Search Results - (( evolution activation function algorithm ) OR ( wave adaptation optimization algorithm ))*

Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks by Ukasyah bin Mahamod

Enhanced weight-optimized recurrent neural networks based on sine cosine algorithm for wave height prediction by Alqushaibi, A., Abdulkadir, S.J., Rais, H.M., Al-Tashi, Q., Ragab, M.G., Alhussian, H.

“…Therefore, the wind plays an essential role in the oceanic atmosphere and contributes to the formation of waves. This paper proposes an enhanced weight-optimized neural network based on Sine Cosine Algorithm (SCA) to accurately predict the wave height. …”
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Water wave optimization with deep learning driven smart grid stability prediction by Mustafa Hilal, Anwer, Hassan Abdalla Hashim, Aisha, G. Mohamed, Heba, Alamgeer, Mohammad, K. Nour, Mohamed, Abdelrahman, Anas, Motwakel, Abdelwahed

“…In this background, the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction (WWOODL-SGSP) model. …”
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Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

“…These algorithms can be used successfully in many applications requiring the optimization of a certain multi-dimensional function. …”
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VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS. by FAROOQ, HUMERA

“…The Genetic Algorithm is an area in the field of Artificial Intelligence that is founded on the principles of biological evolution. …”
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Levy slime mould algorithm for solving numerical and engineering optimization problems by J. J., Jui, M. A., Ahmad, M. I. M., Rashid

“…The proposed Levy Slime Mould Algorithm (LSMA) is a novel metaheuristic algorithm that integrates the Levy distribution into a new metaheuristic called Slime Mould Algorithm (SMA) for solving numerical and engineering problems. …”
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Adaptive multi-parent crossover GA for feature optimization in epileptic seizure identification by Al-Sharhan, Salah, Bimba, Andrew

“…Thus, we propose an adaptive multi-parent crossover Genetic Algorithm (GA) for optimizing the features used in classifying epileptic seizures. …”
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An improved grey wolf with whale algorithm for optimization functions by Asgher, Hafiz Maaz

“…The performance of the proposed algorithm is tested and evaluated on five benchmarked unimodal and five multimodal functions. …”
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Cybernetics approaches in intelligent systems for crops disease detection with the aid of IoT by Rathinam R., Kasinathan P., Govindarajan U., Ramachandaramurthy V.K., Subramaniam U., Garrido S.

Characterization of oil palm fruitlets using artificial neural network by Olukayode, Ojo Adedayo

“…To further validate the generalization accuracy of the LSB_ANN, its performance was compared with that of a Multi-ANFIS network as well as those of three different ANN training algorithms: Levenberg Marquardt (LM) algorithm, Resilient Backpropagation (RP) algorithm and Gradient Descent with Adaptive learning rate (GDA). …”
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Adaptive Initial Contour and Partly-Normalization Algorithm for Iris Segmentation of Blurry Iris Images by Jamaludin, Shahrizan, Mohamad Ayob, Ahmad Faisal, Mohd Norzeli, Syamimi, Mohamed, Saiful Bahri

“…Next, the adaptive initial contour (AIC), δ, and stopping function were integrated to create the adaptive Chan-Vese active contour (ACVAC) algorithm. …”
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Dynamic smart grid communication parameters based cognitive radio network by Haider H.T., Muhsen D.H., Shahadi H.I., See O.H., Elmenreich W.

“…The proposed system helps to drive optimal communication parameters to realize power saving, maximum throughput and minimum bit error rate communication modes. A differential evolution algorithm is used to select the optimal transmission parameters for given communication modes based on a fitness function that combines multiple objectives based on appropriate weights. …”

Y-type Random 2-satisfiability In Discrete Hopfield Neural Network by Guo, Yueling

“…Additionally, the proposed Hybrid Differential Evolution Algorithm was implemented into the training phase to ensure that the cost function of the Discrete Hopfield Neural Network is minimized. …”
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Handover Parameter for Self-optimisation in 6g Mobile Networks: A Survey by Ukasyah Mahamod, Ibraheem Shayea, Hafizal Mohamad, Marinah Othman, Fauzun Abdullah Asuhaimi

The PID controller parameter tuning based on a modified differential evolutionary optimization algorithm for the intelligent active vibration control of a combined single link robo... by Moloody, Abbas, As’arry, Azizan, Hong, Tang Sai, Raja Kamil, ., Zolfagharian, Ali

“…In this strategy, a Strictly Bearish Distributed Exponential Function (SBDEF) has been used as the main target and the criteria and indicators for evaluating and measuring the optimal performance of differential evolution are the Integral Absolute Error (IAE) rate and the PID controller parameter values. …”
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VHF adaptive antenna using a rear defogger by Abdullah, Noorsaliza

“…Downhill simplex method is used as an algorithm to form the adaptive beam for the proposed antenna. …”
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Segmentation of Retinal Vasculature using Active Contour Models (Snakes) by Pang, Kee Y ong

“…The detection of the retinal vessel is achieved by performing image enhancement using CLAHE followed by Bottom-hat morphological transformation. Active contour model (snake) that based on level sets, techniques of curve evolution, and Mumford-Shah functional for segmentation is then used to segment out the detected retinal vessel and produce a complete retinal vasculature. …”
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Energy-efficient power allocation for downlink non orthogonal multiple access networks based on game theory and genetic algorithm / Reem Mustafa Mah’d Al Debes by Reem Mustafa , Mah’d Al Debes

“…Furthermore, the research explores advanced applications such as integrating NOMA with Millimeter-Wave technology and optimizing user association strategies, enhancing system capacity and overall performance. …”
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Computational electromagnetic modeling and optimization techniques to enhance the accuracy and efficiency of a 2.45 GHz pyramidal horn antenna by Newton-Raphson method for IEMI tes... by Hamamah, Fuad, Ahmad, Wfh F.H.W., Gomes, C., Mohd Isa, M., Homam, M. J.

“…Using CST Microwave Studio and optimization techniques, the return losses for the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Interior Point Quadratic Newton (IPQN), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) were-29.61dB,-30.45dB,-26.2dB, and-30.24dB, respectively. …”
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Development of a State-Space Observer for Active Noise Control Systems by Muhssin, Mazin T.

“…This is realized by artificially generating canceling (secondary) source(s) of sound through detecting the unwanted (primary) noise and processing it by an electronic controller, so that when the secondary wave is superimposed on the primary wave the two destructively interfere and cancellation occurs at the observation point. …”
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