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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Prabira Kumar Sethy
Sambalpur University

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

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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.
Rice false smut detection based on faster R-CNN Prabira Kumar Sethy; Nalini Kanta Barpanda; Amiya Kumar Rath; Santi Kumari Behera
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i3.pp1590-1595

Abstract

Rice false smut is one of the most dangerous diseases in rice at the ripening phase caused by Ustilaginoidea Virens. It is one of the most important grain diseases in rice production worldwide. Its epidemics not only lead to yield loss but also reduce grain quality because of multiple mycotoxins generated by the causative pathogen. The pathogen infects developing spikelets and specifically converts individual grain into rice false smut ball. Rice false smut balls seem to be randomly formed in some grains on a panicle of a plant in the paddy field. In this study, we suggest a novel approach for the detection of rice false smut based on faster R-CNN. The process of faster R-CNN comprises regional proposal generation and object detection. The both tasks are done in same convolutional network. Because of such design it is faster for object detection. The faster R-CNN is able to detect the RFS using rectangular labelling from on-field images. The proposed approach is the initial steps to make a prototype for the automatic detection of RFS.
Adaptation issues of machine learning in safety digitization Gyana Ranjana Panigrahi; Nalini Kanta Barpanda; Komma Anitha; Shanti Rathore; Preesat Biswas; Prabira Kumar Sethy
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1802-1808

Abstract

The internet community is the only set of irreplaceable spaces in today’s world and is used by millions for knowledge acquittance via the digital exchange between the landed gentry. The torrent of available e-contents in the Internet community attracts corporates and researchers to find the factual weightage of formed data. It is high time for digital diversification, which is the objective of using various learning-based machine learning (ML) systems for hands-on fortification. The main idea is to make stylistic communication more understandable. Here, the authors try to adapt the factual weightage procedure of formed data through the Internet community using machine learning schemes. Hence, the authors have chosen to emphasize cyber security, which is not well discussed and concerned with ethical contemplation from hackers' forums amidst internet communities. There are disparities in the continual growth of connotations, acronyms, spellings, and even technical jargon, which need periodic re-learning and their prototype implications through the proposed model.
Co-Authors Amiya Kumar Rath Dayal Kumar Behera Gyana Ranjana Panigrahi Komma Anitha Madhabananda Das Nalini Kanta Barpanda Preesat Biswas Santi Kumari Behera Shanti Rathore Subhra Swetanisha