-
1
Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering
Published 2022“…This paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. …”
-
2
Efficient Approximate Conformance Checking Using Trie Data Structures
Published 2021“…We show how our algorithm supports the definition of a budget for alignment computation and also augment it with strategies for meta-heuristic optimization and pruning of the search space. …”
Get full text
Get full text
Get full text
-
3
Towards Multimedia Fragmentation
Published 2006“…Database fragmentation is a process for reducing irrelevant data accesses by grouping data frequently accessed together in dedicated segments. …”
Get full text
Get full text
Get full text
Get full text
conferenceObject -
4
K Nearest Neighbor OveRsampling approach: An open source python package for data augmentation
Published 2022“…This paper introduces K Nearest Neighbor OveRsampling (KNNOR) Algorithm — a novel data augmentation technique that considers the distribution of data and takes into account the k nearest neighbors while generating artificial data points. …”
-
5
-
6
Oversampling techniques for imbalanced data in regression
Published 2024“…For tabular data we conducted a comprehensive experiment using various models trained on both augmented and non-augmented datasets, followed by performance comparisons on test data. …”
-
7
The use of semantic-based predicates implication to improve horizontal multimedia database fragmentation
Published 2007“…We particularly discuss multimedia primary horizontal fragmentation and focus on semantic-based textual predicates implication required as a pre-process in current fragmentation algorithms in order to partition multimedia data efficiently. …”
Get full text
Get full text
Get full text
Get full text
conferenceObject -
8
Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks
Published 2025“…At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R<sup>2</sup> = 0.05 to 0.99 in a representative thiophene set. …”
-
9
KNNOR: An oversampling technique for imbalanced datasets
Published 2021“…The proposed technique called K-Nearest Neighbor OveRsampling approach (KNNOR) performs a three step process to identify the critical and safe areas for augmentation and generate synthetic data points of the minority class. …”
-
10
-
11
-
12
Unsupervised Deep Learning for Classification Of Bats Calls Using Acoustic Data
Published 2021Get full text
doctoralThesis -
13
-
14
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights
Published 2021“…A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. …”
Get full text
article -
15
Privacy-Preserving Framework for Blockchain-Based Stock Exchange Platform
Published 2022“…Furthermore, to assess the overhead of the proposed privacy algorithms on the trading execution time, we conduct several experiments considering different anonymity levels <i>k</i> . …”
-
16
-
17
Generative AI revolution in cybersecurity: a comprehensive review of threat intelligence and operations
Published 2025“…<p dir="ltr">Cyber threats are increasingly frequent in today’s world, posing challenges for organizations and individuals to protect their data from cybercriminals. On the other hand, Generative Artificial Intelligence (GAI) technology offers an efficient way to automatically address these issues with the help of AI models and algorithms. …”
-
18
-
19
Barriers of Adopting Artificial Intelligence Tools in Engineering Construction Projects
Published 2023“…The situation may cause concern and trepidation about integrating AI technologies and lack understanding of their optimal deployment and operation. Construction data management and integration are difficult. AI algorithms depend on data for training and analysis. …”
Get full text
-
20
Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework
Published 2022“…After selecting the most appropriate lattice map (32 × 32) in 750,000 iterations using SOMs, the data points below the dark blue region are mapped onto the data frame to get the videos. …”