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Journal articles on the topic 'Probabilistic Voting-based Filtering Scheme'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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1

Nam, Su Man, and Tae Ho Cho. "Discrete event simulation–based energy efficient path determination scheme for probabilistic voting–based filtering scheme in sensor networks." International Journal of Distributed Sensor Networks 16, no. 8 (2020): 155014772094913. http://dx.doi.org/10.1177/1550147720949134.

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In wireless sensor networks, sensors are extremely vulnerable to false positive and false negative attacks due to their stringent energy and computational constraints. Several en-route filtering schemes mainly focus on saving energy through early detection of false data within a short distance against these attacks; however, they cannot immediately block the false data injected by compromised nodes. A security scheme uses context-aware architecture for a probabilistic voting–based filtering scheme to detect the compromised nodes and block the injection of false data, unlike security protocols.

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KIM, MUN SU, and TAE HO CHO. "AN EN-ROUTE FILTERING METHOD IN SENSOR NETWORKS USING DECISION FUNCTION." International Journal of Information Acquisition 04, no. 04 (2007): 357–64. http://dx.doi.org/10.1142/s0219878907001411.

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Security in sensor networks is a major issue. Sensor networks use symmetric cryptography protocol since sensor nodes have resource constrained hardware. Such netowrks are also deployed in hostile environments. Therefore, an attacker can get all information after any nodes get compromised. The adversary can inject false sensing reports or false Message Authentication Codes into real reports. A probabilistic voting-based filtering scheme is proposed but in several cases it is inefficient in terms of energy consumption and filtering effectiveness. We proposed a new method that uses a decision fun

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Li, Feng, Avinash Srinivasan, and Jie Wu. "PVFS: A Probabilistic Voting-based Filtering Scheme in Wireless Sensor Networks." International Journal of Security and Networks 3, no. 3 (2008): 173. http://dx.doi.org/10.1504/ijsn.2008.020091.

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Cho, Tae Ho, Su Man Nam, and Shahzad Muhammad K. "GAFS: GENETIC ALGORITHM-BASED FILTERING SCHEME FOR IMPROVING DETECTION POWER IN SENSOR NETWORKS." International Journal of Research -GRANTHAALAYAH 3, no. 12 (2015): 100–116. http://dx.doi.org/10.29121/granthaalayah.v3.i12.2015.2894.

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Wireless sensor networks (WSNs) have stringent energy and computational requirements. Security has become very crucial issue with the widespread acceptance of the WSNs in numerous decision-critical and hostile environments. Since sensor nodes are left unattended, they can be compromised by adversaries to launch various application layer attacks. Effective countermeasures against these attacks can lead to improved security. A probabilistic voting-based filtering scheme (PVFS) uses probabilistic filtering based on the distance to counter attacks of fabricated reports with false votes and real re

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Nam, Su Man, and Tae Ho Cho. "Context-Aware Architecture for Probabilistic Voting-based Filtering Scheme in Sensor Networks." IEEE Transactions on Mobile Computing 16, no. 10 (2017): 2751–63. http://dx.doi.org/10.1109/tmc.2016.2641219.

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Tae, Ho Cho, Man Nam Su, and K. Shahzad Muhammad. "GAFS: GENETIC ALGORITHM-BASED FILTERING SCHEME FOR IMPROVING DETECTION POWER IN SENSOR NETWORKS." International Journal of Research – Granthaalayah 3, no. 12 (2017): 100–116. https://doi.org/10.5281/zenodo.848950.

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Wireless sensor networks (WSNs) have stringent energy and computational requirements. Security has become very crucial issue with the widespread acceptance of the WSNs in numerous decision-critical and hostile environments. Since sensor nodes are left unattended, they can be compromised by adversaries to launch various application layer attacks. Effective countermeasures against these attacks can lead to improved security. A probabilistic votingbased filtering scheme (PVFS) uses probabilistic filtering based on the distance to counter attacks of fabricated reports with false votes and real rep

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Jae, Kwan Lee1 Su Man Nam2 and Tae Ho Cho3. "ENSP: ENERGY EFFICIENT NEXT HOP SELECTION IN A PROBABILISTIC VOTING-BASED FILTERING SCHEME USING FUZZY LOGIC." Informatics Engineering, an International Journal (IEIJ) 02, dec (2014): 01–12. https://doi.org/10.5121/ieij.2014.2401.

