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p-Index From 2021 - 2026
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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) IJOT
Sharma, Rohini
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Automotive Engineering Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Design and Analysis of Mobile Locomation Approach Verma, Shokendra Dev; Bhatia, Kirti; Bhadola, Shalini; Sharma, Rohini
International Journal on Orange Technologies Vol. 4 No. 6 (2022): IJOT
Publisher : Research Parks Publishing LLC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31149/ijot.v4i6.3308

Abstract

One of the most difficult tasks for a robotic system is to determine the best path through the workspace. The main purpose is to prevent obstructions and create an optimum path. As a result, a mobile robot's free configuration space must be managed very carefully for course planning and navigation. The path planning work will be easier, faster, and more efficient if the configuration space is partitioned. In addition, the data perceived by the sensor contains some noise. As a result, we construct an approach to produce an optimal prediction state in order to build a map that aids in the effective management of the environment in order to locate the most efficient paths to target. We use the modified Kalman Filter (MKF) to determine the most reliable sensor data prediction, and then the K-means clustering method to identify the subsequent landmarks while evading barriers.
A Study on Image Categorization Techniques Renu; Princy; Bhatia, Kirti; Sharma, Rohini
International Journal on Orange Technologies Vol. 5 No. 5 (2023): International Journal on Orange Technologies
Publisher : Research Parks Publishing LLC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31149/ijot.v5i5.4438

Abstract

Image segmentation is the act of splitting a picture into meaningful and non-overlapping parts. It is a crucial step in comprehending natural scenes and has become a hotbed of research in the fields of image processing and computer vision. Even after decades of work and several successes, feature extraction and model design remain difficult. In this article, we carefully review the development in image segmentation techniques. Three crucial stages of image segmentation—classical segmentation, collaborative segmentation, and semantic segmentation based on deep learning—are primarily examined in accordance with segmentation principles and image data characteristics. We compare, contrast, and briefly discuss the benefits and drawbacks of segmentation models as well as their applicability. We also elaborate on the primary algorithms and critical strategies in each stage.
Real-time emotion prediction system using big data analytics Kaur Dhaliwal, Manpreet; Sharma, Rohini; Kaur, Rajbinder
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp869-879

Abstract

Emotions are an inseparable part of human existence. Emotions have a big impact on the success and failure of the human race. Comprehending human emotions could prove beneficial in creating improved systems for education, security, market sales, production, healthcare and other areas. Big data analytics applied to streamlined real time emotion sensor’s data can give new insights to anticipate stress before it arises and help in making significant choices that improve people's quality of life. This work proposes a framework for big data management and analysis of GSR sensor’s data in real-time for predicting emotions in human participants. Supervised learning techniques, ensemble boosted tree, neural network, Naïve Bayes, support vector machine, decision tree, K-nearest neighbor, and quadratic discriminant analysis are applied to the collected data. Two distinct criteria have been utilized for testing on real-time data one is trained on all participant data, resulting in a generalized system, while the other is trained on participant-specific data, resulting in a personalized system. Hence, the personalized system achieves an accuracy of up to 80.64% across all classes and 100% for binary classes as compare to generalized system achieves 78.12% accuracy. It is concluded that for the purpose of predicting emotions, the personalized model performs better than the generalized model.
Co-Authors Bhadola, Shalini Bhatia, Kirti Kaur Dhaliwal, Manpreet Kaur, Rajbinder Princy Renu Verma, Shokendra Dev