Search Results - (( data replication testing algorithm ) OR ( variable detection mining algorithm ))
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Potential norms detection in social agent societies
Published 2023“…In this paper, we propose a norms mining algorithm that detects a domain's potential norms, which we called the Potential Norms Mining Algorithm (PNMA). …”
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Building norms-adaptable agents from Potential Norms Detection Technique (PNDT)
Published 2023“…This technique enables an agent to update its norms even in the absence of sanctions from a third-party enforcement authority as found in some work, which entail sanctions by a third-party to detect and identify the norms. The PNDT consists of five components: agent's belief base; observation process; Potential Norms Mining Algorithm (PNMA) to detect the potential norms and identify the normative protocol; verification process, which verifies the detected potential norms; and updating process, which updates the agent's belief base with new normative protocol. …”
Short Survey -
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A novel deadlock detection algorithm for neighbour replication on grid environment
Published 2012“…The aim of this research is to handle the deadlock problem in NRG to preserve the consistency of data and increase the throughput. The NRGDD simulation model has been developed to test the algorithm on NRG. …”
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Cancer Prediction Based On Data Mining Using Decision Tree Algorithm
Published 2022“…Many studies have been conducted in order to predict cancer. Different data mining method has showed up with its related algorithms were adopted by different research. …”
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Undergraduates Project Papers -
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Building norms-adaptable agents from Potential Norms Detection Technique (PNDT)
Published 2013“…This technique enables an agent to update its norms even in the absence of sanctions from a third-party enforcement authority as found in some work, which entail sanctions by a third-party to detect and identify the norms. The PNDT consists of five components: agent’s belief base; observation process; Potential Norms Mining Algorithm (PNMA) to detect the potential norms and identify the normative protocol; verification process, which verifies the detected potential norms; and updating process, which updates the agent’s belief base with new normative protocol. …”
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Exploration of machine learning forecasting methods in M4 competition / Muhammad Halim Hamdan and Shuzlina Abdul-Rahman
Published 2021“…Each technique was replicated, trained and tested accordingly. M4 competition dataset was used in this research, with 100,000 time series data and multiple data frequency, which is enough to replicate the real-world situation. …”
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Sentiment-analysis to detect early depressive symptom in Bangla language from social media: a review study
Published 2021“…The review shows that the mostly used method in Sentiment-analysis is Machine Learning in the field of Opinion-mining. Furthermore, we have identified another variable that can be included to improve the existing algorithm.…”
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An integrated anomaly intrusion detection scheme using statistical, hybridized classifiers and signature approach
Published 2015“…Detection methods based on statistical and data mining techniques are widely deployed as anomaly-based detection system (ADS). …”
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Finite element modeling and updating for dynamic study of exhaust structure
Published 2020“…Hence, this paper is carried out to reduce the discrepancies between numerical prediction results with measured test data on FE model of exhaust structure by implementing FE model updating using SOL200 algorithm. …”
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Conference or Workshop Item -
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A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping
Published 2014“…An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. …”
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Toward Predicting Student�s Academic Performance Using Artificial Neural Networks (ANNs)
Published 2023“…No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. …”
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Rockfall source identification using a hybrid Gaussian mixture-ensemble machine learning model and LiDAR data
Published 2019“…Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.…”
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A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification
Published 2023“…Additionally, the Wilcoxon rank test was used to perform the significance analysis between the proposed SCSOKNN method and six other algorithms for a p-value less than 5.00E-02. …”
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Enhancing clustering algorithm with initial centroids in tool wear region recognition
Published 2020“…K-means clustering has become the most applied algorithm in discovering classes in an unsupervised scenario. …”
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Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…Then, the classifier (support vector machine (SVM) and data mining (DM) algorithm, decision tree (DT) were applied on each fusion image and their accuracy were evaluated. …”
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Securing IoT networks using machine learning-resistant physical unclonable functions (PUFs) on edge devices
Published 2026“…The NIST statistical test suite was applied to the CRP data of the RO PUF to evaluate its randomness quality. …”
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A self-adaptive agent-based simulation modelling framework for dynamic processes
Published 2023“…Case studies for simulating dynamic processes of new student registration (administrative domain), transport request (transportation domain) and crime investigation (security domain) with real data collected through interview and document reviews to test the proposed idea. …”
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Finger-Vein Biometric Identification Using Convolutional Neural Network
Published 2016“…An identification rate of 100.00% is achieved, with an 80/20 percent ratio for separation of training and test samples, respectively. An additional number of subjects have also been tested, in which for 81 subjects an accuracy of 99.38% is achieved.…”
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Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease
Published 2021“…Infected palms are asymptomatic throughout the disease's early stages, making disease detection challenging. The survival of affected trees must detect BSR at the mild infected (T1) stage. …”
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