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In wireless sensor networks, sensor nodes are easily compromised due to their restricted hardware resources. These compromised nodes inject fabricated votes into legitimate reports, and generate false report and false vote injection attacks. These attacks deplete energy resources and block report transmission. A probabilistic voting-based filtering scheme was proposed to detect the bogus votes in reports en-route to protect against attacks. Although this method detects false votes in intermediate nodes, the sensor network needs to be effectively operated in consideration of a node's condit

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Lee, Jae Kwan, Su Man Nam, and Tae Ho Cho. "ENSP: Energy Efficient Next Hop Selection in A Probabilistic Voting-Based Filtering Scheme Using Fuzzy Logic." Informatics Engineering, an International Journal 2, no. 4 (2014): 1–12. http://dx.doi.org/10.5121/ieij.2014.2401.

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Lee, Jae Kwan, and Tae Ho Cho. "Ensf: Energy-Efficient Next-Hop Selection Method Using Fuzzy Logic In Probabilistic Voting-Based Filtering Scheme." International Journal of Ambient Systems and Applications 2, no. 4 (2014): 19–32. http://dx.doi.org/10.5121/ijasa.2014.2403.

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Lim, Sang-hyeok, and Tae-ho Cho. "Report Verification Technique for Improvement of the Energy Efficiency in a Probabilistic Voting-based Filtering Scheme of WSNs." International Journal of Computer Applications 171, no. 3 (2017): 21–25. http://dx.doi.org/10.5120/ijca2017914992.

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Lim, Sang-hyeok, and Tae-ho Cho. "Automatic Threshold Reset Scheme using a Double Fuzzy System for Improvement of Detection Rate in a Probabilistic Voting-based Filtering Scheme of WSNs." International Journal of Computer Applications 176, no. 1 (2017): 38–43. http://dx.doi.org/10.5120/ijca2017915513.

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12

Ahn, Jungsub, Sanghyeok Lim, and Taeho Cho. "Fuzzy Logic-based Efficient Message Route Selection Method to Prolong the Network Lifetime in WSNs." International journal of Computer Networks & Communications 13, no. 6 (2021): 73–91. http://dx.doi.org/10.5121/ijcnc.2021.13605.

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Recently, sensor networks have been used in a wide range of applications, and interest in sensor node performance has increased. A sensor network is composed of tiny nodes with limited resources. The sensor network communicates between nodes in a configured network through self-organization. An energyefficient security protocol with a hierarchy structure with various advantages has been proposed to prolong the network lifetime of sensor networks. But due to structural problems in traditional protocols, nodes located upstream tend to consume relatively high energy compared to other nodes. A net

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Cho, Tae Ho, and Su Man Nam. "MODELING AND SIMULATION OF THRESHOLD ANALYSIS FOR PVFS IN WIRELESS SENSOR NETWORKS." International Journal of Research -GRANTHAALAYAH 4, no. 8 (2016): 1–10. http://dx.doi.org/10.29121/granthaalayah.v4.i8.2016.2555.

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Wireless sensor networks (WSNs) suffer serious damage from false positive and negative attacks due to their hardware restrictions. The sensor network causes both unnecessary energy consumption and information loss through false reports and normal reports, which include false message authentication codes (MACs). A probabilistic voting-based filtering scheme (PVFS) effectively detects the two types of attacks through a pre-defined threshold, which is the number of detected false MACs in a report. Since the threshold significantly influences the ability to detect attacks, the sensor network shoul

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Man Nam, Su, and Youn Kyoung Seo. "A Method of Improving Energy Efficiency Through Geofencing and False Data Blocking In Context-Aware Architecture for Probabilistic Voting-Based Filtering Scheme of WSNs." International journal of Computer Networks & Communications 13, no. 3 (2021): 97–107. http://dx.doi.org/10.5121/ijcnc.2021.13306.

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In wireless sensor networks, sensor nodes have the disadvantage of being vulnerable to several attacks due to the use of wireless communication and constrained energy. Adversaries exploit vulnerable characteristics of these nodes to capture them and generate false positive and false negative attacks. These attacks result in false alarms in a base station and information loss in intermediate nodes. A context-aware architecture for a probabilistic voting-based filtering scheme (CAA-PVFS) identifies compromised nodes that cause the damage. Although this method immediately detects the compromised

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Sang-Hyeok, Lim, and Cho Tae-Ho. "WSN Lifetime Extension Using GA Optimised Fuzzy Logic." International Journal of Computer Science & Information Technology (IJCSIT) 9, no. 5 (2017): 1–14. https://doi.org/10.5121/ijcsit.2017.9501.

