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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Kumar Chintakayala, Kushal
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Computer Science & IT Engineering

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Optimized feature selection approaches for accident classification to enhance road safety Sobhana, Mummaneni; Venkatesh Mendu, Gnana Siva Sai; Vemulapalli, Nihitha; Kumar Chintakayala, Kushal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3283-3290

Abstract

In the modern era, the issue of road accidents has become an increasingly critical global concern, requiring urgent attention and innovative solutions. This investigation has compiled an extensive dataset of 10,356 accident occurrences that occurred between the years 2018 and 2022 in Ernakulam district. By utilizing advanced feature selection methodologies, such as genetic algorithm and coyote optimization, this research has identified pivotal accident determinants. The study harnesses the potential of deep learning techniques, encompassing recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), and multilayer perceptron (MLP) for classifying accidents according to severity (categorized as fatal, grievous, and severe). Eight predictive models are trained using the dataset, and the top two are ensembled. Integrating deep learning and optimization strategies, this research aims to create a robust accident classification system. The system will help in developing proactive policies that can reduce the frequency and severity of accidents in Ernakulam district.  
Enhancing ultrasound-guided brachial plexus nerve localization with ResNet50 and support vector machine Mummaneni, Sobhana; Kumar Chintakayala, Kushal; Mukund Yarlagadda, Lalith Sai; Naga Raju Ala, Venkata Siva; Vemulapalli, Nihitha
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.pp4939-4947

Abstract

Medical image segmentation and classification plays a vital role in nerve block/region identification, particularly for anesthesiologists relying on instinctual judgments. However, due to patient-specific anatomical variations, these methods sometimes lack precision. This research focuses on addressing the problem, by incorporating novel ensembling method of ResNet-50 and support vector machine (SVM) to achieve segmentation of dataset images and classification of nerve blocks respectively. The said novel ensemble model is trained on a publicly available dataset consisting of more than 16,800 images. The sole purpose of this work is to address the problem of peripheral nerve blocking (PNB) with the usage of ensemble modelling, while achieving the highest possible accuracy. This research will help practitioners in accurately identifying the location of brachial plexus and distinguishing the type of nerve block to be injected – interscalene and supraclavicular. The model, which integrates ResNet50 and SVM classifier, achieved a commendable 99.27% accuracy in identifying and classifying the brachial plexus region.
Co-Authors Mukund Yarlagadda, Lalith Sai Mummaneni, Sobhana Naga Raju Ala, Venkata Siva Sobhana, Mummaneni Vemulapalli, Nihitha Venkatesh Mendu, Gnana Siva Sai