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All Journal International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
Subbiah Murugan
Saveetha University
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Bayesian decision model based reliable route formation in internet of things Mohanavel Jothish Kumar; Suman Mishra; Elangovan Guruva Reddy; Madasamy Rajmohan; Subbiah Murugan; Narayanasamy Aswin Vignesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1665-1673

Abstract

Security provisioning has become an important issue in wireless multimedia networks because of their crucial task of supporting several services. This paper presents Bayesian decision model based reliable route formation in internet of things (BDMI). The main objective of the BDMI approach is to distinguish unreliable sensor nodes and transmit the data efficiently. Active and passive attack recognition methods identify unreliable node sensor nodes. Remaining energy, node degree, and packet transmission rate parameters to observe their node possibilities for recognizing the passive unreliable nodes. In active recognition, the base station (BS) confirms every sensor node identity, remaining energy, supportive node rate, node location, and link efficiency parameters to detect active unreliable sensor nodes. The Bayesian decision model (BDM) efficiently isolates an unreliable sensor node in the multimedia network. Simulation outcomes illustrate that the BDMI approach can efficiently enhance unreliable node detection and minimize the packet loss ratio in the network.
Multilevel local region sparse shape composition model for liver cancer classification Balasubramanian Sakthisaravanan; Ramakrishnan Meenakshi; Thirugnanasambandam Akila; Saravanan Durga Devi; Subbiah Murugan
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp932-943

Abstract

Machine learning and computer-assisted disease detection are two technological innovations that have significantly improved medical advancement, and recent research has demonstrated their effectiveness. Liver cancer is one of the important causes of cancer-related deaths internationally. Tumor detection in the liver and its classification is challenging due to poor accuracy and high processing requirements available in the existing techniques. In these, there is a loss of edge information and the quality of the images considered. To address these issues in the segmentation and classification of liver cancer, a novel feature extraction method and algorithm called the novel multilevel local region (MLR)-based sparse shape composition model (NMLR-SSC) were developed. This innovative technique identifies the existence of tumors on abdominal computed tomography (CT) images. The input images were obtained from the 3D-IRCADb-01 dataset. The algorithm showed an improvement of 0.22%. When compared to other classifiers. The proposed algorithm showed 100% specificity, sensitivity, and a 98% accuracy rate in detecting the liver tumor.
Co-Authors Balasubramanian Sakthisaravanan Elangovan Guruva Reddy Madasamy Rajmohan Mohanavel Jothish Kumar Narayanasamy Aswin Vignesh Ramakrishnan Meenakshi Saravanan Durga Devi Suman Mishra Thirugnanasambandam Akila