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A wireless sensor network (WSN) consists of multiple sensor nodes and base stations that collect information from widely deployed sensors. However, the disadvantage is that WSNs are randomly distributed in an open environment, which makes them difficult to manage individually and more easily found and compromised by an attacker. An attacker can execute a false report insertion or invalid vote insertion attack through a compromised node. The Probabilistic Voting Filtering System (PVFS) is a system that prevents these two types of attacks. Before sending a report, the proposed method probabilist

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Su, Man Nam1 and Tae Ho Cho2. "A SECURITY METHOD FOR MULTIPLE ATTACKS IN SENSOR NETWORKS: AGAINST FALSE-REPORT INJECTION, FALSE-VOTE INJECTION, AND WORMHOLE ATTACKS." A SECURITY METHOD FOR MULTIPLE ATTACKS IN SENSOR NETWORKS: AGAINST FALSE-REPORT INJECTION, FALSE-VOTE INJECTION, AND WORMHOLE ATTACKS 02, dec (2014): 01–17. https://doi.org/10.5121/ieij.2014.2402.

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In a large-scale wireless sensor network, damage spreads rapidly in the network when under false report injection, false votes injection, or wormhole attacks. These attacks cause finite energy resources to be drained, legitimate reports to be dropped, and data to be intercepted by adversary nodes. A probabilistic voting-based filtering scheme (PVFS) and localized encryption and authentication protocol (LEAP) can be used to cope with these attacks. When multiple attacks occur simultaneously, PVFS and LEAP should be operated together. But the concurrent application of PVFS and LEAP provides inef

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Pulkkinen, Seppo, V. Chandrasekar, and Ari-Matti Harri. "Stochastic Spectral Method for Radar-Based Probabilistic Precipitation Nowcasting." Journal of Atmospheric and Oceanic Technology 36, no. 6 (2019): 971–85. http://dx.doi.org/10.1175/jtech-d-18-0242.1.

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AbstractNowcasts (short-term forecasts) of heavy rainfall causing flash floods are highly valuable in densely populated urban areas. In the Collaborative Adaptive Sensing of the Atmosphere (CASA) project, a high-resolution X-band radar network was deployed in the Dallas–Fort Worth (DFW) metroplex. The Dynamic and Adaptive Radar Tracking of Storms (DARTS) method was developed as a part of the CASA nowcasting system. In this method, the advection field is determined in the spectral domain using the discrete Fourier transform. DARTS was recently extended to include a filtering scheme for suppress

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18

Liao, Xiaofeng, Bo Li, and Bo Yang. "A Novel Classification and Identification Scheme of Emitter Signals Based on Ward’s Clustering and Probabilistic Neural Networks with Correlation Analysis." Computational Intelligence and Neuroscience 2018 (November 5, 2018): 1–15. http://dx.doi.org/10.1155/2018/1458962.

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The rapid development of modern communication technology makes the identification of emitter signals more complicated. Based on Ward’s clustering and probabilistic neural networks method with correlation analysis, an ensemble identification algorithm for mixed emitter signals is proposed in this paper. The algorithm mainly consists of two parts, one is the classification of signals and the other is the identification of signals. First, self-adaptive filtering and Fourier transform are used to obtain the frequency spectrum of the signals. Then, the Ward clustering method and some clustering val

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Ouala, Said, Ronan Fablet, Cédric Herzet, et al. "Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature." Remote Sensing 10, no. 12 (2018): 1864. http://dx.doi.org/10.3390/rs10121864.

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The forecasting and reconstruction of oceanic dynamics is a crucial challenge. While model driven strategies are still the state-of-the-art approaches in the reconstruction of spatio-temporal dynamics. The ever increasing availability of data collections in oceanography raised the relevance of data-driven approaches as computationally efficient representations of spatio-temporal fields reconstruction. This tools proved to outperform classical state-of-the-art interpolation techniques such as optimal interpolation and DINEOF in the retrievement of fine scale structures while still been computat

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20

Fong, Li Wei, Pi Ching Lou, and Kung Ting Lin. "On-Line Bayesian Classifier Design for Measurement Fusion." Advanced Materials Research 461 (February 2012): 826–29. http://dx.doi.org/10.4028/www.scientific.net/amr.461.826.

