Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Applied Engineering and Technological Science (JAETS)
S. Revathy
Sathyabama Institute of Science and Technology
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Smart pools of data with ensembles for adaptive learning in dynamic data streams with class imbalance Radhika Vikas Kulkarni; S. Revathy; Suhas Haribhau Patil
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp310-318

Abstract

Streaming data incorporates dynamicity due to a nonstationary environment where data samples may endure class imbalance and change in data distribution over the period causing concept drifts. In real-life applications learning in dynamic data streams, is vitally important and challenging. A combined solution to adapt to class imbalance and concept drifts in dynamic data streams is rarely addressed by researchers. With this motivation, the current communication presents the online ensemble model smart pools of data with ensembles for class imbalance adaptive learning (SPECIAL) to learn in skewed and drifting data streams. It employs an ageing-based G-mean maximization strategy to adapt to dynamicity in data streams. It employs smart data-pools with the local expertise ensemble to classify samples lying in the same data-pool. The empirical and statistical study on different evaluation metrics exhibits that SPECIAL is more adaptive to class imbalanced dynamic data streams than the state-of-the-art algorithms.
Fuzzy Genetic Particle Swarm Optimization Convolution Neural Network Based On Oral Cancer Identification System R Dharani; S. Revathy; K. Danesh
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v5i1.2874

Abstract

Oral cancer is the eighth most common type of cancer in the world. Every year, 130,000 people in India die from mouth cancer. Getting a diagnosis from a clinical exam by skilled doctors and a biopsy takes time. When a problem is found early, it is always easier to treat. The primary goal of this work is to recognise disease-affected oral regions in a given oral image and classify the oral cancer disorder. This study employs unique Deep Learning algorithms to detect the location of disease-affected oral areas. This work employs the most effective feature extraction techniques, including appearance and patter-based features. Following feature extraction, the Bee Pulse Couple Neural Network (BeePCNN) algorithm is used to choose the best feature. Finally, Deep Learning is used to classify these attributes. An innovative FGPSOCNN reduces the computational complexity of CNN. On an additional real-time data set from Arthi Scan Hospital, a secondary evaluation is conducted. The experimental results indicate that the innovative FGPSOCNN performs better than existing methods.  
Co-Authors K. Danesh R Dharani Radhika Vikas Kulkarni Suhas Haribhau Patil