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) Indonesian Journal of Electrical Engineering and Computer Science
Dayal Kumar Behera
KIIT Deemed to be University
Computer Science & IT Engineering

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Hybrid model for movie recommendation system using content K-nearest neighbors and restricted Boltzmann machine Dayal Kumar Behera; Madhabananda Das; Subhra Swetanisha; Prabira Kumar Sethy
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp445-452

Abstract

One of the most commonly used techniques in the recommendation framework is collaborative filtering (CF). It performs better with sufficient records of user rating but is not good in sparse data. Content-based filtering works well in the sparse dataset as it finds the similarity between movies by using attributes of the movies. RBM is an energy-based model serving as a backbone of deep learning and performs well in rating prediction. However, the rating prediction is not preferable by a single model. The hybrid model achieves better results by integrating the results of more than one model. This paper analyses the weighted hybrid CF system by integrating content K-nearest neighbors (KNN) with restricted Boltzmann machine (RBM). Movies are recommended to the active user in the proposed system by integrating the effects of both content-based and collaborative filtering. Model efficacy was tested with MovieLens benchmark datasets.
SHAP-enhanced ensemble learning for yield prediction: insights from CY-Bench data on Indian wheat Soma Gupta; Dayal Kumar Behera; Satarupa Mohanty; Subhra Swetanisha; Ritik Mallik; Namita Panda
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.pp3411-3420

Abstract

Predicting crop yield accurately is essential for providing food security and improving agricultural practices. This study examines the use of ensemble machine learning models combined with Shapley additive explanations (SHAP) feature selection to improve wheat yield prediction in India. The study uses CY-Bench data, incorporating normalized difference vegetation index (NDVI), meteorological data, and soil moisture data for yield prediction. Various ensemble techniques, including voting, stacking, and boosting are evaluated. Boost m1 ensemble model consistently outperforms other models in the prediction. Additionally, the integration of SHAP-based feature selection with the best ensemble model significantly improves the model accuracy and interpretability by identifying the most influential features affecting yield. The results show the effectiveness of ensemble boosting model, in capturing the complex relationships within agricultural data particularly when combined with feature selection. This method improves the transparency and actionability of machine learning models for agronomists, policymakers, and farmers.
Co-Authors Madhabananda Das Namita Panda Prabira Kumar Sethy Ritik Mallik Satarupa Mohanty Soma Gupta Subhra Swetanisha Subhra Swetanisha