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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Shivashetty, Vrinda
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Device-to-device based path selection for post disaster communication using hybrid intelligence Balakrishna, Yashoda Mandekolu; Shivashetty, Vrinda
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp796-810

Abstract

Public safety network communication methods are concurrence with emerging networks to provide enhanced strategies and services for catastrophe management. If the cellular network is damaged after a calamity, a new-generation network like the internet of things (IoT) is ready to assure network access. In this paper, we suggested a framework of hybrid intelligence to find and re-connect the isolated nodes to the functional area to save life. We look at a situation in which the devices in the hazard region can constantly monitor the radio environment to self-detect the occurrence of a disaster, switch to the device-to-device (D2D) communication mode, and establish a vital connection. The oscillating spider monkey optimization (OSMO) approach forms clusters of the devices in the disaster area to improve network efficiency. The devices in the secluded area use the cluster heads as relay nodes to the operational site. An oscillating particle swarm optimization (OPSO) with a priority-based path encoding technique is used for path discovery. The suggested approach improves the energy efficiency of the network by selecting a routing path based on the remaining energy of the device, channel quality, and hop count, thus increasing network stability and packet delivery.
Proactive cervical cancer risk assessment using data-driven analytics Sreelatha, Sreelatha; Shivashetty, Vrinda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4301-4311

Abstract

This study introduces a sophisticated predictive model integrating clinical and lifestyle data addressing the critical public health challenge of cervical cancer, particularly in regions lacking routine screenings. Leveraging data driven analytics, the proposed model undergoes comprehensive preprocessing, including exploratory data analysis, missing value imputation, and feature extraction. Feature selection is carried out using the XGBoost classifier to ensure model efficacy. Data normalization and class balance via oversampling techniques are applied, with model validation conducted through stratified cross-validation. The optimized feature vector is then employed to train a LightGBM model. Utilizing a retrospective dataset of 858 patients from the Hospital Universitario de Caracas, Venezuela, comprising demographic, lifestyle, and medical history data, the LightGBM model achieves an impressive accuracy of 98%, outperforming similar existing approaches. The study outcome demonstrates the effectiveness of the proposed data modelling framework and feature selection, along with the choice of LightGBM as a suitable classifier. The proposed predictive framework can efficiently aid healthcare professionals in prioritizing high-risk patients for further evaluation and intervention.
Deep ensemble learning with uncertainty aware prediction ranking for cervical cancer detection using Pap smear images Sreelatha, Sreelatha; Shivashetty, Vrinda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1450-1460

Abstract

This paper proposes a novel deep ensemble learning framework designed for the efficient detection and classification of cervical cancer from Pap smear images. The proposed study implements three advanced learning models namely DenseNet201, Xception, and a classical convolutional neural network (CNN) customized with optimal hyperparameters to automate feature extraction and cervical cancer detection process. The proposed study also introduces a novel ensemble learning to enhance the classification of cervical cancer. The proposed ensemble mechanism is based on the confidence aggregation followed by uncertainty quantification and prediction ranking scheme, thus ensuring that more reliable predictions have a proportionally greater influence on the final outcome. The primary goal is to leverage the collective intelligence of the ensemble in a manner that prioritizes reliability and minimizes the impact of less certain predictions. The experimental analysis is carried out on two dataset one with whole slide images (WSI) and another on cropped images. The proposed ensemble model achieves an accuracy rate 100 and 97% for dataset with WSI and with cropped images respectively.
Co-Authors Balakrishna, Yashoda Mandekolu Sreelatha, Sreelatha