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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Sathishkumar Mani
GITAM University
Computer Science & IT Engineering

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An investigation of wine quality testing using machine learning techniques Sathishkumar Mani; Reshmy Avanavalappil Krishnankutty; Sabaria Swaminathan; Prasannavenkatesan Theerthagiri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp747-754

Abstract

Quality is the most determining factor for any product. Optimal care and best measures are to be taken in assessing the quality of any product. This work deals with determining the quality of wine using intelligence-based learning techniques. In order to estimate the quality of wine, several experiments are performed on wine datasets. The main purpose of our work is to study and discover an efficient machine learning (ML) model that could determine the quality of wine given some Physico-chemical features. This study establishes that selecting important features to evaluate rather than all of them can lead to improved forecasts. According to the results, this approach may provide people who are not wine experts a greater opportunity to choose a fine wine.
A machine learning framework for predicting and optimizing return on investment across marketing channels Chandra Chathura; Keerthan Saya; Sathishkumar Mani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3528-3536

Abstract

In the current fast-paced competitive marketing environment, firms require data-centric methods to maximize their investments in several avenues. This research work applies machine learning techniques to estimate the return on investment (ROI) for marketing costs, which helps organizations in budgeting more effectively. Four models including random forest, extreme gradient boosting (XGBoost), gradient boosting, and linear regression were utilized for their accuracy in making predictions. Results showed that the highest accuracy was achieved by linear regression at 99.39%, random forest at 99.07%, gradient boosting at 99.01%, and XGBoost at 98.81%. It was further noted that digital marketing avenues such as social media and online stores gave the highest ROI, indicating that companies should prioritize digital marketing more than traditional marketing. On a practical level, this approach helps marketing team for choosing high performing channels since it estimates expected returns from each marketing channels and make smarter budget allocation. Yet, the study is done by using Kaggle dataset. In order to improve its generality, future research may use larger real-world datasets and extensive visualization techniques.
Co-Authors Chandra Chathura Keerthan Saya Prasannavenkatesan Theerthagiri Reshmy Avanavalappil Krishnankutty Sabaria Swaminathan