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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Yadav, Uma
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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.
Primary phase Alzheimer's disease detection using ensemble learning model Dasarwar, Priya; Yadav, Uma; Chavhan, Nekita
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.pp1531-1539

Abstract

Alzheimer's disease (AD) is a noteworthy problem for public health. Older people are most impacted by this neurological disease. It leads to memory loss and various cognitive impairments, eventually hindering communication. As a result, research on early AD detection has intensified in recent years. In current research work, we propose an ensemble learning strategy to identify AD by classifying brain images into two groups: AD brain and normal brain. Researchers have recently explored various machine learning (ML) and deep learning techniques to improve early disease detection. Patients with AD can recover from it more successfully and with less damage if they receive early diagnosis and therapy. This research presents an ensemble learning model to predict AD using decision trees (DT), logistic regression (LR), support vector machines (SVM), and convolutional neural networks (CNN). The open access series of imaging studies (OASIS) dataset is used for model training, and performance is measured in terms of various kinds of outcome namely accuracy, precision, recall, and F1 score. Our results demonstrated that, for the AD dataset, the CNN achieved the maximum validation accuracy of 90.32%. Thus, by accurately detecting the condition, ensemble algorithms can potentially significantly reduce the annual mortality rates associated with AD.
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.
Optimized convolution neural network with ant colony algorithm for accurate plant disease detection Bondre, Shweta V.; Yadav, Uma; Bondre, Vipin D.; Agrawal, Poorva
IAES International Journal of Artificial Intelligence (IJ-AI) 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/ijai.v14.i5.pp3724-3733

Abstract

In India, agriculture is the primary source of income for half the people. Even in situations of fast population growth, agriculture supplies nourishment for all people. To provide food for the entire population, it is advised to detect plant diseases at an early stage. Plant leaf diseases are recognized using images of the affected leaves. Deep learning (DL) research seems to offer several opportunities for increased accuracy. Ant colony optimization with convolution-neural-network (ACO-CNN), a new deep learning technique for identifying and categorizing diseases, is presented in this article. Ant colony optimization (ACO) was used to examine the efficacy of disease diagnostics in plant leaves. The convolution neural network (CNN) classifier is used to remove texture, color, and leaf arrangement geometry from the input images. The ACO-CNN model outperformed the support vector machine (SVM) and CNN models in terms of precision, recall, and accuracy. CNN's rate is 81.6% as compared to SVM's 80% accuracy level. In the “ACO-CNN” approach, the F1-score, recall, and precision have higher rates as compared to other models, and the “F1-score” has the highest rate compared with other models since the ACO-CNN model has an accuracy rate of 91.00%.
Co-Authors Agrawal, Poorva Bondre, Shweta V. Bondre, Vipin D. Chavhan, Nekita D. Bondre, Vipin Dasarwar (Maidamwar), Priya Dasarwar, Priya Rashmi Sharma Thakre, Bhakti Thakur, Shruti V. Bondre, Shweta