STM Journals Peer-reviewed research from Consortium e-Learning Network

3 publications

  • Published Subscription Review Article

    Textual Clues to Stress: A Machine Learning Approach

    By K. Purushotam Naidu, M. Prasanthi, Y.S.P. Kousalya, Trisha Jenna

    Abstract: Nowadays, numerous individuals utilize social media platforms to share tweets about their daily lives, which often reflect their mental well-being. Recognizing and managing stress is essential before it becomes a serious issue. Each day, a significant volume of informal messages is posted on discussion forums, blogs, and social networking sites. This study introduces a method for detecting stress using information gathered from social media, with a focus on Twitter. The …

    Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 72–76 Read article

  • Published Subscription Review Article

    Parkinson’s Disease Detection on Spiral Images Using CNN with Meta-Classifiers

    By S. Sumahasan, K. Purushotam Naidu, V. Lakshmana Rao, A. Udaya Kumar

    Abstract: In this work, we provide a detailed method for identifying Parkinson’s Disease (PD) by integrating Convolutional Neural Network (CNN) and meta-classifiers. Through the utilization of a varied dataset consisting of handwritten spiral images, our methodology demonstrates commendable accuracy across a range of models. Specifically, our CNN model with meta-classifiers surpasses alternative approaches, achieving an impressive accuracy rate of 95.07%. By utilizing pre-established VGG16 and ResNet50 architectures as bases, the region-based …

    Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 55–66 Read article

  • Published Subscription Review Article

    A Hybrid Machine Learning Approach for Cardiovascular Disease Prediction

    By K. Purushotam Naidu, V. Lakshmana Rao, Esha Thaniya Malla, Indu Kola, Renuka Sai Reddi, Bharathi Kolluru, Raj Tanuja Pentapati

    Abstract: Heart disease ranks among the top causes of death globally. Accurately predicting cardiovascular conditions has become a key challenge in the realm of clinical data analysis. It has been shown that machine learning is an effective means of assisting with predicting and decision-making based on the large volume of data produced by the medical industry. In this study, we describe a unique approach that increases the prediction accuracy of heart-related …

    Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 69–75 Read article

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