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Correlation Clustering via s-Club Cluster Edge Deletion
Published 2023“…In this work, we implement three approaches to tackle the 2-club clustering via edge deletion: a heuristic approach based on the influence of the edge to resolve maximum conflicts, a parameterized algorithm in which by deleting a maximum of k edges, the graph can be transformed into a 2-club cluster based on a branching algorithm, and the approach in Integer Linear Programming to find the optimized solution in an integer formulation. …”
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masterThesis -
42
Complexity Avoidance using Biological Resemblance of Modular Multivariable Structure
Published 2014“…Multivariable structure of any fuzzy rule based system would add complexity when modeling the behavior of the system. …”
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Detection of statistically significant network changes in complex biological networks
Published 2017“…<h3>Background</h3><p dir="ltr">Biological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. …”
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Distributed DRL-Based Downlink Power Allocation for Hybrid RF/VLC Networks
Published 2021“…Additionally, we quantify the performance of the DDPG-based algorithm to prevail amid DRL-based algorithms at the expense of higher implementation complexity.…”
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Performance of artificial intelligence models in estimating blood glucose level among diabetic patients using non-invasive wearable device data
Published 2023“…One of the key aspects of WDs with machine learning (ML) algorithms is to find specific data signatures, called Digital biomarkers, that can be used in classification or gaging the extent of the underlying condition. …”
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47
Content-Aware Adaptive Video Streaming Using Actor-Critic Deep Reinforcement Learning
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doctoralThesis -
48
Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network
Published 2024“…Its purpose was to estimate shear and residual stress levels. Additionally, the multi-objective genetic algorithm (MOGA) was utilised to extract the most optimal parameters for the injection moulding process, aiming to minimise shear and residual stress and thereby increase the resistance of the final product. …”
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Boosting the visibility of services in microservice architecture
Published 2023“…These assessments can be performed by means of a live health-check service, or, alternatively, by making a prediction of the current state of affairs with the application of machine learning-based approaches. In this research, we evaluate the performance of several classification algorithms for estimating the quality of microservices using the QWS dataset containing traffic data of 2505 microservices. …”
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Image-Based Air Quality Estimation Using Convolutional Neural Network Optimized by Genetic Algorithms: A Multi-Dataset Approach
Published 2025“…This paper proposes a new approach using convolutional neural networks with genetic algorithms for estimating air quality directly from images. …”
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53
Predicting Plasma Vitamin C Using Machine Learning
Published 2022“…<p dir="ltr">Precision Nutrition makes use of personal information about individuals to produce nutritional recommendations that have more utility than general population level recommendations. …”
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54
Approximation algorithms inspired by kernelization nethods
Published 2017“…Kernelization algorithms in the context of Parameterized Complexity are often based on a combination of reduction rules and combinatorial insights. …”
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conferenceObject -
55
Improvement Algorithm for Limited Space Scheduling
Published 2001“…The model characterizes resource space requirements over time and establishes a time-space relationship for each activity in the schedule, based on alternative resource levels. An example illustrates the presented algorithm that generates a feasible space schedule.…”
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Decision-level Gait Fusion for Human Identification at a Distance
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doctoralThesis -
58
Empirical comparison of regression test selection algorithms
Published 2001“…These criteria are: number of selected test cases, execution time, precision, inclusiveness, preprocessing requirements, type of maintenance, level of testing, and type of approach. The empirical results show that the five algorithms can be used for different requirements of regression testing. …”
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Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks
Published 2024“…Nowadays, deep learning algorithms are essential for ensuring the reliable, safe, and efficient operation of these complex energy systems. …”
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A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition
Published 2025“…The proposed method divides multiple regions (different data lengths) into the feature space, allowing the algorithm to learn more complex decision boundaries. …”