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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) International Journal of Advances in Applied Sciences
Solanke, Anjali
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Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Comparative analysis of feature descriptors and classifiers for real-time object detection Nandeshwar, Vikas J.; Bhatlawande, Sarvadnya; Solanke, Anjali; Sathe, Harsh; Satao, Shivanand; Satpute, Safalya; Saste, Atharva
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp89-99

Abstract

Detecting objects within complex environments, such as urban settings, holds significant importance across various applications, including driver assistance systems, traffic monitoring, and obstacle detection systems. Particularly crucial for these applications is the accurate differentiation between cars and roads. This study introduces a novel approach that leverages traditional feature descriptors and classifiers for real-time object detection. It conducts an exhaustive comparative analysis of feature descriptors and classifiers to identify the most effective model for real-time object detection. Handcrafted features of images are extracted using algorithms such as scale invariant feature transform (SIFT), oriented fast and brief (ORB), fast retina key-point (FREAK), and local binary pattern (LBP). Seven classifiers are employed, including support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), decision tree (DT), logistic regression (LR), Naive Bayes, and extreme gradient boosting (XGBoost). The performance of the 28 generated combinations of feature descriptors and classifiers is evaluated based on the parameters of accuracy, precision, F1 score, and recall. The model utilizing LBP and XGBoost achieves the highest accuracy, reaching 83.59%. The system architecture comprises a camera, a high-speed computing unit, a display, and an audio subsystem, with the algorithm implemented on a Raspberry Pi 4B (8 GB).
A gamified online learning environment with comprehensive assessments and software integration Shilaskar, Swati; Bhatlawande, Shripad; Deshpande, Rupali; Shinde, Shivam; Madake, Jyoti; Solanke, Anjali
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp416-429

Abstract

The National Achievement Survey (NAS), conducted by the Ministry of Education, India, highlighted a concerning decline in mathematics proficiency among students in Maharashtra as they advance through grades. This trend is further aggravated by the limited availability of online resources in Marathi, hindering their learning progress. To address this, a pilot study was proposed to develop a specialized online platform tailored for Marathi medium students, integrating gamification and artificial intelligence (AI)-driven feedback to enhance engagement and comprehension. The pilot project, conducted at a Marathi medium school with approval from the principal, focused on polynomial division tests for 8th-grade students over four days. Results revealed that despite the easy level test's higher difficulty, students scored higher on the medium level test, possibly due to an adjustment period to the online platform. Notably, some students performed better on the hard-level test, indicating the platform's potential to improve performance. While promising, the study's limitations, including a small sample size, highlight the need for further research with a larger cohort and the integration of automatic suggestions for concept-specific games and assessments in future iterations to optimize the platform's effectiveness.
Co-Authors Bhatlawande, Sarvadnya Bhatlawande, Shripad Deshpande, Rupali Madake, Jyoti Nandeshwar, Vikas J. Saste, Atharva Satao, Shivanand Sathe, Harsh Satpute, Safalya Shilaskar, Swati Shinde, Shivam