, Chenguye Li, Floris Voskuil, T. S. Nijboer, Max J. H. Witjes, and Kenneth M. Tichauer. "Rapid detection of lymph node micro metastasis using widefield paired-agent fluorescent imaging techniques in the angular domain." In Optical Molecular Probes, Imaging and Drug Delivery. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/omp.2023.om3e.5.

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In head and neck squamous cell carcinoma surgical cases, rapid detection of lymph node metastases is needed. Here we present a methodology for rapid lymph node assessment in 1 cm nodes with > 99% sensitivity.
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Zhang, Dong, Liangqing Wu, Changlong Sun, Shoushan Li, Qiaoming Zhu, and Guodong Zhou. "Modeling both Context- and Speaker-Sensitive Dependence for Emotion Detection in Multi-speaker Conversations." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/752.

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Recently, emotion detection in conversations becomes a hot research topic in the Natural Language Processing community. In this paper, we focus on emotion detection in multi-speaker conversations instead of traditional two-speaker conversations in existing studies. Different from non-conversation text, emotion detection in conversation text has one specific challenge in modeling the context-sensitive dependence. Besides, emotion detection in multi-speaker conversations endorses another specific challenge in modeling the speaker-sensitive dependence. To address above two challenges, we propose a conversational graph-based convolutional neural network. On the one hand, our approach represents each utterance and each speaker as a node. On the other hand, the context-sensitive dependence is represented by an undirected edge between two utterances nodes from the same conversation and the speaker-sensitive dependence is represented by an undirected edge between an utterance node and its speaker node. In this way, the entire conversational corpus can be symbolized as a large heterogeneous graph and the emotion detection task can be recast as a classification problem of the utterance nodes in the graph. The experimental results on a multi-modal and multi-speaker conversation corpus demonstrate the great effectiveness of the proposed approach.
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Cao, Song, Genquan Duan, and A. I. Haizhou. "Fast human detection using Node-Combined Part Detector." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6116493.

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Yang, Heming, Xinfang Zhang, Ji Zhou, and Jun Yu. "A Hierarchy of Oct-Sphere Model and its Application in Collision Detection." In ASME 1994 Design Technical Conferences collocated with the ASME 1994 International Computers in Engineering Conference and Exhibition and the ASME 1994 8th Annual Database Symposium. American Society of Mechanical Engineers, 1994. http://dx.doi.org/10.1115/detc1994-0042.

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Abstract Collision and interference detection among 3-D moving objects is an important issue in the simulation of their behavior. This paper presents a new model for representing 3-D objects and a corresponding effective algorithm for detecting collisions and interferences among moving objects. Objects can be represented for efficient collision and interference detection by a hierarchy of oct - sphere model (HOSM). Algorithms are given for building the HOSM and for detecting collisions and interferences between moving objects. On the basis of HOSM, the algorithm checks only intersections between the nodes of the models which are on the surfaces of the objects. Furthermore, because a node of HOSM represents a spherical region, the collision between the two nodes can be easily found just by calculating the distance between the centers of the two spheres corresponding to them no matter how the objects move. Finally, we discuss the efficiency of the algorithm through an example.
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Sei, Yuichi, and Shinichi Honiden. "Reporter node determination of replicated node detection in wireless sensor networks." In the 3rd International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1516241.1516340.

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Veeraiah, Neenavath, and B. T. Krishna. "Selfish node detection IDSM based approach using individual master cluster node." In 2018 2nd International Conference on Inventive Systems and Control (ICISC). IEEE, 2018. http://dx.doi.org/10.1109/icisc.2018.8399109.

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Tran, Thi Minh Chau, Bjorn Scheuermann, and Martin Mauve. "Node presence detection with reduced overhead." In 2009 Sixth International Conference on Wireless On-Demand Network Systems and Services (WONS). IEEE, 2009. http://dx.doi.org/10.1109/wons.2009.4801831.

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Taranum, Fahmina, Ayesha Sarvat, Nooria Ali, and Shamekh Siddiqui. "Detection and Prevention of Blackhole node." In 2020 4th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech). IEEE, 2020. http://dx.doi.org/10.1109/iementech51367.2020.9270072.

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Reports on the topic "NODE DETECTION"

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Cavalli, Luciane R. Detection of Genetic Alterations in Breast Sentinel Lymph Node by Array-CGH. Fort Belvoir, VA: Defense Technical Information Center, October 2005. http://dx.doi.org/10.21236/ada444833.

