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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Panda, Supriya P.
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HybridCSF model for magnetic resonance image based brain tumor segmentation Kataria, Jyoti; Panda, Supriya P.
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1845-1852

Abstract

The human brain comprises a complex interconnection of nerve cells and vital organs, which regulates crucial bodily processes. Although neurons commonly undergo developmental stages, they may occasionally experience abnormalities, leading to abnormal growths known as brain tumors. The objective of brain tumor segmentation is to produce precise boundaries of brain tumor regions. This study extensively analyzes deep learning methods for brain tumor detection, evaluating their effectiveness across diverse datasets. It introduces a hybrid model, which is proposed by the name HybriCSF: hybrid convolutional-SVM-fuzzy C-means model combining convolutional neural network (CNN) with the classifier support vector machine (SVM) and clustering technique fuzzy C-means (FCM). The proposed model was implemented on Br35H, BraTs 2020 and BraTs2021 datasets. The suggested model outperformed the existing methods by achieving 98.6% of accuracy on Br35H dataset and dice score of 0.63, 0.87, 0.81 on BraTs 2020 dataset for enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively. The achieved dice scores on the BraTs 2021datasets are 0.89, 0.95, and 0.89 for ET, WT, and TC, respectively. The results show that the suggested model HybriCSF outperforms the other CNN-based models in terms of accuracy.
Machine learning-based intelligent result compilation RPA bot for higher education institutions Yadav, Neelam; Panda, Supriya P.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp587-594

Abstract

Educators are essential for societal progress, and well-educated students are pivotal for a promising future. Higher education faces challenges such as budget constraints, limited time, and a shortage of trained personnel, leading to faculty stress. Emerging technologies such as artificial intelligence (AI), machine learning (ML), and block chain provide solutions, with robotic process automation (RPA) bots a notable advanced AI subfield-automating repetitive tasks, thereby freeing teachers to focus on more essential responsibilities. RPA bots automate various educational processes, including examinations, admissions, marks updating, student record management, result compilation, human resources, resume screening, and administration. This research examines robotic automation in higher education institutions (HEIs), selecting and prioritizing RPA tasks through a survey involving subject matter experts (SMEs) from different HEIs, including professors and RPA experts. The research aims to develop a “virtual software bot” for automating “result compilation” post-examination. Using tools like XPATH, Whisper, and the web-based automation program Selenium web in Python, the bot automates this process. The ML library “Whisper” addresses the reCAPTCHA problem. The automated bot generates comma separated values (CSV) files in specific formats, completing the task 58 times faster than humans and saving 43 man-hours by compiling results for 653 students in 45 minutes.
Performance analysis of neuro linguistic programming techniques using confusion matrix Kumar, Arun; Panda, Supriya P.
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1696-1702

Abstract

During numerous qualitative surveys, swish patterns and visual kinesthetic dissociation (V/KD) were employed to examine attitudes and past occurrences. Neuro-linguistic programming (NLP) workshops in both hypnotic and non-hypnotic experimental sessions were held for forty days. Results demonstrated that negative sentiments and various emotional factors were significantly higher in 10-days’ workshop sessions as compared to 40 days’ sessions. Following the qualitative sentiments recollection, NLP workshops with various activities in the fear and stress indexing segment were increased in length. The NLP procedure was followed by the decreased negative emotional intensity in both groups; also, the results have been improved when using swish patterns and V/KD techniques. The performance analysis shows the results of improving emotional and sentimental factors in various NLP workshops. The workshops ranged in length from five to forty days. The specifications for workshops were selected based on the human mind's pre-determined conditions. The performance factors of two significant NLP techniques used in NLP workshops were compared and both techniques' performance factors were found to be adequate in terms of modifying behavior patterns. Using the confusion matrix, the overall accuracy percentage between V/KD and swish patterns is calculated, and an increase from 0.65 to 0.83 in the stressed parameters is shown.
Co-Authors Kataria, Jyoti Kumar, Arun Yadav, Neelam