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A neural-network-based classifier design for adaptive Kalman filtering is introduced to fuse the measurements extracted from multiple sensors to improve tracking accuracy. The proposed method consists of a group of parallel Kalman filters and a classifier based on Radial Basis Function Network (RBFN). By incorporating Markov chain into Bayesian estimation scheme, a RBFN is used as a probabilistic neural network for classification. Based upon data compression technique and on-line classification algorithm, an adaptive estimator to measurement fusion is developed that can handle the switching pl

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21

Li, Na, Lei Zou, and Chenxi Wang. "Multi-sensor fusion estimation subject to random sensor failures under binary encoding scheme: A federated-filtering-based method." Journal of Physics: Conference Series 2898, no. 1 (2024): 012029. http://dx.doi.org/10.1088/1742-6596/2898/1/012029.

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Abstract This paper explores the issue of fusion estimation in multi-sensor systems experiencing random sensor failures via a binary coding scheme (BCS). The occurrence of sensor failures is modeled using random variables with predetermined probability distributions. To avert the potential signal distortions during network-based communication, the BCS is utilized to transform the measurement signals into bit strings. A novel federated-filtering-based fusion estimation approach is developed to obtain the desired state estimates. The optimal estimator parameters are achieved by solving a pair of

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22

M, Shamila, G. Rekha, and K. Vinuthna Reddy. "Hybrid Filtering and Probabilistic Techniques for Privacy-Preserving Community Detection in OSNs." International Journal of Experimental Research and Review 41, Spl Vol (2024): 180–94. http://dx.doi.org/10.52756/ijerr.2024.v41spl.015.

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Online Social Networks (OSNs) face the major challenge of protecting participant's privacy, due to the high dimensionality and volume of the data. In real-time social networks, where hundreds of personal details of people are shared every day, there remains a significant threat to privacy. Privacy preservation is challenging for a community detection problem due to the high computational complexity and memory requirements, especially in larger real-world OSN graphs. Although weighted nodes provide better results, as they allow capturing the frequencies of the values, the privacy preservation o

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23

Chao, Yichao, and Rui Han. "A Hierarchical Cache Architecture-Oriented Cache Management Scheme for Information-Centric Networking." Future Internet 17, no. 1 (2025): 17. https://doi.org/10.3390/fi17010017.

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Information-Centric Networking (ICN) typically utilizes DRAM (Dynamic Random Access Memory) to build in-network cache components due to its high data transfer rate and low latency. However, DRAM faces significant limitations in terms of cost and capacity, making it challenging to meet the growing demands for cache scalability required by increasing Internet traffic. Combining high-speed but expensive memory (e.g., DRAM) with large-capacity, low-cost storage (e.g., SSD) to construct a hierarchical cache architecture has emerged as an effective solution to this problem. However, how to perform e

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Bolisetty, V. Vijayasari, U. Yedukondalu, and I. Santiprabha. "Speech enhancement using modified wiener filter based MMSE and speech presence probability estimation." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 2 (2020): 63. http://dx.doi.org/10.11591/ijict.v9i2.pp63-72.

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In the present-day communications speech signals get contaminated due to various sorts of noises that degrade the speech quality and adversely impacts speech recognition performance. To overcome these issues, a novel approach for speech enhancement using Modified Wiener filtering is developed and power spectrum computation is applied for degraded signal to obtain the noise characteristics from a noisy spectrum. In next phase, MMSE technique is applied where Gaussian distribution of each signal i.e. original and noisy signal is analyzed. The Gaussian distribution provides spectrum estimation an

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V., Vijayasri Bolisetty, Yedukondalu U., and Santiprabha I. "Speech enhancement using modified wiener filter based MMSE and speech presence probability estimation." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 2 (2020): 63–72. https://doi.org/10.11591/ijict.v9i2.pp63-72.

