Search Results - (( evolution optimization protocol algorithm ) OR ( variable classification modified algorithm ))
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1
A hybrid-based modified adaptive fuzzy inference engine for pattern classification
Published 2011“…A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data set. …”
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2
Differential evolution optimization for constrained routing in Wireless Mesh Networks
Published 2014“…However, this problem is NP-complete, hence, this paper proposes fast convergent Differential Evolution metaheuristic algorithm with bandwidth and delay constraints for minimum routing cost. …”
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Proceeding Paper -
3
Dealing with Routing Hole Problem in Multi-hop Hierarchical Routing Protocol in Wireless Sensor Network
Published 2019“…The challenging issue of routing protocols is to reduce the communication overhead for data transmission by determining an optimal path. …”
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4
A secure trust aware ACO-Based WSN routing protocol for IoT
Published 2022“…The performance of the proposed routing algorithm is demonstrated through MATLAB. Based on the proposed system, to find the secure and optimal path while aiming at providing trust in IoT environment, the average energy consumption is minimized by nearly 50% even as the number of nodes has increased, as compared with the conventional ACO algorithm, a current ant-based routing algorithm for IoT-communication, and a present routing protocol RPL for IoT.…”
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5
Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification
Published 2020“…All algorithms showed more stability and accuracy when training size applied is more than 6% by the Equal Sample Rate (ESR) method with six variables. …”
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6
Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff
Published 2017“…The results showed that the QEEA algorithm outperformed the other algorithms as it could achieve up to 18% of maximum throughput, 27% reduction in ECR, and 36% improvement in EE in terms of radius ranging from 200 m to 1000 m. …”
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7
Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data
Published 2022“…However, it cannot be applied when the sample size is less than the number of predictor variables. In addressing this problem, some robust procedures for high dimensional dataset via the RFCH algorithm are developed. …”
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8
A modified single-stacked ensemble framework for prognostic analysis of leukemia relapse
Published 2026“…Bone marrow transplant (BMT) is a life-saving treatment for acute leukemia, though relapse remains a risk due to donor compatibility, clinical variability, and healthcare resource limitations. This chapter introduces a modified single-stacked ensemble prognostic algorithm optimized for interpretability, transparency, and reliability, especially for small and imbalanced datasets. …”
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Book Chapter -
9
A comparative study for parameter selection in online auctions
Published 2009“…Hence, this work attempts to improve an existing bidding strategy by taking into accounts the evolution of various model of generic algorithm in optimizing the parameter of the bidding strategies. …”
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10
An ensemble of neural network and modified grey wolf optimizer for stock prediction
Published 2019“…Additionally, the research restricts the number of variables through feature selection to enhance the performance of the algorithm. …”
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11
Boosting and bagging classification for computer science journal
Published 2023“…In the DT algorithm, both variables are altered, whereas, in the GNB algorithm, just the estimator's value is modified. …”
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Predicting mortality of Malaysian patients with acute coronary syndrome (ACS) subtypes using machine learning and deep learning approaches / Muhammad Firdaus Aziz
Published 2022“…ML algorithms were used to examine significant variables utilising feature selection methods. …”
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14
Predicting motorcycle customization preferences using machine learning
Published 2025“…The classification model was developed using the Random Forest algorithm, Support Vector Machine and Logistic Regression with 5-fold Cross validation. …”
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Conference or Workshop Item -
15
Information Theoretic-based Feature Selection for Machine Learning
Published 2018“…The problem with many existing feature selections that evaluate features based on mutual information is that they are designed to handles classification tasks only. And the few existing ones that can work for regression tasks were recently found to underestimate mutual information between two strongly dependent variables. …”
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