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241
Detecting Arabic Cyberbullying Tweets in Arabic Social Using Deep Learning
Published 2023“…Pre-processing is a vital part of the data preparation process for cyberbullying detection. …”
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242
Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model
Published 2024“…The proposed approach of DDN is trained with proper data sequences used for communication and the training phase is conducted with the norms of numerous channel variants. …”
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243
Evacuation of a highly congested urban city
Published 2017“…As the evacuation route planning is computationally challenging, an evacuation scheduling algorithm was adopted to expedite the solution process. …”
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conferenceObject -
244
Sentiment Analysis for Arabic Social media Movie Reviews Using Deep Learning
Published 2022“…For sentiment analysis, pre-processing is a crucial step in the data preparation process. …”
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245
Shuffled Linear Regression with Erroneous Observations
Published 2019“…Existing methods are either applicable only to data with limited observation errors, work only for partially shuffled data, sensitive to initialization, and/or work only with small dimensions. …”
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conferenceObject -
246
High-order parametrization of the hypergeometric-Meijer approximants
Published 2023“…<p>In this work, we introduce an extension to the hypergeometric algorithm we developed before for the resummation of divergent series.The extension overcome the time-consuming problem we face in the parametrization process of the hypergeometric approximants. …”
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247
Semantics-based approach for detecting flaws, conflicts and redundancies in XACML policies
Published 2015“…First, our approach resolves the complexity of policies by elaborating an intermediate set-based representation to which the elements of XACML are automatically converted. Second, it allows to detect flaws, conflicts and redundancies between rules by offering new mechanisms to analyze the meaning of policy rules through semantics verification by inference rule structure and deductive logic. …”
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248
Student advising decision to predict student's future GPA based on Genetic Fuzzimetric Technique (GFT)
Published 2015“…Decision making and/or Decision Support Systems (DSS) using intelligent techniques like Genetic Algorithm and fuzzy logic is becoming popular in many new applications. …”
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conferenceObject -
249
Deep Reinforcement Learning for Resource Constrained HLS Scheduling
Published 2022“…The two main steps in HLS are: operations scheduling and data-path allocation. In this work, we present a resource constrained scheduling approach that minimizes latency and subject to resource constraints using a deep Q learning algorithm. …”
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masterThesis -
250
A Novel Encryption Method for Dorsal Hand Vein Images on a Microcomputer
Published 2019“…Second, the pre- and post-processed images were encrypted with a new encryption algorithm in the microcomputer environment. …”
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251
The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review
Published 2022“…The study selection process was carried out in three phases: study identification, study selection, and data extraction. …”
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252
Newton-Raphson based adaptive inverse control scheme for tracking of nonlinear dynamic plants
Published 2006“…Adaptive tracking of nonlinear dynamic plants is an essential element of many control applications. The main difficulty felt in establishing the tracking of nonlinear dynamic plants is the computational complexity in controller design. …”
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253
A Full-System Approach of the Elastohydrodynamic Line/Point Contact Problem
Published 2008“…These attempts suffered from a major drawback which is the almost full Jacobian matrix of the nonlinear system of equations. This work presents a new approach for solving the fully coupled isothermal elastohydrodynamic problem using a finite element discretization of the corresponding equations. …”
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254
Topology and parameter estimation in power systems through inverter-based broadband stimulations
Published 2015“…Broadband stimulation signals are injected from distributed generators and their effects are measured at various locations in the grid. To process and evaluate this data, a novel aggregation method based on weighed least squares will be proposed in this study. …”
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255
Benchmark on a large cohort for sleep-wake classification with machine learning techniques
Published 2019“…However, the largest experiments conducted to date, have had only hundreds of participants. In this work, we processed the data of the recently published Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study to have both PSG and actigraphy data synchronized. …”
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256
Improving Rule Set Based Software Quality Prediction
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257
On the complexity of multi-parameterized cluster editing
Published 2017“…As a byproduct, we obtain a kernelization algorithm that delivers linear-size kernels when the two edge-edit bounds are small constants.…”
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258
Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort
Published 2023“…However, these BEMSs often suffer from a critical limitation—they are primarily trained on building energy data alone, disregarding crucial elements such as occupant comfort and preferences. …”
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259
An Improved Genghis Khan Optimizer based on Enhanced Solution Quality Strategy for Global Optimization and Feature Selection Problems
Published 2024“…Feature selection (FS) is the activity of defining the most contributing feature subset among all used features to improve the superiority of datasets with a large number of dimensions by selecting significant features and eliminating redundant and irrelevant ones. Therefore, this process can be seen as an optimization process. The primary goals of feature selection are to decrease the number of dimensions and enhance classification accuracy in many domains, such as text classification, large-scale data analysis, and pattern recognition. …”
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260
A Survey of Deep Learning Approaches for the Monitoring and Classification of Seagrass
Published 2025“…By synthesizing findings across various data sources and model architectures, we offer critical insights into the selection of context-aware algorithms and identify key research gaps, an essential step for advancing the reliability and applicability of AI-driven seagrass conservation efforts.…”