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In the present-day communications speech signals get contaminated due to various sorts of noises that degrade the speech quality and adversely impacts speech recognition performance. To overcome these issues, a novel approach for speech enhancement using Modified Wiener filtering is developed and power spectrum computation is applied for degraded signal to obtain the noise characteristics from a noisy spectrum. In next phase, MMSE technique is applied where Gaussian distribution of each signal i.e. original and noisy signal is analyzed. The Gaussian distribution provides spectrum estimation an

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Kurovsky, Stanislav V., Denis A. Mishin, and Daria A. Kastilskaya. "APPLICATION OF BENFORD’S LAW IN THE ANALYSIS OF THE RELIABILITY OF FINANCIAL STATEMENTS USING MACHINE LEARNING METHODS." SOFT MEASUREMENTS AND COMPUTING 6, no. 91 (2025): 52–63. https://doi.org/10.36871/2618-9976.2025.06.005.

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The purpose of the study is to build an algorithm for assessing the reliability of financial statements based on the integration of digital patterns identified by Benford's law with anomaly analysis procedures based on machine learning. The research materials include empirical models of the distribution of numbers, methods of primary filtering of financial data, as well as tools for detecting deviations based on isolation trees, support vectors and probabilistic estimates. The paper uses logical and structural analysis, conceptual design, and comparative formalization of data discovery procedu

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Ekblom, Madeleine, Lauri Tuppi, Olle Räty, Pirkka Ollinaho, Marko Laine, and Heikki Järvinen. "Filter Likelihood as an Observation-Based Verification Metric in Ensemble Forecasting." Tellus A: Dynamic Meteorology and Oceanography 75, no. 1 (2023): 69–87. https://doi.org/10.16993/tellusa.96.

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<strong>Abstract</strong> In numerical weather prediction (NWP), ensemble forecasting aims to quantify the flow-dependent forecast uncertainty. The focus here is on observation-based verification of the reliability of ensemble forecasting systems. In particular, at short forecast lead times, forecast errors tend to be relatively small compared to observation errors and hence it is very important that the verification metric also accounts for observational uncertainties. This paper studies the so-called <em>filter likelihood score</em> which is deep-rooted in Bayesian estimation theory and fits

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Fetzer, Toni, Frank Ebner, Markus Bullmann, Frank Deinzer, and Marcin Grzegorzek. "Smartphone-Based Indoor Localization within a 13th Century Historic Building." Sensors 18, no. 12 (2018): 4095. http://dx.doi.org/10.3390/s18124095.

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Within this work we present an updated version of our indoor localization system for smartphones. The pedestrian’s position is given by means of recursive state estimation using a particle filter to incorporate different probabilistic sensor models. Our recently presented approximation scheme of the kernel density estimation allows to find an exact estimation of the current position, compared to classical methods like weighted-average. Absolute positioning information is given by a comparison between recent measurements of nearby access points and signal strength predictions. Instead of using

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Agha-mohammadi, Ali-akbar, Eric Heiden, Karol Hausman, and Gaurav Sukhatme. "Confidence-rich grid mapping." International Journal of Robotics Research 38, no. 12-13 (2019): 1352–74. http://dx.doi.org/10.1177/0278364919839762.

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Representing the environment is a fundamental task in enabling robots to act autonomously in unknown environments. In this work, we present confidence-rich mapping (CRM), a new algorithm for spatial grid-based mapping of the 3D environment. CRM augments the occupancy level at each voxel by its confidence value. By explicitly storing and evolving confidence values using the CRM filter, CRM extends traditional grid mapping in three ways: first, it partially maintains the probabilistic dependence among voxels; second, it relaxes the need for hand-engineering an inverse sensor model and proposes t

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30

Botvinko, Anatoly Y., and Konstantin E. Samouylov. "Evaluation of firewall performance when ranging a filtration rule set." Discrete and Continuous Models and Applied Computational Science 29, no. 3 (2021): 230–41. http://dx.doi.org/10.22363/2658-4670-2021-29-3-230-241.

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This article is a continuation of a number of works devoted to evaluation of probabilistic-temporal characteristics of firewalls when ranging a filtration rule set. This work considers a problem of the decrease in the information flow filtering efficiency. The problem emerged due to the use of a sequential scheme for checking the compliance of packets with the rules, as well as due to heterogeneity and variability of network traffic. The order of rules is non-optimal, and this, in the high-dimensional list, significantly influences the firewall performance and also may cause a considerable tim

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Padmanabhan, Preethi, Chao Zhang, and Edoardo Charbon. "Modeling and Analysis of a Direct Time-of-Flight Sensor Architecture for LiDAR Applications." Sensors 19, no. 24 (2019): 5464. http://dx.doi.org/10.3390/s19245464.

