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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Deepthi D. Kulkarni
KJEI’s Trinity Academy of Engineering
Electrical & Electronics Engineering

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Stress and anxiety detection: deep learning and higher order statistic approach Vaishali M. Joshi; Deepthi D. Kulkarni; Nilesh J. Uke
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1567-1575

Abstract

Today's teenagers are dealing with anxiety and stress. Anxiety, depression, and suicide rates have increased in recent years because of increased social rivalry. The research is focused on detecting anxiety in students due to exam pressure to reduce the potential harm to a person's wellness. Research is performed on databases for anxious states based on psychological stimulation (DASPS) and our own database. The measured signal is divided into sub bands that correspond to the electroencephalogram (EEG) rhythms using the Butterworth sixth-order order filter. In higher dimensional space, the nonlinearities of each sub-band signal are analyzed using higher order statistics third-order cumulants (TOC). We have classified stress and anxiety using the support vector machine (SVM), K-nearest neighbor (K-NN), and deep learning bidirectional long short-term memory (BiLSTM) network. In comparison to previous techniques, the proposed system's performance using BiLSTM is quite good. The best accuracy in this analysis was 87% on the DASPS database and 98% on the own database. Finally, subjects with high stress levels had more gamma activity than subjects with little stress. This could be an important attribute in the classification of stress.
Improved feature-based hybrid deep learning for multiclassification of ultrasound thyroid nodules Mayuresh Gulame; Deepthi D. Kulkarni; Priya Khune; Nilesh N. Thorat; Ashwini G. Shahapurkar; Vijaya S. Patil; Sumit Arun Hirve; Aarti Pimpalkar
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10447

Abstract

Ultrasonography is frequently used to identify thyroid nodules. Because of their internal features, variable appearances, and ill-defined borders, it might be difficult for a hospitalist to distinguish amongst benign and malignant forms of the nodule based solely on visual inspection. Although deep learning, a subset of artificial intelligence, has significantly advanced medical image recognition, challenges remain in achieving accurate and efficient diagnosis of thyroid nodules. To identify and classify thyroid nodules, this study uses an innovative hybrid DL-assisted multi-classification technique. A median blur eliminates salt-and-pepper noise, and this is followed by segmentation using a method based on enhanced pooling integrated U-Net (EPIU-Net). To produce a single histogram series, features are recovered from the segmented image, including multi-texton, and local ternary pattern (LTP) based patterns. Following feature extraction, the data is expanded and input into a fusion classification model utilizing Deep Maxout and convolutional neural network (CNN) to categorize nodules. This work uses 2 types of datasets and for both datasets, we achieved great results with our hybrid technique across all performance criteria. 0.976, 0.008, 0.992, and 0.017 are the corresponding values for accuracy, false discovery rate (FDR), sensitivity, false negative rate (FNR). Moreover proposed work is verified by k-fold method.
Co-Authors Aarti Pimpalkar Ashwini G. Shahapurkar Mayuresh Gulame Nilesh J. Uke Nilesh N. Thorat Priya Khune Sumit Arun Hirve Vaishali M. Joshi Vijaya S. Patil