Search Results - (( data navigation learning 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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Advancements and challenges in mobile robot navigation: a comprehensive review of algorithms and potential for self-learning approaches by Al Mahmud, Suaib, Kamarulariffin, Abdurrahman, Mohd Ibrahim, Azhar, Haja Mohideen, Ahmad Jazlan

“…In this review paper, a comprehensive review of mobile robot navigation algorithms has been conducted. The findings suggest that, even though the self-learning algorithms require huge amounts of training data and have the possibility of learning erroneous behavior, they possess huge potential to overcome challenges rarely addressed by the other traditional algorithms. …”
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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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A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms by Sariman, Muhammad Shazmin, Othman, Maisara, Mat Akir, Rohaida, Mahamad, Abd Kadir, Ab Rahman, Munirah

“…Based on the findings of this review, we can conclude that an efficient solution for indoor navigation that uses the capabilities of embedded data and technological advances in immersive technologies can be achieved by training the shortest path algorithm with a deep learning algorithm to enhance the indoor navigation system.…”
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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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Prediction of stroke disease using machine learning techniques / Syarifah Adilah Mohamed Yusoff ... [et al.] by Mohamed Yusoff, Syarifah Adilah, Warris, Saiful Nizam, Abu Bakar, Mohd Saifulnizam, Kadar, Rozita

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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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Advancing mobile robot navigation with DRL and heuristic rewards: a comprehensive review by Khan, Mazbahur Rahman, Mohd Ibrahim, Azhar, Al Mahmud, Suaib, Samat, Farah Asyiqin, Jasni, Farahiyah, Mardzuki, Muhammad Imran

“…The advent of Deep Reinforcement Learning (DRL) has spurred significant research into enabling mobile robots to learn effective navigation by optimizing actions based on environmental rewards. …”
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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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Supervisory fuzzy learning control for underwater target tracking by Kia, Chua, Arshad, Mohd Rizal, Adom, Abd Hamid, Wilson, Philip A.

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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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A framework of modified adaptive neuro-fuzzy inference engine by Hossen, Md. Jakir

“…The performance of MANFIE was compared with existing methods in a diversity of practical benchmark applications such as pattern classifications, time series predictions, modeling with inverse learning control and mobile robot navigation. The MANFIE has shown the ability to reduce and form the robust minimal rules (Rules reduced on average 97.95% and 96.90% accuracy for pattern classifications, rules reduced on average 97.15%, 75% and 98.43% for time series predictions, modeling with inverse learning control and mobile robot navigation respectively) to make an appropriate structure and minimize the root mean square error (RMSE - 0.024, 0.149 for time series predictions, 0.007 for modeling with learning control, 0.027 for mobile robot navigation) with the best accuracy. …”
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Heart Attack Prediction with Deep Learning by Syasya Putri binti Mohamad Razauddin

Improvement of an integrated global positioning system and inertial navigation system for land navigation application by Hasan, Ahmed Mudheher

“…The developed navigators utilize artificial intelligence (AI) based on adaptive neuro-fuzzy inference system (ANFIS), to fuse data from both systems and estimate position and velocity errors. …”
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