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Direct time-of-flight (DTOF) is a prominent depth sensing method in light detection and ranging (LiDAR) applications. Single-photon avalanche diode (SPAD) arrays integrated in DTOF sensors have demonstrated excellent ranging and 3D imaging capabilities, making them promising candidates for LiDARs. However, high background noise due to solar exposure limits their performance and degrades the signal-to-background noise ratio (SBR). Noise-filtering techniques based on coincidence detection and time-gating have been implemented to mitigate this challenge but 3D imaging of a wide dynamic range scen

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Querejeta-Azurmendi, Iñigo, David Arroyo Guardeño, Jorge L. Hernández-Ardieta, and Luis Hernández Encinas. "NetVote: A Strict-Coercion Resistance Re-Voting Based Internet Voting Scheme with Linear Filtering." Mathematics 8, no. 9 (2020): 1618. http://dx.doi.org/10.3390/math8091618.

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This paper proposes NetVote, an internet voting protocol where usability and ease in deployment are a priority. We introduce the notion of strict coercion resistance, to distinguish between vote-buying and coercion resistance. We propose a protocol with ballot secrecy, practical everlasting privacy, verifiability and strict coercion resistance in the re-voting setting. Coercion is mitigated via a random dummy vote padding strategy to hide voting patterns and make re-voting deniable. This allows us to build a filtering phase with linear complexity, based on zero knowledge proofs to ensure corre

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Liu, Zheng. "Automatic Image Tagging via a Generative Probabilistic Model." Applied Mechanics and Materials 44-47 (December 2010): 3443–47. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3443.

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We present an approach to tag image automatically via visual topic detecting and initial annotations expanding. Visual topics are detected from corel5k dataset by probabilistic latent semantic analysis (PLSA) model. For an image which is to be tagged, PLSA is used to find visual topic of this image, and then construct initial annotations set. After initial annotations are generated, we use a weighted voting scheme and Flickr API to expand initial annotations. After the above two process, we combine initial annotations and expanded annotations together to construct final annotations. From exper

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Man, Su, and Tae Ho. "Discrete Event Modeling and Simulation of Probabilistic Voting-based Filtering to Find Proper Security Parameters in Wireless Sensor Networks." International Journal of Computer Applications 176, no. 3 (2017): 21–26. http://dx.doi.org/10.5120/ijca2017915556.

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Liu, Zhiqiang, Jianqin Yin, Jinping Li, Jun Wei, and Zhiquan Feng. "A new action recognition method by distinguishing ambiguous postures." International Journal of Advanced Robotic Systems 15, no. 1 (2018): 172988141774948. http://dx.doi.org/10.1177/1729881417749482.

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One of the most important aspects of promoting the intelligence of home service robots is to reliably recognize human actions and accurately understand human behaviors and intentions. In the task of action recognition, there are many common ambiguous postures, which affect the recognition accuracy. To improve the reliability of the service provided by home service robots, this article presents a method of probabilistic soft-assignment recognition scheme based on Gaussian mixture models to recognize similar actions. First, we generate a representative posture dictionary based on the standard ba

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Lu, Erl Huei, Henry Ker Chang Chang, Shu Hwang Liaw, and Pin Chang Su. "Proven Security and Efficiency of Gap Diffie-Hellman Group Blind Signature in E-Commerce." Applied Mechanics and Materials 284-287 (January 2013): 3522–26. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3522.

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Recently bilinear pairings have found various applications in cryptosystems. However, a natural open question is to construct a secure and efficient pairing system without the MapToPoint hash function. The new scheme offers a less order of security and efficiency than the existing other signature schemes based on discrete logarithm. Furthermore, our scheme upholds all desirable properties of previous blind signature schemes, and requires general cryptographic hash function instead of MapToPoint hash function that is inefficient and probabilistic. It can apply in more critical systems like e-vo

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Tsapatsoulis, Nicolas, Marilena Agathokleous, Constantinos Djouvas, and Fernando Mendez. "On the Design of Social Voting Recommendation Applications." International Journal on Artificial Intelligence Tools 24, no. 03 (2015): 1550009. http://dx.doi.org/10.1142/s0218213015500098.

