Search Results - (( learning implementation models algorithm ) OR ( evolution optimization sensor algorithm ))

Cluster head selection optimization in wireless sensor network via genetic-based evolutionary algorithm by Vincent Chung, Hamzarul Alif Hamzah, Norah Tuah, Kit, Guan Lim, Min, Keng Tan, Kenneth Tze Kin Teo

“…Genetic-based evolutionary algorithms such as Genetic Algorithm (GA) and Differential Evolution (DE) have been popularly used to optimize cluster head selection in WSN to improve energy efficiency for the extension of network lifetime. …”
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A centralized localization algorithm for prolonging the lifetime of wireless sensor networks using particle swarm optimization in the existence of obstacles by Abdulhasan Al-Jarah, Ali Husam

“…In a previous research, a relocating algorithm for mobile sensor network had been introduced and the goal was to save energy and prolong the lifetime of the sensor networks using Particle Swarm Optimization (PSO) where both of sensing radius and travelled distance had been optimized in order to save energy in long-term and shortterm. …”
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Design, optimization and fabrication of a climbing six articulated-wheeled robot using artificial evolution and 3D printing by Lim, Shun Hoe, Teo, Jason Tze Wi

“…In this research, an artificial evolution approach utilizing Single-Objective Evolutionary Algorithm (SOEA) and Multi-Objective Evolutionary Algorithm (MOEA) respectively are investigated in the automatic design and optimization of the morphology of a Six Articulated-Wheeled Robot (SAWR) with climbing ability. …”
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An improved gbln-pso algorithm for indoor localization problem in wireless sensor network by Muhammad Shahkhir, Mozamir

“…Then, we compared the result with Particle Swarm Optimization (PSO), Differential Evolution Particle Swarm Optimization (DEPSO), Health Particle Swarm Optimization (HPSO) and Global best Local Neigborhood Particle Swarm Optimization (GbLN-PSO) algorithm. …”
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Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

“…EMA belongs to the evolutionary computation group of nature-inspired metaheuristic algorithms and offers a promising solution. A comparative analysis is conducted with other well-known algorithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Biogeography-Based Optimization (BBO), Teaching-Learning Based Optimization (TLBO), and Beluga Whale Optimization (BWO). …”
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Dealing with Routing Hole Problem in Multi-hop Hierarchical Routing Protocol in Wireless Sensor Network by Sama, Najm Us

“…In the proposed work, the focused problem is how to reduce the communication energy consumption and to avoid the routing hole problem by optimized routing algorithms. First, a routing hole detection algorithm is proposed prior to designing the routing protocol which decreases about 30 percent energy consumption rate, detection time and detection overhead. …”
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A secure trust aware ACO-Based WSN routing protocol for IoT by Sharmin, Afsah, Anwar, Farhat, Motakabber, S. M. A., Hassan Abdalla Hashim, Aisha

“…The performance of the proposed routing algorithm is demonstrated through MATLAB. Based on the proposed system, to find the secure and optimal path while aiming at providing trust in IoT environment, the average energy consumption is minimized by nearly 50% even as the number of nodes has increased, as compared with the conventional ACO algorithm, a current ant-based routing algorithm for IoT-communication, and a present routing protocol RPL for IoT.…”
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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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Mobile machine vision for railway surveillance system using deep learning algorithm by Kit, Guan Lim, Daniel Siruno, Min, Keng Tan, Chung, Fan Liau, Sha, Huang, Tze, Kenneth Kin Teo

“…In this paper, object detection model is developed and implemented with deep learning algorithm. …”
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Automatic design, optimization and In-Situ Fabrication of Heterogeneous Swarm Robot bodies using 3-D printing and multi-objective evolutionary algorithms by Teo, Jason Tze Wi, Johnny Koh, Chin, Kim On, Chua, Bih Lii, Willey Liew, Noor Ajian Mohd. Lair, Lim, Shun Hoe

“…In this research, an artificial evolution approach utilizing Single-Objective Evolutionary Algorithm (SOEA) and Multi-Objective Evolutionary Algorithm (MOEA) respectively are investigated in the automatic design and optimization of the morphology of a Six Articulated-Wheeled Robot (SAWR) with climbing ability. …”
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An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines by Bala, A., Ismail, I., Ibrahim, R., Sait, S.M., Oliva, D.

“…This problem is often formulated as a typical optimization problem. Metaheuristic algorithms are known to be excellent tools for solving optimization problems. …”
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Smart agriculture: precision farming through sensor-based crop monitoring and control system by Mohamad Hakhrani, Asyful Azhim, Abdul Hamid, Syamsul Bahrin

“…Subsequently, four distinct algorithms are trained with the collected dataset to ascertain the most optimal algorithm for predicting crop growth and harvesting time, resulting in the selection of the Random Forest Regression model, which attains the highest model score of 86%. …”
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Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.] by Mohd, Thuraiya, Jamil, Syafiqah, Masrom, Suraya, Ab Rahim, Norbaya

“…In the cycle of Industrial Revolution 4.0 (IR 4.0), many issues in the industries can be solved with implementation of artificial intelligence approaches, including machine learning models. …”
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Evolution, design, and future trajectories on bipedal wheel-legged robot: A comprehensive review by Zulkifli, Mansor, Irawan, Addie, Mohammad Fadhil, Abas

“…The analysis encompasses optimization techniques, sensor integration, machine learning, and adaptive control methods, evaluating their impact on robot capabilities. …”
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Training functional link neural network with ant lion optimizer by Mohmad Hassim, Yana Mazwin, Ghazali, Rozaida

“…This paper proposed the implementation of Ant Lion Algorithm as learning algorithm to train the FLNN for classification tasks. …”
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Object based segmentation and analysis using deep learning algorithm for cats and dogs images by N.Rajandhiran, Shreevishal

“…The deep learning algorithm implemented is the Unet architecture and the Segnet architecture respectively. …”
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Optimizing in-car-abandoned children’s sounds detection using deep learning algorithms / Nur Atiqah Izzati Md Fisol by Md Fisol, Nur Atiqah Izzati

“…To address this problem, an optimized in-car-abandoned children's sounds detection model using deep learning algorithms is proposed. …”
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Car dealership web application by Yap, Jheng Khin

“…Hence, two transfer learning algorithms were proposed and implemented to provide initial performance boost to the River adaptive random forest regressor and classifier, respectively. …”
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Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network by Gao, Yuan, Mohd Kasihmuddin, Mohd Shareduwan, Chen, Ju, Zheng, Chengfeng, Romli, Nurul Atiqah, Mansor, Mohd. Asyraf, Zamri, Nur Ezlin

“…This study introduced a novel ant colony optimization algorithm that implements the population selection strategy of the Estimation of Distribution Algorithm and a new pheromone updating formula. …”
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Rapid software framework for the implementation of machine learning classification models by Rahman, A.S.A., Masrom, S., Rahman, R.A., Ibrahim, R.

“…However, to implement a complete machine learning model involves some technical hurdles such as the steep learning curve, the abundance of the programming skills, the complexities of hyper-parameters, and the lack of user friendly platform to be used for the implementation. …”
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