Yazar "Kottursamy, Kottilingam" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    4x-expert systems for early prediction of osteoporosis using multi-model algorithms
    (Elsevier Sci Ltd, 2021) Prakash, U.; Kottursamy, Kottilingam; Cengiz, Korhan; Kose, Utku; Bui Thanh Hung
    Osteoporosis occurs due to micro-architectural deterioration of the bone tissues with an increased risk of bone fragility, which can cause fractures in the bone without much pressure applied to it. The T-score of a person's bone density report can be used to calculate the difference between BMD to that of healthy bones. Currently, osteoporosis is detected using conventional methods like DXA scans or high computational power requiring FEA tests. Considering individual approaches and mono-prediction techniques leads to omission of micro-fractional prediction parameters. In this paper, we have proposed a 4x-expert system for suspected osteoporosis patients, which is designed using multi model machine learning algorithms for improving prediction and accuracy through the various computational process. The experiment results shows, that the 4x-expert system covers the extensive prediction and accuracy of any suspected bone disorder patients, ranging from 75% to 97%.
  • Küçük Resim Yok
    Öğe
    Adaptive Swarm Intelligence Algorithms for Wireless Sensor Networks in IoT Preface
    (Igi Global, 2022) Kottursamy, Kottilingam; Cengiz, Korhan
    [Abstract Not Available]
  • Küçük Resim Yok
    Öğe
    Dynamic Polygon Generation for Flexible Pattern Formation in Large-Scale UAV Swarm Networks
    (IEEE, 2020) Raja, Gunasekaran; Kottursamy, Kottilingam; Theetharappan, Ajay; Cengiz, Korhan; Ganapathisubramaniyan, Aishwarya; Kharel, Rupak; Yu, Keping
    A UAV swarm network is a network formed by aggregating a large number of UAVs and coordinate them to execute a specific mission, especially in areas where human intervention is not physically possible or economically viable. The process of coordinating and maintaining a UAV swarm network has various phases. The pattern formation phase is one of the important phases and is highly significant in missions where geography is an important aspect of the mission. For the purpose of automating the pattern generation process, this paper proposes the dynamic polygon generation (DPGen) algorithm that can generate convex polygonal pattern with any number of vertices in linear time. The DPGen algorithm generates a pattern dynamically for any number of drones which increases the scalability of the UAV swarm networks, increasing the magnitude of use-cases of the swarm. The DPGen algorithm contains a mechanism to use this algorithm in a decentralized manner while balancing the load on all the UAVs in the network. The usage of DPGen algorithm reduces the network traffic in the UAV swarm network by 78.26% and decreases the power requirement of the leader drone by 74.39%.