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Voting Advice Applications (VAAs) are online tools that match the policy preferences of voters with the policy positions of political parties or candidates. Designed to enhance the political competence of citizens, VAAs have become increasingly popular and institutionally embedded in a growing number of European countries. While the traditional VAA relied on the stated position or academically coded position of parties/candidates, a recent innovation has been to introduce a social vote recommendation borrowing the basic principles of collaborative filtering. The latter takes advantage of the c

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Onan, Aytug. "Hybrid supervised clustering based ensemble scheme for text classification." Kybernetes 46, no. 2 (2017): 330–48. http://dx.doi.org/10.1108/k-10-2016-0300.

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Purpose The immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. The purpose of this paper is to provide diversity among the classification algorithms of ensemble, which is a key issue in the ensemble design. Design/methodol

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Emperuman, Malathy, and Srimathi Chandrasekaran. "Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network." Sensors 20, no. 3 (2020): 745. http://dx.doi.org/10.3390/s20030745.

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Sensor devices in wireless sensor networks are vulnerable to faults during their operation in unmonitored and hazardous environments. Though various methods have been proposed by researchers to detect sensor faults, only very few research studies have reported on capturing the dynamics of the inherent states in sensor data during fault occurrence. The continuous density hidden Markov model (CDHMM) is proposed in this research to determine the dynamics of the state transitions due to fault occurrence, while neural networks are utilized to classify the faults based on the state transition probab

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Isikli Esener, Idil, Semih Ergin, and Tolga Yuksel. "A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis." Journal of Healthcare Engineering 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/3895164.

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A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD) system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA) project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI) images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed

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Volpe, Manuela, Stefano Lorito, Jacopo Selva, Roberto Tonini, Fabrizio Romano, and Beatriz Brizuela. "From regional to local SPTHA: efficient computation of probabilistic tsunami inundation maps addressing near-field sources." Natural Hazards and Earth System Sciences 19, no. 3 (2019): 455–69. http://dx.doi.org/10.5194/nhess-19-455-2019.

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Abstract. Site-specific seismic probabilistic tsunami hazard analysis (SPTHA) is a computationally demanding task, as it requires, in principle, a huge number of high-resolution numerical simulations for producing probabilistic inundation maps. We implemented an efficient and robust methodology using a filtering procedure to reduce the number of numerical simulations needed while still allowing for a full treatment of aleatory and epistemic uncertainty. Moreover, to avoid biases in tsunami hazard assessment, we developed a strategy to identify and separately treat tsunamis generated by near-fi

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ZHANG, BAILING. "RELIABLE IMAGE CLASSIFICATION BY COMBINING FEATURES AND RANDOM SUBSPACE SUPPORT VECTOR MACHINE ENSEMBLE." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 03 (2014): 1450005. http://dx.doi.org/10.1142/s0218001414500050.

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We investigate the implementation of image categorization algorithms with a reject option, as a mean to enhance the system reliability and to attain a higher classification accuracy. A reject option is desired in many image-classification applications for which the system should abstain from making decisions on the most uncertain images. Based on the random subspace (RS) ensemble learning model, a highly reliable image classification scheme is proposed by applying RS support vector machine (SVM) ensemble. Being different to previous classifier ensembles which focus on increasing classification

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Moshfeghi, Yashar, and Alvaro Francisco Huertas-Rosero. "A Game Theory Approach for Estimating Reliability of Crowdsourced Relevance Assessments." ACM Transactions on Information Systems 40, no. 3 (2022): 1–29. http://dx.doi.org/10.1145/3480965.

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In this article, we propose an approach to improve quality in crowdsourcing (CS) tasks using Task Completion Time (TCT) as a source of information about the reliability of workers in a game-theoretical competitive scenario. Our approach is based on the hypothesis that some workers are more risk-inclined and tend to gamble with their use of time when put to compete with other workers. This hypothesis is supported by our previous simulation study. We test our approach with 35 topics from experiments on the TREC-8 collection being assessed as relevant or non-relevant by crowdsourced workers both

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Matushkin, Dmytro. "PHOTOVOLTAIC GENERATION FORECASTING MODELS: CONCEPTUAL ENSEMBLE ARCHITECTURES." System Research in Energy 2024, no. 4 (2024): 56–64. https://doi.org/10.15407/srenergy2024.04.056.

