Search Results - (( normal optimization sensor algorithm ) OR ( variable training based algorithm ))

Whale optimization algorithm based on tent chaotic map for feature selection in soft sensors by AlRijeb, Mothena Fakhri Shaker, Othman, Mohammad Lutfi, Ishak, Aris, Hassan, Mohd Khair, Albaker, Baraa Munqith

“…One of the powerful optimization algorithms that is used for feature selection is the Whale Optimization Algorithm (WOA), which is a nature-inspired metaheuristic optimization algorithm that mimics the social behavior of humpback whales. …”
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Energy Efficient Multi Hierarchy Clustering Protocol for Wireless Sensor Network (EMHC) by Nazir, Babar, Hasbullah, Halabi

“…Clustering in normal sensor nodes is done by optimizing energy efficiency as well as coverage. …”
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A real-time algorithm of optical tactile sensor for surface characterization / Nurul Fathiah Mohamed Rosli by Mohamed Rosli, Nurul Fathiah

“…Many advances have been made in terms of sensor technology for sensitivity optimization. Mostly available optical tactile sensors are bulky, inflexible and lack dexterity for applications with limited or space constraint. …”
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Dynamic positioning base station for wireless sensor network using particle swarm optimization (PSO) by Nurul Adilah Abdul Latiff

“…The positioning of base station is one of the methods to improve the overall performance of wireless sensor network. The base station is normally located far from the sensing area. …”
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Modelling of multi-robot system for search and rescue by Poy, Yi Ler

“…In this project, this sensor-based algorithm is known as the Obstacle Avoidance Algorithm. …”
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Normalized advancement based totally opportunistic routing algorithm with void detection and avoiding mechanism for underwater wireless sensor network by Rahman, Ziaur, Hashim, Fazirulhisyam, A. Rasid, Mohd Fadlee, Othman, Mohamed, Alezabi, Kamal Ali Ahmed

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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

“…The focus of the paper is to propose a hybrid approach for the selection of the most influential input variables for the training and testing of neural network based hybrid models. …”
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Optimal forwarding routing protocol in ipv6-based wireless sensor network by Jamil, Ansar

“…WSNs become very important and being used widely especially in embedded applications. Normally, these applications require numerous low cost, low power and low data sensor nodes that communicating over multiple hop to cover a large geographical area. …”
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A new sinkhole attack detection algorithm for RPL in wireless sensor networks (WSN) by Ali, Mohammed Qasim

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Effect of input variables selection on energy demand prediction based on intelligent hybrid neural networks by Islam, B., Baharudin, Z., Nallagownden, P.

“…The efficacy of these models depends upon many factors such as, neural network architecture, type of training algorithm, input training and testing data set and initial values of synaptic weights. …”
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Improving Network Consistency and Data Availability Using Fuzzy C Mean Clustering Algorithm in Wireless Sensor Networks by Mujahid Tabassum

Power plant energy predictions based on thermal factors using ridge and support vector regressor algorithms by Afzal, Asif, Alshahrani, Saad, Alrobaian, Abdulrahman, Buradi, Abdulrajak, Khan, Sher Afghan

“…Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. …”
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QoS BASED ENERGY EFFICIENT ROUTING IN WIRELESS SENSOR NETWORK by NAZIR, BABAR

“…Clustering within normal sensor nodes is done by optimizing the network/coverage lifetime through a cluster-head-selection algorithm and a sleep/wake scheduling algorithm. …”
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Multidimensional Minimization Training Algorithms for Steam Boiler Drum Level Trip Using Artificial Intelligence Monitoring System by Ismail, F. B., Al-Kayiem, Hussain H.

“…The selection of the relevant variables for the neural networks is based on merging between theoretical analysis base and the plant operator experience. …”
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IoT-Enabled Waste Tracking and Recycling Optimization : Enhancing Sustainable Waste Management by Eugine Teh, Yin Jie, Chee Soon, Chong, Rozaimi, Ghazali, Hazriq Izzuan, Jaafar, Muhamad Fadli, Ghani, Howe Cheng, Teng, Nur Farhanah, Zulkipli, Siaw Hong, Liew

“…Advanced data preprocessing, such as augmentation and normalization, ensures robust model training, while optimized algorithms guide waste sorting based on classification results. …”
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Estimation of core size distribution of magnetic nanoparticles using high-Tc SQUID magnetometer and particle swarm optimizer-based inversion technique by Mohd Mawardi, Saari, Mohd Herwan, Sulaiman, Kiwa, Toshihiko

“…In this work, the core size estimation technique of magnetic nanoparticles (MNPs) using the static magnetization curve obtained from a high-Tc SQUID magnetometer and a metaheuristic inversion technique based on the Particle Swarm Optimizer (PSO) algorithm is presented. The high-Tc SQUID magnetometer is constructed from a high-Tc SQUID sensor coupled by a flux transformer to sense the modulated magnetization signal from a sample. …”
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Artificial neural controller synthesis for TORCS by Shi, Jun Long

“…The results showed: (1) DE hybrid FFNN could generate optimal controllers, (2) the proposed fitness function had successfully generated the required car's racing controllers, (3) the proposed minimization algorithm had been successfully minimize the number of RF sensors used, (4) the PDE algorithm could be implemented to generate optimal solutions for car racing controllers, and (5) the combination of components for average car speed and distance between the car and track axis is very important compared to other components. …”
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Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA) by Mohiuddin, A. K. M., Ashour, Ahmed Aly Ibrahim Shaaban, Yap, Haw Shin

“…The primary concern is to acquire the clear picture of the implementation of Multi-Objective Genetic Algorithm and the essential of variable valve timing effects on the engine performances in various engine speeds. …”
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Neural network based model predictive control for a steel pickling process by Kittisupakorn, P., Thitiyasook, P., Hussain, Mohd Azlan, Daosud, W.

“…The Levenberg-Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. …”
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Development Of Water Quality Index Prediction Model For Penang Rivers Using Artificial Neural Network by Mohd Hamdan, Eleena Yasmeen

“…Prior to the development of ANN-based WQI prediction model, the BR algorithm was chosen with two-, three-, four-, five- and six-neuron architectures for 60% and 70% training. …”
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