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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
D. Bondre, Vipin
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Intelligent cervical cancer detection: empowering healthcare with machine learning algorithms Yadav, Uma; D. Bondre, Vipin; Bondre, Shweta V.; Thakre, Bhakti; Agrawal, Poorva; Thakur, Shruti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp298-306

Abstract

Cervical cancer remains a significant global health issue, particularly in underdeveloped nations, where it contributes to high mortality rates. Early detection is critical for improving treatment outcomes and survival rates. This study employs machine learning (ML) algorithms to predict cervical cancer risk using a dataset from the University of California at Irvine (UCI), which includes demographic and clinical attributes such as age, sexual history, smoking habits, and medical history. After applying data preprocessing techniques, several classification algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree, adaptive boosting (AdaBoost), and artificial neural networks (ANN), were trained and evaluated. The models were assessed using classification metrics such as precision, recall, and F1 score. Among the models, the ANN demonstrated the highest accuracy, achieving a score of 0.95. In addition, correlation analysis revealed significant relationships between various risk factors, providing insights into cervical cancer mechanisms and potential preventive measures. The study highlights the potential of ML in improving cervical cancer detection and patient outcomes, suggesting that advanced ML techniques can be valuable tools in healthcare research and clinical applications.
Optimized electric vehicle charging: solar-driven wireless power transfer system D. Bondre, Vipin; V. Bondre, Shweta; Yadav, Uma; Dasarwar (Maidamwar), Priya; Sharma, Rashmi
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9831

Abstract

Wireless power transfer (WPT) is emerging as a transformative solution to overcome the limitations of conventional plug-in charging for electric vehicles (EVs). This study aims to design and implement an efficient and reliable wireless charging system using inductive coupling with low requirements on the primary circuit. The proposed system integrates an Arduino-controlled high-frequency converter along with sensors and relays to optimize power flow, ensure safety, and reduce energy wastage. The methodology involves experimental rearrangement of transformers and frequency elements to achieve maximum efficiency while maintaining compact circuit design. Results demonstrate that the system can achieve efficient energy transfer suitable for short charging intervals, particularly beneficial for shuttle buses at stations and rental taxis at parking hubs. The findings highlight that wireless charging not only reduces total charging time but also supports cost-effective battery sizing, enabling improved vehicle turnaround and operational efficiency. In conclusion, this work contributes a weather-resistant, safe, and economically viable charging approach that sets new standards for EV infrastructure. Its implications lie in redefining charging stations along predetermined routes and stops, ultimately advancing sustainable and user-friendly electric transportation.
Co-Authors Agrawal, Poorva Bondre, Shweta V. Dasarwar (Maidamwar), Priya Rashmi Sharma Thakre, Bhakti Thakur, Shruti V. Bondre, Shweta Yadav, Uma