Search Results - (( using optimization based algorithm ) OR ( using deep problems algorithm ))
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Multiobjective deep reinforcement learning for recommendation systems
Published 2022“…The results demonstrated that deep reinforcement learning approaches has superiority performance in MO optimization, and its capability of recommending precise item along with achieving high novelty and diversity against the benchmark that using probabilistic based multi-objective approach based on evolutionary algorithm (PMOEA). …”
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2
Multi-objective deep reinforcement learning for recommendation systems
Published 2022“…The results demonstrated that deep reinforcement learning approaches has superiority performance in MO optimization, and its capability of recommending precise item along with achieving high novelty and diversity against the benchmark that using probabilistic based multi-objective approach based on evolutionary algorithm (PMOEA). …”
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Optimizing in-car-abandoned children’s sounds detection using deep learning algorithms / Nur Atiqah Izzati Md Fisol
Published 2023“…Children abandoned in vehicles is a critical issue that has led to numerous fatal injuries worldwide. To address this problem, an optimized in-car-abandoned children's sounds detection model using deep learning algorithms is proposed. …”
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Overhead view based person counting using deep learning
Published 2022“…Third, the accurate tracking of each detected person is performed using the deep learning based tracking framework, known as DeepSORT. …”
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Final Year Project / Dissertation / Thesis -
5
Development of deep reinforcement learning based resource allocation techniques in cloud radio access network
Published 2022“…The first proposed algorithm aims to optimize the EE by controlling the on/off status of RRH via a deep Q network (DQN) and subsequently solving a power optimization problem. …”
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Flock optimization algorithm-based deep learning model for diabetic disease detection improvement
Published 2024“…Hence, the research objective is to create an improved diabetic disease detection system using a Flock Optimization Algorithm-Based Deep Learning Model (FOADLM) feature modeling approach that leverages the PIMA Indian dataset to predict and classify diabetic disease cases. …”
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Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
Published 2022“…However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. …”
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Thesis -
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GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE
Published 2020“…This paper will report on an initial study of the usage of Genetic Algorithm (GA) merged with Deep Neural Network based surrogate model to optimize simulation for electromagnetic structure. …”
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Final Year Project -
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A Hybrid Method Based on Cuckoo Search Algorithm for Global Optimization Problems
Published 2018“…However, it undergoes the premature convergence problem for high dimensional problems because the algorithm converges rapidly. …”
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
Published 2023“…Lastly, a new hybrid technique suggests tackling the current image encryption application problem by using the estimated parameters of chaotic systems with an optimization algorithm, the SKF algorithm. …”
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Improved whale optimization algorithm for feature selection in Arabic sentiment analysis
Published 2019“…The proposed algorithm is compared with six well-known optimization algorithms and two deep learning algorithms. …”
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
Published 2025“…The effectiveness of the proposed LSA + LSTM model is assessed using battery aging data from the NASA dataset. In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. …”
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Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach
Published 2022“…The most popular method to solve parameter estimation problem is using optimization algorithm that easily trap to local minima and poor in exploitation to find the good solutions. …”
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Conference or Workshop Item -
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An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines
Published 2020“…Metaheuristic algorithms are known to be excellent tools for solving optimization problems. …”
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Optimize and deploy machine learning algorithms on embedded devices for manufacturing applications
Published 2025“…This proposal discusses the techniques of optimizing and deploying machine learning algorithms on embedded devices for manufacturing applications; We investigate problems of printed circuit board (PCB) defects and artificial intelligence in embedded system. …”
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Final Year Project / Dissertation / Thesis -
17
Unsupervised Deep Learning Algorithm to Solve Sub-Surface Dynamics for Petroleum Engineering Applications
Published 2020“…The solution provided by deep learning for a differential equation is in a closed analytical form which is differentiable and could be used in any subsequent computation. …”
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Maximizing deep learning-based energy efficiency in 5G downlink MIMO-NOMA systems by using MLP-CNN.
Published 2024“…It can be utilized with multiple convolutional and hidden layers, trained using specific algorithms to solve power allocation problems. …”
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Interference reduction by null-forming optimization based MVDR technique using gravitational search algorithm
Published 2017“…In the present work, Gravitational Search Algorithm (GSA) is a new modern metaheuristic optimization technique used for optimizing the MVDR null-forming level by controlling the excitation weight coefficients in a linear antenna array radiation pattern synthesis. …”
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Forecasting of fine particulate matter based on LSTM and optimization algorithm
Published 2024“…Therefore, this study uses hybrid deep learning models to forecast air pollution based on the concentration of particulate matter with diameter size of less than 2.5 ?…”
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