A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI حسب Oishi Jyoti (21593819)

"…After handling missing values, balancing the dataset, and analyzing the classifier’s performance, it is found that tree-based algorithms, particularly RF, perform better for all the datasets. …"

Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain حسب Farshad Rahimi Ghashghaei (20880995)

"…<p dir="ltr">This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. …"

Uplink Noma in UAV-Assisted IoT Networks حسب Mrad, Ali

"…Given the complexity of the problem and the incomplete knowledge about the environment, the problem is divided into two subproblems: the first models the UAV trajectory and the selection of the first device in the NOMA cluster at each time slot as a Markov Decision Process, and uses Proximal Policy Optimization to solve it. The second device is then selected using a heuristic algorithm based on prioritizing devices with higher bit rate requirements and strict deadlines. …"
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Impacts of On-Grid Solar PV on Distribution Networks and Potential Solutions: A Case Study in the Region of Zahle حسب Korkmaz, Jessica

"…The siting and sizing methodology is conducted by considering five different optimization algorithms, namely the single-objective genetic algorithm (SOGA), the combined SOGA and loss sensitivity factor algorithm (SOGA-LSF), the multi-objective genetic algorithm (MOGA), the combined MOGA-LSF and the CAPADD algorithm of OpenDSS. …"
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AI-Augmented Metasurface Synthesis for Dynamic Beam Steering in Reconfigurable Antenna Arrays حسب Bostani, Ali

"…As compared to the conventional heuristic methods, for example, genetic algorithms (GA), particle swarm optimization (PSO), the approach based on DRLs has rapid policy convergence, has relatively less computational latency, and is autonomous to adapt to dynamic wireless conditions. …"
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Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management حسب Alhusin, Mohammed Omer Alamin

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Boosting the Training of Neural Networks Through Hybrid Metaheuristics حسب Abou Doush, Iyad

"…In this paper, six versions of memetic algorithms (MAs) are proposed to replace gradient descent learning mechanism of MLP where adaptive β�-hill climbing (Aβ�HC) as a local search algorithm is hybridized with six population-based metaheuristics which are hybrid flower pollination algorithm, hybrid salp swarm algorithm, hybrid crow search algorithm, hybrid grey wolf optimization (HGWO), hybrid particle swarm optimization, and hybrid JAYA algorithm. …"
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A METHODOLOGY FOR NETWORK TOPOLOGY DESIGN USING FUZZY EVALUATIONS حسب Khan, Salman A.

"…In this paper, we present a methodology to address design issues. This methodology is based on two algorithms, namely, fuzzy simulated evolution algorithm and the augmenting path algorithm. …"
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A linear programming approach for the weighted graph matchingproblem حسب Almohamad, H.A.

"…A linear program is obtained by formulating the graph matching problem in L1 norm and then transforming the resulting quadratic optimization problem to a linear one. The linear program is solved using a simplex-based algorithm. …"
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GENETIC SCHEDULING OF TASK GRAPHS حسب Benten, M. S.

"…The problem of assigning tasks to processing elements as a combinatorital optimization is formulated, and a heuristic based on a genetic algorithm is presented. …"
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A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworks حسب Sakib Mahmud (15302404)

"…We present a structured taxonomy covering value-based, policy-based, actor-critic, model-based, and advanced multi-agent and multi-objective approaches, and link algorithms to tasks such as dispatch, microgrid coordination, real-time pricing, load balancing, and demand–response. …"

A LINEAR-PROGRAMMING APPROACH FOR THE WEIGHTED GRAPH MATCHING PROBLEM حسب Al-Mohamad, HA

"…A linear program is obtained by formulating the graph matching problem in L1 norm and then transforming the resulting quadratic optimization problem to a linear one. The linear program is solved using a Simplex-based algorithm. …"
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Final exams scheduling for univeristies. (c2001) حسب Timany, Mazen Nuhad

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Power system output feedback stabilizer design via geneticalgorithms حسب Abdel-Magid, Y.L.

"…A digital simulation of the power system is then used in conjunction with the genetic algorithm to determine the output feedback gains. In the second method, the problem of selecting the output feedback gains is converted to a simple optimization problem with an eigenvalue based objective function, which is solved by a genetic algorithm. …"
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Definition and selection of fuzzy sets in genetic‐fuzzy systems using the concept of fuzzimetric arcs حسب Kouatli, Issam

"…Design/methodology/approach – The design was based on two principles: selection and optimization. …"
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Data Generation for Path Testing حسب Mansour, Nashat

"…These algorithms are based on an optimization formulation of the path testing problem which include both integer- and real-value test cases. …"
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Protein structure prediction in the 3D HP model حسب Abu-Khzam, Faisal

"…In this paper, we present a Particle Swarm Optimization (PSO) based algorithm for predicting protein structures in the 3D HP model. …"
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FAST FUZZY FORCE-DIRECTED/SIMULATED EVOLUTION METAHEURISTIC FOR MULTIOBJECTIVE VLSI CELL PLACEMENT حسب Sait, Sadiq M.

"…SE is hybridized with force directed algorithm to speed-up the search. The proposed schemes are compared with previously presented SE based heuristics. …"
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2022 IEEE Congress on Evolutionary Computation (CEC) حسب El-Abd, Mohammed

"…RL is used to select the best-performing action among three of them in the optimization process to evolve a set of solution based on the population state and reward value. …"
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Cost-Benefit Analysis of Genotype-Guided Interruption Days in Warfarin Pre-Procedural Management حسب Islam, Eljilany

"…The benefit of the interventional algorithm was based on estimated reduction in the probabilities of clinical events and their cost, added to the avoided cost because of canceled procedures. …"
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