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41
Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review
Published 2023Subjects: -
42
Three-phase approach for curriculum-based course timetabling problem. (c2012)
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masterThesis -
43
Auto-indexing Arabic texts based on association rule data mining. (c2015)
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masterThesis -
44
A neural networks algorithm for data path synthesis
Published 2003“…This paper presents a deterministic parallel algorithm to solve the data path allocation problem in high-level synthesis. …”
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45
Data reductions and combinatorial bounds for improved approximation algorithms
Published 2016“…Kernelization algorithms in the context of Parameterized Complexity are often based on a combination of data reduction rules and combinatorial insights. …”
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46
Allocating data to distributed-memory multiprocessors by genetic algorithms
Published 2016“…We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. …”
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47
A Hash-Based Assessment and Recovery Algorithm for Distributed Healthcare Systems Using Blockchain Technology
Published 2020“…The improvement of information technology in the past few years has been encouraging the healthcare sector to share medical data online without any barriers and between different parties. …”
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masterThesis -
48
Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review
Published 2023Subjects: -
49
A Fusion-Based Approach for Skin Cancer Detection Combining Clinical Images, Dermoscopic Images, and Metadata
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doctoralThesis -
50
Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization
Published 2023“…However, they might not perform as expected, take much time in training, or not have enough input data to generalize well. To that end, deep transfer learning (DTL) and deep domain adaptation (DDA) have recently been proposed as promising solutions to alleviate these issues. …”
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51
Deep Reinforcement Learning for Resource Constrained HLS Scheduling
Published 2022“…In this work, we present a resource constrained scheduling approach that minimizes latency and subject to resource constraints using a deep Q learning algorithm. The actions and rewards for the proposed algorithm are selected carefully to guide the agent to its objective. …”
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masterThesis -
52
Data damage assessment and recovery algorithm from malicious attacks in healthcare data sharing systems
Published 2016“…Hence, this presents a need for fast and efficient damage assessment and recovery algorithms. In this paper, we present an efficient data damage assessment and recovery algorithm to delete malicious transactions and recover affected transactions in a data source in a health care system based on the concept of the matrix. …”
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53
Cyberbullying Detection in Arabic Text using Deep Learning
Published 2023“…If conducted automatically, rather than relying on human moderators, the process will be faster, enabling the early detection of cyberbullying before severe harm is caused. Data-driven approaches, such as machine learning (ML), particularly deep learning (DL), have shown promising results. …”
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54
Variable Selection in Data Analysis: A Synthetic Data Toolkit
Published 2024“…Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). …”
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55
Parallel physical optimization algorithms for allocating data to multicomputer nodes
Published 1994“…Three parallel physical optimization algorithms for allocating irregular data to multicomputer nodes are presented. …”
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56
Physical optimization algorithms for mapping data to distributed-memory multiprocessors
Published 1992“…We present three parallel physical optimization algorithms for mapping data to distributed-memory multiprocessors, concentrating on irregular loosely synchronous problems. …”
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masterThesis -
57
Artificial intelligence-based methods for fusion of electronic health records and imaging data
Published 2022“…In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. …”
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A Survey of Deep Learning Approaches for the Monitoring and Classification of Seagrass
Published 2025“…The collection of digital images for monitoring underwater habitats, such as seagrass meadows, has increased significantly as recent progress in imaging technology has made it easier to collect high-resolution data. The increase in imagery data has in turn created a demand for automated detection and classification using deep neural network-based techniques. …”
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60
Hybrid Deep Learning-based Models for Crop Yield Prediction
Published 2022“…In this study, we developed deep learning-based models to evaluate how the underlying algorithms perform with respect to different performance criteria. …”