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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ramanna Lamani, Manjunath
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Computer Science & IT Engineering

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Change detection and classification of satellite images using convolutional neural network Srinivasaiah, Raghavendra; Kumar Jankatti, Santosh; Ramanna Lamani, Manjunath; Jinachandra, Niranjana Shravanabelagola
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp329-337

Abstract

Satellite and airborne imagery, collectively known as earth observation imagery, are images of the earth collected from spaceborne or airborne platforms such as satellites and aircraft. Over the last 100 years, with the fast development of aviation, space exploration, and imaging technologies, the coming together of these technologies has been inevitable. Earth observation imagery has many applications in regional planning, geology, reconnaissance, fishing, meteorology, oceanography, agriculture, biodiversity conservation, forestry, landscape, intelligence, cartography, education, and warfare. With the rise in the number of these airborne and spaceborne imaging platforms being deployed by government and private entities alike, the capability to sift through and analyze vast amounts of data generated by these platforms is the need of the hour. With the exponential improvement in the computational capabilities of computers over the last half a century, analysts are exceedingly moving towards the practice of artificial intelligence, machine learning (ML), and computer vision solutions to automate a large part of the processes employed in analyzing earth observation imagery. This work recommends a workflow to perceive and classify changes in earth observation imagery of a given area by utilizing the vast flexibility that convolutional neural networks (CNN) provide.
Attribute optimization to improve breast cancer prediction using machine learning techniques Srinivasaiah, Raghavendra; Kumar Jankatti, Santosh; Shravanabelagola Jinachandra, Niranjana; Ramanna Lamani, Manjunath; Vijaya Lakshmi, Bellam; Bhelwa, Rishita
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1327-1338

Abstract

Breast cancer (BC) arises when cells grow out of control. It affects women more than men. Seeking cancer treatment can be both costly and time consuming, with test results spanning from a few hours to several weeks. The duration of these tests depends on the number of attributes within the dataset. This research paper endeavors to optimize the dataset attributes and find the accuracy of the optimized dataset. The primary goal is to reduce features using recursive feature elimination to minimize the time taken for the test result. This work discusses the machine learning technique and the random forest (RF) algorithm, which helps determine the parameter accuracy on the Wisconsin BC diagnostic dataset. The method achieves an accuracy of 96.49% with only eighteen attributes. It has aided the healthcare industry in finding BC in less time and improving the treatment.
Co-Authors Bhelwa, Rishita Jinachandra, Niranjana Shravanabelagola Kumar Jankatti, Santosh Shravanabelagola Jinachandra, Niranjana Srinivasaiah, Raghavendra Vijaya Lakshmi, Bellam