Search Results - (( mobile evaluation bat algorithm ) OR ( panel classification based algorithm ))*

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    A comprehensive overview of classical and modern route planning algorithms for self-driving mobile robots by Wan Daud, Wan Mohd Bukhari, Abu, Nur Syuhadah, Omar, Siti Nashayu, Sohaimeh, Shahirul Ashraf, Adli,, M. H.

    Published 2022
    “…Classical or traditional methods, such as Roadmaps (Visibility Graph and Voronoi Diagram), Potential Fields, and Cell Decomposition, and modern methodologies such as heuristic-based (Dijkstra Method, A* Algorithms, and D* Algorithms), metaheuristics algorithms (such as PSO, Bat Algorithm, ACO, and Genetic Algorithm), and neural systems such as fuzzy neural networks or fuzzy logic (FL) and Artificial Neural Networks (ANN) are described in this report. …”
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    Bat optimized link state routing protocol for energy-aware mobile ad-hoc networks by Jubair M.A., Mostafa S.A., Muniyandi R.C., Mahdin H., Mustapha A., Hassan M.H., Mahmoud M.A., Al-Jawhar Y.A., Al-Khaleefa A.S., Mahmood A.J.

    Published 2023
    “…The symmetry between OLSR of MANET and Bat Algorithm (BA) is that both of them use the same mechanism for finding the path via sending and receiving specific signals. …”
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    Delay and energy-aware routing for efficient data collection in wireless sensor networks / Ihsan Ali by Ihsan , Ali

    Published 2024
    “…Both Bat Algorithm parameters and AFW parameters are adaptively tuned to balance exploration and exploitation throughout the optimization process. …”
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    Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff by Mohd Yusoff, Nurulanis

    Published 2017
    “…Basically, the QEEA is based on the Time Domain (TD) and Frequency Domain (FD) scheduling where it is dependent on the QoS requirements to allocate resources. The proposed algorithm is compared against other scheduling algorithms, namely, the Channel and QoS Aware (CQA), Priority Set Scheduler (PSS), Proportional Fair (PF), Maximum Throughput (MT) and Blind Average Throughput (BAT). …”
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    Development of electronic nose for classification of aromatic herbs using Artificial Intelligent techniques by Che Soh, Azura, Mohamad Radzi, Nur Fadzilah, Mohamad Yusof, Umi Kalsom, Ishak, Asnor Juraiza, Hassan, Mohd Khair

    Published 2018
    “…Two classification methods, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used in order to investigate the performance of classification accuracy for this E-nose system. …”
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