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optimizations algorithm » optimization algorithms (Expand Search)
monkey optimizations » policy optimization (Expand Search), motor optimization (Expand Search)
level optimization » global optimization (Expand Search), based optimization (Expand Search)
optimizations algorithm » optimization algorithms (Expand Search)
monkey optimizations » policy optimization (Expand Search), motor optimization (Expand Search)
level optimization » global optimization (Expand Search), based optimization (Expand Search)
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61
Software defect prediction. (c2019)
Published 2019“…One that focuses on predicting defect in software modules using a hybrid heuristic - a combination of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). We compare our approach to 9 well known machine learning techniques and results show the advantages of our model over the other techniques. …”
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masterThesis -
62
Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition
Published 2021“…<p>Different aggregation levels of the electric grid's big data can be helpful to develop highly accurate deep learning models for Short-term Load Forecasting (STLF) in electrical networks. …”
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63
Genetic Fuzzimetric Technique (GFT)
Published 2012“…Integration of fuzzy systems with genetic algorithm has been identified by researchers as a useful technique of optimizing systems under uncertainty. …”
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conferenceObject -
64
An automated temporal partitioning tool for a class of DSP applications
Published 1998“…This restructuring transformation is performed on the temporally partitioned behavior and results in near-optimization of throughput. Case study on the Joint Photographic Experts Group (jpeg) image compression algorithm demonstrates the e ectiveness of our approach.…”
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conferenceObject -
65
Energy-Aware Physical Synthesis of Deep Neural Networks for Edge-AI Applications in Robotics and VLSI Systems
Published 2025“…Energy-aware physical synthesis is essential for deploying deep neural networks (DNNs) in edge-AI applications for robotics and VLSI systems, where stringent power, area, and latency constraints prevail. Beyond algorithm-and software-level optimizations such as network compression and compiler techniques, the physical effects during VLSI imple-mentation including clock tree synthesis (CTS), routing congestion, IR-drop, cell sizing, and placement significantly influence energy consumption and timing closure. …”
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66
Peak Loads Shaving in a Team of Cooperating Smart Buildings Powered Solar PV-Based Microgrids
Published 2021“…The algorithm has been tested through a case study to demonstrate its performance and effectiveness.…”
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67
An automated temporal partitioning and loop fission approach for FPGA based reconfigurable synthesis of DSP applications
Published 2017“…We present an automated temporal partitioning and loop transformation approach for developing dynamically reconfigurable designs starting from behavior level specifications. An Integer Linear Programming (ILP) model is formulated to achieve near-optimal latency designs. …”
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conferenceObject -
68
Improving Active Resonance Damping and Unbalanced Voltage Mitigation Based on Combined DDSRF and Washout Filter in Islanded Microgrids
Published 2024“…Finally, Level 4 integrates a controller based on extracting positive and negative sequence components using the dual decoupled synchronous reference frame (DDSRF) algorithm. …”
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69
A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security
Published 2023“…Then, the Conjugate Self-Organizing Migration (CSOM) optimization algorithm is deployed to select the most relevant features to train the classifier, which also supports increased detection accuracy. …”
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70
Inferential sensing techniques in industrial applications
Published 0007“…System delays are obtained by approximating the model by a linear model. Genetic algorithm, which is a heuristic optimization technique, is used to ¯nd the system delays of the linear model, which are used in dynamical neural network model. …”
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masterThesis -
71
Autonomous Demand-Side Management in the Future Smart Grid
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doctoralThesis -
72
A method for data path synthesis using neural networks
Published 2017“…Presents a deterministic parallel algorithm to solve the data path allocation problem in high-level synthesis. …”
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conferenceObject -
73
Communication-Based Adaptive Overcurrent Protection for Distribution Systems with DistributedGenerators
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doctoralThesis -
74
Developing a Cooperative Behavior for Multi Agents System Application to Robot Soccer
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doctoralThesis -
75
A novel approach for real time flows scheduling
Published 2006“…A plethora of packet-scheduling algorithms have been proposed in the literature in order to meet the stringent time constraints of real time flows at an IP router level. …”
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conferenceObject -
76
Resource allocation scheme for eMBB and uRLLC coexistence in 6G networks
Published 2023“…The proposed solution was compared with three puncturing baseline reference algorithms and the performance was evaluated in terms of eMBB Sum throughput and Fairness level. …”
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77
Improving the Resilience of Smart Distribution Networks against Cyber Attacks
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doctoralThesis -
78
A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems
Published 2021“…To be more precise, a greedy randomized adaptive search procedure (GRASP) algorithm is developed and its performance is evaluated against optimal solutions acquired using the MIP. …”
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79
Student advising decision to predict student's future GPA based on Genetic Fuzzimetric Technique (GFT)
Published 2015“…Looking at the historical data of students, fuzzy logic can be used to develop rules based on these data. Genetic Algorithm would be used to optimize the performance of the system.…”
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conferenceObject -
80
Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information
Published 2019“…This basically follows either a feature-level or decision-level strategy. In all likelihood, while features from several modalities may enhance the classification performance, they might exhibit high dimensionality and make the learning process complex for the most used machine learning algorithms. …”
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