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Cavalli, Luciane R. Detection of Genetic Alterations in Breast Sentinel Lymph Node by Array-CGH. Fort Belvoir, VA: Defense Technical Information Center, October 2006. http://dx.doi.org/10.21236/ada460808.

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Tzonev. PR-396-143702-R01 Feasibility of a Petroleum Leak Detection Cable Utilizing Polymer Absorption Sensor. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 2015. http://dx.doi.org/10.55274/r0010849.

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Several high-profile new pipeline projects in Canada and the USA have triggered investigation of auxiliary leak detection systems outside of the pipeline. Although external leak detection technologies have existed for decades, a comprehensive solution that is widely applicable for both new and existing pipeline deployment still does not exist. The goal of PRCI project PL-1H was to evaluate the feasibility of interconnecting multiple underground Polymer Absorption (PA) hydrocarbon sensor nodes through a single cable that provides both power and data communication inductively to each node. Installing the cable alongside a pipeline would create a continuous underground sensor network that could detect the migrating hydrocarbons from the leak and therefore would have excellent sensitivity for underground hydrocarbon releases. PL-1H developed a comprehensive theoretical model of the cable, created a system topology, established manufacturability, and constructed a proof of concept that was tested under a variety of conditions. The results indicate that the cable concept is technologically feasible and could be practical. It was concluded that if commercialized, the solution could help prepare the industry for the future by delivering a new leak detection tool with small leak detection capabilities.
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Tzonev, Nick. PR-396-163702-R02 Petroleum Pipeline Polymer Absorption Sensor Leak Detection Cable Phase 2. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), October 2021. http://dx.doi.org/10.55274/r0012183.

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The goal of this Project is to validate and commercialize Syscor's External Leak Detection System (ELDS) cable and wireless sensor node. Syscor's ELDS cable system will be able to detect leaks smaller than 5 barrels (bbl) and will be suitable for deployment in new construction projects as well as existing underground pipelines. The capability of Syscor's ELDS to be plowed in place at a safe distance from the pipeline differentiates the solution from competing approaches as it is more economically viable for existing pipelines. The proposed leak detection cable will be able to recognize leaks that would previously remain unnoticed for long periods of time, reducing the release of chemicals into the environment and remediation costs to pipeline operators.
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ZHAO, JIE, LIANHUA YE, WEI WANG, YANTAO YANG, ZHENGHAI SHEN, and SUNYIN RAO. Surgical Prognostic Factors of Second Primary Lung Cancer: A Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0047.

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Review question / Objective: The objective of this study was to explore the effects of different surgical strategies and potential prognostic factors on the prognosis of patients with SPLC through a systematic review and meta-analysis.Prognostic factors included surgical approach, type of SPLC(Synchronous and metachronous),histology,disease-free interval (DFI),tumor size,CT morphology, lymph node metastasis status, smoking status, gender. Condition being studied: With the development of imaging technology and better survival after primary lung cancer, the detection rate of second primary lung cancer (SPLC) has been increasing. At present, the staging and treatment of the second primary lung cancer are still controversial. Although surgery is widely accepted as the main treatment method, there is no unified diagnostic criteria and diagnosis and treatment strategy. The objective of this study was to explore the effects of different surgical strategies and potential prognostic factors on the prognosis of patients with SPLC through a systematic review and meta-analysis.
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Lewis, Nathan S., Rodney M. Goodman, and Robert H. Grubbs. A Conducting Polymer-Based Electronic Nose for Landmine Detection. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada396394.

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De Loecker, Jan. A Note on Detecting Learning by Exporting. Cambridge, MA: National Bureau of Economic Research, November 2010. http://dx.doi.org/10.3386/w16548.

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Singh, Ishwar, Prabhjot Singh, and Brajesh Choudhary. NOνA Far Detector νe Sideband Study Technical Note. Office of Scientific and Technical Information (OSTI), June 2023. http://dx.doi.org/10.2172/1988491.

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Koutcher, Jason. Non-Invasive Detection of Axillary Nodes by Contrast Enhanced Magnetic Resonance Imaging. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada382736.

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Sanseverino, Giuseppe, Dominik Krumm, Wolfgang Kilian, and Stephan Odenwald. Body-attached Sensor Nodes for Automatic Detection of Hike Events and Parameters. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317538.

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