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  • Open Access

    ARTICLE

    Prediction of Link Failure in MANET-IoT Using Fuzzy Linear Regression

    R. Mahalakshmi1,*, V. Prasanna Srinivasan2, S. Aghalya3, D. Muthukumaran4

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1627-1637, 2023, DOI:10.32604/iasc.2023.032709

    Abstract A Mobile Ad-hoc NETwork (MANET) contains numerous mobile nodes, and it forms a structure-less network associated with wireless links. But, the node movement is the key feature of MANETs; hence, the quick action of the nodes guides a link failure. This link failure creates more data packet drops that can cause a long time delay. As a result, measuring accurate link failure time is the key factor in the MANET. This paper presents a Fuzzy Linear Regression Method to measure Link Failure (FLRLF) and provide an optimal route in the MANET-Internet of Things (IoT). This work aims to predict link… More >

  • Open Access

    ARTICLE

    Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

    M. Manikandan1,*, S. Sakthivel2, V. Vivekanandhan1

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3169-3185, 2023, DOI:10.32604/iasc.2023.029232

    Abstract Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network. This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms. The efficiency of the sensor node is energy bounded, acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors. Network management plays a significant role in wireless sensor networks, which was obsessed with the factors like the reliability of the network, resource management, energy-efficient routing, and scalability of services. The topology of… More >

  • Open Access

    ARTICLE

    An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

    Yaning Xiao, Xue Sun*, Yanling Guo, Sanping Li, Yapeng Zhang, Yangwei Wang

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 815-850, 2022, DOI:10.32604/cmes.2022.019198

    Abstract Gorilla troops optimizer (GTO) is a newly developed meta-heuristic algorithm, which is inspired by the collective lifestyle and social intelligence of gorillas. Similar to other metaheuristics, the convergence accuracy and stability of GTO will deteriorate when the optimization problems to be solved become more complex and flexible. To overcome these defects and achieve better performance, this paper proposes an improved gorilla troops optimizer (IGTO). First, Circle chaotic mapping is introduced to initialize the positions of gorillas, which facilitates the population diversity and establishes a good foundation for global search. Then, in order to avoid getting trapped in the local optimum,… More >

  • Open Access

    ARTICLE

    Determination of Cup to Disc Ratio Using Unsupervised Machine Learning Techniques for Glaucoma Detection

    R. Praveena*, T. R. GaneshBabu

    Molecular & Cellular Biomechanics, Vol.18, No.2, pp. 69-86, 2021, DOI:10.32604/mcb.2021.014622

    Abstract The cup nerve head, optic cup, optic disc ratio and neural rim configuration are observed as important for detecting glaucoma at an early stage in clinical practice. The main clinical indicator of glaucoma optic cup to disc ratio is currently determined manually by limiting the mass screening was potential. This paper proposes the following methods for an automatic cup to disc ratio determination. In the first part of the work, fundus image of the optic disc region is considered. Clustering means K is used automatically to extract the optic disc whereas K-value is automatically selected by algorithm called hill climbing.… More >

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