Multiclass feature selection with metaheuristic optimization algorithms: a review حسب Abu Zitar, Raed

"…Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. …"
احصل على النص الكامل

Enhanced climate change resilience on wheat anther morphology using optimized deep learning techniques حسب Arifa Zahir (20748764)

"…The primary objective of this study is to explore how Deep Learning algorithms can be beneficial in categorizing agricultural records, particularly in monitoring and identifying variations in spring wheat germplasm. …"

An Improved Genghis Khan Optimizer based on Enhanced Solution Quality Strategy for Global Optimization and Feature Selection Problems حسب Abdel-Salam, Mahmoud

"…To assess the efficacy of the I-GKSO, it has been subjected to comparisons with multiple different algorithms. The trials conducted using FS datasets yield a quantitative consideration of the I-GKSO's capacity to attain the most optimal subset of features. …"
احصل على النص الكامل

Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas حسب Marwan Dhuheir (19170898)

"…We conducted extensive simulations and compared our approach with two state-of-the-art models using traditional RL algorithms represented by a deep Q-network algorithm, a Particle Swarm Optimization (PSO) algorithm, and one greedy solution. …"

Deep and transfer learning for building occupancy detection: A review and comparative analysis حسب Aya Nabil Sayed (17317006)

"…Moreover, the paper conducted a comparative study of the readily available algorithms for occupancy detection to determine the optimal method in regards to training time and testing accuracy. …"

Intelligent route to design efficient CO₂ reduction electrocatalysts using ANFIS optimized by GA and PSO حسب Majedeh Gheytanzadeh (17541927)

"…The primary purpose of this study is to establish a new model through machine learning methods; namely, adaptive neuro-fuzzy inference system (ANFIS) combined with particle swarm optimization (PSO) and genetic algorithm (GA) for the prediction of *CO (the key intermediate) adsorption energy as the efficiency metric. …"