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All Journal International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
Ajit Kumar Nayak
Siksha ‘O’ Anusandhan (deemed to be University)
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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A novel machine learning based hybrid approach for breast cancer relapse prediction Ghanashyam Sahoo; Ajit Kumar Nayak; Pradyumna Kumar Tripathy; Jyotsnarani Tripathy
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1655-1663

Abstract

The second leading cause of death for women is breast cancer, which is growing. Some cancer cells may remain in the body, so relapse is possible even if treatment begins soon after diagnosis. Since there are now many machine learning (ML) approaches to recurrence prediction in breast cancer, it is important to compare and contrast them to find the most effective one. Datasets with many features often lead to incorrect predictions because of this. In this study, correlation-based feature selection (CFS) and the flower pollination algorithm (FPA) are used to improve the quality of the wisconsin prognostic breast cancer (WPBC) and University Medical Centre, Institute of Oncology (UMCIO) breast cancer relapse datasets respectively. Data imputation, scaling, pre-process raw data. The second stage uses CFS to select discriminative features based on important feature correlations. The FPA chose the optimum attribute combination for the most precise answer. We tested the approach using 10-fold cross-validation stratification. Various trials show 84.85% and 83.92% accuracy on the WPBC and UMCIO breast cancer relapse datasets, respectively. The hybrid method performed well in feature selection, increasing the accuracy of the relapse classification for breast cancer.
Assessment of deep learning based Hindi Odia bidirectional machine translation system Subhashree Satpathy; Smitaprava Mishra; Ajit Kumar Nayak
International Journal of Advances in Applied Sciences 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/ijaas.v15.i2.pp687-695

Abstract

India is a vast nation with a diverse range of cultures and languages. Most Indians choose to use their native languages when communicating with machines. To integrate smart technologies into every facet of Indian society, efficient systems that can positively identify Indian languages must be established. Machine translation (MT) studies comprise most of the natural language processing (NLP) in the era of multilingual computer-human interaction. Till now, less emphasis has been placed to develop MT systems among Indian languages. Yet again, building a qualitative and quantitative corpus in these languages is challenging. This work focuses on two Indic languages for the development of a Hindi to Odia bidirectional machine translation system (HOBMT). Bilingual evaluation understudy (BLEU), word error rate (WER), character error rate (CER), and metric for evaluation of translation with explicit ordering (METEOR) evaluation metrics are used to assess the accuracy of the translation. The most advanced sequential deep learning (DL) models, such as recurrent neural network (RNN), long short term memory (LSTM), and gated recurrent unit (GRU), are used in this study. In this research, RNN is observed with improved translation results due to its sequential data handling with context preservation.
Co-Authors Ghanashyam Sahoo Jyotsnarani Tripathy Pradyumna Kumar Tripathy Smitaprava Mishra Subhashree Satpathy