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381
The Assessment and Allocation of Public Private Partnership Risks in the UAE
Published 2022Get full text
doctoralThesis -
382
A Cyber-Physical System and Graph-Based Approach for Transportation Management in Smart Cities
Published 2021“…To efficiently process the incoming big data streams, the proposed architecture uses the Apache GraphX tool with several parallel processing nodes, along with Spark and Hadoop that ultimately provide better performance against various state-of-the-art solutions. …”
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Evolutionary support vector regression for monitoring Poisson profiles
Published 2023“…The proposed monitoring scheme is revealed to be superior to its counterparts, including the likelihood ratio test (LRT), multivariate exponentially weighted moving average (MEWMA), LRT-EWMA and other machine learning-based schemes. The simulation results show superiority of the proposed method in profiles with fixed explanatory variables and non-parametric models in nearly all situations while it is not able to be the best in all the simulations when there are with random explanatory variables. …”
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386
Integrated Energy Optimization and Stability Control Using Deep Reinforcement Learning for an All-Wheel-Drive Electric Vehicle
Published 2025“…A tailored multi-term reward function is structured to penalize excessive yaw rate error, sideslip angle, tire slip deviations beyond peak grip regions, and power losses based on a realistic electric machine efficiency map. …”
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387
Frontiers and trends of supply chain optimization in the age of industry 4.0: an operations research perspective
Published 2024“…It contributes to the literature by identifying the four OR innovations to typify the recent advances in SC optimization: new modeling conditions, new inputs, new decisions, and new algorithms. Furthermore, we recommend four promising research avenues in this interplay: (1) incorporating new decisions relevant to data-enabled SC decisions, (2) developing data-enabled modeling approaches, (3) preprocessing parameters, and (4) developing data-enabled algorithms. …”
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388
Various Faults Classification of Industrial Application of Induction Motors Using Supervised Machine Learning: A Comprehensive Review
Published 2025“…In current literature, there are a number of papers that address all these faults using different methods, and this paper compiles the information from the written works for ease of access. Machine learning algorithms are a set of data-driven rules that are able to classify specific faults in induction motors, which will be explained further in this review paper. …”
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389
Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review
Published 2023“…<p dir="ltr">The widespread penetration of inverter-based resources has profoundly impacted the electrical stability of power systems (PSs). …”
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390
A novel hybrid methodology for fault diagnosis of wind energy conversion systems
Published 2023“…Feature selection pre-processing is an important step to increase the accuracy of the classification algorithm and decrease the dimensionality of a dataset. …”
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391
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LNCRI: Long Non-Coding RNA Identifier in Multiple Species
Published 2021“…To overcome these challenges we developed LNCRI (Long Non-Coding RNA Identifier), a novel machine learning (ML)-based tool for the identification of lncRNA transcripts. …”
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394
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Automated systems for diagnosis of dysgraphia in children: a survey and novel framework
Published 2024“…This work discusses the data collection method, important handwriting features, and machine learning algorithms employed in the literature for the diagnosis of dysgraphia. …”
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396
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Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization
Published 2023“…DL algorithms require data labeling and high-performance computers to effectively analyze and understand surveillance data recorded from fixed or mobile cameras installed in indoor or outdoor environments. …”
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A FeedForward–Convolutional Neural Network to Detect Low-Rate DoS in IoT
Published 2022“…LR DoS attacks are difficult to detect as their attack signature is similar to benign network traffic. The existing AI-based detection algorithms in the literature are signature-based, and their efficacy in detecting unknown LR DoS attacks was not explored. …”