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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Angadi, Sanjeevkumar
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Notice of Retraction An NFMF-DBiLSTM model for human anomaly detection system in surveillance videos Angadi, Sanjeevkumar; Moorthy, Chellapilla V. K. N. S. N.; Tripathi, Mukesh Kumar; Tingare, Bhagyashree Ashok; Kadam, Sandeep Uddhavrao; Misal, Kapil
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp647-656

Abstract

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles. We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.-----------------------------------------------------------------------In response to the increasing demand for an intelligent system to avoid abnormal events, many models for detecting and locating anomalous behaviors in surveillance videos have been proposed. Nevertheless, significant flaws of inadequate discriminating ability are present in the majority of these models. A novel newton form and monotonic function based deep bidirectional long short-term memory (NFMF-DBiLSTM) human anomaly recognition system was discussed in this paper to tackle those issues. Initially, videos are transformed into frames; after that, the duplicate frames are removed, and by utilizing the shannon entropy centered contrast limited adaptive histogram equalization (SE-CLAHE) algorithm, the contrast has been elevated. By using the probabilistic matrix factorization kernel density estimation (PMF-KDE) technique, the background is subtracted after estimating only the motion of the object. After this, the silhouette function is performed utilizing the dirac depth silhouette function (DDSF). In addition, clustering is done by sorting and average-based K-means (SA-KM). The features are extracted from the suspected human and are then chosen by utilizing Poisson Eurasian oystercatcher optimization (PEOO). For classifying normal or anomaly, the selected features are subjected directly into the NFMF-DBiLSTM. When contrasted with the prevailing methodologies, the proposed model is found to be more efficient.
Identification of soluble solid content and total acid content using real-time visual inspection system Moorthy, C. H. V. K. N. S. N.; Tripathi, Mukesh Kumar; Hudagi, Manjunath R.; Hadimani, Lingaraj A.; Chavan, Gayatri Sanjay; Angadi, Sanjeevkumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp238-246

Abstract

This paper presents the framework for identifying materials using a fused descriptor-based approach, leverage computer vision techniques. The system is structured into three phases: derivation, extraction, and portrayal. Initially, the system employs K-means gathering techniques for establishing derivation. Following derivation, the system utilizes variety, texture, and shape-based feature extraction methods to extract relevant features from the soluble solid content and total acid content using real-time visual inspection system. A “consolidating” fusion feature is explored in the final phase using classification algorithms like C4.5, support vector machines (SVM), and k-nearest neighbors (KNN). The performance evaluation of the recognition system demonstrates promising results, with accuracy rates of 97.89%, 94.60%, and 90.25% achieved by using C4.5, SVM, and KNN separately. This indicates that the proposed fusion strategy effectively supports accurately recognizing materials using a fused descriptor-based approach.
Co-Authors Chavan, Gayatri Sanjay Hadimani, Lingaraj A. Hudagi, Manjunath R. Kadam, Sandeep Uddhavrao Misal, Kapil Moorthy, C. H. V. K. N. S. N. Moorthy, Chellapilla V. K. N. S. N. Tingare, Bhagyashree Ashok Tripathi, Mukesh Kumar