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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Mehta, Pradnya Samit
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Computer Science & IT Engineering

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Pothole detection model for road safety using computer vision and machine learning Bidve, Vijaykumar S.; Kakakde, Kiran S.; Bhole, Rahul H.; Sarasu, Pakiriswamy; Shaikh, Ashfaq; Mehta, Pradnya Samit; Borde, Santosh P.; Kediya, Shailesh O.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4480-4487

Abstract

Potholes pose significant threats to vehicular movement, causing damage to vehicles and risking the safety of drivers and pedestrians. The escalating issue of potholes has led to substantial financial losses for vehicle owners and drivers. Traditional methods of pothole detection are impractical, necessitating an innovative approach. The study focuses on implementing a detection system capable of accurately identifying potholes, empowering vehicles to adapt their speed or halt to prevent damage. The transformative solution presented in this research leverages cutting-edge technologies, specifically computer vision and machine learning, aiming to enhance road safety and streamline maintenance efforts. By addressing the interdependence of modern civilization on road networks, the Pothole Detection Model promises improved road safety, efficient maintenance practices, and the emergence of an era in intelligent transportation systems. The integration of technology into transportation infrastructure highlights the proactive measures needed to combat road imperfections, ensuring a safer and more efficient road network for the benefit of society.
Unveiling precision: Eye cancer detection redefined with particle swarm optimization and genetic algorithms Narwadkar, Sanved; Mehta, Pradnya Samit; Patil, Rutuja Rajendra; Kadam, Kalyani; Bidve, Vijaykumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1087-1095

Abstract

Eye cancer detection is rare. The study introduces a holistic swarm intelligence method for the timely identification and categorization of three significant eye disorders: glaucoma, diabetic retinopathy, and cataract. Glaucoma is distinguished by elevated pressure within the eye and harm to the optic nerve, potentially leading to permanent loss of vision. Diabetic patients experience diabetic retinopathy primarily due to the presence of high blood sugar levels. The early detection and classification of cataracts can be achieved by combining swarm intelligence algorithms such as particle swarm optimization (PSO) and genetic algorithms (GA). In the case of diabetic retinopathy diagnosis, swarm intelligence is employed to optimize the parameters of deep learning models, thereby enhancing the accuracy of lesion segmentation and classification. Cataract detection used to improve the evaluation of lens opacity and cloudiness, providing a robust diagnostic mechanism. The accuracy obtained with a PSO is 85.79%, F1 score 83.45%, and recall 82.43%. The accuracy obtained with a GA is 82.10%, F1 score 81.16%, and recall 81.51%. The comparison of GA, convolution neural network, and PSO algorithms proves that the accuracy to detect the eye cancer is achieved with PSO and GA algorithm.
Co-Authors Bhole, Rahul H. Bidve, Vijaykumar Bidve, Vijaykumar S. Borde, Santosh P. Kadam, Kalyani Kakakde, Kiran S. Kediya, Shailesh O. Narwadkar, Sanved Patil, Rutuja Rajendra Sarasu, Pakiriswamy Shaikh, Ashfaq