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The decisions regarding power regulation, energy resource planning, and integrating “green” energy into the electrical grid hinge on precise probabilistic forecasts. One of the potential strategies to enhance forecast accuracy is the utilization of ensemble forecasting methods. They represent an approach where multiple models collaborate to achieve superior results compared to what a single model could produce independently. These methods can be categorized into two main categories: competitive and collaborative ensembles. Competitive ensembles harness the diversity of parameters and data to c

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Ghaleb, Fuad A., Mohammed Alsaedi, Faisal Saeed, Jawad Ahmad, and Mohammed Alasli. "Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning." Sensors 22, no. 9 (2022): 3373. http://dx.doi.org/10.3390/s22093373.

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Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are either vulnerable to web defacement attacks or created and managed by hackers such as fraudulent and phishing websites. Detecting malicious websites is essential to prevent the spreading of malware and protect end-users from being victims. However, most existing solutions rely on extracting features from the website’s content which can be harmful to

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Widyaningrum, Elyta, Ben Gorte, and Roderik Lindenbergh. "Automatic Building Outline Extraction from ALS Point Clouds by Ordered Points Aided Hough Transform." Remote Sensing 11, no. 14 (2019): 1727. http://dx.doi.org/10.3390/rs11141727.

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Many urban applications require building polygons as input. However, manual extraction from point cloud data is time- and labor-intensive. Hough transform is a well-known procedure to extract line features. Unfortunately, current Hough-based approaches lack flexibility to effectively extract outlines from arbitrary buildings. We found that available point order information is actually never used. Using ordered building edge points allows us to present a novel ordered points–aided Hough Transform (OHT) for extracting high quality building outlines from an airborne LiDAR point cloud. First, a Ho

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Kalinina, Irina A., and Aleksandr P. Gozhyj. "Modeling and forecasting of nonlinear nonstationary processes based on the Bayesian structural time series." Applied Aspects of Information Technology 5, no. 3 (2022): 240–55. http://dx.doi.org/10.15276/aait.05.2022.17.

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The article describes an approach to modelling and forecasting non-linear non-stationary time series for various purposes using Bayesian structural time series. The concepts of non-linearity and non-stationarity, as well as methods for processing non-linearity’sand non-stationarity in the construction of forecasting models are considered. The features of the Bayesian approach in the processing of nonlinearities and nonstationaryare presented. An approach to the construction of probabilistic-statistical models based on Bayesian structural models of time series has been studied. Parametric and n

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"Enhancing Probabilistic Voting-based Filtering Scheme against Injection Attacks." KSII Transactions on Internet and Information Systems 19, no. 6 (2025). https://doi.org/10.3837/tiis.2025.06.011.

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Sanghyeok, Lim. "GEOGRAPHIC INFORMATION-BASED ROUTING OPTIMIZATION USING GA FOR CLUSTER-BASED WSNS." June 30, 2018. https://doi.org/10.5121/ijwmn.2018.10302.

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ABSTRACT Wireless sensor networks are used for data collection and event detection in various fields such as home networks, military systems, and forest fire monitoring, and are composed of many sensor nodes and a base station. Sensor nodes have limited computing power, limited energy, are randomly distributed in an open environment that operates independently, and have difficulties in individual management. Taking advantage of those weaknesses, attackers can compromise sensor nodes for various kinds of network attacks. Several security protocols have been proposed to prevent these attacks. Mo

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Informatics, Engineering an International Journal (IEIJ). "ENSP: ENERGY EFFICIENT NEXT HOP SELECTION IN A PROBABILISTIC VOTING-BASED FILTERING SCHEME USING FUZZY LOGIC." January 9, 2025. https://doi.org/10.5281/zenodo.14619539.

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In wireless sensor networks, sensor nodes are easily compromised due to their restricted hardware resources. These compromised nodes inject fabricated votes into legitimate reports, and generate false report and false vote injection attacks. These attacks deplete energy resources and block report transmission. A probabilistic voting-based filtering scheme was proposed to detect the bogus votes in reports en-route to protect against attacks. Although this method detects false votes in intermediate nodes, the sensor network needs to be effectively operated in consideration of a node's conditions

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